Achieving a truly sustainable energy transition requires progress across multiple dimensions beyond climate change mitigation goals. This article reviews and synthesizes results from disparate strands of literature on the coeffects of mitigation to inform climate policy choices at different governance levels. The literature documents many potential cobenefits of mitigation for nonclimate objectives, such as human health and energy security, but little is known about their overall welfare implications. Integrated model studies highlight that climate policies as part of well-designed policy packages reduce the overall cost of achieving multiple sustainability objectives. The incommensurability and uncertainties around the quantification of coeffects become, however, increasingly pervasive the more the perspective shifts from sectoral and local to economy wide and global, the more objectives are analyzed, and the more the results are expressed in economic rather than nonmonetary terms. Different strings of evidence highlight the role and importance of energy demand reductions for realizing synergies across multiple sustainability objectives.

This document describes the REMIND model in its version 1.5. REMIND is an integrated assessment model of the energy-economy-climate system. REMIND stands for “Regional Model of Investments and Development.”

The new scenario framework facilitates the coupling of multiple socioeconomic reference pathways with climate model products using the representative concentration pathways. This will allow for improved assessment of climate impacts, adaptation and mitigation. Assumptions about climate policy play a major role in linking socioeconomic futures with forcing and climate outcomes. The paper presents the concept of shared climate policy assumptions as an important element of the new scenario framework. Shared climate policy assumptions capture key policy attributes such as the goals, instruments and obstacles of mitigation and adaptation measures, and introduce an important additional dimension to the scenario matrix architecture. They can be used to improve the comparability of scenarios in the scenario matrix. Shared climate policy assumptions should be designed to be policy relevant, and as a set to be broad enough to allow a comprehensive exploration of the climate change scenario space.

This document describes the REMIND model in its version 1.6. REMIND is an integrated assessment model of the energy-economy-climate system. REMIND stands for “Regional Model of Investments and Development.”

We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert interviews that we conducted with modelers in the context of climatic change. From the interviews we deduce that generically, experts hold a much more informed opinion on the marginals of uncertain parameters rather than on their correlations. Accordingly, we specify the class by prescribing precise measures for the marginals while letting the correlation structure subject to complete ignorance. For sake of transparency, our discussion focuses on the tutorial example of a linear two-dimensional Gaussian model. We operationalize Bayesian learning for that class by various updating rules, starting with (a modified version of) the generalized Bayes' rule and the maximum likelihood update rule (after Gilboa and Schmeidler). Over a large range of potential observations, the generalized Bayes' rule would provide non-informative results. We restrict this counter-intuitive and unnecessary growth of uncertainty by two means, the discussion of which refers to any kind of imprecise model, not only to our class. First, we find our class of priors too inclusive and, hence, require certain additional properties of prior measures in terms of smoothness of probability density functions. Second, we argue that both updating rules are dissatisfying, the generalized Bayes' rule being too conservative, i.e., too inclusive, the maximum likelihood rule being too exclusive. Instead, we introduce two new ways of Bayesian updating of imprecise probabilities: a "weighted maximum likelihood method" and a "semi-classical method." The former bases Bayesian updating on the whole set of priors, however, with weighted influence of its members. By referring to the whole set, the weighted maximum likelihood method allows for more robust inferences than the standard maximum likelihood method and, hence, is better to justify than the latter. Furthermore, the semi-classical method is more objective than the weighted maximum likelihood method as it does not require the subjective definition of a weighting function. Both new methods reveal much more informative results than the generalized Bayes' rule, what we demonstrate for the example of a stylized insurance model. -- Generalized Bayes rule ; imprecise probabilities ; known marginals ; maximum likelihood update ; modeling expert opinions ; robust Bayesians ; unknown correlation structure ; updating under complex uncertainty