Measuring Environmental and Economic Performance of Air Pollution Control for Province-Level Areas in China

Health Damage of Air Pollution, Governance Uncertainty and Economic Growth

The evaluation of environmental and health governance processes is an important part of the innovation and perfection of modern governance systems. Based on the macropanel samples, this paper analyzes the impact of the health damage caused by air pollution (APHD) on economic growth and the related mechanisms accordingly using the moderate model and the threshold model. The results can be concluded as follows: (1) After locking in the health damage perspective, the APHD has a negative impact on economic growth. When other conditions are met, economic growth will significantly drop by 1.233 percent for each unit increase in the APHD index. (2) There is a moderate effect of governance uncertainty in APHD on economic growth with different characteristics. The combination of governance uncertainty and APHD can significantly inhibit economic growth, and this moderating effect has different impacts due to heterogeneous conditions. Spatially, this inhibitory effect is significantly obvious in the eastern, central, and western regions, while the negative effect is significant in areas north of the Huai River with medium and low self-defense ability. Additionally, compared with the delegating of governance power at the municipal level, when the governance power is delegated at the county level, the interaction between the governance uncertainty constructed by income fiscal decentralization and APHD has a less negative economic effect. (3) There is a threshold effect under the conditions of a low level of decentralization of prevention and control, a high level of investment in governance, and a low level of APHD. However, under the condition of a certain APHD level, when the decentralization level of pollution control is higher than 7.916 and the input level of pollution control in GDP is lower than 1.77%, the negative moderating effect can be effectively reduced.

Urban and industrial environmental pollution control in China: An analysis of capital input, efficiency and influencing factors

With rapid urbanization and industrialization, environmental pollution caused by such activities has drawn much attention due to its adverse impacts on environmental quality and public health. Therefore, under the current background of China's ecological civilization construction, promoting the precise and scientific treatment of environmental pollution holds great significance. This paper proposes an improved perpetual inventory method to systematically measure the capital stock of urban and industrial pollution control. The efficiency of urban and industrial pollution control is measured by adopting the global data envelopment analysis (DEA) model. Then, the influencing factors of pollution control efficiency are empirically analyzed by using the spatial Tobit regression model. The results reveal that, first, the growth rate of the capital input scale of urban pollution control is greater than that of industrial pollution control, and the spatial distribution of capital input is unbalanced. Second, the efficiency of urban and industrial pollution control from 1991 to 2019 was generally low. The current efficiency values of urban and industrial pollution control are less than 0.2 and 0.5, respectively, indicating that urban and industrial pollution control are far from efficient. Third, the efficiency of urban and industrial pollution control is significantly positively related to the level of urbanization and industrialization, has a U-shaped relationship with the economic development level, and has heterogeneous effects on technology, energy intensity, government influence and foreign trade. On this basis, we provide constructive suggestions for optimizing the performance of pollution control.

Efficiency of Water Pollution Control Based on a Three-Stage SBM-DEA Model

With the growing severity of water pollution issues, the prevention and control of water pollution became highly complicated and challenging, and the investment in water pollution control has been constantly increased. Scientific evaluation of efficiency is critical to recognize whether the investments in water pollution control are effective. However, most studies could not exclude the influences of external environmental and random factors when evaluating the efficiency of water pollution control, resulting in biased results. To overcome this shortcoming, this study employed a three-stage SBM-DEA (slacks-based measure-data envelopment analysis) model to determine the efficiency of water pollution control efforts in a city of China from 2003 to 2017. The results showed that water quality in the study area has been significantly improved due to those pollution control efforts. The influences from external environmental and stochastic factors have led to an underestimation of the efficiency of water pollution control in the first stage. After excluding these effects in the second stage, the adjusted efficiency of water pollution control showed a fluctuating upward trend in the third stage, reflecting the true effectiveness of efforts to prevent and control water pollution in the study cities, with an average efficiency of 0.87. Finally, several suggestions for enhancing the efficiency of water pollution control in Chengde were proposed.

