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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.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

This paper aims to present the case for critical realism (CR) as a framework in steering the anticipatory and participatory activities an appropriate analysis of complex problems, aiming toward a transformative change.

Through observation, interview and facilitation for Circular Design Lab and Thai Clean Air Network, this paper unpacks their foresight activities, their key findings and subsequently connects to the Morphogenesis analysis based on an alternative foresight epistemology of CR.

Foresight based on CR philosophy provides a deeper understanding of the complexity and invisibility of air pollution issues in Thailand. Acknowledging the transitive reality beyond this study’s perception, the activity design applies the iceberg models to investigate problem framing and illustrate the stratified reality in three domains: the empirical based on emission reports and legislative regulations; the actual based on patterns of farmers practice and industrial development, activated by causal mechanisms; the real based on structural and mental models, driven by cultural and belief systems in Thailand. At the bottom layer of the iceberg, the real lies the generative mechanisms of pre-existing structural and cultures that constrain Thai citizen from acting on social change.

CR’s emancipatory theory provides an immanent critique towards social improvement by illustrating comprehensive causal explanations of complex problems such as air pollution; while morphogenesis theory elaborates on the unconscious domination of the existing social structures, agencies, and cultures. Thus, the ethical inquiry of CR research is committed to the emancipation of false beliefs and creating conditions for “human prosperity”. However, this non-neutral value commitment is debated in the futures studies field.

The anticipatory activities on air pollution in Thailand bring to light the reality of power and oppression beyond human perception and illustrate the connection to the belief systems and its consequential action or lack thereof in dealing with the issues. The insight to power relationship provides an unconventional way to empower citizens in creating transformative change.

Modern foresight practice has developed under western cultures and societies. Recent efforts are made to investigate the epistemology underlying this field, for the future issues are ever more complex and interrelated across multiple sectors. This requires this study’s consideration of the meaning of knowledge and knowing, influencing the research paradigm. This paper proposes CR as a suitable foresight approach to emancipate this study from the widely accepted epistemologies and examine this study’s presupposition about social reality by a philosophical explanation based on the elements of ontology, causation, structure and persons.

Despite differing strategies towards environmental sustainability in developed and developing nations, the manufacturing sector in these regional domains faces substantial environmental issues. The purpose of this study is to examine the green-lean-six sigma (GLSS) enablers and outcomes for enhancing environmental sustainability of manufacturing firms in both, a developed and developing country context by using an environment-centric natural resource-based view (NRBV).

First, a framework of GLSS enablers and outcomes aligned with the NRBV strategic capabilities is proposed through a systematic literature review. Second, this framework is used to empirically investigate the GLSS enablers and outcomes of manufacturing firms through in-depth interviews with lean six sigma and environmental consultants from New Zealand (NZ) and Pakistan (PK) (developed and developing nations).

Analysis from both regional domains highlights the use of GLSS enablers and outcomes under different NRBV capabilities of pollution prevention, product stewardship and sustainable development. A comparison reveals that NZ firms practice GLSS to comply with environmental regulatory requirements, avoid penalties and maintain their clean-green image. Conversely, Pakistani firms execute GLSS to reduce energy use, satisfy international customers and create a green image.

This paper provides new insights on GLSS for environmental sustainability which can assist industrial experts and academia for future strategies and research.

This is one of the early comparative studies that has used the NRBV to investigate GLSS enablers and outcomes in manufacturing firms for enhancing environmental performance comparing developed and developing nations

This research is aimed to examine how environmental pollution affects consumers' preference for self-improvement products.

Through a series of three experimental studies, this research substantiates our hypotheses by employing various manipulations of environmental pollution and examining different types of self-improvement products.

The research demonstrates that environmental pollution enhances consumers' preference for self-improvement products via the mediation of perceived environmental responsibility. And the effect is negatively moderated by social equity sensitivity.

The recurrent incidence of environmental pollution has elicited significant concern among the general public and academic scholars. An overwhelming majority of research examining the impact of pollution on consumer behavior has concentrated on its influence on environmentally friendly and healthy consumption patterns. Nevertheless, the current research proposes that pollution fosters a preference for products associated with self-improvement, mediated by perceived environmental responsibility, with the effects being moderated by social equity sensitivity.

