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This article presents a model of development, civil war and climate change. There are multiple interactions. Economic growth reduces the probability of civil war and the vulnerability to climate change. Climate change increases the probability of civil war. The impacts of climate change, civil war and civil war in the neighbouring countries reduce economic growth. The model has two potential poverty traps – one is climate-change-induced and one is civil-war-induced – and the two poverty traps may reinforce one another. The model is calibrated to sub-Saharan Africa and a double Monte Carlo analysis is conducted in order to account for both parameter uncertainty and stochasticity. Although the IPCC Special Report on Emission Scenarios (SRES) is used as the baseline, thus assuming rapid economic growth in Africa and convergence of African living standards to the rest of the world, the impacts of civil war and climate change (ignored in SRES) are sufficiently strong to keep a number of countries in Africa in deep poverty with a high probability.

Nature-based solutions may actively reduce hydro-meteorological risks in urban areas as a part of climate change adaptation. However, the main reason for the increasing uptake of this type of solution is their many benefits for the local inhabitants, including recreational value. Previous studies on recreational value focus on studies of existing nature sites that are often much larger than what is considered as new NBS for flood adaptation studies in urban areas. We thus prioritized studies with smaller areas and nature types suitable for urban flood adaptation and divided them into four common nature types for urban flood adaptation: sustainable urban drainage systems, city parks, nature areas and rivers. We identified 23 primary valuation studies, including both stated and revealed preference studies, and derived two value transfer functions based on meta-regression analysis on existing areas. We investigated trends between values and variables and found that for the purpose of planning of new NBS the size of NBS and population density were determining factors of recreational value. For existing NBS the maximum travelling distance may be included as well. We find that existing state-of-the-art studies overestimate the recreational with more than a factor of 4 for NBS sizes below 5 ha. Our results are valid in a European context for nature-based solutions below 250 ha and can be applied across different NBS types and sizes.

Shifts in rainfall patterns and increasing temperatures associated with climate change are likely to cause widespread forest decline in regions where droughts are predicted to increase in duration and severity. One primary cause of productivity loss and plant mortality during drought is hydraulic failure. Drought stress creates trapped gas emboli in the water transport system, which reduces the ability of plants to supply water to leaves for photosynthetic gas exchange and can ultimately result in desiccation and mortality. At present we lack a clear picture of how thresholds to hydraulic failure vary across a broad range of species and environments, despite many individual experiments. Here we draw together published and unpublished data on the vulnerability of the transport system to drought-induced embolism for a large number of woody species, with a view to examining the likely consequences of climate change for forest biomes. We show that 70% of 226 forest species from 81 sites worldwide operate with narrow (<1 megapascal) hydraulic safety margins against injurious levels of drought stress and therefore potentially face long-term reductions in productivity and survival if temperature and aridity increase as predicted for many regions across the globe. Safety margins are largely independent of mean annual precipitation, showing that there is global convergence in the vulnerability of forests to drought, with all forest biomes equally vulnerable to hydraulic failure regardless of their current rainfall environment. These findings provide insight into why drought-induced forest decline is occurring not only in arid regions but also in wet forests not normally considered at drought risk.

AbstractAnalyzing the politics and policy implications in Brazil of attributing extreme weather events to climate change, we argue for greater place-based sensitivity in recommendations for how to frame extreme weather events relative to climate change. Identifying geographical limits of current recommendations to emphasize the climate role in such events, we explore Brazilian framings of the two tragic national disasters, as apparent in newspaper coverage of climate change. We find that a variety of contextual factors compel environmental leaders and scientists in Brazil to avoid and discourage highlighting the role of climate change in national extreme events. Against analysts’ general deficit-finding assumptions, we argue that the Brazilian framing tendency reflects sound strategic, socio-environmental reasoning, and discuss circumstances in which attributing such events to climate change—and, by extension, attribution science—can be ineffective for policy action on climate change and other socio-environmental issues in need of public pressure and preventive action. The case study has implications beyond Brazil by begging greater attention to policies and politics in particular places before assuming that attribution science and discursive emphasis on the climate role in extreme events are the most strategic means of achieving climate mitigation and disaster preparedness. Factors at play in Brazil might also structure extreme events attribution politics in other countries, not least some other countries of the global South.

AbstractTropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83−85% of the surveys presented losses in forest biomass and tree species richness, functional traits, and conservation value. On average, forest fragments have 25−32% less biomass, 23−31% fewer species, and 33, 36, and 42% fewer individuals of late-successional, large-seeded, and endemic species, respectively. Biodiversity and biomass erosion are lower inside strictly protected conservation units, particularly in large ones. We estimate that biomass erosion across the Atlantic Forest remnants is equivalent to the loss of 55−70 thousand km2of forests or US$2.3−2.6 billion in carbon credits. These figures have direct implications on mechanisms of climate change mitigation.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.