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A Framework for Considering Climate Change in Transportation and Land Use Scenario Planning

III. Data Requirements

Scenario planning is a data-intensive process and, accordingly, the Pilot Project depended on robust data for each of its major elements: creating the baseline of existing conditions, developing the performance indicators for evaluation and projecting future conditions. Data were collected from state and other GIS databases, federal resources, the Cape Cod Commission, and towns. As the project progressed, some critical data sets that didn't currently exist had to be created from scratch.

This section provides a summary of the data needed to develop performance indicators ̶ specifically, measures of mitigation and adaptation ̶ as well as baseline land use and transportation data required for the development of the scenarios. Additional details on data collection, limitations, and development can be found in the various appendices referenced and the Technical Scenario Report.

Selection of Performance Indicators

Performance indicators, or measures of performance, allow participants to compare the effects or consequences of different land use and transportation decisions. Selection of the performance indicators was an important early consideration for the Pilot Project. The Pilot Project began with identifying performance indicators that matched the goals of the project and incorporating these into the RFP for the scenario planning consultant. These five key performance indicators were:

Other indicators of interest expressed early in the process included impacts on habitat; energy, air pollution, water, and waste reduction targets; and sustainability, livability, physical activity, and economic development measures. Due to the Pilot Project's focus on climate change adaptation, percentage of development vulnerable to climate change effects also became an important indicator once the expert elicitation process, described below, resulted in a data layer that could support this measure.

Although cost to implement the scenario was proposed as a potential indicator, it was ultimately not included in the analysis due to the lack of sufficient data and the inability to model specific GHG emission strategies. Additionally, because the Pilot Project focused on the process by which development and transportation investment decisions are made at the regional level, the scale was not conducive to developing cost estimates for specific capital or operational projects.

As part of its response to the Volpe Center's RFP, the consultant team provided the Planning Group with a list of over 60 possible indicators (see Appendix D: List of Potential Performance Indicators) for use in the project's scenario planning model, including indicators that matched the RFP request. Based on data availability and input on importance, or priority ranking, from the Planning and Technical Committees, the initial list was edited down to the following eleven performance measures:

Details on the methodology for the GHG mitigation and SLR indicators are included below as they are the focus of the Pilot Project. The methodology for how the additional indicators were developed, measured, and impacted by the scenario development process is included in the Technical Scenario Report. Section V discusses the performance of the resulting scenarios based on these indicators.

Sea-Level Rise and Other Climate Change Impacts

The project team determined that no regional SLR impact estimates existed for Cape Cod. Several computer-based models exist to conduct regional-level estimates but lack the specificity at the local level that was desired and require investment of significant time and resources. Thus, the Pilot Project team decided to organize and facilitate a consensus-/group-based expert elicitation (EE) with local and regional coastal experts at Woods Hole, Massachusetts in July 2010. The coastal experts included staff from the following agencies:

The project team coordinated with the USGS to identify several GIS layers in advance for reference during the EE, including orthophotography, landform and geologic maps, elevation data, and FEMA flood areas. The EE also referenced the literature review that had been conducted (see Appendix B: Literature Review), in particular the USGS coastal vulnerability assessment of Cape Cod.

The initial goal for the EE was to develop SLR impact projections for specific areas of Cape Cod for three time horizons (20, 50, and 100 years), and, if feasible, for three scenarios (low, medium, and high SLR estimates). However, during the EE, the experts acknowledged that this level of detail, and focusing on inundation only, was not possible. The limiting factors included the dynamic conditions influencing SLR at local levels, a lack of robust data sources, and the feasibility of the requisite analysis and modeling within the scope and scale of the Pilot Project.

Due to these limitations, the EE workshop focused on identifying vulnerable areas, or areas of concern, for SLR and other climate-related impacts on Cape Cod. Experts identified specific areas that they considered vulnerable based on the following criteria:

The EE workshop resulted in an indexed map of the vulnerable areas with a key to specific explanations of why certain areas were marked as potentially vulnerable. The map and key are provided in Appendix E: Vulnerable Area Map and Key.

Transportation Greenhouse Gas Mitigation Strategies

The Technical Committee developed a preliminary list of mitigation strategies based on a literature review, in particular the report Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions.[17] That study, commissioned by a group of federal agencies and a diverse set of interest groups, assesses the potential effectiveness of transportation strategies to reduce GHG emissions through reduced travel activity or improved vehicle and system operations.

Drawing upon Moving Cooler, the Technical Committee created an initial list of strategies and then tailored specific strategies to address the regional context. Through a series of conference calls, the group revised the initial list of strategies, adding ones that might be pursued and deleting those that were deemed infeasible for Cape Cod. For example, while increasing the gas tax may be an effective mitigation strategy, regional and local agencies in Massachusetts do not have the authority to amend the gas tax rate. The group focused only on strategies that could be implemented at the local level; policies that require federal or state action were not included.

