Spatial heterogeneity and interacting intensity of drivers for trade-offs and synergies between carbon sequestration and biodiversity

Abstract

Global crises of biodiversity loss and climate change highlighted the urgent need to understand the trade-off and synergy between carbon sequestration and biodiversity and the driving mechanisms behind them. However, existing research on driving mechanisms primarily focused on the influence of independent variables on dependent variables, neglecting the interaction between independent variables. The InVEST and PLUS models were utilized in this study to evaluate the dynamics of carbon sequestration and biodiversity in the Chishui River Basin, Southwest of China. By integrating the factor detection of the Geographic Detector and the Multi-scale Geographically Weighted Regression (MGWR) model, this study identified the key drivers affecting trade-off and synergy and revealed their spatial heterogeneity. Furthermore, utilizing the interaction detection of the Geographic Detector and the Geographic Cross-Convergence Mapping (GCCM) model, this study analyzed the interaction direction and interaction intensity among key drivers. The results showed that: (1) From 2012–2035, the total values of carbon sequestration and biodiversity exhibited an overall increasing trend, with 58.49 % of the area showing trade-off effects, 23.86 % showing positive synergy, and 17.65 % showing negative synergy. (2) Elevation, slope, topographic relief, temperature, and land use intensity were identified as key factors influencing trade-off and synergy, and these factors displayed significant spatial heterogeneity. (3) When multiple drivers interacted, their explanatory power for trade-off and synergy increased significantly. The interaction patterns among drivers presented a “topography➙climate↔human activity↔distance➙soil” structure, where human activity interacted bidirectionally with other factors, with the strongest interaction occurring between climate factors and human activity. This study advanced the understanding of the driving mechanisms behind trade-off and synergy through MWGR and GCCM model. Based on the supply-demand theory, a synergistic management framework was proposed to emphasize the “pattern-process-service-feedback” nexus. The hotspot areas identified through scenario simulation could be used for synergistic enhancement of carbon sequestration and biodiversity.