What determines the performance of low-carbon cities in China? Analysis of the grouping based on the technology-Organization-Environment framework.


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: 24 03 2023
accepted: 08 07 2023
medline: 17 8 2023
pubmed: 15 8 2023
entrez: 15 8 2023
Statut: epublish

Résumé

Addressing climate change and reducing greenhouse gas emissions have emerged as shared global objectives. Enhancing the development performance of low-carbon cities has become an urgent and widely acknowledged concern for both government policy-making departments and academics. Drawing upon the complex grouping perspective and resource allocation theory, this study investigates how varying conditions related to technology, organization, and environment in Chinese low-carbon pilot cities can effectively allocate resources to shape the governance performance of low-carbon cities. This paper employs a comprehensive grouping analysis perspective, treating the research object as a combination of various ways between condition variables. It integrates the advantages of case studies and variable studies, and investigates the collective relationships between elemental groupings and outcomes using the fsQCA analysis method. This approach facilitates the understanding of multiple concurrent causal relationships within the technology-organization-environment (TOE) framework, accounting for different performance levels in Chinese low-carbon pilot cities, as well as addressing complex causal issues such as asymmetry and multiple scenario equivalence. Data from 30 representative low-carbon pilot cities in China were employed to validate the TOE theoretical framework. No single element alone can be considered a necessary condition for low-carbon city governance performance. However, environmental enhancement plays a more prominent role in the governance performance of low-carbon cities. Additionally, the presence of "multiple concurrent" technical, organizational, and environmental conditions leads to a diverse range of governance performance in Chinese low-carbon pilot cities. In other words, the driving paths of low-carbon city performance exhibit distinct pathways. The findings of this study can assist low-carbon pilot city managers in generating effective governance ideas, facilitating the successful implementation of low-carbon city pilot projects, and drawing valuable lessons from the experience of low-carbon city development in China.

Sections du résumé

BACKGROUND AND OBJECTIVES
Addressing climate change and reducing greenhouse gas emissions have emerged as shared global objectives. Enhancing the development performance of low-carbon cities has become an urgent and widely acknowledged concern for both government policy-making departments and academics. Drawing upon the complex grouping perspective and resource allocation theory, this study investigates how varying conditions related to technology, organization, and environment in Chinese low-carbon pilot cities can effectively allocate resources to shape the governance performance of low-carbon cities.
METHODS AND DATA
This paper employs a comprehensive grouping analysis perspective, treating the research object as a combination of various ways between condition variables. It integrates the advantages of case studies and variable studies, and investigates the collective relationships between elemental groupings and outcomes using the fsQCA analysis method. This approach facilitates the understanding of multiple concurrent causal relationships within the technology-organization-environment (TOE) framework, accounting for different performance levels in Chinese low-carbon pilot cities, as well as addressing complex causal issues such as asymmetry and multiple scenario equivalence. Data from 30 representative low-carbon pilot cities in China were employed to validate the TOE theoretical framework.
CONCLUSION
No single element alone can be considered a necessary condition for low-carbon city governance performance. However, environmental enhancement plays a more prominent role in the governance performance of low-carbon cities. Additionally, the presence of "multiple concurrent" technical, organizational, and environmental conditions leads to a diverse range of governance performance in Chinese low-carbon pilot cities. In other words, the driving paths of low-carbon city performance exhibit distinct pathways.
CONTRIBUTION
The findings of this study can assist low-carbon pilot city managers in generating effective governance ideas, facilitating the successful implementation of low-carbon city pilot projects, and drawing valuable lessons from the experience of low-carbon city development in China.

Identifiants

pubmed: 37582071
doi: 10.1371/journal.pone.0289160
pii: PONE-D-23-08832
pmc: PMC10427000
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

e0289160

Informations de copyright

Copyright: © 2023 Chen 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

Comput Intell Neurosci. 2022 Jun 15;2022:6366061
pubmed: 35755745
Int J Environ Res Public Health. 2021 Dec 02;18(23):
pubmed: 34886436
J Environ Manage. 2023 Apr 15;332:117363
pubmed: 36736083
Sci Adv. 2018 Jun 27;4(6):eaaq0390
pubmed: 29963621
Sci Total Environ. 2019 Jul 20;675:472-482
pubmed: 31030153
Int J Environ Res Public Health. 2023 Mar 14;20(6):
pubmed: 36981993

Auteurs

Weidong Chen (W)

Department of Management and Economics, Tianjin University, Tianjin, China.

Quanling Cai (Q)

Department of Management and Economics, Tianjin University, Tianjin, China.
College of Politics and Public Administration, Qinghai Minzu University, Xining, Qinghai Province, China.

Kaisheng Di (K)

Department of Management and Economics, Tianjin University, Tianjin, China.
College of Politics and Public Administration, Qinghai Minzu University, Xining, Qinghai Province, China.
Department of Party Committee, Party School of the Qinghai Provincial Committee of CPC, Xining, Qinghai Province, China.

Dongli Li (D)

Department of Management and Economics, Tianjin University, Tianjin, China.
College of Chunming, Hainan University, Haikou, Hainan Province, China.

Caiping Liu (C)

Department of Management and Economics, Tianjin University, Tianjin, China.
College of Politics and Public Administration, Qinghai Minzu University, Xining, Qinghai Province, China.

Mingxing Wang (M)

College of Finance and Economics, Qinghai University, Xining, Qinghai Province, China.

Sichen Liu (S)

College of Economics and Management, Wuhan University, Wuhan, Hubei Province, China.

Zhensheng Di (Z)

College of Politics and Public Administration, Qinghai Minzu University, Xining, Qinghai Province, China.

Qiumei Shi (Q)

Health Education Services Department, Xining Aier Eye Hospital, Xining, Qinghai Province, China.

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