The final list of potential GHG emission reduction strategies, which can be found in Appendix F: Potential GHG Reduction Strategies, is organized into seven categories:

  1. Pricing strategies. These strategies raise the costs associated with the use of some components of the transportation system relative to others.
  2. Land use and smart growth strategies. These strategies create more transportation-efficient land use patterns (i.e., fewer and shorter vehicle trips).
  3. Non-motorized transportation strategies. These strategies encourage greater levels of walking and bicycling as alternatives to driving.
  4. Public transportation strategies. These strategies encourage greater use of, and aim to expand the availability of, public transportation.
  5. Regional ride-sharing, car-sharing, and commuting strategies. These strategies expand services and provide incentives to travelers to choose transportation options other than driving alone.
  6. Operational and intelligent transportation system (ITS) strategies. These strategies improve the operation of the transportation system to make better use of existing capacity.
  7. Vehicle efficiency and alternative fuel strategies. These strategies improve the fuel efficiency of vehicles and increase the use of alternative fuels.

The project team discussed the possibility of developing regionally specific estimates of the GHG reduction potential of the mitigation measures presented in Moving Cooler. However, the group eventually elected not to pursue this option due to limitations in the availability of necessary data, time, and resources. Instead, stakeholders discussed and voted on the most important and feasible strategies at the Pilot Project's November 2010 workshop, described in more detail in Section IV. The results of this poll are presented in Appendix G: Priority Transportation Strategies for Cape Cod.

Baseline Data for Scenario Development

The model used to develop the scenarios required baseline transportation and growth data.

Transportation Data

Transportation data required for the Pilot Project consisted of the following existing baseline data, which were used to estimate how changes in specific factors would result in decreased VMT:

Typically, approaches to estimating regional VMT are done using four-step regional transportation models that consist of the following steps:

  1. Trip generation - estimation of the number of trips that occur daily within the study area.
  2. Trip distribution - development of assumptions about trip origins and destinations.
  3. Modal choice or split - estimation of the percentage of trips made by different modes (e.g., personal vehicle, mass transit, bicycle, walking).
  4. Trip assignment - assignment of the trips calculated in steps 1-3 to specific transportation routes.

Both the Cape Cod Commission and MassDOT have four-step models that cover Cape Cod and that were used to generate baseline data for the Pilot Project.

However, these regional models may not capture local effects of changes in the urban design and planning of neighborhoods that can also decrease travel demand. Consequently, traditional models may be augmented by other approaches, such as the 5D estimation method,[18] which was used for the Pilot Project. This method enables the estimation and comparison of local effects on VMT due to five factors: design, density, diversity, destination accessibility, and distance to transit. These factors can be measured in a number of ways based on available data. In the case of the Pilot Project, design was measured as street network density (road miles per square mile), density as household density (units per acre), diversity as ratio of population to jobs, destination accessibility as distance of neighborhoods to other regional destinations, and distance to transit as number of people served by transit service areas.

The 5D method consisted of the following steps:

  1. Measure the factor in the first scenario.
  2. Measure the factor in the second scenario.
  3. Calculate the percent change.
  4. Multiply by a specific variable elasticity to calculate related decreases in VMT.

Generally, elasticity is a ratio used to measure the change of one variable due to another variable. The 5D method measures the responsiveness of changes in VMT to one of the 5D variables. So, for every percent increase in any one D, there is a related decrease in VMT. The elasticity assumptions used were taken from a meta-analysis completed by Reid Ewing and Robert Cervero.[19] Cape Cod-specific assumptions were not available at the time of the study, but these elasticities are easily adjustable within the scenario planning tool (CommunityViz) analysis.

For mode choice, the transportation model available included the following mode categories:

  1. Passenger Vehicles
  2. Light Duty Vehicles
  3. Medium Trucks
  4. Heavy Trucks

Bicycle, pedestrian, and transit were not included. Due to the lack of available regional mode share data, no assumptions were made for these missing modes and no change in mode share was assumed for any of the scenarios. However, VMT reductions due to people shifting from driving to biking or walking for short trips were implicitly captured in the 5D analysis. Assumptions about transit mode shift can be revisited in the future. The Technical Scenario Report contains more information on this topic.

Growth Assumptions

Future projected growth values for households and employment were held constant across scenarios to provide a one-to-one comparison of any differences among the scenarios. The growth values were derived from U.S. Census 2000 projections for 2030 new growth in population, employment, and households (see Table 1). These figures were the best available data at the time and were vetted with the Cape Cod Commission, which did not have alternative estimates. However, the projections were based on previous periods of rapid growth and are thus considered overestimated. The 2010 Census, which became available after the project was complete, provides an update to the growth trend and shows a decrease in growth for the study area over the past 10 years.[20] The assumption for growth is variable and can be changed in the analysis for future use.

