Decomposition and decoupling analysis of multi-sector CO2 emissions based on LMDI and Tapio models: Case study of Henan Province, China.

The peak carbon dioxide emissions at the provincial level is the foundation for achieving the national target of carbon emission peak, thus it is important to analyze the characteristics of provincial CO2 emissions. However, there is a lack of comprehensive analysis and research on quantifying the contributions of the driving factors to decoupling at the provincial level. Therefore, taking Henan Province as the research object, this study establishes the decoupling effort model by combining the traditional LMDI model and Tapio model based on compiling the CO2 emission inventories from 2006 to 2019. The results showed that total CO2 emissions increased from 2006 to 2011, and decreased after 2011 in Henan Province. Raw coal was the primary fuel source of Henan's CO2 emissions, and the sector of "power and heat production" was the major industrial source, accounting for above 45% of the total emissions. Economic output and energy intensity were the major factors promoting and restraining the increase in Henan's CO2 emissions, respectively. In terms of the decoupling state, Henan achieved the transformation from weak decoupling to strong decoupling from 2006 to 2019. Industry presented a strong decoupling condition, while weak decoupling was detected in the agriculture sector during the study period. The changing trend of energy intensity decoupling effort was consistent with that of total decoupling effort, indicating that energy intensity is a crucial factor in achieving decoupling. This study is helpful to grasp the CO2 emission characteristics of Henan Province and provide the theoretical basis for formulating emission mitigation measures of peak carbon dioxide emissions in Henan and other provinces.

[Influence of COVID-19 Prevention and Control Measures on PM2.5 Concentration, Particle Size Distribution, Chemical Composition, and Source in Zhengzhou, China].

The COVID-19 lockdown was a typical occurrence of extreme emission reduction, which presented an opportunity to study the influence of control measures on particulate matter. Observations were conducted from January 16 to 31, 2020 using online observation instruments to investigate the characteristics of PM2.5 concentration, particle size distribution, chemical composition, source, and transport before (January 16-23, 2020) and during (January 24-31, 2020) the COVID-19 lockdown in Zhengzhou. The results showed that the atmospheric PM2.5 concentration decreased by 4.8% during the control period compared with that before the control in Zhengzhou. The particle size distribution characteristics indicated that there was a significant decrease in the mass concentration and number concentration of particles in the size range of 0.06 to 1.6 µm during the control period. The chemical composition characteristics of PM2.5 showed that secondary inorganic ions (sulfate, nitrate, and ammonium) were the dominant component of PM2.5, and the significant increase in PM2.5 was mainly owing to the decrease in NO3- concentration during the control period. The main sources of PM2.5 identified by the positive matrix factorization (PMF) model were secondary sources, combustion sources, vehicle sources, industrial sources, and dust sources. The emissions from vehicle sources, industrial sources, and dust sources decreased significantly during the control period. The results of analyses using the backward trajectory method and potential source contribution factor method indicated that the effects of transport from surrounding areas on PM2.5 concentration decreased during the control period. In summary, vehicle and industrial sources should be continuously controlled, and regional combined prevention and control should be strengthened in the future in Zhengzhou.

Eco-efficiency assessment of industrial parks in Central China: a slack-based data envelopment analysis.

Industrial parks have made important contributions to China's economic development, but they have caused serious pollution to the environment. With the promotion of China's sustainable development, improving the eco-efficiency of industrial parks has gradually become the focus of attention. In this study, a slack-based data envelopment analysis (SBM-DEA) model, which included three input indicators and six output indicators, was applied to assess the eco-efficiencies of 18 industrial parks in Central China. The ecological development level of different industrial parks in Central China was uneven, and their efficiency scores ranged from 0.06 to 1. Next, the most sensitive input and output variables are identified by sensitivity analysis, and it is concluded that land and water consumption will have a significant impact on the evaluation results of the model. Then, the influencing factors of eco-efficiency are discussed, and it was found that a reasonable energy structure and industrial structure, as well as high industrial added value, would increase the eco-efficiency of industrial parks. Finally, based on the findings, policy recommendations for improving the eco-efficiency of industrial parks are put forward, including fulfilling government responsibilities, adjusting energy and industrial structures, and improving the high-quality development of the parks.

Uncovering the characteristics of air pollutants emission in industrial parks and analyzing emission reduction potential: case studies in Henan, China.

Industrial parks contribute greatly to China's economic development while emitting huge air pollutants. It is necessary to study the characteristics of air pollutant emissions in industrial parks. In this study, emission inventories for 11 industrial parks were established. Meanwhile, the source emission and spatial distribution characteristics of the industrial park were analyzed. The cluster analysis was used to classify these parks into "4Hs", "Mixed" and "4Ls" parks. "4Hs", "Mixed" and "4Ls" represent that the levels of energy intensity, economic proportion of energy-intensive industries, coal proportion and pollution performance value are high, medium and low in turn. Then three emission reduction measures were set up to estimate the emission reduction potential and environmental impacts. The results show that: (1) the emissions of SO2, NOx, CO, PM10, PM2.5, VOCs and NH3 of 11 industrial parks in 2017 were 11.2, 23.1, 30.8, 8.3, 3.5, 5.1, and 1.1 kt, respectively. (2) Power plants were the largest source of SO2 and NOx emissions, and industrial processes were the largest emission source of CO, PM10, PM2.5, VOCs and NH3. (3) "4Hs" parks with traditional energy-intensive industries as the leading industries should be the emphasis of air pollutant emission reduction. (4) Through the optimal emission reduction measures, SO2, NOx, PM10, PM2.5 and VOCs were reduced by 81, 46, 51, 46 and 77%, respectively. Environmental impact reductions include 1.6 kt SO2eq acidified gas emissions, 1.4 kt PO43-eq eutrophication substances, 4.2 kt PM10eq atmospheric particulate emissions, 7.0 kt 1,4-DCEeq human toxic substances, and 5.2 kt PM2.5 eq breathing Inorganic. This study is helpful to understand the characteristics of air pollutants emissions in industrial parks and promotes the proposal and implementation of air pollutant emissions reduction strategies.

Life cycle assessment of environmental impact on municipal solid waste incineration power generation.

Municipal solid waste (MSW) incineration power generation is an important treatment technology, which has been widely concerned in recent years. It is of great significance to evaluate the environmental impact. This study conducted the environmental life cycle assessment of MSW incineration power plant in Yongcheng city, Henan province, China. After that, the comprehensive environmental impacts of MSW incineration power plant, landfill, and coal-fired power plant are compared. Furthermore, the energy conservation and emission reduction benefits brought by MSW incineration power plant in Yongcheng city are quantitatively analyzed. The results show that (1) the main environmental impact categories of MSW incineration power plant are human toxicity potential and acidification potential, which together account for 72.8% of the total comprehensive environmental impact. In addition, incineration is the main process of pollutant generation, which contributes 94.1% to the comprehensive environmental impact. (2) As an effective supplement to landfill and coal-fired power generation, MSW incineration power generation produces lower environmental impact. (3) Significant energy-saving and emission reduction benefits can be brought by MSW incineration power plant. Particularly, it could save energy 2.75×104 tce, reduce greenhouse gas emissions 3.43×105 t CO2-eq, and effectively reduce the emissions of various air pollutants for the local area annually.