The feasibility of achieving climate stabilization consistent with the objective of 2°C is heavily influenced by how the effort in terms of mitigation and economic resources will be distributed among the major economies. This paper provides a multi-model quantification of the mitigation commitment in 10 major regions of the world for a diversity of allocation schemes. Our results indicate that a policy with uniform carbon pricing and no transfer payments would yield an uneven distribution of policy costs, which would be lower than the global average for OECD countries, higher for developing economies and the highest, for energy exporters. We show that a resource sharing scheme based on long-term convergence of per capita emissions would not resolve the issue of cost distribution. An effort sharing scheme which equalizes regional policy costs would yield an allocation of allowances comparable with the ones proposed by the Major Economies. Under such a scheme, emissions would peak between 2030 and 2045 for China and remain rather flat for India. In all cases, a very large international carbon market would be required.

The availability of technology plays a major role in the feasibility and costs of climate policy. Nonetheless, technological change is highly uncertain and capital intensive, requiring risky efforts in research and development of clean energy technologies. In this paper, we introduce a two-track method that makes it possible to maintain the rich set of information produced by climate-economy models while introducing the dimension of uncertainty in innovation efforts, without succumbing to computation complexity. In particular, we solve the problem of an optimal R&D portfolio by employing Approximate Dynamic Programming, through multiple runs of an integrated assessment model (IAM) for the purpose of computing the value function, and expert elicitation data to quantify the relevant uncertainties. We exemplify the methodology with the problem of evaluating optimal near-term innovation investment portfolios in four key clean energy technologies (solar, biofuels, bioelectricity and personal electric vehicle batteries), taking into account the uncertainty surrounding the effectiveness of innovation to improve the performance of these technologies. We employ an IAM (WITCH) which has a fairly rich description of the energy technologies and experts' beliefs on future costs for the abovementioned technologies. Focusing on Europe and its short-term climate policy commitments, we find that batteries in personal transportation dominate the optimal public R&D portfolio. The resulting ranking across technologies is robust to changes in risk-aversion, R&D budget limitation and assumptions on crowding out of other investments. These results suggest an important upscaling of R&D efforts compared to the recent past.

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