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The purpose of this paper is to offer an accessible and interdisciplinary research strategy in organisational ethnography, called action ethnography, that acknowledges key concepts from action research and engaged and immersive ethnography. It aims to encourage methodological innovation and an impact turn in ethnographic practice.

A working definition of “action ethnography” is provided first. Then, to illustrate how an action ethnography can be designed by considering impact from the outset, the author draws on a study she is undertaking with a grassroots human rights monitoring group, based in England, and then discusses advantages and limitations to the approach.

The author suggests three main tenets to action ethnography that embrace synergies between action research and ethnography: researcher immersion, intervention leading to change and knowledge contributions that are useful to both practitioners and researchers.

This paper provides researchers who align with aspects of both action research and ethnography with an accessible research strategy to employ, and a better understanding of the interplay between the two approaches when justifying their research designs. It also offers an example of designing an action ethnography in practice.

Whereas “traditional” ethnography has emphasised a contribution to theoretical knowledge, less attention has been on a contribution to practice and to those who ethnographers engage with in the field. Action ethnography challenges researchers to consider the impact of their research from the outset during the research design, rather upon reflection after a study is completed.

The present research attempts to delve into the climate-agriculture-migration nexus to ascertain whether the variation in agriculture output due to climatic factors has a significant influence on the emigration flows from the Asian and African continents.

The present study draws upon a rich panel of data from 27 countries (14 African and 13 Asian) between 1995 and 2020. The first stage least square, OLS and 2SLS techniques have been employed to examine the relationships between climatic factors and international migration, climatic factors and agriculture output, and agriculture output and international migration, respectively.

The results exhibit a positive relationship between temperature and international migration. The influence of temperature on agriculture output is negative but insignificant while precipitation promotes agriculture output. In addition, agriculture output negatively influences international migration, and these findings establish a climate-agriculture-migration relationship.

To counteract the climate-agriculture-migration nexus, it is incumbent upon governments to conduct extensive field trials and data collection exercises to assess the influence of climate in separate agro-ecological zones and devise policies accordingly.

The literature concerning the interrelationship between climatic factors, agriculture and migration is scarce and what is available pertains to different contexts. Moreover, no studies are based on Asia and Africa; the continents have a high dependence on agriculture and outmigration rates, and the present research covers this important gap in the literature.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

The burgeoning literature on climate-related finance suggests that climate change influences financial markets. Building on this foundation, the present study aims to investigate the time-varying predictive power of news related to physical and transition climate risks for financial instability across the financial systems of the US, EU, and the ASEAN+3 countries (comprising the Association of Southeast Asian Nations plus China, Japan, and South Korea), from January 2003 to August 2022, on a monthly basis.

In this study, we use the VAR-based Granger-causality test in the presence of instabilities introduced by Rossi and Wang (2019), and combine it with the innovative rolling and recursive bootstrap time-varying Granger-causality approach of Shi et al. (2020). These methods were chosen for their capacity to effectively capture the dynamic influence of climate risk-related news on financial instability over time, offering an advantage over traditional constant parameter regressions and standard Granger causality methods. Additionally, we make use of the Media Climate Change Concerns indices recently developed by Ardia et al. (2022), coupled with regional financial stress indices.

Our findings indicate that the predictive power of climate change news for financial instability is substantial but varies over time. This influence becomes especially pronounced during periods that align with specific local and global events. In the US and EU, the predictive power is influenced by a combination of global and local macroeconomic, political, health, and climate-related factors. In contrast, ASEAN+3 financial systems show a stronger response to regional and local events, with comparatively less sensitivity to global events.

The results of this study are noteworthy for investors, highlighting increased market instability during periods with prevalent climate change news. Investors can adjust their strategies to mitigate risks and respond to macro-events that trigger climate news-related market instability, while considering regional sensitivities. Similarly, these findings are significant for policymakers, emphasizing the need to consider the influence of climate news on financial markets when designing regulatory frameworks. This could involve enacting measures to stabilize the financial system during periods of significant climate news. Policymakers might consider developing macroprudential regulations to bolster financial institutions’ resilience against climate change news effects.

This study pioneers the exploration of how climate change news affects financial system stability at the macro level. It extends beyond traditional research, typically focusing on direct effects of climate change in banking and asset markets, by examining broader implications of climate risk-related news for financial system instability. Furthermore, this study enhances our understanding of the predictors of global financial stability by examining the financial systems of the US, the EU, and ASEAN+3. It specifically investigates the impact of climate change news, a topic not extensively explored in previous research focusing mainly on macro-factors such as financial liberalization and business cycles.

