A Study on the Effect of Socioeconomic and Demographic Factors on Energy Consumption in Residential, Commercial, and Industrial Settings
A Case Study of West Virginia
DOI:
https://doi.org/10.55632/pwvas.v97i1.1107Keywords:
Energy Consumption, Socioeconomic FactorsAbstract
This research investigates the effects of socioeconomic and demographic factors on energy consumption across residential, commercial, and industrial settings with a focus on poverty, illiteracy, underserved and rural counties, and the prevalence of Black and Hispanic populations. The study aims to shed light on how these factors contribute to energy consumption patterns. The study was done using multiple linear regression and data from national surveys and census. The results indicate that socioeconomic and demographic factors, particularly poverty, educational attainment, and the proportion of the Black population, significantly influence energy consumption in the residential sector. Specifically, poverty and a higher Black population are positive predictors of energy use, while illiteracy is a negative predictor. Similar trends are observed in commercial energy consumption; however, industrial energy use does not appear to be affected by these factors. This suggests that other elements, such as geographical location, scale of production, operational practices, governmental policies, and others, play a role in industrial energy consumption. By identifying the connections between socioeconomic and demographic factors and energy consumption, this research contributes to the broader discussion on energy equity and sustainability and highlights the urgent need for energy policies for reliable and affordable energy for all communities.
References
Administration, U. S. E. I. (2015). Residential Energy Consumption Survey (RECS): 2015, Housing Characteristics (U.S. Energy Information Administration. https://www.eia.gov/consumption/residential/data/2015/index.php?view=characteristics
Administration, U. S. E. I. (2021). State Energy Data System (SEDS): 2021 Edition (U.S. Energy Information Administration. https://www.eia.gov/state/seds/archive/seds2021.pdf
Agency, U. S. E. P. Pollution Prevention (P2) National Emphasis Areas (NEAs). U.S. Environmental Protection Agency. https://www.epa.gov/p2/p2-national-emphasis-areas-neas#:~:text=The%20Pollution%20Prevention%20(P2)%20Program%20has
Alcendor, D. J. (2020). Racial disparities-associated COVID-19 mortality among minority populations in the US. Journal of clinical medicine, 9(8), 2442.
Assessment, G. G. E. (2011). Global energy assessment. Laxenburg: IIASA (im Erscheinen).
Benson, C., Bishaw, A., & Glassman, B. (2023). Persistent poverty in counties and census tracts. US Census Bureau.
Berman, Y., Ben-Jacob, E., & Shapira, Y. (2016). The dynamics of wealth inequality and the effect of income distribution. PloS one, 11(4), e0154196.
Bohvalovs, Ģ., Kalnbaļķīte, A., Pakere, I., Vanaga, R., Kirsanovs, V., Lauka, D., Prodaņuks, T., Laktuka, K., Doļģe, K., & Zundāns, Z. (2023). Driving sustainable practices in vocational education infrastructure: A case study from Latvia. Sustainability, 15(14), 10998.
Bureau, U. S. C. (1960). Current Population Survey, Annual Social and Economic Supplements (CPS ASEC) (U.S. Census Bureau. https://www2.census.gov/programs-surveys/cps/techdocs/cpsmar23.pdf
Bureau, U. S. C. (2016a). ACS 1-Year Estimates Data Profiles
ACS DEMOGRAPHIC AND HOUSING ESTIMATES (American Community Survey. https://data.census.gov/table/ACSDP1Y2016.DP05?q=DP05&y=2016
Bureau, U. S. C. (2016b). ECNSVY Business Patterns County Business Patterns
Geography Area Series: County Business Patterns by Employment Size Class and Legal Form of Organization: 2016 (Economic Surveys. https://data.census.gov/table/CBP2016.CB1600CBP?g=010XX00US$0500000&y=2016&n=N0000.00
Bureau, U. S. C. (2019). Annual Estimates of Housing Units for Counties: April 1, 2010 to July 1, 2019 (CO-EST2019-ANNHU) (U.S. Census Bureau. https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-housing-units.html
Bureau, U. S. C. (2020). Decennial Census of Population and Housing: 2020 Redistricting Data (PL 94-171). U.S. Census Bureau. https://data.census.gov/table/DECENNIALPL2020.P1?t=Populations%20and%20People:Race%20and%20Ethnicity&g=040XX00US54&y=2020&d=DEC%20Redistricting%20Data%20(PL%2094-171)
Bureau, U. S. C. (2023). QuickFacts: West Virginia. U.S. Census Bureau. https://www.census.gov/quickfacts/fact/table/WV/PST045223
Charlier, D., & Kahouli, S. (2019). From residential energy demand to fuel poverty: income-induced non-linearities in the reactions of households to energy price fluctuations. The Energy Journal, 40(2), 101-138.
Chester, L., & Morris, A. (2011). A New Form of Energy Poverty Is the Hallmark of Liberalised Electricity Sectors. Australian Journal of Social Issues, 46(4), 435-459. https://doi.org/10.1002/j.1839-4655.2011.tb00228.x
Dallas, J., Hopkins, B., Cloney, M., Dhillon, E., Texakalidis, P., Nguyen, V., Ordon, M., El Tecle, N., Chen, T., Hsieh, P., Liu, J., Koski, T., & Dahdaleh, N. (2023). Using Machine Learning and Big Data for the Prediction of Venous Thromboembolic Events After Spine Surgery: A Single-Center Retrospective Analysis of Multiple Models on a Cohort of 6869 Patients. Journal of Craniovertebral Junction and Spine, 14(3), 221. https://doi.org/10.4103/jcvjs.jcvjs_69_23
Energy justice towards racial justice. (2020). Nature Energy, 5(8), 551-551. https://doi.org/10.1038/s41560-020-00681-w
Evans, M., & Chapman, A. (2024). Residential Immobility and Racial and Ethnic Disparities in Housing Quality. Sociology of Race and Ethnicity, 10(1), 31-50.
