The impact of fiscal decentralization on CO2 emissions in China

被引：123

作者：

Cheng, Shulei ^{[1
]}

Fan, Wei ^{[2
]}

Chen, Jiandong ^{[1
]}

Meng, Fanxin ^{[3
,4
]}

Liu, Gengyuan ^{[4
]}

Song, Malin ^{[5
]}

Yang, Zhifeng ^{[6
]}

机构：

[1] Southwestern Univ Finance & Econ, Sch Publ Adm, Chengdu 611130, Sichuan, Peoples R China

[2] Southwestern Univ Finance & Econ, Sch Publ Finance & Taxat, Chengdu 611130, Sichuan, Peoples R China

[3] Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100875, Peoples R China

[4] Yale Univ, Sch Forestry & Environm Studies, New Haven, CT 06511 USA

[5] Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Anhui, Peoples R China

[6] Guangdong Univ Technol, Inst Environm Ecol Engn, Guangzhou 510006, Guangdong, Peoples R China

来源：

ENERGY | 2020年 / 192卷 / 192期

关键词：

Fiscal decentralization; Energy consumption; CO2; emissions; LMDI decomposition; Dynamic panel regression; GREENHOUSE-GAS EMISSIONS; CARBON-DIOXIDE EMISSIONS; ENVIRONMENTAL ENFORCEMENT; REGULATORY COMPETITION; ENERGY; INTENSITY; ACHIEVE; CONSUMPTION; REDUCTION; EUROPE;

D O I：

10.1016/j.energy.2019.116685

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

This paper evaluates the impact of fiscal decentralization on CO2 emissions in China. We build an equation to combine fiscal decentralization and CO2 emissions and decompose it using the logarithmic mean Divisia index (LMDI) decomposition technique. Then, we examine the nonlinear impact of fiscal decentralization on CO2 emissions using a dynamic panel regression model and Chinese interprovincial data from 1997 to 2015. The empirical results show that the direct impact of fiscal decentralization on CO2 emissions is nonlinear, and the higher the per capita fiscal expenditure, the more fiscal decentralization can reduce CO2 emissions. Moreover, the government's emissions reduction policies effectively reduce CO2 emissions only when they are conducted at the provincial and city levels, and some differences among regions remain. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：15

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