Considering the world population, an additional 415.1 billion m² of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.

A mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.

This study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.

This study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces.

This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis.

Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively.

To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE.

To the best of the authors’ knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE.

The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages.

This study aims to examine the historical development, present state and potential future directions of the integration between building information modeling (BIM) and life cycle assessment (LCA) in the field of construction. Additionally, this paper identifies current problems while offering insight into worldwide BIM research trends.

This study uses text mining on unstructured abstracts, a novel approach not previously documented in BIM research. By conducting a comprehensive systematic assessment of academic literature, this work uses advanced bibliometric approaches to examine the developmental trajectory of the integration of BIM and LCA. The research incorporates co-citation and keyword co-occurrence mapping, providing a complex visual depiction of the interconnectedness of information across different periods.

The results of this analysis reveal the historical development of the integration of BIM and LCA, including its roots and the initial research that established the foundation for further investigations. The aforementioned seminal works signify the inception of the discipline, serving as a source of inspiration for current scholarly investigations. Currently, there is a complex network of interdisciplinary cooperation that can be observed, combining knowledge and perspectives from the fields of design, engineering, construction and sustainability.

This research contributes novelty to the scholarly discourse by offering a holistic and up-to-date panorama of the dynamic BIM and LCA research landscape. It identifies emerging trends, influential contributors and uncharted territories, thus providing a foundation for scholars to contribute meaningfully to the advancement of knowledge in sustainable construction practices.

This study aims to understand the impact of extreme weather events on fast-moving consumer goods (FMCG) consumption and to examine the role of anticipated product scarcity and FMCG types on such behavior.

This paper conducted five studies, combining archival data analysis with behavioral experiments. The archival data included sales data from a supermarket chain and weather data from the National Weather Service. The experiments were designed to test the effect of extreme weather cues on consumption, the psychological mechanism behind this effect and moderators.

This research found that consumers’ anticipation of extreme weather events significantly increases their consumption of FMCGs. This research further discovered that these behaviors are driven by anticipated product scarcity and moderated by consumers’ altruisms and FMCG types.

Limitations of the research include the reliance on reported sales data and self-reported measures, which could introduce biases. The authors also primarily focused on extreme weather events, leaving other types of disasters unexplored. Furthermore, cultural differences in disaster response might influence results, yet the studies do not fully address these nuances. Despite these limitations, the findings provide critical insights for FMCG retailers and policymakers, suggesting strategies for managing demand surges during disasters. Moreover, understanding consumer behavior under impending disasters could inform intervention strategies, potentially mitigating panic buying and helping ensure equitable resource distribution. Last, these findings encourage further exploration of environmental influences on consumer behavior.

The findings have practical implications for products, brand managers and retailers in managing stock levels and product distribution during disasters. Furthermore, understanding the psychological mechanisms of these behaviors could inform policymakers’ designs of public interventions for equitable resource allocation during extreme weather events.

The research provides significant social implications by highlighting how extreme weather events impact FMCG consumption. This understanding can guide public policymakers in creating efficient disaster management plans. Specifically, anticipating surges in FMCG purchases can inform policies for maintaining price stability and preventing resource shortages, mitigating societal stress during crises. Moreover, these findings encourage public education around responsible purchasing during disasters, potentially reducing panic buying. By collaborating with FMCG manufacturers and retailers, governments can ensure a steady supply of essentials during extreme weather events. Thus, the research can play a crucial role in enhancing societal resilience in the face of impending disasters.

To the best of the authors’ knowledge, this is the first study to integrate the impact of extreme weather events on consumption behavior with the psychological theory of anticipated product scarcity. The unique focus on FMCGs offers a novel perspective on consumer behavior literature.

Extreme weather events are on the rise around the globe. Nevertheless, it is unclear how these extreme weather events have impacted the supply chain sustainability (SCS) framework. To this end, this paper aims to identify and analyze the aspects and criteria to enable manufacturing firms to navigate shifts toward SCS under extreme weather events.

The Best-Worst Method is deployed and extended with the entropy concept to obtain the degree of significance of the identified framework of aspects and criteria for SCS in the context of extreme weather events through the lens of managers in the manufacturing firms of a developing country-Nigeria.

The results show that extreme weather preparedness and economic aspects take center stage and are most critical for overcoming the risk of unsustainable patterns within manufacturing supply chains under extreme weather events in developing country.

This study advances the body of knowledge by identifying how extreme weather events have become a significant moderator of the SCS framework in manufacturing firms. This research will assist decision-makers in the manufacturing sector to position viable niche regimes to achieve SCS in the context of extreme weather events for expected performance gains.