Exploring the spatiotemporal impacts of urban form on CO2 emissions: Evidence and implications from 256 Chinese cities

Abstract

Understanding how urban form (UF) affects CO2 emissions is important to reduce emissions in the practice of urban planning and management. This study investigates the spatiotemporal impacts of UF on CO2 emissions by using Geographically and Temporally Weighted Regression (GTWR) model, in which UF is characterized by metrics across three dimensions underpinning sustainable development, namely environmental-aspect urban form (EnUF), economic-aspect urban form (EcUF) and social-aspect (SUF) urban form. The data are collected from 256 Chinese cities in 2000, 2005, 2010, 2015 and 2018. The results reveal that: (1) On average, EnUF has two-sided effects on CO2 emissions; SUF and EcUF exert negative and positive effects on CO2 emissions respectively. (2) There is significant spatiotemporal heterogeneity in terms of the impacts of UF variables on CO2 emissions. (3) As for EnUF variables, only the eastern coastal Chinese cities effectively show the benefits from urban compactness in terms of CO2 reduction. Increased urban complexity and sprawling lead to more CO2 emissions. The higher the diversity of carbon sink land, the more conducive to CO2 emission reduction. (4) With respect to SUF variables, population density and road density present “U-shaped” and “inverted U-shaped” relationship with CO2 emissions respectively. The accessibility of green space and water bodies contributes significantly to CO2 reduction. (5) As regards EcUF variables, the impact of the degree of economic agglomeration and related industry diversity on CO2 emissions changes from positive to negative in some cities. The impact of unrelated industry diversity on CO2 emissions is spatially characterized as “negative coastal areas surround the positive inland areas”. This study casts a new light on the impacts of UF on CO2 emissions and also proposes differentiated policy implications for carbon emission reduction in different Chinese cities.