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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.

A study of the production of prompt J/ψ mesons contained in jets in proton-proton collisions at s=8TeV is presented. The analysis is based on data corresponding to an integrated luminosity of 19.1 fb−1 collected with the CMS detector at the LHC. For events with at least one observed jet, the angular separation between the J/ψ meson and the jet is used to test whether the J/ψ meson is part of the jet. The analysis shows that most prompt J/ψ mesons having energy above 15 GeV and rapidity |y|<1 are contained in jets with pseudorapidity |ηjet|<1. The differential distributions of the probability to have a J/ψ meson contained in a jet as a function of jet energy for a fixed J/ψ energy fraction are compared to a theoretical model using the fragmenting jet function approach. The data agree best with fragmenting jet function calculations that use a long-distance matrix element parameter set in which prompt J/ψ mesons are predicted to be unpolarized. This technique demonstrates a new way to test predictions for prompt J/ψ production using nonrelativistic quantum chromodynamics. Physics Letters B, 804 ISSN:0370-2693 ISSN:0031-9163 ISSN:1873-2445

The production of palm oil, soy, beef and timber are key drivers of global forest loss. For this reason, over 470 companies involved in the production, processing or distribution of these commodities have issued commitments to eliminate or reduce deforestation from their supply chains. However, the effectiveness of these commitments is uncertain since there is considerable variation in ambition and scope and there are no globally agreed definitions of what constitutes a forest. Many commitments identify high conservation value forests (HCVFs), high carbon stock forests (HCSFs) and forests on tropical peatland as priority areas for conservation. This allows for mapping of the global extent of forest areas classified as such, to achieve an assessment of the area that may be at reduced risk of development if companies comply with their zero deforestation commitments. Depending on the criteria used, the results indicate that between 34% and 74% of global forests qualify as either HCVF, HCSF or forests on tropical peatland. However, we found that the total extent of these forest areas varies widely depending on the choice of forest map. Within forests which were not designated as HCVF, HCSF or forests on tropical peatland, there is substantial overlap with areas that are highly suitable for agricultural development. Since these areas are unlikely to be protected by zero-deforestation commitments, they may be subject to increased pressure resulting from leakage of areas designated as HCVF, HCSF and tropical peatland forests. Considerable uncertainties around future outcomes remain, since only a proportion of the global market is currently covered by corporate commitments. Further work is needed to map the synergies between corporate commitments and government policies on land use. In addition, standardized criteria for delineating forests covered by the commitments are recommended.

AbstractWe quantify global and regional aggregate damages from global warming of 1.5 to 4 °C above pre-industrial levels using a well-established integrated assessment model, PAGE09. We find mean global aggregate damages in 2100 of 0.29% of GDP if global warming is limited to about 1.5 °C (90% confidence interval 0.09–0.60%) and 0.40% for 2 °C (range 0.12–0.91%). These are, respectively, 92% and 89% lower than mean losses of 3.67% of GDP (range 0.64–10.77%) associated with global warming of 4 °C. The net present value of global aggregate damages for the 2008–2200 period is estimated at $48.7 trillion for ~ 1.5 °C global warming (range $13–108 trillion) and $60.7 trillion for 2 °C (range $15–140 trillion). These are, respectively, 92% and 90% lower than the mean NPV of $591.7 trillion of GDP for 4 °C warming (range $70–1920 trillion). This leads to a mean social cost of CO2 emitted in 2020 of ~ $150 for 4 °C warming as compared to $30 at ~ 1.5 °C warming. The benefits of limiting warming to 1.5 °C rather than 2 °C might be underestimated since PAGE09 is not recalibrated to reflect the recent understanding of the full range of risks at 1.5 °C warming.