Optimal environmental road pricing

Johansson‐Stenman, Olof

doi:10.1016/j.econlet.2005.08.008

Cited by 26 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, only a few studies have simultaneously considered some of these interactions in the design process, in terms of either the bilateral relationships between macro-level (especially, environmental) effects and travel (and location) choices [57,178,223,311,332,418], or the endogenous changes in time value perceptions [88,240]. The holistic treatment of these interactions should take into account the inter-personal as well as the intrapersonal variability of perceptions and attitudes, together with the behavioral and macro-level effects over a 'predictable' time horizon.…”

Section: Evaluation and Treatment Of The Impacts Of Road Network Pricingmentioning

confidence: 99%

Design and evaluation of road pricing: state-of-the-art and methodological advances

2009

View full text Add to dashboard Cite

show abstract

Section: Evaluation and Treatment Of The Impacts Of Road Network Pricingmentioning

confidence: 99%

Design and evaluation of road pricing: state-of-the-art and methodological advances

2009

View full text Add to dashboard Cite

show abstract

“…Much attention among previous studies has focused on the effects of road pricing and driving restrictions, the results of which are contradictory in various cities [5][6][7][42][43][44]. Johansson-Stenman [45] reviewed the theory of congestion pricing for roads and suggests that improved public transport or frequent use of public transport are potentially important policy complements to road pricing. As for other road and transport policies, Wang et al [46] found that road patterns have a significant effect on PM 2.5 concentrations.…”

Section: Literature Reviewmentioning

confidence: 99%

Do Urban Subway Openings Reduce PM2.5 Concentrations? Evidence from China

Hong-you

Zhu

et al. 2018

Sustainability

View full text Add to dashboard Cite

With the fast-growing consumption of automobiles in China, vehicle fumes appear to be one of the major contributors to PM 2.5 (fine particles with a diameter of 2.5 µm or less) emissions. As a type of green transportation, Chinese urban subway transit has developed rapidly in recent years. This paper estimates the effect of urban subway openings on PM 2.5 concentrations in Chinese cities. Using daily air quality data for 29 Chinese cities from 2013 to 2018, and using regression discontinuity design, we find that new subway openings reduce PM 2.5 concentrations by an average of 18 µg/m 3 . The potential mechanism for this emission reduction is due to the offset in on-road transportation, and the use of cleaner energy. Also, central heating in winter, population scale, and city tier play crucial roles in the emission-reducing effect of subway openings. A series of robustness tests are conducted to support the fundamental empirical results. Targeted policy implications, such as optimizing the planning of urban subway construction and road network, fostering awareness of green travel, and promoting the innovation of emission-reducing technologies, are suggested for sustainable development in Chinese cities.

show abstract

“…However, it may be noted that emission pricing defined in this article is different from congestion pricing, as in emission pricing the objective is to reduce emissions and congestion. Johansson-Stenman (2006) has shown that negative externalities due to indirect environmental costs are overlooked while performing conventional congestion pricing. Hence a new model was proposed for considering environmental concern in traditional optimal steady-state road pricing models.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Transportation Systems-Enabled Optimal Emission Pricing Models for Reducing Carbon Footprints in a Bimodal Network

Sharma

Mishra

2012

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Scientists and policymakers intend worldwide emissions reduction of up to 80% of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) in the next four decades to stabilize atmospheric concentrations. Henceforth, an immediate response from the transportation sector, one of the largest producers of GHGs (up to 30% in the United States), is critical for GHGs reduction. Recent advancement in intelligent transportation systems (ITS) offers a technical solution to implement emission pricing effectively in a reasonable period of time. Further, this strategy can foster demand for efficient vehicles and high transit ridership while reducing GHGs emission and generating revenue. Therefore, in this study, we propose models for understanding the reduction of GHGs emission and shifts of private vehicle trips to transit by implementing ITS-based optimal emission pricing to reduce GHGs emission by a certain percentage in a composite transportation network (transit and highway network). The bilevel models presented in this study take into account the planner's policy decision and the road user's response to such policies in a simple and methodologically robust framework. The complex decision of choosing transit over private vehicle and road user behavior in the study has been studied by mode split functions and the classical user equilibrium principle. The performance of proposed models is compared to the base case (do nothing); reductions in total GHGs emission by optimal emission pricing shows efficacy of the models. The presented methodology in this article is generalizable and can be applied to any transportation network.

show abstract

Optimal environmental road pricing

Cited by 26 publications

References 9 publications

Design and evaluation of road pricing: state-of-the-art and methodological advances

Design and evaluation of road pricing: state-of-the-art and methodological advances

Do Urban Subway Openings Reduce PM2.5 Concentrations? Evidence from China

Intelligent Transportation Systems-Enabled Optimal Emission Pricing Models for Reducing Carbon Footprints in a Bimodal Network

Contact Info

Product

Resources

About