L1D85 (1.5) |
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L1T82 (26%): energy_consumption (10%), energy_intensity (7%) L1T81 (8%): china (20%), china_s (15%) L1T4 (7%): respectively (6%), reduction (5%), increase (4%), decrease (4%) L1T27 (5%): energy (66%) L1T88 (5%): model (29%), forecasting (18%) |
title | abstract |
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Decoupling China's carbon emissions increase from economic growth: An economic analysis and policy implications (2000) 🗎🗎 | As the world's second largest carbon emitter, China has long been criticized as a "free-rider" benefiting from other countries' efforts to reduce greenhouse gas emissions but not taking responsibility for its own emissions. China has been singled out as one of the major targets at the subsequent negotiations after the Kyoto meeting. Bq analyzing the historical contributions of interfuel switching, energy conservation, economic growth and population expansion to China's CO2 emissions during 1980 -97, this article clearly demonstrates that the above criticism is unjustified. Moreover, given the fact that the role of China is an issue of perennial concern at the international climate change negotiations, the article envisions some efforts and commitments that could be expected from China until its per capita income catches lip with the level of middle-developed countries. By emphasizing the it win-win strategies, these efforts and commitments are unlikely to jeopardize China's economic development and, at the same time, would give the country more leverage at the international climate change negotiations subsequent to the Buenos sires meeting. (C) 2000 Elsevier Science Ltd. All rights reserved. |
The natural and social properties of CO2 emission intensity (2003) 🗎🗎 | The formation of CO2 emission intensity reflects two properties: The natural property reveals CO2 emission intensity as being derived from energy intensity. The social property reveals the impact of energy and environment policy on the fuel mix, and further affects the numerical value of the index Of CO2 emissions/total primary energy supply (TPES). This paper emphasizes that understanding the above two properties can help us in making energy and environmental policy for achieving the Kyoto Protocol and other environmental targets. (C) 2002 Elsevier Science Ltd. All rights reserved. |
Accuracy of past projections of US energy consumption (2005) 🗎🗎 | Energy forecasts play a key role in development of energy and environmental policy. Evaluations of the accuracy of past projections can provide insight into the uncertainty that may be associated with current forecasts. They can also be used to identify sources of inaccuracies, and potentially lead to improvements in projections over time. Here we assess the accuracy of projections of US energy consumption produced by the Energy Information Administration over the period 1982-2000. We find that energy consumption projections have tended to underestimate future consumption. Projections 10-13 years into the future have had an average error of about 4%, and about half that for shorter time horizons. These errors mask much larger, offsetting errors in the projection of GDP and energy intensity (EI). GDP projections have consistently been too high, and El projection consistently too low, by more than 15% for projections of 10 years or more. Further work on the source of these sizable inaccuracies should be a high priority. Finally, we find no evidence of improvement in projections of consumption, GDP, or El since 1982. (C) 2003 Elsevier Ltd. All rights reserved. |
Global Climate Change: The Empirical Study of Sensitivity Model in China's Sustainable Development (2009) 🗎🗎 | This study employs logical, scientific understanding and prediction of a Sensitivity Model which is an integer tool in the assessment of climate change for China. It simulated a smooth environmental sustainable transition for the next ten years, given the 11 simulated variables. The positive coefficient for GDP growth variable suggests that estimated emissions initially rise with GDP growth, and eventually fall; the impact of population on emissions has been more pronounced in lower rather than in higher income ranges, contradicting Environmental Kuznet Curve. The role of energy efficiency on emissions has been the greatest when CO(2) emission is at its peak. |
An Analysis of Taiwan's Energy Ecological Efficiency and the Effectiveness of the Kyoto Protocol (2010) 🗎🗎 | This paper examines and discusses core issues relating to ecological pressure and climate policy performance in Taiwan, given Taiwan's status as a non-signer of the Kyoto Protocol. Taiwan's CO, emissions account for 0.9 percent of the world's total, making it the 23rd largest producer of global CO, emissions. To better understand Taiwan's performance in energy efficiency, this study calculates Taiwan's energy ecological footprint (EEF) and its ecological debt and eco-energy efficiency (EEE) from 1990 to 2005. These measures are then used in cross-national comparisons of EEF and EEE between Taiwan and Annex 1 and non-Annex-1 countries that have ratified the Kyoto Protocol. In addition, a regional comparison of the same indices is made between Taiwan, Japan and the Republic of Korea. The results suggest that Taiwan would experience a decline in its EEF and an increase in its eco-energy efficiency if it were to sign the Kyoto Protocol and to implement policies for better management of energy resources. Copyright (C) 2009 John Wiley & Sons, Ltd and ERP Environment. |
Copenhagen commitments and implications: A comparative analysis of India and China (2010) 🗎🗎 | Dynamic targets have been long advocated as a participatory tool for developing countries in climate change mitigation. Copenhagen commitments of India and China resume this trend after the unsuccessful attempt of Argentina a decade ago. However, linear intensity targets are prone to 'hot air' problems or non-compliance risks. Intensity targets of India and China are analyzed using their elasticity parameters. The relationship of these parameters to the structural nature of emissions and GDP profiles has been demonstrated and a method of comparing the probability indices of target achievement has been formulated in this paper, showing a lower probability for China compared to India. Similarly, a method of defining stringency factor for linear targets has been suggested and stringency factors evaluated for India (40%) and China (90%). which shows the relative stability of India's targets. This paper evaluates an energy-GDP-emissions index (EYE index) to indicate the extent of coupling/decoupling of economic growth from emissions. The three indices developed in this paper, namely, elasticity parameter, stringency factor and EYE index can be effectively used to analyze the economy-emissions relationships for policy making and target setting. (C) 2010 Elsevier Ltd. All rights reserved. |
Source decomposition of changes in income inequality: the integral-based approach and its approximation by the chained Shapley-value approach (2011) 🗎🗎 | This paper proposes source decomposition of the change in income inequality between two time points using the integral-based approach (IBA) and the (chained) Shapley-value approach as its approximation. In comparison to static Shapley-value decompositions and traditional decompositions for the square of the coefficient of variation and the Gini index, the new dynamic Shapley-value approach is intuitively appealing as a decomposition procedure for changes in inequality. It is expected to yield relatively small differences among various inequality measures, essentially maintaining consistency with income source classification. Path dependency, a possible drawback of the new decompositions, is not expected to be a particular problem in the usual cases. The properties are illustrated for application to the increase in US family income inequality during 1979-1996. In this empirical study, the new decompositions showed a tendency that was clearly different from those of the existing decompositions, indicating that the proposed procedures shed new light on analysis of the causes of inequality changes. An extension to incorporate other factors such as family structure is also successful without loss of the desirable properties. |
CHALLENGES TO PHASING OUT FOSSIL FUELS AS THE MAJOR SOURCE OF THE WORLD'S ENERGY (2011) 🗎🗎 | Energy-related data for China, India, the United States, and the world were analyzed for the period 2005-2035 to gain insight on (1) the evolution of energy intensity, (2) the pattern of carbon-dioxide equivalent (CO2) emitted per unit of GDP, (3) reductions in the carbon intensity required to achieve CO2 emissions comparable to the 1990 Kyoto Protocol's baseline year, (4) key obstacles to transitioning to a world's economy less dependent on fossil fuels. Key findings are: (1) the world's total primary energy use is expected to increase by 56% in the period 2005-2035, (2) the world's rate of GDP growth outpaces its rate of increase in energy use because of a decrease in the energy/GDP ratio, (3) the world's carbon intensity in 2035 must undergo a near 4-fold reduction to achieve emissions equal to those of 1990, (4) there are major obstacles to transitioning to a world much less reliant on fossil fuels. |
Study on China's low carbon development in an Economy-Energy-Electricity-Environment framework (2011) 🗎🗎 | Emissions mitigation is a major challenge for China's sustainable development. We summarize China's successful experiences on energy efficiency in past 30 years as the contributions of Energy Usage Management and Integrated Resource Strategic Planning, which are essential for low-carbon economy. In an Economy-Energy-Electricity-Environment (E4) framework, the paper studies the low-carbon development of China and gives an outlook of China's economy growth, energy-electricity demand, renewable power generation and energy conservation and emissions mitigation until 2030. A business-as-usual scenario is projected as baseline for comparison while low carbon energy and electricity development path is studied. It is defined as low carbon energy/electricity when an economy body manages to realize its potential economic growth fueled by less energy/electricity consumption, which can be characterized by indexes of energy/electricity intensity and emissions per-unit of energy consumption (electricity generation). Results show that, with EUM, China, could save energy by 4.38 billion ton oil equivalences (toes) and reduce CO2 emission by 16.55 billion tons: with IRSP, China, could save energy by 1.5 Btoes and reduce CO2 emission by 5.7 Btons, during 2010-2030.10 realize the massive potential, China has to reshape its economic structure and rely much on technology innovation in the future. (C) 2011 Elsevier Ltd. All rights reserved. |
Update of indicators for climate change mitigation in Greece (2011) 🗎🗎 | This paper analyses the factors affecting greenhouse gas (GHG) emissions in Greece, (i.e. the drivers of pressures on climate change), using environmental indicators related to energy, demographics and economic growth. The analysis is based on the data of 2008 and considers types of fuel and sectors. The Kaya identity is used to identify the relationship between drivers and pressures, using annual time series data of National GHG emissions, population, energy consumption and gross domestic product. The analysis shows that over the period 2000-2008, GHG emissions show a slight variation, but they are almost stabilised, with a total increase of 1.6%. Despite the economic growth over that period, this stabilisation may be considered as a combination of reductions in the energy intensity of GDP and the carbon intensity of energy, which are affected by improvements in energy efficiency and introduction of "cleaner" fuels, such as natural gas and renewables in the energy mixture of the country. (C) 2011 Elsevier Ltd. All rights reserved. |
China has been experiencing industrialization and urbanization since reform and opening of its economy in 1978. Energy consumption in the country has featured issues such as a coal-dominated energy mix, low energy efficiency and high emissions. Thus, it is of great importance to explore the factors driving the increase in energy consumption in the past two decades and estimate the potential for decreasing energy demands in the future. In this paper a hybrid energy input-output model is used to decompose driving factors to identify how these factors impact changes in energy intensity. A modified RAS approach is applied to project energy requirements in a BAU scenario and an alternative scenario. The results show that energy input mix, industry structure and technology improvements have major influences on energy demand. Energy demand in China will continue to increase at a rapid rate if the economy develops as in the past decades, and is projected to reach 4.7 billion tce in 2020. However, the huge potential for a decrease cannot be neglected, since growth could be better by adjusting the energy mix and industrial structure and enhancing technology improvements. The total energy demand could be less than 4.0 billion tce in 2020. (C) 2011 Elsevier Ltd. All rights reserved. | |
The rapid growth of domestic oil consumption in Saudi Arabia and the opportunity cost of oil exports foregone (2012) 🗎🗎 | We analyze the rapid growth of Saudi Arabia's domestic oil consumption, a nine-fold increase in 40 years, to nearly 3 million barrels per day, about one-fourth of production. Such rapid growth in consumption - 5.7% annually, which is 37% faster than its income growth of 4.2% - will challenge Saudi Arabia's ability to increase its oil exports, which are relied upon in long-term world oil projections by the International Energy Agency (IEA), US Department of Energy (DOE) and British Petroleum (BP). However, these institutions assume unprecedented slowdowns in Saudi oil consumption - from 5.7% annual growth historically to less than 2% in the future - allowing them to project increases in Saudi oil exports. Using 1971-2010 data, we estimate that the income responsiveness (elasticity) of oil consumption is at least 1.5-using both Ordinary Least Squares regression and Cointegration methods. We believe that continued high growth rates for domestic oil consumption are more likely than the dramatic slowdowns projected by IEA, DOE and BP. This will have major implications for Saudi production and export levels. (C) 2012 Elsevier Ltd. All rights reserved. |
The characteristics of China's energy structure and the distribution of its coal resources make coal transportation a very important component of the energy system; moreover, coal transportation acts as a bottleneck for the Chinese economy. To insure the security of the coal supply, China has begun to build regional strategic coal reserves at some locations, but transportation is still the fundamental way to guaranty supply security. Here, we study China's coal transportation quantitatively with a linear programming method that analyses the direction and volume of China's coal flows with the prerequisite that each province's supply and demand balance is guaranteed. First, we analyse the optimal coal transportation for the status quo coal supply and demand given the bottleneck effects that the Daqin Railway has on China's coal flow; second, we analyse the influence of future shifts in the coal supply zone in the future, finding that China's coal flows will also change, which will pressure China to construct railways and ports; and finally, we analyse the possibility of exploiting Yangtze River capacity for coal transportation. We conclude the paper with suggestions for enhancing China's coal transportation security. (C) 2012 Elsevier Ltd. All rights reserved. | |
What drives the change in China's energy intensity: Combining decomposition analysis and econometric analysis at the provincial level (2012) 🗎🗎 | We employ decomposition analysis and econometric analysis to investigate the driving forces behind China's changing energy intensity using a provincial-level panel data set for the period from 1995 to 2009. The decomposition analysis indicates that: (a) all of the provinces except for a few experienced efficiency improvement, while around three-fourths of the provinces' economics became more energy intensive or remained unchanged; (b) consequently the efficiency improvement accounts for more than 90% of China's energy intensity change as opposed to the economic structural change. The econometric analysis shows that the rising income plays a significant role in the reduction of energy intensity while the effect of energy price is relatively limited. The result may reflect the urgency of deregulating the price and establishing a market-oriented pricing system in China's energy sector. The implementation of the energy intensity reduction policies in the Eleventh Five-Year Plan (FYP) has helped reverse the increasing trend of energy intensity since 2002. Although the Chinese Government intended to change the industry-led economic growth pattern, it seems that most of the policy effects flow through the efficiency improvement as opposed to the economic structure adjustment. More fundamental changes to the economic structure are needed to achieve more sustainable progress in energy intensity reduction. (C) 2012 Elsevier Ltd. All rights reserved. |
Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model (2012) 🗎🗎 | Analyses and forecasts of carbon emissions, energy consumption and real outputs are key requirements for clean energy economy and climate change in rapid growth market such as China. This paper employs the nonlinear grey Bernoulli model (NGBM) to predict these three indicators and proposes a numerical iterative method to optimize the parameter of NGBM. The forecasting ability of NGBM with optimal parameter model, namely NGBM-OP has remarkably improved, compared to the GM and ARIMA. The MAPEs of NGBM-OP for out-of-sample (2004-2009) are ranging from 1.10 to 6.26. The prediction results show that China's compound annual emissions, energy consumption and real GDP growth is set to 4.47%, -0.06% and 6.67%, respectively between 2011 and 2020. The co-integration results show that the long-run equilibrium relationship exists among these three indicators and emissions appear to be real output inelastic and energy consumption elastic. The estimated values cannot support an EKC hypothesis, and real output is significantly negative impact on emissions. In order to promote economic and environmental quality, the results suggest that China should adopt the dual strategy of increasing energy efficiency, reducing the loss in power transmission and distribution and stepping up energy conservation policies to reduce any unnecessary wastage of energy. (C) 2012 Elsevier Ltd. All rights reserved. |
European CO2 emission trends: A decomposition analysis for water and aviation transport sectors (2012) 🗎🗎 | A decomposition analysis is used to investigate the main factors influencing the CO2 emissions of European transport activities for the period 2001-2008. The decomposition method developed by Sun [1] has been used to investigate the carbon dioxide emissions intensity, the energy intensity, the structural changes and the economy activity growth effects for the water and the aviation transport sectors. The analysis is based on Eurostat data and results are presented for 14 Member States, Norway and EU27. Results indicate that economic growth has been the main factor behind the carbon dioxide emissions increase in EU27 both for water and aviation transport activities. (C) 2012 Elsevier Ltd. All rights reserved. |
Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system (2012) 🗎🗎 | A novel three-dimensional energy-saving and emission-reduction chaotic system is proposed, which has not yet been reported in present literature. The system is established in accordance with the complicated relationship between energy-saving and emission-reduction, carbon emissions and economic growth. The dynamic behavior of the system is analyzed by means of Lyapunov exponents and bifurcation diagrams. With undetermined coefficient method, expressions of homoclinic orbits of the system are obtained. The Silnikov theorem guarantees that the system has Smale horseshoes and the horseshoes chaos. Artificial neural network (ANN) is used to identify the quantitative coefficients in the simulation models according to the statistical data of China, and an empirical study of the real system is carried out with the results in perfect agreement with actual situation. It is found that the sooner and more perfect energy-saving and emission-reduction is started, the easier and sooner the maximum of the carbon emissions will be achieved so as to reduce carbon emissions and energy intensity. Numerical simulations are presented to demonstrate the results. (C) 2012 Elsevier Ltd. All rights reserved. |
A PSO-GA optimal model to estimate primary energy demand of China (2012) 🗎🗎 | To improve estimation efficiency for future projections, the present study has proposed a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO-GA EDE) model, for China. The coefficients of the three forms of the model (linear, exponential, and quadratic) are optimized by PSO-GA using factors, such as GDP, population, economic structure, urbanization rate, and energy consumption structure, that affect demand. Based on 20-year historical data between 1990 and 2009, the simulation results of the proposed model have greater accuracy and reliability than other single optimization methods. Moreover, it can be used with optimal coefficients for the energy demand projections of China. The departure coefficient method is applied to get the weights of the three forms of the model to obtain a combinational prediction. The energy demand of China is going to be 4.79, 4.04, and 4.48 billion tce in 2015, and 6.91, 5.03, and 6.11 billion tce ("standard" tons coal equivalent) in 2020 under three different scenarios. Further, the projection results are compared with other estimating methods. (C) 2011 Elsevier Ltd. All rights reserved. |
CO2 emissions and economic development: China's 12th five-year plan (2012) 🗎🗎 | For the period of the 12th Five-Year Plan (2011-2015), the Chinese government has decided to reconsider and adjust its policies on economic development because of the pressures of CO2 emissions and fossil energy consumption. The current paper adopts the logarithmic Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to simulate the relationship between CO2 emissions and other economic development factors in China. Three groups of outliers are found using samples from 1989 to 2008 and the Partial Least Square (PLS) regularity test method. The outlier analysis reveals three important areas for CO2 reduction: (a) decreasing the share of coal to the total energy consumption and replacing it with non-fossil energies; (b) controlling vehicles used in the cities as well as (c) adjusting industrial structure. Furthermore, based on the social and economic realities of China, the current paper designs six feasible development scenarios for the period covered by the 12th Five-Year Plan and predicts the values of each factor in each scenario. The values can test the implementation of China's CO2 control development concept. The experiences obtained by outlier analysis can be of significant reference value for realizing the predicted scenarios. (C) 2011 Elsevier Ltd. All rights reserved. |
Coupling model of energy consumption with changes in environmental utility (2012) 🗎🗎 | This study explores the relationships between metropolis energy consumption and environmental utility changes by a proposed Environmental Utility of Energy Consumption (EUEC) model. Based on the dynamic equilibrium of input-output economics theory, it considers three simulation scenarios: fixed-technology, technological-innovation, and green-building effect. It is applied to analyse Hong Kong in 1980-2007. Continual increase in energy consumption with rapid economic growth degraded environmental utility. First, energy consumption at fixed-technology was determined by economic outcome. In 1990, it reached a critical balanced state when energy consumption was 22 x 10(9) kWh. Before 1990 (x(1) <22 x 10(9) kWh), rise in energy consumption improved both economic development and environmental utility. After 1990 (x(1) > 22 x 10(9) kWh), expansion of energy consumption facilitated socio-economic development but suppressed environmental benefits. Second, technological-innovation strongly influenced energy demand and improved environmental benefits. The balanced state remained in 1999 when energy consumption reached 32.33 x 10(9) kWh. Technological-innovation dampened energy consumption by 12.99%, exceeding the fixed-technology condition. Finally, green buildings reduced energy consumption by an average of 17.5% in 1990-2007. They contributed significantly to energy saving, and buffered temperature fluctuations between external and internal environment. The case investigations verified the efficiency of the EUEC model, which can effectively evaluate the interplay of energy consumption and environmental quality. (C) 2012 Elsevier Ltd. All rights reserved. |
The improvement of CO2 emission reduction policies based on system dynamics method in traditional industrial region with large CO2 emission (2012) 🗎🗎 | Some traditional industrial regions are characterized by high industrial proportion and large CO2 emission. They are facing dual pressures of maintaining economic growth and largely reducing CO2 emission. From the perspective of study of typological region, taking the typical traditional industrial region-Liaoning Province of China as a case, this study establishes a system dynamics model named EECP and dynamically simulates CO2 emission trends under different conditions. Simulation results indicate, compared to the condition without CO2 emission reduction policies, CO2 emission intensity under the condition of implementing CO2 emission reduction policies of "Twelfth Five-Year Plan" is decreased by 11% from 2009 to 2030, but the economic cost is high, making the policies implementation faces resistance. Then some improved policies are offered and proved by EECP model that they can reduce CO2 emission intensity after 2021 and decrease the negative influence to GDP, realizing the improvement objects of reducing CO2 emission and simultaneously keeping a higher economy growth speed. The improved policies can provide reference for making and improving CO2 emission reduction policies in other traditional industrial regions with large CO2 emission. Simultaneously. EECP model can provide decision-makers with reference and help for similar study of energy policy. (C) 2012 Elsevier Ltd. All rights reserved. |
Planning carbon emission trading for Beijing's electric power systems under dual uncertainties (2013) 🗎🗎 | In this study, a full-infinite interval-stochastic mixed-integer programming (FIMP) method is developed for planning carbon emission trading (CET) under dual uncertainties. FIMP has advantages in uncertainty reflection and policy analysis, particularly when the input parameters are provided as crisp and functional intervals as well as probabilistic distributions. The developed FIMP is applied to a real case study for managing carbon dioxide (CO2) emissions with trading scheme of Beijing's electric power system (EPS). Electric power industry is one of the major sources of CO2 emission in China. It is essential to accumulate relevant experience to provide a reliable basis for establishing a regional or national CET market, so as to prepare for docking with the international market. This is the first attempt to introduce CET scheme into Beijing's EPS to mitigate CO2 emissions. The solutions for energy supply, electricity generation, carbon-quota allocation, and capacity expansion are obtained. They cannot only be used for formulating CO2-reduction policies and assessing the associated economic implications in purchasing emission permits or bearing economic penalties, but also facilitate analyzing various policies when pre-regulated electricity-generation plans and pre-defined CO2-emission scheines are violated. (C) 2013 Elsevier Ltd. All rights reserved. |
Reform of refined oil product pricing mechanism and energy rebound effect for passenger transportation in China (2013) 🗎🗎 | Improving energy efficiency is the primary method adopted by the Chinese government in an effort to achieve energy conservation target in the transport sector. However, the offsetting effect of energy rebound would greatly reduce its real energy-saving potentials. We set up a Linear Approximation of the Almost Ideal Demand System Model (LA-AIDS model) to estimate the rebound effect for passenger transportation in China. Real energy conservation effect of improving energy efficiency can also be obtained in the process. The result shows that the rebound effect is approximately 107.2%. This figure signifies the existence of 'backfire effect', indicating that efficiency improvement in practice does not always lead to energy-saving. We conclude that one important factor leading to the rebound effect, is the refined oil pricing mechanism. China's refined oil pricing mechanism has been subjected to criticism in recent years. The results of simulation analysis show that the rebound could be reduced to approximately 90.7% if the refined oil pricing mechanism is reformed. In this regard, we suggest further reforms in the current refined oil pricing mechanism. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved. |
Decomposition of Ireland's carbon emissions from 1990 to 2010: An extended Kaya identity (2013) 🗎🗎 | In recent decades, Ireland has been an important example of a development pathway where rapid economic growth was accompanied by rising energy demand and increasing carbon emissions. Understanding the driving forces of carbon emissions is necessary for policy formulation and decomposition analysis is widely used for this purpose. This study uses an extended Kaya identity as the scheme and applies the log mean Divisia index (LMDI I) as the decomposition technique. Change in carbon emissions is decomposed from 1990 to 2010 and includes a measure of the effect of renewable energy penetration. Results illustrate that scale effects of affluence and population growth act to increase emissions and are countered primarily by energy intensity and fossil fuel substitution. Renewable energy penetration has a minor effect but has been increasing in recent years. Policy will need to significantly reduce intensity and increase renewables if applicable targets are to be reached. This requires not only a comprehensive suite of policies and measures but emphasis on the development path and 'non-technical' change for optimal outcomes. (c) 2013 Elsevier Ltd. All rights reserved. |
Decomposition analysis and Innovative Accounting Approach for energy-related CO2 (carbon dioxide) emissions intensity over 1996-2009 in Portugal (2013) 🗎🗎 | 'Complete decomposition' technique was used to examine CO2 emissions intensity and its components, considering 36 economic sectors and the 1996-2009 period. Additionally, Innovative Accounting Approach was implemented, that includes forecast error variance decomposition and impulse response functions, applied to factors in which emissions intensity was decomposed. It is shown that CO2 emissions intensity diminished significantly. Energy intensity of economic sectors is the most important effect in the determination of CO2 emissions intensity. The technologies used could be more efficient and less polluting, for the same amount of fuel used. This means that there was a substitution between fossil fuels in favour of less polluting fuels, but the technologies related to fossil fuels may still have a significant role. The industry (in particular 5 industrial sectors) is contributing largely to the effects of variation of CO2 emissions intensity. There is bidirectional causality between CO2 emissions intensity and the share of fossil fuels in total energy consumption. Emissions by fossil fuel and energy intensity affect the structure of the economy in favour of less energy intensive sectors. Emissions intensity reacts more significantly to shocks in the weight of fossil fuels in total energy consumption compared to shocks in other variables. (c) 2013 Elsevier Ltd. All rights reserved. |
China's energy demand and its characteristics in the industrialization and urbanization process: A reply (2013) 🗎🗎 | Zhang and Qin (2013) argued that in Jiang and Lin (2012), the equation form and variable selection should be altered, and it was problematic to use regression equation to project the future. In this reply, we disagree with and will refute some of the points raised in their comments. The model that we established was based on the mature economic theory; with the variable selections all having economic implications. Considering the economic development stage, China's urbanization will speed up and this will have significant effect on energy consumption. Therefore, urbanization is an indispensable variable for analyzing energy demand in China. The scenario design only in terms of the GDP is sufficient for illustrating energy demand trend in China to be understood in a way by most of the people. Although energy forecast is not that precise, it has an important implication for energy policy design, especially for China which is in transition. And China's energy demand will keep high growth in the mid-term. (C) 2013 Elsevier Ltd. All rights reserved. |
An Adaptive-Network-Based Fuzzy Inference System-Data Envelopment Analysis Algorithm for Optimization of Long-Term Electricity Consumption, Forecasting and Policy Analysis: The Case of Seven Industrialized Countries (2013) 🗎🗎 | This article presents an adaptive-network-based fuzzy inference system (ANFIS)-data envelopment analysis (DEA) algorithm for improvement of long-term electricity consumption forecasting and analysis. Six models are proposed to forecast annual electricity demand. Six different membership functions and several linguistic variables are considered in building ANFIS. The proposed models consist of two input variables, namely, gross domestic product and population. All trained ANFIS are then compared with respect to mean absolute percentage error. To meet the best performance of the intelligent-based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). DEA is used to optimize the electricity consumption as well as examine the behavior of electricity consumption. To show the applicability and superiority of the ANFIS-DEA algorithm, actual electricity consumption in the USA, Canada, Germany, United Kingdom (UK), Japan, France and Italy from 1980-2007 is considered. Electricity consumption is then forecasted up to 2015. The unique features of the ANFIS-DEA algorithm are: behavioral analysis and optimization in complex, non-linear and uncertain environments. |
Comparing climate policies to reduce carbon emissions in China (2013) 🗎🗎 | Currently, China is the largest carbon emitter mainly due to growing consumption of fossil fuels. In 2009, the Chinese government committed itself to reducing domestic carbon emissions per unit of GDP by 40-45% by 2020 compared to 2005 levels. Therefore, it is a top priority for the Chinese government to adopt efficient policy instruments to reduce its carbon intensity. Against this background, this paper develops a general equilibrium model and seeks to provide empirical contributions by comparing the potential impacts of several different policy options to reduce China's carbon emissions. The main findings are as follows. Firstly, these climate policies would affect the structure of economy and contribute to carbon emissions reduction and carbon intensity reduction. Secondly, there would be significant differences in the economic and environmental effects among different climate policies and hence, the government would trade-off among different economic objectives to overcome any potential resistances. Thirdly, there would be considerable differences in the emissions effects of absolute and intensity-based carbon emissions controls, implying that the government might adopt different climate policies for absolute or intensity-based carbon emissions controls. Looking ahead, the government should trade-off among different objectives when designing climate reforms. (C) 2013 Elsevier Ltd. All rights reserved. |
