Humanity faces the unprecedented global challenge of climate change. The sheer complexity and uncertainty of this problem renders mere intuitive reasoning insufficient. To aid global climate negotiations, Integrated Assessment Models (IAMs) are used to analyze the interplay between climate and the economy. More specifically, IAMs account for how greenhouse gas emissions affect climate change, how climate change affects economic production, and how economic production affects GHG emissions. We can use IAMs to project trends in emissions and gross domestic product, assess the costs and benefits of climate policies, and estimate the social carbon cost required to achieve stated emissions reduction targets. Although IAMs are central to informing decision-making to avoid catastrophic consequences, policy recommendations resulting from IAMs commonly prompt a very heterogeneous distribution of risks and benefits across the globe. During the recent 2021 United Nations Climate Change Conference (COP26), it became clear that equity is a central issue in the climate action debate. Emerging economies consider currently suggested abatement policies unjust in light of the historical CO2 generation of high-income countries and the strongly increasing need for energy in low-income countries. The term double inequality has been coined to describe the inverse relationship between the distributions of risks and responsibilities. In fact, the regions that are the least responsible for historical and mostly current CO2 emissions around the world, exhibit the highest degree of vulnerability to climate damages. In order to enable international cooperation and have a shot at meeting the Paris Agreement target, we require policies that promote more equitable mitigation pathways. Equity is therefore an eminently pressing topic, yet most IAM studies largely neglect it due to the implicit use of a utilitarian social welfare function that aggregates risks and benefits over space and time, thus losing sight of distributional consequences.
In order to account for distributional justice, we transform the RICE model into a simulation model and embed it in a many-objective simulation-optimization setup such that we can find Pareto-optimal climate mitigation pathways for different problem formulations. Next to using four ethical premises (rooted in utilitarianism, sufficientarianism, egalitarianism, and prioritarianism), we also direct particular attention to the disaggregation of utility and disutility within each of these ethical premises. The reason for this disaggregation is based on the incommensurability of these two. Usually, IAMs maximize aggregate variables such as welfare. If we also consider the minimization of welfare loss, which is based on economic damages as one of the objectives, we can enable a potentially fairer distribution of not only consumption but economic damages. We argue that we can find climate justice behind the veil of aggregation. What we mean by this is that more equitable policy recommendations are obscured and lie hidden behind a bulwark of highly aggregated variables. If we look beyond this obstruction by the means of disaggregation, we are better equipped to find climate justice. In order to get to the bottom of this, we ask the following question:
How are Pareto-optimal climate abatement pathways affected by the disaggregation of utility and disutility in alternative ethical problem formulations when using an integrated assessment model under deep uncertainty?
To answer this question, we use a framework that is called multi-scenario multi-objective robust decision-making. For each of the eight problem formulations (4 ethical premises x 2 levels of aggregation), we use a multi-objective evolutionary algorithm to find Pareto-optimal policies. We reevaluate their performances under uncertainty by comparing their climate abatement pathways across the problem formulations. On a high-level, we can summarize our key findings as:
- dominance of aggregation levels over ethical premises
- correlation between low welfare and high welfare loss
- general dominance of egalitarian aggregated Pareto-optimal policies
- shared misery via egalitarian disaggregated Pareto-optimal policies
The effect of disaggregating utility and disutility is stronger than originally expected. Using disaggregated problem formulations yields substantially different pathways even within the same ethical premise. These results are promising as we could transfer these insights to other more complex IAMs such as IMAGE and MESSAGE. Overall, this could be also good news for the equity debate. Using alternative ethical premises and disaggregating incommensurate objectives such as utility and disutility can offer alternative policy recommendations and resulting climate abatement pathways which could in turn enable more equity. What we likely need now, is a stronger dialogue between the modelers and policy analysts on the one side and the stakeholders and decision-makers on the other side. The latter ones should not just blindly trust in the magical outputs of a model but they need to be involved to decide what problem formulations we need to use as there is no correct way to frame a complex real-world problem. As unmitigated climate damages exhibit an independent impact on a region's well-being, we could render IAMs more useful for climate policy if we a) acknowledge that the classical notion of welfare is obsolete, b) use a multi-objective approach, and c) let the decision-makers decide how they want to trade-off the various objectives in post. In this manner, we could use IAMs to advance into the direction of enabling a transition of more climate justice.