Shao Jinhua, Fang Sheng, Zhao Meiling, Qian Wanxin, Wang Cai
School of Humanities and Social Sciences, Anhui University of Science and Technology, Huainan, China.
College of Tourism, Hainan Tropical Ocean University, Sanya, China.
Front Public Health. 2025 Mar 25;13:1550395. doi: 10.3389/fpubh.2025.1550395. eCollection 2025.
Tourism development is important for the formulation of the national carbon reduction policy. China has put forward the goals of carbon peaking and carbon neutrality. Studying the impact of China's tourism industry on carbon emissions is of great significance in scientifically formulating emission reduction policies and helping China to realize its carbon reduction goals. In this study, we simulate the complex relationship between the tourism industry and carbon emissions in China using machine learning models. This study is the first to employ interpretable machine learning to analyze the impact of the tourism industry on carbon emissions in China. Our findings demonstrate that sparrow search algorithm and random forest (SSA-RF) hybrid model can model the relationship between carbon emissions and tourism factors with low error. The expansion of the tourism industry positively contributes to the increase in carbon emissions. Our study highlights the need to consider tourism factors when formulating national carbon reduction policy.
旅游业发展对国家碳减排政策的制定至关重要。中国已提出碳达峰和碳中和目标。研究中国旅游业对碳排放的影响，对于科学制定减排政策以及助力中国实现碳减排目标具有重要意义。在本研究中，我们使用机器学习模型模拟中国旅游业与碳排放之间的复杂关系。本研究首次采用可解释机器学习来分析中国旅游业对碳排放的影响。我们的研究结果表明，麻雀搜索算法与随机森林（SSA-RF）混合模型能够以较低误差对碳排放与旅游因素之间的关系进行建模。旅游业的扩张对碳排放的增加有正向贡献。我们的研究强调了在制定国家碳减排政策时考虑旅游因素的必要性。
