Industrial agglomeration measured by plants’ distance and CO2 emissions: Evidence from 268 Chinese prefecture-level cities

Yanting Zheng, Huidan Yang, Jinyuan Huang, Qi Cui and Jinyan Zhan

Technological Forecasting and Social Change, 2022, vol. 176, issue C

Abstract: Industrial agglomeration has a significant effect on carbon emissions. However, studies based on economic indicators seldom adequately addressed the underlying mechanisms (e.g., infrastructure and technology sharing, joint administration), which are often determined by the physical distance between plants. By calculating the distances between plants, the factory scatter index (FSI) was developed to reflect industrial agglomeration. Using a spatial econometric model, we investigated the relationship between the FSI and CO2 emissions in 268 Chinese cities during 1998–2013. The results demonstrate that increasing the FSI leads to an increase in total CO2 emissions and CO2 emission intensity. Differences between this result and those obtained with conventional indicators (e.g., factory density) suggest that the FSI may be more appropriate as a method of formulating spatial optimization guidelines for carbon reduction. Further spatial coupling analyses divided this spatial relationship into four types and explained their changes over time. The results may provide local governments with another possible option to reduce carbon emissions by shortening inter-factory distance, and could additionally help them to understand the implications of the spatial pattern of factories on current carbon emissions and the sensitivity of future carbon emission reduction to the likely pattern of future industrialization.

Keywords: CO2 emissions; Carbon mitigation; Industrial agglomeration; Spatial optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162522000014

DOI: 10.1016/j.techfore.2022.121469

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