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This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices. • Assessed impact of policies on key polluters in 259 Chinese cities. • Employed DID and spatial Durbin models for in-depth policy impact analysis. • Found significant reduction in urban air pollution from policies. • Revealed spatial spillover effects, aligning with "pollution haven" hypothesis. • Proposed nuanced approaches for local and spatial policy impacts. [ABSTRACT FROM AUTHOR]

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public. [Display omitted] • Examines diverse air pollution sources, including industry, transport, and natural events. • Comprehensively assesses global health risks, especially disease burden from particulate matter and chemicals. • Analyzes economic impacts of air pollution, including healthcare costs and productivity losses. • Discusses recent advances in interventions like clean energy and urban planning to address air pollution. [ABSTRACT FROM AUTHOR]

This paper studies how centralizing environmental administration affects air pollution in China. China launched a reform in 2016 to empower upper-level environmental protection bureaus to administer lower-level bureaus vertically through personnel control. Exploiting a stacked difference-in-differences strategy, we find that the reform significantly reduced air pollution. The effect was stronger in places where pollution was less likely to be affected by spillovers from other provinces, where local governments initially paid less attention to environmental protection, and where there was less economic importance. Further analysis shows that the reform reduced pollution by strengthening the pollution reduction incentives of local environmental officials, increasing the intensity of local environmental inspection, and promoting environmental compliance by polluting firms. [ABSTRACT FROM AUTHOR]

There is a knowledge gap on how ground-level particulate pollution affects labor productivity in emerging nations due to a lack of study, especially when it comes to outdoor work settings like couriers in the express delivery industry. Combining findings from two research projects, this paper examines the socioeconomic consequences of particulate matter and ground-level particulate pollution. Special panel dataset from China's express delivery companies are used, we study how particulate pollution affects courier productivity. The instrumental variable of our analysis was built by particulate pollution data from upwind towns. Moreover, a comparable rise in particulate levels during the 30 days caused a significant 23.7% decline in worker productivity. This draws attention to a neglected area of the economic effects of particulate pollution, especially in underdeveloped countries. Our results also highlight the wider health hazards connected to outdoor activities in high-pollution locations, drawing comparisons on outdoor exercisers and particulate matter concentrations. The critical need for coordinated policy attention addressing both ground-level Particulate and particle matters in developing nations is highlighted by the increased risk of lung function impairment among outdoor exercisers owing to excessive particulate matter concentrations. The interrelated risks that air pollution poses to public health and economic productivity are clarified by this Comprehensive viewpoint. • Examines the socioeconomic consequences of particulate matter and ground-level ozone pollution. • Using a special panel dataset from one of China's top express delivery companies. • Air pollution poses to public health and economic productivity are clarified. [ABSTRACT FROM AUTHOR]

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance. • Environmental performance index is constructed based on endogenous directions. • A Euclidean distance function is used to identify endogenous directions. • Optimization pathways and environmental performance show heterogeneity among cities. • Mitigating NO x emissions can improve environmental performance significantly. [ABSTRACT FROM AUTHOR]

Current urban and transport planning practices have significant negative health, environmental, social and economic impacts in most cities. New urban development models and policies are needed to reduce these negative impacts. The Superblock model is one such innovative urban model that can significantly reduce these negative impacts through reshaping public spaces into more diverse uses such as increase in green space, infrastructure supporting social contacts and physical activity, and through prioritization of active mobility and public transport, thereby reducing air pollution, noise and urban heat island effects. This paper reviews key aspects of the Superblock model, its implementation and initial evaluations in Barcelona and the potential international uptake of the model in Europe and globally, focusing on environmental, climate, lifestyle, liveability and health aspects. We used a narrative meta-review approach and PubMed and Google scholar databases were searched using specific terms. The implementation of the Super block model in Barcelona is slow, but with initial improvement in, for example, environmental, lifestyle, liveability and health indicators, although not so consistently. When applied on a large scale, the implementation of the Superblock model is not only likely to result in better environmental conditions, health and wellbeing, but can also contribute to the fight against the climate crisis. There is a need for further expansion of the program and further evaluation of its impacts and answers to related concerns, such as environmental equity and gentrification, traffic and related environmental exposure displacement. The implementation of the Superblock model gained a growing international reputation and variations of it are being planned or implemented in cities worldwide. Initial modelling exercises showed that it could be implemented in large parts of many cities. The Superblock model is an innovative urban model that addresses environmental, climate, liveability and health concerns in cities. Adapted versions of the Barcelona Superblock model are being implemented in cities around Europe and further implementation, monitoring and evaluation are encouraged. The Superblock model can be considered an important public health intervention that will reduce mortality and morbidity and generate cost savings for health and other sectors. [ABSTRACT FROM AUTHOR]

