During the just concluded 13th Five-Year Plan, China continued to maintain the momentum of rapid economic development, but still faced environmental pollution problems caused by this. Finding the relationship between Nitrogen Dioxide pollution and economic development is helpful and significant in better achieving and optimizing sustainable environmental development. Taking China’s 333 prefecture-level cities as samples from 2016 to 2018, the spatial lag model (SAR) was used to study the impact of economic growth on urban Nitrogen Dioxide pollution. The results show that Nitrogen Dioxide has strong positive characteristics of spatial spillover, but there is a linear relationship between economic growth and Nitrogen Dioxide concentration that slowly rises, and there is no inverted U-shaped relationship, which does not support the Environmental Kuznets Curve (EKC) hypothesis; The results also show the impact of per capita GDP, natural gas consumption, residential natural gas consumption, industrialization, and transportation development on the increase of Nitrogen Dioxide concentration, and the impact of green coverage on the decrease of Nitrogen Dioxide concentration. However, there is no significant relationship between technological investment and Nitrogen Dioxide concentration. The above conclusions are still valid after the robustness test, and recommendations are put forward to reduce Nitrogen Dioxide pollution.
The degree of industrial agglomeration in China has contributed to the reduction of nitrogen dioxide pollution because of financial support, the allocation of environmental governance efficiency, and technological advantages. However, the intensity and scope of the spatial effect of this contribution needs to be studied in depth. Based on the influence mechanism and intermediate mechanism of the spatial pattern, this paper uses the panel data of 282 prefecture-level and above cities in China from 2015 to 2018, draws on the STIRPAT model, and uses the Spatial Panel Durbin and Panel Threshold models to investigate the effects of industrial agglomeration on nitrogen dioxide. The study finds that 1) industrial agglomeration has a significant spatial spillover effect on the reduction of nitrogen dioxide pollution, and the increase in the level of local industrial agglomeration can greatly reduce the concentration of nitrogen dioxide in the surrounding area. 2) This kind of spatial overflow has a threshold boundary. Within 100 km, it is a dense area of overflow and reaches the threshold boundary beyond 150 km. 3) Under the influence of the three intermediate mechanisms of industrial agglomeration, the increase in car ownership, and the level of economic development, the impact of industrial agglomeration on the reduction of nitrogen dioxide pollution has gradually increased. The above conclusion is still valid after various robustness tests.
Industrial agglomeration in a region changes the economic structure, strategic layout and resource status of a city, and has an important impact on sustainable economic development. The relationship between industrial agglomeration, air pollution and economic sustainability is a key issue concerning the high-quality development of national economy. China is a developing country that once experienced severe air pollution. Now, the Chinese government is aiming to achieve the goal of sustainable and high-quality economic development in China. In this paper, a spatial Dubin model was developed to study the relationship between industry, environment and the economy. The statistical analysis used the air pollutant data of 273 prefecture-level cities in China from 2015 to 2018. The results showed that: (1) there was a positive U-shaped nonlinear relationship between industrial agglomeration and sustainable economic development, and there was a spatial spillover effect. (2) There was a positive U-shaped nonlinear relationship between air pollution and sustainable economic development, and there was a spatial spillover effect between them. (3) The effect of industrial agglomeration on sustainable economic development was influenced by air pollution, an intermediary variable. The existence of air pollution weakens the promoting effect of industrial agglomeration on sustainable economic development.
This paper proposes a feedback, rolling and adaptive operation decision-making mechanism for coupling and nesting of time scales. It is aimed at the change of time scale and the dynamics in the operation process, considering the relationship between operation period and multi-time scales. The key point is to integrate forecasting and operation in order to adapt to the multi-time scales dynamic change in the operation process. The operation process is divided into different time scales; forecasting and operation model method libraries are constructed, and the progressive updating and nesting mechanism are used to realize the process dynamic operation, according to the regulation period or operation period of the reservoir. Taking the Miyun Reservoir in Beijing, China as the research object, the operation mechanism is integrated into the operation process, and the complex forecasting operation and control mechanism are integrated, based on the integrated platform and using modern information technology. The forecasting and operation method uses classic different models, which can be selected based on different goals. The forecasting inflow is used as input, and the output is the water distribution plan, more importantly, the mechanism in the operation process is the key point. This is a rolling modification of the inflow process in the next stage, and the operation plan also changes accordingly. The feasibility, effectiveness, rationality and flexibility of the reservoir dynamic and adaptive operation are verified, so that the reservoir operation is dynamically changing and adapting to the changing demand. The proposed operation mechanism has scientific value and guiding significance to improve the reservoir operation theory, and it provides decision support for the actual reservoir operation and operation business.
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