Dynamic Incentive Allocation for City-scale Deep Decarbonization

TL;DR

This paper introduces a data-driven, dynamic incentive allocation model for city-scale decarbonization that maximizes carbon reduction while considering equity, outperforming traditional schemes by up to 32%.

Contribution

It presents a novel optimization framework using multi-armed bandits to allocate incentives efficiently and equitably for decarbonization at the city level.

Findings

Achieves up to 32.23% higher carbon reductions than baseline schemes.

Can incorporate equity constraints, reaching 78.84% of optimal reductions.

Demonstrates effectiveness using real city data and future price scenarios.

Abstract

Greenhouse gas emissions from the residential sector represent a significant fraction of global emissions. Governments and utilities have designed incentives to stimulate the adoption of decarbonization technologies such as rooftop PV and heat pumps. However, studies have shown that many of these incentives are inefficient since a substantial fraction of spending does not actually promote adoption, and incentives are not equitably distributed across socioeconomic groups. 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 city-wide carbon reduction. We leverage techniques for 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 framework to a city in the Northeast U.S., using real household energy data, grid carbon intensity data, and future price scenarios. We show that our learning-based technique significantly outperforms an example status quo incentive scheme, achieving up to 32.23% higher carbon reductions. We show that our framework can accommodate equity-aware constraints to equitably allocate incentives across socioeconomic groups, achieving 78.84% of the carbon reductions of the optimal solution on average.