Plastic waste trade has grown considerably in the last decades and has led to severe environmental problems in recipient countries. Being the largest recipient, China has permanently banned the imports of plastic waste since 2018. This paper examines the causal effect of plastic waste imports on air pollution by exploiting China's experience of importing plastic waste and the recent import ban. By combining data on plastic waste imports with PM 2.5 data at the city level for the years 2000–2011, we find that plastic waste imports increased PM 2.5 density significantly in recipient cities. To evaluate the impact of the import ban on air quality, we employ daily data on air pollution between 2015 and 2020. Our difference-in-differences results show that affected cities, relative to other cities, experienced considerable improvement in air quality following the ban. Further analysis reveals that increased incineration of non-recycled waste is the main channel. These findings provide insights into the costs of importing plastic waste and the potential environmental gains from banning such imports in other countries. • We assess the environmental impact of plastic waste trade in the context of China. • Chinese cities that imported more plastic waste experienced an increase in PM2.5 density between 2000 and 2011. • The primary factor contributing to worsened air quality was the increased incineration of non-recycled waste. • China's import ban in 2018 effectively improved air quality. • The findings highlight the importance of designing effective policies to regulate international trade in waste and domestic waste management. [ABSTRACT FROM AUTHOR]

With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution. • A new AQI prediction model for Stacking ensemble learning is proposed. • The ensemble model is combined with the error correction model. • Using RSA algorithm to optimize the parameters of the Stacking ensemble models. • Based on the indicator, 5 models are selected for ensemble to obtain better prediction results. [ABSTRACT FROM AUTHOR]

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e. , pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM 2.5 , NO 2 , and SO 2 were 1.016 (95% confidence interval, CI: 1.006–1.026), 1.063 (1.045–1.090), and 1.048 (1.031–1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities. • Industrial PM 2.5 , NO 2 , and SO 2 levels were estimated by a chemical transport model. • Childhood asthma onset rates decreased with decreased industrial pollutants levels. • The strongest association by industrial sector was with PM 2.5 from metal smelters. [ABSTRACT FROM AUTHOR]

Information about a company's environmental, social and governance (ESG) performance has become increasingly important in the decision-making process of financial institutions. The financial implications of environmental challenges (e.g. water stress), negative social impacts (e.g. health impacts in local communities) or poor corporate governance (e.g. breaching legislation) all continue to increase. Accordingly, there is a need for financial institutions to incorporate information on ESG risks, opportunities and impacts in decisions that relate to risk management, investments, credit, strategy, and reporting. ESG information is typically disseminated through ESG ratings, which combine the three constituents into a single rating, or ascribe them separate scores. The compilation of ESG ratings and the identification of appropriate data sources is an inherently complex process; as such, there is no single standard for data collection or reporting. This has led to a divergence in the underlying data sources used by different rating providers, as well as in the determination of factors that are deemed worthy of measurement in the first place. For example, when assessing a company's environmental impact, one rating provider may rely on company-provided data, while another may incorporate independent third-party assessments. Unfortunately, there is currently no clear mechanism for effectively resolving such disagreements to establish a standardised approach to ESG rating assessments. However, geospatial data and analyses offer several key advantages for ESG assessments, including consistency, the potential for enhanced accuracy, and the ability to identify and assess environmental impacts at a detailed physical asset level, in addition to evaluating the broader spatial context. By incorporating geospatial information (obtained through manually processing remotely sensed data, or by using existing products) rating methodologies can be improved, and disparities can be addressed more effectively. This would enable a more comprehensive understanding of the environmental considerations of ESG assessments, promoting a more informed and precise decision-making process. Within this context, a few institutions (e.g. the University of Oxford, the WWF, and a few others) are pioneering thought leadership around spatial finance, including the assessment of ESG issues utilising geospatial intelligence, but there are no consistent frameworks for incorporating geospatial data into ESG ratings and analysis. This paper explores the opportunity for such a geospatial environmental scoring framework, defining a variety of methods in which open data with broad geographic coverage could be incorporated into ESG analysis, generalisable to a range of assets and sectors. The proposed framework is organised into two categories: localised effects, which directly impact the immediate vicinity of an asset, and delocalised effects, which contribute to global climate change and atmospheric pollution. Sub-scores are defined within these categories, which capture both the localised effects on land use, biodiversity, soils, and hydrology, and the global impacts resulting from atmospheric emissions. The approaches for handling geospatial data to generate both these sub-scores and the final E-score are presented, including a test case, and the complete methodology is made available in open repositories. • First geospatial framework for corporates and investors to assess environmental impacts of a study area. • Transparent and accessible framework, making use of open access data and tools. • Practical description of the framework with an industrial test case. [ABSTRACT FROM AUTHOR]