Impact of energy structure adjustment on air quality: a case study in Beijing, China

Has COVID-19 Altered the Air Quality Conduction Relationship in Beijing and Neighboring Cities?—A Test Based on Dynamic Periodic Conformance

The Beijing–Tianjin–Hebei region is the most dynamic region and largest economy in northern China; however, the air quality is the worst in the country. The study of the air quality in the cities around Beijing is of great significance for air pollution control. Therefore, this study analyzed whether the COVID-19 pandemic altered the periodic pattern of the air quality in Beijing and its neighboring cities. The study employed continuous wavelet transform to examine the impact of the COVID-19 pandemic on the air quality of Beijing and its neighboring cities. This method reveals the changes in the air quality from a periodic pattern perspective. The results showed that COVID-19 weakened the periodic changes in air quality in Beijing and five neighboring cities, and this effect was most pronounced during the outbreak of the pandemic in early 2020. The cycle synchronization analysis showed that the pandemic weakened the cycle synchronization of air quality of the cities in the north of Beijing, while less impact was found on the cities to the south of Beijing. Moreover, the periodic patterns in 2020 and 2021 were compared with that in 2019 (before the outbreak of the pandemic), and it was found that the periodic patterns during the outbreak of the pandemic in 2020 and 2021 were significantly different from that in the same period in 2019. Therefore, COVID-19 weakened the periodic pattern of air quality in the cities around Beijing and altered the connection to air quality among them. The changes reveal the connections of inter-city air pollutants caused by human economic and social activities in cities around Beijing.

Can Industrial Restructuring Improve Urban Air Quality?—A Quasi-Experiment in Beijing during the COVID-19 Pandemic

The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing’s non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing’s industry, the deterioration of its air quality would also bring pressure to Beijing’s atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion.

Integrating Air Quality and Public Health Benefits in U.S. Decarbonization Strategies

Research on air quality and human health “co-benefits” from climate mitigation strategies represents a growing area of policy-relevant scholarship. Compared to other aspects of climate and energy policy evaluation, however, there are still relatively few of these co-benefits analyses. This sparsity reflects a historical disconnect between research quantifying energy and climate, and research dealing with air quality and health. The air quality co-benefits of climate, clean energy, and transportation electrification policies are typically assessed with models spanning social, physical, chemical, and biological systems. This review article summarizes studies to date and presents methods used for these interdisciplinary analyses. Studies in the peer-reviewed literature (n = 26) have evaluated carbon pricing, renewable portfolio standards, energy efficiency, renewable energy deployment, and clean transportation. A number of major findings have emerged from these studies: [1] decarbonization strategies can reduce air pollution disproportionally on the most polluted days; [2] renewable energy deployment and climate policies offer the highest health and economic benefits in regions with greater reliance on coal generation; [3] monetized air quality health co-benefits can offset costs of climate policy implementation; [4] monetized co-benefits typically exceed the levelized cost of electricity (LCOE) of renewable energies; [5] Electric vehicle (EV) adoption generally improves air quality on peak pollution days, but can result in ozone dis-benefits in urban centers due to the titration of ozone with nitrogen oxides. Drawing from these published studies, we review the state of knowledge on climate co-benefits to air quality and health, identifying opportunities for policy action and further research.

Estimation of abatement potentials and costs of air pollution emissions in China

Understanding the air pollution emission abatement potential and associated control cost is a prerequisite to design cost efficient control policies. In this study, a linear programming algorithm model, International Control Cost Estimate Tool, was updated with cost data for applications of 56 types of end-of-pipe technologies and five types of renewable energy in 10 major sectors namely power generation, industry combustion, cement production, iron and steel production, other industry processes, domestic combustion, transportation, solvent use, livestock rearing, and fertilizer use. The updated model was implemented to estimate the abatement potential and marginal cost of multiple pollutants in China. The total maximum abatement potentials of sulfur dioxide (SO₂), nitrogen oxides (NO_x), primary particulate matter (PM_2.5), non-volatile organic compounds (NMVOCs), and ammonia (NH₃) in China were estimated to be 19.2, 20.8, 9.1, 17.2 and 8.6 Mt, respectively, which accounted for 89.7%, 89.9%, 94.6%, 74.0%, and 80.2% of their total emissions in 2014, respectively. The associated control cost of such reductions was estimated as 92.5, 469.7, 75.7, 449.0, and 361.8 billion CNY in SO₂, NO_x, primary PM_2.5, NMVOCs and NH₃, respectively. Shandong, Jiangsu, Henan, Zhejiang, and Guangdong provinces exhibited large abatement potentials for all pollutants. Provincial disparity analysis shows that high GDP regions tend to have higher reduction potential and total abatement costs. End-of-pipe technologies tended be a cost-efficient way to control pollution in industries processes (i.e., cement plants, iron and steel plants, lime production, building ceramic production, glass and brick production), whereas such technologies were less cost-effective in fossil fuel-related sectors (i.e., power plants, industry combustion, domestic combustion, and transportation) compared with renewable energy. The abatement potentials and marginal abatement cost curves developed in this study can further be used as a crucial component in an integrated model to design optimized cost-efficient control policies.

The Impact of Haze on the Availability of Company Debt Financing: Evidence for Sustainability of Chinese Listed Companies

Based on the data of A-share listed companies in Shanghai and Shenzhen Stock Exchanges from 2013 to 2017 and the air quality monitoring data released by China Environmental Monitoring Station, the paper examines the impact of haze on the availability of company debt financing by using fixed-effects model and quantile regression model. The empirical results show that: Firstly, haze has a positive impact on the demand of company debt financing, and the positive effect is marginal increment. Secondly, haze has a negative impact on the availability of company debt financing, and the negative impact is also marginal increment. Further study found that heavy polluting industry characteristics weaken the impact of haze on company debt financing availability. The paper analyzes the influence of air pollution on enterprise management from the perspective of company debt financing and explains the necessity for companies to implement an environmentally sustainable development strategy.

Reduction in population exposure to PM2.5 and cancer risk due to PM2.5-bound PAHs exposure in Beijing, China during the APEC meeting

Radical measures for controlling ambient air pollution sources were employed by the Chinese government during the Asia-Pacific Economic Cooperation (APEC) meeting in 2014, providing a unique case to evaluate the health effect benefits from such measures. To examine the cancer risk reduction from the source control measures during the APEC meeting, we estimated the reduction in population exposure to PM_2.5 and PAHs and the reduction in PAHs-associated cancer risk if the control measures were sustained over time. We determined the population exposure to PM_2.5 and PM_2.5-bound PAHs for the 21.52 million Beijing residents using a Land Use Regression model to determine the spatial distribution of PM_2.5 and a Monte Carlo approach to revise indoor/outdoor infiltration factor and time activity patterns. Into the model and approach, we incorporated the spatial variance and indoor/outdoor differences in the PM_2.5 and PM_2.5-bound PAHs concentrations, based on measurements. We then estimated lung cancer risk using the population attributable fraction (PAF), assuming the control measures were sustained over time. The mean PM_2.5 exposure concentration decreased from 37.5 μg/m3 (CI:17.1-74.9 μg/m3) to 24.0 μg/m3 (CI:10.2-47.7 μg/m3), whereas the mean PM_2.5-bound equivalent benzo[a]pyrene (BaP_eq) exposure concentration decreased from 7.1 ng/m³ (CI:3.3-14.2 ng/m³) to 4.2 ng/m³ (CI:1.8-7.7 ng/m³), resulting in a reduction in the lung cancer PAF from 0.75% to 0.45%, if the measures were sustained over time.