Dynamic Incentive Allocation for City-scale Deep Decarbonization

2025-02-13

AI Summary

This study addresses the low efficiency and inequitable distribution of incentive policies in deep decarbonization of the urban residential sector. Methodologically, it proposes a dynamic incentive allocation framework that directly optimizes city-wide carbon reduction, integrating a multi-armed bandit model to capture household adoption behavior, fairness-aware constraints, and high-resolution datasets on energy consumption, grid carbon intensity, and socioeconomic attributes. Its key contribution lies in being the first to embed city-level carbon reduction targets directly into incentive design—enabling precise, adaptive, and equitable allocation at the household level. Empirical evaluation in a northeastern U.S. city demonstrates that the framework achieves 32.23% greater emissions reduction than current incentives; even under stringent fairness constraints, it retains 78.84% of the optimal reduction performance, effectively balancing efficiency and equity.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizes dynamic incentive allocation
• Maximizes city-wide carbon reduction
• Ensures equitable incentive distribution

Innovation

• Dynamic incentive allocation optimization
• Multi-armed bandits for human factors
• Equity-aware decarbonization incentive framework