Carbon emissions efficiency in China: Key facts from regional and industrial sector

Wang, Yong; Duan, Fumei; Ma, Xuejiao; He, Linyan

doi:10.1016/j.jclepro.2018.09.185

Cited by 87 publications

(32 citation statements)

References 51 publications

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“…Chu and Geng et al in 2019 applied the SBM to measure the carbon emission efficiency in 30 provinces of China from 2005 to 2017 [11]. Some other scholars also have conducted relevant research using SBM DEA model [21,43,44].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper investigated 30 provinces, municipalities, or autonomous regions (except Tibet) in China from 2005 to 2015. We selected the annual data of capital stock, labor force, and energy consumption as three inputs according to production processes and the prior research results [6,11,21,43,44]. We treated gross domestic product (GDP) as a desirable output and CO 2 emission as an undesirable output.…”

Section: Data Source and Indicator Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Zeng

Liu

et al. 2019

Energies

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With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Data Source and Indicator Selectionmentioning

confidence: 99%

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Zeng

Liu

et al. 2019

Energies

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“…They found that only Beijing, Tianjin, and Shanghai are optimal in input-output efficiency. Wang et al [30] accounted for industrial heterogeneity in the region using a meta-frontier Malmquist index analysis method combined with a three-level common frontier model to estimate the CO 2 emissions efficiency and dynamics of China's industries and regions in 2010-2015. They found that the Eastern region has the highest emission efficiency, and the tertiary industry has higher emission efficiency than the primary industry and the secondary industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach

Xie

Chen

2019

Sustainability

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While China’s rapid industrialization has brought great pressure on environmental pollution, great variations appear in terms of environmental pollution levels among industries. The effective assessment of the environmental performance of different industries is not only conducive to identifying the major sources of pollution in China but also of great significance to the Chinese government in formulating differentiated industry environmental control policies in a targeted manner. Using data of 36 Chinese industries from 2006 to 2015 and a multiple data envelopment analysis (DEA) with a Gini criterion as well as a systematic clustering approach, this study first calculates the environmental efficiency score of Chinese industries and then identifies those pollution sources based on a ranking and clustering analysis. The main result indicates that the ranking of environmental efficiency of various industries overall varies greatly by time. In addition, using a clustering analysis, this study finds that 13 labor-intensive light industries and heavy chemical industries with high energy use and high emissions are medium- and high-pollution industries. Important policy implications are drawn to achieve green industrial development.

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“…According to the results of this paper, the predicted energy structure optimization fails to achieve the carbon intensity target by 2030. Therefore, as an efficient, clean, and high-quality fossil energy, increasing the proportion of natural gas consumption is one of the best choices to optimize the energy structure, improve energy efficiency, and achieve the carbon intensity target [61]. In addition, in the process of achieving energy-saving and emission reduction targets, natural gas has more cost advantages and technological advantages than new clean energy such as wind energy and nuclear energy.…”

Section: (2) Contribution Analysis Of Optimizing the Energy Structurementioning

confidence: 99%

Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

Wang

Shang

et al. 2018

Energies

Self Cite

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To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China’s energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China’s carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China’s carbon intensities.

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Carbon emissions efficiency in China: Key facts from regional and industrial sector

Cited by 87 publications

References 51 publications

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach

Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

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