A Statistical Study on the Efficacy of Energy Policy within the United States

Authors

  • Chunting Zhong Shenzhen College of International Education
  • Neil Agarwal

DOI:

https://doi.org/10.47611/jsrhs.v13i3.7355

Keywords:

Two-Sample T-test, Energy Policies Evaluation, Ethanol Production, Ethanol Consumption

Abstract

This study evaluates the effectiveness of three key energy policies promoting ethanol as a biofuel: The Energy Policy Act of 2005, The Energy Independence and Security Act of 2007, and The Bipartisan Budget Act of 2018. Using rigorous statistical analysis, including two-sample t-tests with Python’s pandas and scipy.stats libraries, we assess the impact of these policies on ethanol production and consumption.

Our comprehensive methodology includes temporal segmentation and statistical measurements, exploring each policy’s provisions, industry alignment, stakeholder engagement, regulatory frameworks, timing, policy synergy, and external influences. Findings reveal the Energy Independence and Security Act as the most effective, significantly boosting ethanol production and consumption. The Energy Policy Act also shows a notable impact, though less pronounced. Conversely, the Bipartisan Budget Act exhibits limited correlation with ethanol metrics, with some significance at a 90% confidence level.

The study underscores the importance of clear objectives, expert engagement, strategic timing, tailored provisions, stakeholder alignment, and robust regulatory frameworks in crafting effective ethanol policies. By providing a thorough evaluation of these policies, the research informs future policy-making efforts, contributing to a more sustainable and greener energy landscape.

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Author Biography

Neil Agarwal

"Neil T. Agarwal is a distinguished Operations Research Analyst and Principal Data Scientist with a robust background in both government and consulting sectors. Currently serving with the Federal Aviation Administration in Washington D.C., Neil has been instrumental in developing innovative statistical methodologies to assess aviation demand. His role extends beyond modeling, as he also advises on aviation policy and implements advanced machine learning techniques to improve operational efficiencies.

Before joining the FAA, Neil enhanced the Department of Energy's capabilities in energy consumption analysis and petroleum product pricing from January 2017 to October 2019. His contributions included developing analytical tools and statistical models that significantly improved data processing and economic forecasting.

In addition to his analytical prowess, Neil thrives in a mentoring role. As an adjunct lecturer at the London School of Economics and Political Science, he passionately imparts his expertise in data analysis and business analytics to undergraduates. His courses—focused on management decision-making and applied modeling—are complemented by practical training in data visualization using Tableau and Excel, fostering a hands-on learning environment."

References or Bibliography

Bipartisan Budget Act of 2018, Pub. L. No. 115-123 Stat. 79 (2018). https://www.govinfo.gov/content/pkg/PLAW-115publ123/pdf/PLAW-115publ123.pdf

British Medical Journal [BMJ]. (2020, October 28). 2. Mean and standard deviation. The Bmj. https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/2-mean-and-standard-deviation

International Energy Agency [IEA]. (2021, August 24). Energy Policy Act of 2005 (Energy Bill). IEA. https://www.iea.org/policies/1492-energy-policy-act-of-2005-energy-bill

Lakens, D. D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 3. https://doi.org/10.3389/fpsyg.2013.00863

United States Environmental Protection Agency [EPA]. (2024, April 4). Summary of the Energy Independence and Security Act. United States Environmental Protection Agency. https://www.epa.gov/laws-regulations/summary-energy-independence-and-security-act

University of California San Diego [UCSD]. (n.d.). (Student’s) t-tests. UCSD Psyc 201ab / CSS 205 / Psyc 193. https://vulstats.ucsd.edu/t-tests.html

U.S. Energy Information Administration [EIA]. (2024). Table 10.3 Fuel Ethanol Overview [Dataset]. In TOTAL ENERGY. U.S. Energy Information Administration. https://www.eia.gov/totalenergy/data/monthly/index.php

Zeldow, B., Leavitt, T., & Hatfield, L. (n.d.). Difference-in-Differences. Difference-in-Differences. https://diff.healthpolicydatascience.org/

Published

08-31-2024

How to Cite

Zhong, C., & Agarwal, N. (2024). A Statistical Study on the Efficacy of Energy Policy within the United States. Journal of Student Research, 13(3). https://doi.org/10.47611/jsrhs.v13i3.7355

Issue

Section

HS Research Articles