Impacts of Energy Efficiency and Economic Growth on Air Pollutant Emissions: Evidence from Angara–Yenisey Siberia

Environmental problems of urban and rural areas are now high on the agenda of industrialized countries, becoming a key challenge for regional-level policymaking. The mutual influence of population growth, economic and technological development, and the anthropogenic pressure on the environment is still insufficiently studied in many countries, including Russia. In this paper, this relationship is studied for the municipalities of Angara–Yenisey Siberia using an ensemble of the STIRPAT-like regression models, adapted according to the available data. We found that population size and gross municipal product were positively associated with pollutant emissions (p < 0.01), while energy efficiency had no significant impact on air pollution. In addition to the poor national data quality and completeness issues, which can distort statistical conclusions, the cause of the observed lack of spatial correlation between energy efficiency and air pollutant emissions may be path dependence and an insufficient pace of transition to a greener economy. This leaves room for institutional transformations aimed at intensifying energy efficiency to reduce the environmental burden.

Can technology R&D continuously improve green development level in the open economy? Empirical evidence from China's industrial sector

Applying a global DEA model based on non-radial directional distance function, this paper constructs a comprehensive efficiency index to estimate green development level and further identifies the influencing mechanism of technology R&D on green development in China's industrial sector. The results demonstrate that the level of green development in China's industrial sector declined year by year and the average was 0.27, and it also shows significant regional characteristics within the sample period. Besides, the environment pollution transferred from the east to the central and the west. In addition, the results also indicate that there is a threshold effect for the impact of technology R&D on China's industrial green development. Based on the volume of the trade openness, this effect presents a "N"-type characteristic that tilts to the right. According to the research results, the corresponding policy recommendations are put forward, which may be of great importance to improve the green development level in China's industrial sector.

An Optimal Pollution Control Model for Environmental Protection Cooperation between Developing and Developed Countries

With the continuous increase in greenhouse gas emissions in the world and the United States announcing withdrawal from the Paris Agreement, the conflicts between environmental protection and economic growth of developing and developed countries have become increasingly challenging. In this paper, following the principle of “common but differentiated responsibilities” specified in the Kyoto Protocol and the Paris Agreement, we develop an optimal pollution control model based on a dynamic system for both developing and developed countries. We analyze how different perspectives of the developing and developed countries affect their investments in pollution control and how to determine their responsibilities based on the principle of common but differentiated responsibilities. Our aim is to obtain a stable equilibrium mechanism to maximize the social welfare between the developing and developed countries and explore the optimal pollution control and economic growth path. Our results show that it is optimal for the developed countries to help developing countries with pollution control in their initial stage of economic growth. Once the developing countries reach a certain economic development level, they can contribute more to pollution control, while the developed countries can reduce their environmental investment. We show that by following this optimal path, the developing and developed countries can effectively control environment pollution without significant loss of social welfare.

The Spatiotemporal Dynamics and Socioeconomic Factors of SO2 Emissions in China: A Dynamic Spatial Econometric Design

With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO2 emissions to peak in China. By exploratory spatial data analysis (ESDA) and econometric approaches, this study explores the spatiotemporal characteristics of SO2 emissions and reveals how the socioeconomic determinants influence the emissions in China’s 30 provinces from 1995 to 2015. The study first identifies the overall space- and time-trend of regional SO2 emissions and then visualizes the spatiotemporal nexus between SO2 emissions and socioeconomic determinants through the ESDA method. The determinants’ impacts on the space–time variation of emissions are also confirmed and quantified through the dynamic spatial panel data model that controls for both spatial and temporal dependence, thus enabling the analysis to distinguish between the determinants’ long- and short-term spatial effects and leading to richer and novel empirical findings. The study emphasizes close spatiotemporal relationships between SO2 emissions and the socioeconomic determinants. China’s SO2 emissions variation is the multifaceted result of urbanization, foreign direct investment, industrial structure change, technological progress, and population in the short run, and it is highlighted that, in the long run, the emissions are profoundly affected by industrial structure and technology.