Path analysis and mediating effects of influencing factors of land use carbon emissions in Chang-Zhu-Tan urban agglomeration
以长株潭城市群为案例，利用1995-2018年遥感与能源数据，通过PLS-SEM模型识别经济、人口、能源等七个潜变量对土地利用碳排放的直接与间接影响路径，发现土地是最大正向因素，经济为负向因素，技术等变量通过完全中介间接影响碳排放。
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.