[Spatiotemporal Variation Characteristics of Ozone and Identification of Key Influencing Factors Based on Random Forest Model： A Case Study of Chuzhou City].

The cause of ozone pollution is a complex scientific problem. Studying the spatiotemporal variation characteristics of O3 at different time scales and analyzing the key influencing factors of O3 concentration is of great significance for the precise formulation of urban air pollution control measures and the improvement of urban air quality. Based on the analysis of the spatiotemporal variation characteristics of O3 concentration in Chuzhou City, we studied the 12 ozone-influencing factors of meteorology and pollutants at multiple time scales using Spearman correlation analysis and a random forest model. The results showed that： ① The O3 pollution level of Chuzhou City showed an aggravating trend, and the O3 concentration distribution showed a spatial pattern of "high in the southeast and low in the northwest." ② From February to May, SO2 concentration had a strong impact on the increase in O3 concentration. From June to September, PM2.5 and PM10 were significantly positively correlated with ozone and had a greater impact. ③ Relative humidity, temperature, and wind speed had a significant impact on O3, whereas barometric pressure and hourly rainfall had a weak impact. ④ The O3 pollution mechanism in Chuzhou City changed from "pollutant-controlled" to "meteorology-controlled." ⑤ Among meteorological and pollutant factors, the three influencing factors that had the greatest influence on O3 concentration were temperature, wind speed, and relative humidity, with PM10 concentration, PM2.5 concentration, and SO2 concentration also contributing. All of the above six influencing factors had a significant nonlinear relationship with the O3 concentration.

[Research on the Evaluation Method and Application of Provincial Differentiated Carbon Peaking in China].

Promoting regions with favorable conditions to take the lead in reaching a carbon peak is an inevitable step towards achieving the dual carbon goals under the "nationwide coordinated action" plan. Considering the differences among Chinese provinces, this study measured the peaking pressure of each province based on the spatial distribution of carbon emissions. We then constructed a provincial peaking capacity evaluation system based on five dimensions, namely, peaking pressure, emission reduction status, economic development, policy support, and resource endowment, to comprehensively evaluate the carbon peaking capacity of 30 provincial administrative regions in China, excluding Hong Kong, Macau, Taiwan, and Tibet, using the entropy value method to determine the index weights. The 30 provinces were divided into five peaking tiers according to the evaluation results. The results showed that:① 18 regions, such as Hainan and Beijing, displayed a surplus in carbon emission space; eight regions, including Hebei and Shandong, showed a deficit in carbon emission space; and the carbon emission spaces allocated to Zhejiang, Anhui, Henan, and Hubei were comparable to their respective actual emissions. ② Developed regions generally had a higher carbon peaking capacity than that of less developed regions, with Beijing and Shanghai showing outstanding carbon peaking capacity, whereas Jiangxi and Guizhou had more room to improve their capacity. Finally, differentiated peaking targets and priority actions were proposed according to the provinces' different peaking tiers and local conditions.

[Synergistic Paths of Reduced Pollution and Carbon Emissions Based on Different Power Demands in China].

Nowadays, China is faced with two strategic tasks:improving ecological environmental quality and realizing carbon neutrality and carbon peaking. Synergy to reduce pollution and carbon emissions has become an inevitable choice for the comprehensive green transition of economic and social development in China. The electric power sector will play an important role in the transition process. Based on different power demand scenarios, a multi-objective model was constructed to achieve carbon peaking and carbon neutrality at a low cost, and the optimal path scheme of carbon emission reduction synergy was obtained. The results showed that under the premise of achieving carbon peaking and carbon neutrality as scheduled, pollution reduction and carbon reduction had good synergies, and their synergistic control could effectively facilitate the realization of the low-carbon transition. Optimizing the power generation structure of the electric power sector was the key measure to achieving the synergistic effect of pollution reduction and carbon reduction. During the study period, the proportion of thermal power decreased continuously, and the proportion of clean power exceeded 92.5%. The emissions of carbon dioxide and major air pollutants were significantly different under different power demands. Carbon dioxide emissions were most affected by power demand. The peak carbon dioxide emissions under low power demand, medium power demand, and high power demand were 9.416 billion, 10.409 billion, and 10.746 billion t, respectively. The emissions of sulfur dioxide, nitrogen oxide, and particulate matter also showed an increasing trend in the low power demand, medium power demand, and high power demand scenarios. The increase in power demand only increased the pressure of power generation structure adjustment within the electric power sector, without affecting the output and activity level of other sectors, that is, the pressure of emission reduction in the electric power sector caused by power demand did not show the trend of transmission between sectors.

