China's domestic industry redistribution facilitates carbon emissions mitigation

Revealing the hidden carbon flows in global industrial Sectors-Based on the perspective of linkage network structure

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000-2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network.

Does industrial co-agglomeration promote green energy efficiency? Evidence from spatial panel data of 284 cities in China

Industrial co-agglomeration (IC) plays a pivotal role in the development of local and adjacent green energy efficiency across 284 Chinese cities, encompassing both resource-based and non-resource-based urban centers. Based on the panel data of 284 cities in China from 2005 to 2020, this study employs spatial econometric methods to empirically assess the influence of IC and its spillover effects on green energy efficiency, employing a spatial Durbin model. Additionally, the study categorizes the 284 Chinese cities into resource-based and non-resource-based categories, utilizing spatial econometric methods to delve into the heterogeneity of their effects and spillover impacts. The key findings are as follows: (1) The average green energy efficiency across the 284 Chinese cities from 2005 to 2020 stands at 0.5834. The trend in IC indicates growth and concentration towards the central areas, increasing from 2.7396 in 2005 to 2.7658 in 2020. (2) The IC, with a coefficient of 0.0918, promotes the local green energy efficiency. (3) There are spillover effects of local IC on the green energy efficiency in adjacent areas with a coefficient of 0.2550 and an Indirect Effect of 0.4567. (4) In resource-based cities, IC positively impacts local green energy efficiency with a coefficient of 0.1056 but negatively affects green energy efficiency in adjacent areas with a coefficient of -0.1368. In non-resource-based cities, IC enhances green energy efficiency in adjacent cities with a coefficient of 0.1335. Consequently, the study offers pertinent policy recommendations aimed at improving energy efficiency in light of these findings.