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The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

As the intensity of natural disasters increases, there is a need to develop policies and procedures to assist various agencies with moving aid to affected areas. One of the biggest limitations to this process is damage to transportation networks, in particular waterways. To keep waterways safe, aids to navigation (ATONs) are placed in various areas to guide mariners and ships to their final destination. If the ATONs are damaged, then the waterways are left unsafe, making it difficult to move supplies and recover from a disaster. The aim of this paper is to explore the effectiveness of pre‐positioning strategies for port recovery in response to a natural disaster.

A stochastic facility location model is presented to determine where teams and commodities should be pre‐positioned in order to maximize the number of ATONs repaired, given a constraint on response time. The first stage decisions focus on determining the location of resources. The second stage decisions consist of the distribution of supplies and teams to affected areas.

Results show the benefit of pre‐positioning and the value of coordination toward the responsiveness of restoring waterways. Furthermore, the relationship between resources, repair time, and response is characterized.

There has been extensive work addressing pre‐positioning as it relates to responding to the needs of populations affected by disasters. However, little has been done to explore pre‐positioning in the context of business recovery from severe weather events.

A number of severe weather events have influenced a shift in UK policy concerning how climate-induced hazards are managed. Whist this shift has encouraged improvements in emergency management and preparedness, the risk of climate change is increasingly becoming securitised within policy discourses, and enmeshed with broader agendas traditionally associated with human-induced threats. Climate change is seen as a security risk because it can impede development of a nation. The purpose of this paper is to explore the evolution of the securitisation of climate change, and interrogates how such framings influence a range of conceptual and policy focused approaches towards both security and climate change.

Drawing upon the UK context, the paper uses a novel methodological approach combining critical discourse analysis and focus groups with security experts and policymakers.

The resulting policy landscape appears inexorably skewed towards short-term decision cycles that do little to mitigate longer-term threats to the nation’s assets. Whilst a prominent political action on a global level is required in order to mitigate the root causes (i.e. GHG emissions), national level efforts focus on adaptation (preparedness to the impacts of climate-induced hazards), and are forming part of the security agenda.

These issues are not restricted to the UK: understanding the role of security and its relationship to climate change becomes more pressing and urgent, as it informs the consequences of securitising climate change risks for development-disaster risk system.

Agriculture is a sector highly dependent on climate, and thus it will experience multiple impacts from climate change. In contrast, agriculture is also one of the main contributors of climate change, emitting greenhouse gases, mainly related to land use, fertiliser application and livestock production. Higher temperature and atmospheric CO₂ concentration, changes in precipitation patterns and more frequent extreme weather events are expected to have a negative impact on crop productivity, water and soil resources. Coordinated mitigation and adaptation practices have to be a worldwide priority in order to maintain productivity levels and food production.

The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.

A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.

The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.

This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.

In recent years, the application of credit scoring in urban microfinance institutions (MFIs) became popular, while rural MFIs, which mainly lend to agricultural clients, are hesitating to adopt credit scoring. The purpose of this paper is to explore whether microfinance credit scoring models are suitable for agricultural clients, and if such models can be improved for agricultural clients by accounting for precipitation.

This study merges two data sets: 24,219 loan and client observations provided by the AccèsBanque Madagascar and daily precipitation data made available by CelsiusPro. An in- and out-of-sample splitting separates model building from model testing. Logistic regression is employed for the scoring models.

The credit scoring models perform equally well for agricultural and non-agricultural clients. Hence, credit scoring can be applied to the agricultural sector in microfinance. However, the prediction accuracy does not increase with the inclusion of precipitation in the agricultural model. Therefore, simple correlation analysis between weather events and loan repayment is insufficient for forecasting future repayment behavior.

The results should be verified in different countries and climate contexts to enhance the robustness.

By applying scoring models to agricultural clients as well, all clients can benefit from an improved risk assessment (e.g. faster decision making).

To the best of the authors’ knowledge, this is the first study investigating the potential of microfinance credit scoring for agricultural clients in general and for Madagascar in particular. Furthermore, this is the first study that incorporates a weather variable into a scoring model.

According to the most recent report from the Intergovernmental Panel on Climate Change (Intergovernmental Panel on Climate Change [IPCC], 2007), 11 out of the last 12 years have been the hottest on record since 1850. It is also estimated that the average global surface temperature from 1850–1899 to 2001–2005 has increased by 0.76°C. Global sea level increased at an average rate of 1.8mm per year over the period 1961–2003 and, over the 20th century, sea levels rose by 0.17m. Since the middle of 20th century, human activities have contributed to global warming, a phenomenon that is expected to continue at an increasingly faster rate in the 21st century if there is no effort to address it.

Although businesses face various types of risks because of climate change, the level of concern among managers seem to lag behind the institutional pressure to deal with the climate change issue. This paper aims to bridge this gap in perceptions by presenting a framework to assist business leaders in translating the climate change issue into a format that managers can appreciate.

Drawing from the supply chain literature, this paper presents a model that shows how climate change-related policy and resource risks affect a firm’s supply, operations and demand domains and the risk management approaches appropriate for each type of risk. Excerpts from 10-K annual reports filed by US automotive and food retailers are used to show how the model works.

Although majority of companies examined do not report climate change-related risks, the evidence from those that do affirm the framework’s ability to translate these risks into manager-friendly supply chain terminology.

Managers can participate in sustainability actions by focusing on the risks and effects of climate change. Business leaders, researchers and policymakers can adopt supply chain risk management terminology to connect with otherwise indifferent managers.

Tourism is a rapidly growing economic sector that contributes significantly to national and local economies globally. Tourism growth in any destination largely depends on the weather and climate, considered prime factors affecting global tourist flows. Global South countries are said to be particularly vulnerable to climate change, owing to their limited adaptation capacity, placing them at greater risk of the impacts of climate change. This adaptive capacity is mainly attributed to a lack of capital intensity and technological flexibility, which is less effective than in developed countries. In addition to a lack of capacity to adjust to the direct hazards of climate change, developing countries are at additional risk due to their heavy reliance on economic sectors and resources sensitive to climate change, such as tourism. An enhanced understanding of climate change's impacts and adaptations to climate change is critical for determining strategic actions for tourism planning and development. This chapter provides a theoretical review of tourism and adaptation strategies, challenges and the dimensions of vulnerability in a tourism context, as well as the implications of climate change on tourism planning in the future. This chapter discusses the impact of climate change on tourism in the Global South, examining case studies and policy frameworks for adaptation and mitigation. It further explores opportunities for sustainable tourism development and partnerships for climate-resilient tourism. Overall, the chapter focuses on the challenges and opportunities for sustainable tourism in the Global South in the face of climate change.