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Artificial Intelligence and Systemic Resilience: Energy, Finance, and Logistics in Sustainable Supply Chains

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

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 2344

Special Issue Editors

Dr. Blanka Tundys

E-Mail Website
Guest Editor
Faculty of Economy, Finance and Management, Institute of Management, University of Szczecin, Cukrowa 8, 71-004 Szczecin, Poland
Interests: green supply chain; sustainable supply chain; close-loop chain; eco-innovation; city logistics; logistics; transport systems; city economics; strategy in logistics; transportation; performance measurement in logistics and supply chain; efficiency in logistics; circular economy; smart city; risk management in the supply chain; uncertainty; vulnerability of the supply chain; resilience in supply chain; uncertainty supply chain; energy poverty
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Magdalena Zioło

E-Mail Website
Guest Editor
Institute of Management, Pomeranian University in Słupsk, 76-200 Słupsk, Poland
Interests: finance and banking; sustainable finance and green banking; sustainable development; public finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of climate change, energy transition, geopolitical uncertainty, and digital transformation, the concept of systemic resilience has become central to the effective functioning of supply chains and energy systems. Systemic resilience encompasses energy, financial, and operational dimensions, and is crucial for ensuring business continuity, risk management, and sustainability across industries. This Special Issue seeks to explore how Artificial Intelligence (AI) and related digital technologies (machine learning, big data analytics, digital twins, IoT) can be applied to enhance the resilience of complex supply chains—both within and beyond the energy sector. The aim is to highlight integrative approaches that combine energy resilience, financial robustness, and ESG goals, contributing to long-term stability and efficiency. We invite contributions that address the multi-dimensional nature of resilience in supply chains, focusing on the role of AI in risk anticipation, resource optimization, and real-time adaptation. We are particularly interested in papers that present theoretical models, practical applications, and case studies at the intersection of energy systems, financial stability, and sustainable logistics.

In the context of climate change, geopolitical uncertainty, and advancing digitalization, research on the energy and financial resilience of entire logistics and production systems is gaining increasing importance. Particular challenges concern energy management in supply chains, including in transport, industry, warehousing, and services.

Topics of interest include, but are not limited to: AI for energy and operational resilience in supply chains Financial resilience and ESG compliance in logistics and energy-intensive sectors Predictive models of energy and financial shocks using AI and machine learning Sustainable and adaptive supply chain networks supported by digital technologies The role of AI in mitigating disruptions in transport, storage, and distribution Resilience indicators and measurement frameworks in the context of SDGs AI-based tools for decarbonization and energy efficiency in supply chains Intelligent resource allocation in response to energy price volatility AI for early warning systems and dynamic response to crisis events Case studies of resilient supply chains in the energy and manufacturing sectors.

Dr. Blanka Tundys
Prof. Dr. Magdalena Zioło
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

  • supply chains
  • energy and financial resilience
  • energy management in supply chains
  • artificial intelligence and systemic resilience

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Published Papers (2 papers)

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Research

39 pages, 1204 KB  
Article
Artificial Intelligence for Energy and Cost Resilience in Sustainable Supply Chains: A Dynamic LCA/TCO Approach to Multimodal Transport
by Tomasz Neumann and Paweł Wierzbicki
Energies 2026, 19(9), 2169; https://doi.org/10.3390/en19092169 - 30 Apr 2026
Viewed by 189
Abstract
The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; [...] Read more.
The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; however, they often rely on static assumptions and averaged data, limiting their ability to capture real-world variability. This study proposes an AI-enhanced LCA–LCC/TCO framework for the integrated evaluation of decarbonised multimodal Door-to-Port transport systems. Artificial intelligence is embedded directly into the life cycle inventory and cost inventory stages to generate scenario-specific estimates of energy consumption, greenhouse gas emissions, and operational costs. The framework is demonstrated through a case study of a multimodal Door-to-Port transport chain comprising road pre-haulage, rail line-haul, and port terminal operations. Three scenarios are analysed: conventional, partially decarbonised, and fully decarbonised configurations. The results indicate that partial decarbonization reduces greenhouse gas emissions by more than 60% compared to the baseline while achieving the lowest total cost of ownership. Full decarbonization achieves emission reductions exceeding 95% but is associated with slightly higher costs under current assumptions. Sensitivity analysis verifies the robustness of the relative scenario ranking under different energy prices, carbon pricing, and electricity carbon intensity. The proposed framework provides a structured decision-support framework for logistics operators, port authorities, and policymakers seeking cost-effective pathways to low-emission multimodal transport systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Systemic Resilience: Energy, Finance, and Logistics in Sustainable Supply Chains)
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23 pages, 942 KB  
Article
Who Wins the Energy Race? Artificial Intelligence for Smarter Energy Use in Logistics and Supply Chain Management
by Blanka Tundys and Tomasz Wiśniewski
Energies 2026, 19(2), 305; https://doi.org/10.3390/en19020305 - 7 Jan 2026
Cited by 1 | Viewed by 1752
Abstract
Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, [...] Read more.
Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, highlighting both its potential to enhance energy efficiency and reduce greenhouse gas emissions, as well as its inherent environmental costs associated with digital infrastructures such as data centers. The findings reveal the dual character of digitalization: while predictive algorithms and digital twin applications facilitate demand forecasting, process optimization, and real-time adaptation to market fluctuations, they simultaneously generate additional energy demand that must be offset through renewable energy integration and intelligent energy balancing. The analysis underscores that the effectiveness of AI deployment cannot be captured solely through economic metrics but requires a holistic evaluation framework that incorporates environmental and social dimensions. Moreover, regional disparities are identified, with advanced economies accelerating AI-driven green transformations under regulatory and societal pressures, while developing economies face constraints linked to infrastructure gaps and investment limitations. The analysis emphasizes that AI-driven predictive models and digital twin applications are not only tools for energy optimization but also mechanisms that enhance systemic resilience by enabling risk anticipation, adaptive resource allocation, and continuity of operations in volatile environment. The contribution of this study lies in situating AI within the digital–green synergy discourse, demonstrating that its role in logistics decarbonization is conditional upon integrated energy–climate strategies, organizational change, and workforce reskilling. By synthesizing emerging evidence, this article provides actionable insights for policymakers, managers, and scholars, and calls for more rigorous empirical research across sectors, regions, and time horizons to verify the long-term sustainability impacts of AI-enabled solutions in supply chains. Full article
(This article belongs to the Special Issue Artificial Intelligence and Systemic Resilience: Energy, Finance, and Logistics in Sustainable Supply Chains)
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