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Study of Past and Future Spatiotemporal Patterns and Impact on Electricity Consumption for Sustainable Planning: A Case Study of El Paso, Texas

Author

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  • Joanne M. Moyer

    (Civil Engineering Department, University of Texas at El Paso, El Paso, TX 79968, USA)

  • Adeeba A. Raheem

    (Civil Engineering Department, University of Texas at El Paso, El Paso, TX 79968, USA)

Abstract

As cities continue to grow, their urban form continues to evolve. As a consequence of urban growth, the demand for infrastructure increases to meet the needs of a growing population. Understanding this evolution and its subsequent impingement on resources allows for planners, engineers, and decision-makers to plan for a sustainable community. Patterns and rate of urban expansion have been studied extensively in various cities throughout the United States (U.S.), utilizing remote sensing and geographic information system (GIS). However, minimal research has been conducted to understand urban growth rates and patterns for cities that possess borders, geological attributes, and/or protected areas that confine and direct the cities’ urban growth, such as El Paso, Texas. This study utilizes El Paso, Texas, as a case study to provide a basis for examining growth patterns and their possible impact on the electricity consumption resource, which lies on the U.S./Mexico and New Mexico borders, contains the largest urban park in the nation (Franklin Mountains State Park), and Ft. Bliss military base. This study conducted a change analysis for El Paso County to analyze specific areas of concentrated growth within the past 15-years (2001–2016). The results indicate that county growth has primarily occurred within the city of El Paso, in particular, Districts 5 (east side), 1 (west side), and 4 (northeast), with District 5 experiencing substantial growth. As the districts expanded, fragmentation and shape irregularity of developed areas decreased. Utilizing past growth trends, the counties’ 2031 land-use was predicted employing the Cellular Automata (CA)-Markov method. The counties’ projected growth was evenly distributed within El Paso city and outside city limits. Future growth within the city continues to be directed within the same districts that experienced past growth, Districts 1, 4, and 5. Whereas projected growth occurring outside the city limits, primarily focused within potential city annexation areas in accordance with the cities’ comprehensive plan, Plan El Paso. Panel data analysis was performed to investigate the relationship between urban dynamic growth patterns and electricity consumption. The findings suggest that, as urban areas expanded and fragmentation decreased, electricity consumption increased. Further investigation to include an expansion of urban pattern metrics, an extension of the time period studied, and their influence on electricity consumption is recommended. The results of this study provided a basis for decision-makers and planners with an understanding of El Paso’s concentrated areas of past and projected urban growth patterns and their influence on electricity consumption to mitigate possible fragmentation growth through informed decisions and policies to provide a sustainable environment for the community.

Suggested Citation

  • Joanne M. Moyer & Adeeba A. Raheem, 2020. "Study of Past and Future Spatiotemporal Patterns and Impact on Electricity Consumption for Sustainable Planning: A Case Study of El Paso, Texas," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8480-:d:428069
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    1. repec:cup:cbooks:9781108422536 is not listed on IDEAS
    2. Chen Liping & Sun Yujun & Sajjad Saeed, 2018. "Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-23, July.
    3. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Jie Zhao & Nguyen Xuan Thinh & Cheng Li, 2017. "Investigation of the Impacts of Urban Land Use Patterns on Energy Consumption in China: A Case Study of 20 Provincial Capital Cities," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    5. Brooks,Chris, 2019. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781108436823.
    6. Barry Kew & Brian D. Lee, 2013. "Measuring Sprawl across the Urban Rural Continuum Using an Amalgamated Sprawl Index," Sustainability, MDPI, vol. 5(5), pages 1-23, April.
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    Cited by:

    1. Long, Dengjie & Du, Junhua & Xin, Yongrong, 2023. "Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China," Resources Policy, Elsevier, vol. 85(PB).
    2. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).

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