What factors lead to the decline of energy intensity in China's energy intensive industries?

被引：156

作者：

Tan, Ruipeng ^{[1
]}

Lin, Boqiang ^{[2
]}

机构：

[1] Xiamen Univ, Sch Econ, China Ctr Energy Econ Res, Xiamen 361005, Fujian, Peoples R China

[2] Xiamen Univ, China Inst Studies Energy Policy, Sch Management, Collaborat Innovat Ctr Energy Econ & Energy Polic, Xiamen 361005, Fujian, Peoples R China

来源：

ENERGY ECONOMICS | 2018年 / 71卷

关键词：

China's energy intensive industries; Energy intensity; Technology improvement; Factor substitution; Index decomposition analysis; Production decomposition analysis; STRUCTURAL DECOMPOSITION ANALYSIS; CARBON-DIOXIDE EMISSIONS; CO2; EMISSIONS; EMPIRICAL-ANALYSIS; UNITED-STATES; CONSUMPTION; INDEX; EFFICIENCY; LMDI; URBANIZATION;

D O I：

10.1016/j.eneco.2018.02.019

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper seeks to investigate the main factors causing the decline in energy intensity of China's energy intensive industries. Index Decomposition Analysis and Production Decomposition Analysis methods are combined to complete the decomposition analysis. Overall, seven factors are related to the decline in the energy intensity and technology improvement effect is the most significant factor. Technical efficiency effect is positively related to the decline in twelve provinces but negatively related in seventeen provinces. Capital-energy substitution effect is beneficial to the decline in twenty provinces. Labor-energy substitution effect undermines the decline and substitution effect among different categories of energy can be ignored. Considering provincial contribution, only Xinjiang Province has a negative contribution. Liaoning, Hebei and Shanghai provinces make the largest contributions to the decline in energy intensity. The main policy implications include enhancing investments in research and development in China's energy intensive industries; transforming the intensive development model of the energy intensive industries; gradually reforming energy price; and improving the layout of energy intensive industries. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：213 / 221

页数：9

共 40 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] A new energy decomposition method: perfect in decomposition and consistent in aggregation [J].

Ang, BW ;

Liu, FL .

ENERGY, 2001, 26 (06) :537-548

[5]

[Anonymous], 2006, OR MANAG, DOI DOI 10.19744/J.CNKI.11-1235/F.2006.10.010

[6]

Baiocchi G., 2010, Understanding changes in the UK's CO2 emissions: A global perspective

[7] 2 EXACT, NON-ARBITRARY AND GENERAL-METHODS OF DECOMPOSING TEMPORAL CHANGE [J].

BETTS, JR .

ECONOMICS LETTERS, 1989, 30 (02) :151-156

[8] 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

[9] A bottom-up analysis of China's iron and steel industrial energy consumption and CO2 emissions [J].

Chen, Wenying ;

Yin, Xiang ;

Ma, Ding .

APPLIED ENERGY, 2014, 136 :1174-1183

[10] Spillover effects of FDI on innovation in China: Evidence from the provincial data [J].

Cheung, KY ;

Lin, P .

CHINA ECONOMIC REVIEW, 2004, 15 (01) :25-44

← 1 2 3 4 →