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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.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

The recent meeting of G-20 world leaders, held in New Delhi, in 2023, highlighted that the physical effect of climate change has considerable macro-economic costs at the national and global levels and they have also pledged to accelerate the clean, sustainable and inclusive energy transition along a variety of pathways. Climate change could pose various emerging risks to the firm’s operational and financial activities, specifically for those which are belonging to the energy sector. Thus, this study aims to investigate the impact of climate risks on the financial performance of select energy companies from G-20 countries.

The study considered 48 energy companies from G-20 countries as the sample for the period of 2017 to 2021. To measure the climate change-related physical risks, the study has considered the ND-GAIN climate vulnerability score and the firm’s financial performance has been measured by return on assets, return on equity, return on capital used and price-to-book ratio. To examine the impact of climate risks on the financial performance of the sample companies, the authors have used pooled ordinary least squares (OLS) and fixed/random effect regression analysis and required data diagnosis tests are also performed.

The empirical results suggested that climate risks negatively impacted the financial performance of the sample companies. The market performances of the firms are also being impacted by the physical climate change. The results of panel data regression analysis also confirmed the robustness of the empirical results derived from the pooled OLS analysis suggesting that firms that operated in a less climate-risky country, financially performed better than the firms that operated in a more climate-risky country.

The paper has significant practical implications like it could be helpful for the policymakers, investors, suppliers, researchers and other stakeholders in developing deeper insights about the impact of climate risks on the energy sectors from an international perspective. This study may also help the policymakers in developing policies for the management of climate risk for the energy sector.

This study adds insights to the existing literature in the area of climate risks and firm’s financial performance. Moreover, this may be the first study that attempts to evaluate the impact of climate risks on the financial performance of select energy companies from the G-20’s perspective.

The goal of this study is to predict the farmers’ concerns about agricultural index-insurance (AII) for weather risk mitigation of horticultural crops in hilly regions. The key impetus of analysis is to prioritize the AII requirements based on the farmers’ perspectives using the requirements prioritization approach.

The integrated approach has been applied in this paper. Initially, the MoSCoW prioritization technique has been employed to prioritize the AII attributes utilizing a four-dimensional agriculture insurance scale. Later, the rank sum weighting method was deployed to assign the ultimate rank to AII attributes based on the farmers’ responses.

Findings specified that out of 15 AII attributes, majority of 11 attributes were placed in “must have” and “should have” categories that related to claim, design, premium and grievance management dimensions. However, three AII attributes are placed in the “could have” category. Additionally, findings of rank-sum weighting method-based ranking can help insurers in redesigning farmers-oriented AII services for risk mitigation of horticulture crops by incorporating these ranks as per their priority level.

The prioritized AII attributes are helpful for insurers and managers in order to solve the problems associated with design, premium, claim and grievance management of AII.

Findings deliver significant insights to insurers to incorporate the prioritized AII attributes ranked by farmers.

This is the initial known analysis that integrated the MoSCoW and rank sum weighting method to prioritize the AII requirements prioritization among Indian farmers.

Although climate change-related risks affect all stakeholders along the supply chain, the potential impact on small and micro-sized suppliers is incredibly excessive. The corresponding toll of these climate risk threats on the mental health and well-being of owners of small and micro-sized suppliers can adversely affect their participation in sustainability efforts, ultimately impacting the firm's performance. This often-overlooked dynamic forms the core of our research. We probe into two pivotal aspects: how industry dynamism and climate risk affect the mental health and well-being of owners of small and micro-sized suppliers and how, in turn, dictate involvement and, consequently, supply chain sustainability performance. This is further nuanced by the moderating role of the abusive behavior of buyers.

Our study is built on resource dependency theory and the supporting empirical evidence is fortified by a mixed-methods sequential explanatory design. This study comprises three phases. In the first phase, our experiment examines the effect of industry dynamism and climate risk exposure on sustainable supply chain management performance. Hypotheses H1a and H1b are tested in the first phase. The second phase involves using a survey and structural equation modeling to test the comprehensiveness of the model. Here, the relationship between industry dynamism, climate risk exposure, mental health and well-being of owners of small and micro-sized supplier firms, supplier involvement and sustainable supply chain management (H2–H7) is tested in the second phase. In the third phase, we adopt a qualitative approach to verify and provide descriptive explanations of phase two findings.

Our findings underscore the significance of small and micro-sized suppliers in sustainability, offering invaluable insights for both theoretical understanding and practical implementation. Our study highlights that buyers must allocate sufficient resources to support small and micro-sized supplier firms and collaborate closely to address climate change and its impacts.

The key takeaway from this study is that buyer firms should consider SDG 3, which focuses on the good health and well-being of their employees and the mental health and well-being of owners of small and micro-sized suppliers in their upstream supply chain. This approach enhances sustainability performance in supply chains.

This is one of the first studies that shows that industry dynamism and climate risk exposure can negatively impact small and micro-sized suppliers in the presence of a contextual element, i.e. abusive behavior of buyers, and ultimately, it negatively impacts sustainable supply chain performance dimensions.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

Farmland is an important property sector that has attracted the attention of institutional investors globally in recent years. This paper examines the risk-adjusted performance and portfolio diversification benefits of Australian farmland in a portfolio over the eight-year period of Q2:2015–Q2:2023, highlighting the unique property management dimensions to this property sector for its effective role in an institutional investor's property portfolio.

Using the quarterly ANREV Australian farmland index over Q2:2015–Q2:2023, the risk-adjusted performance and portfolio diversification potential of Australian farmland is assessed. Constrained mixed-asset portfolios are used to assess the potential added-value role of Australian farmland in a mixed-asset portfolio. Analyses are also done for the farmland sub-sectors of annual farmland and permanent farmland.

Australian farmland is seen to show strong risk-adjusted performance but at a much higher risk level than that seen for direct property. Diversification benefits from Australian farmland are also evident, with an important role by Australian farmland seen in the mixed-asset portfolio. Specific farmland property management strategies are identified for the effective inclusion of farmland in an institutional investor's property portfolio, including the potential benefits towards net zero carbon strategies.

This is the first research that provides an independent empirical examination of the strategic importance of Australian farmland property for institutional investors using the institutional investment-grade ANREV Australian farmland database, from both an investment and environmental perspective. The unique property management implications for Australian farmland property are also highlighted, including the potential role of Australian farmland in net zero carbon strategies by institutional investors.

A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.