• Energy Research
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Pour une grande majorité des économistes, la taxe carbone, légitimée par ses fondements théoriques, devait se situer au coeur des politiques de lutte contre le changement climatique. Cependant, comme le notait déjà en 2015 l'économiste de l'environnement Jean-Charles Hourcade, « le passage de la théorie à la pratique est un exercice difficile en matière de taxe carbone ». Nous verrons dans un premier temps pourquoi et comment l’économie du bien-être en est venue à considérer l’efficacité comme unique critère et comment la taxe carbone est devenue une mesure phare pour les économistes de l’environnement. Nous verrons ensuite les difficultés que ceux-ci rencontrent lorsqu’ils essaient de concilier les fondamentaux aux exigences des réalités sociales et politiques au moment de la mise en place de la mesure. Enfin, nous verrons comment des approches normatives qui avaient pu sembler reléguées au second rang de la discipline, mobilisent ces évènements pour montrer l’insuffisance du critère d’efficacité et la nécessité de tenir compte de critères éthiques dans le choix des instruments des politiques publiques. For a large majority of economists, the carbon tax, legitimized by its theoretical foundations, should be at the heart of policies to combat climate change. However, as environmental economist Jean-Charles Hourcade already noted in 2015, “the transition from theory to practice is a difficult exercise when it comes to carbon taxes”. We will first see why and how well-being economics came to consider efficiency as the sole criterion and how the carbon tax became a flagship measure for environmental economists. We will then see the difficulties they encounter when they try to reconcile the fundamentals with the demands of social and political realities at the time of implementing the measure. Finally, we will see how normative approaches which may have seemed relegated to the second rank of the discipline, mobilize these events to show the insufficiency of the criterion of effectiveness and the need to take into account ethical criteria in the choice of public policy instruments. .

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.

A double-null configuration is being considered for the EU-DEMO, due to its potential benefits for power exhaust arising from the use of two active divertors and magnetically disconnected low- and high-field sides. Using systematic parameter scans in fluid simulations, we have investigated the divertor power exhaust in the EU-DEMO in a connected double-null configuration, and compared the edge plasma properties to those obtained in a single-null configuration under detached conditions anticipated for reactor operation. Neglecting drift effects and kinetic behaviour of the neutrals, no clear benefits of the double-null configuration could yet be identified for the radiation pattern and power mitigation on open field lines. Future work should address the aforementioned physics as well as the effect of the additional X-point on core radiation.

AbstractFundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories – the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis – are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

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.