The impacts of carbon tax on energy intensity and economic growth - A dynamic evolution analysis on the case of China (2013) 🗎🗎 | This paper examines the impacts of carbon tax on energy intensity and economic growth in a novel four-dimensional energy-saving and emission-reduction system with carbon tax constraints. Based on Lyapunov exponents and bifurcation diagrams, the dynamic behavior of the system is analyzed. The quantitative coefficients of the actual system are identified by artificial neural network. A scenario study is undertaken by observing the dynamic evolution behavior of energy intensity and economic growth in reality. The concept of turning point of energy intensity in the four-dimensional dynamic system is put forward for the first time. By adjusting the correlation coefficients of the four-dimensional system, more effective methods being performed to steadily and diligently reduce energy intensity. Take for instance the situation in China, the problem of when and how to introduce carbon tax are settled within the framework of the four-dimensional dynamic system. The results show that, as the tax levy point of carbon tax grows larger, the energy intensity of the four-dimensional system could be controlled better. It is both important and necessary to note the inhibition effect of these changes on economic growth. The best time to levy carbon tax and the best tax levy point are achieved after a comprehensive analysis within the framework of the four-dimensional dynamic system. The more appropriate time carbon tax is started, the higher growth rate of carbon tax is adopted, the better corresponding policies and laws are made, the easier the carbon emissions could be controlled and the more energy intensity could be declined, so as to achieve the goal of reducing the carbon dioxide emissions and keeping proper energy intensity. Numerical simulations are carried out to demonstrate the results. (C) 2013 Elsevier Ltd. All rights reserved. |
The decline of sectorial components of the world's energy intensity (2013) 🗎🗎 | The world's primary energy consumption in the last 40 years has been increasing at 2.2%/year while GDP growth has been 3.4%/years over the same period. The decline of the energy intensity (I=E/GDP) has been, therefore, of 1.2%/year. In order to reduce the world's consumption growth proposal have been made to reduce the world's energy intensity by 40% by 2030 which corresponds to a reduction of 2.5%/year, roughly the double of the historical decline. Our analysis shoes that such goal could only be achieved by an unprecedented reduction of the energy intensity of "services" (which represent less than half the world energy consumption) since energy intensity of industry has remained practically constant in the last 40 years. (C) 2012 Elsevier Ltd. All rights reserved. |
Optimum estimation and forecasting of renewable energy consumption by artificial neural networks (2013) 🗎🗎 | Increasing energy consumption has led to release of pollutants such as greenhouse gases that affects on human health, agriculture, natural ecosystems, and earth temperature. Accurate estimation and forecasting of renewable energy is vital for policy and decision-making process in energy sector. This paper presents an Artificial Neural Network (ANN) approach for optimum estimation and forecasting of renewable energy consumption by considering environmental and economical factors. The ANN trains and tests data with Multi Layer Perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where there are no available measurement equipments. To show the applicability and superiority of the proposed ANN approach, monthly available data were collected for 11 years (1996-2006) in Iran. Complete sensitivity analysis is conducted to choose the best model for prediction of renewable energy consumption. The acquired results have shown high accuracy of about 99.9%. The results of the proposed model have been compared with conventional and fuzzy regression models to show its advantages and superiority. The outcome of this paper provides policymakers with an efficient tool for optimum prediction of renewable energy consumption. This study bypasses previous studies with respect to several distinct features. (C) 2013 Elsevier Ltd. All rights reserved. |
Correlation between Chinese and international energy prices based on a HP filter and time difference analysis (2013) 🗎🗎 | To establish a reasonable system and mechanism for Chinese energy prices, we use the Granger causality test, Hodrick-Prescott (HP) filter and time difference analysis to research the pricing relationship between Chinese and international energy prices. We find that Chinese and international crude oil prices changed synchronously while Chinese refined oil prices follow the changes of international oil prices with the time difference being about 1 month to 2 months. Further, Australian coal prices Granger causes Chinese coal prices, and there is a high correlation between them. The U.S. electricity price is influenced by the WTI crude oil price, the U.S. gasoline price and the HenryHub gas price. Due to the unreasonable price-setting mechanism and regulation from the central government, China's terminal market prices for both electricity and natural gas do not reflect the real supply-demand situation. This paper provides quantitative results on the correlation between Chinese and international energy prices to better predict the impact of international energy price fluctuations on China's domestic energy supply and guide the design of more efficient energy pricing policies. Moreover, it provides references for developing countries to improve their energy market systems and trading, and to coordinate domestic and international energy markets. (C) 2013 Elsevier Ltd. All rights reserved. |
The impact of domestic trade on China's regional energy uses: A multi-regional input-output modeling (2013) 🗎🗎 | To systematically reveal how domestic trade impacts on China's regional energy uses, an interprovincial input-output modeling is carried out to address demand-derived energy requirements for the regional economies in 2007 based on the recently available data. Both the energy uses embodied in final demand and interregional trade are investigated from the regional and sectoral insights. Significant net transfers of embodied energy flows are identified from the central and western areas to the eastern area via interregional trade. Shanxi is the largest energy producer and interregional embodied energy deficit receiver, in contrast to Guangdong as the largest energy user and surplus receiver. By considering the impacts of interregional trade, the energy uses of most eastern regions increase remarkably. For instance, Shanghai, Hainan, Zhejiang, Beijing, Jiangsu and Guangdong have their embodied energy requirements 87.49, 19.97, 13.64, 12.60, 6.46 and 6.38 times of their direct energy inputs, respectively. In contrast, the embodied energy uses of some central and western regions such as Inner Mongolia, Shanxi, Xinjiang, Shaanxi and Guizhou decrease largely. The results help understand the hidden network linkages of interregional embodied energy flows and provide critical insight to amend China's current end-reduction-oriented energy policies by addressing the problem of regional responsibility transfer. (C) 2013 Elsevier Ltd. All rights reserved. |
The dynamics of natural gas consumption and GDP in Bangladesh (2013) 🗎🗎 | Reserves of natural gas in Bangladesh are very large and total demand has increased secularly in recent years. This paper examines the causal relationship between the consumption of natural gas and GDP in Bangladesh over the period 1980 to 2010. We find that there is a positive unidirectional causality running from GDP to natural gas consumption: movements in GDP affect the consumption of natural gas but not vice-versa. While our results rest on several statistical assumptions, they support the pursuit of policies that are in line with energy conservation. Implementing these policies will be of particular significance in light of the fact that Bangladesh's current reserves of natural gas will not meet its current level of consumption demand beyond the next two decades. (C) 2013 Elsevier Ltd. All rights reserved. |
Model projections and policy reviews for energy saving in China's service sector (2013) 🗎🗎 | Energy efficiency of buildings in the service sector is becoming increasingly important in China due to the structural shift of the economy from industry to services. This paper employs a bottom-up cohort model to simulate current energy saving policies and to make projections for future energy use and CO2 emissions for the period 2000-2030 in the Chinese service sector. The analysis shows that energy demand in the service sector will approximately triple in 2030, far beyond the target of quadrupling GDP while only doubling energy use. However, it is feasible to achieve the target of emission reduction by 40% in 2020 even under the poor state of compliance rate of building standard. This paper also highlights four crucial aspects of designing optimal energy saving policies for China's service sector based on the model results. (C) 2013 Elsevier Ltd. All rights reserved. |
Energy saving and emission reduction of China's urban district heating (2013) 🗎🗎 | China's carbon dioxide (CO2) emission ranks highest in the world. China is committed to reduce its CO2 emission by 40% to 45% from the 2005 levels by 2020. To fulfill the target, China's CO2 emission reduction must exceed 6995 million tons. Energy consumption and CO2 emission of China's urban district heating (UDH) are increasing. The current policy implemented to improve UDH focuses on replacing coal with natural gas to reduce energy consumption and CO2 emission to some extent. This paper proposes that heat pump heating (HPH) could serve as a replacement for UDH to help realize energy-saving and emission-reduction goals to a greater extent. The paper also analyzes the impact of this replacement on the heating and power generation sectors. The results show that replacing coal-based UDH with HPH decreases energy consumption and CO2 emission by 43% in the heating sector. In the power generation sector, the efficiency of power generation at the valley electricity time increases by 0.512%, and the ratio of peak-valley difference decreases by 16.5%. The decreases in CO2 emission from the heating and power generation sectors cumulatively account for 5.55% of China's total CO2 emission reduction target in 2020. (C) 2012 Elsevier Ltd. All rights reserved. |
Carbon Flow Tracing Method for Assessment of Demand Side Carbon Emissions Obligation (2013) 🗎🗎 | As increasing number of nations, industries, and individuals are involved in the carbon mitigation initiative, it becomes significant to answer what amount of carbon emissions social entities are responsible for, especially from electricity service. In this paper, a carbon flow tracing method is presented to determine carbon emissions obligation from electricity consumption. The method traces energy sources of electricity consumption across the electricity network to determine the indirect carbon emissions caused. From a "generation-to-consumption" perspective, the transmission characteristic of electricity supply and locational energy mix are reflected. The method is employed to address two important issues uniformly, i.e., carbon accounting at the regional level and locational carbon intensity assessment at the user level, respectively. Test results from two examples show that the method is a preferable choice to solve the two problems. Additionally, the method may contribute to carbon reduction cooperation and end user participation in carbon mitigation. |
Analysis of South Korea's economic growth, carbon dioxide emission, and energy consumption using the Markov switching model (2013) 🗎🗎 | Recently, many countries have been making an effort to reduce their carbon dioxide (CO2) emission, and as part of such effort, the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Kyoto Protocol in 1997. South Korea is very likely to be included in the second batch of countries that must reduce their greenhouse gas emission after the end of the implementation of the Kyoto Protocol in 2012. Reducing the country's CO2 emission, however, can have an impact on the economy. Therefore, in this study, the correlations between South Korea's economic growth, CO2 emission, and energy consumption were analyzed. The analysis period was from QI 1991 to Q4 2011, and the analysis methods that were used were regression analysis for the relational analysis among the various overall indices, and the Markov switching model for a more detailed analysis. The results of the analyses showed that South Korea's economic growth and CO2 emission were coincidental. The correlation analysis of the country's economic growth and energy consumption showed a significant correlation between economic growth and fossil fuels, which emit CO2, such as coal in the industrial sector, petroleum products in the industrial and transportation sectors, and liquefied natural gas (LNG) in the residential/ commercial and industrial sectors. It is expected that the results of this study will pave the way for the conduct of various researches on controlling the country's CO2 emission management, and for suggestions for such to be given, such as policies for reducing the energy consumption in each sector, using the methodology proposed in this study. (C) 2012 Elsevier Ltd. All rights reserved. |
China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model (2013) 🗎🗎 | Although China became the world's largest CO2 emitter in 2007, the country has also taken serious actions to reduce its energy and carbon intensity. This study uses the bottom-up LBNL China End-Use Energy Model to assess the role of energy efficiency policies in transitioning China to a lower emission trajectory and meeting its 2020 intensity reduction goals. Two scenarios - Continued Improvement and Accelerated Improvement - were developed to assess the impact of actions already taken by the Chinese government as well as planned and potential actions, and to evaluate the potential for China to reduce energy demand and emissions. This scenario analysis presents an important modeling approach based in the diffusion of end-use technologies and physical drivers of energy demand and thereby help illuminate China's complex and dynamic drivers of energy consumption and implications of energy efficiency policies. The findings suggest that China's CO2 emissions will not likely continue growing throughout this century because of saturation effects in appliances, residential and commercial floor area, roadways, fertilizer use; and population peak around 2030 with slowing urban population growth. The scenarios also underscore the significant role that policy-driven efficiency improvements will play in meeting 2020 carbon mitigation goals along with a decarbonized power supply. Published by Elsevier Ltd. |
Energy efficiency and CO2 emissions in Swedish manufacturing industries (2013) 🗎🗎 | This paper analyses the trends in energy consumption and CO2 emissions as a result of energy efficiency improvements in Swedish manufacturing industries between 1993 and 2008. Using data at the two-digit level, the performance of this sector is studied in terms of CO2 emissions, energy consumption, energy efficiency measured as energy intensity, value of production, fuel sources, energy prices and energy taxes. It was found that energy consumption, energy intensity and CO2 emission intensity, measured as production values, have decreased significantly in the Swedish manufacturing industries during the period studied. The results of the decomposition analysis show that output growth has not required higher energy consumption, leading to a reduction in both energy and CO2 emission intensities. The role of structural changes has been minor, and the trends of energy efficiency and CO2 emissions have been similar during the sample period. A stochastic frontier model was used to determine possible factors that may have influenced these trends. The results demonstrate that high energy prices, energy taxes, investments and electricity consumption have influenced the reduction of energy and CO2 emission intensities, indicating that Sweden has applied an adequate and effective energy policy. The study confirms that it is possible to achieve economic growth and sustainable development whilst also reducing the pressure on resources and energy consumption and promoting the shift towards a low-carbon economy. |
Estimation on oil demand and oil saving potential of China's road transport sector (2013) 🗎🗎 | China is currently in the stage of industrialization and urbanization, which is characterized by rigid energy demand and rapid growth of energy consumption. Therefore, energy conservation will become a major strategy for China in a transition to low-carbon economy. China's transport industry is of high energy consumption. In 2010, oil consumption in transport industry takes up 38.2% of the country's total oil demand, of which 23.6% is taken up by road transport sector. As a result, oil saving in China's road transport sector is vital to the whole nation. The co-integration method is developed to find a long-run relationship between oil consumption and affecting factors such as GDP, road condition, labor productivity and oil price, to estimate oil demand and to predict future oil saving potential in China's transport sector under different oil-saving scenarios. Monte Carlo simulation is further used for risk analysis. Results show that under BAU condition, oil demand of China's road transport sector will reach 278.5 million ton of oil equivalents (MTOE) in 2020. Oil saving potential will be 86 MTOE and 131 MTOE under moderate oil-saving scenario and advanced oil-saving scenario, respectively. This paper provides a reference to establishing oil saving policy for China's road transport sector. (C) 2013 Elsevier Ltd. All rights reserved. |
Simulation Analysis of China's Energy and Industrial Structure Adjustment Potential to Achieve a Low-carbon Economy by 2020 (2013) 🗎🗎 | To achieve a low-carbon economy, China has committed to reducing its carbon dioxide (CO2) emissions per unit of gross domestic product (GDP) by 40%-45% by 2020 from 2005 levels and increasing the share of non-fossil fuels in primary energy consumption to approximately 15%. It is necessary to investigate whether this plan is suitable and how this target may be reached. This paper verifies the feasibility of achieving the CO2 emission targets by energy and industrial structure adjustments, and proposes applicable measures for further sustainable development by 2020 through comprehensive simulation. The simulation model comprises three sub-models: an energy flow balance model, a CO2 emission model, and a socio-economic model. The model is constructed based on input-output table and three balances (material, value, and energy flow balance), and it is written in LINGO, a linear dynamic programming language. The simulation results suggest that China's carbon intensity reduction promise can be realized and even surpassed to 50% and that economic development (annual 10% GDP growth rate) can be achieved if energy and industrial structure are adjusted properly by 2020. However, the total amount of CO2 emission will reach a relatively high level-13.68 billion tons-which calls for further sound approaches to realize a low carbon economy, such as energy utilization efficiency improvement, technology innovation, and non-fossil energy's utilization. |
Walking away from a low-carbon economy? Recent and historical trends using a regional decomposition analysis (2013) 🗎🗎 | Using the latest available data, this brief article attempts to provide the first regional decomposition analysis of CO2 emissions from fuel combustion. Covering eight regions of the world, determinants are estimated in relative and absolute terms for the period 1971-2010. We use the unparalleled 2010 global surge in CO2 emissions as a reference and entry point for the analysis. Overall, results show that most regions have recently performed worse than their historical trends and lack of meaningful progress is identified. Whereas specific drivers for certain regions suggest some level of continuous improvement (e.g. reduced energy intensity in Asia, decarbonisation of of energy supply in OECD Europe), they are incapable of offsetting the effects of economic growth and increased energy use. With the exception of Africa, most regions appear to have missed the low-carbon economy opportunity' provided by the 20082009 global financial crisis. Results suggest a lack of serious environmental effectiveness of regional policy portfolios aiming at reducing CO2 emissions. Highly ambitious energy efficiency and renewable energy policies across all regions are immediately needed. Additionally, absolute reductions in energy use from fossil fuels and resulting CO2 emissions are urgently required in rich regions if we are to align production and consumption patterns with maintaining global warming below the 2 degrees C threshold. (C) 2013 Elsevier Ltd. All rights reserved. |
Retrospective and prospective decomposition analysis of Chinese manufacturing energy use and policy implications (2013) 🗎🗎 | Aims: The industrial sector dominates the China's total energy consumption, accounting for about 70% of energy use in 2010. Hence, this study aims to investigate the development path of China's industrial sector which will greatly affect future energy demand and dynamics of not only China, but the entire world. Scope: This study analyzes energy use and the economic structure of the Chinese manufacturing sector. The retrospective (1995-2010) and prospective (2010-2020) decomposition analyses are conducted for manufacturing sectors in order to show how different factors (production growth, structural change, and energy intensity change) influenced industrial energy use trends in China over the last 15 years and how they will do so up to 2020. Conclusions: The forward looking (prospective) decomposition analyses are conducted for three different scenarios. The scenario analysis indicates that if China wants to realize structural change in the manufacturing sector by shifting from energy-intensive and polluting industries to less energy-intensive industries, the value added average annual growth rates (AAGRs) to 2015 and 2020 should be more in line with those shown in scenario 3. The assumed value added AAGRs for scenario 3 are relatively realistic and are informed by possible growth that is foreseen for each subsector. Published by Elsevier Ltd. |
Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas (2013) 🗎🗎 | Energy consumption and efficiency are important in regional low-carbon economic development and environmental protection. This paper provides a system dynamics and fuzzy multi-objective programming integrated approach for the prediction of energy consumption and CO2 emissions at a regional level. First, a general system dynamics model is constructed to analyze an economy-energy system. To deal with the uncertainties and optimize the parameters in the system, a fuzzy multiple objective programming model is used. This decision support model is then applied to predict the energy consumption of a world heritage area in China during 2010-2020. The results reveal that the energy consumption and CO2 emissions increase dramatically with rapid economic growth. In the scenario herein considered, which is based on the proposed optimization model, a 1932% reduction was found in the energy intensity decreases from 0.88 tce/10(4) RMB to 0.71 tce/10(4) RMB, and a 23.26% reduction in the CO2 emission intensity, from 2.15 t/10(4) RMB to 1.65 t/10(4) RMB. Policy suggestions such as adjusting the industrial structure, enhancing local hydro-power and renewable energy and increasing investment in energy technology and efficiency are indicated for the promotion of a low-carbon oriented economy in world heritage areas. (C) 2012 Elsevier Ltd. All rights reserved. |
The Aggregated Energy Security Performance Indicator (AESPI) at national and provincial level (2014) 🗎🗎 | The energy security performance of Thailand has been assessed using the "Aggregated Energy Security Performance Indicator (AESPI)" for the period 1986-2030. During 1986-1991, AESPI had a sharply decreasing trend from level 9 to level 7 (maximum is 10), implying that the country's energy security status reduced during that period. The energy conservation programmes contributed in maintaining AESPI (at higher than level 6) during 1992-2009. Of the 25 indicators, energy and electricity consumption per capita, final energy intensity (including industrial and transportation sector), losses in transformation, RPR of crude oil and natural gas, net energy import dependency and carbon-di-oxide emission per capita (and per GDP) have a high correlation among them and also have a high weighting factor for AESPI indicator. The use of current policy scenario and low carbon society scenario to estimate AESPI during 2010-2030 showed that AESPI under the low carbon society scenario has an increasing trend at 1.3% annual average improvement rate compared to 0.6% of current policy scenario. AESPI was also developed for Phuket, a province of Thailand, for the period 2001-2009. The results show that Phuket had low AESPI compared to the national level, and this can be improved by promoting electricity conservation, energy efficient equipment use and cost-effective renewable energy projects. The advantages (and differences) of AESPI compared to other energy security indicators in the literature have also been presented and discussed. (C) 2014 Elsevier Ltd. All rights reserved. |
Reduction potential of CO2 emissions in China's transport industry (2014) 🗎🗎 | Energy saving and carbon dioxide emission reduction in China is drawing increasing attention worldwide. China is currently in the stage of industrialization and urbanization, which is characterized by rapid growth of energy consumption. China's transport industry is highly energy-consuming and highly polluting. In 2010, oil consumption in China's transport industry was 38.2% of the country's total oil demand, and accordingly had given rise to increasing amounts of carbon dioxide emissions. This paper explores the main factors affecting carbon dioxide emissions using the Kaya identity. Co-integration method is developed to examine the long-run relationship between carbon dioxide emissions and affecting factors of GDP, urbanization rate, energy intensity and carbon intensity in the transport industry. Both carbon dioxide emission and reduction potential are estimated under different emission-reduction scenarios. Monte Carlo simulation is further used for risk analysis. Results show that under BAU (Business As Usual) scenario, carbon dioxide emission in China's transport industry will reach 1024.24 million tons (Mt) in 2020; while its reduction potential will be 304.59 Mt and 422.99 Mt under moderate emission-reduction scenario and advanced emission-reduction scenario, respectively. Considering this huge potential, policy suggestions are provided to reduce the level of CO2 emissions in China's transport industry. (C) 2014 Elsevier Ltd. All rights reserved. |
China's regional disparities in energy consumption: An input-output analysis (2014) 🗎🗎 | While most of previous studies on China's energy conservation took the huge country as a whole, this manuscript revealed the obvious regional disparities in energy consumption of China's 30 provinces. Based on a hybrid energy input output model, the total energy consumption of different regions was decomposed and compared using three measurements of embodied energy in inter-regional trade: 1) only considered inter-regional energy trade; 2) considered embodied energy in flow-out of final goods and services; 3) considered embodied energy in flow-in of final goods and services. Based on the second and third measurements, the 30 regions were categorized into four groups by their energy intensity and per capita GDP (gross domestic production). Common characteristics of decomposed regional energy intensity are discussed, and policy implication for regional energy conservation is provided. For developed regions with low energy intensities, such as Shanghai, energy conservation should focus on promoting low energy-consuming life style. For under-developed regions with low energy intensities, such as Guangxi, economic development is more urgent than energy conservation. For developing and energy absorbing regions, improving energy efficiency in industries is significant For developing and energy exporting regions, transforming primary energy into high value-added products would be beneficial for economic development and energy conservation. (C) 2014 Elsevier Ltd. All rights reserved. |
Promoting carbon emissions reduction in China's chemical process industry (2014) 🗎🗎 | The chemical process industry is the second largest carbon-emitting sector in China. Therefore, it is extremely urgent and crucial to explore how to reduce carbon emissions in the sector. This paper employs the co-integration method and scenario analysis to investigate how to reduce carbon emissions in the sector. The granger causality test is conducted and the result indicates that all the variables except InPare granger-causes InCE. Moreover, comparing the JJ (Johansen-Juselius) co-integration with the EG (Engle-Granger) co-integration based on the squared residual, the fitting effect and the prediction effect, we find that the EG co-integration method is better. Furthermore, we adopt the EG co-integration and scenario analysis and find that the emission reduction potential of the sector will be 63.9 Mt in 2015 and 180 Mt in 2020 under the middle scenario; and 121.4 Mt in 2015 and 327.9 Mt in 2020 under the advanced scenario. Finally, the paper provides some policy implication. (C) 2014 Elsevier Ltd. All rights reserved. |
The Geopolitical Energy Security Evaluation Method and a China Case Application Based on Politics of Scale (2014) 🗎🗎 | Combining the theories of politics of scale from political geography, security theory from international relations, and energy security theory, and putting the scale conversion of energy contention, geographical relationship and geo-structure in geo-setting, and the three properties of safety in consideration, this paper rebuilds a geo-energy security evaluation model and uses the model to quantitatively evaluate China's geo-oil energy security in the Russian Pacific oil pipeline construction from 1995 to 2010. Five results could be drawn as follows: (1) from the aspect of time, an up-surging Geo-oil Safety Index of China in the Russian Pacific oil pipeline construction indicated an increasingly disadvantage of China in the geo-oil contention by politics of scale. If the United States and South Korea are involved, the competition would be further intensified; (2) from the aspect of geopolitical relationship, a general decrease occurred in the Sino-Japan Energy Competition Index, but a specific increase appeared in the competition of energy imports from Russia, by China and Japan individually; (3) from the aspect of regional strategy of energy export, an obvious downward tendency in Energy Export Strategy Index showed that Russia has changed its export destination off of Europe; (4) from the aspect of geo-security, a relatively steady proportion of China's oil consumption, and a friendly comprehensive strategic partnership of cooperation between China and Russia, reduced the worries of China's geo-oil energy security to some extent; (5) from the aspect of geopolitical structure, the increasing comprehensive national power in China, driven by rapid economic growth, will intensify the geo-oil competition in Northeast Asia. |
Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption (2014) 🗎🗎 | To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individual models (BP, GM(1, 1), triple exponential smoothing model, and polynomial trend extrapolation model) and combination forecasting models to predict China's energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecasting model, it is more effective to evaluate the performance of forecasting model. Firstly, the forecasting model's performances rank in line with ACD. Then ACD is firstly proposed to choose individual models for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based on ACD shows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China's energy consumption will be up to 5.7988 billion tons of standard coal in 2018. |
Environmental and economic consequences of the overexploitation of natural capital and ecosystem services in Xilinguole League, China (2014) 🗎🗎 | The evaluation of natural capital and environmental services has always been an important step in the implementation of sustainable development concepts and policies. The results presented in this study address the demand for environmental support of the economy of Xilinguole League in the Inner Mongolia Autonomous Region as well as the value of its natural and human-made capital. The results show that the reliance of the economy of Xilinguole League on local and imported non-renewable resources (coal and minerals) decreases both the environmental and economic sustainability of the area. Emergy-based performance indicators of the Xilinguole League economy show a low sustainability index (ESI=0.79), though it is higher than for the Chinese economy as a whole (ESI=0.47), as well as a low percentage of renewable resources being used (%REN=0.16, though this is higher than for all of China, 0.09). In contrast, the grassland-based livestock sector shows a higher renewability index (%REN=0.67) and sustainability (ESI=9.61). The emergy exchange ratios (exported emergy/imported emergy) are calculated to be 4.38 for the livestock system and 4.28 for the Xilinguole economy, which is much higher than the value of 1.74 for the overall Chinese economy, indicating uncompensated overexploitation of local systems (meat and coal, respectively). Intensified coal exploitation and intensive cattle grazing are discussed to support the decision-making process for setting local energy policy and ecological compensation. It is proposed that conservation of coal resources (avoiding misuse and moderating excess extraction and trade) and protection of natural grassland capital are more advantageous in emergy terms than the "blind" pursuit of accelerated, economic growth. (c) 2013 Elsevier Ltd. All rights reserved. |
Forecasting energy consumption in China following instigation of an energy-saving policy (2014) 🗎🗎 | China is in a key stage of industrialization and urbanization, which brings a high economic growth rate accompanied by high energy consumption. To alleviate the unsustainable demand for energy consumption, China's government has instigated an energy-saving policy to decrease energy consumption per unit gross domestic product (GDP) so as to improve energy efficiency. Based on analysing historical trends of energy consumption and GDP, we have applied an optimized single-variable discrete grey forecasting model [OSDGM (1, 1)] to measure the instigation effects of the energy-saving policy and forecast whether the planned reduction rate of energy consumption per unit GDP in the implementation stage could be accomplished or not. The results illustrate that China's government has made major progress on energy saving even though the task is tough in the long run. The forecasting results indicate that it is difficult to accomplish the planned reduction rate of energy consumption per unit GDP at both the national and provincial levels. According to the economic growth rate of 2011 and 2012, nearly half of the provinces could not reach their planned reduction rate objectives. These conclusions are very important for China's government both in terms of policy monitoring and development. |
Peak energy consumption and CO2 emissions in China (2014) 🗎🗎 | China is in the processes of rapid industrialization and urbanization. Based on the Kaya identity, this paper proposes an analytical framework for various energy scenarios that explicitly simulates China's economic development, with a prospective consideration on the impacts of urbanization and income distribution. With the framework, China's 2050 energy consumption and associated CO2 reduction scenarios are constructed. Main findings are: (1) energy consumption will peak at 5200-5400 million tons coal equivalent (Mtce) in 2035-2040; (2) CO2 emissions will peak at 9200-9400 million tons (Mt) in 2030-2035, whilst it can be potentially reduced by 200-300 Mt; (3) China's per capita energy consumption and per capita CO2 emission are projected to peak at 4 tce and 6.8 t respectively in 2020-2030, soon after China steps into the high income group. (C) 2014 Elsevier Ltd. All rights reserved. |