[Evaluation Method and Application for Urban Carbon Peaking & Neutrality Performance].

As the key unit of greenhouse gas emission sources, cities have the most direct and fundamental significance to achieve the national carbon peaking carbon neutrality goal. In order to evaluate the current performance of urban carbon peaking and neutrality, a set of urban peaking and carbon neutrality action index evaluation systems consisting of three criterion layers, seven elements, and fourteen specific index layers were developed based on the analytic hierarchy process considering the preferences of decision makers, through the steps of influencing factor determination, indicator selection, and scoring principle determination, as well the indicator weightings. Thus, a relatively comprehensive scientific evaluation method was formed to fully evaluate the attitude of the government towards the goal of carbon peaking and neutrality, the state of social economy, energy consumption, industrial structure, transportation, and other aspects, as well as the actual effect of emission reduction efficiency and trends. Through the central city evaluation application study, it was found that the first-tier economically developed and low-carbon pilot cities had a more outstanding comprehensive performance in reaching the peak and neutrality. The comprehensive scores of Beijing, Shenzhen, Wuhan, Shanghai, Qingdao, Guangzhou, Chengdu, Xiamen, Kunming, and Lanzhou all exceeded 60 points. Beijing, Xiamen, Ningbo, Shenzhen, and Qingdao had significant climate ambitions, whereas Haikou, Guangzhou, Chengdu, Nanning, and Beijing had a better low-carbon status. Kunming, Lanzhou, Luoyang, Daqing, Jilin, and other cities showed significant emission reduction trends. Most cities still had problems such as insufficient willingness to reach the peak and lack of statistical information disclosure system. The evaluation method could be optimized by improving the index system, updating the empowerment, and forming the annual evaluation mechanism next step. It is suggested to formulate the local carbon reduction work plan by coordinating the whole country at different levels, improve the urban energy and greenhouse gas statistics and information disclosure system, and organize the carbon peaking pilot construction in areas where conditions permit.

[Pollution and Carbon Reduction Effect of OFDI in China and Its Mechanism].

Under the "Going out" strategy and the Belt and Road Initiatives, the trade in goods and services and flow of production factors between China and the rest of the world have become more frequent, and the total amount of outward foreign direct investment (OFDI) is considerable and growing significantly. Therefore, along with the extensive economic growth and the substantial growth of foreign investment, the environmental impact of OFDI has become noteworthy. Here, through theoretical analysis and logical deduction, three possible pathways of the impact of OFDI in China on the environment were presented as hypotheses, which included the industrial structure, the technological innovation progress, and the economic-scale expansion. Using Chinese provincial data from 2004 to 2019, an environmental load index including main environmental pollutant emissions and carbon emissions was constructed. Taking this as the dependent variable, an intermediary effect method was constructed to analyze the home pollution and carbon reduction effect and the influence mechanism of OFDI in China. The results showed that ① OFDI in China reduced the environmental load, and each 1% increase in OFDI reduced the environmental load by 0.051%-0.076%. ② The OFDI in China reduced the environmental load by advancing the industrial structure and technological innovation progress, and a 1% increase in OFDI resulted in a 0.060% and 0.006% reduction in environmental load through their indirect effects, respectively, whereas OFDI increased the environmental load by 0.009% through the path of economic-scale expansion. The contributions of leading environmental load changes mediated by advancing industrial structure, technological innovation progress, and economic-scale expansion were 65.9%-84.5%, 6.6%-8.5%, and -12.7%- -9.9%, respectively, and the contribution of OFDI directly driving the environmental load to change was 19.7%-37.4%. Based on this, policy recommendations, including promoting Chinese enterprises and capital going abroad, encouraging relatively disadvantaged domestic industries to explore foreign markets, strengthening reverse technology spillover effects, and forming a sustainable low-carbon development mode, have been proposed.