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author:

Li, K. (Li, K..) [1] | Luo, Z. (Luo, Z..) [2] | Hong, L. (Hong, L..) [3] | Wen, J. (Wen, J..) [4] | Fang, L. (Fang, L..) [5]

Indexed by:

Scopus

Abstract:

Based on the panel data from 283 prefecture-level cities in China between 2009 and 2019, this study examines the impact of China's carbon emissions trading system on reducing carbon emissions and its mechanisms, using the PSM-DID method. The findings demonstrate that the carbon emissions trading system can effectively decrease the total carbon emissions in pilot cities in China, and has a positive spatial spillover effect on neighboring cities of the pilot areas. The carbon emission trading system primarily reduces carbon emission by incentivizing businesses to implement eco-friendly practices and improve the industrial structure of pilot cities. Increased financial marketisation can promote the carbon emission reduction effects of the trading system. The impact of the carbon emission trading system on old industrial base cities and inland cities is more significant than those on non-old industrial base cities and coastal cities. © 2023 The Authors

Keyword:

Carbon emission reduction Carbon emission trading system Prefecture-level cities in China

Community:

  • [ 1 ] [Li K.]College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
  • [ 2 ] [Luo Z.]College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
  • [ 3 ] [Hong L.]College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
  • [ 4 ] [Wen J.]College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
  • [ 5 ] [Fang L.]School of Economics and Management, Fuzhou University, Fuzhou, 350108, China

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Source :

Heliyon

ISSN: 2405-8440

Year: 2024

Issue: 1

Volume: 10

3 . 4 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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