Over-tourism and green investments: spatial MMQR insights on China’s coastal pollution and carbon emissions

Introduction Controlling over-tourism has emerged as a pressing concern, attracting significant recent attention. Investigating this issue through the analysis of the impacts of marine green energy investment (MGEI), fintech (FT), and tourism concentration (TC) on carbon footprint (CF) and coastal water pollution (CWP) at tourist destinations is crucial. Methods This study employs the Spatial Method of Moment Quantile Regression (SMMQR) model to examine the effects of these indicators on two environmental metrics in coastal regions of China, validated through Moran's I analysis, Local Indicators of Spatial Association (LISA) Cluster Maps, and robustness checks. Results Results reveal strong positive spatial autocorrelation, with dominant High-High (HH) clusters for both environmental indicators, concentrated in areas such as Shanghai, Guangzhou, and Sanya, indicating significant environmental pressures. TC and FT exacerbate CF (6.215-13.185 and 0.715-2.110) and CWP (5.210-10.145 and 2.045-4.570), whereas MGEI exhibits mixed CF (-3.078-4.042) and CWP impacts (-3.038-6.858), driven by spatial dependencies ranging from 0.275-0.312. Discussion These findings bolster recent research on tourism and FT's environmental impacts, expanding the analysis by incorporating spatial dynamics and investment, and pinpointing over-tourism risks in high-impact areas. The study proposes setting an over-tourism threshold to better manage this issue moving forward.