Table 1: Population, Employment and Household Estimates Used in Analysis

Growth Type Base Year (2008) Horizon Year (2030) Delta (2030 - 2008)
Population 224,335 284,335 60,000
Households 95,660 123,660 28,000
Employment 91,238 107,738 16,500

The baseline data and the estimates for future growth did not include the summer and winter fluctuations on the Cape. Neither Census projections nor the supplied transportation modeling results included summer population and employment. The scenario planning consultant did develop some multiplier assumptions by town for summer population increases based on available summer population estimates and vacant home data from the U.S. Census. However, similar data on seasonal employment were not available. Thus, the Pilot Project did not incorporate seasonal and therefore recreational data but did identify ways in which that data could be validated and used in the future.

Observations and Recommendations

Assessing Data Requirements

Observation 7: Scenario planning is an inherently data-intensive process and can require data beyond what some regions already have. Introducing a climate change mitigation and adaptation focus to the Pilot Project added a layer of complexity and uncertainty to the data collection. As a pilot project, it was initially unclear what data would be necessary to complete the preliminary scenario development and indicator analysis as well as what data would be available and feasible to access or develop within the time and resource constraints of the project.

Assessing Sea-Level Rise Impacts

Observation 8: Models that anticipate the effects of SLR on coastal areas are generally not location-specific and do not take into account coastal dynamics at a highly localized scale. The Pilot Project was able to account for the specific factors affecting Cape Cod by creating a locally-defined vulnerability map through an expert elicitation, but the resulting map did not provide projections on the magnitude of the impact. The Technical Committee and coastal and climate scientists involved in the expert elicitation ultimately determined that producing specific estimates of SLR for sub-regions of Cape Cod was not feasible given currently available data and that collecting the necessary data was beyond the scope and budget of the Pilot Project.

Developing GHG Mitigation Measures

Observation 9: Not all GHG mitigation measures will be feasible - politically, financially, or otherwise. Furthermore, local agencies in the project area may not have the authority or ability to implement certain measures. Strategies to reduce GHG emissions can focus on VMT reduction, fuel efficiency, vehicle technology, or operational efficiency. With the exceptions of VMT reduction, technology of local government fleet vehicles, development of alternative fuel stations, and some implementation of ITS, these are primarily the purview of the state and federal governments. VMT reduction is the primary mechanism through which local and regional land use and transportation investment decisions can have an impact. The impact of density, land use mix, and transit access on VMT can be modeled and assessed easily in scenario planning. Actions that aim to change behavior through pricing, incentives and other means are more difficult to model, and need additional time and resources to integrate into the scenario planning process.

Observation 10: General estimates for the emission reduction potential of mitigation measures exist. The report Moving Cooler, for instance, presents national and some regional estimates for a variety of GHG emissions mitigation measures. However, such estimates are not transferable to the unique characteristics of Cape Cod. Development of Cape Cod-specific estimates would have required significant investment of time and resources and the availability of detailed data sets. Therefore, GHG mitigation measures other than VMT reduction were not incorporated into the scenario development process.

Developing Baseline Data

Observation 11: The Pilot Project originally intended to focus on a place characterized by the presence of both gateway communities and seasonal recreational travel. The peak summer population of Cape Cod is believed to be triple the year-round population. Accounting for a more precise interpretation of this trend into the Pilot Project's refined scenario would have had a drastic impact on VMT and GHG emissions. However, seasonal fluctuations in Cape Cod's population and travel patterns were not sufficiently captured in the scenario planning process due to lack of data and the difficulty in quantifying recreational travel demand.

Observation 12: The transportation model used for the Pilot Project did not have data on bicycle, walking, or transit mode share. The 5D analysis used was able to account for some mode shift to bicycle and walking, but proposed changes to transit service frequency did not result in any changes in assumptions about mode split within the scenario planning model.

Observation 13: During the scenario planning workshop, participants expressed concern over the accuracy of baseline data, particularly with the amount of growth in jobs and housing that they were being asked to allocate. Participants suggested that the growth figures being used were too high, as the projections were based on previous periods of rapid growth. Additionally, in some instances, participants' questions regarding the presentation of the information on potential areas vulnerable to SLR diverted their concentration from evaluating the tradeoffs between growth placement, climate change adaptation and mitigation, resource protection, and identifying opportunities for the future.

Another key strength of the scenario planning process is the ability of participants to consider the implications of their actions for a specific set of indicators. When participants in a scenario planning exercise focus too intently on the accuracy of the data - as occurred to some degree in the Pilot Project - it can compromise their decision-making process. Validating projection figures with stakeholders prior to the scenario planning should prevent this from happening and ensure that the exercise is realistic. Addressing these issues will save time, avoid confusion, and make participants more informed and effective in their actions.

Updated: 12/23/2016
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