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Chinese Journal of Management Science ›› 2022, Vol. 30 ›› Issue (7): 252-263.doi: 10.16381/j.cnki.issn1003-207x.2019.0073

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Disaggregating China’s National Carbon-reduction Target Based on Optimizing the Total Cost of Emission Reduction

DU Juan, PAN Meng, WANG Yun-feng   

  1. School of Economics and Management, Tongji University, Shanghai 200092, China
  • Received:2019-01-12 Revised:2019-05-06 Online:2022-08-05 Published:2022-08-05
  • Contact: 杜娟 E-mail:dujuan@tongji.edu.cn

Abstract: In order to take an active role in fighting greenhouse effect and global warming, China is vigorously promoting the ecological civilization construction and a new mode of harmonious development between man and nature. One of the important measures is to establish the national market for trading carbon emissions. How to equitably allocate the national carbon reduction target at the provincial level is one of the key factors to ensure the orderly running of the carbon trading market. Under the frame of efficiency analysis, directional distance function (DDF) is used to assess for each provincial region the quantitative trend between its GDP loss and carbon emission reduction. Based on the fitted regression equations, the GDP decreases directly caused by emission reductions can be calculated and viewed as the costs of cutting carbon. Then a programming method is developed to disaggregate China’s national reduction target of carbon dioxide emissions at the provincial level by minimizing total reduction cost.The results indicate that different provincial regions tend to share different levels of reduction target depending on their economic conditions, industrial structures and energy structures. All 30 provincial regions are divided into four clusters by economic development and emissions level. Provinces belonging to the same cluster exhibit similar trend in carbon reduction. Detailed discussions are provided for each cluster on the economic and industrial status, and on how to achieve sustainable development. Under the stepwise regression analysis, three factors, namely GDP, per capita GDP and carbon dioxide emissions have a significant positive impact on the allocation results, while the second industry proportion and the third industry proportion influence them in a negative way.

Key words: carbon dioxide emission reduction; emission allowance; cost of emission reduction; directional distance function; data envelopment analysis

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