Strategies for dealing with climate change must incorporate and quantify all the relevant uncertainties, and be designed to manage the resulting risks 1 . Here we employ the best available knowledge so far, summarized by the three working groups of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5; refs 2-4), to quantify the uncertainty of mitigation costs, climate change dynamics, and economic damage for alternative carbon budgets. We rank climate policies according to di erent decision-making criteria concerning uncertainty, risk aversion and intertemporal preferences. Our findings show that preferences over uncertainties are as important as the choice of the widely discussed time discount factor. Climate policies consistent with limiting warming to 2• C above preindustrial levels are compatible with a subset of decision-making criteria and some model parametrizations, but not with the commonly adopted expected utility framework.Many of the uncertainties surrounding climate change are difficult to quantify and depend on the judgement of experts and on the type of model used to generate future scenarios. Each model produces a distribution over the possible states of nature (for example, cost of mitigation, temperature increase, or economic damage from climate change), and these distributions might differ from model to model. How should we select climate policy in the face of these uncertainties? This paper addresses this question using a framework that accounts for both state uncertainty (for example, the distribution over states of nature) and model uncertainty (for example, the different models (or experts) which generate distributions over states) 5 . We investigate a variety of preferences and assumptions over these two types of uncertainty. A special case is the subjective expected utility 6 framework, traditionally used in economic evaluations. However, an expected utility setting might not work when the information is incomplete and ambiguous, which is clearly the case for climate change 7 . Moreover, people are known to approach risks and uncertainties differently 8 . The proposed setting allows us to explore additional decision-making criteria to deal with uncertainty, in the spirit of refs 7,9. Alternative statistical techniques, consistent with Bayesian approaches, have been developed to cope with model uncertainty 10 . Model weighting is an active topic in climate research 11 , where historical observations provide a basis for model evaluation, although it is not commonly used 12 . Although our framework is sufficiently flexible to accommodate different prior probability measure over the set of possible models, our baseline model assumes a uniform prior with equal model weights.The literature on the role of uncertainty in climate policy making has mostly relied on either analytical or simplified integrated assessment models (IAMs), such as DICE (ref. 13). In such contexts, different decision-making criteria and preferences over risks have been shown to have a sig...

Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely underexplored. Until now, research has focused either on pure economic approaches such as computable general equilibrium (CGE) and partial equilibrium (PE) models, or on (physical) energy supply scenarios. These latter could be used to evaluate the environmental impacts of various energy saving or cleaner technologies via life cycle assessment (LCA) methodology. These modelling efforts have, however, been pursued in isolation, without exploring the possible complementarities and synergies. In this study, we have undertaken a practical combination of these approaches into a common framework: on the one hand, by coupling a CGE with a PE model, and, on the other hand, by linking the outcomes from the coupling with a hybrid inputoutput−process based life cycle inventory. The methodological framework aimed at assessing the environmental consequences of two energy policy scenarios in Luxembourg between 2010 and 2025. The study highlights the potential of coupling CGE and PE models but also the related methodological difficulties (e.g. small number of available technologies in Luxembourg, intrinsic limitations of the two approaches, etc.). The assessment shows both environmental synergies and trade-offs due to the implementation of energy policies. For example, despite the changes in technologies towards the reduction of greenhouse gas emissions, only marginal improvements are observed in the climate change mitigation scenario as compared to the business-as-usual. The energy related production and imports are indeed expected to increase over time and represent a large contribution to the country's impacts. Interestingly, side effects on other impacts than climate change or fossil resource depletion (e.g. ionising radiation and water depletion) may also occur mainly due to the use of nuclear energy in neighbouring countries.

Highlightsd Implementing the Paris Agreement targets will entail shifts in energy jobs d Globally, we find an increase in direct global energy jobs under well-below 2 Cd Over 80% of energy jobs by 2050 are expected to be in renewables d Solar and wind manufacturing sectors will provide millions of jobs globally