Climate Risk Management最新文献

Climate-related risks pose challenges to communities globally as changing climatic conditions alter the patterns of natural hazards which threaten human lives and infrastructure. In Longyearbyen, Norway, in the High Arctic Svalbard archipelago, climatic changes presently occur at rates well in excess of global means, with corresponding changes to climate-related risks requiring new and improved risk governance strategies. Here, we present the results from a literature review investigating how recent advances in climate-related risk governance can help inform risk governance strategy development in Longyearbyen. The literature identified in our work indicates recent research into the governance of climate-related risks has focused to a large extent on flooding or landslides. Successful risk governance in the reviewed literature often included data collection of both environmental and social information and emphasized local, context-specific knowledge via bi-directional risk communication throughout the risk governance process. We identified knowledge gaps in the literature review. First, there is a missing societal safety perspective on climate changes and natural hazards: much of the identified literature views the climatic changes and natural hazards either through a physical process-based perspective rooted in the natural sciences, or focuses on physical mitigation measures, without considering the interaction of nature, technology, and society. Second, there is a lack of research on data collection and analysis strategies that combine the acquisition of local knowledge via a discourse-based approach with data and knowledge generated from sensors or physical models via a technical approach. Third, more research is required on uncertainty assessment and handling in the risk governance process. Fourth, there is missing consideration of short-term disaster handling approaches – especially in relation to relatively more frequent consideration of long-term climate adaptation strategies. Finally, as none of the reviewed works specifically addressed risk governance in an Arctic setting, we discuss how the results from this literature review and the proposed risk governance framework can help transfer knowledge to Longyearbyen’s context. Our results help clarify current knowledge related to the governance of climate-related risks and provide a foundation for future work in Arctic locations.

While migration is often conceptualized as an adaptive response to climate hazards, migration can also present severe risks to people on the move. In this paper, we attempt to operationalize the Representative Key Risks (RKR) framework of the Sixth Assessment Report of Working Group II of the Intergovernmental Panel on Climate Change (IPCC) for human mobility. First, we provide a framework for understanding how mobility risks emerge by engaging with the concept of habitability. We argue that uninhabitability occurs where the physical environment loses suitability and where there is a loss of agency in local populations. The severity of the risk from the loss of habitability is then represented by the high potential for human suffering. When climate hazards affect physical suitability and agency, the forms of migration that occur undermine human wellbeing and the right to self-determination: forced displacement, community relocation/resettlement, and involuntary immobility. Second, we show how such forms of mobility are more or less likely along different Shared Socioeconomic Pathways (SSPs). This paper asserts a central concern around human suffering to recentre scenario discourse on where, and how, adaptation, changes to development patterns, and government policies can reduce this suffering. Proactive governance at local, national, and international levels that attends to people’s adaptation and mobility needs can avert the more frequent emergence of severe risks related to mobility in a changing climate.

As socio-natural phenomena, wildfires are exacerbated by climate change and socioeconomic dynamics. However, the role of socioeconomic uncertainty in shaping future wildfire risk and management remains largely neglected. Building on the notion that risk emerges at the intersection of hazard, exposure and vulnerability, we conduct an integrative literature review to identify the most significant socioeconomic drivers of wildfire risk in the European geographical and institutional context and bring this together with the Shared Socioeconomic Pathways (SSP) perspectives on plausible socioeconomic dynamics. To our knowledge, this is the first study to bridge the gap between wildfire research and socioeconomic scenarios to establish a conceptual understanding of future wildfire risk. The resulting wildfire risk scenario space has two main applications: (i) it acts as a qualitative navigator for factoring socioeconomic uncertainty in model-based wildfire risk assessments, and (ii) it sets the boundary conditions for evaluating the feasibility of management strategies. Sustainable land use practices and profitable agricultural value chains can reduce future wildfire risk (e.g. SSP1), whereas land degradation (e.g. SSP4), and socioeconomic disparities (e.g. SSP3) may increase it. As a result, challenges to future wildfire risk management differ significantly across scenarios, leading to paradoxical situations. In scenarios where vulnerability reduction has significant potential to lower risk, socioeconomic challenges reduce the feasibility of implementing the necessary measures to achieve risk reduction. Similar dilemmas may arise in the context of hazard and exposure. By considering multiple plausible futures, this paper emphasizes the importance of accounting for socioeconomic dynamics in shaping wildfire risk and keeping the design of risk management strategies open and flexible in the face of changing circumstances.

Climate change has direct and indirect consequences for the military and defence sector. Direct impacts are for example the damaging of military infrastructure due to more extreme climatic conditions or the impact on operational capabilities when on missions. Indirect impacts include the increase of global instability and insecurity, or migration due to changing climatic conditions. Climate change risks are often seen as a threat multiplier. Whilst the impacts of climate change on the military and defence sector is gaining attention in both political and scientific realms, the way this sector adapts to these risks is, however, still pretty much unknown.

