Dynamic Incentive Allocation for City-Scale Deep Decarbonization
Author(s)
Sitaraman, Anupama
Lechowicz, Adam
Bashir, Noman
Liu, Xutong
Hajiesmaili, Mohammad
Shenoy, Prashant
Date Issued
July 16, 2025
Journal
ACM Journal on Computing and Sustainable Societies
Publisher
ACM
Citation
Anupama Sitaraman, Adam Lechowicz, Noman Bashir, Xutong Liu, Mohammad Hajiesmaili, and Prashant Shenoy. 2025. Dynamic Incentive Allocation for City-Scale Deep Decarbonization. ACM J. Comput. Sustain. Soc. 3, 3, Article 22 (September 2025), 25 pages.
Version
Final published version
Abstract
Greenhouse gas emissions from the residential sector represent a large fraction of global emissions and must be significantly curtailed to achieve ambitious climate goals. To stimulate the adoption of relevant technologies such as rooftop PV and heat pumps, governments and utilities have designed incentives that encourage adoption of decarbonization technologies. However, studies have shown that many of these incentives are inefficient since a substantial fraction of spending does not actually promote adoption. Further, these incentives are not equitably distributed across socioeconomic groups. In this paper, we present a novel data-driven approach that adopts a holistic, emissions-based, and city-scale perspective on decarbonization.
We propose an optimization model that dynamically allocates a total incentive budget to households to directly maximize the resultant carbon emissions reduction -- this is in contrast to prior work, which focuses on metrics such as the number of new installations. We leverage techniques from the multi-armed bandits problem to estimate human factors, such as a household's willingness to adopt new technologies given a certain incentive. We apply our proposed dynamic incentive framework to a city in the Northeast U.S., using real household energy data, grid carbon intensity data, and future price scenarios. We compare our learning-based technique to two baselines, one "status-quo' baseline using incentives offered by a state and utility, and one simple heuristic baseline. With these baselines, we show that our learning-based technique significantly outperforms both the status-quo baseline and the heuristic baseline, achieving up to 37.88% higher carbon reductions than the status-quo baseline and up to 28.76% higher carbon reductions compared to the heuristic baseline. Additionally, our incentive allocation approach is able to achieve significant carbon reduction even in a broad set of environments, with varying values for electricity and gas prices, and for carbon intensity of the grid. Finally, we show that our framework can accommodate equity-aware constraints to preserve an equitable allocation of incentives across socioeconomic groups while achieving 83.34% of the carbon reductions of the optimal solution on average.
MIT Department
MIT Center for Energy and Environmental Policy Research
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1145/3736650
