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Countries worldwide consider solid waste collection and recycling necessary due to the recent emphasis on conservation of resources and environmental protection. Due to the constraints from resource depletion and the need for sustainable economic growth, solid waste recycling has become a critical issue in China. Several indigenous researchers in China have studied the potential benefits of solid waste recycling. However, most studies limited the environmental assessment of solid waste recycling to greenhouse gas (GHG) emissions and considered only one type of solid waste (paper or plastic). Therefore, the present study analyzed the energy (electricity) and environmental (GHG and air pollutant emissions) benefits of recycling steel, nonferrous metal, plastic, and paper wastes from 2005 to 2017 in China. The study used the formulation of model equations method to estimate the electrical energy and environmental benefits. Prominent findings show that the total amount of electricity saved by recycling solid waste from 2005 to 2017 was 3743.3 Mtce. On average, solid waste recycling during the period led to a 43.2% saving on electricity. Solid waste recycling avoided 4765.9 billion kg of carbon dioxide emission and 22.502 billion kg of methane emission. It was also found that the recycling of solid waste saved a total amount of 10,669.8 M kg of NO X emission but had a burden of −6263.2 M kg of VOCs emission on the environment. Solid waste recycling avoided the emission of CO 2 , CH 4 , NO X , and SO X, but the recycling of steel, plastics, and paper waste had negative impacts on the environment in terms of VOCs and PM emissions. Proper measures such as installing air pollution control devices should be put in place to minimize the emission of pollutants during the recycling of these solid wastes. • The energy and environmental benefits of solid waste recycling is assessed. • The formulation of model equations method was used for the assessment. • On average, the recycling of solid waste saved 43.2% of electricity. • Recycling of waste avoided a significant amount of GHG and air pollutant emissions. • Solid waste recycling had no essential benefit on the emission of VOCs and PM. [ABSTRACT FROM AUTHOR]

Due to variations in economic scale, economic structure, and technological advancement across different Chinese provinces and cities, the cost of air pollution reduction differs significantly. Therefore, the total reduction cost can be decreased by capitalizing on these regional discrepancies in reduction cost to carry out cooperative emission reduction. In this paper, taking NO x reduction in North China as an example, a regional cooperative reduction game (CRG) model was constructed to minimize the total cost of emission reduction while achieving future emission reduction targets. The fair allocation of benefits from cooperation plays a crucial role in motivating regions to participate into the cooperation. A comprehensive mechanism of benefits allocation was proposed to achieve fair transferred compensation. The mechanism combines the consumption responsibility principle based on input-output theory and the Shapley value method based on game theory. Compared to the cost before the optimized collaboration, the CRG model will save 20.36% and 13.71% of the total reduction cost in North China, respectively, under the target of 17.68% NO x reduction by 2025 and 66.44% NO x reduction by 2035 relative to 2020. This method can be employed in other regions to achieve targets for air pollution reduction at minimum cost, and to motivate inter-regional cooperation with this practical and fair way of transferred compensation. [Display omitted] • A regional cooperative reduction game model for air pollution was constructed. • We projected NO x reduction cost savings in North China under two future scenarios. • An integrated approach was used to allocate cooperative benefits with fairness. • The model helps achieve air pollution reduction targets at the minimum cost. [ABSTRACT FROM AUTHOR]

The pollution control effect of seasonal environmental regulation policies in developing countries still lacks empirical evidence. In 2017, China implemented its first Atmospheric Environmental Policy in Autumn and Winter (AEPAW) to coordinate efforts among cities in reducing air pollutant emissions. Taking the daily panel data of 174 cities in northern China from July 2017 to July 2020 as samples, this paper empirically examines the pollution control effect of the AEPAW using a difference-in-differences model, a difference-in-difference-in-differences model, and a regression discontinuity design. The results show that the AEPAW significantly improves air quality in autumn and winter, with the air quality index decreasing by 5.6% on average by reducing PM 2.5 , PM 10 , SO 2 , and O 3 emissions. However, the AEPAW only creates a short-term "policy-induced blue sky", and there exists a phenomenon of "retaliatory pollution" after the AEPAW ends. Besides, the pollution control effect of the AEPAW is moderated by the heterogeneity of the national "Two Sessions" and the Central Environmental Protection Inspection. The implementation of the AEPAW also has a significant spillover effect on air pollution control in surrounding areas. The net benefit from the AEPAW is estimated to be approximately US$ 670 million per year. These findings not only have practical significance for strengthening the comprehensive control of air pollution in China, but also give some important references for other developing countries. • We examine the pollution control effect of the Atmospheric Environmental Policy in Autumn and Winter (AEPAW). • We take the daily panel data of 174 cities in northern China as samples. • We find that the AEPAW significantly improves air quality in autumn and winter. • The AEPAW only creates a short-term "policy-induced blue sky". • The AEPAW has a significant spillover effect on air pollution control in surrounding areas. [ABSTRACT FROM AUTHOR]

