Medium Complexity Earth System Risk Management (ERM)

Climate change is a major challenge to long-term sustainable development. More research needs to be done to come up with feasible mitigation policies consistent with emissions targets. These should aim to minimize the negative impacts of climate change on humans and environmental well-being while ensuring that other elements of well-being, most notably economic prosperity, are not compromised to an unacceptable degree. Integrated Assessment Models (IAMs) have been put forward as a tool to account for the entire causal loop between economic growth, emissions, and global warming with its negative impacts. Stylized IAMs are models with a low complexity that enable us to test a large set of policies and choose the best one among them. A prominent example of stylized IAMs is the DICE (Dynamic Integrated model of Climate and the Economy) model developed by W. Nordhaus. Following Nordhaus, mainstream research focuses only on a limited number of climate-economy paths generated by so-called optimal policies. The problem is that climate change caused by economic growth is an extremely complex problem that cannot simply be reduced to the selection of the best policy with respect to one rather arbitrary utility criterion, as occurs now. The mainstream approach leads to four major limitations of current generation IAMs. [1] The range of multiple near-optimal solutions is ignored, which prevents tradeoffs being analyzed within a set of possible decisions that are practically indistinguishable from each other, from the decision makers perspective. [2] The decision-making process suffers from an inconsistency between short-term actions and a long-term target. [3] IAMs produce policies that are virtual, i.e., that are not proved to be realistically possible and effective, despite uncertainty, but are made by models to appear to be so. [4] Even such a broadly accepted stylized IAM as DICE drastically simplifies carbon-climate feedback beyond the realism needed for reliable analysis. Our ambition is to push stylized IAMs to a higher level of applicability to inform the real-world policy process related to climate change. To make this possible, we propose the Medium Complexity Earth System Risk Management (ERM), a coherent methodology addressing all four limitations. Innovations include transferring the concept from mathematical control theory to IAMs. Further innovations include decision procedure ensuring viable policies that are in line with evidence-based risk aversion. ERM applied to the DICE model results in a model of the next generation, ERM-DICE, with climate and carbon modules of DICE being recalibrated to better represent real climate processes on Earth. ERM-DICE will be freely available as a software tool to be used for further analysis.

Climate change is a major challenge to long-term sustainable development. Feasible climate mitigation policies should aim to minimize the negative impacts of climate change on humans and the environment while ensuring that economic development is not undermined. To quantify these trade-offs, Integrated Assessment Models (IAMs) have been put forward which account for the entire causal loop between economic growth, emissions, and global warming with its negative impacts. Stylized IAMs are models with low complexity to assess a large set of potential policies, and to ultimately select the best one among them. The DICE model (Dynamic Integrated model of Climate and the Economy) developed by W. Nordhaus is probably the most influential stylized IAM. However, mainstream research focuses only on a small number of climate-economy pathways reflecting so-called "optimal policies". Assessing only the "best" policy with respect to a rather arbitrary utility criterion, as in the mainstream current-generation IAMs, ignores the valuable insights gained from alternative, close-to-optimal strategies. To deal with this shortcoming, in this project, we developed new ideas and models that have resulted in significant impacts on climate policy research. Here are the key highlights: -- we came up with an actionable policy framework called Carbon Removal Obligations (CROs) that constrains emissions to comply with the remaining carbon budget without the need for detailed IAM modeling. Additionally, a pricing mechanism can be used to steer the emissions path and reduce climate risks. -- we developed a state-of-the-art probabilistic carbon-climate model (Pathfinder), which we integrated with DICE, and which combines low (reduced) complexity and high accuracy for modeling climate change. -- we proposed a new quantitative definition of sustainability using ideas of control theory and analyzed the tradeoffs between mitigation capacity needed versus delayed abatement. We also proposed a numerical metric to quantify the loss of sustainability. These results were in line with our ambition to push stylized IAMs to a higher level of applicability to inform the real-world policy process related to climate change.