Driving Factors of Carbon Emission Intensity for China's Planting: A Combination of LMDI and PDA

被引:0
作者
Yang, Fuxia [1 ]
Fan, Dongshou [1 ]
Xu, Fei [2 ]
机构
[1] Huazhong Agr Univ, Coll Econ & Management, Wuhan, Peoples R China
[2] Anhui Normal Univ, Sch Econ & Management, Wuhu, Peoples R China
来源
FRONTIERS IN CLIMATE | 2022年 / 3卷
基金
英国科研创新办公室; 中国国家自然科学基金;
关键词
planting industry; carbon emission intensity; decomposition; LMDI-PDA; driving factors; DECOMPOSITION ANALYSIS; ENERGY-CONSUMPTION; ECONOMIC-GROWTH; CO2; EMISSIONS; AGGREGATE;
D O I
10.3389/fclim.2021.798339
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
It is important to explore the driving factors of the carbon emission intensity (CI) for China's planting under the dual pressures of adequate food supply and carbon neutrality. Previous studies separately investigate the impact of technical or structural factors on the total carbon emissions of China's agricultural sector, but few studies assess the comprehensive effects of these two. To this end, this paper incorporates the production-theoretical decomposition analysis (PDA) into the logarithmic mean Divisia index (LMDI) and decomposes the changes of CI into seven components, namely, two technical effects, four structural ones, and one regional layout effect. Based on the panel data of the agricultural sector for 31 provinces in China from 2001 to 2018, the contribution rates of the seven components to the changes of China's planting CI are computed. The results indicate that China's planting CI presents a downward trend with an average annual decreasing rate of 11.4% over the whole study period. The improvement in technical efficiency (TEFF) plays a dominant role in the decline of CI for planting with a contribution rate of 83.19%, followed by the output structure (OS) change (27.28%). In contrast, technical change (TECH) (8.00%) promotes the increase of CI. Further, the effects present significant regional heterogeneities. Specifically, TEFF contributes the highest share to the decline of CI for producing-sales balance areas (BA), and OS plays the greatest role in the decrease of CI for main grain-sales areas (MCA) during the entire study period. Accordingly, some policy recommendations are put forward on how to reduce the CI of China's planting.
引用
收藏
页数:11
相关论文
共 42 条
[1]   LMDI decomposition approach: A guide for implementation [J].
Ang, B. W. .
ENERGY POLICY, 2015, 86 :233-238
[2]   The LMDI approach to decomposition analysis: a practical guide [J].
Ang, BW .
ENERGY POLICY, 2005, 33 (07) :867-871
[3]   Decomposition analysis for policymaking in energy: which is the preferred method? [J].
Ang, BW .
ENERGY POLICY, 2004, 32 (09) :1131-1139
[4]   Decomposition analysis of factors driving CO2 emissions in Chinese provinces based on production-theoretical decomposition analysis [J].
Chen, Liyun ;
Duan, Qi .
NATURAL HAZARDS, 2016, 84 :S267-S277
[5]   Food systems are responsible for a third of global anthropogenic GHG emissions [J].
Crippa, M. ;
Solazzo, E. ;
Guizzardi, D. ;
Monforti-Ferrario, F. ;
Tubiello, F. N. ;
Leip, A. .
NATURE FOOD, 2021, 2 (03) :198-209
[6]   Study on decoupling analysis between energy consumption and economic growth in Liaoning Province [J].
Dong, Bai ;
Zhang, Ming ;
Mu, Hailin ;
Su, Xuanming .
ENERGY POLICY, 2016, 97 :414-420
[7]   Drivers of China's Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches [J].
Dong, Feng ;
Gao, Xinqi ;
Li, Jingyun ;
Zhang, Yuanqing ;
Liu, Yajie .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2018, 15 (12)
[8]   Drivers of carbon emission intensity change in China [J].
Dong, Feng ;
Yu, Bolin ;
Hadachin, Tergel ;
Dai, Yuanju ;
Wang, Ying ;
Zhang, Shengnan ;
Long, Ruyin .
RESOURCES CONSERVATION AND RECYCLING, 2018, 129 :187-201
[9]   Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework [J].
Du, Kerui ;
Lin, Boqiang .
ENERGY, 2015, 90 :570-577
[10]   Rebound effect of energy efficiency in China's construction industry: a general equilibrium analysis [J].
Du, Qiang ;
Li, Zhe ;
Li, Yi ;
Bai, Libiao ;
Li, Jingtao ;
Han, Xiao .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2019, 26 (12) :12217-12226