Currently, air pollution is primarily characterized by PM 2.5 and O 3. Therefore, the co-control of PM 2.5 and O 3 has become an important task of atmosphere pollution prevention and control in China. However, few studies have been conducted on the emissions from vapor recovery and processes, which is an important source of VOCs. This paper analyzed the VOC emissions of three vapor process technologies in service stations and first proposed key pollutants for priority control based on the coordinated reactivity of O 3 and SOA. The concentration of VOCs emitted from the vapor processor was 3.14–9.95 g m−3, compared to 631.2–717.8 g m−3 for uncontrolled vapor. Alkanes, alkenes, and halocarbons accounted for a high proportion of the vapor both before and after control. Among the emissions, i -pentane, n -butane, and i -butane were the most abundant species. Then, the species of OFP and SOAP were calculated through the maximum incremental reactivity (MIR) and fractional aerosol coefficient (FAC). The average source reactivity (SR) value of the VOC emissions from three service stations was 1.9 g g−1, while the OFP ranged from 8.2 to 13.9 g m−3 and SOAP ranged from 0.18 to 0.36 g m−3. By considering the coordinated chemical reactivity of O 3 and SOA, a comprehensive control index (CCI) was proposed for the control of key pollutant species that have multiplier effects on environment. For adsorption, trans -2-butene and p -xylene were the key co-control pollutants, while toluene and trans -2-butene were the most important for membrane and condensation + membrane control. A 50% emission reduction of the top two key species that emission account for 4.3% averagely will reduce O 3 by 18.4% and SOA by 17.9%. [Display omitted] • The organized emissions of VOCs from three vapor processors were studied in service stations. • The species composition of VOCs before and after control were analyzed. • The OFP and SOAP were calculated to evaluate the secondary pollution. • The co-control of key pollutants based on SOA and O 3 has great environmental benefits. [ABSTRACT FROM AUTHOR]

Production restriction is an environmental regulation adopted in China to curb the air pollution of industrial enterprises. Frequent production restrictions may cause economic losses for enterprises and further hinder their green transformation. Polluting enterprises are faced with the dilemma of choosing environmental protection or economic development. Using panel data on industrial enterprises in China from 2016 to 2019, this paper evaluates the impact of production restrictions on both enterprises' environmental and economic performance with regression models. The results show that production restrictions significantly drop the concentrations of SO2 and NOx emitted from polluting enterprises. Meanwhile, production restrictions have significant negative effects on operating income, financial expenses, net profit, and environmental protection investment. The mechanism analysis reveals that production restrictions mitigate air pollutant concentrations by increasing the number of green patents and improving total factor productivity, which also verifies the Porter hypothesis. However, there is a masking mediating effect of environmental investment, which indicates that the reduction of environmental investment hinders the enterprise's efforts to control air pollution. In addition, heterogeneous analysis shows that the economic shock on microenterprises is larger than that on small enterprises. Implementing production restrictions for microenterprises may be a way to eliminate their backwards production capacity. • Production restriction dropped air pollutants of polluting enterprises. • Production restriction has negative effects on business performance of enterprises. • TFP and green innovation are the mediating variables of pollution reduction. • The reduction of environmental investment hinders the pollution control. • The shocks to the business performance of microenterprises are larger. [ABSTRACT FROM AUTHOR]