Analysis of China's new energy conservation policy and the provincial decomposition of the energy consumption target (2014) 🗎🗎 | To compensate for the limitation of the energy intensity indicator and to constrain energy consumption growth more directly and severely, China had decided to implement the Total Energy Consumption Control (TECC) policy during the 12th Five-Year Plan (FYP). This paper analyzes the situation of energy consumption in 3 scenarios based on the energy consumption trend during the 11th FYP, the energy intensity reduction target during the 12th FYP, and the TECC policy. The Energy Conservation Comprehensive Evaluation System (ECCES) which includes 5 indicators reflecting energy conservation responsibility and 3 indicators reflecting energy conservation difficulty has been built, and Analytic Hierarchy Process (AHP) and Entropy methods are used to determine the weights of 8 basic indicators. According to the ranking of comprehensive evaluation scores of ECCES, 30 provinces are divided into 5 clusters, which are assigned 3%, 3.5%, 4.2%, 5.5%, and 6.8% of controlled energy consumption growth rate, respectively. By comparing and analyzing energy consumption situations in 3 scenarios, we find that the TECC policy has the strongest constraint effect, and can achieve unification of provincial and national energy conservation goals. The provincial decomposition result of energy consumption controlled target of TECC policy roughly remains consistent with the current economic and energy development layout of China that the provinces in southeast coastal developed areas are assigned lower controlled energy consumption growth rate while the backward inland provinces are assigned higher growth rates. (C) 2014 AIP Publishing LLC. |
Government control or low carbon lifestyle? - Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system (2014) 🗎🗎 | This paper explores a novel selective-constrained energy-saving and emission-reduction (ESER) dynamic evolution system, analyzing the impact of cost of conserved energy (CCE), government control, low carbon lifestyle and investment in new technology of ESER on energy intensity and economic growth. Based on artificial neural network, the quantitative coefficients of the actual system are identified. Taking the real situation in China for instance, an empirical study is undertaken by adjusting the parameters of the actual system. The dynamic evolution behavior of energy intensity and economic growth in reality are observed, with the results in perfect agreement with actual situation. The research shows that the introduction of CCE into ESER system will have certain restrictive effect on energy intensity in the earlier period. However, with the further development of the actual system, carbon emissions could be better controlled and energy intensity would decline. In the long run, the impacts of CCE on economic growth are positive. Government control and low carbon lifestyle play a decisive role in controlling ESER system and declining energy intensity. But the influence of government control on economic growth should be considered at the same time and the controlling effect of low carbon lifestyle on energy intensity should be strengthened gradually, while the investment in new technology of ESER can be neglected. Two different cases of ESER are proposed after a comprehensive analysis. The relations between variables and constraint conditions in the ESER system are harmonized remarkably. A better solution to carry out ESER is put forward at last, with numerical simulations being carried out to demonstrate the results. (C) 2014 Elsevier Ltd. All rights reserved. |
Electricity demand and conservation potential in the Chinese nonmetallic mineral products industry (2014) 🗎🗎 | As the high energy-consuming manufacturing industry, electricity consumption of nonmetallic mineral products in China accounted for 7.93% of industrial, 5.84% of national and 1.33% of global electricity consumption in 2010. This study attempts to specify the determinants of sectoral electricity demand, forecast future electricity consumption by creating a model using the Johansen cointegration methodology and estimate the sectoral electricity conservation potential. Results indicate that GDP per capita is the leading force explaining the sectoral electricity consumption increase, while value-added per worker, R&D intensity and electricity price are the main factors contributing to the sectoral electricity consumption decrease. Results demonstrate that sectoral electricity consumption in 2020 will be 369.79-464.83 billion kWh under the low-growth scenario and 530.14-666.39 billion kWh under the high-growth scenario. Moreover, under the low-growth scenario, the sectoral electricity conservation potential in 2020 will be 33.72-95.03 billion kWh, accounting for 0.45-1.26% of China's total electricity demand in 2020; under the high-growth scenario, the sectoral electricity conservation potential in 2020 will be 48.34-136.24 billion kWh, accounting for 0.26-0.74% of world's total electricity consumption in 2010 respectively. Finally, we provide some policy recommendations for encouraging energy conservation in China's nonmetallic mineral products industry. (C) 2014 Elsevier Ltd. All rights reserved. |
Energy demand in China: Comparison of characteristics between the US and China in rapid urbanization stage (2014) 🗎🗎 | China's energy demand has shown characteristics of rigid growth in the current urbanization stage. This paper applied the panel data model and the cointegration model to examine the determinants of energy demand in China, and then forecasts China's energy demand based on the scenario analysis. Results demonstrate an inverted U-shaped relationship between energy demand and economic growth in the long term. In business as usual scenario, China's energy consumption will reach 6493.07 million tons of coal equivalent in 2030. The conclusions can be drawn on the basis of the comparison of characteristics between the US and China. First, energy demand has rigid growth characteristics in the rapid urbanization stage. Second, coal-dominated energy structure of China will lead to the severe problems of CO2 emissions. Third, rapid economic growth requires that energy prices should not rise substantially, so that energy conservation will be the major strategy for China's low-carbon transition. Major policy implications are: first, urbanization can be used as an opportunity for low-carbon development; second, energy price reform is crucial for China's energy sustainability. (C) 2013 Elsevier Ltd. All rights reserved. |
Energy intensity, target level of energy intensity, and room for improvement in energy intensity: An application to the study of regions in the EU (2014) 🗎🗎 | While the previous literature shows that a decline in energy intensity represents an improvement in energy use efficiency, it does not provide a target level of energy intensity, nor what room for improvement in terms of energy intensity could entail. This study establishes an indicator of such room for improvement in terms of energy intensity by measuring the difference between the target level of energy intensity and the actual energy intensity and thereby monitors energy use efficiency. The traditional indicator of energy intensity, defined as energy use over GDP, mainly estimates energy use efficiency, but is a partial effect between the energy input and GDP output. However, our proposed indicator of the room for improvement in terms of energy intensity is the total-factor effects based on the multiple-inputs model. By taking the 27 EU members to investigate their energy use efficiency using the indicator of the room for improvement in terms of energy intensity, this study concludes that an improvement in energy intensity does not fully depend on a decline in energy intensity, and we instead need to confirm whether the room for improvement in terms of energy intensity decreases. This finding is particularly relevant for energy policy-makers. (C) 2013 Elsevier Ltd. All rights reserved. |
China's energy consumption under the global economic crisis: Decomposition and sectoral analysis (2014) 🗎🗎 | It is now widely recognized that there is a strong relationship between energy consumption and economic growth. Most countries' energy demands declined during the economic depression of 20082009 when a worldwide economic crisis occurred. As an export-oriented economy, China suffered a serious exports decline in the course of the crisis. However, it was found that energy consumption continued to increase. Against such a background, this paper aims to assess and explain the factors causing the growth of energy consumption in China. First, we will explain the impact of domestic final use and international trade on energy consumption by using decomposition analysis. Second, embodied energy and its variation across sectors are quantified to identify the key sectors contributing to the growth. Lastly, the policy implications for long-term energy conservation are discussed. The results show that the decline in exports was one of the driving forces for energy consumption reduction in the crisis, but that the growth of domestic demand in manufacturing and construction, largely stimulated by economic stimulus plans, had the opposite effect on energy consumption. International trade contributed to decreasing energy consumption of China during and after the crisis because the structure of exports and imports changed in this period. (C) 2013 Elsevier Ltd. All rights reserved. |
Modelling tools to evaluate China's future energy system - A review of the Chinese perspective (2014) 🗎🗎 | Research efforts to analyse China's future energy system increased tremendously over the past decade. One prominent research area is China's first binding CO2 emission intensity target per unit of GDP (Gross Domestic Product) and its impact on the country's economy and energy system. This paper compares 18 energy modelling tools from ten Chinese institutions. These models have been described in English language publications between 2005 and 2013, although not all are published in peer-reviewed journals. When comparing the results for three main energy system indicators across models, this paper finds that there are considerable ranges in the reference scenarios: (i) GDP is projected to grow by 630-840% from 2010 to 2050, (ii) energy demand could increase by 200-300% from 2010 to 2050, and (iii) CO2 emissions could rise by 160-250% from 2010 to 2050. Although the access to the modelling tools and the underlying data remains challenging, this study concludes that the Chinese perspective, independently from the modelling approach and institution, suggests a rather gradual and long-term transition towards a low carbo`n economy in China. Few reference scenarios include an emission peak or stabilisation period before 2040. While policy scenarios frequently suggest efficiency improvements, a short-term and large-scale introduction of non-fossil power technologies is rarely recommended. (C) 2014 Elsevier Ltd. All rights reserved. |
Comparing World Economic and Net Energy Metrics, Part 1: Single Technology and Commodity Perspective (2015) 🗎🗎 | We translate between biophysical and economic metrics that characterize the role of energy in the economy. Specifically, using data from the International Energy Agency, we estimate the energy intensity ratio (EIR), a price-based proxy for a power return ratio (PRR similar to P-out/P-invested). The EIR is a useful metric, because for most countries and energy commodities, it can indicate the biophysical trends of net energy when data are too scarce to perform an original net energy analysis. We calculate EIR for natural gas, coal, petroleum and electricity for forty-four countries from 1978 to 2010. Global EIR values generally rise from 1978 to 1998, decline from 1998 to 2008 and then slightly rebound. These trends indicate one interpretation of the net energy of the world economy. To add perspective to our recent, but short, time series, we perform the same calculations for historical England and United Kingdom energy prices to demonstrate that a given energy price translates to different PRRs (EIR in this case) depending on the structure of the economy and technology. We review the formulation of PRRs and energy return ratios (ERR similar to E-out/E-invested) to indicate why PRRs translate to (the inverse of) energy prices and ERRs translate to (the inverse of) energy costs. We show why for any given value of an ERR or PRR, there is not a single corresponding energy cost or price, and vice versa. These principles in turn provide the basis to perform better modeling of future energy scenarios (e.g., low-carbon transition) by considering the relationship between economic metrics (cost and price) and biophysical metrics (energy and power return ratios) based on energy, material and power flows. |
A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model (2015) 🗎🗎 | To accurately predict the consumption of clean energy in China, a grey dynamic model is constructed by taking economic growth and environmental regulation as exogenous variables. The Nash equilibrium idea-based optimization method is proposed to solve the parameters of the model so as to obtain better modeling effects than that of the traditional model. The empirical results show that: (1) a spontaneous increasing mechanism of the clean energy consumption has not yet formed in China; (2) both GDP and effluent charge play a positive role in accelerating clean energy consumption in China, but effluent charge has a stronger effect than GDP; (3) clean energy consumption in China is expected to stably increase at an annual rate of 5.73 % averagely in 2012-2020. By 2020, clean energy consumption in China is expected to reach 454.55 million tons of standard coal. The study also provides some policy suggestions of promoting clean energy consumption based on the empirical analysis conclusions. |
Urban energy consumption and CO2 emissions in Beijing: current and future (2015) 🗎🗎 | This paper calculates the energy consumption and CO2 emissions of Beijing over 2005-2011 in light of the Beijing's energy balance table and the carbon emission coefficients of IPCC. Furthermore, based on a series of energy conservation planning program issued in Beijing, the Long-range Energy Alternatives Planning System (LEAP)-BJ model is developed to study the energy consumption and CO2 emissions of Beijing's six end-use sectors and the energy conversion sector over 2012-2030 under the BAU scenario and POL scenario. Some results are found in this research: (1) During 2005-2011, the energy consumption kept increasing, while the total CO2 emissions fluctuated obviously in 2008 and 2011. The energy structure and the industrial structure have been optimized to a certain extent. (2) If the policies are completely implemented, the POL scenario is projected to save 21.36 and 35.37 % of the total energy consumption and CO2 emissions than the BAU scenario during 2012 and 2030. (3) The POL scenario presents a more optimized energy structure compared with the BAU scenario, with the decrease of coal consumption and the increase of natural gas consumption. (4) The commerce and service sector and the energy conversion sector will become the largest contributor to energy consumption and CO2 emissions, respectively. The transport sector and the industrial sector are the two most potential sectors in energy savings and carbon reduction. In terms of subscenarios, the energy conservation in transport (TEC) is the most effective one. (5) The macroparameters, such as the GDP growth rate and the industrial structure, have great influence on the urban energy consumption and carbon emissions. |
NEW APPROACH TO ENERGY INTENSITY IN THE EU - TOTAL ENERGY AND CARBON COST APPROACH (2015) 🗎🗎 | The main objective of this manuscript is to look anew at Energy intensity, an indicator often used as measure of efficient economic development, which currently does not include any environmental component. The authors compared results obtained on the same sample by using Energy Intensity, a well known indicator, and Index of Energy Intensity Cost, an improved indicator suggested by the authors. The new indicator includes carbon emission cost, since 96% of carbon emission is result of energy consumption. Besides the introduction of a new component, the authors changed the nature of the indicator itself. Namely, traditional Energy intensity is based on physical values. The new indicator is expressed in monetary values - more suitable for monitoring economic development. Monitoring in the EU27 region, by using two chosen indicators, shows opposite results. Measuring of Energy Intensity, EU27 region showed positive trends. Measuring of Index of Energy Intensity Cost showed negative trends. Further modifications to Energy intensity are needed. |
Green growth in oil producing African countries: A panel data analysis of renewable energy demand (2015) 🗎🗎 | Renewable energy has been considered as the solution to the hydra-headed problems of energy security, energy access and climate change, especially in Africa. In addition, renewable energy sources, such as the sun, wind, wave and waste abound in Africa are in need of investment. In order to provide both policy and investment guide, this study investigates the drivers of renewable energy demand in oil-producing African countries. Three panel data models - a random effect model, a fixed effects model and a dynamic panel data model - are used to estimate renewable energy demand with a comprehensive set of determinants. The estimation results indicate that the main drivers of renewable energy in oil-producing African countries are real income per capita, energy resource depletion per capita, carbon emissions per capita and energy prices. The study recommends that policies should encourage the consumption of commercial sources of renewable energy to attract the needed investments. (C) 2015 Elsevier Ltd. All rights reserved. |
Structural Decomposition Analysis of Carbon Emissions and Policy Recommendations for Energy Sustainability in Xinjiang (2015) 🗎🗎 | Regional carbon dioxide emissions study is necessary for China to realize the emissions mitigation. An environmental input-output structural decomposition analysis (IO-SDA) has been conducted in order to uncover the driving forces for the increment in energy-related carbon dioxide emissions in Xinjiang from both production and final demands perspectives from 1997 to 2007. According to our research outcomes, emissions increase can be illustrated as a competition between consumption growth (per capita GDP) and efficiency improvement (carbon emission intensity). Consumption growth have caused an increase of 109.98 Mt carbon dioxide emissions during 1997 to 2007, and efficiency improvement have caused a 97.03 Mt decrease during the same period. Per capita GDP is the most important driver for the rapid emission growth, while carbon emission intensity is the significant contributor to offset these increments. In addition, production structure changes performed as a new major driver for the steep rise in carbon dioxide emissions in recent years (2002-2007), indicating that the rapid emission growth in Xinjiang is the result of structural changes in the economy making it more carbon-intensive. From the viewpoint of final demands, fixed capital formation contributed the highest carbon dioxide emission, followed by inter-provincial export and urban residential consumption; while inter-provincial imports had the biggest contributions to offset emission increments. Based on our analysis results, Xinjiang may face great challenges to curb carbon dioxide emissions in the near future. However, several concrete mitigation measures have been further discussed and then raised by considering the regional realities, aiming to harmonize regional development and carbon dioxide emissions reduction. |
Clean-energy policies and electricity sector carbon emissions in the U.S. states (2015) 🗎🗎 | State governments in the United States have enacted various clean-energy policies to decarbonize electric utilities, diversify energy supplies, and stimulate economic development. With a panel data set for 48 continental states from 1990 to 2008, fixed-effect panel regressions are estimated to test the impacts of clean-energy policies on total carbon emissions, electricity consumption, and carbon intensity. The results indicate that supply-side policy tools, such as RPS and EERS, are negatively correlated with carbon intensity in the electricity sector. More aggressive policies are needed to reduce total carbon emissions. (C) 2015 Elsevier Ltd. All rights reserved. |
Modelling and forecasting the demand for natural gas in Pakistan (2015) 🗎🗎 | This study examines both the short and long-term dynamics of natural gas consumption in Pakistan through an econometric model, sector-specific income, price and cross price elasticities of natural gas demand are estimated over the period 1978-2011. The estimated income elasticities indicate that real GDP per capita exerts a larger impact on gas consumption as compared to its price. The price and cross price elasticities are relatively low, indicating consumers' indifference in Pakistan towards price escalation. They neither decrease gas consumption nor try to explore less expensive substitutes for natural gas. A validation of the estimated demand equations is performed, showing high degree of accuracy through tracking the historical data. In order to determine the future outlook of natural gas demand, sectoral equations are simulated as baseline. Both moderate and extreme demand scenarios are projected for the period 2012-2020. Ex-post simulation, resulting through baseline scenario, suggests that the power sector is more likely to occupy top position in terms of natural gas consumption. It is expected that the natural gas consumption would reach 734,062 MMCFT by 2020, followed by the transport sector (238,943 MMCFT); industrial sector (541,869 MMCFT); residential sector (304,821 MMCFT) and the commercial sector (72,784 MMCFT). Furthermore, simulation results based on moderate and extreme scenarios reveal that a deliberate increase in natural gas prices reduces per capita natural gas consumption significantly over the forecast time horizon (2012-2020). The findings of this study have significant implications with respect to energy conservation and economic development. Particularly, price and income elasticities have practical relevance for appropriate pricing and income policies. Additionally, forecast results can provide useful support for designing an appropriate infrastructure and investment plan with reference to gas market in future. (C) 2015 Elsevier Ltd. All rights reserved. |
Decomposing changes in CO2 emission inequality over time: The roles of re-ranking and changes in per capita CO2 emission disparities (2015) 🗎🗎 | This paper analyzes the effects of changes in country ranking and per capita CO2 emissions on the change in CO2 emission inequality over time. For this purpose, we introduce a three-term decomposition of the change occurring in the Gini index of per capita CO2 emissions when moving from an initial to a final per capita CO2 emission distribution. The decomposition explains the link between the inequality trend and the changes in country ranking, population size, and per capita CO2 emission disparities. We show that all components of inequality change can be further decomposed by subgroup. This provides analysts with a decomposition technique detecting the within-group and between-group contributions to each component of inequality change. The decomposition is used to analyze the change in per capita CO2 emission inequality in Europe over the 1991-2011 period. (c) 2015 Elsevier B.V. All rights reserved. |
Threshold characteristic of energy efficiency on substitution between energy and non-energy factors (2015) 🗎🗎 | The elasticity of substitution between energy and non-energy is a key parameter in quantifying distributional impacts of energy and environmental policies. However, the empirical results of energy substitution are contradictory. The source of discrepancies in the results remains controversial, which provides ample motivation for further interpretation. The present paper is to identify the discrepancies of energy and non-energy substitution from the perspective of energy efficiency. The case study is based on the elasticities between energy and non-energy of China's 36 industrial sectors during the period 1994-2008. The results show that there is overwhelming evidence of a threshold effect which separates the substitution of energy and non-energy, on the basis of energy efficiency. The findings imply that when the same energy-saving capital is invested to the industries, the energy intensive sectors are of more energy-saving potential. (c) 2015 Elsevier B.V. All rights reserved. |
How might China achieve its 2020 emissions target? A scenario analysis of energy consumption and CO2 emissions using the system dynamics model (2015) 🗎🗎 | According to-the Chinese government's CO2 reduction commitment, the production and consumption of traditional and renewable energy in China from 2001 to 2012, this paper forecasts the energy consumption, gross CO2 emissions and CO2 emission intensity in China from 2013 to 2020 via system dynamics simulation. Coupled with the energy system and renewable energy policy factors, the effects of different economic growth rates and policy factors on the energy consumption were estimated. The results showed that in different economic growth rates scenarios, total energy consumption and CO2 emissions increased by 36,140.75 kWh and more than 10,000 billion kg in 10% GDP growth rate scenario than in the 7% scenario in 2020. The CO2 emission per GDP decreased by 52% in 2020 as compared to 2005 under the scenario of 10% GDP growth rate. Under different renewable energy policy factors' scenarios, the coal energy consumption decreased by 2.3% in scenario of 5% policy factor as compared to 1% policy factor scenario. The CO2 emissions reduced by 3321.94 billion kg in 2020 under the scenario of 5% policy factor as compared to that in the scenario of 1% policy factor. The CO2 emission per GDP reduced by 47-50% based on the 2005 level in different scenarios of renewable energy policies. We found that with an increase in GDP, total energy consumption and CO2 emissions increased in the base scenario. Higher economic growth rates led to an increase in energy consumption, including both the traditional and renewable energy resources. In stark contrast to total CO2 emission, however, CO2 emission intensity decreased with an increase in economic growth rate. The renewable energy policy improved the renewable energy development, reduced CO2 emissions and CO2 emission intensity. It was More effective in energy saving and CO2 reduction than the high growth rate scenarios. The results indicated that China is highly likely to achieve its CO2 emission reduction goal under different simulated scenarios. Besides, the energy distribution was similar in different scenarios. Coal Was the major energy source which amounted to more than 70% of the total energy consumption. Finally, we conclude with important policy implications according to our simulations results. (C) 2015 Elsevier Ltd. All rights reserved. |
Study on the Optimization of the Industrial Structure in a Mining Economic Region: Taking Carbon Emissions as a Restriction (2015) 🗎🗎 | In the first decade of the 21st century, as a typical coal province and mining economic region, Shanxi province made a great contribution to the national economic construction and reform. At the same time, coal mining has caused serious damage to the ecological environment, excessive use of resources, the deterioration of the ecological environment and a decline in the sustainable development capacity. Overreliance on a resource-based economy leads to problems such as a poorly developed economy and a single industrial structure. In this context, Shanxi province has to take actions to transform its industrial structure into a low-carbon development model as soon as possible. This paper measures the values of the consumption coefficients of capital investments, electric power and CO2 emissions by establishing a Grey Model (1, 1) using the data from 2007 to 2011 and designing the optimization scheme of the three industrial structures from 2015 to 2020 by establishing a grey dynamic linear programming model for Shanxi province. The results show that the industrial structure in Shanxi province needs to be improved. It is revealed that the proportion of three industries in Shanxi province should change from 6:46:48 in 2015 to 6:41:54 in 2020. At the same time, among the seven largest sectors in terms of carbon emissions in the secondary industry, Shanxi government may continue to promote the development of the coal-bed methane and the coal chemical industry in the coal industry, while the other six sectors should be limited. |
Carbon emissions trading scheme exploration in China: A multi-agent-based model (2015) 🗎🗎 | To develop a low-carbon economy, China launched seven pilot programs for carbon emissions trading (CET) in 2011 and plans to establish a nationwide CET mechanism in 2015. This paper formulated a multi-agent-based model to investigate the impacts of different CET designs in order to find the most appropriate one for-China. The proposed bottom-up model includes all main. economic agents in a general equilibrium framework. The simulation results indicate that (1) CET would effectively reduce carbon emissions, with a certain negative impact on the economy, (2) as for allowance allocation, the grand-fathering rule is relatively moderate, while the benchmarking rule is more aggressive, (3) as for the carbon price, when the price level in the secondary CET market is regulated to be around RMB 40 per metric ton, a satisfactory emission mitigation effect can be obtained, (4) the penalty rate is suggested to be carefully designed to balance the economy development and mitigation effect, and (5) subsidy policy for energy technology improvement can effectively reduce carbon emissions without an additional negative impact on the economy. The results also indicate that the proposed novel model is a promising tool for CET policy making and analyses. (C) 2015 Elsevier Ltd. All rights reserved. |
Research on a Carbon Reduction Optimization Model for a Megalopolis Based on Land-Use Planning and ICCLP Method (2015) 🗎🗎 | For the primary purpose of minimizing carbon dioxide emissions in a megalopolis, an optimization model that remarkably reduces carbon emissions for the megalopolis, which is based on the inexact chance-constrained linear programming (ICCLP) method and incorporates interval linear programming (ILP), and chance constrained programming (CCP), has been constructed. The corresponding net emissions of carbon dioxide results in probability levels of default equalling p(i)=0.01, 0.05, 0.1 are [1,383.379, 1,825.311]x 10(4), [1,357.728, 1,800.841]x10(4), [1,338.671, 1,780.060]x10(4) tons in the megalopolis in 2015. Besides, the areas of different types of carbon-sinkable land of various cities within planned regions are obtained. The volume of energy consumption of dominating energy consumption industries in planned regions equals [965.52, 1,136.79]x10(4) tons, which is reduced by [14.97, 22.09]%, while the intensity of energy consumption is decreased by [18.00, 20.00]% compared with that in 2010. Meanwhile, the intensities of carbon emissions are reduced by 20.00%, 19.00%, and 18.08%, respectively, under the conditions of p(i)=0.01, 0.05, 0.1. It meets the requirements that carbon intensity shall be cut down by 17.00% in 2015 compared with that in 2010, which was proposed by "The 12th Five-Year Initiative of Controlling Greenhouse Gas Emissions!' The annual average GDP growth rate is 12.20%, reaching 9.79x 10(11) yuan in total, higher than the expected annual growth rate of 10% in accordance with the development objective of "12th Five-Year" plan. |
Energy conservation potential in China's petroleum refining industry: Evidence and policy implications (2015) 🗎🗎 | China is currently the second largest petroleum refining country in the world due to rapid growth in recent years. Because the petroleum refining industry is energy-intensive, the rapid growth in petroleum refining and development caused massive energy consumption. China's urbanization process will guarantee sustained growth of the industry for a long time. Therefore, it is necessary to study the energy conservation potential of the petroleum industry. This paper estimates the energy conservation potential of the industry by applying a cointegration model to investigate the long-run equilibrium relationship between energy consumption and some factors such as energy price, enterprise scale, R&D investment and ownership structure. The results show that R&D investment has the greatest reduction impact on energy intensity, and the growth of market participants (i.e. the decline of the share of state-owned companies) can improve energy efficiency of this industry. Under the advanced energy-saving scenario, the accumulated energy conservation potential will reach 230.18 million tons of coal equivalent (tce). Finally, we provide some targeted policy recommendations for industrial energy conservation. (C) 2014 Elsevier Ltd. All rights reserved. |
How to Move China toward a Green-Energy Economy: From a Sector Perspective (2016) 🗎🗎 | With China's rapid economic growth, energy-related CO2 emissions have experienced a dramatic increase. Quantification of energy-related CO2 emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended "Kaya identity" and "IPAT model" with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO2 emissions in China during 1995-2009. Empirical results show that the main obstacle that hinders China's transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO2 emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China. |
Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods (2016) 🗎🗎 | The 2012 Doha meeting established the continuation of the Kyoto protocol, the legally-binding global agreement under which signatory countries had agreed to reduce their carbon emissions. Contrary to this assumed obligation, all G20 countries with the exception of France and the UK saw significant increases in their CO2 emissions over the last 25 years, surpassing 300% in the case of China. This paper attempts to forecast the evolution of carbon dioxide emissions in Bahrain over the 2012-2021 decade by employing seven Automated Forecasting Methods, including the exponential smoothing state space model (ETS), the Holt-Winters Model, the BATS/TBATS model, ARIMA, the structural time series model (STS), the naive model, and the neural network time series forecasting method (NNAR). Results indicate a reversal of the current decreasing trend of pollution in the country, with a point estimate of 2309 metric tons per capita at the end of 2020 and 2317 at the end of 2021, as compared to the 1934 level achieved in 2010. The country's baseline level corresponding to year 1990 (as specified by the Doha amendment of the Kyoto protocol) is approximately 25.54 metric tons per capita, which implies a maximum level of 20.96 metric tons per capita for the year 2020 (corresponding to a decrease of 18% relative to the baseline level) in order for Bahrain to comply with the protocol. Our results therefore suggest that Bahrain cannot meet its assumed target. |
Will the Steam Coal Price Rebound under the New Economy Normalcy in China? (2016) 🗎🗎 | The steam coal price in China has been continuously decreasing since the second half of 2012. Constant low price of coal will accelerate the development of thermal power, cause more serious air pollution problems, and bring adverse influence to China's energy reformation in the future. Therefore, analyzing the factors underlying the phenomenon of the decreasing steam coal price is significant. In this study, we first qualitatively analyze five main factors, namely, economy, supply, demand, substitutes, and port stocks. On the basis of the relationships among these five factors, we obtain the causality diagram and the system flow diagram of coal price for further quantitative research. Then, we conduct an empirical analysis using the system dynamics (SD) method and determine the simulated price from 2012 to 2017. Finally, we discuss the running results and come to the conclusion that the steam coal price will continue to decrease under the combined actions of the five main factors and it will not rebound in the near future. |
