Aviation big data-driven tourism carbon efficiency evaluation: evidence from China
利用航空大数据和DEA模型评估中国旅游碳效率，发现东部沿海效率高、中西部低，为区域旅游可持续发展提供政策建议。
The impact of tourism on driving economic growth and CO2 emissions has been considered the key issue for realizing sustainable development. While existing research has examined tourism-related CO2 emissions and their underlying factors, the regional heterogeneity of carbon efficiency remains underexplored. Traditional evaluation approaches based on statistical data often lack granularity and cannot accurately reflect actual performance. To address these gaps, this study develops an aviation big data-driven carbon efficiency evaluation framework. This framework integrates an enhanced bottom-up approach with the three-stage data envelopment analysis model to assess tourism carbon efficiency and its regional variations in case of China. The findings reveal distinct spatial patterns: high carbon efficiency is clustered in eastern China, particularly in coastal areas. The central and western regions exhibit low efficiency due to geographical constraints and limitations related to production scale. These insights provide practical implications for tourism sustainable development. Policymakers could enhance accessibility by optimizing diversified transportation networks and promote high-value-added sectors to balance market expansion with emission reductions. Regional agglomeration should be encouraged to leverage their unique market advantages to generate economies of scale. Technological innovation for efficient energy usage is essential for improving carbon efficiency.