Scaling behavioral incentives for low-carbon mobility through digital platforms

2025-11-12

AI Summary

How can low-carbon mobility be scaled to support global carbon neutrality? This study presents the first large-scale empirical evaluation of a carbon-credit incentive mechanism within a Mobility-as-a-Service (MaaS) platform, leveraging real-world travel data from 390 million users and 4.8 billion multimodal trips. By embedding dynamic carbon accounting into the MaaS digital infrastructure—enabling real-time emission feedback and incentive redemption—the intervention significantly enhanced user engagement with sustainable transport: monthly public transit and cycling usage increased by 20.3%, while daily gasoline vehicle usage declined by 1.8%, yielding an annual emissions reduction of 94,000 tons CO₂e—equivalent to 5.7% of Beijing’s certified emission reductions. The findings provide the first robust, large-scale evidence that digitally mediated behavioral interventions can drive persistent, scalable decarbonization in urban transportation systems.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Meeting global carbon reduction targets requires large-scale behavioral shifts in everyday travel. Yet, real-world evidence on how to motivate such large-scale behavioral change remains scarce. We evaluate a carbon incentive program embedded in a MaaS platform in Beijing, China, using data from 3.9 million participants and 4.8 billion multimodal trips over 395 days. The program increased reported public transport and bike travel by 20.3% per month and reduced gasoline car use by 1.8% per day, yielding an annual carbon reduction of ~94,000 tons, or 5.7% of certified reductions in Beijing's carbon market. Although effects diminished over time, participants still made 12.8% more green trips per month after eight months, indicating persistence. These results provide the first large-scale empirical evidence of carbon incentives in MaaS and highlight their potential to inform targeted, city-specific interventions that can scale to support global low-carbon mobility transitions.

Problem

• Evaluating carbon incentive program effectiveness in mobility platforms
• Measuring large-scale behavioral shifts towards low-carbon transportation modes
• Assessing persistence of green travel habits over extended periods

Innovation

• Carbon incentives embedded in MaaS platform
• Analyzed 4.8 billion multimodal trips data
• Increased public transport and bike travel