Hybridization of CMIP6 and spatiotemporal models for assessing solar energy dynamics and transition risks in Guangxi under “dual-carbon” goals

Under the global dual-carbon goals, assessing regional solar potential is vital for the energy transition. This study evaluates solar resource potential and spatiotemporal redistribution risks in Guangxi, China, using ECMWF ERA5 and CMIP6 multi-scenario data. We develop an interactive spatiotemporal regression model by integrating optimal parameter geographic detectors with geographically weighted regression to quantify drivers' synergistic effects. Key findings: (1) solar resources exhibit strong path dependency, with the highest growth rate occurring under the medium-emission scenario due to the atmospheric purification effect.; (2) dominant drivers shift with scenarios: topography (low emissions), cloud-aerosol interactions (medium), and multi-factor synergy (high); and (3) The resource center migrates from the southwestern coast to the northeastern interior, with rising spatial-instability risks. This work supports optimized solar deployment and regional energy transition in Guangxi.