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The purpose of this paper is to investigate whether air pollution has significant impact on corporate cash holdings and financial leverage.

The data of 199 firms listed on Istanbul Stock Exchange during the period 2009–2020 is analyzed by using pooled ordinary least squares and two-step system generalized method of moments models.

The results indicate that firms in regions with high air pollution tend to increase cash level. In addition, the positive effect of air pollution on cash level is stronger and more significant for environmentally sensitive firms and firms with low operational and distress risk. The results also show insignificant effect of air pollution on financial leverage.

Firms in regions with high air pollution should conduct proactive environmental protection procedures and enhance their eco-efficiency instead of holding excess cash that could negatively affect financial performance. In this context, policymakers should provide financial facilities to firms located in regions with high air pollution and that have low ability to finance environmental investments. On the other hand, the environmental laws and regulations introduced by regulatory authorities can enhance the economic development and firm performance by decreasing the adverse influences of air pollution on corporate financial policies.

To the best of the author’s knowledge, this research is one of few that examines the impact of air pollution on corporate cash holdings and financial leverage in emerging markets.

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.

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 objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

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 paper aims to interrogate the accountabilities of the foreign companies which have directly invested in the Iraqi oil and gas industry.

Using both qualitative and quantitative methodologies, the authors first map the stakeholder accountabilities (qualitative) of foreign oil and gas companies and second, the authors seek to demonstrate quantitatively – through structural break tests and publicly available sustainability reports – whether these companies have accounted for their environmental and social impacts both to Iraqi people and to the global community.

The authors find that the Western democratic values embedded in stakeholder theory, in terms of sustainability, do not hold the same meaning in cultural contexts where conceptions and application of Western democratic values are deeply problematic. This paper identifies a crucial problem in the global oil supply chain and problematises the application of traditional theoretical approaches in the context of the Iraqi oil and gas industry.

Implications of this study include the refocus of attention onto the local and global environmental impacts of the Iraqi oil and gas industry by foreign direct investments. Such a refocus highlights the reasons and ways that decision makers should accommodate these less salient stakeholders.

The primary contribution is the critique of the lack of environmental accountability of foreign direct investment companies in the Iraqi oil and gas industry. The authors also make theoretical and methodological contributions via the problematisation of the cultural bias inherent in traditional stakeholder theories, and by introducing a quantitative method to evaluate the accountabilities of companies.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

It is claimed that port supply chain integration (PSCI), thanks to its attributes, holds a potential to trigger a port to strategically sustain performance- and competitiveness-measures through strengthening and/or reinforcing an array of port sustainability aspects. This paper aims to empirically investigate the existent influence of PSCI on economic and environmental pillars of port sustainability.

Based on a resource-based view, this paper hypothesises that PSCI has a certain impact on port “economic” sustainability (PECS) and port “environmental” sustainability (PENS). Measures of PSCI, PECS and PENS are refined and validated using data collected from the maritime logistics industry in Egypt, and structural equation modelling is employed to test the hypothetical relationships.

The results indicate that a port having adopted an integrative strategy into supply chains could enhance its cost and operational efficiency, financial and investment situation, while offering high-quality services to its customers. Similarly, PSCI is emphatically correlated with water and air pollution management, energy efficiency and green port management practices.

Having identified the acute potential of PSCI for sustainable development in maritime logistics and supply chains, this line of research allows port operators and/or authorities to better understand strategic options with which they are able to improve their sustainability practices. This paper is, however, limited only to two dimensions of sustainability by not exploring the “social” aspect of port sustainability due to data-related issues.

This line of research could be regarded as an extended application from other industrial sectors to the port industry in a way to empirically examine the inclusive relationship of PSCI with economic and environmental parameters. The findings from this research make a due contribution to the field of port sustainability in general and Egyptian ports in particular.