Technical change, inter-factor and inter-fuel substitution possibilities in Pakistan: a trans-log production function approach (2016) 🗎🗎 | Energy consumption in Pakistan increased significantly over the last two decades primarily due to industrialization. In order to meet the growing energy needs, the government adopted short-term policies by setting up thermal power projects, which are more expensive both in terms of fiscal cost and environmental damages as compared to hydro power projects. The former has exposed Pakistan to international oil price shocks and environmental degradations. This study, therefore, attempts to analyze technical change using trans-log production function by employing non-energy factors (i.e. capital and labor) and energy factors (i.e. petroleum and natural gas) to estimate elasticity of substitution. The purpose of the study is to provide policy suggestions to the government on how to achieve high economic growth vis-a-vis improved energy security and environmental sustainability. The results reveal that capital energy and labor energy are substitutes, thereby suggesting the need for an increased focus on technological advancement and skilled employment generation to conserve energy and reduce CO2 emission. A gradual elimination of energy subsidies, which will make energy price reflects its true cost, is required to further discourage increased energy consumption and promote capital intensive production methods. A scenario analysis based on the elasticity of substitution between capital energy and petroleum gas shows the potential of energy conservation and emission mitigation. The results further suggest the importance of a diversified energy supply mix characterized by abundant hydro and other renewable energy resources alongside natural gas. (C) 2016 Elsevier Ltd. All rights reserved. |
Impact of International Oil Price on Energy Conservation and Emission Reduction in China (2016) 🗎🗎 | In the context of "new normal" economy and frequent "haze", the strategy of energy conservation and emission reduction aiming to lower costs and reduce pollution is currently still a major strategic direction in China and the world, and will remain so for some time in the future. This paper uses the annual data of West Texas Intermediate (WTI) crude oil price in 1987-2014 as samples. We firstly present the direction and mechanism of the influence of oil price change on total consumption of every kind of energy by path analysis, and then consider establishing a Structural Vector Autoregression model of energy conservation and emission reduction in three statuses. Research shows that if the international oil price increases by 1%, the energy consumption per GDP and carbon dioxide emission increase by 0.092% and 0.053% respectively in the corresponding period. In the status of high energy consumption and high emission, if the international oil price increases by 1%, the energy consumption per GDP and carbon dioxide emission increase by 0.043% and 0.065% respectively in the corresponding period. In the status of low energy consumption and low emission, if the international oil price increases by 1%, the energy consumption per GDP per unit increases by 0.067% and carbon dioxide emission decreases by 0.085% in the corresponding period. |
Analysis of the Driving Factors and Contributions to Carbon Emissions of Energy Consumption from the Perspective of the Peak Volume and Time Based on LEAP (2016) 🗎🗎 | Studying the driving factors and contributions of carbon emissions peak volume and time is essential for reducing the cumulative carbon emissions in developing countries with rapid economic development and increasing carbon emissions. Taking Jilin Province as a case study, four scenarios were set in this paper respectively: Business as Usual Scenario (BAU), Energy-Saving Scenario (ESS), Energy-Saving and Low-Carbon Scenario (ELS), and Low-Carbon Scenario (LCS). Furthermore, the carbon emissions were predicted according to the energy consumption based on the application of LEAP system. The research result showed that the peak time of carbon emissions would appear in 2045, 2040, 2035 and 2025 under the four different scenarios, respectively. The peak volumes would be 489.8 Mt, 395.2 Mt, 305.3 Mt and 233.6 Mt, respectively. The cumulative emissions by 2050 are respectively 15.632 Bt, 13.321 Bt, 10.971 Bt and 8.379 Bt. According to the forecasting, we analyzed the driving factors of and contributions to carbon emissions peak volume and time. On the premise of moderate economic growth, the "structural emission reduction", namely the adjustment of industrial structure and energy structure, and "technology emission reduction", namely the reduction of energy intensity and carbon emission coefficient could make the peak volume reduced by 20%-52% and cumulative carbon emissions (2050) reduced by 15%-46% on the basis of BAU. Meanwhile, controlling the industrial structure, energy structure and energy intensity could make carbon emissions reach the peak 5-20 years ahead of the time on the basis of BAU. Controlling GDP, industrial structure, energy structure, energy intensity and coefficient of carbon emissions is the feasible method to adjust the carbon emissions peak volume and time in order to reduce the cumulative emissions. |
Carbon dioxide-emission in China's power industry: Evidence and policy implications (2016) 🗎🗎 | The Logarithmic Mean Divisia Index (LMDI) and scenario analysis have been applied respectively to analyze the impact of CO2-emission and its potential reduction in China's power industry. According to the results of LMDI, there are six factors that affect the carbon emission in the power industry. Electricity intensity (El), and economic activity (EA) are the primary driving factors for the increment in emissions, accounting for 42.33% and 57.05% of the total increment during 1985 to 2011. Results also demonstrated that energy efficiency (EE) contributed 13.54% abatement during 1985 to 2011, and will play a key role in emission abatement in the future. Furthermore, the paper estimate the trend of power sector's carbon dioxide emission under three scenarios (basic, moderate and optimum) in order to determine the mitigation potential. The potential mitigation rate will equal to 22.03% and 37.57% in 2020 in Case A and Case B respectively in 2020. (C) 2016 Elsevier Ltd. All rights reserved. |
Sustainable energy development scenario forecasting and energy saving policy analysis of China (2016) 🗎🗎 | The development of renewable energy, which is an important way to solve the environmental protection, has now become a research focus around the world. As a developing country with rapid growth, China gains the rapid development of economy, but the environmental pollution is becoming increasingly serious in recent years. Electricity industry plays an important role for energy-saving and emission-reduction in China, in order to reduce the environment pollution, it needs to consider how the electricity consumption affects the carbon emissions. In this paper, a stochastic impacts by regression on population, affluence, and technology (STRIPAT) model was built, which estimates the relationship between the carbon emissions, population, GDP per capita, electricity consumption and energy consumption. It found that population, GDP per capita and the ratio of electricity consumption in energy consumption are change 1%, the carbon emissions will change 1.207%, 0.901% and 1.188% respectively. Based on the relationship, there electricity energy development scenarios were assumed and analyzed, and it found that the way to reduce carbon emissions should consider improving technical ability, which should accounting for improving the ratio of electricity power ratio in the energy consumption, improving the efficiency of using fossil energy and the development of renewable energy. (C) 2016 Elsevier Ltd. All rights reserved. |
A structural decomposition analysis of global energy footprints (2016) 🗎🗎 | Understanding the drivers of past and present energy consumption trends is important for a range of stakeholders, including governments, businesses and international development organizations, in order to prepare for impacts on global supply chains caused by changes in future energy price or availability shocks. In this paper we use environmentally-extended input-output tables to: (a) quantify the long-term drivers that have led to diversified energy footprint profiles of 186 countries around the world from 1990 to 2010; (b) identify which countries and sectors recorded an increase or decrease in energy footprints during this time period; (c) highlight the effect of international outsourcing of energy intensive production processes by decomposing the structural and spatial change in energy footprints; and (d) discuss the implications for national economic policy for the identified drivers. To this end, we use a detailed Multi-Regional Input-Output database and three prevalent structural decomposition analysis methods. To reduce biases in the results due to time lapse and currency variations, we convert input-output tables to common US$ and 1990-constant prices. This study provides a broad overview of the magnitude and distribution of the drivers for energy footprints across countries. The results of this study demonstrate that for almost all countries affluence and population growth are driving energy footprints worldwide, which is in part counteracted by the retarding effect of industrial energy intensity. In particular, this study demonstrates that with increasing per-capita GDP, the total energy footprint of a country is increasingly concentrated on imports or consumption. (C) 2015 Published by Elsevier Ltd. |
Estimation of the energy efficiency in Chinese provinces (2016) 🗎🗎 | China is one of the largest energy consumers and CO2 emitters globally. The growth rate of energy consumption in China is about 6 % per year, and it consumed 21 % of the world's total energy in 2012. In recent years, the Chinese government decided to introduce several energy policy instruments to promote energy efficiency. For instance, the reduction targets for the level of energy intensity have been defined for provinces in China. However, energy intensity is not an accurate proxy for energy efficiency because changes in energy intensity are a function of changes in several socioeconomic factors. In this paper, we present an empirical analysis on the estimation of the persistent and transient energy efficiency of Chinese provinces by employing a log-log aggregate energy demand frontier model. The model is estimated by using data on 29 provinces observed over the period 2003 to 2012. Several econometric model specifications for panel data are used: the random effects model and the true random effects model along with other versions of these models. Our analysis shows that energy intensity cannot measure accurately the level of efficiency in the use of energy in Chinese provinces. Further, our empirical analysis shows that the average value of the persistent energy efficiency is around 0.81 whereas the average value of the transient energy efficiency is relatively high and shows a value of approximately 0.97. By improving the level of efficiency in the use of energy to 100 %, the total energy consumption in China would decrease by approximately 1000 Mtce, which corresponds to 25 % of total energy consumption in 2012. |
Forecasting the Allocation Ratio of Carbon Emission Allowance Currency for 2020 and 2030 in China (2016) 🗎🗎 | Many countries and scholars have used various strategies to improve and optimize the allocation ratios for carbon emission allowances. This issue is more urgent for China due to the uneven development across the country. This paper proposes a new method that divides low-carbon economy development processes into two separate periods: from 2020 to 2029 and from 2030 to 2050. These two periods have unique requirements and emissions reduction potential; therefore, they must involve different allocation methods, so that reduction behaviors do not stall the development of regional low-carbon economies. During the first period, a more deterministic economic development approach for the carbon emission allowance allocation ratio should be used. During the second period, more adaptive and optimized policy guidance should be employed. We developed a low-carbon economy index evaluation system using the entropy weight method to measure information filtering levels. We conducted vector autoregressive correlation tests, consulted 60 experts for the fuzzy analytic hierarchy process, and we conducted max-min standardized data processing tests. This article presents first- and second-period carbon emission allowance models in combination with a low-carbon economy index evaluation system. Finally, we forecast reasonable carbon emission allowance allocation ratios for China for the periods starting in 2020 and 2030. A good allocation ratio for the carbon emission allowance can help boost China's economic development and help the country reach its energy conservation and emissions reduction goals. |
Analysis and Modeling for China's Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission (2016) 🗎🗎 | The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure. |
Energy needs for Morocco 2030, as obtained from GDP-energy and GDP-energy intensity correlations (2016) 🗎🗎 | We present forecasts of the energy consumption of Morocco towards 2030. Two models have been developed and their results compared: one based on the energy intensity (IE) and another one on a link with the country urbanization rate (URB). The IE model allowed to segment energy consumption in four posts while the URB model only in two posts. For the sensitivity analysis to economic growth, three future GDP evolution scenarios are proposed. The retrospective correlations of both models are excellent but their future extrapolations finish in slightly different results. Through their correlation to electricity consumption, peak power forecasts are also presented. A forecast of the country energy intensity is commented. As the average yearly increase of electricity should still be between 4.9% and 7.1% during 2020-2030, the electric equipment program continuation after 2020 must soon be clarified and avoid the former implementation delays. As the white combustibles needs should yearly increase between 6.3% and 7.8% in 2020-2030, electrical equipment programs should also make provisions for the case of deployment of electric cars. Butane subsidies widen the gap with other fuels and must be removed very soon possible to reduce the growth of its consumption and energy intensity. (C) 2015 Elsevier Ltd. All rights reserved. |
This paper performs energy model hindcasting which compared the historical energy simulation results with the,observations. We used one of the Integrated Assessment Models and simulated global historical energy consumption from 1981 to 2010 associated with exogenous socioeconomic assumptions, as is typically performed for future scenario. The simulation period was chosen with consideration of data availability and structural constancy of the model. Based on comparison with observations, there are three main findings. First, the global aggregated primary energy shows high reproducibility. In terms of energy source specific results, the fitness in electricity, coal, and biomass consumption were high. However, that of crude oil and natural gas is lower than others. This could be due to the price elasticity assumption, implying that the model can be improved with regard to this element. Second, the reproducibility increases as the simulation is close to the base year 2005. Third, although the global aggregated information shows high reproducibility, some disaggregated regions have lower reproducibility. Furthermore, high income countries tend to show higher reproducibility than in low income countries. Given the uncertainties in the ability of IAMs to reproduce certain aspects of the energy system, forecasts must be treated with caution. (C) 2016 Elsevier Ltd. All rights reserved: | |
Decomposition of the factors influencing export fluctuation in China's new energy industry based on a constant market share model (2017) 🗎🗎 | In this study, we investigate the factors influencing the fluctuation in the export trade of China's new energy industry, and the export trade fluctuations in its subdivision industries, using a constant market share (CMS) model and UN Comtrade export data for China's new energy industry from 1996 to 2014. The study reveals that the import demands of the international market for China's new energy industry dominate the fluctuations in the export trade of the industry. Moreover, there are different reasons for the fluctuations in the different export markets of China's new energy industry as well as the export trade fluctuations in the subdivision industries. To promote the development of China's new energy industry, numerous policy suggestions are proposed. These include culturing domestic markets, solving problems in the grid-connection and consumption of new energies, and carrying out international cooperation. Some corresponding suggestions for subdivision industries of the new energy industry are also presented. |
Theoretical Explanations for the Inverted-U Change of Historical Energy Intensity (2017) 🗎🗎 | Historical experience shows that the economy-wide energy intensity develops nonmonotonically like an inverted U, which still lacks direct theoretical explanations. Based on a model of structural change driven by technological differences, this paper provides an attempt to explore the underlying mechanisms of energy intensity change and thus to explain the above empirical regularity accompanied by structural transformation, through introducing a nested constant elasticity of substitution production function with heterogeneous elasticities of substitution. According to some reasonable assumptions, this extended model not only describes the typical path of structural change but also depicts the inverted-U development of economy-wide energy intensity. With the availability of Swedish historical data, we take calibration and simulation exercises which confirm the theoretical predictions. Furthermore, we find that: (1) elasticities of substitution may affect the shapes and peak periods of the inverted-U curves, which can explain to a certain extent the heterogeneous transitions of economy-wide energy intensity developments in different economies; and (2) over long periods of time, the economy-wide energy intensity determined by the initial industrial structure and sectoral energy intensity tends to grow upward, while structure change among sectors provides a driving force on reshaping this trend and turning it downward. |
Influencing factors and prediction of carbon dioxide emissions using factor analysis and optimized least squares support vector machine (2017) 🗎🗎 | As the energy and environmental problems are increasingly severe, researches about carbon dioxide emissions has aroused widespread concern. The accurate prediction of carbon dioxide emissions is essential for carbon emissions controlling. In this paper, we analyze the relationship between carbon dioxide emissions and influencing factors in a comprehensive way through correlation analysis and regression analysis, achieving the effective screening of key factors from 16 preliminary selected factors including GDP, total population, total energy consumption, power generation, steel production coal consumption, private owned automobile quantity, etc. Then fruit fly algorithm is used to optimize the parameters of least squares support vector machine. And the optimized model is used for prediction, overcoming the blindness of parameter selection in least squares support vector machine and maximizing the training speed and global searching ability accordingly. The results show that the prediction accuracy of carbon dioxide emissions is improved effectively. Besides, we conclude economic and environmental policy implications on the basis of analysis and calculation. |
The Low-Carbon Transition toward Sustainability of Regional Coal-Dominated Energy Consumption Structure: A Case of Hebei Province in China (2017) 🗎🗎 | CO2 emission resulted from fossil energy use is threatening human sustainability globally. This study focuses on the low-carbon transition of Hebei's coal-dominated energy system by estimating its total end-use energy consumption, primary energy supply and resultant CO2 emission up to 2030, by employing an energy demand analysis model based on setting of the economic growth rate, industrial structure, industry/sector energy consumption intensity, energy supply structure, and CO2 emission factor. It is found that the total primary energy consumption in Hebei will be 471 and 431 million tons of coal equivalent (tce) in 2030 in our two defined scenarios (conventional development scenario and coordinated development scenario), which are 1.40 and 1.28 times of the level in 2015, respectively. The resultant full-chain CO2 emission will be 1027 and 916 million tons in 2030 in the two scenarios, which are 1.24 and 1.10 times of the level in 2015, respectively. The full-chain CO2 emission will peak in about 2025. It is found that the coal-dominated situation of energy structure and CO2 emission increasing trend in Hebei can be changed in the future in the coordinated development scenario, in which Beijing-Tianjin-Hebei area coordinated development strategy will be strengthened. The energy structure of Hebei can be optimised since the proportion of coal in total primary energy consumption can fall from around 80% in 2015 to below 30% in 2030 and the proportions of transferred electricity, natural gas, nuclear energy and renewable energy can increase rapidly. Some specific additional policy instruments are also suggested to support the low-carbon transition of energy system in Hebei under the framework of the coordinated development of Beijing-Tianjin-Hebei area, and with the support from the central government of China. |
The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China (2017) 🗎🗎 | This paper attempts to explore the effect of energy construction adjustment on the energy-saving and emission-reduction (ESER) dynamical evolution system. Based on the nonlinear dynamics theory, the dynamic behavior of the novel system is discussed. The quantitative coefficients of the actual system are identified with the aid of genetic algorithm-back propagation neural network. Scenario analysis results show that, energy construction adjustment could effectively control energy intensity. To clarify this further, an example of 4D ESER system with new energy constraints is demonstrated. Investigation results show that, government control can effectively control energy intensity, while brings inhibiting impact on economic growth and people's livelihood. Economic investment is the key variable affecting energy construction adjustment and ESER, the ESER system will crash when the investment is too low. Energy construction adjustment could effectively reduce energy intensity. However, the ESER system will also crash if the development of energy construction adjustment is too fast. The interesting thing is that the ESER system should be pulled back to steady state as the investment getting bigger. Energy intensity could be controlled in expected range by taking adequate measures. Full use of the role of energy structure adjustment should be made to promote the development of new energy, while government control is used only when necessary. (C) 2016 Elsevier Ltd. All rights reserved. |
Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model (2017) 🗎🗎 | Forecasting CO2 emissions in China always has been of great significance as it could help the government to improve energy policies and plans. To this end, a novel grey multivariable model is designed in this paper. Compared with the conventional grey multivariable model, which has certain drawbacks of inaccurate prediction and poor adaptability that restrict their applications in practical cases, the proposed model can make three improvements: first, an optimized grey model having a modified background value is proposed to predict the trends of the driving variables. Second, the novel grey multivariable model is established, combined with the changing trends of driving variables. Third, the adjustment coefficient in the new model is optimized to obtain optimal values for the time response function. To demonstrate its efficacy, the proposed model is employed to reproduce and predict the CO2 emissions from fuel combustion compared with four benchmark models-the results show that the new model yields more accurate forecasting results than the competing models. Eventually, the new model will be used to quantify future Chinese CO2 emissions from fuel combustion from 2014 to 2020, and the forecasted results can provide a solid basis for formulating environmental policies and energy consumption plans. (C) 2017 Elsevier Ltd. All rights reserved. |
The Coordinated Development Path of Renewable Energy and National Economy in China Considering Risks of Electricity Market and Energy Policy (2017) 🗎🗎 | The long-standing over-reliance on fossil fuels brings urgent environmental issues. To reduce emissions andmaintain a sustained economic growth, many countries seek for energy revolution. With the help of smart grid technology, renewable energy eventually plays an indispensable role in energy production and consumption. Electricity market mechanisms and energy policies are thus developed rapidly and can pose more risks to national economy. This paper proposes a modified computable general equilibrium (CGE) model for China to evaluate these risks. Based on several economic evaluation indices, the coordinated development path of renewable energy and national economy is put forward. Industrial restructuring is considered in the modified CGE model since it is one of the key economic policies in today's China. Numerical studies are conducted based on real-world data, and sensitivity analysis further illustrates how different factors affect the results. |
Carbon emission flow in the power industry and provincial CO2 emissions: Evidence from cross-provincial secondary energy trading in China (2017) 🗎🗎 | The accurate calculation of CO2 emissions in every province in China is the basis for developing regional energy policies. There is a huge fossil-fuel reserve and production capacity in western China, whereas their underdeveloped social and economic status means that there is lower demand. The more developed eastern coastal regions of China show dynamic economic momentum and, hence, higher energy demand. Based on the carbon emission flows in network theory, this paper proposes an approach to recalculate provincial CO2 emissions from the perspective of secondary energy consumption. This approach attributes CO2 emissions to final energy consumers after considering cross-provincial secondary energy trading, especially cross-provincial electric power trading in the regional power grid. Given the uneven distribution of energy resources and the imbalance of energy consumption among regions, cross-provincial secondary energy trading in China is significant, especially in the power industry. By adopting the approach proposed in this paper, the provincial carbon intensity and the corresponding energy policy can be modified to make energy end users pay rather than the primary producer. (C) 2017 Published by Elsevier Ltd. |
Has energy efficiency performance improved in China?-non-energy sectors evidence from sequenced hybrid energy use tables (2017) 🗎🗎 | We conduct a comparative analysis of two energy efficiency indicators for China: heating value energy intensity (HEI) and economic value energy intensity (MEI). We formulate 1997-2002-2007-2012 hybrid energy comparable sequence use tables in an input-output accounting framework, and compare the two indicators using a randomized block ANOVA. The results show that MEIs and HEIs have significantly different variability patterns among sectors and are evolutionarily divergent over time. The directional changes in MEI and HEI are found to be inconsistent at both the sectoral and national levels. A further analysis with a LMDI index decomposition model shows that the difference between HEI LMDI and HEI LMDI is principally caused by energy prices. Based on the evidence from the two indicators and their relationship to energy prices, we are unconvinced about China's purported improvements in energy efficiency in recent years. (C) 2017 Elsevier B.V. All rights reserved. |
Examining the driving forces in moving toward a low carbon society: an extended STIRPAT analysis for a fast growing vast economy (2017) 🗎🗎 | Amidst worldwide concern for global warming, this is now a big challenge to move toward low carbon society. Energy efficiency, energy mix and lifestyles are likely to play significant role in this journey. Using an extended STIRPAT model, namely STIRDEFPAT (stochastic impacts by regression on energy demand, energy mix, fossil fuel intensity, population, affluence and technology), this study aims to analyze the environmental impact of affluence and lifestyle changes through decomposition of energy demand, energy mix and fossil fuel intensity in a fast growing economy like India. Data period of this study is 1990-2016. The study employs ridge regression to fit the extended STIRPAT model. Empirical results show that all the impact factors included in the model have significant positive influence on carbon emission. However, the largest contributing factor is affluence having around 24% promoting impact on carbon emission. The positive attitude toward propagation of environmentally sensitive and green choice adaptive behavior along with clean technology and green energy mix is likely to be an effective strategy to restrain adverse impact on the environment. |
Allocation and simulation study of carbon emission quotas among China's provinces in 2020 (2017) 🗎🗎 | China will form its carbon market in 2017 to focus on the allocation of regional carbon emission quota in order to cope with global warming. The rationality of the regional allocation has become an important consideration for the government in ensuring stable growth in different regions that are experiencing disparity in resource endowment and economic status. Based on constructing the quota allocation indicator system for carbon emission, the emission quota for each province in different scenarios and schemes in 2020 is simulated by the multifactor hybrid weighted Shannon entropy allocation model. The following conclusions are drawn: (1) The top 5 secondary-level indicators that influence provincial quota allocation in weight are as follows: per capita energy consumption, openness, per capita carbon emission, per capita disposable income, and energy intensity. (2) The ratio of carbon emission in 2020 is different from that in 2013 in many scenarios, and the variation is scenario 2 > scenario 1 > scenario 3, with Hubei and Guangdong the provinces with the largest increase and decrease ratios, respectively. (3) In the same scenario, the quota allocation varies in different reduction criteria emphases; if the government emphasizes reduction efficiency, scheme 1 will show obvious adjustment, that is, Hunan, Hubei, Guizhou, and Yunnan will have the largest decrease. The amounts are 4.28, 8.31, 4.04, and 5.97 million tons, respectively. |
Rebound effect of improved energy efficiency for different energy types: A general equilibrium analysis for China (2017) 🗎🗎 | This paper explores the rebound effect of different energy types in China based on a static computable general equilibrium model. A one-off 5% energy efficiency improvement is imposed on five different types of energy, respectively, in all the 135 production sectors in China. The rebound effect is measured both on the production level and on the economy-wide level for each type of energy. The results show that improving energy efficiency of using electricity has the largest positive impact on GDP among the five energy types. Inter-fuel substitutability does not affect the macroeconomic results significantly, but long-run impact is usually greater than the short-run impact. For the exports-oriented sectors, those that are capital-intensive get big negative shock in the short run while those that are labour-intensive get hurt in the long run. There is no "backfire" effect; however, improving efficiency of using electricity can cause negative rebound, which implies that improving the energy efficiency of using electricity might be a good policy choice under China's current energy structure. In general, macro-level rebound is larger than production-level rebound. Primary energy goods show larger rebound effect than secondary energy goods. In addition, the paper points out that the policy makers in China should look at the rebound effect in the long term rather than in the short term. The energy efficiency policy would be a good and effective policy choice for energy conservation in China when it still has small inter-fuel substitution. (C) 2017 Elsevier B.V. All rights reserved. |
Energy overview for globalized world economy: Source, supply chain and sink (2017) 🗎🗎 | Energy use of the globalized world economy is comprehensively overviewed by means of a systems input-output analysis based on statistics of 2010. Emphases are put on the sources of primary energy exploitation, interregional trade imbalance of energy use via global supply chains, and sinks of energy use in final demand. The largest final, user turns out to be the United States, compared with China as the leading energy exploiter. The global trade volume of energy use is shown in magnitude up to about 90% of the global primary energy exploited. The United States is recognized as the world's biggest energy use importer, in contrast to Russia as the biggest exporter. Approximately one third of global primary energy exploited is shown to be embodied in inter-regional net trade. Japan and Russia are respectively illustrated to be the world's leading net importer and leading net exporter of energy use. For China as the leading energy exploiter, about 30% of its exploited energy is for foreign regions' final use, and 70% for its own final use. For the European Union as the largest sink region, nearly 80% of the energy required in its final use is from foreign regions, led by Russia. As reflected in the results, the conventional perspective based only on the direct energy consumption by region inevitably leads to inter-regional "energy grabbing" and "carbon leakage", which raises a serious concern of "regional decrease at the expense of global increase". In current context of energy shortage and climate change, this global energy overview can provide essential strategic implications at the international, national and regional scales for sustainable energy policy making. |
Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models (2017) 🗎🗎 | Much theoretical and empirical research has verified the non-linear and uncertain relationships between carbon emissions and economic growth. To forecast the carbon emissions from fossil energy consumption, this paper introduces the power exponential term of the relevant variables as exogenous variables into a multivariable grey model. Under the target of minimisation of the mean absolute percentage error, two non-linear programming models are constructed to solve the unknown parameters of the non-linear grey multivariable model. In addition, to enhance the adaptability of the grey model to large sample sizes, we divide the data of Chinese gross domestic product and carbon emissions from fossil energy consumption of 1953-2013 into 15 stages. The empirical results show that the non-linear grey multivariable model can reflect the mechanism of the non-linear effects of gross domestic product on carbon emissions from fossil energy consumption, and has higher forecast accuracy than the traditional grey model and the autoregressive integrated moving average models. In three schemes economic growth at low, medium, and high, speeds we use the non-linear grey model to quantify future Chinese carbon emissions from fossil energy consumption from 2014 to 2020, and the predicted results can provide the basis for energy planning and the formulation of environmental policy. (C) 2016 Elsevier Ltd. All rights reserved. |