Gómez-Navarro, T., Calero-Pastor, M., Pellicer-Sifres, V., Lillo-Rodrigo, P., Alfonso-Solar, D., & Pérez-Navarro, Á. (2021). Fuel poverty map of Valencia (Spain): Results of a direct survey to citizens and recommendations for policy making. Energy Policy, 151, 112162.
Guez, I., Focht, G., Greer, M. L. C., Cytter-Kuint, R., Pratt, L.-t., Castro, D. A., Turner, D., Griffiths, A. M., & Freiman, M. (2021). Development of a Multimodal Machine-Learning Fusion Model to Non-Invasively Assess Ileal Crohn’s Disease Endoscopic Activity. https://doi.org/10.1101/2021.08.29.21262424
He, P., Liang, J., Qiu, Y., Li, Q., & Xing, B. (2020). Increase in Domestic Electricity Consumption From Particulate Air Pollution. Nature Energy, 5(12), 985-995. https://doi.org/10.1038/s41560-020-00699-0
Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3(6), 497-526.
Hosier, R. H., & Dowd, J. (1987). Household fuel choice in Zimbabwe: an empirical test of the energy ladder hypothesis. Resources and energy, 9(4), 347-361.
Katunský, D., Korjenic, A., Katunská, J., & Lopušniak, M. (2011). Evaluation of energy consumption for heating of industrial building in-situ. Engineering, 3(5), 470.
Kent, A. H., & Ricketts, L. (2021). Wealth gaps between White, Black and Hispanic families in 2019. Federal Reserve Bank of St. Louis On the Economy Blog.
Liu, E., Judd, B., & Santamouris, M. (2017). Challenges in Transitioning to Low Carbon Living for Lower Income Households in Australia. Advances in Building Energy Research, 13(1), 49-64. https://doi.org/10.1080/17512549.2017.1354780
Low, H. E. E. C. I. (2016). Lifting the High Energy Burden in America’s Largest Cities.
Ma, O., Cardoso de Oliveira, R. P., Rosenlieb, E., & Day, M. H. (2019). Sector-Specific Methodologies for Subnational Energy Modeling.
Ortiz, J., Jiménez Martínez, M., Alegría-Sala, A., Tirado-Herrero, S., González Pijuan, I., Guiteras Blaya, M., & Canals Casals, L. (2021). Tackling energy poverty through collective advisory assemblies and electricity and comfort monitoring campaigns. Sustainability, 13(17), 9671.
Pachauri, S., & Spreng, D. (2004). Energy use and energy access in relation to poverty. Economic and Political weekly, 271-278.
Patel, A. V., Shah, S., Patel, K., Mehta, P., Amin, N., Shah, C. C., & Prajapati, S. (2018). Prevalence of Post-Stroke Anxiety and Its Association With Socio-Demographical Factors, Post-Stroke Depression, and Disability. Neuropsychiatria I Neuropsychologia, 13(2), 43-49. https://doi.org/10.5114/nan.2018.79604
Qing, G., Luo, Y., Huang, W., Wang, W., Yue, Z., Wang, J., Li, Q., Jiang, S., & Sun, S. (2021). Driving factors of energy consumption in the developed regions of developing countries: A case of Zhejiang Province, China. Atmosphere, 12(9), 1196.
Rogulj, I., Peretto, M., Oikonomou, V., Ebrahimigharehbaghi, S., & Tourkolias, C. (2023). Decarbonisation Policies in the Residential Sector and Energy Poverty: Mitigation Strategies and Impacts in Central and Southern Eastern Europe. Energies, 16(14), 5443. https://doi.org/10.3390/en16145443
Ross, L., Drehobl, A., & Stickles, B. (2018). The high cost of energy in rural America: Household energy burdens and opportunities for energy efficiency. Washington DC: American Council for an Energy Efficient Economy.
U.S. Census Bureau, S. A. I., & Program, P. E. (2016). 2016 Poverty and Median Household Income Estimates - Counties, States, and National (U.S. Census Bureau. https://www.census.gov/data/datasets/2016/demo/saipe/2016-state-and-county.html
U.S. Department of Education, N. C. f. E. S. PIAAC Small Area Estimation (SAE) dataset: SAE_website_dataset.c15d59a2d7e219fcb6d1 (U.S. Department of Education. https://nces.ed.gov/surveys/piaac/sae
Vega-Perkins, J., Newell, J. P., & Keoleian, G. A. (2023). Mapping Electric Vehicle Impacts: Greenhouse Gas Emissions, Fuel Costs, and Energy Justice in the United States. Environmental Research Letters, 18(1), 014027. https://doi.org/10.1088/1748-9326/aca4e6
Xia, S., Yang, Y., Qian, X., & Xu, X. (2022). Spatiotemporal interaction and socioeconomic determinants of rural energy poverty in China. International Journal of Environmental Research and Public Health, 19(17), 10851.
Zhang, X., Xue, M., Lv, P., Wang, H., Li, Z., Zhou, J., & Li, S. (2023). Industrial Development Layout and Competitiveness Evaluation Based on Correlation Analysis between Power and Industry. SHS Web of Conferences,
Zheng, J., Yan, H., Zhong, Y., Que, H., & Shu, A. (2022). A Study on the Impact Mechanism of Social Mobility and Economic Conditions on Income Inequality of the United States. 19-27. https://doi.org/10.2991/978-94-6463-010-7_4
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Proceedings of the West Virginia Academy of Science applies the Creative Commons Attribution-NonCommercial (CC BY-NC) license to works we publish. By virtue of their appearance in this open access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.
