Analysis of driving factors and allocation of carbon emission allowance in China.

China's "13th Five-Year Plan" proposes that carbon emissions per unit of GDP be reduced by 18% from their 2015 levels. In this context, the present study uses grey relational analysis to explore the correlation degree between carbon emissions and economic, energy and population effects. This study also quantitatively analyzes the contribution rate of each driving factor by using logarithmic mean Divisia index. Results show that per capita GDP and energy consumption per unit of GDP are the key factors that lead to changes in carbon emissions. Then, the input-oriented BCC model in data envelopment analysis (DEA) is used to evaluate the efficiency of the primary carbon emission allowance allocation scheme in the "13th Five-Year Plan". The results show that the average score of technical efficiencies is only 0.7409. Finally, the zero-sum-gains DEA model is used to adjust and optimize the scheme under the premise of constant total carbon emissions. Thereafter, a scientific carbon-emission allowance allocation scheme is proposed. We verified that the optimal scheme can ensure the compatibility of the carbon emission allowances of provincial-level administrative regions with their economy, energy, and population from the perspective of efficiency.

[1]  Y. Kaya.  Impact of carbon dioxide emission control on GNP growth : Interpretation of proposed scenarios , 1989 .

[2]  Stefano Galmarini,et al.  Drivers in CO2 emissions variation: A decomposition analysis for 33 world countries , 2016 .

[3]  Yi-Ming Wei,et al.  Integrated weighting approach to carbon emission quotas: an application case of Beijing-Tianjin-Hebei region , 2016 .

[4]  B. W. Ang,et al.  Assessing the role of international trade in global CO2 emissions: An index decomposition analysis approach , 2018 .

[5]  Peng Zhou,et al.  Decoupling and attribution analysis of industrial carbon emissions in Taiwan , 2016 .

[6]  Boqiang Lin,et al.  Emissions reduction in China׳s chemical industry – Based on LMDI , 2016 .

[7]  O. Ngwenyama,et al.  Managing emissions allowances of electricity producers to maximize CO2 abatement: DEA models for analyzing emissions and allocating emissions allowances , 2018, International Journal of Production Economics.

[8]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[9]  Yue-Jun Zhang,et al.  The allocation of carbon emission quotas to five major power generation corporations in China , 2018, Journal of Cleaner Production.

[10]  Fujun Hou,et al.  Driving factors of electric carbon productivity change based on regional and sectoral dimensions in China , 2018, Journal of Cleaner Production.

[11]  Bin Chen,et al.  Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city , 2018, Applied Energy.

[12]  F. Cucchiella,et al.  Efficiency and allocation of emission allowances and energy consumption over more sustainable European economies , 2018 .

[13]  Andrew Simpson,et al.  Pollution responsibility allocation in supply networks: A game-theoretic approach and a case study , 2019, International Journal of Production Economics.

[14]  Jin Yang,et al.  Analyzing the impact factors of energy-related CO2 emissions in China: What can spatial panel regressions tell us? , 2017 .

[15]  Dezhi Li,et al.  Carbon emissions and policies in China's building and construction industry: Evidence from 1994 to 2012 , 2016 .

[16]  Peng Zhou,et al.  Carbon dioxide emissions allocation: A review , 2016 .

[17]  Xin Yao,et al.  Decomposition analysis of factors affecting carbon dioxide emissions across provinces in China , 2017 .

[18]  J. M. Cansino,et al.  Analysis of the main drivers of CO2 emissions changes in Colombia (1990–2012) and its political implications , 2018 .

[19]  Y. Geng,et al.  Efficient allocation of CO2 emissions in China: a zero sum gains data envelopment model , 2016 .

[20]  Liang Liang,et al.  CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China , 2016 .

[21]  Fei Ye,et al.  Carbon dioxide emissions quotas allocation in the Pearl River Delta region: evidence from the maximum deviation method. , 2018 .

[22]  R. Inglesi‐Lotz.  Decomposing the South African CO2 emissions within a BRICS countries context: Signalling potential energy rebound effects , 2018 .

[23]  Lixin Miao,et al.  Quantification and driving force analysis of provincial-level carbon emissions in China , 2017 .

[24]  L. Miao,et al.  Sector decomposition of China’s national economic carbon emissions and its policy implication for national ETS development , 2017 .

[25]  Rui Zhao,et al.  Allocation of carbon emissions among industries/sectors: An emissions intensity reduction constrained approach , 2017 .

[26]  A. Chiu,et al.  Electricity trading and its effects on global carbon emissions: A decomposition analysis study , 2018, Journal of Cleaner Production.

[27]  Gongbing Bi,et al.  Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA , 2015 .

[28]  B. W. Ang,et al.  Assessing drivers of economy-wide energy use and emissions: IDA versus SDA , 2017 .

[29]  Rongrong Li,et al.  Decomposition and decoupling analysis of carbon emissions from economic growth: A comparative study of China and the United States , 2018, Journal of Cleaner Production.

[30]  B. W. Ang,et al.  Multiplicative decomposition of aggregate carbon intensity change using input–output analysis , 2015 .

[31]  Fei Teng,et al.  Sharing emission space at an equitable basis: Allocation scheme based on the equal cumulative emission per capita principle , 2014 .

[32]  Qingxian An,et al.  Allocation of carbon dioxide emission permits with the minimum cost for Chinese provinces in big data environment , 2017 .

[33]  Qian Shi,et al.  Driving factors of the changes in the carbon emissions in the Chinese construction industry , 2017 .

[34]  Fengyan Fan,et al.  Factor analysis of energy-related carbon emissions: a case study of Beijing , 2017 .

[35]  Yue-Jun Zhang,et al.  Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method , 2014 .

[36]  Eliane Gonçalves Gomes,et al.  Olympic ranking based on a zero sum gains DEA model , 2003, Eur. J. Oper. Res..

[37]  R. Xie,et al.  The effects of urban agglomeration economies on carbon emissions: Evidence from Chinese cities , 2018 .

[38]  Yue-Jun Zhang,et al.  The decomposition of energy-related carbon emission and its decoupling with economic growth in China , 2015 .

[39]  Zhaohua Wang,et al.  Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing, China , 2016 .