Technology advance and the carbon dioxide emission in China - Empirical research based on the rebound effect (2017) 🗎🗎 | At present, technology advance is the greatest contributor to the carbon dioxide mitigation. However, the real effect of technology advance on mitigation is worth further studying due to the existence of rebound effect (RE). A key issue is how to quantify the relationship between technology advance and carbon dioxide emission accurately. This paper figures out a comprehensive and modified framework involving around the RE of carbon emission from the macroeconomic perspective. Using this framework, this paper quantitatively evaluates the relationship between technical change and carbon emission based on the data of 30 provinces in China. It is founded that: (1) the carbon RE is about 10-60% in Chinese provinces; (2) the RE of carbon emission differs among the regions in China; (3) carbon reduction and environment issues should be solved step by step regionally in China. (4) According to our results, a reasonable control on total energy consumption and fossil energy pricing adjustment, should be taken as the supplementary policy in China; at the same time, carbon financing, carbon trading and other aspects of institutional innovation should be taken into account at the appropriate time. |
Identifying primary energy requirements in structural path analysis.: A case study of China 2012 (2017) 🗎🗎 | Primary energy requirements have close interaction with resource, technology, environment, infrastructure, as well as the socio-economic development. This study links the entire supply chain of the Chinese economy from energy extraction to final consumption by using input-output analysis and structural path analysis. The results show that the domestic primary energy input amounted to 3318.7 Mtce in 2012, of which 49.5% was induced by investment demands. Despite being one of the world's largest energy importers, embodied energy uses (EEUs) in China's exports were equivalent to about one fourth of its total domestic supply. All Manufacturing sectors accounted for 44.3% of the total EEUs, followed by Construction for 33.3%, Services for 11.6% and Power &' Heat for 3.9%. After examining the embodied energy paths, critical economic sectors such as Construction of Buildings, Construction Installation Activities, Transport Via Road, Production and Supply of Electricity and Steam and Processing of Steel Rolling Processing, and supply chain routes starting from final uses to resource extraction such as "Capital formation -> Construction of Buildings -> Production and Supply of Electricity and Steam -> Production and Supply of Electricity and Steam -> Mining and Washing of Coal", were identified as the main contributors to China's raw coal and other primary energy requirements. Restructuring Chinese economy from manufacturing industries to construction and services with huge economic costs cannot fundamentally conserve energy, owing to their almost identical structures in higher production tiers; more appropriate policies on technology efficiency gains, energy mix improvement, economic structure adjustment and green consumption deserve to be considered in the light of upstream and downstream responsibilities from a systematic viewpoint. (C) 2017 Elsevier Ltd. All rights reserved. |
Quantifying the impacts of decarbonisation in China's cement sector: A perspective from an integrated assessment approach (2017) 🗎🗎 | China has been the world's largest cement producer and the cement production has led to increased energy consumption and CO2 emissions, which greatly affect the country's social and economic development. In this paper, an integrated assessment framework was developed by combining the Stock-based model and the Integrated MARKAL-EFOM System model of China (China TIMES), and it was used in China's cement sector to simulate the trends of energy consumption and CO2 emissions during 2010-2050, under a reference scenario and three carbon tax scenarios. The modeling results show that: (1) the cement production in China will increase from 1.8 billion tons in 2010, to a peak of 2.5 billion tons in around 2017, and then gradually decrease to 1.5 billion tons in 2050; (2) Through the adoption of three alternative abatement measures (i.e., switching the fuel types, implementing the energy-efficient measures and CCS), China's cement sector could potentially achieve a great reduction in CO2 emissions; (3) In the near future, the cement sector's decarbonisation will mainly rely on energy efficiency improvement; while the use of alternative fuel use and CCS will be of great significance from a long-term perspective. Additionally, based on the modeling results, we build the energy-saving and emission-reduction technologies roadmap for China's cement sector. (C) 2016 Elsevier Ltd. All rights reserved. |
A comparison of carbon dioxide (CO2) emission trends among provinces in China (2017) 🗎🗎 | As the world leader in CO2 emissions, China is a key focus for climate change mitigation. In this paper, we conducted a cross-province comparison of CO2 emission trends in China from 2006 to 2012. We determined effects of CO2 emission factor (EMF), energy mix change (EIVIX), potential energy intensity change (PEI), industrial structure (STR), economic activity (EAT), technological change (BPC) and energy efficiency change (EC) as underlying forces of CO2 emission changes with production-based decomposition. Compared to other production-theory decomposition analyses (PDA), the method used in this paper can overcome the weakness of PDA on the measurement of structural changes and energy mix effect. The results provided strong evidence that EAT is the main driver behind rising emissions, while changes in PEI, EMX and EC have led to CO2 emission reductions in most provinces/municipalities in China. In particular, we introduced the global benchmark technology to establish the relationship between CO2 emissions and energy use technology. The potential CO2 reductions in China were further measured under the scenarios of contemporaneous technology and global technology. The principal empirical implication is that the promotion of energy conservation technology and reductions in inter-regional technological disparity would be effective in reducing CO2 emissions in technically inefficient regions. |
The prospects of China's long-term economic development and CO2 emissions under fossil fuel supply constraints (2017) 🗎🗎 | This paper presents an energy-environment-economy model that described technology-specific information and integrated resource depletion to simulate China's long-term CO2 emissions and economic development under fossil fuel supply constraints towards 2050. The modeling approach and findings not only support the theoretical of relationship between physical resources depletion and economic growth to some extent, but also provide practical significance for policy making that can be shared with other developing countries. The results indicate that energy supply constraints will play a crucial role in China's future economic development, causing a 7.9% decrease in GDP compared to the 2050 baseline and a peak of CO2 emissions at 11.2 Gt around 2034 under a resource constraint scenario, which can be considered as a new baseline considering fossil fuel depletion. Moreover, under a low carbon scenario considering low carbon measures, economic growth is less dependent on fossil fuel consumption, and CO2 emissions will peak earlier in 2030, and the negative impact on GDP from finite fossil fuel supply can be alleviated by 5.5% by 2050. The low carbon scenario is a good way to achieve both CO2 mitigation and low-carbon growth, which may lead to a complete restructuring of the China's energy-economic system. To achieve the economic restructure towards low carbon economy, Chinese government should take into account the crucial role of fossil fuels supply constraints, set reasonable and moderate future GDP growth targets, and strictly implement the low carbon measures including accelerating technical progress, non-fossil fuel development, energy structure improvement and the upgrading of industrial structure and household consumption patterns. (C) 2016 Elsevier B.V. All rights reserved. |
Analyzing the impact factors of energy-related CO2 emissions in China: What can spatial panel regressions tell us? (2017) 🗎🗎 | China produces a huge amount of CO2 emissions that need to be reduced to comply with international commitments. Identifying the driving factors is critical for achieving mitigation targets and transiting toward a low-carbon economy in China. This study is one of the first few attempts to apply panel data and spatial econometrics models to analyze the spatial dependence of province-level CO2 emission intensity and the economic drivers of such emissions. First, we conducted a systematical review of the development of spatial econometrics. Second, we generated a panel data set about energy and socioeconomic development of 30 provinces and municipalities in China from 2005 to 2012. Next, we chose the most appropriate model to explain the predominant factors of energy-related CO2 emissions based on a series of tests of the spatial econometrics models. The results suggested that technology improvement, adjustment of industrial and energy structures, and environmentally friendly behaviors and patterns of consumption of habitants play an important role in CO2 emissions mitigation. The results also revealed that CO2 emissions had strong spatial spillover effects, which indicate that policies adopted in one province will affect policy-making in neighboring provinces, implying that provinces need take combined action to balance climate change mitigation and economic policy goals. (c) 2017 Elsevier Ltd. All rights reserved. |
Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization (2017) 🗎🗎 | In the prevailing low-carbon economy, China is under enormous pressure to control CO2 emissions, therefore, of great significance is the study to analyze what influential factors mainly contribute to emissions, so as to forecast emissions accurately and harness the growth from the source. In this paper, basing on 22 influencing factors identified by bivariate correlation analysis, factor analysis is then adopted to extract the latent factors which essentially affect emissions and 8 special factors transformed by scoring coefficients are acquired. Extreme learning machine (ELM) whose input weights and bias threshold were optimized by particle swarm optimization (PSO), hereafter referred as PSO-ELM, is established to predict CO2 emissions and testify the availability of the factor analysis. Case studies reveal that the factor analysis which generates 8 factors as input can highly improve prediction accuracy. And the simulation results demonstrate that the built model PSO-ELM outperforms the compared ELM and back propagation neural network in forecasting CO2 emissions. Eventually, the analysis made in this study can provide valuable policy implications for Hebei's CO2 emissions reduction and strategic low carbon development. (C) 2017 Elsevier Ltd. All rights reserved. |
Study on the Evolution Mechanism and Development Forecasting of China's Power Supply Structure Clean Development (2017) 🗎🗎 | The clean development of China's power supply structure has become a crucial strategic problem for the low-carbon, green development of Chinese society. Considering the subsistent developments of optimized allocation of energy resources and efficient utilization, the urgent need to solve environmental pollution, and the continuously promoted power market-oriented reform, further study of China's power structure clean development has certain theoretical value. Based on the data analysis, this paper analyzes the key factors that influence the evolution process of the structure with the help of system dynamics theory and carries out comprehensive assessments after the construction of the structure evaluation system. Additionally, a forecasting model of the power supply structure development based on the Vector Autoregressive Model (VAR) has been put forward to forecast the future structure. Through the research of policy review and scenario analysis, the paths and directions of structure optimization are proposed. In this paper, the system dynamics, vector autoregressive model (VAR), policy mining, and scenario analysis methods are combined to systematically demonstrate the evolution of China's power structure, and predict the future direction of development. This research may provide a methodological and practical reference for the analysis of China's power supply structure optimization development and for theoretical studies. |
Total-Factor Energy Efficiency in BRI Countries: An Estimation Based on Three-Stage DEA Model (2018) 🗎🗎 | The Belt and Road Initiative (BRI) is showing its great influence and leadership on the international energy cooperation. Based on the three-stage DEA model, total-factor energy efficiency (TFEE) in 35 BRI countries in 2015 was measured in this article. It shows that the three-stage DEA model could eliminate errors of environment variable and random, which made the result better than traditional DEA model. When environment variable errors and random errors were eliminated, the mean value of TFEE was declined. It demonstrated that TFEE of the whole sample group was overestimated because of external environment impacts and random errors. The TFEE indicators of high-income countries like South Korea, Singapore, Israel and Turkey are 1, which is in the efficiency frontier. The TFEE indicators of Russia, Saudi Arabia, Poland and China are over 0.8. And the indicators of Uzbekistan, Ukraine, South Africa and Bulgaria are in a low level. The potential of energy-saving and emissions reduction is great in countries with low TFEE indicators. Because of the gap in energy efficiency, it is necessary to distinguish different countries in the energy technology options, development planning and regulation in BRI countries. |
GDP Per Capita and Protest Activity: A Quantitative Reanalysis (2018) 🗎🗎 | Our research suggests that the relation between GDP per capita and sociopolitical destabilization is not characterized by a straightforward negative correlation; it rather has an inverted U-shape. The highest risks are typical for the countries with intermediate values of GDP per capita, not the highest or lowest values. Thus, until a certain value of GDP per capita is reached, economic growth predicts an increase in the risks of sociopolitical destabilization. This positive correlation is particularly strong (r = .94, R-2 = .88) and significant for the intensity of antigovernment demonstrations. This correlation can be observed in a very wide interval (up to 20,000 of international 2014 dollars at purchasing power parities [PPPs]). We suggest that it is partially accounted for by the following regularities: (a) GDP growth in authoritarian regimes strengthens the pro-democracy movements, and, consequently, intensifies antigovernment demonstrations; (b) in the GDP per capita interval from the minimum to $20,000, the growth of GDP per capita correlates quite strongly with a declining proportion of authoritarian regimes and a growing proportion of intermediate and democratic regimes; and, finally, (c) GDP growth in the given diapason increases the level of education of the population, which, in turn, leads to a higher intensity of antigovernment demonstrations. |
Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation (2018) 🗎🗎 | Primary energy plays a critical role in the socio-economic development of China, and accurate energy consumption forecasting can help the government to formulate energy policies. To do this, the present study aims to apply a self-adaptive multi-verse optimizer (AMVO) to optimize the parameters of the support vector machine (SVM). It employs a rolling cross-validation scheme to predict China's primary energy consumption in which the independent variables are gross domestic product (GDP) per capita, population, the urbanization rate, the share of the industry in GDP and coal's share of primary energy consumption. The results indicate that the hybrid AMVO-SVM model has higher precision than other models. Finally, we apply the hybrid AMVO-SVM model to predict the energy consumption of China between 2017 and 2030 in five scenarios. In the reference scenario, China's primary energy consumption will reach 4839.3 Mtce in 2020 and 5656.2 Mtce in 2030. (C) 2018 Elsevier Ltd. All rights reserved. |
Industrial sectors' energy rebound effect: An empirical study of Yangtze River Delta urban agglomeration (2018) 🗎🗎 | As the most prosperous area and one of China's major economic centers, Yangtze River Delta urban agglomeration (YRDUA) shows the largest concentration of adjacent metropolitan areas in the world. Its energy conservation and emissions reduction efforts are critical for developing a low carbon economy in China. Based on panel data of 14 cities from 2003 to 2013, this study estimates the rebound effect's magnitude in YRDUA's industrial sectors using dynamic ordinary least squares (DOLS) and seemingly unrelated regression (SUR) methods. The empirical results are as follows: (1) Significant substitute relationships exist between energy and capital factors and between energy and labor factors. (2) The own price elasticity of labor is the most elastic, followed by those of energy and capital. (3) The rebound effect is approximately 40.04%. Evidence suggests that promoting financial development, conducting structural reform in the supply side, and establishing reasonable controls of industrial growth and scale expansion are conducive for energy conservation and pollution alleviation of YRDUA's industrial sectors. (C) 2018 Elsevier Ltd. All rights reserved. |
DRIVING MECHANISMS AND PEAK LEVELS OF CO2 EMISSIONS IN CHINA: EVIDENCE FROM A SIMULTANEOUS EQUATION MODEL (2018) 🗎🗎 | As climate change is an important environmental problem around the world, the development trends and the time of peak carbon emissions in China arouse extensive international attention. In this study, a simultaneous equation model is constructed from a systems perspective to investigate the dynamic action mechanisms of economic scale, industrial structure, energy mix, and energy efficiency or their internal disturbance factors on carbon emissions. The results specify that rapid economic development is accompanied by large amounts of carbon emissions. Under the premise of a low-carbon economy, adjustment of energy mix and industrial structure is the main way to reduce carbon emissions. From the efficiency standpoint, the increase in energy utilization rate could also effectively mitigate carbon emissions. Based on a simultaneous equation model reasonable predictions of the endogenous and exogenous variables are produced in the system, and the development trend of carbon emissions in China in the near future is analyzed. The simulation analysis determines that under the assumption of 5.5% economic growth, carbon emissions could reach a peak at 11.2 billion tons in China by 2030. Finally, according to simulations of the carbon emissions development trend, policy suggestions for energy savings and emission reduction in China are provided. These provide a reference point for policy-making to achieve the goal of peak carbon emissions in 2030 in China. |
Metal supply constraints for a low-carbon economy? (2018) 🗎🗎 | Low-carbon energy systems are more metal-intensive than traditional energy systems. Concerns have been expressed that this may hamper the transition to a low-carbon economy. We estimate the required extraction of Fe, Al, Cu, Ni, Cr, In, Nd, Dy, Li, Zn, and Pb until 2050 under several technology-specific low-carbon scenarios. Annual metal demand for the electricity and road transportation systems may rise dramatically for indium, neodymium, dysprosium, and lithium, by factors of more than three orders of magnitude. However, in the base year 2000 the dominant uses were often in other sectors. Since growth in these other, previously dominant sectors has been less pronounced, the overall growth in society's metal needs is much less dramatic than in the electricity and transportation sectors. Total annual demand for the researched metals would rise by a factor of 3-4.5, corresponding to compound growth rates of between 2% and 3%. Such growth rates are similar or lower compared with historical growth rate levels over the last few decades. Prolonged higher levels have existed for copper, for example, with production rising by 8% per year from 1992 to 2006. Yet this state of affairs does not give cause for complacency. The richest resources may have been used, production is showing a tendency towards becoming very large-scale, and development times have increased, all leading to greater risks of disruption. It is therefore crucial, when developing specific technologies, that the resource-specific constraints are analyzed and options for substitution are developed where risks are high. |
The dynamic linkage effect between energy and emissions allowances price for regional emissions trading scheme pilots in China (2018) 🗎🗎 | China's emissions trading scheme (CETS) pilots are emerging and fragmented markets; these emissions allowances markets may exhibit a different fundamental price process from the European Union's (EU) and the United States' (US) emissions trading scheme (ETS). The dynamic linkage effects between energy and emissions allowances prices are investigated for regional ETS pilots in China using cointegration techniques. The empirical results confirm that, in the long run, coal, oil and natural gas prices are the main determinant factors of regional emissions allowances prices, except for the second phase Beijing ETS pilots; however, their long-run cointegration relationships in the Beijing and Shanghai pilots are not exactly in line with the Guangdong and Hubei pilots. In the short run, the changes in oil and natural gas prices in the second phase Beijing ETS pilot, the natural gas prices in the Shanghai ETS pilot, and the coal prices in the Hubei ETS pilot are found to have significant effects on regional emissions allowances prices in China. Finally, regional energy pricing inefficiency, regional emissions trading market overreaction and their inefficient fundaments may provide major obstacles in reducing the effectiveness of the fossil energy price pass-through to emissions allowances prices in regional ETS pilots. |
Carbon overhead: The impact of the expansion in low-carbon electricity in China 2015-2040 (2018) 🗎🗎 | China has embarked on a massive program of low-carbon electricity (LE) deployment, in order to reduce its current dependence on coal. The cumulative installed capacity of LE in 2015 was almost four times of that in 2002. Moreover, China has a target of 20% for non-fossil fuels in primary energy consumption by 2030. LE provides substantial carbon savings in the use phase, but LE infrastructure tends to require more materials than their fossil-fuel electricity counterpart. Here we estimate the carbon 'overhead' from infrastructure expansion during China's transition to LE. We report estimates of the learning curves of the carbon intensity of LE installation, calculated from regional historical data in the period 2002-2012. We combine this information with the predicted cumulative installed capacity from well-known scenarios from national and international bodies. We then project the trends of carbon impacts from LE investments up to 2040. Our results show that, under all scenarios and every year, the annual carbon impact of LE investments never exceeds 4% of China's total carbon emissions, and that the carbon impacts of the expansion in LE infrastructure show either a steady decline or a peak during 2030-2035 before declining further. |
Carbon emission and abatement potential outlook in China's building sector through 2050 (2018) 🗎🗎 | The carbon dioxide generated by building sector accounts for approximately 30% of the total CO2 emissions in China. The building sector plays a significant role in Chinese low-carbon development. This study develops the CAS bottom-up model system to predict the future trend of.carbon emissions in China's building sector. Firstly, we sets three scenarios: business as usual (BAU), policy scenario, and synergistic emission reduction (SER) scenario, which consider the influence of low-carbon building policies and emission factors (i.e. power and heat emission factor (PEF and HEF)). Then we develop an emission reduction potential model to assess the CO2 abatement potential of the building sector in 2016-2050. The results reveal that low-carbon policies of building sector in policy scenario can only slow down but not curb the CO2 emission completely. The CO2 emissions will reach its peak before 2030 in the SER scenario, taking into account the impact of PEF and HEF. The analysis demonstrates that the synergistic reduction effect of inter-department will be better than that of one sector. Furthermore, green buildings, renewable energy building and energy conservation policies for district heating have a great influence on emission abatement in the building sector. |
Peak energy consumption and CO2 emissions in China's industrial sector (2018) 🗎🗎 | China's industrial sector accounts for more than half of the country's final energy demand. Thus, controlling its energy consumption and carbon dioxide (CO2) emissions is critical for achieving China's Paris Agreement target. We developed two scenarios for this sector's energy consumption and CO2 emissions up to 2050 by inputting China's current macroeconomic, industrial de-capacity, energy efficiency, and policy requirements into a modified global change assessment model. Our quantitative analysis indicated that the industrial sector's energy consumption and CO2 emissions growth will peak by 2025, subsequently declining. In the first reference scenario, peak energy consumption and CO2 emission values in 2025 will be 2.42 gigatons of coal equivalent (Gtce) and 4.43 gigatons of CO2 (GtCO(2)), respectively. In the second low-carbon scenario, these peak values in 2025 will be 2.28 Gtce and 4.13 GtCO(2), respectively, that is, 5% and 7% lower than the respective values in the first scenario. After 2025, energy consumption and CO2 emissions will decrease gradually up to 2050. Measures required to achieve low-carbon peak targets include policies for adjusting and optimizing the industrial structure, promoting low-carbon energy, and capping energy and coal use, and CO2 emissions in this sector. (C) 2018 Elsevier Ltd. All rights reserved. |
Estimating energy service demand and CO2 emissions in the Chinese service sector at provincial level up to 2030 (2018) 🗎🗎 | China has been through a rapid development process of expanding tertiary industries and urbanization. Due to the aggressive expansion of service building space, energy consumption and CO2 emissions from the service sector are increasing significantly. In this study, we concentrate on China's service sector and analyze CO2 emissions from six sub-sectors: offices, mercantile, food and lodging, education, healthcare and others. Considering the gap of tertiary industry development status and climate diversity across regions, we base our study on regional analyses and the study subjects include 31 provincial regions of mainland China. We first estimate floor area and energy service demand of each sub-sector based on difference-influencing indicators. Then we conduct a scenario analysis and use an AIM/Enduse bottom-up cost optimization model to evaluate the CO2 reduction potential brought about by efficient technologies in China's service buildings. The results show that: 1) office and mercantile are two major sub-sectors in the service sector, while most of the floor area increase in the service sector is due to office buildings; 2) by 2030 a considerable part of energy consumption will be replaced by renewable energy, which leads to no-regrets CO2 emissions reductions; 3) renewable energy penetration potential is smaller in regions with larger tertiary industry GDP and CO2 emission reductions in cold and warm regions are caused by consumption decrease of coal and electricity respectively; 4) the optimization analysis allocates more reduction potential to regions with a smaller tertiary GDP share. |
Analysis of technological progress and input prices on electricity consumption: Evidence from China (2018) 🗎🗎 | This paper establishes a translog cost function model with productivity growth equations to study the influence of technological progress and price changes on electricity production factors and consumption. Within a unified analytic framework, regression is applied to the panel data for seven regions in China during the period 1997 to 2013. The functions of production costs on factor substitution are analyzed based on the regression. The influence of electricity price and technological progress on electricity productivity and the rebound effect of electricity are discussed. Electricity price can significantly and positively influence electricity productivity (expressed by the total value of output consumed by unit electricity); however, the stimulation of technological progress is not notable. The rebound effect of electricity computed based on an electricity price breakdown showed that values of the rebound effect in Southwestern China and Central China were higher than 100% while annual average values of the rebound effect in Northeastern China and Southern China were -60.39% and -81.47%, respectively. The area with the lowest annual average value for the rebound effect was Northwestern China at 14.96%. (C) 2018 Elsevier Ltd. All rights reserved. |
Driving forces of energy embodied in China-EU manufacturing trade from 1995 to 2011 (2018) 🗎🗎 | In this study, an empirically validated Environmental Input-Output Life Cycle Assessment (EIO-LCA) model was applied to calculate and decompose the amount of energy embodied in the manufacturing trade between China and the European Union (EU) in 1995-2011. The main findings are as follows: China's entry to the WTO (World Trade Organization) in 2001 has spurred the growth of energy embodied in exports from China to the EU, while environmental policies issued by Chinese government since 2006 have pulled it down. The export sectoral structure change and energy consumption intensity decrease are two key drivers of China's embodied energy export changes. China's embodied energy exports are mainly contributed by coal and other fossil fuels. Germany is the largest importer of China's embodied energy among the EU member states. This study can provide data support and reference basis for international trade and climate negotiations, and help Chinese government to improve its policies on industrial structure, primary energy structure and export state structure. |
Trends of energy demand in the Middle East: A sectoral level analysis (2018) 🗎🗎 | The Middle East region is a key player in the world energy market today. It holds approximately over 50% of the world's proven fossil fuel reserves. Yet, the region is significantly challenged by the large dependence on finite fossil fuel resources in its primary energy supply. The intricate relationship between climate change mitigation and the development of energy systems underlines great uncertainty over the future of energy development in the Middle East. Such uncertainty is greatly linked to growing energy demands and the region's capacity to transition to low-carbon energy systems. Over the past 20years, the total primary energy demand in the Middle has almost tripled due to rapid population growth and economic development. Notably, most of the growing energy demand was concentrated in 5 countries, Iran, Saudi Arabia, Iraq, Kuwait, and the UAE. These 5 countries represented around 82% of the total primary energy demand in 2015, with Saudi Arabia and Iran alone accounted for 60%. The core question of this paper is what are the possible implications of growing energy demands in these countries and which sectors will entail significant increases in the projected energy requirements? The significance of the work presented here stems from analyzing 4 major countries that constitute the largest share in Middle East's total energy consumption and associated emissions. Examining these 4 countries together is important to highlight how future increase in these countries could largely affect the overall energy demand from the Middle East region in the next 20years. Thus, the scope of the paper is looking at energy demand implications in 4 countries, Iran, Saudi Arabia, Kuwait, and the United Arab Emirates (UAE). Iraq is excluded from the analysis due to the large political uncertainty associated with Iraq's energy development. Here, a regression model is used to forecast energy demand from 5 economic sectors across the 4 countries using projected increase in population and gross domestic product (GDP) by 2030. Results indicate that most of the projected energy demand will be from Iran and Saudi Arabia. In addition, industry and transportation sectors will witness the largest increase among the 5 sectors examined in the paper. For instance, industry and transportation sector will collectively account for 52% and 67% of the projected energy demand in Iran and Saudi Arabia, respectively. Such results are important to highlight when ascertaining sectoral level implications of future energy demands and to determine potential areas where energy savings can be made. |
Analysis of the energy intensity of Kazakhstan: from data compilation to decomposition analysis (2018) 🗎🗎 | There are large gaps in energy consumption data and consequently in the estimates of CO2 emissions from fuel combustion in Kazakhstan. This study provides the first comprehensive review of energy consumption trends in Kazakhstan, discusses several important discrepancies in energy statistics and presents an improved versions of Energy Balances, developed using additional data. The results indicate that Kazakhstan's energy intensity of gross domestic product (GDP) declined by 30% from 1.14 to 0.8 toe/thousand 2005USD between 2000 and 2014. To understand factors influencing this decline, the change in energy intensity of GDP was decomposed using the Logarithmic Mean Divisia Index I method. The upstream sector (mainly oil and gas) played the most important role in the observed GDP energy intensity change. Although the share of this sector in total GDP increased, causing an increase in energy intensity due to inter-sectoral structural effects, the consequences were counteracted by a twofold decline in the sector's energy intensity, resulting in a net decrease. On the contrary, the power and heat, transport and household sectors saw an increase in energy intensity between 2000 and 2014. The results clearly demonstrate that there is an urgent need for policies and measures to be put in place in the power and heat, household and transport sectors, to support renewable energy development, increase buildings' energy efficiencies, replace inefficient stoves and improve heating systems and encourage changes in public transportation systems. Furthermore, improving energy statistics and setting appropriate sectoral energy intensity reduction targets are crucial for achieving real efficiency improvements in the economy. |
Forecasting China's total energy demand and its structure using ADL-MIDAS model (2018) 🗎🗎 | Forecasting total energy demand and its structure is the basis for energy planning and industrial policy formulation. However, existing research on the forecast of energy structure remains inadequate. This study aims at constructing an ADL-MIDAS model to identify the optimal model to forecast China's energy demand and its structure, and offer a reasonable judgement on future carbon emission and energy scenarios in China and other developing countries. Thus, this study adopts mixed frequency data for quarterly GDP, quarterly added value, and annual energy demand of various industries to construct an ADL-MIDAS model. Then, the optimal model to forecast China's energy demand is selected from various model combinations that employ different weight functions and forecasting methods. The model forecasts China's total energy demand and its structure as proposed in the 13th Five Year Plan. The in-sample prediction results show that, in the optimal model, the smallest prediction error is 0.02%, while the largest of the four future periods is 2%, indicating that the ADL-MIDAS model is effective in forecasting energy demand. Further, the forecast results suggest that, by 2020, China's total energy demand will reach approximately 4.65 billion tonnes of standard coal equivalent; the demand for coal, natural gas, and non-fossil fuel will be 57%, 7.6%, and 18%, respectively, contingent on economic growth conditions. Given these forecast results, the energy planning targets set under the 13th Five Year Plan are attainable. However, in the case of natural gas demand, considerable marketing is required to promote its use. (C) 2018 Elsevier Ltd. All rights reserved. |
