U.S. State Energy Transition Dynamics: Renewable Adoption, Fossil Displacement, and Forecasting with Ensemble Machine Learning

References

[1] U.S. Energy Information Administration, “State Energy Data System (SEDS),” U.S. Department of Energy, 2024. https://www.eia.gov/state/seds/

[2] M. A. Delmas and M. J. Montes-Sancho, “U.S. state policies for renewable energy: Context and effectiveness,” Energy Policy, vol. 39, no. 5, pp. 2273–2288, 2011. https://doi.org/10.1016/j.enpol.2010.10.036

[3] R. Wiser, G. Barbose, J. Heeter, T. Mai, L. Bird, D. Millstein, D. Reiter, and E. Lantz, “Assessing the costs and benefits of U.S. renewable portfolio standards,” Environmental Research Letters, vol. 12, no. 9, p. 094023, 2017. https://doi.org/10.1088/1748-9326/aa7937

[4] G. Kavlak, J. McNerney, and J. E. Trancik, “Evaluating the causes of cost reduction in photovoltaic modules,” Energy Policy, vol. 123, pp. 700–710, 2018. https://doi.org/10.1016/j.enpol.2018.08.015

[5] R. York and S. E. Bell, “Energy transitions or additions? Why a transition from fossil fuels may be more challenging than other energy transitions,” Energy Research & Social Science, vol. 51, pp. 40–43, 2019. https://doi.org/10.1016/j.erss.2019.01.020

[6] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774, 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

[7] F. W. Geels, “Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study,” Research Policy, vol. 31, no. 8–9, pp. 1257–1274, 2002. https://doi.org/10.1016/S0048-7333(02)00062-8

[8] J. Markard, R. Raven, and B. Truffer, “Sustainability transitions: An emerging field of research and its prospects,” Research Policy, vol. 41, no. 6, pp. 955–967, 2012. https://doi.org/10.1016/j.respol.2012.02.013

[9] M. Arman, M. N. Hasan, and I. H. Rasel, “Clean energy transition in the USA: Big data analytics for renewable energy forecasting and carbon reduction,” J. Management World, vol. 2024, no. 3, pp. 192–206, 2024. https://doi.org/10.53935/jomw.v2024i4.1196

[10] G. P. Herrera, M. Constantino, B. M. Tabak, H. Pistori, J. Su, and A. Naranpanawa, “Data on forecasting energy prices using machine learning,” Data in Brief, vol. 25, p. 104122, 2019. https://doi.org/10.1016/j.dib.2019.104122

[11] [X. Guo, R. Kou, and X. He, “Towards carbon neutrality: Machine learning analysis of vehicle emissions in Canada,” Sustainability, vol. 16, no. 23, p. 10526, 2024. https://doi.org/10.3390/su162310526

[12] Y. Hao and Q. He, “Forecasting U.S. fossil energy consumption: Advancing accuracy with multilayer perceptron and residual learning,” J. Energy Research and Reviews, vol. 16, no. 6, pp. 13–26, 2024. https://doi.org/10.9734/jenrr/2024/v16i6354

[13] I. Nica, I. Georgescu, and J. Kinnunen, “Evaluating renewable energy’s role in mitigating CO₂ emissions: A case study of solar power in Finland using the ARDL approach,” Energies, vol. 17, no. 16, p. 4152, 2024. https://doi.org/10.3390/en17164152

[14] J. G. MacKinnon, “Numerical distribution functions for unit root and cointegration tests,” J. Applied Econometrics, vol. 11, no. 6, pp. 601–618, 1996. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6%3C601::AID-JAE417%3E3.0.CO;2-T

[15] N. E. Benti, M. D. Chaka, and A. G. Semie, “Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects,” Sustainability, vol. 15, no. 9, p. 7087, 2023. https://doi.org/10.3390/su15097087

[16] S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples),” Biometrika, vol. 52, no. 3–4, pp. 591–611, 1965. https://doi.org/10.2307/2333709

[17] R. B. D’Agostino, A. Belanger, and R. B. D’Agostino Jr., “A suggestion for using powerful and informative tests of normality,” The American Statistician, vol. 44, no. 4, pp. 316–321, 1990. https://doi.org/10.1080/00031305.1990.10475751

[18] D. A. Dickey and W. A. Fuller, “Distribution of the estimators for autoregressive time series with a unit root,” J. Amer. Stat. Assoc., vol. 74, no. 366, pp. 427–431, 1979. https://doi.org/10.2307/2286348

[19] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, “Testing the null hypothesis of stationarity against the alternative of a unit root,” J. Econometrics, vol. 54, no. 1–3, pp. 159–178, 1992. https://doi.org/10.1016/0304-4076(92)90104-Y

[20] W. H. Kruskal and W. A. Wallis, “Use of ranks in one-criterion variance analysis,” J. Amer. Stat. Assoc., vol. 47, no. 260, pp. 583–621, 1952. https://doi.org/10.2307/2280779

[21] T. S. Breusch and A. R. Pagan, “A simple test for heteroscedasticity and random coefficient variation,” Econometrica, vol. 47, no. 5, pp. 1287–1294, 1979. https://doi.org/10.2307/1911963

[22] G. C. Chow, “Tests of equality between sets of coefficients in two linear regressions,” Econometrica, vol. 28, no. 3, pp. 591–605, 1960. https://doi.org/10.2307/1910133

[23] H. White, “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity,” Econometrica, vol. 48, no. 4, pp. 817–838, 1980. https://doi.org/10.2307/1912934

[24] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 785–794, 2016. https://doi.org/10.1145/2939672.2939785

[25] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30, pp. 3146–3154, 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf

[26] C. Odedina, “Impact of big data on marketing strategy and consumer behavior analysis in the US,” SSRN Working Paper, 2023a. https://dx.doi.org/10.2139/ssrn.4520361

[27] C. Odedina, “How business analytics can help improve supply chain efficiency in the US,” SSRN Working Paper, 2023. https://dx.doi.org/10.2139/ssrn.4520339

@article{1716160,
      author = {Christopher Odedina, Tochuckwu Akaegbusi},
      title = {U.S. State Energy Transition Dynamics: Renewable Adoption, Fossil Displacement, and Forecasting with Ensemble Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3073-3087},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716160.pdf},
      abstract = {The study investigates the dynamics of the United States’ energy transition in all 50 states (including the District of Columbia), over the period 1990 to 2024, using the United States Energy Information Administration’s State Energy Datasets. Using a combination of panel econometrics, clustering, and machine learning, this study aims to identify major patterns in the uptake of renewable energy sources in the United States, along with their economic effects. The preliminary results show significant cross-state heterogeneity and structural change in 2008 as the major turning point in the transition path. In addition, four major archetypes of states are identified, namely: early movers, progressive large-scale adopters, gradually transitioning states, and fossil-locked states, which are characterized by their high dependence on conventional sources of energy. Furthermore, a higher share of renewable energy reduces total energy expenditure, taking into account factors such as energy demand and prices. Based on the machine learning models, this study demonstrates a high degree of predictive accuracy (R² ≈ 0.77). Also, the interpretability analysis of the results demonstrates that lagged renewable share is the dominant predictor of future adoption. This implies a high degree of path dependence in which historical energy structures strongly constrain current transition dynamics. Thus, the U.S. energy transition is heterogeneous, cost-reducing, and structurally persistent, with inertia in renewable adoption representing a key barrier to accelerated decarbonization.},
      keywords = {Energy transition; Renewable energy; Fossil fuels; U.S. state panel data; XGBoost; LightGBM; SHAP; Structural break; Panel regression; Decarbonisation.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716160}
  }