Path analysis and mediating effects of influencing factors of land use carbon emissions in Chang-Zhu-Tan urban agglomeration

Xu Yang and Xianzhao Liu

Technological Forecasting and Social Change, 2023, vol. 188, issue C

Abstract: Land use carbon emission is one of the main sources of greenhouse gases. In order to achieve carbon reduction, it is crucial to clarify the pathways of land use carbon emission influencing factors. The article takes Chang-Zhu-Tan urban agglomeration as the study area, combines the land use remote sensing data and energy consumption statistics from 1995 to 2018, and uses the STIRPAT extended model to identify the economic, population, energy, technology, policy, land and opening-up level as the latent variables affecting the land use carbon emission in the study area, to establish PLS-SEM to explore the influence of latent variables on dependent variables and the interaction between latent variables. The results show that the seven selected latent variables had good explanatory power on land use carbon emission with R2 reaching 0.983. Economy, population, energy and land — the latent variables in this study — can directly influence land use carbon emission. This study found that land was the most important factor in increasing land use carbon emissions, with a path coefficient of 1.471, while economy played a negative role, with a path coefficient of −1.401. Latent variables other than energy and land all had mediating effects on the dependent variable or the remaining latent variables directly or indirectly. Indirect effects included complete mediation and partial mediation. For example, technology and opening-up level cannot directly influence the dependent variable, but can have a completely indirect effect by affecting energy (complete mediation). The fitness and superiority of the model developed in this paper were extremely high, with values of 0.067 and 0.846 for SRMR and GOF, respectively. This study helps to clarify the influence paths of land use carbon emission and figuratively depicts the path direction and degree of influence among influencing factors, which will help Chang-Zhu-Tan urban agglomeration achieve carbon emission reduction.

Keywords: Land use carbon emission; STIRPAT; PLS-SEM; Path analysis; Mediating effects; Influencing factors (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:188:y:2023:i:c:s0040162522007892

DOI: 10.1016/j.techfore.2022.122268

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