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The Role of Energy Systems and AI in Energy Transition, Economic Resilience, and Sustainable Development

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 3443

Special Issue Editors

Dr. Mohga Bassim
*
E-Mail Website
Guest Editor
Department of Economics and International Studies, University of Buckingham, Buckingham MK18 1EG, UK
Interests: energy economics; development economics; econometrics; environmental economics
* Chief Guest Editor
Dr. Mohamed M. Elheddad

E-Mail Website
Guest Editor
Department of Economics and International Studies, University of Buckingham, Buckingham MK18 1EG, UK
Interests: energy economics; development economics; econometrics; environmental economics

Special Issue Information

Dear Colleagues,

The increasing pursuit of decarbonisation, energy security, and sustainable economic development is reshaping the global energy landscape. Transitioning from fossil fuels to renewable energy sources requires strategies to incorporate innovations and developments in technology, regulations, and policies. Artificial intelligence (AI) emerges as a potent tool for enhancing efficiency and optimising resource allocation, enhancing and accelerating the deployment of renewable energy systems.

This Special Issue explores the role of energy systems and AI in transitioning to renewable energy within the broader contexts of energy security, energy economics, and sustainable development. It aims to bring together cutting-edge research that examines, for example, how AI-driven innovations are reshaping energy markets, improving grid resilience, and supporting policy decision-making. It comes at a time of growing global concerns about the dangers of climate change and the urgent need for decarbonisation. The issue is also expected to address the ethical and regulatory implications of AI’s adoption in energy systems, ensuring that technological advancements align with societal and environmental goals.

This Special Issue focuses on the strategies necessary to integrate energy systems, technological advancements, policy reforms, and economic incentives for energy security, economics, and sustainable development. It also focuses on the role of artificial intelligence (AI) in accelerating energy transition, improving energy grid management, supporting data-driven policymaking, enhancing resource allocation, improving the efficiency of energy generation, distribution, and consumption, and optimising the integration of renewable energy. Furthermore, the issue explores the synergies between energy systems and AI in the context of energy security, economic development, and environmental sustainability.

The scope of this Special Issue covers a broad range of interdisciplinary topics at the intersection of energy systems, technology, economics, energy security, and economic development. It is expected to cover topics related to energy transition, energy poverty, energy security, conflicts, the use of AI in improving energy efficiency, forecasting demand and integrating renewable energy sources into traditional energy infrastructures, and the economics of other alternative fuels in future energy systems (e.g., hydrogen). Additionally, the issue will explore the role of foreign direct investments, trade integration, and the impact of environmental regulations on renewable energy transition. The issue also aims to offer new empirical evidence, case studies, insights, and recommendations.

This Special Issue aims to contribute to the ongoing academic and policy discussions and literature on energy transition. It is expected to offer perspectives on AI’s role in energy systems and integrate AI-driven solutions for energy transition, energy security, economic feasibility, and sustainability discussions.

We invite researchers and practitioners to contribute to this Special Issue and help to advance this field’s growing knowledge. The issue provides a platform for sharing insights, strategies, and solutions that address the challenges and opportunities of the global energy transition.

Dr. Mohga Bassim
Dr. Mohamed M. Elheddad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy systems
  • renewable energy
  • AI
  • energy transition
  • energy economics
  • energy security
  • energy poverty
  • sustainable development
  • technological innovation
  • decarbonisation

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Published Papers (3 papers)

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Research

21 pages, 1342 KB  
Article
Sustainable Entrepreneurial Capacity at the Macro Level: Macroeconomic and Energy Conditions in Selected European Economies
by Anna Misztal, Anita Fajczak-Kowalska and Magdalena Kowalska
Energies 2026, 19(6), 1399; https://doi.org/10.3390/en19061399 - 10 Mar 2026
Viewed by 356
Abstract
The paper explores the association between macroeconomic conditions (MCI), energy-related factors (ENI), and sustainable entrepreneurial capacity at the macro level (SEI) in Denmark, Germany, the Netherlands, Poland, and Sweden over the period 2008–2024. SEI is constructed as a composite index that reflects the [...] Read more.
The paper explores the association between macroeconomic conditions (MCI), energy-related factors (ENI), and sustainable entrepreneurial capacity at the macro level (SEI) in Denmark, Germany, the Netherlands, Poland, and Sweden over the period 2008–2024. SEI is constructed as a composite index that reflects the macro-level economic, social, and environmental dimensions of enterprise activity rather than firm-level entrepreneurial behaviour. Both MCI and ENI capture multidimensional external macroeconomic and energy-related conditions. The study employs correlation analysis, ordinary least squares (OLS), and seemingly unrelated regressions (SUR). The results indicate cross-country heterogeneity (p < 0.05). MCI has heterogeneous effects that support SEI in more advanced countries and have weaker or context-dependent effects in less advanced countries. ENI has heterogeneous effects that support SEI in systems based on renewables and weaken SEI in situations with high costs and fossil fuels. SUR results indicate strong systemic interdependence among the economic, social, and environmental pillars, highlighting their joint and mutually reinforcing role at the macro level. In general, the results indicate that macroeconomic and energy policies are related to SEI in country-specific ways, underscoring the importance of policy frameworks tailored to national structural characteristics. Full article
(This article belongs to the Special Issue The Role of Energy Systems and AI in Energy Transition, Economic Resilience, and Sustainable Development)
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12 pages, 727 KB  
Article
A New Lens on the Sustainability of the AI Revolution
by Pierluigi Contucci, Godwin Osabutey and Filippo Zimmaro
Energies 2026, 19(2), 525; https://doi.org/10.3390/en19020525 - 20 Jan 2026
Viewed by 612
Abstract
We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological [...] Read more.
We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise in scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today’s AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: these countries account for a large share of global GDP and energy use and are therefore expected to be most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth. Full article
(This article belongs to the Special Issue The Role of Energy Systems and AI in Energy Transition, Economic Resilience, and Sustainable Development)
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29 pages, 6058 KB  
Article
Machine Learning-Based Carbon Compliance Forecasting and Energy Performance Assessment in Commercial Buildings
by Aditya Ramnarayan, Felipe de Castro, Andres Sarmiento and Michael Ohadi
Energies 2025, 18(15), 3906; https://doi.org/10.3390/en18153906 - 22 Jul 2025
Viewed by 1778
Abstract
Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along [...] Read more.
Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along with relevant ambient weather data and building characteristics, with the objective of predicting the buildings’ energy performance through the year 2040. Using the forecasted emission results, the portfolio of buildings is analyzed for the incurred carbon non-compliance fees based on their on-site fossil fuel CO2e emissions to assess and pinpoint facilities with poor energy performance that need to be prioritized for decarbonization. The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. To comply with these regulations, the building portfolio would need to reduce on-site fossil fuel CO2e emissions by an average of 58,246 metric tons (22.10 kg/sq. ft.) annually, totaling 640,708 metric tons (22.10 kg/sq. ft.) over a period of 11 years. This study demonstrates the potential for robust machine learning models to generate accurate forecasts to evaluate carbon compliance and guide prompt action in decarbonizing the built environment. Full article
(This article belongs to the Special Issue The Role of Energy Systems and AI in Energy Transition, Economic Resilience, and Sustainable Development)
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