Decoupling, decomposition and forecasting analysis of China's fossil energy consumption from industrial output (2018) 🗎🗎 | Industries are a major fossil energy consumer and economic development contributor in China. The Chinese government is performing the industrial reforms to diminishing the dependence of industrial output on fossil energy consumption. To guide the present industrial policy adjustment, this study employs the Tapio decoupling index, a structural decomposition algorithm, a hybrid forecasting model, and industry-related data from 2001 to 2014 to evaluate, decompose and forecast the relationship between China's industrial output and fossil energy consumption. Empirical results show that the decoupling index of fossil energy consumption from the value added by China's industry was less than 0 in 2015. Consequently, the industrial fossil energy consumption reached its peak at that time and will gradually decrease in the future, even though the added value continuously increased. The mitigation goals set by the Chinese government for industrial fossil energy intensity by 2020 are expected to be achieved ahead of schedule. The attainment of these goals will strongly support the realization of China's fossil energy-related prospects for 2030. To reduce industrial fossil energy consumption, China should develop strategies for non-fossil energy electricity generation, implement electricity price bidding, prevent overheated investments to real estate and infrastructure, and eliminate backward capacity and establish market-access rules for acetic acid, calcium carbide, ethyl alcohol, and dimethyl ether. (C) 2018 Elsevier Ltd. All rights reserved. |
A system dynamics model of China's electric power structure adjustment with constraints of PM10 emission reduction (2018) 🗎🗎 | Recently, Chinese state environmental protection administration has brought out several PM10 reduction policies to control the coal consumption strictly and promote the adjustment of power structure. Under this new policy environment, a suitable analysis method is required to simulate the upcoming major shift of China's electric power structure. Firstly, a complete system dynamics model is built to simulate China's evolution path of power structure with constraints of PM10 reduction considering both technical and economical factors. Secondly, scenario analyses are conducted under different clean-power capacity growth rates to seek applicable policy guidance for PM10 reduction. The results suggest the following conclusions. (1) The proportion of thermal power installed capacity will decrease to 67% in 2018 with a dropping speed, and there will be an accelerated decline in 2023-2032. (2) The system dynamics model can effectively simulate the implementation of the policy, for example, the proportion of coal consumption in the forecast model is 63.3% (the accuracy rate is 95.2%), below policy target 65% in 2017. (3) China should promote clean power generation such as nuclear power to meet PM10 reduction target. |
Hydropower and potential for interfuel substitution: The case of electricity sector in Malaysia (2018) 🗎🗎 | The electricity sector in Malaysia is dominated by fossil fuels. This has immensely increased the amount of CO2 emissions and other pollutants. The objective of this paper is to investigate the potential for inter fuel substitution between the four major fuels of coal, gas, oil, and hydropower that are currently being used in the generation of electricity in Malaysia. Using a translog production function, the study adopted a ridge regression procedure to estimate the parameters. The results suggest a potential for substitution among the fuels. Hydropower is observed to be a substitute for other fossils fuels which is an indication that the country can gradually move towards adopting a cleaner fuel in the generation of electricity. We also extended the analysis to Thailand and China to show the consistency of the method when applied to different countries. (C) 2018 Elsevier Ltd. All rights reserved. |
Bioenergy industry and the growth of the energy sector in the EU-28 region: Evidence from panel cointegration analysis (2018) 🗎🗎 | This study attempts to find whether the bioenergy industry contributed to the growth of the energy sector in the EU-28 region from 1990 to 2013. This study adopts the framework of the conventional production function to identify the influence of the bioenergy industry on the growth of the energy totality industries in the EU-28 region. To this goal, the authors apply the unit root test, panel cointegration test, cointegration estimate analysis, and heterogeneous panel causality test. The key finding of this study is that the bioenergy industry's determinant consumption and labour and capital inputs have an important and positive effect on the outgrowth of the energy section. This study showed that there is a significant long-run balanced correlation between the bioenergy industry and the outgrowth of the energy sector in the EU-28 region. Heterogeneous panel causality results show that bioenergy consumption and capital input of the bioenergy industry have an important and positive correlation with the growth of the energy sector in EU28. Also, the results show that bioenergy consumption, capital input of bioenergy, and labour input of the bioenergy industry have important and positive relationships with the growth of the energy sector in EU28 developed states. Moreover, heterogeneous panel causality results show that bioenergy consumption and labour input of the bioenergy industry have important and positive correlations with the growth of the energy sector in EU28 developing states. With reference to the studied panel dataset implicating all 3 models, the findings boost the growth hypothesis. The implications of such analysis for the EU energy policy makers are related to competitiveness, sustainable development, and the security of energy supply. The results showed that the bioenergy industry have an important effect on the growth of the energy sector in the EU-28 region. Published by AIP Publishing. |
Energy intensity and energy conservation potential in China: A regional comparison perspective (2018) 🗎🗎 | Increasing energy demand and the associated environmental pressures have ignited the Chinese government's concerns regarding energy conservation. Using provincial-level panel data covering the period of 2000-2015, this study first identifies the drivers of energy intensity across China's regions, employing a series of econometric techniques allowing for cross-sectional dependence and slope homogeneity. Based on the estimation results and scenario analysis, this study forecasts the possible energy conservation potential at the regional level by 2030. The panel augmented mean group (AMG) estimator provides similar estimation results for the three regions: economic structure and urbanization rate are the deterministic factors increasing energy intensity, while R&D investment and relative energy price reduce it. The results of scenario analysis indicate that, under the advanced scenario, the energy conservation potential in the eastern, central, and western regions in 2030 will be 1,209.53 million tons of coal equivalent (tce), 664.23 million tce, and 774.48 million tce, respectively. At the national level, the advanced scenario can save 2,648.24 million tce of energy consumption by 2030, accounting for 43.3% of the energy demand under the business-as-usual (BAU) scenario. Finally, these findings offer several targeted policy suggestions for reducing energy intensity and promoting energy conservation potential at the national and regional levels. (C) 2018 Elsevier Ltd. All rights reserved. |
China's long-term low carbon transition pathway under the urbanization process (2019) 🗎🗎 | This study develops a comprehensive analysis framework and socio-economic energy system model that interlinks demographic change and energy system in order to analyze the urbanization process and its relation with China's long-term CO2 emissions trend as China' economy enters the "new normal" stage. The results show that, around 300 million people are expected to migrate from rural areas to urban areas by 2050 following a trend, in which people are moving gradually from small and medium city groups to large and super city groups. The migration trend together with the improvement in living standard will promote China's infrastructure construction, industry production, and energy service demand growth. Under the Business as Usual (BAU) scenario, total primary energy consumption in China will reach 8.4 Gtce by 2050, energy-related CO2 emissions will increase to 17.6 Gt, which is 83% higher than the 2013 level. While in the Low Carbon Transition (LCT) scenario with technology innovation, the total primary energy demand for China in 2050 could be controlled at similar to 6 Gtce; CO2 emissions would peak during 2020-2025, and be reduced by 78% by 2050 compared to the BAU scenario. In the transition process, non-fossil fuel power generation and energy efficiency technologies have the largest mitigation potentials. Industry and power sectors would peak first before 2020, followed by the building and transport sectors which are projected to peak around 2030. The total additional capital investment required for LCT would account for 1.5% of GDP. Therefore, it is technologically and economically feasible for China to implement new urbanization strategy. |
Structural path and decomposition analysis of aggregate embodied energy and emission intensities (2019) 🗎🗎 | Aggregate energy and emission intensities have respectively been widely used to measure the overall performance of energy consumption and environmental pollution from the production perspective. Recently, Su and Ang (2017) propose the aggregate embodied intensity (AEI) indicator, defined as the ratio of embodied energy (or emissions) to embodied value added, to analyze the relationship between energy (or emissions) and value added or GDP from the demand perspective using the input-output (I-O) framework. Besides I-O analysis, structural path analysis (SPA) can be used to split the I-O analysis results into different layers to extract the important paths in terms of energy consumption and the resulting emissions. This paper incorporates the SPA technique with the AEI indicators and structural decomposition analysis (SDA) technique in the context of energy and emission studies. An empirical study using China's 2007 and 2012 datasets is presented to illustrate the AEI at the detailed transmission layers, show their relationships with the AEI indicators at different levels, and further investigate the driving forces to the changes of these AEI indicators. The proposed multi-level AEI framework can also be applied to other indicators and extended to multi-country/region analysis. (C) 2019 Elsevier B.V. All rights reserved. |
Comparing electricity consumption trends: A multilevel index decomposition analysis of the Genevan and Swiss economy (2019) 🗎🗎 | Switzerland, including the canton of Geneva, aims to reduce its electricity consumption following its decision to phase out nuclear electricity production. To investigate whether national policies and a regional programme, both of which aim at improving electricity efficiency, may have had an effect, we disentangle the effects of changes in economic structure, overall economic activity and structure-corrected energy intensity (SCEI) on the electricity consumption in the canton of Geneva and in Switzerland on multiple aggregation levels. The primary sector being negligibly small, we define the economy as the secondary and tertiary sector. Our analysis shows that changes in electricity consumption in the Genevan and Swiss economy were mainly caused by changes in activity and SCEI, although structural changes were not negligible. Specifically, we have shown that correcting for structural change may significantly impact comparisons between regional Sal trends. Our analysis shows that economy-wide electric energy efficiency improved both in Geneva and Switzerland. This is the case both for the time period 2000-2007 (first period, before implementation of the energy efficiency programme [EEP] eco21 in Geneva) and for the period 2008-2014 (second period, after implementation of the EEP, excluding 2015). In Switzerland, the average reductions in SCEI in the study periods were similar: 1.2% per year in the first period and 1.0% per year in the second period. The average reduction in SCEI in Geneva was faster in the second period (2.6% per year) than in the first period (1.5% per year). These findings suggest that national energy efficiency policies that have been in place since 2001 have been effective. A strong increase in SCEI by 4.6% occurred in Geneva in 2015, diminishing the gap between the Genevan and Swiss SCEI. We found that this increase was likely to be caused by weather effects. When including the year 2015, the average reduction in SCEI in the second period was 1.7% for Geneva and 1.1% for Switzerland. Moreover, we have shown that weather effects were more strongly correlated with SCEI than with absolute electricity consumption, highlighting the importance of correcting for structure and weather effects. Further, weather effects made it difficult to identify contributions from the EEP to the reduction of the Genevan SCEI. We found that the applicability IDA for tracing the effectiveness of EEPs needs to be critically assessed in view of data availability, confounding influences such as weather effects as well as the maturity and size of the EEP. (C) 2019 Elsevier B.V. All rights reserved. |
The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China's Industries (2019) 🗎🗎 | China's industrial sectors have an approximate consumption amounting to 70% of the aggregate power of the entire country. Investigating the driving forces of the decline in the energy intensity is essential for accelerating China's conversion into a low-carbon economy. Nowadays, there has been no agreement as yet when it comes to the impacts of China's industrial sectors on energy intensity. The current research work studies the impacts of key driving forces, in particular foreign as well as indigenous innovations, on China's industrial energy intensity in 34 industrial sectors between 2000 and 2010. Linear and nonlinear analysis methodologies are put to use. The linear empirical findings show that indigenous innovation primarily contributes to driving down the industrial energy intensity across the sampling duration. The foreign innovations, which take the shape of FDI as well as imports, are seen as benefiting the decline in industrial energy intensity; on the other hand, exports ramp up the industrial energy intensity. An additional investigation, on the basis of the panel threshold framework, indicates that the impact of foreign innovations by means of openness as well as industrial energy intensity has an association with the technological absorptive potential. The empirical evidence puts forward some pivotal inferences for policymakers with regard to China's declining industrial energy intensity-for instance, exploitation of the maximum benefit associated with the technology spillovers; in addition, it is important to take into consideration the attributes and scenarios that impact industrial energy intensity. |
Decomposition of Cameroon's CO2 emissions from 2007 to 2014: an extended Kaya identity (2019) 🗎🗎 | To effectively combat global warming, an enormous reduction in CO2 emissions is required. Cameroon, which is currently the largest emitter of CO2 in the CEMAC subregion, has committed to reducing its greenhouse gas emissions by 32% by 2035. However, previous studies in Cameroon have only addressed the relationship between economic growth, energy consumption, and CO2 emissions without estimating all causal relationships at the same time. Moreover, no study has yet decomposed this country's CO2 emissions to date. To fill these research gaps and further assess the determinants of these CO2 emissions, an extended Kaya identity and the Logarithm Mean Divisia Index (LMDI I) have been applied in this paper to identify, quantify, and explain the main drivers of Cameroon's CO2 emissions from 2007 to 2014. Seven effects were measured and the main findings show that carbon intensity and the emission factor increased by 0.57% and 107.50% respectively. Regarding contributions to the increase of CO2 emissions, the population effect was the most positive followed by the activity effect, whereas the energy intensity, the substitution of fossil fuels and the penetration of renewable energies have contributed to reduce the CO2 emission. To enable Cameroon to not only achieve the goals of its vision but also develop a low-carbon economy, this paper provides some proposed avenues that should be considered by policymakers. |
Estimating the impacts of emissions trading scheme on low-carbon development (2019) 🗎🗎 | As the main climate policy, emissions trading scheme (ETS) has been proved by scholars to have significant impacts on carbon dioxide (CO2) emissions and energy consumption reduction worldwide. However, existing studies have focused mainly on the simulated impacts of ETS and few studies have poured specific attention into the actual impacts and their dynamical change, which may lead to an ambiguous understanding of ETS's performance. Here, taking China as the case study, this study investigated the net dynamic impacts of ETS policy on low-carbon development with respect to CO2 emissions, carbon intensity, energy consumption and energy intensity using a difference-in-differences method. The findings indicate that there is a positive relationship to some extent between carbon trading system and low-carbon transformation. The effect of ETS policy on low-carbon development will gradually increase over the time. When the variables population, gross domestic product, ratio of the secondary industry, technical level, and income level are controlled, these conclusions are also robust. In addition, common trend hypothesis and counterfactual test were used to confirm the reliability of the conducted difference-in-differences models. The findings are conductive to the future ETS policy-making and the research framework proposed in this study is also applicable to the assessment of global climate policy. (c) 2019 Elsevier Ltd. All rights reserved. |
A synthesized factor analysis on energy consumption, economy growth, and carbon emission of construction industry in China (2019) 🗎🗎 | Climate change calls for worldwide public concern and effort to cut down carbon dioxide (CO2) emissions and globally realize sustainable development. As one of the largest energy consumers, the construction industry plays a crucial role in achieving the national carbon emission reduction goal. This paper, by employing an improved Kaya model, explored the relationships and trends among carbon emission, energy consumption, and GDP growth rate and carbon emission intensity of the Chinese construction industry. Results showed that the carbon emission of the construction industry is mainly affected by GDP growth of construction scale. The energy consumption was the main driver to the increase of carbon emissions. The carbon emissions of unit area and carbon intensity showed a decreased trend with the development of economy and the increase of construction scale. The dependence of construction activities on the fossil fuel remained despite noticeable decrease. Energy intensity helps to reduce construction carbon emissions. Low carbon economy development of construction industry calls for technological innovation, alternative energy and new technical support for further breakthrough. These findings provide scientific evidence of carbon emissions in the Chinese construction industry and useful inputs for industry-specific emission regulations. |
Energy saving and emission reduction of fossil energy based on low carbon economy and its consumption structure optimization (2019) 🗎🗎 | Energy saving and emission reduction have been not only a slogan but also a policy in this modern society where the phenomenon of greenhouse is exacerbated. In this study, calculation method of carbon emission and integrated parallel acquisition technique (IPAT) scenario prediction model were combined to predict the changes of total carbon emissions, energy structure distribution, and carbon emission intensity under three measures of energy saving and emission reduction in the next ten years in Shandong, China. The results showed that the total carbon emission increased year by year, and the coal ratio and carbon emission intensity decreased under the natural scenario; the total carbon emission in the weakly constrained scenario would increase annually until 2029, the amplitude was smaller than that of the natural scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than that of the natural scenario. Under the strongly constrained scenario, the total carbon emission would increase annually before 2025, and the amplitude was smaller than the weakly constrained scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than the weakly constrained scenario. |
Carbon Emission Evaluation Based on Multi-Objective Balance of Sewing Assembly Line in Apparel Industry (2019) 🗎🗎 | Apparel manufacturing is an industry with high energy consumption and carbon emissions. With the development of the low-carbon economy, low-carbon production in the apparel manufacturing industry become more and more imperative. The apparel industry is encountering great challenges in reducing carbon emissions. Garment sewing comprises a large number of processes, machines and operators. However, the existing studies lack quantitative analysis of carbon sources in the sewing process. This study analyzed the carbon emission characteristics in garment sewing production. Evaluation models of carbon emission were established for the sewing process in this research and the factors of fabrics, accessories, sewing machines and operators were included in the models. The results showed that fabrics and accessories were the main sources of carbon emissions in garment sewing production. The second largest carbon emission source was sewing machines, followed by operators. According to the evaluation models, the number of machines, operators and the utilization rate of the machines were related to the balance of the assembly line. A multi-objective optimization model aimed at minimizing the time loss rate and smoothness index of the assembly line was established, and a fast and elitist multi-objective genetic algorithm was used to obtain the solution for carbon emission reduction. The men's shirt assembly lines, based on three types of workstation layouts (the order of processes, the type of machines and the components of the garment), were applied to verify the effectiveness of the model and algorithm. The results indicated that the total carbon emissions of the three assembly lines based on balance optimization were less than that of the normal assembly line. The assembly line of the workstations arranged in the order of processes was the best assembly line since it had the highest efficiency and the lowest carbon emissions. |
Assessing Ghana's carbon dioxide emissions through energy consumption structure towards a sustainable development path (2019) 🗎🗎 | The CO2 emission that accompanies significant consumption of energy raises concern as for whether economies that require such path like Ghana can also achieve sustainable economic development. In this paper, the driving factors of energy-related CO2 emissions and its future trend for Ghana is studied using a time series data spanning from 1980 to 2016 in an Autoregressive Distributed Lag Model with Extended Kaya Identity framework. First Kaya identity was adopted and extended to decomposed CO2 emission driving factors into carbon intensity, energy intensity, economic activity, and substitution effect. The estimates were further used to forecast CO2 emissions through to 2030. The results indicate the major driver for historical CO2 emissions increase in Ghana has been the transition from biomass to petroleum fuel consumption. This is followed by the energy intensity of economic output, carbon intensity changes and overall economic activity. Carbon-free energy consumption currently does not lead to a reduction of CO2 in Ghana. The forecasting results show the current trend of energy consumption and economic development path have the potential for CO2 emissions reduction. Some targeted policy suggestions in relation to the estimate results are also provided. (C) 2019 Elsevier Ltd. All rights reserved. |
Scenario Analysis on Energy Consumption and CO2 Emissions Reduction Potential in Building Heating Sector at Community Level (2019) 🗎🗎 | Energy consumption and carbon emissions of building heating are increasing rapidly. Taking Liaobin coastal economic zone as an example, two scenarios are built to analyze the potential of energy consumption and CO2 emissions reduction from the aspects of laws, regulations, policies and planning. The baseline scenario refers to the traditional way of energy planning and the community energy planning scenario seeks to apply community energy planning within the zone. Energy consumption and CO2 emission are forecast in two scenarios with the driving factors including GDP growth, changes in population size, energy structure adjustment, energy technology progress, and increase of energy efficiency. To improve accuracy of future GDP and population data prediction, an ARIMA (Autoregressive Integrated Moving Average model) (1,1,1) model is introduced into GDP prediction and a logistics model is introduced into population prediction. Results show that compared with the baseline scenario, energy consumption levels in the community energy planning scenario are reduced by 140% and CO2 emission levels are reduced by 45%; the short-term and long-term driving factors are analyzed. Policy implications are given for energy conservation and environmental protection. |
Factor analysis of projected carbon dioxide emissions according to the IPCC based sustainable emission scenario in Turkey (2019) 🗎🗎 | The Greenhouse gas emissions one of the hot topics all around the world which are causing climate change. In the face of anthropogenic climate change, increasing the carbon dioxide emissions as well as the development of Turkey seems to be a serious challenge for the success of global carbon dioxide emission reduction efforts. A decomposition analysis of historical and projected carbon dioxide emissions from fossil fuel combustion of Turkey has been investigated by using the Logarithmic Mean Divisia Index technique by considering in particular carbon intensity, energy mix, energy intensity, affluence and population effects. The projected data have been obtained from International Panel on Climate Change based sustainable emission scenario. The results show that the all effects on carbon dioxide emissions were positive for historical evaluation. On the other hand, carbon intensity, energy mix and energy intensity effects on carbon dioxide emissions were negative while affluence and population effects were still positive for projected evaluations. Therefore, decision makers should be reconsider the carbon dioxide emissions reduction targets and some related policies. (C) 2018 Published by Elsevier Ltd. |
Meeting 2030 primary energy and economic growth goals: Mission impossible? (2019) 🗎🗎 | To meet climate change mitigation objectives, international institutions have adopted targets aimed at reducing or ending growth of primary energy consumption. Simultaneously, continued economic growth is forecasted to meet human development goals. Together, declining energy consumption and rising gross domestic product (GDP) is called "absolute decoupling." However, absolute decoupling is unprecedented for the world economy as a whole (since at least 1971). Is absolute decoupling "Mission impossible?" Given the high stakes, we need a clearer understanding of the extent of future energy-GDP decoupling. To gain that understanding, we perform societal exergy analyses using a novel Physical Supply Use Table framework to assess historical and future trends of primary energy consumption and economic growth for one medium human development index country and one very high human development index country, Ghana and the United Kingdom (UK), respectively. Three key results are obtained. First, we find that it will be very difficult to absolutely decouple primary energy consumption from economic activity. This is particularly true for Ghana's rapidly growing economy, where projected economic growth of 5.0 %/year will require growth of primary energy consumption of around 2.0 %/year. It is also true for the UK, where at best primary energy consumption appears constant into the future to provide a projected GDP growth of 2.7 %/year. Second, we find that energy efficiency is not an effective means to reduce primary energy consumption and associated carbon dioxide emissions due to economy-wide feedback effects, placing greater importance on decarbonizing the primary energy supply. Third, we find primary energy intensity is not an appropriate metric to measure energy reduction progress, because meeting primary energy intensity targets does not ensure absolute decoupling will occur. At present, absolute decoupling appears to be mission impossible. |
Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China (2019) 🗎🗎 | China has initiated various dedicated policies on clean energy substitution for polluting fossil-fuels since the early 2010s to alleviate severe carbon emissions and environmental pollution and accelerate clean energy transformation. Using the autoregressive integrated moving average (ARIMA) regression, we project the potentials of substituting coal and oil with clean energy for different production sectors in China toward the year 2030. Based on the projections, a dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine: the impacts of future clean energy substitution on China's energy production, outputs of non-energy sectors, macro-economy, and CO2 emissions. First, we found that most production sectors are projected to replace polluting fossil-fuels with clean energy in their terminal energy consumption in 2017-2030. Second, clean energy substitution enables producing green co-benefits that would enable improvements in energy production structure, reductions in national CO2 emissions, and better real GDP and employment. Third, technological progress in non-fossil-fuel electricity could further benefit China's clean and low-carbon energy transformation, accelerating the reduction in CO2 emissions and clean energy substitution. Furthermore, the most beneficiary are energy-intensive and high carbon-emission sectors owing to the drop in coal and oil prices, while the most negatively affected are the downstream sectors of electricity. Through research, various tentative improvement policies are recommended, including financial support, renewable electricity development, clean energy utilization technology, and clean coal technologies. |
Forecasting of CO2 emissions in Iran based on time series and regression analysis (2019) 🗎🗎 | Iran has become one of the most CO2 emitting countries during the last decades. The country ranks after Japan and Germany in terms of CO2 emissions. However, from an economic viewpoint, the gross domestic product (GDP) of Iran is lower than the summation of Berlin and Tokyo GDP. Moreover, a large proportion of Iran's revenue comes from the crude oil export; therefore, this level of CO2 emission cannot be economically driven and is as a result of high energy intensity in this country. This is while the government also has not a clear program in this regard. The Sixth Five-year Development Plan of Iran, in addition, sets a number of ambitious targets mostly regarding the energy intensity, GDP growth, and renewable energies, but does not mention to CO2 emission issue. Therefore, prospects for an early settlement of the dispute are seemingly dim. Our aim is to predict Iran's CO2 emissions in 2030 under assumptions of two scenarios, i.e. business as usual (BAU) and the Sixth Development Plan (SDP), using multiple linear regression (MLR) and multiple polynomial regression (MPR) analysis. Findings suggest that Iran most likely will not meet its commitment to the Paris Agreement under the BAU's assumptions; however, full implementation of the ambitiously shaped SDP could have met the target by end 2018. (C) 2019 Published by Elsevier Ltd. |
Assessing net energy consumption of Australian economy from 2004-05 to 2014-15: Environmentally-extended input-output analysis, structural decomposition analysis, and linkage analysis (2019) 🗎🗎 | This paper provides a comprehensive analysis of Australian net energy consumption between 2004-05 and 2014-15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system. |
Application of artificial neural networks for testing long-term energy policy targets (2019) 🗎🗎 | The paper analyses a model of the EU energy system by means of artificial neural networks. This model is based on the prediction of CO2 emissions until 2050 taking into account the current Energy Policy of the EU. The results show that artificial neural networks model this system very well and that this model has the ability to predict the behaviour of CO2 emissions. This will also enable timely response and correction of energy and economic strategy by changing the value of the relevant indicators in order to achieve the ambitious planned reductions of CO2 emissions by 2050. These plans are specified in the Energy Roadmap 2050 document of the European Commission from 2012 and promote economically cost-effective scenarios that will adapt the European Union's economy to the needs of environmental protection and the reduction of energy consumption. Several structures of Artificial Neural Networks were analysed in order to select the best one for modelling large energy systems. It was determined that the model with the Cascade Forward Back Propagation structure with numerous specific indicators can model such energy systems and predict of CO2 emissions with acceptable accuracy. (C) 2019 Elsevier Ltd. All rights reserved. |
Drivers of CO2 emissions from power generation in China based on modified structural decomposition analysis (2019) 🗎🗎 | Currently, over 20% of global electricity and 30% of global CO2 emissions from fuel combustion are generated in China. Understanding the driving forces of CO2 emissions from power generation is critical for both decarbonizing the power sector and achieving national carbon reduction targets. The objectives of this study were to identify critical driving forces behind changes in CO2 emissions from the power sector and to propose appropriate decarbonization pathways. First, the generation and demand structures of the power sector were introduced into a CO2 emission accounting model and decomposition analysis. Instead of traditional input output analysis, structural decomposition analysis modified using a power transmission table was used to investigate the impacts of five driving factors of CO2 emissions from China's power generation. The five driving factors comprised the proportion of thermal power, power generation technology, power generation structure, power demand structure and power demand, whereby the latter was divided further into nine detailed parts. Considering five regional power grids in China, the contributions of these factors were analyzed at both national and regional level. The results showed that the majority of the increase in CO2 emissions during 2007-2012 could be attributed to electricity generation (96.2%) driven by changes in power demand, which should be the key to power sector decarbonization. By contrast, 30.7% of emissions were offset by changes in the proportion of thermal power and technology, demonstrating the obvious effects of China's policy on clean energy transition. Additionally, all power grids exhibited an increase in CO2 emissions from electricity generation, with the east and central grids accounting for 64% of the national increase. Power transmission structure had only a small impact on CO2 emissions from power generation. By using the electricity transmission table, we modified SDA to overcome the time lag issues and eliminate the reliance on 10 data, and continuous annual data rather than aggregated five-yearly data can be used to capture the structural effects, thus providing more precise results for the driving forces of emission changes. Our case study shows that there is huge potential in extending the 10-based SDA method to other trade-related studies. (C) 2019 Elsevier Ltd. All rights reserved. |
