Although performance targets imposed by high levels of governments are an important vertical result-oriented influence on local governments’ policy adoption, no studies have examined the impacts of performance targets on policy processes. Using the adoption of atmospheric pollutant emission standards by Chinese provinces in the 2000 to 2015 period, this study runs spatial autoregression models to investigate the effects of mandatory performance targets on environmental policy adoption processes in China. The results show that top-down performance targets can drive the adoption of atmospheric pollutant emission standards in Chinese provinces. Furthermore, the influence of vertical environmental performance targets on the adoption of local atmospheric pollutant emission standards is stronger in Chinese provinces that have adopted more similar standards in the previous period. The results show that top-down performance management shapes the policy adoption processes and policy adoption of Chinese provinces under vertical result-oriented performance target pressure is path-dependent.

China relies on the total pollutant emission control and environmental target responsibility system to curb environmental pollution and improve energy conversation. How the central government breaks down environmental targets among provincial governments lies at the core, but little research has been done to explore the determinants of environmental target-setting empirically. This work models the decomposition process of environmental targets by focusing on the roles of historical performance and provinces’ political status. With the method of hierarchical linear model, data on five kinds of environmental obligatory targets (energy consumption per unit GDP and other four kinds of pollutants) during China’s “12th Five-year Plan” period is used to test the hypotheses. The results show that provincial historical structural performance is negatively significantly correlated with their environmental target levels, while the effects of historical scale performance and intensity performance are not significant. Besides, provinces with higher political rankings tend to be allocated higher targets, which is in accordance with the model effect hypothesis rather than the bargaining effect hypothesis.

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM_2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM_2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM_2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM_2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM_2.5 concentrations in adjacent areas weakened the temporal convergence of PM_2.5 concentrations. This paper introduced policy implications for haze prevention and control.

Although haze pollution with PM_2.5 as the chief pollutant has become a critical threat worldwide, little research has examined the effects of PM_2.5 concentrations on subjective well-being. Based on a longitudinal aggregated panel dataset from Chinese provinces, this study investigates the effects of PM_2.5 concentrations on levels of happiness and the inequality of happiness. The results showed that high ground-level PM_2.5 concentrations decreased the average level of happiness and high PM_2.5 concentrations had stronger negative effects on the happiness of persons with high income than those with low income. In addition, PM_2.5 concentrations were also significantly negatively related to inequality of happiness in Chinese provinces. Further empirical tests showed that the negative effects of PM_2.5 concentrations on the inequality of happiness could be explained by the stronger influence of PM_2.5 concentrations on the subjective well-being of individuals with a higher initial level of happiness than those with a lower initial level of happiness. This confirms that PM_2.5 pollution can do harm to subjective well-being and reduce variations in the subjective well-being of individuals. The policy implications of controlling haze pollution and improving well-being are discussed.

Happiness studies generally investigate average levels of happiness rather than happiness inequality between regions, and studies of social inequality usually measure it based on the distribution of life opportunities (e.g., income) rather than life results (e.g., happiness). Inspired by the Kuznets curve, which illustrates the inverted U-shaped correlation between income inequality and economic growth, this study investigates whether there is a subjective wellbeing Kuznets curve. It uses data from ten waves of the Chinese General Social Survey to construct a panel data set and runs panel data models to investigate the hypothesized curvilinear relationship between happiness inequality and economic growth. The results show that happiness inequality, measured as the standard deviations of respondents’ self-reported happiness, first increases and then decreases as per-capita GDP increases in Chinese provinces. These findings strongly support the subjective wellbeing Kuznets curve hypothesis and suggest that strategies for reducing happiness inequality must consider stages of economic development.