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The purpose of the present paper is to review studies on weather index-insurance as a tool to manage the climate change impact risk on farmers and to explore the study gaps in the currently existing literature by using a systematic literature review.

This study analyzed and reviewed the 374 articles on weather index insurance (WII) based on a systematic literature search on Web of Science and Scopus databases by using the systematic literature review method.

WII studies shifted their focus on growing and emerging areas of climate change impact risk. The finding shows that the impact of climate change risk significantly influenced the viability of WII in terms of pricing and design of WII. Therefore, the cost of WII premium increases due to the uncertainty of climate change impact that enhances the probability of losses related to insured weather risks. However, WII has emerged as a risk management tool of climate insurance for vulnerable agrarian communities. The efficacy of WII has been significantly influenced by repetitive environmental disasters and climate change phenomena.

This study will be valuable for scholars to recognize the missing and emerging themes in WII.

This study will help the policy planners to understand the influence of climate change impact on WII viability.

This study is the original work of the author. An attempt has been made in the present study to systematically examine the viability of WII for insuring the climate change risk.

The paper aims to assess the impact of weather-induced shocks on household food consumption in the rural Vietnamese Mekong Delta (VMD) through the case of Long An province and evaluate the effectiveness of widely used coping strategies in mitigating weather-related shock impacts.

The system generalized method of moments (GMM) estimation method is applied to explore information on shock incidence, recovery, and time occurrences. The paper uses a sample of 272 repeated farming households from 5-wave survey data from 2008 to 2016, resulting in 1,360 observations.

The paper confirms the robust negative effect of a natural shock on food consumption. Additionally, using savings proves to be the most potent measure to smooth food consumption. Other favorable coping strategies are “getting assistance from relatives, friends” or “getting assistance from the Government, and non-government organizations (NGOs).” The mitigating effects are also traced in the current analysis.

Using caution when generalizing the results from Long An to the whole VMD is reasonable. The rather limited observations of coping strategies do not allow the authors to analyze any specific strategy.

The proposed approach employs the GMM technique and controls for endogenous coping strategies and thus provides accurate estimates of the effects of weather-related shocks and the mitigation effectiveness in the rural VMD.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

The increasing frequency and intensity of the extreme weather events could cause devastating consequences in tourism. Climate change–related extreme weather events and their relation to tourism is an emerging field for education and research. The purpose of this study is to categorize the impact of climate change on tourist destinations with regard to extreme weather-related risks in outdoor recreation and tourism. Managerial implications for policymakers and stakeholders are discussed.

To outline the risks from climate change associated with tourism, this study uses the Prisma analysis for identification, screening, checking for eligibility and finding relevant literature for further categorization.

Based on a thoroughly examination of relevant literature, risks and threats posed by climate change could be categorized into following four areas: reduced experiential value in outdoor winter recreation; reduced value in beach scenery and comfort; land degradation and reduced biodiversity; and reduced value in personal safety and comfort in tourism. It also focuses on the significance of using big data applications in catastrophic disaster management and risk reduction. Recommendations with technology and data analytics to continuously improve the disaster management process in tourism education are provided based on findings of this study.

Primary contributions of this study include the following: providing a summarized overview of the risks associated with climate change in terms of tourist experiential value for educational implications; and revealing the role of data analytics in disaster management in the context of tourism and climate change for tourism education.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

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.

The survivorship of firms under extreme weather poses an essential question about the local economy's health. Over 90% of agricultural banks are categorized as community banks, which are important financial institutions promoting local growth. While previous studies suggest that climate change and weather shocks adversely impact community banks' resiliency, studies on whether these institutions engage in risk-reducing management strategies have been limited. In this study, the authors examine strategic choices of local community banks when facing flood events which include (1) safety net increase, (2) portfolio diversification, and (3) branch opening. These strategic choices are the coping mechanisms banks can take to survive while affecting the local competitive lending market.

The authors use panel-fixed effect regressions based on the storm data from National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) and the call reports from the Federal Deposit Insurance Corporation (FDIC). The authors focus on community banks' account variable characteristics and the number of offices to examine whether community banks take an active role in managing flood risk.

Results suggest that community banks do employ the selected strategic choices to a certain degree, as it is found that there is an increase in the core capital that absorbs shocks and portfolio diversification. However, the magnitudes of these activities are rather small and not large enough to fully mitigate the climate risk. Also, the authors do not find any evidence of branch expansion associated with local floods.

This study contributes to the literature by examining different strategic choices of community banks in the face of natural uncertainty. Even though concerns of climate risk have been raised in the regulatory setting, a lack of guidance or assessment tools could contribute to the passive action of these community banks, even though climate risks can have a significant economic impact. Thus, the evidence documented from this study calls for further guidelines and the importance of highlighting climate risks on community banks so that they can actively engage in risk-reducing strategies.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.