Driving mechanisms and peak pathways of tourism carbon emissions: a case study of Hunan based on the STIRPAT model

Under the global context of climate change and the pursuit of carbon peaking and neutrality goals, scientifically assessing the dynamic evolution, driving mechanisms, and future evolution pathways of tourism-related carbon emissions at the provincial scale is critical for formulating differentiated emission reduction strategies.

Taking Hunan Province as a case study, this paper develops a hybrid accounting framework that integrates top-down and bottom-up approaches to systematically measure the magnitude and structure of tourism carbon emissions from 2006 to 2025. An extended STIRPAT model combined with ridge regression is employed to identify key driving factors, while the Tapio decoupling model and multi-scenario simulation are coupled to project peak emission pathways.

1) In terms of emission scale, tourism carbon emissions in Hunan reached a staged historical peak of 17.814 3 million tons in 2019, rebounding strongly to 16.889 7 million tons from 2023 to 2025, with transportation and sightseeing activities accounting for 74%-84% of total emissions; 2) Regarding driving factors, tourist volume is the primary positive driving factor (elasticity coefficient 0.648 7), while energy intensity shows a relatively marginal contribution (elasticity coefficient 0.106 6), indicating that current growth remains dominated by scale expansion, with technological mitigation effects not yet fully materialized; 3) In terms of decoupling trends, the dominant decoupling state from 2006 to 2025 is weak decoupling (9 occurrences), with strong decoupling occurring only once in 2008 due to external shocks; the abnormally high decoupling index in 2022 reflects exceptional circumstances, suggesting that low-carbon transition stability still requires consolidation; 4) Multi-scenario prediction indicate that under the stringent low-carbon scenario with enhanced policy constraints, emissions will peak at 19.849 million tons in 2030 and then decline gradually, contrasting with the persistent growth trajectories under the baseline and moderate low-carbon scenarios.

The relative rate of change between tourist volume and energy intensity is the decisive factor for achieving the peak; only by simultaneously curbing tourist growth and reducing energy intensity can a trend reversal be realized. This study establishes a systematic analytical framework encompassing aggregate accounting, driver decomposition, and scenario simulation, providing quantitative decision-making references for the formulation of low-carbon tourism development policies at the provincial level.