Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction

Abstract

As the digital economy becomes the new engine of economic growth, China has introduced a series of smart city policies aimed at promoting high-quality and sustainable urban development. This paper aims to evaluate the green development effects of China’s “Smart City Pilot” policy and to explore the heterogeneity of policy effects across different types of cities. Using panel data from 283 prefecture-level cities in China from 2006 to 2020, this study examines the relationship between smart city construction policy and urban green development efficiency using the green total factor productivity (GTFP). We employ the Causal Forest and mediation effect models to estimate the impact of smart city pilot policy on GTFP and explore the underlying mechanisms. The main results are: (1) The smart city pilot policy significantly enhances urban GTFP, a finding consistent across diverse policy evaluation approaches. (2) The influence of the policy on green development varies among cities, and such heterogeneity is effectively captured by the Causal Forest. (3) This varied impact primarily stems from urban location factors and inherent characteristics. Notably, the policy effect in Eastern China outpaces that in other regions. The policy yields greater green benefits with financial development and medical capital rises, but excessive government public expenditure curtails its positive influence. (4) The mediation mechanisms through which the smart city pilot policy promotes green development exhibit certain differences between the “high-effect group” and the “low-effect group”. The former predominantly leverages innovation-driven and agglomeration effects, while the latter chiefly relies on industrial structural advancement and rationalization.

Impact Analysis of Regional Smart Development on the Risk of Poverty among the Elderly

“…The specific regression results are shown in Table 4. Considering that the collected data may have heteroscedasticity problems [20], the clustering robust standard error corrected based on individual ID coding is used according to the method of Chengfeng Yu [26]. In Model (1), no control variables are added, and on the assumption that the individual fixed effects are controlled, the impact of regional smart development level on the risk of poverty in old age passes the 1% statistical test, and the regression result is significantly negative, with the regression coefficient being −2.153.…”