The impacts of the low-carbon city policy on urban air pollution in China

Abstract

Declining levels of social welfare caused by climate warming and air pollution place increasing constraints on high-quality, sustainable global development. To achieve global climate-governance goals, it is essential to accelerate the process of peaking carbon emissions and meeting air-quality standards. Despite growing awareness of the impact of low-carbon city policies on the environment, few studies have focused on their impact on urban air quality. Based on panel data drawn from 275 cities between 2011 and 2017, the present study evaluates the effects of a low-carbon city policy on urban sulfur-dioxide emissions, using the low-carbon city policy as a quasi-natural experiment. The findings reveal that urban sulfur-dioxide emissions have obvious spatial-autocorrelation characteristics, showing obvious spatial clustering. The low-carbon city policy not only significantly reduced urban sulfur-dioxide emissions in pilot cities, but also suppressed sulfur-dioxide emissions in surrounding cities through an indirect rebound effect. This paper provides a theoretical reference for collaborative governance, which can help to achieve peak carbon emissions and air-quality standards. To reach those goals, nations must abandon territorial prevention-and-control methods based on administrative divisions and to fully activate cross-city regional joint prevention-and-control measures. This study proposes key policies, including promoting inter-city regional coordination mechanisms, strengthening the collaborative governance in relation to carbon-dioxide and sulfur-dioxide emissions, and promoting the construction of inner-city green facilities.

Digitalization, Spillover and Environmental, Social, and Governance Performance: Evidence From China

“…To visualize the relationship between digitization and ESG performance, a spatial correlation analysis is performed using the standard deviation ellipse (SDE), a method that shows the spatial contours between different variables (Gu, 2023c). The results are shown in Figure 1.…”

“…Therefore, it is necessary to further test the spatial correlation of the explanatory variables (Gu, 2023a). Here, spatial autocorrelation tests, including Moran’s I (Gu, 2023c), Geary’s c (Yamada, 2021) and Getis and Ord’s G (Griffith, 2021) are performed on the explanatory variables, and the results are reported in Table 2. All tests are at the 1% significance level; therefore, the original hypothesis of no spatial autocorrelation of the explanatory variables is rejected (Gu, 2016).…”

Official Turnovers and Economic Growth: Evidence from China

“…Neighboring cities are often not independent of each other in terms of economic and social development, and have strategic interactions (Gu, 2023c;Shi, 2020). A city's economic and social development often affects the development of neighboring cities, which is termed as the spatial spillover effect (Gu, 2024;Li et al, 2022).…”

Exploring the Spatial Correlation Network and Influencing Factors of Green Economic Development in Typical Urban Agglomerations in China: A Case Study of the Yangtze River Delta

“…Climate change and environmental issues have become the focus of global attention and have farreaching effects on human survival and development. The increasing global pollution emissions have led to the rising temperature of the earth, triggering extreme weather, ecological damage and other problems, bringing great risks to human society and natural ecosystems [1]. In this context, all countries are exploring effective paths for green development, actively taking measures to reduce environmental pollution, and promoting green economic development (hereinafter referred to as GED).…”

“…However, the existing studies still have the following problems: (1) The existing studies mainly explain or measure GED from a static resource or factor perspective, and lack the measurement and spatial and temporal analysis of the dynamic changes of green economic development. (2) Focusing on neighboring relationships while ignoring the spillover effects of nonneighboring regions.…”