Digital Spatial Governance and Built-Up Land Carbon Intensity: Evidence from China’s Smart City Pilot Program

Abstract

Smart city policies may affect urban carbon emissions and developed land at the same time, but the two may not change in the same direction. This study examines city-level carbon dioxide (CO2) emissions per unit of built-up land area, whose natural logarithm is denoted as lnCLCI using panel data for 40 cities in the middle reaches of the Yellow River Basin and surrounding areas from 2011 to 2022. The main policy sample includes 37 cities after removing three cases where the Smart City Pilot designation covered only a sub-prefectural area. Treatment timing is defined using the actual adoption year of each city. Group-time average treatment effects are used as the main estimates, while a two-way fixed-effects model serves as a benchmark. The analysis also separates the responses of carbon emissions and built-up land, tests spatial policy exposure and pre-policy heterogeneity, and evaluates prediction performance in cities excluded from model training. The unadjusted group-time average treatment effect is 0.026 with a p-value of 0.708. After adjustment for pre-treatment covariates, the estimate is −0.040 with a p-value of 0.673. The TWFE benchmark gives an estimate of 0.008 with a p-value of 0.909. These estimates do not provide evidence that the Smart City Pilot policy reduced lnCLCI on average. Estimates for log carbon emissions (lnCO2) and log built-up land area (lnBUA) are also statistically imprecise, and the pre-policy interaction tests show no consistent pattern of treatment heterogeneity. Estimates of spatial policy exposure are sensitive to the choice of spatial weight matrix. In the 2013–2022 prediction sample, Random Forest achieves a held-out-city R2 of 0.568, compared with 0.767 for OLS. Taken together, the results show that changes in carbon emissions per unit of built-up land should not be interpreted as changes in absolute emissions alone. They also show the importance of keeping predictive performance separate from estimates of policy effects.