Sulfur dioxide pollution and energy justice in Northwestern China embodied in West-East Energy Transmission of China (2019) 🗎🗎 | Severe air pollution in China is primarily caused by heavy demands for energy, especially from fossil fuels. Having the majority of China's energy resources, northwestern China played an increasingly significant role in China's energy supply over the past two decades, but the associated environmental consequences and energy justice are almost unknown or ignored. Here we conduct extensive model simulations using a multi-regional input-output (MRIO) model to measure sulphur dioxide (SO2) emissions resulting from the interprovincial trade associated with the west-east energy transmission in China. We examine the environmental consequences using a coupled weather forecast-atmospheric chemistry model. We show that SO2 emissions from the virtual west-east energy transmission accounted for over 40% of total SO2 emissions in northwestern China in the 2000s. Accordingly, 35-52% of SO2 atmospheric concentrations in this region could be attributed to the virtual west east energy transmission for the same period. At some of the large-scale national energy and chemical industry bases in northwestern China, SO2 concentrations induced by the energy supply due to the demand from eastern China exceeded 60%. A tagging technique was employed to identify the source-receptor relationship of SO2 emissions embodied in west-east energy transmission by estimating the sensitivity and efficiency of the energy demanding regions to energy and heavy industry products. The results discerned eastern China to be a major sensitive energy demand region causing SO2 emission from the energy and high energy consuming industries in northwestern China. We propose that the Chinese authorities should subsidize the environmental losses in northwestern China subject to the virtual west-east energy transmission to promote energy justice. |
Is carbon emission decline caused by economic decline? Empirical evidence from Russia (2019) 🗎🗎 | Russia's energy-related carbon emission decreased by roughly 30% between 1992 and 2017. Previous studies reported that economic recession led to carbon emission reduction in Russia during 1990s. This paper aims to examine whether the economic recession remains to lead to a decline in Russia's carbon emission for 1992-2017. The results show that not economic recession, but improving energy efficiency is the most significant contributor to decreasing Russia's carbon emission from 1992 to 2017. Economic recession is the major contributor to the decrease in Russian carbon emission only before the new century and then reversed to the leading contributor to the increase in carbon emission. This research also finds that a shift to less carbon-intensive fuel and decrease in population also contribute to offsetting carbon emission in Russia. Thus, this research argues that the cause for the decline in Russia's carbon emission for 1992-2017 is not economic recession. Indeed, Russia's economic activity and change in carbon emission have been delinked since the new century. It can be concluded that the reduction in Russia's carbon emission during 1992-2017 arises from a combination of improving energy efficiency, a shift to less carbon-intensive fuel, and decrease in population. |
The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model (2019) 🗎🗎 | This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020-2036) and analyze the relationships among causal factors by applying a structural equation modeling/vector autoregressive model with exogenous variables (SEM-VARIMAX Model). This model is effective for analyzing relationships among causal factors and optimizing future forecasting. It can be applied to contexts in different sectors, which distinguishes it from other previous models. Furthermore, this model ensures the absence of heteroskedasticity, multicollinearity, and autocorrelation. In fact, it meets all the standards of goodness of fit. Therefore, it is suitable for use as a tool for decision-making and planning long-term national strategies. With the implementation of the Sustainable Development Policy for Energy Consumption under Environmental Law (S.D.EL), the forecast results derived from the SEM-VARIMAX Model indicate a continuously high change in energy consumption from 2020 to 2036the change exceeds the rate determined by the government. In addition, energy consumption is predicted to have an increased growth rate of up to 185.66% (2036/2020), which is about 397.08 ktoe (2036). The change is primarily influenced by a causal relationship that contains latent variables, namely, the economic factor (ECON), social factor (SOCI), and environmental factor (ENVI). The performance of the SEM-VARIMAX Model was tested, and the model produced a mean absolute percentage error (MAPE) of 1.06% and a root-mean-square error (RMSE) of 1.19%. A comparison of these results with those of other models, including the multiple linear regression model (MLR), back-propagation neural network (BP model), grey model, artificial neural natural model (ANN model), and the autoregressive integrated moving average model (ARIMA model), indicates that the SEM-VARIMAX model fits and is appropriate for long-term national policy formulation in various contexts in Thailand. This study's results further indicate the low efficiency of Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand. The predicted result for energy consumption in 2036 is greater than the government-established goal for consumption of no greater than 251.05 ktoe. |
Impact of China-Pakistan economic corridor on Pakistan's future energy consumption and energy saving potential: Evidence from sectoral time series analysis (2019) 🗎🗎 | China-Pakistan Economic Corridor is a strategic economic project to enhance economic connectivity between Pakistan and China. We estimate the impact of CPEC related economic activities on overall energy consumption and its saving potential for Pakistan by 2030. Johansen cointegration is employed to evaluate the long run relationship between energy consumption and its determinants at aggregate and sectoral levels and forecast future energy demand using scenario analysis. Baseline scenario results indicate that aggregate energy consumption will approximately be 41% higher in 2030 compared to 2013 levels. Energy consumption in industrial and commercial sectors in 2030 will increase by 136% and 414% under baseline scenario. The Monte Carlo Simulations incorporating future uncertainty support the scenario analysis results. Energy saving potential suggests that energy conservation should be part of overall energy policies and sector specific energy intensity targets should be implemented to harness the energy saving potential. |
The impact of British Columbia's carbon tax on residential natural gas consumption (2019) 🗎🗎 | We estimate the effect of British Columbia's (BC) carbon tax on per capita residential natural gas consumption using panel data regression and synthetic control models. We use province and state-level data documenting annual natural gas consumption from 1990 through 2014. Results from the panel data regression model suggest that the carbon tax substantially reduced residential natural gas consumption. Our preferred approach is the synthetic control method that we use to select a group of provinces and states against which BC's residential natural gas consumption trends can be compared. Using the synthetic control approach we find that the BC carbon tax reduced per capita residential natural gas consumption by approximately 7%. (C) 2018 Elsevier B.V. All rights reserved. |
Using machine learning tools for forecasting natural gas consumption in the province of Istanbul (2019) 🗎🗎 | Commensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods. (C) 2019 Elsevier B.V. All rights reserved. |
Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging (2020) 🗎🗎 | The accident risk of severe (>= 5 fatalities) accidents in fossil energy chains (Coal, Oil and Natural Gas) is analyzed. The full chain risk is assessed for Organization for Economic Co-operation and Development (OECD), 28 Member States of the European Union (EU28) and non-OECD countries. Furthermore, for Coal, Chinese data are analysed separately for three different periods, i.e., 1994-1999, 2000-2008 and 2009-2016, due to different data sources, and highly incomplete data prior to 1994. A Bayesian Model Averaging (BMA) is applied to investigate the risk and associated uncertainties of a comprehensive accident data set from the Paul Scherrer Institute's ENergy-related Severe Accident Database (ENSAD). By means of BMA, frequency and severity distributions were established, and a final posterior distribution including model uncertainty is constructed by a weighted combination of the different models. The proposed approach, by dealing with lack of data and lack of knowledge, allows for a general reduction of the uncertainty in the calculated risk indicators, which is beneficial for informed decision-making strategies under uncertainty. |
Does China's carbon emissions trading scheme really work? A case study of the hubei pilot (2020) 🗎🗎 | China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei's industrial carbon dioxide (CO2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO2 emissions, gross domestic product (GDP), energy consumption intensity, and CO2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS. (C) 2020 Elsevier Ltd. All rights reserved. |
Will China Achieve Its Ambitious Goal?-Forecasting the CO2 Emission Intensity of China towards 2030 (2020) 🗎🗎 | China has set out an ambitious target of emission abatement; that is, a 60-65% reduction in CO2 emission intensity by 2030 compared with the 2005 baseline level and emission peak realisation. This paper aimed to forecast whether China can fulfil the reduction target of CO2 emission intensity and peak by 2030 based on the historical time series data from 1990 to 2018. Four different forecasting techniques were used to improve the accuracy of the forecasting results: the autoregressive integrated moving average (ARIMA) model and three grey system-based models, including the traditional grey model (1,1), the discrete grey model (DGM) and the rolling DGM. The behaviours of these techniques were compared and validated in the forecasting comparisons. The forecasting performance of the four forecasting models was good considering the minimum mean absolute percentage error (MAPE), demonstrating MAPE values lower than 2%. ARIMA showed the best forecasting performance over the historical period with a MAPE value of 0.60%. The forecasting results of ARIMA indicate that China would not achieve sufficient reductions despite its projected emission peak of 96.3 hundred million tons by 2021. That is, the CO2 emission intensity of China will be reduced by 57.65% in 2030 compared with the 2005 levels. This reduction is lower than the government goal of 60-65%. This paper presents pragmatic recommendations for effective emission intensity reduction to ensure the achievements of the claimed policy goals. |
Life Cycle Cost, Energy and Carbon Assessments of Beijing-Shanghai High-Speed Railway (2020) 🗎🗎 | The Beijing-Shanghai High-Speed Railway (HSR) is one of the most important railways in China, but it also has impacts on the economy and the environment while creating social benefits. This paper uses a life cycle assessment (LCA) method and a life cycle cost (LCC) analysis method to summarize the energy consumption, carbon emissions and costs of the Beijing-Shanghai HSR from the perspective of life cycle, and proposes some corresponding suggestions based on the results. The research objective of this paper is to analyse the carbon emissions, energy consumption, and costs of the rail system which includes the structure of the track and earthwork of the Beijing-Shanghai HSR during four stages: conception stage, construction stage, operation and maintenance stage, and disposal stage. It is concluded that the majority of the carbon emissions and energy consumption of the entire rail system are from the construction stage, accounting for 64.86% and 54.31% respectively. It is followed by the operation and maintenance stage with 31.60% and 35.32% respectively. In contrast, the amount of carbon emissions and energy consumption from the conception stage is too small to be considered. Furthermore, cement is the major contributor to the carbon emissions and energy consumption during the construction stage. As for the cost, the construction stage spends the largest amount of money (US$4614.00 million), followed by the operation and maintenance stage (US$910.61 million). Improving production technologies and choosing construction machinery are proposed to reduce the cost and protect the environment. |
Study on the forecast model of electricity substitution potential in Beijing-Tianjin-Hebei region considering the impact of electricity substitution policies (2020) 🗎🗎 | In recent years, China has introduced a series of energy substitution policies, aiming to achieve a fundamental change in the way of energy development. Firstly, based on the world energy model constructed by IEA, this paper determines the main driving factors in the field of power substitution, including economy, technology, policy, energy price and environmental factors. Secondly, based on the principle of system dynamics, a causal relationship diagram of five driving factors is constructed to clarify the feedback and linkage relationship between the factors. Finally, this paper proposes a hybrid prediction model combining Salp Swarm Algorithm (SSA) and Least Squares Support Vector Machines (LSSVM), namely SSA-LSSVM, which fully considers the influence of relevant factors of electric energy substitution, while avoiding the subjectivity of model parameter setting. The results show that the future development of electric energy substitution in the Beijing-Tianjin-Hebei region has maintained a rapid pace. The potential for electric energy substitution in the industrial production sector is large, and there is a large space for mining and operation optimization in the residential heating and transportation sectors. This paper provides a basis for the future development and improvement of the strategic plan of electric energy substitution in Beijing-Tianjin-Hebei region. |
Determinants of carbon inequality in China from static and dynamic perspectives (2020) 🗎🗎 | Carbon inequality (CI) is one of the core issues concerning social welfare and economic development. To overcome the disadvantages of the widely used Theil economic inequality index, which does not consider technical efficiency and technological change, we developed static and dynamic inequality analysis tools by incorporating production-theoretical decomposition analysis, a logarithmic mean Divisia index, and a Shapley/Sun method into the Theil index decomposition model. We then investigated the determinants of China's CI during the period 2003-2015 using the original and modified Theil index decomposition models. Our key findings are: 1) national CI experienced a downward trend with some fluctuations over time; 2) intragroup inequality was the main driver of these changes using the original approach; 3) the disparities in industrialization, investment scale, income, population share, potential carbon factor, energy usage technical efficiency, and CO2 emission technological change accounted for the decreases in CI, whereas the disparities in investment rate, potential energy intensity, industrial investment-output share, energy usage technological change and CO2 emission technical efficiency contributed to increases in CI. We offer some policy proposals based on our results. (C) 2020 Elsevier Ltd. All rights reserved. |
Targeting carbon emissions mitigation in the transport sector - A case study in Urumqi, China (2020) 🗎🗎 | Promoting changes in the transport energy mix is critical to reducing carbon dioxide emissions from the transport sector. The development of zero-carbon energy or low-carbon energy has thus become an accelerating global trend. This work develops a systematic integrated framework to support carbon reduction for an urban transport system. Firstly, a transport metabolism model is proposed. Secondly, based on the Intergovernmental Panel on Climate Change (IPCC) carbon accounting approach, carbon emissions of passenger transport systems are calculated. According to carbon emissions reduction policies and the transport metabolism, four hypothetical scenarios are investigated. Under the constraints of carbon emissions targets and transport demands, the carbon emission pinch analysis (CEPA) method is used to determine the minimum demand for zero-carbon energy and optimize the energy mix of transport modes. Passenger transport in Urumqi, China is analyzed as an illustrative case study. For the optimal scenario, the zero-carbon energy (electricity) demand is 1.07x10(9) kWh. The optimal energy mix for private cars is that electricity, gasoline, and compressed natural gas usage accounting for 14.5%, 25.5%, and 60%, respectively. The unit emission reduction cost of policies is evaluated for the specific policies. The implementation priority of the policies is then determined. The potential transport mode shift is determined by a marginal cost diagram. The transporteenergy nexus is also considered. Results show that the most effective measure of carbon emissions reduction in the transport sector is fuel switching. General policy implications of these results for other urban centers are then discussed. (C) 2020 Elsevier Ltd. All rights reserved. |
Sectoral peak CO2 emission measurements and a long-term alternative CO2 mitigation roadmap: A case study of Yunnan, China (2020) 🗎🗎 | China has established national CO2 peak emission targets and launched a series of low-carbon pilot programs to tackle climate change. Research on regional peak CO2 emission targets and the establishment of mitigation roadmaps based on local characteristics is fundamental for achieving the national goals. This study analyzed the peak CO2 emission targets by applying the stochastic impacts by regression on population, affluence, and technology (STIRPAT) and long-range energy alternatives planning system (LEAP) models to data from Yunnan province, China. This study also simulated and measured the effectiveness of the CO2 abatement potential by decomposing the peak CO2 emissions for 14 subscenarios. The results showed that peak emissions will reach 200 Mt CO(2)e in Yunnan over a prolonged period from 2024 to 2028, and differences in the social-economic stages will result in different peak emissions from various sectors. Our research also found notable differences between different mitigation effects. Promoting hydro power, saving energy in the steel manufacturing and chemical sectors, employing highly efficient combustion engines in vehicles, developing public transport, and conserving urban residential energy should be considered as short-term primary actions. Clean-energy vehicles and saving energy in public buildings should be considered in the long term. Therefore, this study can provide a suitable reference to policymakers and stakeholders for identifying the most effective policy measures and actions for different sectors in low-carbon pilot planning programs. (C) 2019 Published by Elsevier Ltd. |
Decomposing factors affecting CO2 emissions in Pakistan: insights from LMDI decomposition approach (2020) 🗎🗎 | Carbon emissions have turned out to be one of the key alarming and complex issues which drive a long-lasting debate over climate change. The increasing trend in the usage of fossil fuels for the curse of economic development and at the same time reducing carbon emissions has become a significant phenomenon worldwide. In this study, we evaluate carbon emissions (CO2) during 1972-2016 by employing logarithmic mean Divisia index (LMDI) method. The results from decomposition using LMDI method indicate that the economic development factor is the main driving force for the increase of per capita carbon emissions in the country; the energy structure and energy efficiency are the restraining factor for per capita carbon emissions. Therefore, Pakistan should continue to upgrade energy structure from traditional sources to renewable energy sources to curb the increase of carbon emissions, and also, improve the efficiency of energy use and save energy to cope with environmental challenges. Finally, the study concludes with some policy suggestions. |
Modeling carbon emission trajectory of China, US and India (2020) 🗎🗎 | Developing more effective carbon mitigation strategies requires accurate forecasting of carbon emissions. This is especially true for China, the United States and India, which produce over half of the world's carbon emissions. China, the United States, and India represent three different types of carbon emissions data. The first data type is typical for China: rapid grows which is thereafter slowing down. The US carbon emissions data show volatile growth and decline. The third data type is common for India's data: accelerated growth. To deal with the volatility of the data and to improve the forecasting accuracy, this paper combines Metabolic Nonlinear Grey Model (MNGM) with Autoregressive Integrated Moving Average (ARIMA) to develop the combined MNGM-ARIMA model, and MNGM with Back Propagation Neural Network (BPNN) to develop the new combined MNGM-BPNN model. In this way, ARIMA and BPNN could correct the modelling residual error of MNGM, to decrease the forecasting error. Through the quantitative data study of the countries in focus, we have made the following findings: First, the mean relative percent errors of the proposed MNGM-ARIMA and MNGM-BPNN models are 1.35% and 1.57%, which are lower than the traditional MNGM, ARIMA, and BPNN models. Besides, it is projected that during the period of 2019-2030, the US' carbon emissions will keep a downward trend, while carbon emissions in China and India will continue to grow. Finally yet importantly, in the future periods of time, the carbon emissions growth rate in India will be faster than that for China. The above findings have led us to the following conclusions: (1) Error values of those two proposed approaches have proven that the strategy of correcting the forecasting error of the previous models with the latter models can effectively improve the forecasting accuracy. (2) The proposed two combined forecasting techniques have shown sound performance for the three types of data. It is reasonable to believe that new approaches will help to improve insight into better forecasting carbon emissions in other countries and regions of the world. (3) China and India will remain the major sources of global carbon emissions, and should therefore continue their efforts on carbon emission reduction. (C) 2020 Elsevier Ltd. All rights reserved. |
Multitemporal LMDI Index Decomposition Analysis to Explain the Changes of ACI by the Power Sector in Latin America and the Caribbean between 1990-2017 (2020) 🗎🗎 | This paper analyzes the drivers behind the changes of the Aggregate Carbon Intensity (ACI) of Latin America and the Caribbean (LAC) power sector in five periods between 1990 and 2017. Since 1990 the carbon intensity of the world has been reduced almost 8.8% whereas the carbon intensity of LAC countries only decreased 0.8%. Even though by 2017 the regional carbon intensity is very similar to the one observed by 1990, this index has showed high variability, mainly in the last three years when the ACI of LAC fell from 285 gCO(2)/kWh in 2015 to 257.7 gCO(2)/kWh. To understand what happened with the evolution of the carbon intensity in the region, in this paper a Logarithmic Mean Divisia for Index Decomposition Analysis (IDA-LMDI) is carried out to identify the accelerating and attenuating drivers of the ACI behavior along five periods. The proposal outperforms existing studies previously applied to LAC based upon a single period of analysis. Key contributions are introduced by considering the type of fuel used in power plants as well as specific time-series of energy flows and CO2 emissions by country. Results reveal structural reasons for the increase of the ACI in 1995-2003 and 2008-2015, and intensity reasons for the decrease of the ACI in 1990-1995, 2003-2008 and 2015-2017. |
Exploring the efficiency of new energy generation: Evidence from OECD and non-OECD countries (2020) 🗎🗎 | In this study, we defined new energy generation inputs as the installed capacity of solar energy, wind power, geothermal energy and biofuel production, and we defined electricity from new energy as an output indicator. Based on panel data in OECD and non-OECD countries from 2007 to 2016, we used stochastic frontier analysis to calculate the efficiency of new energy generation and analyzed the influencing factors. We found the following results: the efficiency of global new energy generation is improving; the energy price, technological progress and education level have positive impacts on the efficiency of new energy generation; and industrial structure and opening up have a negative impact on the efficiency of new energy generation. Based on our study results, we offer some recommendations to promote the development of new energy generation. |
Energy use embodied in international trade of 39 countries: Spatial transfer patterns and driving factors (2020) 🗎🗎 | The energy embodied in international trade is transferred globally through trade links. The understanding of the energy flows embodied in international trade and what drives the variations in embodied energy use is of great significance for achieving the global goal of saving energy and reducing energy-related emissions. Thus, this research, in its first stage, calculated the energy use embodied in international trade of 39 countries from 1995 to 2011 by building a multiregional input-output model and described the spatial transfer patterns of energy flows using geo-visualization techniques. In the second stage, this paper applied the Logarithmic Mean Divisia Index (LMDI) approach to identify the driving factors of embodied energy use. The findings are as follows. (1) The aggregated embodied energy use of these 39 countries significantly increased during the sample period. ( 2) Regarding the flows of embodied energy use, these 39 countries can be classified into 3 groups, namely, energy-rich countries with net outflows (Group 1), developed countries (Group 2) and developing countries with net inflows (Group 3). (3) From the decomposition results of the LMDI method, both energy intensity and economic output are the main driving factors that affect embodied energy outflow and inflow changes in international trade. The improvement of energy intensity is the main contributor to reducing the increase in energy use embodied in international trade. Moreover, increases in embodied energy use are attributed to the growth of imports and exports, economic output, and population. On the other hand, upgrading industrial systems and optimizing industrial structure can contribute to reducing embodied energy use growth. Accordingly, policy recommendations are given. International trade plays a crucial role in assigning different shares of responsibility for energy-related emissions reduction. To formulate effective and efficient environmental policies, embodied energy use should be considered. Moreover, solutions to alleviate environmental pollution are improved energy use and extensive use of clean energy. (C) 2020 Elsevier Ltd. All rights reserved. |
Path Analysis of Beijing's Dematerialization Development Based on System Dynamics (2020) 🗎🗎 | Dematerialization is a phenomenon in which resource consumption and pollutant discharge decrease during economic development. In order to explore the optimal paths of Beijing's dematerialization, this study combines material flow analysis method and the Tapio decoupling model to construct a city dematerialization evaluation model, and establishes a system dynamics model to simulate the comprehensive dematerialization levels and the dematerialization levels of eight materials under four scenarios. The results show that the key factors affecting the dematerialization levels of resource and discharge end were non-metals consumption and CO2 emissions respectively. During 2016-2030, Beijing would achieve weak decoupling state under four scenarios, but the degree of dematerialization would be different. For the comprehensive dematerialization level, during 2017-2024, an industrial restructuring (IR) scenario, which would strengthen R&D investment and optimize the industrial structure, would be the optimal choice. During 2025-2030, an environmental governance (EG) scenario, which means increasing the investment in pollution control, would bring about the best dematerialization level. There would be differences in the optimal dematerialization paths for eight materials. For example, economic sustainable degrowth (ESD) and EG scenarios would be the optimal paths for dematerialization of atmospheric pollutants in the period 2017-2021 and 2022-2030, respectively. |
Inequality and convergence in energy intensity in the European Union (2020) 🗎🗎 | Disparities in energy intensity across the European Union member countries are large, especially after the enlargement that took place in 2004 when Central and Eastern European countries with high energy intensities accessed the European Union. All member countries have committed themselves to the target of reducing energy intensity, however the rate of change in energy intensity differs across countries. In this context, monitoring the convergence process among member countries is crucial to assess the progress in achieving the energy saving targets stated by the European Union. In this article, the convergence process in energy intensity is examined by using an approach based on inequality decomposition. The change in inequality is broken down into two components measuring-convergence in energy intensity and the re-ranking of countries within the energy intensity distribution, respectively. Since the change in inequality measures the relative variation of energy intensity dispersion, the inequality change in itself is a measure of-convergence. Moreover, the inequality change and its components are further decomposed to detect the spatial components of convergence and reranking. The convergence in energy intensity in the European Union from 2003 to 2014 is investigated. Results show that convergence mainly occurs in the first years of the period considered, whereas there is a slowdown of the convergence process in the following years. In this second phase, spatial effects on convergence and reranking are more evident. |
CO2 response to business cycles: New evidence of the largest CO2- Emitting countries in Asia and the Middle East (2020) 🗎🗎 | This paper examines CO2 response to business cycles of six large CO2- emitting countries in Asia and the Middle East, namely China, India, Japan, Iran, Saudi Arabia, and South Korea by using a Markov-switching autoregressive models. Emissions are cyclically more volatile than GDP in a typical country, except for Iran. It is observed that emissions are procyclical in all studied countries except for India, but the elasticity of the emission with respect to GDP significantly depends on regimes. In Japan and South Korea, elasticity of CO2 emissions with respect to GDP during expansions is significantly larger than during recessions. In Iran and Saudi Arabia, elasticity of CO2 emissions with respect to GDP is significantly smaller than 1, but the CO2 emissions response to GDP during recessions is significantly larger than during expansions. In China, elasticity of CO2 emissions to GDP is significantly larger than 1 during recessions and normal and CO2 emissions response to GDP during recessions is significantly larger than during expansions. These results have implications for environmental policy makers; the optimal response of pollution-abatement costs in Japan and South Korea should be high (low) during expansions (recessions) and in China, Iran and Saudi Arabia should be low (high) during expansions (recessions) after releasing a positive productivity shock. (C) 2019 Elsevier Ltd. All rights reserved. |
Quantile partial adjustment model with application to predicting energy demand in China (2020) 🗎🗎 | As the largest energy consumer, it is urgent for China to implement demand side management (DSM). This requires the accurate predictions of long-run energy demand and its short-run dynamic mechanism. To this end, we extend the conventional partial adjustment model into the framework of quantile regression and label it as quantile partial adjustment (QPA). The QPA model is able to investigate heterogeneous effects of drivers on energy demand, as well as to capture its whole conditional distribution. We conduct an empirical study on China's energy demand using annual data from 1990 to 2017. The empirical results show that there exists obvious heterogeneous effects, for instance, the inverse-U shaped adjustment speed. Moreover, we design three different scenarios to produce conditional density forecasts of energy demand for the next 12 years. We notice that bimodal curves or even multimodal curves emerge under three different scenarios. These findings imply that there are several possible intervals for long-run energy demand, which leaves enough space to formulate rational and sustainable energy policies in China. The further discussion at the provincial level obtains similar results and shows the obvious heterogeneity across provinces, which highlights the importance to take into account regional differences in energy DSM. (C) 2019 Elsevier Ltd. All rights reserved. |
Is there a decoupling relationship between CO2 emission reduction and poverty alleviation in China? (2020) 🗎🗎 | Whether CO2 emission reduction will inhibit poverty alleviation in a short time still remains unclear. In this paper, the extended linear expenditure system model and the CO2 emission accounting method were applied to measure the values of the poverty alleviation as well as CO2 emission reduction, and then decoupling analysis model was introduced to identify the relationship between CO(2 )emissions and poverty alleviation within Hubei Province, a poverty-stricken area in China. The results show that the poverty indices all remain small although poverty line within Hubei Province is much higher than two dollars per day. Maximum values for head count, poverty gap, and squared poverty gap indices are all lower than 0.25, 0.10, and 0.05 respectively, implying that there are good realistic bases for poverty alleviation. At the same time, CO2 emission and energy intensities within this region are characterized by inverted U-shaped curves and are currently in a declining phase, but CO2 emissions have significantly increased. Results derived from multi-period data analysis show that the decoupling relationship have switched from 'expansive negative decoupling' to 'weak decoupling', indicating that there is a decoupling relationship between CO2 emission reduction and poverty alleviation. The data and results can be used to provide further references for clarifying the relationship between the two and arranging the plan of policy implementation. |
Decomposition Analysis of CO(2)Emission from Electricity Generation: Comparison of OECD Countries before and after the Financial Crisis (2020) 🗎🗎 | The purpose of this study is to analyze the factors that affect CO(2)emissions in the electricity generation sector in 36 OECD countries during the periods 1995-2008 and 2008-2017. This paper utilized Logarithmic Mean Divisia Index method for decomposing CO(2)emission into economic activity, electricity intensity that represents demand policy effort, the share of thermal generation, the mix of thermal generation, thermal efficiency that represent supply policy efforts, and carbon emission coefficient. The results showed that EU nations achieved a higher level of CO(2)reduction compared to that of non-EU nations. Regarding the policy factors, the decrease in the share of thermal generation served as the key driver, followed by the decrease in electricity intensity via improvements in energy consumption efficiency. Most non-EU countries such as South Korea, Chile, Mexico, Turkey, and Japan demonstrated an increasing trend of carbon emission during this period, which could be attributed to the changes in the generation mix on the supply side or the electricity intensity on the demand side. Increase in electricity price was confirmed to cause lower electricity intensity. South Korea had the largest amount of carbon emission among OECD countries and maintained one of the lowest electricity retail prices among OECD countries. |