Air pollution has become a global public health risk factor as rapid urbanization advances. To observe the air pollution situation, air monitoring stations have been established in many cities, which record six air pollutants. Previous studies have identified cities exhibiting similar air pollution characteristics by combining principal component analysis (PCA) with cluster analysis (CA). However, spatial and temporal effects were neglected. In this paper, we focus on the combination of GTWPCA and STCA, which fully incorporates spatio-temporal effects. It is then applied to air pollution data from the top 10 urban agglomerations in China during 2016–2021. Key experimental findings include: 1. GTWPCA provides a more detailed interpretation of local variation than PCA. 2. Compared with CA, STCA highlights the coupling effect in the spatial and temporal dimensions. 3. The combination of GTWPCA and STCA captures similar air pollution characteristics from spatio-temporal perspectives, which has the potential to help environmental authorities take further action to control air pollution. • Proposed new framework combining GTWPCA and STCA. • Identified the main air pollutants at spatial and temporal locations. • Captured similar air pollution behavior over space and time. [ABSTRACT FROM AUTHOR]

The "Air Pollution Prevention and Control Action Plan" is regarded as China's war against air pollution, which is also noted as China's Clean Air Action (CAA policy). Although abundant studies have focused on the impact of CAA policy on social activities, such as air quality, pollutants emission, and economic output, limited studies explore its impact on enterprise competitiveness from a more micro perspective. Improving total factor productivity (TFP) is essential to achieve economic efficiency and sustainable development. Identifying the impact and influence mechanism of the CAA policy on TFP is important for China to realize the coordinated development of environment and economy and promote high-quality economic development. Based on the difference-in-differences model, this paper evaluates the impact of the CAA policy on enterprise productivity. The empirical analysis shows that there is a significantly negative effect from the CAA policy to the TFP of polluting enterprises, indicating that the CAA policy has decreased enterprise productivity. Our finding is robust, after conducting several robustness tests, such as replacing different dependent indicators, adopting different policy measurement variables, considering other policy interventions and using propensity score matching. Besides, this paper also discusses the possible heterogeneities. This paper adds additional empirical evidence to understand the relationship between air pollution control and enterprise competitiveness. The conclusions of this paper have important practical significance for China to further promote air pollution control and perfect the environmental control system. • We analyze the impact of China's Clean Air Action on enterprise competitiveness. • There is a negative effect from the Clean Air Action policy to enterprise total factor productivity. • Possible heterogeneities have been discussed from several aspects. • This paper adds additional empirical evidence to understand China's Clean Air Action. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities. • A dual-scale ensemble framework is proposed for air quality index prediction. • CEEMDAN technology is adopted to obtain multiple intrinsic mode functions. • An improved WOA algorithm based on dimension learning-based hunting is proposed. • Long short-term memory network is used to predict high-frequency components. • Regularized extremum learning machine is used to predict low-frequency components. [ABSTRACT FROM AUTHOR]

To abate the severe air pollution, the Chinese government released the Action Plan of Air Pollution Prevention and Control (APAPPC) in 2013. This paper regards the APAPPC as a quasi-experiment and uses the DID method to investigate the impact of environmental regulation on the growth of green total-factor productivity of China's industries. This article employs the non-radial directional distance function (NDDF) and the global Malmquist index to measure the total-factor carbon productivity of China's industries. Further regressions suggest that the implementation of the APAPPC has significantly promoted the growth of the total-factor carbon productivity in the air pollution-intensive industries, and its marginal effect has steadily increased with time. This result is still valid after using a series of counterfactual tests and robustness tests. The further mechanism study shows that the APAPPC has significantly promoted R&D investment, especially in instruments and equipment, which has effectively promoted technical efficiency and technological advancement. It indicates that stringent and well-designed environmental regulations should lead to a "win-win" situation of environmental improvement and economic development by encouraging enterprises to upgrade their technology and equipment. • This paper uses a typical case in China to demonstrate the porter's hypothesis. • This paper uses the DEA method to measure the total-factor carbon productivity of China's industries. • The Action Plan of Air Pollution Prevention and Control in China has significantly promoted the growth of total-factor carbon productivity. • The regulation promotes productivity by propelling enterprises to increase R&D investment and update their technical equipment. [ABSTRACT FROM AUTHOR]

