The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis

Feng, Chao; Zhang, Hua; Huang, Jianbai

doi:10.1016/j.rser.2016.12.114

Cited by 89 publications

(56 citation statements)

References 78 publications

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“…DEA can simulate the actual production process well (Feng et al, 2017b). Based on the DEA theory, the inefficient areas can be improved and become the efficient areas by adjusting the slack (Shi et al, 2010).…”

Section: Data Envelopment Analysismentioning

confidence: 99%

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Huang

Kwan

et al. 2018

Journal of Cleaner Production

142

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a b s t r a c tCities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000e2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries.

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Section: Data Envelopment Analysismentioning

confidence: 99%

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Huang

Kwan

et al. 2018

Journal of Cleaner Production

142

View full text Add to dashboard Cite

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“…By using Three-hierarchy meta-frontier DEA (Data Envelopment Analysis), Feng et al [54] decompose carbon emission efficiency for different industries, and then calculate carbon reduction potential from the perspectives of management, technology, and structure. Figure 2 shows that due to the relatively high-level technology, the Eastern region has the highest carbon emission efficiency in the primary and tertiary industries; by contrast, low-carbon emission efficiency in the Central and Western regions is mostly caused by technical inefficiency.…”

Section: Peak Carbon Emissions In the Western Regionmentioning

confidence: 99%

“…From a regional perspective, Feng et al [54] argue that the interchange of technology throughout China cannot be ignored. Li [79] suggests that attention should be paid to the mutual influences of technological progress among regions in the process of carbon emission peak, thereby activating the benign interaction of regional technological progress.…”

Section: Further Enhancing the Development Of Fdi And Technology Progmentioning

confidence: 99%

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

Dong

Hua

2018

Sustainability

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China is continually increasing its efforts to reduce carbon emissions due to dual pressure domestically and internationally. Specifically, China has committed to achieving peak carbon emissions no later than 2030. As an international goal, carbon emission peak in China has generated considerable research interest. However, most scholars only focus on a partial aspect of peak carbon emissions. Through a review of literature from numerous sources, this paper provides a more systematic analysis than previously available of how China can reach its peak carbon emissions as early as possible. This study first elaborates the status of peak carbon emissions in China from regional and sectoral perspectives and summarizes the various predictions. Then, five main driving factors of carbon emission peak in China are investigated, i.e., economic development, urbanization, energy-related issues, foreign direct investment and technology, and transportation. Corresponding to these five factors, some specific and practical recommendations are proposed. Furthermore, regional differences and spatial "spillovers" must be taken into account, thereby achieving a "win-win" solution with respect to the integration of emission mitigation and economic development, as well as reaching the desired target of peak carbon emissions.

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“…Undesirable Outputs Production Inputs [34] GDP SO 2 , COD, Nitrogen Labor, capital, energy, water [35] GDP CO 2 Labor, capital, energy [36] GDP CO 2 , SO 2 Labor, capital, coal, crude oil, natural gas [37] Industrial added value CO 2 Labor, capital, energy [38] GDP Labor, capital, energy [39] GDP Waste water, waste gas, solid waste Labor, capital, energy [40] Industrial added value CO 2 , SO 2 Labor, capital, energy [41] Industrial added value NO 2 Capital, electricity [26] GDP CO 2 Labor, capital, energy [42] GDP CO 2 , SO 2 Labor, capital, coal, electricity [4] GDP CO 2 Labor, capital, energy [43] GDP CO 2 Labor, capital, energy [44] GDP Solid waste Labor, capital, coal [45] GDP, primary secondary and tertiary industry PM 10 , SO 2 , NO 2 Coal, oil, gas, electricity, energy investment [46] Industrial added value Waste water, solid waste Labor, capital, coal [22] Industrial added value CO 2 Labor, capital, energy [47] GDP SO 2 , solid waste Labor, capital, energy [48] GDP CO 2 , SO 2 , COD Labor, capital, energy [49] GDP CO 2 , SO 2 , solid waste, industrial dust Labor, capital, energy [50] GDP CO 2 Labor, capital, energy [6] GDP CO 2 Labor, capital, energy [51] GDP CO 2 , SO 2 Labor, capital, energy [52] GDP SO 2 , waste water, solid waste Labor, capital, energy …”

Section: Authors Desirable Outputsmentioning

confidence: 99%

Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis

Zhang

Gao

2018

Sustainability

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There are two categories (i.e., radial and non-radial category) in conventional DEA (Data Envelopment Analysis). Recently, intermediate approach was put forward as a new third category. Intermediate approach is a newly proposed approach and there are quite limited related studies. This study contributes to the DEA studies by putting forward an analytical framework of combining intermediate approach and DEA window analysis along with the concepts of natural and managerial disposability. Such combination is quite meaningful and this new approach has three important features. To the best of our knowledge, such type of research has never been investigated by the existing studies. As an application, this approach is used to evaluate the performance of provinces in China from 2007 to 2014. Furthermore, this study develops a series of performance indices from different perspectives. This study identifies the three important findings. Firstly, eco-technology advancements can achieve economic prosperity and environmental protection simultaneously, and thus should become a new direction of climate policies. Secondly, considerable differences exist in a series of indices that evaluates the performance of various provinces and pollutants from different respective. Then, sufficient attention should be given to the provinces and the pollutants with poor performance. Finally, the Chinese government should promote efficiency improvement by "catching up" for provinces with poor performance in the short term. In addition, the central government should reduce regional disparity in order to promote the social sustainability in the long term.

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The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis

Cited by 89 publications

References 78 publications

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis

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