Abstract
Cities worldwide face the challenges of accommodating a growing population, while reducing emissions to meet climate mitigation targets. Public transit investments are often proposed as a way to curb emissions while maintaining healthy urban economies. However, cities face a system-level challenge in that transportation systems have cascading effects on land use and economic development. Understanding how an improved public transit system could affect urban growth and emissions requires a system-level view of a city, to anticipate side effects that could run counter to policy goals. To address this knowledge gap, we conducted a case study on the rapidly growing Research Triangle, North Carolina (USA) region, which has proposed to build a Light Railway by 2026 along a heavily used transportation corridor between the cities of Durham and Chapel Hill. At the same time, Durham County has set a goal of lowering greenhouse gas emissions by 30% from a 2005 baseline by 2030. In collaboration with local stakeholders, we developed a system dynamics model to simulate how Light Rail transit and concurrent policies could help or hinder these sustainable growth goals. The Durham–Orange Light Rail Project (D–O LRP) model simulates urban–regional dynamics between 2000 and 2040, including feedbacks from energy spending on economic growth and from land scarcity on development. Counter to expectations, model scenarios that included Light Rail had as much as 5% higher regional energy use and CO2 emissions than business-as-usual (BAU) by 2040 despite many residents choosing to use public transit instead of private vehicles. This was largely due to an assumption that Light Rail increases demand for commercial development in the station areas, creating new jobs and attracting new residents. If regional solar capacity grew to 640 MW, this would offset the emissions growth, mostly from new buildings, that is indirectly due to Light Rail. National trends in building and automobile energy efficiency, as well as federal emissions regulation under the Clean Power Plan, would also allow significant progress toward the 2030 Durham emissions reduction goal. By simulating the magnitude of technology and policy effects, the D–O LRP model can enable policy makers to make strategic choices about regional growth.
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Notes
Reinforcing and balancing loops amplify and limit change in the system, respectively. For example, VMT increases road congestion, which decreases VMT in a balancing feedback loop.
This increase is gradually phased in during the 6 years of Light Rail construction (2020–2026). Ten percent was chosen to be a conservative estimate of how Light Rail affects commercial development. Property values of businesses near rail stations are known to increase (Cervero and Duncan 2002; Garrett 2004). Light Rail can also stimulate economic activity; a report about the Dallas Area Rapid Transit (DART) Light Rail line (Clower et al. 2014) calculates that public spending of $4.7B on rail line expansion between 2003 and 2013 resulted in $7.4B of regional economic activity (transactions or spending) over that time period.
Energy use increases slightly despite the 0% demand increase, due to a positive feedback involving employment, earnings, population, and nonresidential sq ft growth.
All electricity use for Light Rail is attributed to Tier 1.
These include a 14% reduction in residential energy use intensity (EUI), an 11% reduction in commercial and industrial EUI, as well as an 80% increase in average vehicle MPG between 2015 and 2040.
640 MW assumes a solar capacity factor of 0.15 for North Carolina. This is the ratio of actual power output to installed (nameplate) capacity over a year.
equivalent to LEED Gold standard, with the water savings representing 60% of the 25% energy savings, http://www.usgbc.org/articles/green-building-facts.
As with CO2 emissions, the model attributes energy spending for all fuel types (electricity, gasoline, etc.) to energy use in the DCHC MPO region, regardless of where the energy was generated or where the emissions were produced (for example, in a power plant outside the region).
When the increase in demand for nonresidential sq ft by LRT is set to zero, there is still a slight increase in GRP, especially in the Light Rail + Redevelopment scenario, due to a positive feedback loop involving growth in employment, earnings, total population, and nonresidential sq ft.
Progress being measured as: emissions reduction due to action X/total emissions reduction between 2030 BAU and Durham GHG goal. In this case, the 2030 BAU assumes no building or vehicle energy efficiency improvements after 2015.
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Acknowledgements
This research was supported by an appointment to the Internship/Research Participation Program at the U.S. Environmental Protection Agency (EPA), Office of Research and Development, Research Triangle Park, NC, USA, administered by the Oak Ridge Institute for Science and Education through an Interagency Agreement between the U.S. Department of Energy and the U.S. Environmental Protection Agency. This project was made possible with help from many individuals including Kiran Alapaty, Tobin Freid, Marilyn ten Brink, and Teresa Hairston. This article has been reviewed in accord with EPA policy and approved for publication. The opinions expressed or statements made herein are solely those of the authors and do not necessarily reflect the views of the agencies mentioned above. Trade names or commercial products cited do not represent an endorsement or recommendation for use.
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Procter, A., Bassi, A., Kolling, J. et al. The effectiveness of Light Rail transit in achieving regional CO2 emissions targets is linked to building energy use: insights from system dynamics modeling. Clean Techn Environ Policy 19, 1459–1474 (2017). https://doi.org/10.1007/s10098-017-1343-z
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DOI: https://doi.org/10.1007/s10098-017-1343-z