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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 designedincentivesthat 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 article, 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 resultantcarbon 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 estimatehuman 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 accommodateequity-awareconstraints to preserve an equitable allocation of incentives across socioeconomic groups while achieving 83.34% of the carbon reductions of the optimal solution on average. 

Motivated by the emerging paradigm of resource allocation that integrates classical objectives, such as cost minimization, with societal objectives, such as carbon awareness, this paper proposes a general framework for the online fair allocation of reusable resources. Within this framework, an online decision-maker seeks to allocate a finite resource with capacityCto a sequence of requests arriving with unknown distributions of types, utilities, and resource usage durations. To accommodate diverse objectives, the framework supports multiple actions and utility types, and the goal is to achieve max-min fairness among utilities, i.e., maximize the minimum time-averaged utility across all utility types. Our performance metric is an (α,β)-competitive guarantee of the form: ALG ≥ α • OPT^*- O(T^β-1),; α, β ∈ (0, 1], where OPT^*and ALG are the time-averaged optimum and objective value achieved by the decision maker, respectively. We propose a novel algorithm that achieves a competitive guarantee of (1-O(√(log C/C)), 2/3) under the bandit feedback. As resource capacity increases, the multiplicative competitive ratio term 1-O(√ logC/C) asymptotically approaches optimality. Notably, when the resource capacity exceeds a certain threshold, our algorithm achieves an improved competitive guarantee of (1, 2/3). Our algorithm employs an optimistic penalty-weight mechanism coupled with a dual exploration-discarding strategy to balance resource feasibility, exploration, and fairness among utilities.