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Armed conflicts trigger region-specific mechanisms that affect land use change. Deforestation is presented as one of the most common negative environmental impacts resulting from armed conflicts, with relevant consequences in terms of greenhouse gas emissions and loss of ecosystem services. However, the impact of armed conflict on forests is complex and may simultaneously lead to positive and negative environmental outcomes, i.e. forest regrowth and deforestation, in different regions even within a country. We investigate the impact that armed conflict exerted over forest dynamics at different spatial scales in Colombia and for the global tropics during the period 1992–2015. Through the analysis of its internally displaced population (departures) our results suggest that, albeit finding forest regrowth in some municipalities, the Colombian conflict predominantly exerted a negative impact on its forests. A further examination of georeferenced fighting locations in Colombia and across the globe shows that conflict areas were 8 and 4 times more likely to undergo deforestation, respectively, in the following years in relation to average deforestation rates. This study represents a municipality level, long-term spatial analysis of the diverging effects the Colombian conflict exerted over its forest dynamics over two distinct periods of increasing and decreasing conflict intensity. Moreover, it presents the first quantified estimate of conflict's negative impact on forest ecosystems across the globe. The relationship between armed conflict and land use change is of global relevance given the recent increase of armed conflicts across the world and the importance of a possible exacerbation of armed conflicts and migration as climate change impacts increase.
The question of whether urbanization contributes to increasing carbon dioxide emissions has been mainly investigated via scaling relationships with population or population density. However, these approaches overlook the correlations between population and area, and ignore possible interactions between these quantities. Here, we propose a generalized framework that simultaneously considers the effects of population and area along with possible interactions between these urban metrics. Our results significantly improve the description of emissions and reveal the coupled role between population and density on emissions. These models show that variations in emissions associated with proportionate changes in population or density may not only depend on the magnitude of these changes but also on the initial values of these quantities. For US areas, the larger the city, the higher is the impact of changing its population or density on its emissions; but population changes always have a greater effect on emissions than population density.
Costs of adaptation in the developing world have been mostly equated to those of climate proofing infrastructure under the assumption of unconstrained knowledge and planning capacities. To correct this, we introduce a cost-scaling methodology estimating sectoral investments to enhance the knowledge and planning capacities of countries based on an empirical collection of 385 climate-related projects. We estimate that circa 9.2 billion USD are required for financing knowledge and planning activities in developing countries in 2015. The agricultural and water sectors demand the higher investments ? 3.8 and 3.5 billion USD, respectively. Average investments between 2015 and 2050 are projected at 7 billion USD per year ? the largest fraction of which (4 billion) in Africa. Investments in this study were found to constitute approximately 40%, 20?60% and 5?15% of previous cost estimates to climate-proof infrastructure in the agricultural, water, and coastal sectors, respectively. The effort to finance the knowledge and planning capacities in developing countries is therefore not marginal relative to the costs of adapting infrastructure. The findings underline the potential of using empirical collections of climate-related projects for adaptation cost assessments as complementary to process and economic models.
Human mortality shows a pronounced temperature dependence. The minimum mortality temperature (MMT) as a characteristic point of the temperature-mortality relationship is influenced by many factors. As MMT estimates are based on case studies, they are sporadic, limited to data-rich regions, and their drivers have not yet been clearly identified across case studies. This impedes the elaboration of spatially comprehensive impact studies on heat-related mortality and hampers the temporal transfer required to assess climate change impacts. Using 400 MMTs from cities, we systematically establish a generalised model that is able to estimate MMTs (in daily apparent temperature) for cities, based on a set of climatic, topographic and socio-economic drivers. A sigmoid model prevailed against alternative model setups due to having the lowest Akaike Information Criterion (AICc) and the smallest RMSE. We find the long-term climate, the elevation, and the socio-economy to be relevant drivers of our MMT sample within the non-linear parametric regression model. A first model application estimated MMTs for 599 European cities ( >100 000 inhabitants) and reveals a pronounced decrease in MMTs (27.8-16 degrees C) from southern to northern cities. Disruptions of this pattern across regions of similar mean temperatures can be explained by socio-economic standards as noted for central eastern Europe. Our alternative method allows to approximate MMTs independently from the availability of daily mortality records. For the first time, a quantification of climatic and non-climatic MMT drivers has been achieved, which allows to consider changes in socio-economic conditions and climate. This work contributes to the comparability among MMTs beyond location-specific and regional limits and, hence, towards a spatially comprehensive impact assessment for heat-related mortality.
Hungry cities: how local food self-sufficiency relates to climate change, diets, and urbanisation
(2019)
Using a newly developed model approach and combining it with remote sensing, population, and climate data, first insights are provided into how local diets, urbanisation, and climate change relates to local urban food self-sufficiency. In plain terms, by utilizing the global peri-urban (PU) food production potential approximately lbn urban residents (30% of global urban population) can be locally nourished, whereby further urbanisation is by far the largest pressure factor on PU agriculture, followed by a change of diets, and climate change. A simple global food transport model which optimizes transport and neglects differences in local emission intensities indicates that CO2 emissions related to food transport can be reduced by a factor of 10.
Colombia's agriculture, forestry and other land use sector accounts for nearly half of its total greenhouse gas (GHG) emissions. The importance of smallholder deforestation is comparatively high in relation to its regional counterparts, and livestock agriculture represents the largest driver of primary forest depletion. Silvopastoral systems (SPSs) are presented as agroecological solutions that synergistically enhance livestock productivity, improve local farmers' livelihoods and hold the potential to reduce pressure on forest conversion. The department of Caquetá represents Colombia's most important deforestation hotspot. Targeting smallholder livestock farms through survey data, in this work we investigate the GHG mitigation potential of implementing SPSs for smallholder farms in this region. Specifically, we assess whether the carbon sequestration taking place in the soil and biomass of SPSs is sufficient to offset the per-hectare increase in livestock GHG emissions resulting from higher stocking rates. To address these questions we use data on livestock population characteristics and historic land cover changes reported from a survey covering 158 farms and model the carbon sequestration occurring in three different scenarios of progressively-increased SPS complexity using the CO2 fix model. We find that, even with moderate tree planting densities, the implementation of SPSs can reduce GHG emissions by 2.6 Mg CO2e ha−1 yr−1 in relation to current practices, while increasing agriculture productivity and contributing to the restoration of severely degraded landscapes.