Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/327617 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Innovation & Knowledge (JIK) [ISSN:] 2444-569X [Volume:] 10 [Issue:] 3 [Article No.:] 100716 [Year:] 2025 [Pages:] 1-12
Publisher: 
Elsevier, Amsterdam
Abstract: 
Integration and synergy of environmental policies is crucial for maximizing carbon reduction effects. This study investigates the heterogeneous impact of environmental policy synergy on carbon neutrality using panel data from 30 Chinese provinces between 2003 and 2022 and employing generalized quantile regression and spatial econometric models. The relevant findings are threefold. (1) The marginal impact of environmental policy synergy on per capita carbon emissions exhibits distinct structural characteristics across different positions in the conditional distribution of China's per capita carbon emissions. (2) Significant heterogeneity is evident in the effects of various environmental policy synergy categories on carbon neutrality. Specifically, synergies between government and enterprise environmental regulation (ER), and government-and public-type ER effectively reduces per capita carbon emissions, contributing to carbon neutrality. In contrast, the synergy between enterprise and public-type ER policies increases per capita carbon emissions, while the synergy between all three types of ER policies does not significantly impact carbon emissions. (3) The direct effect of local government competition on per capita carbon emissions is negligible; however, government and corporate policy synergy results in increased per capita carbon emissions under government competition. This study reveals the heterogeneous effects of policy combinations, providing a dynamic decision-making basis for optimizing policy instruments in achieving carbon peaking and carbon neutrality.
Subjects: 
Environmental regulation
Policy synergy
Carbon neutrality
Local government competition
Generalized quantile regression
Spatial econometric regression
JEL: 
Q54
Q58
H77
C23
D62
O18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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