Strategic Intelligence Capabilities and Green Growth: A Cross-Country Regression Analysis Using World Bank Indicators
Issam EL AZZAOUI, Zineb DRISSI
Abstract. Strategic intelligence is a major factor that keeps getting more and more attention as a key enabler of national energy transitions, but there is very little research on its actual empirical impact on the increase of renewable electricity. This paper examines how three indicators: R&D spending, regulatory quality, and GDP per capita, affect the generation of renewable electricity in the countries of the world at different development levels. The study of panel data from the World Bank (2000–2021) is used to compare the economies of the OECD, the markets that are emerging, and Morocco as a developing country case. By employing both bivariate and multivariate regression models, the findings demonstrate that the factors of R&D and GDP evolution have a substantial effect on the outcomes of renewable energy (β = 15.767, p = 0.015; β = 0.0019, p=0.003), mainly in advanced economies. In emerging situations, economic growth and R&D have an individual negative significant correlation (β = -0.001, p = 0.0227; β = -8.070, p=0.0210) but no significant multivariate correlation, suggesting that these countries prioritize more immediate development challenges over renewable energy investments, whereas in Morocco, R&D seems to be the main driver of adoption even if the institutions are not strong. These results are consistent with the multidimensional conception of strategic intelligence as a capacity that depends on the context and is influenced by foresight, governance, and absorptive capacity. The research is a theoretical and empirical contribution to the understanding of the capabilities of long-term planning on the trajectories of sustainable energy.
Keywords
Strategic Intelligence, Renewable Energy, Innovation Policy, R&D Expenditure, Regulatory Quality, Energy Transition, Economic Development
Published online 4/25/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Issam EL AZZAOUI, Zineb DRISSI, Strategic Intelligence Capabilities and Green Growth: A Cross-Country Regression Analysis Using World Bank Indicators, Materials Research Proceedings, Vol. 64, pp 1082-1090, 2026
DOI: https://doi.org/10.21741/9781644904091-134
The article was published as article 134 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] REN21, Renewables 2023 global status report, REN21 Secretariat, Paris, 2023. https://www.ren21.net/reports/global-status-report/
[2] I. Miles, O. Saritas, The depth of the horizon: Searching, scanning and widening horizons, Foresight 14 (6) (2012) 530–545. https://doi.org/10.1108/14636681211284953
[3] A. Havas, M. Weber, Foresight as a governance tool to help shape the next production revolution, 50 éves a magyar jövőkutatás, Palatia Nyomda és Kiadó Kft, Győr, 2018, pp. 207–216. https://doi.org/10.2139/ssrn.3295746
[4] World Intellectual Property Organization, Global innovation index 2023, WIPO, 2023. https://www.wipo.int/global_innovation_index/en/2023/
[5] D. Czarnitzki, C. Lopes-Bento, Innovation subsidies: Does the funding source matter for innovation intensity and performance? Empirical evidence from Germany, ZEW Discussion Papers, No. 11-053 (2011), ZEW – Leibniz Centre for European Economic Research. https://hdl.handle.net/10419/48878
[6] World Bank, Worldwide Governance Indicators, World Bank Open Data, https://databank.worldbank.org/source/worldwide-governance-indicators
[7] A. Pegels, W. Lütkenhorst, Is Germany’s energy transition a case of successful green industrial policy? Contrasting wind and solar PV, Energy Policy 74 (2014) 522–534. https://doi.org/10.1016/j.enpol.2014.06.031
[8] B.K. Sovacool, How long will it take? Conceptualizing the temporal dynamics of energy transitions, Energy Res. Soc. Sci. 13 (2016) 202–215. https://doi.org/10.1016/j.erss.2015.12.020
[9] D. Popp, Induced innovation and energy prices, Am. Econ. Rev. 92 (1) (2002) 160–180. https://doi.org/10.1257/000282802760015658
[10] F.J. Aguilar, Scanning the business environment, Macmillan, New York, 1967.
[11] H. Lesca, N. Lesca, Weak signals for strategic intelligence: Anticipation tool for managers, ISTE Ltd and Wiley, London, 2011. DOI:10.1002/9781118602775
[12] E. Verdolini, L.D. Anadon, J. Lu, G.F. Nemet, The effects of expert selection, elicitation design, and R&D assumptions on experts’ estimates of the future costs of photovoltaics, Energy Policy 80 (2015) 233–243. https://doi.org/10.1016/j.enpol.2015.01.006
[13] M. Mazzucato, G. Semieniuk, Public financing of innovation: New questions, Oxford Rev. Econ. Policy 33 (1) (2017) 24–48. https://doi.org/10.1093/oxrep/grw036
[14] S. Fankhauser, J.B. Smith, R.S.J. Tol, Weathering climate change: Some simple rules to guide adaptation decisions, Ecol. Econ. 30 (1) (1999) 67–78. https://doi.org/10.1016/S0921-8009(98)00117-7

