The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization

Yu, Shiwei; Zheng, Shuhong; Li, Xia

doi:10.1016/j.eneco.2018.07.017

Cited by 129 publications

(45 citation statements)

References 34 publications

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“…Zhang and Chen [16] used the neural network method to predict China's coal consumption and carbon emissions and concluded that China's coal consumption and carbon emissions would maintain a relatively stable growth trend in the future. Yu [17] use the economy -cost of carbon emissions (ECC) multi-objective optimization model to predict the carbon emissions peak, the results show that China's energy carbon emissions will reach peak between 2025 and 2028, during this period, if the gross domestic product (GDP) to maintain an annual growth of 5.9% to 6.3%, carbon emissions of the average annual growth rate will reach 0.5% 1.1%. Zhao and Luo [18] used the Vector Error Correction Model (VECM) to study the relationship between carbon emission intensity and coal and crude oil consumption and predicted China's future energy consumption structure and carbon emissions.…”

Section: The Carbon Emissions On the Energy Supply-sidementioning

confidence: 99%

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Jijun

2020

PLoS ONE

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In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.

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Section: The Carbon Emissions On the Energy Supply-sidementioning

confidence: 99%

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Jijun

2020

PLoS ONE

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“…The results indicated that that coal consumption played a leading role in economic growth and carbon emissions; the GDP had a two-way relationship with carbon emissions, coal, natural gas and power consumption; it was imperative to change the structure of energy consumption [25]. Yu et al (2018) proposed a new economic-carbon emission-costs (ECC) multi-objective optimization model to measure the peak of CO 2 emissions. The results showed that optimizing the coal-dominated structure of energy consumption would effectively contribute toward ensuring that China's carbon emissions peak by 2030.…”

Section: From the Perspective Of The Supply-sidementioning

confidence: 99%

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis

Song

et al. 2020

PLoS ONE

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In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China’s energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future.

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“…The control variables are crucial factors affecting conditional convergence, referring previous researches, the per capita GDP (PGDP), 41 ratio of secondary industry value-added to GDP (SIND), 42,43 PFDI, 44,45 PDEN, 23 per capita urban green land (PLAND), 46,47 energy intensity (INTEN) is used as the control variables to investigate industrial SO 2 convergences.Especially, the energy intensity is measured using the ratio of total coal consumption to local industrial output, 48,49 and the calculation of coal consumption in prefectural cities is reported in Supplementary Appendix C. In addition, some studies support the EKC hypothesis, 16 thus we also take into account the quadratic term, PGDP 2 , to examine whether there exists an inverted U-shaped curve across China’s prefectural cities.…”

Section: Data and Variablesmentioning

confidence: 99%

Are there industrial SO₂ convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective

Nan

Qin

Cai

et al. 2019

Energy & Environment

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As the world’s largest emitter of sulfur dioxide, China is facing mounting domestic and international pressures to tackle the increasingly serious atmospheric pollution. Convergence is an important inherent characteristic of sulfur dioxide discharge. This study examines the convergence of per capita sulfur dioxide emissions across 280 Chinese prefecture-level cities from 2003 to 2016. Due to the spatial autocorrelation of air pollutants, conventional estimation methods for β convergence ignore the spatial effects and produce biased results. Consequently, spatial econometric models with different weight matrices are employed to control for spatial effects. The empirical results indicate that per capita sulfur dioxide emissions exhibit both absolute β convergence and conditional β convergence, and spatial effect and other socioeconomic factors accelerate the convergence speed. In addition, this study verifies the environmental Kuznets curve hypothesis between sulfur dioxide and gross domestic product. The results highlight the importance of regional cooperation and coordination when formulating environmental and industrial policies.

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The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization

Cited by 129 publications

References 34 publications

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis

Are there industrial SO₂ convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective

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The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization

Cited by 129 publications

References 34 publications

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis

Are there industrial SO2 convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective

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Are there industrial SO₂ convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective