The elasticity and efficiency of carbon reduction strategies in transportation

The elasticity and efficiency of carbon reduction strategies in transportation

WoS Article

Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [-2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [-354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings' interpretation should consider the inherent data limitations.

Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [-2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [-354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings' interpretation should consider the inherent data limitations.

Carbon elasticity; carbon policy; meta-analysis; policy efficiency; transport policy

Carbon elasticity; carbon policy; meta-analysis; policy efficiency; transport policy

Bencekri, Madiha; Ku, Donggyun; Lee, Doyun; Van Fan, Yee; Klemes, Jiri Jaromir; Varbanov, Petar Sabev; Lee, Seungjae

2024

02.10.2023

TAYLOR & FRANCIS INC530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106

TAYLOR & FRANCIS INC530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106

1556-7036

Energy Sources Part A-Recovery Utilization and Environmental Effects

4

45

United States of America

12791

12807

17

https://www.tandfonline.com/doi/full/10.1080/15567036.2023.2276380