Identifying the key regions and industries for carbon emission reduction in the manufacturing industry is crucial for determining the cooperative objects and setting the cooperative mode when each manufacturing sub-sector in each region carries out collaborative emission reduction. This study adopts a holistic carbon governance perspective that emphasizes coordinated action across regions and industries. It constructs a nested network of collaborative carbon emission reduction in manufacturing industry. By analyzing China’s manufacturing carbon emission data from 2010 to 2021, the study examines the spatial and temporal evolution and structural characteristics of the nested network, identifies the key regions and industries for collaborative emission reduction in manufacturing, and finally constructs a cooperative pathway framework of regional-industry collaborative carbon emission reduction in manufacturing. The study results show that: (1) The spatio-temporal evolution trend of the nested network of collaborative emission reduction in manufacturing industry remained stable in the three core structures during the period of 2010–2021. (2) The manufacturing industry cooperative emission reduction nested network shows an obvious “core-edge” spatial distribution pattern. (3) The petroleum processing and coking industry, nonferrous metal smelting and pressing industry, and tobacco processing industry in Central China, East China, and South China are the key nodes of the manufacturing industry cooperative emission reduction nested network, and their contribution rates are 29.86%, 4.45%, and 3.17%, respectively. (4) The pathway framework for regional-industrial collaborative carbon reduction in manufacturing achieves a four-dimensional synergy of technology, industry, policy, and space through the technological penetration of core nodes, the differentiated supplementation of edge nodes, and the systematic linkage of collaborative interfaces, to promote the transition of carbon governance from fragmentation to networking.
Building similarity graph...
Analyzing shared references across papers
Loading...
Meijing Chen
Ting Wang
Min Zhang
Humanities and Social Sciences Communications
Queen's University Belfast
Guizhou University
Building similarity graph...
Analyzing shared references across papers
Loading...
Chen et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d896166c1944d70ce075ca — DOI: https://doi.org/10.1057/s41599-026-07156-5
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: