Exploring the key influencing factors of low-carbon innovation from urban characteristics in China using interpretable machine learning

Exploring the key influencing factors (KIFs) of Urban Low-Carbon Innovation (ULCI) from urban characteristics is essential for devising customized promotion strategies. However, existing studies are hampered by methodological limitations that lead to an inability to effectively discern KIFs among urban characteristics or unravel complex, non-linear relationships, and interaction effects. To address these gaps, this paper adopts a synthetic approach based on interpretable Machine Learning (ML). Firstly, the influencing factors are identified through the Delphi method and a systematic literature review. Subsequently, three single AI models (KNN, SVR, and CART) and three ensemble models (RF, XGBoost, and LBGM) are employed to fit the data. Finally, the SHapley Additive exPlanations (SHAP) algorithm is integrated to identify the KIFs and disentangle their impact effects. The findings indicate that (1) 41 influencing factors are identified, from which 10 KIFs, such as Expenditure on Research, Carbon Emissions, Local General Budgetary Revenue, and Education Expenditure, are determined, (2) the developed interpretable ML model tailored for ULCI's KIFs analysis demonstrates high precision and effectively capturing non-linear relationships (R2 = 0.841, RMSE = 0.591, MAE = 0.463), and (3) the global impact, interactive effects, and individual sample impact of the KIFs are explained, and two categories of KIFs dominated by positive and negative influences are revealed respectively. Results of the KIFs identification can provide policy-makers with insight for designing ULCI enhancement paths and consequently promote emission mitigation in China.