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Optimizing emission reduction task sharing: technology and performance perspectives

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  • Jiasen Sun

    (Soochow University)

  • Guo Li

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Sustainable Development Research Institute for Economy and Society of Beijing)

Abstract

One effective way to achieve emission reduction targets is to allocate overall emission reduction tasks among regions. However, existing AEP optimization models do not consider technology heterogeneity between regions. This study addresses this problem, by first incorporating a meta-frontier technique into the data envelope analysis model (DEA) to measure the level of energy conservation and emission reduction (ECER) technology of different regions in China. Then, the study proposes an optimization model for emission reduction task sharing, by integrating DEA and ECER technology. Compared with previous models, the optimization model proposed in this study considers both technology and efficiency factors. The proposed model was applied to an empirical analysis of 176 cities in China from 2012 to 2016. The empirical results show that the average comprehensive efficiency of all the sample cities is very low. This indicates there is great potential for improving the environmental performance of Chinese cities. The environmental performance results of the sample cities further verify the Kuznets hypothesis: environmental performance and economic development level follow a U-shaped curve. ECER technology levels in China's third- and fourth-tier cities have not significantly changed in recent years. There is an increased reduction in sulfur dioxide (SO2) emissions in Chinese cities, but dust emission reduction is unstable, especially in the third-tier cities. Based on these results, this article also proposes a series of policy recommendations for cities to improve ECER performance.

Suggested Citation

  • Jiasen Sun & Guo Li, 2022. "Optimizing emission reduction task sharing: technology and performance perspectives," Annals of Operations Research, Springer, vol. 316(1), pages 581-602, September.
    • Handle: RePEc:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04273-z
    DOI: 10.1007/s10479-021-04273-z
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