School of Public Affairs, Zhejiang University, 866 Yuhangtang Road, Zhejiang, 310058, China.
China Institute of Urbanization, Zhejiang University, Zhejiang, 310058, China.
Environ Sci Pollut Res Int. 2023 Nov;30(53):114375-114390. doi: 10.1007/s11356-023-30123-5. Epub 2023 Oct 20.
Using a dataset encompassing 228 cities in China spanning from 2005 to 2019, this study explores the nonlinear relationship between air quality and housing prices and devises a strategy that incorporates the instrumental variable and machine learning to address the endogeneity issue. Both traditional models and machine learning models find air pollution affects housing prices in a diminishing manner. The negative impact of air pollution on housing prices decreases when the degree of air pollution intensifies. Such a characteristic is more pronounced in Eastern China and cities with fewer land resource constraints and larger populations. Mechanism analysis also reveals that air pollution could affect residents' perceived air quality and the industrial structure, further contributing to the nonlinear relationship between air quality and housing prices. The further SHapley Additive exPlanations (SHAP) evaluates the importance of air quality in determining housing prices and finds that air quality's contribution outweighs educational and medical resources. The contribution of air quality also shows a distinct regional disparity and has become increasingly important in recent years. The findings refine the benefit assessment accuracy related to air quality improvement.
本研究使用了一个涵盖中国 228 个城市、时间跨度为 2005 年至 2019 年的数据集，探讨了空气质量与房价之间的非线性关系，并提出了一种结合工具变量和机器学习的策略来解决内生性问题。传统模型和机器学习模型都发现，空气污染对房价的影响呈递减趋势。当空气污染程度加剧时，空气污染对房价的负面影响会减小。这种特征在中国东部和土地资源约束较少、人口较多的城市更为明显。机制分析还表明，空气污染会影响居民对空气质量的感知和产业结构，从而进一步导致空气质量与房价之间的非线性关系。进一步的 SHapley Additive exPlanations（SHAP）评估了空气质量在决定房价方面的重要性，发现空气质量的贡献超过了教育和医疗资源。空气质量的贡献也表现出明显的区域差异，近年来变得越来越重要。这些发现提高了与空气质量改善相关的效益评估准确性。