Currently, the National Aeronautics and Space Administration (NASA) and several outer space industry multibillionaire entrepreneurs – e.g., Elon Musk (SpaceX), Jeff Bezos (Blue Origin), and Richard Branson (Virgin Galactic), to name a few – are actively engaged in outer space research that reports innovative advances, such as outer space mining, outer space tourism, outer space medicine labs, outer space terraforming of Mars and moon, and altering celestial bodies and terrestrial humans to enhance extraterrestrial survivability. All these advances induce serious ethical concerns of human identity and dignity and destiny, human rights and privileges over earth and her resources, and cosmic sustainability. Further, the current understanding of sustainability development is highly anthropocentric (i.e., the earth and cosmos are meant solely for man's use) and limited in scope as a terrestrial, temporal, economic, and pro-human project. Critical thinking invites sustainability development to include trans-terrestrial, trans-temporal, trans-economic, and transhuman developments. While outer space research certainly offers great hopes of newer living spaces and resources for mankind already strapped by depleted terrestrial habitable spaces, we believe that this capital-intensive “elitist” unregulated outer space research industry may benefit a chosen few at the expense of polarizing mankind in terms of one's undeserved financial capacities to afford extraterrestrial spaces and privileges while endangering Nature by deploying massive terrestrial energy resources for outer space rocket launches causing trailing cosmic debris and planetary pollution. We frame this complex problem into terrestrial humanist issues versus extraterrestrial transhumanist issues, each domain triggered by pro-planetary versus pro-cosmic breakthrough technologies, thus creating a fourfold framework that enables us to explore a distributed ethical strategic understanding and ethical resolution of outer space ethical concerns.

As the global emphasis on environmental consciousness intensifies, many corporations claim to be environmentally responsible. However, some merely partake in “greenwashing” – a facade of eco-responsibility. Such deceptive behavior is especially prevalent in Chinese heavy-pollution industries. To counter these deceptive practices, this study aims to use machine learning (ML) techniques to develop predictive models against corporate greenwashing, thus facilitating the sustainable development of corporations.

This study develops effective predictive models for greenwashing by integrating multifaceted data sets, which include corporate external, organizational and managerial characteristics, and using a range of ML algorithms, namely, linear regression, random forest, K-nearest neighbors, support vector machines and artificial neural network.

The proposed predictive models register an improvement of over 20% in prediction accuracy compared to the benchmark value, furnishing stakeholders with a robust tool to challenge corporate greenwashing behaviors. Further analysis of feature importance, industry-specific predictions and real-world validation enhances the model’s interpretability and its practical applications across different domains.

This research introduces an innovative ML-based model designed to predict greenwashing activities within Chinese heavy-pollution sectors. It holds potential for application in other emerging economies, serving as a practical tool for both academics and practitioners.

The findings offer insights for crafting informed, data-driven policies to curb greenwashing and promote corporate responsibility, transparency and sustainable development.

While prior research mainly concentrated on the factors influencing greenwashing behavior, this study takes a proactive approach. It aims to forecast the extent of corporate greenwashing by using a range of multi-dimensional variables, thus providing enhanced value to stakeholders. To the best of the authors’ knowledge, this is the first study introducing ML-based models designed to predict a company’s level of greenwashing.

Theory is an essential prerequisite in the development and maturation of any scholarly discipline. This study offers insight into theory development in reverse logistics (RL) studies, provides a synopsis of the theories employed in RL studies, and presents a comprehensive framework for choosing and applying theories in RL studies.

Using the systematic literature review approach, 265 various RL articles were analysed to discover the trend in using theories in RL studies and classify the individual theories employed. The analysis of the theoretical classification is presented to explain the type and frequency of the usage of theories.

The analysis discovered 52 specific theories from the sample. These theories were categorised under various categories: competitive, inventory, economic, decision, etc. The institutional, stakeholder, transaction cost economies, resource-based view, natural resource-based view, dynamic capability, agency and theory of planned behaviour were some of the key theories discovered. Finally, a comprehensive framework is provided to aid researchers in choosing and utilising theories.

This study gives authors, reviewers and editors perspectives on utilising theories in RL studies. It will give them the impetus to develop theories in RL and limit the borrowing or extension of theories from other disciplines to RL studies.

To the best of the researcher's knowledge, this is the first attempt to comprehensively provide an anatomical perspective into theory usage in RL studies. Besides, this study's proposed framework for selecting and using theories is a novelty in the domain of RL.

Over the last two decades, green supply chain management (GSCM) has enabled businesses to operate in an environmentally friendly manner. The present review examines 234 research articles and proposes a methodical literature review on GSCM, focusing on the aspects of sustainable development.

The work examines conceptual, analytical, empirical and non-empirical research articles, analyzing at all levels of the organization, such as firm, dyad, supply chain and network. The objective of the review is to provide insights into the state and scope of existing research in the domain of GSCM, to identify the prevalence of GSCM and to consolidate the trend of future research. The literature review follows a systematic methodology for analyzing the literature.

The findings can support researchers in identifying research areas with significant impact and streamline research on GSCM in the future. Practitioners can utilize this structured classification to strategize their green initiatives in their firms.

The work contributes to providing literature that explores a detailed review in GSCM. The proposed literature review captures critical aspects in the domain of GSCM and offers future research directions.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.