Reexamining the Environmental Kuznets Curve in Chinese Cities: Does Intergovernmental Competition Matter?

Abstract

Since China's central authority began enforcing the environmental target responsibility system and introduced environmental indicators to the official ranking tournament in 2007, an ecological transformation has emerged in the intergovernmental competition (IGC) among localities. Because the extant literature on the environmental Kuznets curve (EKC) remains unclear regarding how that ecological IGC transformation changes the EKC economy-pollution correlation, this research investigates the degree to which the transformed IGC changes the form of the EKC, and how that altered EKC varies for different pollutants (i.e., SO2 and CO2) and in different regions (i.e., the eastern, central, and western regions). The results demonstrate a consistently inverted U-shaped relationship between income and SO2 emissions in all three regions, whereas when CO2 emissions are taken as the pollution indicator, the EKC hypothesis holds only in the eastern and central cities, and a positive linear income-CO2 nexus is found in the western region. Spatial analysis reveals that whereas the IGC flattens the inverted U-shaped curves between income and SO2 emissions, it has led to a higher economic cost, corresponding to the turning point of the EKC for CO2 emissions. The findings indicate that the ecological transformation of the IGC has facilitated a positive up-down yardstick competition in the strategic interactions of sustainable development across local Chinese governments, which can lead to a kind of balance between centralization and decentralization by inspiring local officials' adaptability and activity in reducing pollutant emissions and strengthening the officials' responsiveness to performance rankings. This study elucidates the environmental impacts of IGC in China and provides an institutional explanation for the strategic interactions among local governments when they are tackling the environment-economy nexus under multitask conditions.