Extreme climate events have emerged as a critical threat to the economic resilience and environmental sustainability of urban systems. As a central pillar of the low-carbon transition, improvements in carbon emission efficiency (CEE) are increasingly recognized as a potential pathway to mitigate the occurrence and intensity of such events. Drawing on a balanced panel dataset of 163 cities from 2006 to 2022, this study integrates an Extreme Gradient Boosting (XGBoost) model augmented with SHAP (Shapley Additive Explanations) analysis and a Geographically and Temporally Weighted Regression (GTWR) framework to examine the nonlinear and spatially heterogeneous effects of CEE on the Climate Physical Risk Index (CPRI). The results reveal a distinct two-stage dynamic pattern, in which CEE initially exacerbates and subsequently mitigates climate risk, indicating a nonlinear transition from short-term intensification to long-term alleviation. This relationship shows clear differences across city levels and climate types. The strongest effects appear in peripheral cities and in areas with extreme rainfall dominance (ERD). Spatial analysis based on GTWR also shows a clear north–south pattern. The effect of CEE in reducing risk becomes stronger from the south to the north. Based on these results, the study suggests different land-use policy strategies for different city types and climate conditions. The results give actionable insights for designing targeted carbon governance policies. These policies aim to deal with the growing challenges caused by extreme climate events under ongoing climate change.

Agricultural green transformation is important for achieving economic growth and environmental sustainability. Geographical indication agricultural products (GIAPs) have become a useful tool for improving agricultural green total factor productivity (AGTFP). However, the mechanisms through which GIAPs affect AGTFP and their spatial differences are not yet fully understood. This study uses a balanced panel of 290 Chinese cities from 2008 to 2024 and combines Double Machine Learning (DML), Geographically Weighted Random Forest (GWRF), and Shapley Additive explanations (SHAP) to examine the effects of GIAPs on AGTFP. The results show that GIAPs significantly enhance AGTFP, primarily through agricultural industry agglomeration and the downstream penetration of leading agricultural enterprises. The effects exhibit substantial spatial and category heterogeneity. GWRF-SHAP results further reveal a clear geographical gradient, with GIAPs exhibiting greater predictive importance and contribution in inland than coastal regions. These findings highlight the need for region- and product-specific strategies to leverage GIAPs for agricultural green transformation. This study provides new evidence on the green development effects of geographical indications and offers policy insights for promoting sustainable agricultural development.

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability.

Geographical indication (GI) certification is widely promoted as a market-based tool for rural sustainability. However, causal evidence on whether geographical indication products (GIPs) improve ecological well-being remains scarce. This study uses a balanced panel of 1487 Chinese counties from 2007 to 2022 and a two-way fixed-effects difference-in-differences design with a one-year lagged treatment to examine the effect of GIPs on the Ecological Quality Index (EQI). The result shows that GIPs raise EQI by 0.149 points, which corresponds to 0.31% of the mean and 1.19% of the standard deviation. The effect appears three years after certification and persists thereafter. In addition, mechanism tests identify pollution reduction as the main channel; lower industrial sulfur dioxide emissions and fewer green patent applications provide evidence consistent with the proposed mechanisms. The benefits concentrate in less-developed provinces and the central region. Furthermore, a spatial Durbin model based on county-boundary contiguity detects positive spillovers through spatial feedback, and the estimated total impact incorporating the spatial multiplier is more than five times the non-spatial estimate. GI policy may help reconcile rural development with ecological conservation, although its benefits are context-dependent, lagged, and spatially diffuse.