In order to control air pollution better, China has adopted the central environmental inspection mechanism. This paper adopts the data of 290 prefecture-level cities in China to evaluate the air improvement effect of the central environmental inspection mechanism. Firstly, based on the Mechanism Design Theory and Principal-agent Theory, this paper analyzes the government incentive measures and effort levels under the conditions of information as symmetry and asymmetry. Then, this paper introduces Regression Discontinuity Design (RDD) Model to evaluate the air improvement effect of the central environmental inspection mechanism. Lastly, this paper explores the sustainability of the environmental inspection mechanism through the regression analysis of environmental inspection revisit. The results show that: under the condition of information symmetry, the central government can make the local governments reach the Pareto optimal effort level through the design of incentive contract. In a short period of time, the first round of environmental inspection and environmental inspection revisit have significantly improved air quality, and significantly reduced PM2.5 and PM10 and other major single pollutants. In general, this system has had an immediate effect. The environmental inspection revisit has also significantly reduced the concentrations of AQI, PM2.5 and PM10. Compared with the first round of environmental inspection and environmental inspection revisit, the latter has a higher level of air pollution reduction and better effect. The sustainability of the improvement effect of environmental inspection mechanism is questioned. It is of great significance to evaluate the actual effect of the central environmental inspection system and tap the unique experience of "Chinese style" to explore how to control air pollution and promote green development. • This paper explored the effect of central environmental inspection. • The Regression Discontinuity Design (RDD) Model was used in this paper. • The sustainability of the environmental inspection mechanism was explored. [ABSTRACT FROM AUTHOR]

Since the 2000s, China has enforced regional collaboration as a way of resolving cross-diffused air pollution and jurisdictional limitations. It is, however, a hotly debated issue as to whether authority-driven regional collaboration can be sustained and effective, and no consensus has been achieved by the theoretical discussion. Despite extensive empirical research on the topic, most studies judged the sustainability of collaborations by analyzing the environmental outcomes within a short period after their initiation. To provide more comprehensive empirical evidence to the open question, this paper introduces the criterion of adaptivity to determine whether collaboration is sustainable. Taking the changing collaboration in Beijing-Tianjin-Hebei and its surroundings as a quasi-natural experiment, Difference-in-Difference model and Triple Difference Model are used to verify the adaptivity from two aspects. Results show that authority-driven collaboration adjustment yields better environmental outcomes. Additionally, the better environmental outcome is observed in original collaborators, implying that local governments can learn by doing and form interdependence through repeated collaborative actions. The adaptive collaboration, however, has two limitations. First, the collaboration mechanism is formed primarily based on performance evaluation and target assessment instruments, without showing adaptivity in reducing soft control indicators of air pollution. Second, the different regulation pressure between non-collaborating and collaboration cities results in undesirable spillover effects within a range of 40–100 km. This study not only contributes to empirical and theoretical studies on regional collaboration but also provides support to the improvement of the collaboration system in practice. • Process-adaptivity is introduced as the criterion to evaluate collaboration. • Authority-driven collaboration generates positive environmental outcomes. • Authority-driven collaboration is adaptive but limited to mandatory indicators. • Regional collaboration brings out negative spillovers within 40–100 km. [ABSTRACT FROM AUTHOR]

Chinese officials play an important role in air pollution control. This paper used a sample of 282 prefecture-level cities in China to discuss the impact of promotion incentives of officials on air pollution from the perspectives of heterogeneity, mechanism and spatial effects. We found that the promotion incentives of officials reduced air pollution, and GDP per capita had positive moderating effects. The effects of promotion incentives were more significant in cities with less air pollution, in the central and western regions, for officials with higher education levels, or years after 2007. The promotion incentives could promote the development of green finance and green technology innovation, both of which were conducive to mitigating air pollution. Using the dynamic spatial Durbin model (DSDM), we found that the promotion incentives had negative spatial spillover effects. The promotion incentives in surrounding cities reduced air pollution in the local city; however, it had only short-run effects and no long-run effects. • The promotion incentives of officials could significantly reduce air pollution. • The effects of promotion incentives on air pollution have heterogeneity. • Green finance and green technology innovation have positive mediating effects. • Promotion incentives have negative spatial spillover effects in the short-run and no long-run effects. [ABSTRACT FROM AUTHOR]

The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread "free rider" behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO 2 governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO 2 control and can be extended to the researches and practices of other air pollutants control. • An optimization model is developed by considering removal costs and health benefits. • A benefit allocation model is established based on environmental justice. • The superiority of the benefit allocation model is demonstrated by sensitivity analysis. [ABSTRACT FROM AUTHOR]

