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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. 

We study the cooperative asynchronous multi-agent multi-armed bandits problem, where each agent's active (arm pulling) decision rounds are asynchronous. That is, in each round, only a subset of agents is active to pull arms, and this subset is unknown and time-varying. We consider two models of multi-agent cooperation, fully distributed and leader-coordinated, and propose algorithms for both models that attain near-optimal regret and communications bounds, both of which are almost as good as their synchronous counterparts. The fully distributed algorithm relies on a novel communication policy consisting of accuracy adaptive and on-demand components, and successive arm elimination for decision-making. For leader-coordinated algorithms, a single leader explores arms and recommends them to other agents (followers) to exploit. As agents' active rounds are unknown, a competent leader must be chosen dynamically. We propose a variant of the Tsallis-INF algorithm with low switches to choose such a leader sequence. Lastly, we report numerical simulations of our new asynchronous algorithms with other known baselines. 

We introduce and study spatiotemporal online allocation with deadline constraints (SOAD), a new online problem motivated by emerging challenges in sustainability and energy. In SOAD, an online player completes a workload by allocating and scheduling it on the points of a metric space (X,d) while subject to a deadlineT. At each time step, a service cost function is revealed that represents the cost of servicing the workload at each point, and the player must irrevocably decide the current allocation of work to points. Whenever the player moves this allocation, they incur a movement cost defined by the distance metricd(⋅, ⋅) that captures, e.g., an overhead cost. SOAD formalizes the open problem of combining general metrics and deadline constraints in the online algorithms literature, unifying problems such as metrical task systems and online search. We propose a competitive algorithm for SOAD along with a matching lower bound establishing its optimality. Our main algorithm, ST-CLIP, is a learning-augmented algorithm that takes advantage of predictions (e.g., forecasts of relevant costs) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms in a simulated case study of carbon-aware spatiotemporal workload management, an application in sustainable computing that schedules a delay-tolerant batch compute job on a distributed network of data centers. In these experiments, we show that ST-CLIP substantially improves on heuristic baseline methods.