2959. Optimising sectoral climate mitigation efforts against multiple sustainability indicators: a multi-objective integrated assessment
Invited abstract in session MC-18: OR for Sustainable Development III, stream OR for Sustainable Development.
Monday, 12:30-14:00Room: Esther Simpson 2.09
Authors (first author is the speaker)
| 1. | Alexandros Nikas
|
| Electrical & Computer Engineering, Decision Support Systems Lab, National Technical University of Athens | |
| 2. | Dirk-Jan Van de Ven
|
| Basque Centre for Climate Change (BC3) | |
| 3. | Clàudia Rodés Bachs
|
| Basque Centre for Climate Change | |
| 4. | Theo Rouhette
|
| Basque Centre for Climate Change | |
| 5. | Russell Horowitz
|
| Basque Centre for Climate Change | |
| 6. | Jon Sampedro
|
| Basque Centre for Climate Change | |
| 7. | Natasha Frilingou
|
| National Technical University of Athens | |
| 8. | Kimon Georgiou
|
| National Technical University of Athens | |
| 9. | Xin Zhao
|
| Pacific Northwest National Laboratory | |
| 10. | Abhishek Chaudhary
|
| Civil Engineering, Indian Institute of Technology (IIT) Kanpur | |
| 11. | Konstantinos Koasidis
|
| Institute of Communication & Computer Systems, National Technical University of Athens |
Abstract
The sustainable development agenda involves complex transformations that significantly interact with climate action. Developing long-term sustainable development pathways typically relies on integrated assessment models (IAMs) by exploring co-benefits and trade-offs between climate policy and sustainable development goals (SDGs), and hardcoding policies into models to understand sustainability implications of climate action. However, most IAMs apply least-cost approaches for defining marginal mitigation efforts, disproportionally favouring maximising performance on economic rather than broader sustainability indicators. Here, we introduce an integrated approach leveraging multi-objective optimisation algorithms linked with IAMs, and Monte Carlo simulations to simultaneously optimise mitigation in terms of multiple SDG indicators, while considering stochastic uncertainty. Our aim is to optimise global mitigation effort allocation across economic sectors based on the performance in indicators along multiple SDGs and assuming a wide range of socioeconomic assumptions, towards developing long-term pathways with balanced sustainability performance. We validate this approach with the GCAM model and the AUGMECON-R algorithm, identifying trade-offs in sectoral and sustainability performance, notably between economic and environmental dimensions. Finally, we highlight the advantages of our approach to inform climate policy by comparing our SDG-balanced pathways with least-cost ones.
Keywords
- Multi-Objective Decision Making
- OR in Environment and Climate change
- Sustainable Development
Status: accepted
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