This research aims to assess how the European Union member states’ military and defence sector are adapting to the impacts of climate change. We map and analyse the current policy actions by analysing the defence and climate policy documents (n = 63) and conducting interviews (n = 8) with civil servants of defence ministries across the EU. We find that almost none of the countries have a concrete climate change adaptation plan for their military. Whilst several frontrunner countries do mention climate change in their defence policy documents, they lack concrete policy goals and instruments. Moreover, concrete adaptation measures are not discussed by most of the countries. France is a notable exemption as it offers more detailed policies, but they too are at the groundwork stage. Hardly any reference to the military was found in the climate policy documents of the countries. The results show that the military and defence sector of the EU member states are not well prepared to the impacts of climate change and that concerted action is needed to close the adaptation gap adequately and effectively.

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

Research is increasingly approaching migration as an adaptation to climate risk. Yet our understanding of the migration-adaptation nexus remains limited, as most studies conceptualize migration as either adaptive or maladaptive and focus on specific aspects of vulnerability. To advance a comprehensive understanding of migration’s successful and maladaptive effects, this study employs a two-dimensional conceptualization of migration outcomes, encompassing a range of vulnerability variables at the migrant and household levels and migrants’ well-being. This framework is applied to the case of drought-influenced migration from agro-pastoralist northern Kenya to the City of Nairobi. Based on semi-structured interviews with 40 long-term migrants, we identify quantitative and qualitative migration-induced changes in the examined variables.

The results highlight the complexity of migration outcomes. Effects on the broad range of variables comprising vulnerability’s exposure, sensitivity and adaptive capacity components are mixed. Migrants’ ability to provide their families’ basic needs has improved, although only half of the households could allocate remittances to reconstruct their drought-stricken livelihood sources in northern Kenya. Moreover, the profound change in social-environmental settings induced by migration exposed migrants to unfamiliar risks, such as urban crime, but also to new sources of adaptive capacity, such as knowledge enabling the development of climate-insensitive livelihoods. However, migration’s partial success in reducing vulnerability came at the expense of migrants’ well-being, which diminished drastically. These findings stress the need for fundamental changes in the migration-as-adaptation literature, including a more thorough engagement with the temporalities and scope of migration’s effects on adaptation, greater attention to the tradeoffs that are integral to migration as adaptation, and a shift to analytical frameworks that consider maladaptive effects alongside successful ones. We argue that these changes are essential to develop interventions that maximize migration’s adaptive potential while minimizing its maladaptive effects.

Unlike existing studies that examined the effects of weather variability by relying on the current weather conditions disregarding the long-term influence of historical weather patterns, we jointly estimate the effects of current and past weather variability on rural households’ nutritional status. Using three waves of nationally representative panel data from rural Ethiopia, we show that the nutritional status of farming households, measured by daily intakes of micro-and macronutrients, is more sensitive to past weather variability than the current weather condition. We also find that adverse weather history can trigger responses that are linked to the deterioration of nutritional status.

Based on two surveys among Dutch homeowners, we find evidence for a disconnect between flood risk awareness and intentions to mitigate climate change. In terms of awareness, we find that owners of at-risk properties are 9.8 percentage points more likely to see floods as the main threat to their home. However, at-risk owners are also 4.6 percentage points less likely to consider improving their property’s energy efficiency. Trust in flood protection turns out to be a moderator variable. In particular, at-risk owners with high levels of trust are less likely to consider improving energy efficiency than at-risk owners with low levels of trust in flood protection. We discuss implications for climate risk communication.

Context

Crop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts.

Objective

This paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices.

Method

Satellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information measurement that produces balanced relationships and unbiased weights. The CHF was first generated with data from the past years (2016–2019), and the resulting weights were then applied to the normalized data of the current year (2020).

Results

The current crop insurance scheme, using CHF data instead of yield data, was implemented in the state of West Bengal, India, covering about 500,000 ha of potato across and one thousand insurance units in the 2020 crop season. The CHF and yield data from past years showed similar patterns in the majority of cases. The indemnity level was set at 70 % of the normal CHF, which was the average CHF of past years. Loss assessment and compensation payouts for the current year were determined by the extent of CHF reduction beyond the indemnity level.

Significance

This new index-insurance scheme has many advantages over the conventional yield-based scheme in terms of transparency, objectivity and ease of implementation. There is scope for improving the composite index with additional features. Such technology-driven index-insurance schemes for field crops are expected to bring a paradigm shift in the crop insurance sector, giving rise to new business models and benefitting all the stakeholders.

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three and 12-month periods. The algorithms’ performance was measured using statistical indices. The study revealed that drought distribution within the UDB is not uniform, with a discernible decreasing trend in SPEI values. Notably, the four ML algorithms effectively predicted SPEI values for the specified periods. Random Forest, Voting Regressor, and AdaBoost demonstrated the highest Nash-Sutcliffe Efficiency (NSE) values, ranging from 0.74 to 0.93. In contrast, the K-Nearest Neighbors algorithm produced values within the range of 0.44 to 0.84. These research findings have the potential to provide valuable insights for water resource management experts and policymakers. However, it is imperative to enhance data collection methodologies and expand the distribution of measurement sites to improve data representativeness and reduce errors associated with local variations.