Evaluating the Effectiveness of a School-Based Intervention on Driving-Related Carbon Emissions Using Real-Time Transportation Data

Cordero, Eugene; Centeno, Diana · 2019 · ROSA P / San Jose State University. College of Business. Mineta Transportation Institute

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Summary

This study evaluates the effectiveness of a school-based intervention designed to reduce driving-related carbon emissions by leveraging real-time transportation data and environmental education. Motivated by the fact that transportation is the largest source of carbon emissions in California, the research addresses the gap in strategies that encourage individual drivers to adopt more energy-efficient behaviors. While infrastructure improvements and vehicle efficiency are common mitigation strategies, behavioral change remains underutilized. The authors hypothesized that providing students with real-time feedback on their driving habits within an educational context could foster pro-environmental attitudes and improve driving efficiency. The methodology involved a pilot study conducted in three general education science courses at San José State University during the fall of 2018. The researchers partnered with Zendrive, a smartphone application provider, to collect driving data via iPhone sensors, measuring hard accelerations, hard braking, and time spent over the speed limit. This data was used to calculate a “Green Driving Score,” where higher scores indicated more efficient driving. The curriculum consisted of a two-week baseline period, where students monitored their driving patterns, followed by a conservation period, where they implemented plans to improve their scores. Data was aggregated and visualized on a secure web dashboard for both students and instructors. Participation was limited to iPhone users who consented to data sharing, resulting in a sample size of 37 students. Surveys were also administered to assess student attitudes toward climate change and transportation. The results indicated that the intervention led to modest improvements in driving behavior. Average Green Driving Scores improved by 2% to 5% across the studied classes. Specifically, in the sections taught by Diana Centeno, the median score increased from 79 to 84. In the section taught by Eugene Cordero, initial improvements were negligible, but a second conservation period with an added incentive (a burrito lunch) resulted in a median score increase from 84 to 86. Notably, larger behavioral changes were observed in students who did not initially identify as having strong pro-environmental attitudes. Survey data revealed that while over 90% of students recognized transportation’s role in carbon emissions, only 40% regularly practiced green driving behaviors prior to the intervention. Privacy concerns and device compatibility (iPhone-only) limited participation rates. The study concludes that educational programs utilizing real-time data on driving behavior can effectively encourage more efficient driving and potentially reduce transportation-related carbon emissions. The findings suggest that such interventions are particularly effective for students who lack pre-existing pro-environmental attitudes, indicating that real-time feedback can bridge the gap between knowledge and action. The authors recommend further research to refine these tools, address privacy concerns, and expand accessibility beyond specific smartphone platforms to maximize the educational and behavioral modification potential of real-time transportation data.

Key finding

Average student Green Driving Scores improved by between 2 and 5 percent following the educational intervention, with the largest changes occurring among students who initially lacked pro-environmental attitudes.

Methodology

field_study

Sample size: 37

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• exposure measurement
• eco driving

Information type

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• Applied Guidance: countermeasure evaluation
• Empirical Findings: observational prevalence
• Methodological Resource: dataset resource