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This paper aims to analyze the legal framework of land degradation in Asia and provide market shared liability as a new theory to solve a problem regarding the difficulty for judges to determine the percentage of compensation for corporations responsible for land degradation. This paper aims also presents a theory to solve the problem of the vacuum of legal responsibility theory, which can make corporations proportionally responsible in terms of causing land degradation.

This was done through legal research methods, mainly with systematical interpretation. The approach used in this paper is conceptual, statute and comparative approach.

By analyzing the related legal norms, it can be understood that in Asian countries, such as Indonesia, Thailand and Malaysia, there are regulations regarding land degradation. However, the regulations in these countries are not specific and tend to focus on nature conservation, which has an impact on handling land degradation. Therefore, it needs special regulation to deal with land degradation. One of the things that need to be regulated about land degradation is a market shared liability.

This research is limited to regulation in the Asia region. By analyzing the regulation, this paper will provide an analysis about the land degradation regulation mechanism in Asia and give an analysis about market shared liability as one of the solution to handling land degradation. Having the same ground rules will create synergies between countries in Asia to handle land degradation.

This paper is the first systematic legal research comparing regulations from three nations in Asia on land degradation and the first paper to provide market shared liability as a solution to handling land degradation.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

The study examines the presence of the pollution haven or pollution halo hypothesis in Brazil, Russia, India, China and South Africa (BRICS) and Next-11 economies. Hence, it empirically tests the direct impact of foreign direct investment (FDI) on the ecological footprint. Further, it explores the moderating role of green innovation on the nexus between FDI and ecological footprint.

The study uses the Driscoll–Kraay (DK) standard error panel regression technique to examine the long-run elasticities amongst the variables for the group of emerging countries, BRICS and Next-11, during the period of 1992 to 2018. Further, statistical robustness is demonstrated using the fully modified ordinary least squares technique.

The empirical finding shows that FDI degrades environmental quality by raising the ecological footprint. Thus, it proves that FDI is a source of pollution haven in BRICS and Next-11 countries. However, green innovation negatively moderates the relationship between FDI and ecological footprint. That means the joint impact of green innovation, and FDI proves the presence of the pollution halo hypothesis. Further, renewable energy consumption is reducing the ecological footprint, but economic growth and industrialisation are worsening the environmental quality.

This study offers policy implications for governments and policymakers to promote environmental sustainability by improving green innovation and allowing FDI that encourages clean and advanced technology.

No prior studies examine the moderating role of green innovation on the relationship between FDI and ecological footprint in the context of emerging countries.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

This study aims to examine the relationship between pollution and life expectancy in oil producing communities, where there is a dearth of empirical evidence on how knowledge and coping strategy, agriculture and foreign capital inflows mediate the relationship between pollution and life expectancy.

The study employed a cross sectional survey design to analyze the roles of knowledge and coping strategy, agriculture and foreign capital inflows in the relationship between pollution and life expectancy in Benekuku and Okpai oil producing communities in the Niger Delta. The study employed the modern structural equation modeling (SEM) estimator.

Estimates show the mediating effect of agriculture on air pollution-longevity (coeff. = 0.398; t-value = 4.425; p < 0.05) and (coeff. = −0.120; t-value = −3.862; p < 0.05) mediating effect of foreign capital. The result revealed that agriculture and foreign capital inflows are significant mediators in pollution-life expectancy relations, affirming the Niger Delta as a pollution haven. However, knowledge and coping strategy with estimate of (coeff. = 0.233; t-value = 6.150; p < 0.05) spurs life expectancy.

The study suggests knowledge of hazard identification and reporting and awareness of coping strategy as the panacea to poor life expectancy rate in these local oil producing communities.

The study departs from existing works by estimating the mediating roles of agriculture and foreign capital inflow in air pollution-Life expectancy by controlling for knowledge and coping strategy using the structural equation model with ethical approval from Health Ethics Research Committee.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0734

The purpose of this study is to examine the relationship between pollutant emissions, financial development and IFRS in developed and developing countries between 1998 and 2022.

Data were obtained from World Development Indicators and World Governance Indicators of the World Bank.

Using FGLS and GMM estimators, the results provide evidence that financial development has a significant positive impact on a variety of pollutant emissions. However, this positive impact is moderated by IFRS for the overall sample and country income groups.

Governments and regulatory organizations should support companies’ investments in clean energy and technologies to slow down environmental degradation. Tax credits and subsidies may be helpful to achieve this goal. Also, governments may encourage companies to cooperate with universities and research institutions to develop environment-friendly production and distribution methods to reduce pollution. Although stakeholders may obtain information about environmental issues in financial statements that are prepared in accordance with IFRS, there is a need for standardization of their contents.

Greenhouse gases are major contributors to climate change and global warming. In addition to private costs borne by producers, the production and consumption of products have social costs arising from pollution that affects air, water, and soil. Pollution adversely affects people's physiological and psychological health, which decreases labor productivity, thereby leading to a decrease in economic growth.

According to the author’s knowledge, this is the first study that examines the impact of IFRS on the relationship between financial development and pollutant emissions.

This systematic review examined the emerging threat of indoor and outdoor pollutants to public health in Latin America and the Caribbean (LAC).

Pollutants and pollution levels are becoming an increasing cause for concern within the LAC region, primarily because of the rapid increase in urbanization and the use of fossil fuels. The rise in indoor and outdoor air pollutants impacts public health, and there are limited regional studies on the impact of these pollutants and how they affect public health. A comprehensive literature search was conducted using Google Scholar, PubMed, Scopus, EBSCOhost, Web of Science and ScienceDirect databases. Significant search terms included “indoor air pollution,” “outdoor air pollution,” “pollution,” “Latin America,” “Central America,” “South America” and “Caribbean was used.” The systematic review utilized the Rayyan systematic software for uploading and sorting study references.

Database searches produced 1,674 results, of which, after using the inclusion–exclusion criteria and assessing for bias, 16 studies were included and used for the systematic review. These studies covered both indoor and outdoor pollution. Various indoor and outdoor air pollutants linked to low birth weight, asthma, cancer and DNA impairment were reported in this review. Even though only some intervention programs are available within the region to mitigate the harmful effects of pollution, these programs need to be robust and appropriately implemented, causing possible threats to public health. Significant gaps in the research were identified, especially in the Caribbean.

Limitations of the study include limited available research done within LAC, with most of the research quantifying pollutants rather than addressing their impacts. Additionally, most studies focus on air pollution but neglect water and land pollution’s effects on public health. For this reason, the 16 studies included limited robustness of the review.

Although available studies quantifying pollution threats in LAC were identified in this review, research on the adverse impacts of pollution, especially concerning public health, is limited. LAC countries should explore making cities more energy-efficient, compact and green while improving the transportation sector by utilizing clean power generation. In order to properly lessen the effects of pollution on public health, more research needs to be done and implemented programs that are working need to be strengthened and expanded.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.