Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action
This work addresses the challenge of balancing economic and climate goals for policymakers, though it is incremental as it applies an existing algorithm to a known model.
The paper tackles the problem of optimizing climate-economic trade-offs by extending the DICE model from single-objective to bi-objective optimal control, maximizing social welfare and minimizing temperature deviation, and finds that the multi-objective approach yields a Pareto front with diverse solutions, revealing a lower limit on temperature deviation without technological advances.
One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions.