Driving force for the variation of pollution discharge in the Chinese industrial department: An empirical study based on pollution data during the 12th five-year plan period
KUAI Peng1, SHU Ke-dong1, CHENG Run-he2
1. School of Economics, Hefei University of Technology, Hefei 230002, China; 2. School of Environment, Beijing Normal University, Beijing 100875, China
Abstract：Based on the LMDI model, variations of industrial pollutant emission during the 12th five-year plan period were decomposed into three driving factors in terms of scale, structure and intensity effects. The results showed that scale effect was the main driving force for the reduction of industrial pollutant. Specifically, there was a countrywide movement of reducing the outdated and excess industrial capacities during the past 5 years, which had suppressed the unordered industrial expansion, thus lowered the growth of pollutant emission. The structure effect was not as significant as the scale effect, however, there was only little difference between them, which indicated that the adjustment of industrial structures had also caused positive effects. The intensity effect was relatively not significant. Based on the above results, it was suggested that the government pay more attentions to the scale effects during the 13th five-year plan period. Meanwhile, in consideration of the marginal cost of scale effect that would obviously keep on increasing, long-term mechanism such as industrial structure adjustment or techniques innovation was in need.
蒯鹏, 束克东, 成润禾. 我国工业部门环境污染排放变化的驱动因素——基于“十二五”工业排放数据的实证研究[J]. 中国环境科学, 2018, 38(6): 2392-2400.
KUAI Peng, SHU Ke-dong, CHENG Run-he. Driving force for the variation of pollution discharge in the Chinese industrial department: An empirical study based on pollution data during the 12th five-year plan period. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(6): 2392-2400.
Ang B W. LMDI decomposition approach:A guide for implementation[J]. Energy Policy, 2015,86:233-238.
[3]
Jiang J J, Ye B, Xie D J, et al. Provincial-level carbon emission drivers and emission reduction strategies in China:Combining multi-layer LMDI decomposition with hierarchical clustering[J]. Journal of Cleaner Production, 2017,169:178-190.
[4]
Mousavi B, Lopez N S A, Biona J B M, et al. Driving forces of Iran's CO2 emissions from energy consumption:An LMDI decomposition approach[J]. Applied Energy, 2017,206:804-814.
[5]
Jeong K, Kim S. LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector[J]. Energy Policy, 2013,62(9):1245-1253.
Achour H, Belloumi M. Decomposing the influencing factors of energy consumption in Tunisian transportation sector using the LMDI method[J]. Transport Policy, 2016,52:64-71.
[8]
Chong C H, Liu P, Ma L W, et al. LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram[J]. Energy, 2017,133:525-544.
Ang B W, Choi K H. Decomposition of aggregate energy and gas emission intensities for industry:a refined Divisia index method[J]. Energy Journal, 1997,18(3):59-73.
