A data-driven mobility–energy typology framework for New York State

Clement Rames, AlÄ na M Wilson, Daniel Zimny-Schmitt, Carolina Neri, Joshua Sperling and Patricia Romero-Lankao
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Clement Rames: Institute for Advanced Architecture of Catalonia, Spain
Carolina Neri: 1877University of Colorado Boulder, USA

Environment and Planning B, 2021, vol. 48, issue 8, 2254-2271

Abstract: Exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals. At the same time, mapping key enablers, barriers, and risks for successfully meeting ambitious goals and targets (e.g. by geography, age, income, education, population density) offer important explanatory power as to context-specific challenges and opportunities. This paper explores how a highly geographically resolved understanding of social, economic, techno-infrastructural, environmental, and governance (SET E G) factors shape variations in technology adoption and associated mobility and energy outcomes in diverse communities of New York State, in terms of electric vehicle adoption rates, alternative commute mode choices, vehicles per household, and vehicle fuel economy. Results indicate the range of two to three times higher adoption rates for electric vehicles by more highly educated, wealthier, “Core Urban†populations relative to the other identified typologies, given the labels “Rural†, “Suburban†, and “Urban†populations. Additionally, commute mode choice is closely linked with population and employment density—more than 89% of Core Urbanites use transit or active modes, compared with just 26% and 18% of Suburban and Rural residents, respectively. Household vehicle ownership varies from approximately 1.9 vehicles per household in Rural areas to only 0.6 in Core Urban settings. Findings on differences among the four settlement types, which go beyond simply rural to urban contexts, suggest an important need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems statewide.

Keywords: Energy-efficient mobility; sociodemographics; high-resolution geospatial analysis; New York State (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:48:y:2021:i:8:p:2254-2271

DOI: 10.1177/2399808320974032

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