Identification of factors influencing net primary productivity of terrestrial ecosystems based on interpretable machine learning --evidence from the county-level administrative districts in China

Abstract

Global climate change is rooted in the imbalance between carbon sources and sinks, and net-zero greenhouse gas emissions should focus not only on the source-side drivers but also on the sink-side influencing factors. Taking the county-level administrative districts in China as the sample, this study uses machine learning models to fit the relationship between socioeconomic development (SED) and net primary productivity (NPP) of terrestrial ecosystems. Moreover, it identifies key influencing factors and their effects based on the SHapley Additive exPlanations (SHAP) algorithm. The results show that the districts with low terrestrial NPP show the characteristics of agglomeration distribution. The eight key factors, in order, are as follows: agricultural development level, latitude, population size, longitude, animal husbandry development level, economic scale, time trend and industrialization level. In this study, via SHAP interaction plots, we found that the effects of population, economic growth, and industrialization on terrestrial NPP are regionally heterogeneous; via cluster analysis, we found the stage characteristics of the mode of SED affecting terrestrial NPP. Therefore, the conservation of terrestrial NPP needs to be combined with the stage changes of SED, as well as inter-regional differences, to develop a regionally coordinated and time-coherent ecological carbon sink conservation plan.