L1D85

(1.5)
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%)
titleabstract

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.

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.

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.

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.

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.

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.

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 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.

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.

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.