Using a System Dynamics Modelling Process to Determine the Impact of eCar, eBus and eTruck Market Penetration on Carbon Emissions in South Africa (2020) 🗎🗎 | The complexities that are inherent in electricity value chains are non-linear in nature and they require unconventional modelling methods, such as system dynamics. This paper provides an overview of the system dynamics method applied for obtaining an understanding of the impact of electric-bus, -car, and -truck market penetration on carbon emissions in South Africa, through the development of the electric mobility simulator (eMobiSim). Two scenarios were tested. The World Reference scenario was based on a market penetration of 22% eCars, 19% eTrucks, and 80% eBuses and the Gross Domestic Product (GDP) scenario was based on 2.38% eCars, 1.79% eTrucks, and 12% eBuses. The results indicate that the World Reference scenario is the most optimistic, with a 12.33% decrease in carbon emissions in the transport sector and an increase of 4.32% in the electricity sector. However, if the economic structure that is specific to South Africa is to be considered and the GDP scenario is run, then there would only be a 1.77% decrease of carbon emissions in the transport sector and an increase of 0.64% in the electricity sector. Although the eCar market penetration produces the highest reduction in carbon emissions, the volumes that are required are large and other factors, such as price parity and affordability in the various income deciles, would have to be considered in determining whether this volume is achievable. |
The CO2 Emissions Drivers of Post-Communist Economies in Eastern Europe and Central Asia (2020) 🗎🗎 | CO2 emissions have become a key environmental contaminant that is responsible for climate change in general and global warming in particular. Two geographical groups of countries that previously belonged to the former bloc of socialist countries are used for the estimations of CO2 emissions drivers. The research covers such Eastern European countries as Bulgaria, Czech Republic, Hungary, Russian Federation, Poland, Romania, Slovak Republic, and Ukraine and such Central Asian states as Kazakhstan and Uzbekistan during the period 1996-2018. The main goal of the research is to identify common drivers that determine carbon dioxide emissions in selected states. To control for the time fixed effects (like EU membership), random effect model was used for the analysis of the panel data set. Results: It is found that energy efficiency progress reduces per capita CO2 emissions. Thus, an increase in GDP by 100 USD per one ton of oil equivalent decreases per capita CO2 emissions by 17-64 kg. That is, the more energy-efficient the economy becomes, the less CO2 emissions per capita it produces in a group of selected post-communist economies. Unlike energy efficiency, an increase in GDP per capita by 1000 USD raises CO2 emissions by 260 kg per capita, and the richer the economy becomes, the more CO2 emissions per capita it generates. The increase in life expectancy by one year leads to an increase in CO2 emissions per capita by 200-370 kg, with average values of 260 kg per capita. It was found that an increase in agriculture, forestry, and fishing sector share (as a % of GDP) by one percentage point leads to the decrease in CO2 emissions by 67-200 kg per capita, while an increase in industrial sector share by one percentage point leads to the increase in CO2 per capita emissions by 37-110 kg. Oil prices and foreign direct investment appeared to be statistically insignificant factors in a group of selected post-communist economies. Conclusions: The main policy recommendation is the promotion of energy efficiency policy and the development of green economy sectors. The other measures are the promotion of a less energy-intensive service sector and the modernization of the industrial sector, which is still characterized by high energy and carbon intensity. |
India can increase its mitigation ambition: An analysis based on historical evidence of decoupling between emission and economic growth (2020) 🗎🗎 | This article aims to present historical rate of decoupling and based on that determine the scope for India to increase itsmitigation beyond the NDC commitment. Empirical evidence on nature and rate of decoupling between energy related emission and economic growth for the period 1990-91 to 2012-13 in India is presented. In addition to estimating the magnitude of decoupling elasticity, decomposition analysis is also applied to understand howthe four factors: activity growth, energy intensity change, structural change and fuel mix change, are driving the change in emission in India. Decoupling elasticity and Log Mean Divisia Index (LMDI) methods are used for decomposition. The results indicate presence of relative decoupling in India. The industrial sector leads among the four sectors-agriculture, industry, services and power generation in achieving this relative decoupling, mostly through improvement in energy efficiency and some structural changes. Results show that even in the business as usual scenario if India acts upon individual sector level mitigation potentials, it has the potential to raise mitigation ambition beyond current Nationally Determined Contributions (NDC) without adversely impacting economic growth. With continued high share of coal in the energy mix it is going to be difficult to achieve absolute decoupling. (c) 2020 International Energy Initiative. Published by Elsevier Inc. All rights reserved. |
Supply as a factor in the destabilization of the oil market (2020) 🗎🗎 | The article deals with the fundamental and conjuncture factors of the oil market equilibrium. The purpose of the study is to identify new economic indicators that affect the global pricing model in the oil market, and determine the effectiveness of the existing cartel agreement. The structure of net energy consumption, as well as features of demand for energy resources by regions of the world, is presented in the article. The study provides a comprehensive statistical analysis of the dynamics of changes in oil prices and production indicators of the largest oil producing countries in the world. The analysis highlights the factors of the oil market stabilization from the demand and supply side: production, technological and price factors. Relative indicators, such as the level of technology development and drilling efficiency in the respective segments, were found to have the greatest impact on the stability of energy prices, and long-term price stabilization is possible only if the US oil industry participates in OPEC+ agreement. (C) 2020 The Author(s). Published by Elsevier Ltd. |
Macro-Regional Economic Structural Change Driven by Micro-founded Technological Innovation Diffusion: An Agent-Based Computational Economic Modeling Approach (2021) 🗎🗎 | This paper introduces an agent-based computational economic modeling approach into an input-output analytical framework and proposes diffusion of technological innovation behavior into the simulation models. A large number of heterogeneous firms with macro-regional economic frameworks are included to perform policy simulation scenarios to investigate the impact of diffusing technological innovations on the dynamic changes in the regional economic structures of major global economies (i.e., China, Japan, the United States, Russia, India, and the European Union). This study reveals that process innovation may be more conducive to promoting the transfer of resource elements between regions for China, the EU, Japan, India, and Russia. However, for the U.S., product innovation may facilitate upgrading its industrial structure. Furthermore, from 2012 to 2030, for these six economies, the output share of the primary industry will likely decline by varying degrees, while the output share of the tertiary industry will show an uptrend. The employment share in the tertiary industry in these six economies decreased. Another important finding is that differentiated technological innovation-driven policies must be adopted within the context of global economic governance. Moreover, each economy should choose a technological innovation mode that is suitable for its economic development. Thus, these findings provide an important theoretical basis for formulating global economic governance policies in the future. |
Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand (2021) 🗎🗎 | Cold chain logistics has become one of the main sources of carbon emissions. Meanwhile, the implementation of low-carbon economy has become an inevitable way to promote sustainable development. However, previous studies on the cold chain inventory routing problem (IRP) paid less attention to the cost of carbon emissions. In this paper, a linear programming (LP) model is established, which takes the costs of vehicle transportation, time window and carbon emission into consideration. Although the simple LP model is easy to be solved, it cannot handle the problems with uncertainty. Therefore, in order to overcome the influence of uncertainty, the proposed LP model is developed into three low-carbon robust optimization (RO) models. In addition, this paper takes a cold chain logistics enterprise in Yangtze River Delta as an example for empirical analysis. The results of the case study prove that the RO models can quickly solve the problems with uncertainty and still maintain robustness, while the LP model has failed. Specifically, the R-ellipsoid model produces the best result among the three RO models. It is suggested that when the carbon emission tax increases, the decision makers tend to choose a better path planning scheme, which will not only reduce the total cost, but also obtain environmental benefits. Finally, the findings of this paper generate some implications for the low-carbon transformation of cold chain logistics enterprises. |
The path to a 2025 nuclear-free Taiwan: An analysis of dynamic competition among emissions, energy, and economy (2021) 🗎🗎 | Taiwan intends to be nuclear free by 2025. This study employs the Lotka-Volterra competition model for sustainable development to analyze the emissions-energy-economy (3Es) issue to make appropriate policy suggestions for a nuclear-free transition. It also offers a new approach to naming the 3E relationship. The literature review shows that the environmental Kuznets curve accompanies the feedback and conservation hypotheses. In the 3E dynamics relationship analysis, the model shows a good mean absolute percentage error (<15%) for the model estimation. The key findings are as follows: 1) the fossil fuel-led economy exists; 2) CO(2)emissions are reduced with nuclear energy consumption; 3) renewable energy is far from scale; 4) a complementary effect exists between fossil fuel and nuclear energy consumption; and 5) gas retrofitting and phasing out of nuclear seem imminent. In the energy transition, Taiwan drastically cuts nuclear energy without considering energy diversity due to which troubles might ensue. The priority issue for Taiwan's energy mix is energy security. To deal with these concerns, this study suggests the government could improve energy efficiency, build a smart grid, develop carbon capture and storage, and reconsider putting nuclear energy back into the energy mix before renewable energy is scaled. |
Measurement on carbon lock-in of China based on RAGA-PP model (2021) 🗎🗎 | The measurement indicator system of carbon lock-in was constructed from four dimensions: fixed investment, technique, institution and social behavior; and the projection pursuit evaluation model based on the Real-coded Accelerating Genetic Algorithm, (RAGA-PP model) was established. In this study, China's carbon lock-in level was measured and analyzed based on relevant data from 2003 to 2016. The model application contributes to solving problems such as the subjectivity of weight estimation and premature convergence. The results indicate that, in addition to the increasing degree of social behavior lock-in, for China, the overall carbon lock-in and lock-in levels of fixed input, technique, and institution have decreased dramatically. The overall carbon lock-in level of the eastern provinces is the weakest, followed by the central and northeastern regions, with the western regions being the strongest, and the polarization of provincial carbon lock-in levels being the most significant in the central and western areas. The government should make prudent decisions on the main investment and construction of social infrastructure from a long-term perspective. It is essential to make breakthroughs in the core technologies of complementary and coupled energy utilization to accelerate the formation of new power systems. |
Does fi nancial deepening drive spatial heterogeneity of PM2.5 concentrations in China? New evidence from an eigenvector spatial fi ltering approach (2021) 🗎🗎 | To provide policymakers with a different perspective on reducing PM2.5 concentrations, this paper not only identifies the economic driving factors of PM2.5 concentrations in China but also investigates its spatial heterogeneity with special consideration of financial deepening. A random effect eigenvector spatial filtering (RE-ESF) approach with and without non-spatially varying coefficients (SNVC) is performed by using the provincial panel dataset over the period 2002-2016. The main findings are as follows: First, compared with the non-spatial pooled regression model, the RE-ESF and RE-ESF-SNVC models have increased the goodness-of-fit from 0.8854 to 0.9778 and 0.9834, respectively, indicating that the RE-ESF approach produces a better fit to the data. Second, the global results of the RE-ESF model show that a 1% increase in financial deepening will bring about 0.152% decrease in PM2.5 concentrations. Third, the local results of the RE-ESF-SNVC model indicate that the spatially varying coefficient of financial deepening ranges from -0.3215 to 0.101 with the median value of -0.1315, reflecting significant spatial heterogeneity of PM2.5 concentrations driven by financial deepening. These findings contribute to PM2.5 concentration reduction by identifying financial deepening as a significant economic driving factor, investigating its global and local impacts on PM2.5 concentrations, and providing policymakers with implications for developing appropriate financial policies, such as a more flexible province-specific reserve requirement policy supplemented by higher deposit rates and a cross-provincial allocation of bank credits. (C) 2021 Elsevier Ltd. All rights reserved. |
Analyzing the Driving Forces behind CO2 Emissions in Energy-Resource-Poor and Fossil-Fuel-Centered Economies: Case Studies from Taiwan, Japan, and South Korea (2021) 🗎🗎 | Based on the strong similarities between energy-resource-poor and fossil-fuel-centered economies (e.g., Taiwan, Japan, and South Korea) in terms of economy, culture, and energy usage characteristics, they should be analyzed collectively. This study adopted two-tier input-output structural decomposition analysis to identify the driving forces behind CO2 emissions to these countries to the formulation of effective environmental policy. Data from the World Input-Output Database was used to decompose relative changes in CO2 emissions into a range of technological advances, factor substitution, and final demand effects. Technological advances in energy (direct) contributed to a 77% reduction in Taiwan and a 34% reduction in South Korea. This is a clear indication that improving energy efficiency via technological advances should be a priority. In Japan in particular, there was a 22% reduction in CO2 emissions attributable to technological advances in materials; hence, it is recommended that Taiwan and South Korea work to extensively develop eco-industrial parks to create industry clusters to promote resource/energy efficiency and reductions in CO2 emissions. Decomposition results based on factor substitution revealed that a variety of strategies will be required, such as switching to fuels that are less carbon intensive, promoting the adoption of renewable energies, and implementing clean-coal technologies. |
Effect of income and energy efficiency on natural capital demand (2021) 🗎🗎 | This study explores the driving forces of natural capital demand to help solve the new problems faced by China's regional sustainable development and formulate relevant policies on resource utilization, ecological compensation, and energy structural adjustment. We find a significant and inverted U-shaped relationship between income and natural capital demand. Both economic development and environmental protection can only be improved beyond the inflection point. Energy efficiency is also closely related to natural capital demand, and its continuous improvement can slow down the rise in natural capital demand. This research provides important implications for the spread and allocation of natural capital regionally. It recommends the national allocation of natural capital, formulation of differentiated environmental policies, and improvement in energy efficiency by improving scale, technology, and structure. |
Technological constraints to energy-related carbon emissions and economic growth decoupling: A retrospective and prospective analysis (2021) 🗎🗎 | Decoupling the trends of energy-related carbon emissions from economic growth is an essential precondition and condition for cost-effective climate stabilization. Using an extended Kaya identity and the Tapio decoupling definitions, this study explored the role and magnitude of drivers associated with carbon emissions and economic growth decoupling across 134 countries. The retrospective (1995-2015) decomposition analysis was complemented by exploring the future decoupling projections using assumptions of the Shared Socio-economic Pathways (SSP) framework. The results indicate that less mature developing economies are carbonizing their energy systems at faster rates (about 1-4%/year) compared to developed economies. While emerging developing economies are progressively transitioning towards weak decoupling. Overall the deterioration of structural and intensity technology drivers, including conversion efficiency, emission factor, and fossil fuel mix, are the major pinch points to achieving absolute decoupling among developing economies. The prospective analysis of the SSP assumptions also indicate that developing economies exhibit very minimal signs of decoupling and decarbonization relative to developed economies. The study provides instructive insights about the urgency of addressing global decarbonization, especially among developing economies. (c) 2020 Elsevier Ltd. All rights reserved. |
Do Urbanization and Energy-environment Policies Affect Housing Values? Evidence From Spatial Textual Analysis (2021) 🗎🗎 | This study examines the impacts of urbanization and energy-environment policies on housing prices across 30 provinces in China from 2000 to 2015. Results indicate that current urbanization policy and current housing policy do not significantly affect the housing prices, but the housing prices are determined by current energy-environmental policy. In addition, we argue that the effects of post urbanization policy and post energy-environmental policy remain highly significant, while the implementation of energy-environmental policy results in an increase in housing prices through the reduction of the emission of industrial dust. Finally, the Clean Energy Policy drives up housing prices in highly regulated provinces in China. |
Assessment of mid-to-long term energy saving impacts of nearly zero energy building incentive policies in cold region of China (2021) 🗎🗎 | Nearly zero energy building (NZEB) has become an effective solution in building sector to climate change. In 2017, China first set up a NZEB target of 10 million m(2) by the end of 2020, followed by a series of incentive policies on provincial level issued for NZEB promotion that has expanded NZEB floor area from almost zero to 12 million m(2). However, impact of NZEB policies on building energy reduction in mid to long term and contribution to energy peak reduction remain unknown. This study collected 47 existing policies and analyzed their impact on building energy consumption. A policy-driven model composed of conservative, moderate and propulsive scenarios was established, through which total building energy consumption of representative provinces in cold region of China from 2020 to 2050 was calculated. Results showed that under moderate scenario, with 100% NZEB market penetration in 2050 and "30-3 0-30" mid-term goal by 2030, energy consumption could save 13% by 2030 and 60% by 2050, compare to the conservative scenario. In response to carbon emission target of China, the year 2025, 2030 and 2035 should be set as the governmental enforcement year for ultra-low energy buildings, NZEB and ZEB respectively, according to propulsive scenario. (C) 2021 Elsevier B.V. All rights reserved. |
The driving forces behind the change in energy consumption in developing countries (2021) 🗎🗎 | Economic growth is principally powered by energy fuels. While the potential energy transition pathways in developed countries are clear, they have not been well explored for developing countries. Here, we study the average annual growth rate of energy consumption in 12 aggregated regions during 2001-2017 and the driving factors behind that growth. The countries with high energy consumption growth rates were concentrated in Asia and North Africa and four of the top five regions were in Asia, while the energy consumption in developed countries was stable or even declined in that period. Therefore, based on a comprehensive consideration of factors such as population and economic development, to quantify the role of renewable energy, we analyze the long time series of energy consumption for China, India, Indonesia, Myanmar and Bangladesh since the 1970s. Despite economic development and population growth accelerating energy consumption substantially upward, energy intensity made energy consumption decrease. Coal and oil dominated the energy transition pathway in China and India, while biomass and natural gas dominated in Indonesia, Myanmar and Bangladesh. The amount of CO2 emissions in different countries was closely related to the amount and type of the energy they used. Our research results emphasize the importance of improving energy efficiency and adjusting energy structure to reduce energy consumption and achieve sustainable development. |
The Impacts of Technology Shocks on Sustainable Development from the Perspective of Energy Structure-A DSGE Model Approach (2021) 🗎🗎 | Considering that the effect of different types of energy on sustainable development differs, the optimization of energy structure is commonly seen as a decisive factor for sustainable development. In this study, we focus on energy structure and construct a dynamic stochastic general equilibrium (DSGE) analysis framework including the environment, society, and the economy. Furthermore, we analyze the effect of different technology shocks on sustainable development when the proportion of clean energy is separately set at 10%, 20%, and 40%. To demonstrate the conclusions of the DSGE analysis framework, we construct the sustainability index and measure the relationship between the sustainability index scores and the proportion of clean energy of 68 countries in 2017, and the R-2 of the linear relationship between the sustainability index score and the proportion of clean energy was 0.30. Results show that the technology shock of clean energy exhibits more benefits for sustainable development than that of non-clean energy. Moreover, we find that the optimization of the energy structure can be helpful for the enhancement of sustainable development capacity. This study is helpful to expand the DSGE analysis framework from the perspective of energy structure. This study also provides effective ways and reference suggestions for local governments to optimize energy structure and improve sustainable development capability. |
Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling (2021) 🗎🗎 | The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 h of electricity consumption in a 24 h interval can on average reduce the resulting CO2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day. |
The spatiotemporal characteristics of electrical energy supply-demand and the green economy outlook of Guangdong Province, China (2021) 🗎🗎 | The GDP of Guangdong Province exceeded 10 trillion RMB in 2019. The province has the largest economy in China and also the greatest consumption of electrical energy, the most important driving force for its economic development. However, Guangdong Province has a high degree of external dependence and large internal differences, and consequently, its distribution of electrical energy demand is uneven and its regional growth is unbalanced. Exploration of Guangdong's electrical energy supply and demand structure and elucidation of the rules governing spatial and temporal changes in its electrical energy systems are highly important. This paper collects data on the electrical energy supply and demand in Guangdong Province from 2000 to 2018, employs ArcGIS spatial analysis and Moran Index and beta-convergence test. The main results were as follows. Self-sufficiency for electricity in Guangdong dropped from 100% in 2000 to 69% in 2018. The remainder of electricity was imported. Guangdong mainly relies on thermal electric generation, while other import provinces with the exception of Guizhou mainly rely on hydropower. The proportion of renewable energy fell from 11.6% in 2000 to 5.0% in 2018. The demand for electrical energy in Guangdong is very large, more than half of this demand is from secondary industry. Other modern industries had the highest growth rate in electricity consumption. Electrical energy mainly flows to the Guangdong-Hong Kong-Macao Greater Bay Area, and the increased demand is also mainly concentrated there. The absolute differences in electrical consumption among cities has increased, showing a distinct polarization phenomenon. This paper provides a reference for solving electrical energy supply-demand problems, ensuring the sustainable development of the economy and promoting the green economic transformation of Guangdong. (C) 2020 Elsevier Ltd. All rights reserved. |
CO2 emissions inequality through the lens of developing countries (2021) 🗎🗎 | There is increasing interest in CO2 emissions inequality between and within countries, and concerns about the impacts of COVID-19 on vulnerable groups. In this study, the CO2 emissions inequality based on the different consumption category data of disaggregated income groups in eight developing countries is analyzed with the application of input-output model. We further examine the effects of the COVID-19 outbreak on CO2 emissions inequality based on the hypothetical extraction method, and the results reveal that the outbreak has decreased the CO2 emissions inequality and emissions over time. However, the shared socioeconomic pathway scenario simulation results indicate that long-term CO2 emissions inequality will persist. Targeted poverty elimination measures improve the utility of the lowand lowest-income groups and reduce CO2 emissions inequality. Reducing the excessive consumption on the demand side as well as improving the energy efficiency and increasing the share of renewable energy in the energy consumption on the supply side will provide more informed options to achieve multiple desirable outcomes, such as poverty elimination and climate change mitigation. |
Impacts of oil price uncertainty on energy efficiency, economy, and environment of Malaysia: stochastic approach and CGE model (2021) 🗎🗎 | Uncertainty in global oil prices significantly influences the economic performance of Malaysia as a net oil-exporting country. This study uses an integrated approach, in which a stochastic method is integrated with a computable general equilibrium (CGE) model to examine the impacts of likely change in global oil price on energy efficiency and, consequently, on key economic, energy, and environmental variables of Malaysia. The stochastic method, which is related to the Monte Carlo assessment, is based on historical data of global oil price, during 1980-2017, provides probable changes in oil price and their probability of occurrence. Simulation results show that likely changes in global oil price, with a 90% probability, change energy efficiency in Malaysia between - 0.08 and + 0.06% within which the economic performance of Malaysia changes between - 5.22 and 3.00% and household welfare changes between - 4.81 and 2.92%. Furthermore, the energy demand changes between 1.51 and - 2.93% and CO2 emission changes between 4.21 and - 2.03%. However, the emission of other air pollutants changes between - 2.45 and 2.21%. These economic and environmental changes generate a double dividend effect on the Malaysian economy. The value of the rebound effect also changes between 103.21 and 95.79%. Therefore, the paper highlights a strong interconnection among oil price fluctuation, energy efficiency, energy consumption, CO2 emission, and economic growth and thus the necessity for an integrated policymaking method. |
Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model (2021) 🗎🗎 | A reliable prediction of CO2 emissions from marine fleets plays an important role in the low carbon development of shipping industry. However, CO2 emissions from marine fleets have its own inherent trends and the influencing factors might have interaction effects, and these problems make it difficult to build an accurate prediction model. To this end, a novel multivariable trend interaction grey model, named TIGM(1, N), is proposed in this paper. TIGM(1, N) extends the grey prediction model by integrating three different terms, i.e., interaction, trends, and constant terms into the grey action terms of the classical multivariable grey model. Compared with the classical multivariable grey model, TIGM(1, N) effectively reflects the impact of input variable interactions and trends on the system's behavior. To increase the accuracy, the new model's adjustment coefficient is optimized to obtain optimal time-response function values. The experimental results show that TIGM(1, N) outperforms linear regression models and other variants of the grey prediction models in predicting the CO2 emissions from Chinese marine fleets. Finally, the new model is applied to predict the marine fleets' CO2 emissions during 2016-2018 and the results demonstrates the feasibility of the proposed model in low carbon development plan of shipping industry and its value in formulating environmental policies. (c) 2021 Elsevier B.V. All rights reserved. |
Influencing Factors, Energy Consumption, and Carbon Emission of Central Heating in China: A Supply Chain Perspective (2021) 🗎🗎 | The rapid growth of energy demand in China's central heating sector and the large differences in regions have posed challenges to its energy supply safety, which affected the progress of China's energy transformation. From a supply chain perspective, this study uses the feasible generalized least squares method to conduct empirical research on the central heating data of 17 provinces in China from 2006 to 2017. The results shows that the main factors of central heating includes energy consumption structure, heat generation method, heat transport carrier, heating degree days and heating area; The main factor that increases the energy consumption of central heating in each province is the same, namely Heating area (HA). However, the main factors that reduce energy consumption in each province are different; using gas instead of coal for clean heating can reduce some greenhouse gas emissions while bringing huge gas supply pressure. According to the results, this study provides some policy suggestions. |
China's Energy Transition Policy Expectation and Its CO2 Emission Reduction Effect Assessment (2021) 🗎🗎 | Measuring the expected impact of China's energy transition on carbon dioxide (CO2) mitigation and identifying the key influencing factors in different economic sectors will help to provide better policy recommendations for CO2 emission reduction. Based on the prediction results of China's CO2 emissions in 2030 under the baseline scenario and the target scenario, this study constructs the control group and the treatment group of the energy transition policy quasinatural experiment and then uses the difference in difference (DID) model to evaluate the CO2 emission reduction effect of China's energy transition policy. The results reveal that the energy transition policy has a significant impact on CO2 emission reduction and helps to achieve China's 2030 carbon emission reduction target. The impact of energy structure transition on CO2 emission reduction has significant sectoral heterogeneity, which has a positive reduction effect in the industry sector, wholesale and retail sectors, and accommodation and catering sectors, but its reduction effect is not obvious in transportation, storage, and postal sectors. It is suggested that China should implement the sector-differentiated CO2 mitigation strategy, focus on improving the industrial sector's energy efficiency, and promote the clean, low-carbon transition of energy consumption structure in construction, transportation, storage, and postal industries. |
Simulation on the effectiveness of carbon emission trading policy: A system dynamics approach (2021) 🗎🗎 | As a flexible market mechanism based on the control of total quantity, the carbon emission trading system aims to achieve economic development while reducing carbon emissions and energy consumption. How to improve the effectiveness of carbon emission trading (CET) policy and achieve the established emission reduction targets have attracted widespread attention from scholars. Taking Guangdong Province as an example, this paper constructed a system dynamics (SD) model to investigate the interaction between internal factors of CET system and simulated the effectiveness of CET policy from 2016-2026. The results indicate that (1) Relative errors between historical data and simulated data are controlled within 5%, which indicates that the system model is suitable to simulate real system. (2) The average sensitivity of the quota variation rate, paid ratio and penalty coefficient are 0.42, 0.56 and 0.29, respectively, indicating that these three parameters can be identified as leverage parameters affecting the efficiency of the CET policy. (3) A single type of policy is difficult to achieve emission reduction targets. The policy portfolio scenario will achieve the reduction target in Thirteenth Five-Year plan of Guangdong, that is, the total amount of quota decreases by 0.5% per year, the paid ratio rises 2% in that ratio per year, and the penalty coefficient is triple the carbon price. |
Official development assistance and carbon emissions of recipient countries: A dynamic panel threshold analysis for low- and lower-middle-income countries (2022) 🗎🗎 | Better use of official development assistance (ODA) to mitigate carbon emissions in developing countries requires a better understanding of the effects of ODA on carbon emissions. The dynamic panel threshold regression model is employed to explore the effects of ODA and carbon emissions in 59 low-income and lower-middle-income countries. Urbanization is employed as a threshold variable. The proposed model can better reflect endogenous explanatory variables, which indicate the inertial characteristics and dynamic changes of carbon emissions. The first-order lag coefficients of carbon emissions in the proposed model have a significant positive effect, indicating that the growth of carbon emissions in these countries has a strong path dependence. Moreover, when the urbanization of recipient countries is below the threshold value, a 1% increase in ODA leads to a 0.2259% increase in carbon emissions. When urbanization exceeds the threshold value, a 1% increase in ODA leads to a 0.2281% increase in carbon emission. In addition, the conditional convergence level of carbon emission growth in low-urbanization areas is much greater than that in high-urbanization areas. With the development in urbanization, if no effective measures are developed and implemented, there is a risk that carbon emissions of recipient countries might develop from "low-emission balanced growth" to "high-emission balanced growth." (C) 2021 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers. |