The social cost of carbon driven by green behaviors.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 20 07 2022
accepted: 18 05 2023
medline: 3 7 2023
pubmed: 30 6 2023
entrez: 30 6 2023
Statut: epublish

Résumé

With the change of climate issues and the needs of economic development, the idea of practicing green and low-carbon behaviors sinks deeper and deeper into people's hearts. This paper based on the social cost of carbon (SCC) model, this paper constructs a new carbon social cost model by adding the impact of green low-carbon behavior. Classify climate states, based on Bayesian statistical knowledge, study the posterior probability distribution of climate state transitions, and discuss the optimal carbon policy for different climate states by balancing emission utility costs and utility weighted carbon marginal products. This article also discusses the damage caused by rising temperatures and explores their impact on carbon price policies. then, the paper calculates SCC under four kinds of climate states, which will be visually displayed with graphs. Finally, we compare SCC obtained in this paper with that in other researches. The results show that: (1) Climate status has a significant impact on carbon policy, and carbon price predictions will dynamically change with climate status. (2) Green low-carbon behavior has a positive impact on climate status. (3) There are differences in the impact of the three types of damage caused by rising temperatures on carbon price policies. (4) Green development is conducive to stabilizing the value of SCC. (5) Close monitoring of the climate state helps to update the probability of damage in time so that we can precisely adjust the corresponding policies on SCC. This study provides theoretical and empirical reference for the government to formulate carbon price policies and promote the development of social green behavior.

Identifiants

pubmed: 37390053
doi: 10.1371/journal.pone.0286534
pii: PONE-D-22-18165
pmc: PMC10313057
doi:

Substances chimiques

Carbon 7440-44-0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0286534

Informations de copyright

Copyright: © 2023 Fu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Environ Sci Pollut Res Int. 2019 Feb;26(6):6000-6013
pubmed: 30612378
Proc Natl Acad Sci U S A. 2021 Jan 26;118(4):
pubmed: 33431652
Behav Sci (Basel). 2022 Feb 03;12(2):
pubmed: 35200286
Int J Environ Res Public Health. 2022 Jul 06;19(14):
pubmed: 35886108

Auteurs

Min Fu (M)

Research Institute of Carbon Neutralization Development, School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, P.R. China.
Jiangsu Province Engineering Research Center of Spatial Big Data, School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, P.R. China.

Yixiang Zhang (Y)

Jiangsu Province Engineering Research Center of Spatial Big Data, School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, P.R. China.
Jiangsu Province Engineering Research Center of Industrial Carbon System Analysis, School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, P.R. China.

Lixin Tian (L)

Research Institute of Carbon Neutralization Development, School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, P.R. China.
Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, P.R. China.

Zaili Zhen (Z)

Jiangsu Province Engineering Research Center of Industrial Carbon System Analysis, School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, P.R. China.
Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, P.R. China.

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