Does Smart City Construction Reduce Haze Pollution?

Li Wang; Qian Xie; Fei Xue; Zongxin Li

doi:10.3390/ijerph192416421

Does Smart City Construction Reduce Haze Pollution?

Int J Environ Res Public Health. 2022 Dec 7;19(24):16421. doi: 10.3390/ijerph192416421.

Authors

Li Wang¹, Qian Xie¹, Fei Xue², Zongxin Li¹

Affiliations

¹ School of Economics & Management, Northwest University, Xi'an 710127, China.
² Faculty of applied economics, University of Chinese Academy of Social Sciences, Beijing 102488, China.

Abstract

Smart city construction plays an important role in environmental governance and public health. Based on the panel data of 216 prefecture-level cities across China during the period 2009-2018, this study uses the multi-time difference-in-differences method to evaluate the haze reduction effect of smart city construction. The estimated results demonstrate that the construction of smart cities can reduce haze pollution in pilot cities significantly. The main conduction mechanisms are the technical effects and the environmental regulatory effects that promote the reduction of corporate emissions. The heterogeneity analyses show that the haze reduction effect of smart city construction is more evident in southern cities, inland cities and resource-efficient cities. In addition, the benefit analyses show that smart cities can reduce the carbon emission intensity and promote economic growth. These results provide empirical support for accelerating the construction of a new type of smart city and building a new type of people-oriented urbanization.

Keywords: benefit analysis; haze pollution; multi-time difference-in-differences; smart city.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollution* / analysis
Air Pollution* / prevention & control
China
Cities
Conservation of Natural Resources*
Economic Development
Environmental Policy
Environmental Pollution / analysis
Humans
Urbanization

Grants and funding

This research was funded by the National Social Science Foundation of China, grant number 21&ZD153, the National Natural Science Foundation of China, grant number 71971163, and the Scientific Research Project of Shaanxi Provincial Department of Education, grant number 21JK0379, the Ministry of education of the People’s Republic of China, grant number 20YJC910009, the National Bureau of Statistics of China, grant number 2021LY068.