China is experiencing serious atmospheric pollution, which also exhibits significant spatial heterogeneity. The Chinese government has implemented targeted pollution control measures at the city level, emphasizing coordination among cities to prevent and control air pollution in key regions such as Beijing–Tianjin–Hebei (BTH) urban agglomeration. This study combined an inter-city multi-regional input–output (MRIO) model with an air quality dispersion model consisting of a weather research and forecasting (WRF) model and the CALPUFF model (WRF/CALPUFF) to study the inter-city economic consumption, pollutant emission and concentration among 13 cities in BTH urban agglomeration. NO x is chosen as an example. The combined effects of economic linkage and atmospheric transport show that NO x concentrations in cities in the BTH urban agglomeration are attributable to three consumption sources: a local contribution from the target city's own local economic consumption (average, 25%), and non-local consumption contributions, including other cities in the BTH urban agglomeration (average, 36%) and regions outside of BTH (average, 39%). Compared with the contributions to NO x concentrations calculated using only the MRIO model or atmospheric transport stimulation model, the results of this paper quantify that the consumption outside of a city could provide a greater impact on the city's air quality due to the combined effects of economic linkage and atmospheric transport. To avoid negative impacts of emission reduction targets on economic consumption, governmental regional pollution control policies should consider the combined effects of economic linkage and atmospheric transport. • The inter-city MRIO model is combined with the air quality simulation model. • The non-local consumption could drove a greater impact on a city's air quality in BTH region. • Provide a useful guidance to the inter-city air pollution control policies. [ABSTRACT FROM AUTHOR]

The power industry of China plays an important role in reducing the carbon dioxide (CO 2) and air pollutant emissions. This paper focuses on synergistic CO 2 and air pollution emission reduction by implementing technical and structural measures, and explores the processes aimed at achieving the co-benefits of carbon reduction and air pollution control effectively in the power industry at a local level. The results of the analysis show that the structural measures achieve overall co-benefits, and the technical measures have a good effect in reducing the air pollution, but simultaneously increase CO 2 emissions because of the additional electricity used for running air pollution control devices (APCDs). Based on the analysis of the power generation incremental cost and pollutant emission reduction cost, cost-effective approaches to attain different emission reduction targets are proposed to the power industry in four case regions. These approaches can provide policy makers and stakeholders useful and relevant policy and operation recommendations to reduce CO 2 and air pollution emissions cost-effectively and promote low carbon sustainability in the power industry of China. • China's power industry plays an important role in reducing CO 2 and air pollutants. • Technical and structural measures of reducing CO 2 and air pollutants are analyzed. • Structural measures achieve overall co-benefits. • Technical measures have a good effect in reducing pollutants, but increase CO 2. • Cost-effective approaches are formed to cut CO2 and air pollutants. [ABSTRACT FROM AUTHOR]

The "Joint Prevention and Control of Regional Air Pollution in China" has been put forward to solve serious regional air pollution. However, there is no geographical division covering the whole country that could reveal the similarities and differences among regions to support implementing this policy. This paper applied an air quality model and a multiregional input-output model to analyze the impacts and patterns of atmospheric transmission and trade transfer of regional air pollution, then compared the differences among typical provinces in the northern, central and southern regions. The social network analysis method was used to analyze the interacting pattern of regional atmospheric transmission and trade transfer, and 31 provinces in mainland China were divided into 5 clusters to reveal the interprovincial relationship of atmospheric transmission; then, the 31 provinces were divided into 3 clusters to reveal the interprovincial relationship of trade transfer. Based on these results, the provincial geographical division of "Nine Atmospheric Environmental Transmission Zones" covering mainland China was carried out. In addition, the key regions linking atmospheric transmission and trade transfer were also found. For example, Inner Mongolia is a typical key linkage region. From an atmospheric transmission perspective, it is linked to the northeast area. However, from a trade transfer perspective, it has a close relationship with the southern provinces. The linkage among provinces provides support for the interregional interaction mechanism of air pollution and the formulation of "Joint Prevention and Control of Regional Air Pollution in China". Image 1 • The impact of atmospheric and trade transfer on regional pollution were discussed. • The atmospheric transmission is location-sensitive and trade transfer is not. • The social network analysis was used in air atmospheric environmental zoning. • The 31 provinces were classified into 9 atmospheric environmental transmission zones. • There are four provinces being nodes connecting atmosphere and trade transfer. [ABSTRACT FROM AUTHOR]