AI Is Quietly Rewiring the Clean Energy Transition, Landmark Review Finds

Artificial intelligence has long been pitched as a general-purpose accelerator for science and industry, but a sweeping new review argues that in the clean technology sector it is doing something more profound: changing how energy and environmental systems are designed, operated, and governed in the first place. The study, published in the Journal of Big Data, synthesizes more than 450 research papers across sectors that are usually analyzed in isolation, from renewable power forecasting and grid management to hydrogen production, bioenergy, carbon capture, and recycling. Led by Chukwuebuka Joseph Ejiyi of Chengdu University of Technology together with colleagues in China and the Republic of Korea, the review positions itself as one of the most comprehensive cross-sectoral mappings of artificial intelligence in clean technologies compiled to date, and its central claim is blunt: incremental optimization is no longer the right frame for what machine learning is doing to the energy transition.

The authors begin from a diagnosis of fragmentation. Previous surveys have typically zeroed in on a single application domain, such as short-term forecasting of solar and wind output, smart grid control, or materials discovery for carbon capture, and each of those silos has produced genuinely useful insights. The problem, they argue, is that the clean energy transition is a system-of-systems challenge, in which a forecasting model feeds a trading algorithm, which shapes storage dispatch, which determines whether a hydrogen electrolyzer runs on surplus renewables or on fossil-heavy electricity. By treating these domains as separate literatures, researchers have missed the coupling effects that determine real-world sustainability outcomes. The new review stitches those threads together, assessing applications across renewable energy forecasting, energy storage, hydrogen and bioenergy, carbon capture, utilization, and storage, and recycling pathways within the circular economy.

Technically, the review highlights how data-driven models have matured across each of these domains. In renewable forecasting, machine learning systems now blend satellite imagery, numerical weather prediction, and sensor telemetry to predict generation at horizons ranging from minutes to days, reducing the uncertainty that grid operators must cover with reserves. In storage, algorithms learn charge and discharge strategies that extend battery life while arbitraging price volatility. In hydrogen and bioenergy, models screen catalysts, optimize fermentation and gasification conditions, and predict yield from variable feedstocks. In carbon capture and utilization, learning systems accelerate the search for sorbents and solvents and tune capture processes that were historically too energy-intensive to scale. In recycling and circular economy flows, computer vision and robotics sort waste streams with a speed and accuracy that manual sorting cannot match, while predictive models estimate the recoverable value of end-of-life products before they are disassembled.

One of the review’s most distinctive contributions is its insistence that technical performance alone is an incomplete measure of success. The authors devote substantial attention to how artificial intelligence supports life cycle assessment and techno-economic analysis, the two workhorse methods used to judge whether a clean technology actually reduces environmental burdens and whether it can survive in the market. Life cycle assessment traces the emissions, resource use, and impacts of a product from raw material extraction through disposal, while techno-economic analysis models costs, revenues, and financial risk. Machine learning can automate the data collection and uncertainty quantification that make these analyses slow and expensive, and it can link laboratory-scale innovations to system-level metrics of sustainability and cost-effectiveness. In the authors’ framing, an algorithm that improves a catalyst by a few percent is only interesting if the life cycle and economic models confirm that the improvement survives scaling.

The review is equally candid about the obstacles that keep these tools out of deployment. Data scarcity tops the list: industrial facilities generate sparse, proprietary, and inconsistently labeled records, and many clean technology processes, from novel electrolyzers to pilot capture plants, simply lack the training data that modern deep learning assumes. Model interpretability follows closely. Operators and regulators are reluctant to hand control of critical infrastructure to neural networks whose reasoning they cannot inspect, a concern the authors connect to the growing field of explainable AI. Algorithmic bias can skew models toward the conditions and geographies represented in their training data, quietly disadvantaging regions with different climates, grids, or industrial bases. Cybersecurity risks compound the problem, since increasingly networked energy systems present attack surfaces that legacy supervisory control architectures were never designed to defend, a challenge the co-author team, which includes researchers from a network and data security laboratory in Sichuan, examines explicitly.

Integration with legacy infrastructure emerges as another stubborn barrier. Power grids, refineries, and waste plants were engineered around deterministic control loops and decades-old communication protocols, and retrofitting them with learning-based systems raises questions of compatibility, reliability, and liability that no amount of algorithmic elegance can resolve on its own. The review’s cross-sectoral perspective makes this point sharper: a forecasting model may be accurate enough for a modern control room, yet worthless if the substation it informs runs on equipment that cannot accept probabilistic inputs. The authors argue that deployment pathways must therefore be engineered alongside the algorithms themselves, with transition architectures that let intelligent and conventional controls coexist while infrastructure catches up.

Perhaps the most uncomfortable section of the review turns the lens on artificial intelligence itself. Training large-scale models is energy-intensive, and data center electricity demand is rising rapidly as AI adoption accelerates across the economy. The authors argue that promoting AI as an enabler of sustainability while ignoring its own footprint would be a category error, and they call for sustainable digital practices: efficiency-aware model design, responsible siting of compute, and honest accounting of the emissions attributable to AI systems. This self-critical stance distinguishes the review from much of the enthusiasm-driven literature and aligns it with a growing scholarly debate over whether the computational costs of machine learning can be justified by the operational savings it delivers in energy systems.

To move from diagnosis to action, the authors propose a roadmap resting on four pillars. First, open datasets, so that results can be reproduced and benchmarked across laboratories and industries rather than locked inside proprietary silos. Second, physics-informed and interpretable AI models that embed physical laws and engineering constraints directly into the learning process, improving accuracy in data-poor regimes and giving engineers and regulators a transparent basis for trust. Third, ethical and governance frameworks that address accountability, fairness, and safety before, not after, systems are deployed at scale. Fourth, stronger alignment between technology, policy, and markets, so that algorithms trained to optimize technical objectives are not fighting against regulatory structures and price signals that point in a different direction. The roadmap is explicitly aimed at three audiences at once: researchers who need shared data and evaluation standards, industry leaders who need integration pathways, and policymakers who need decision support grounded in evidence.

The timing of the review gives that policy dimension particular weight. With national commitments to net-zero emissions hardening into legislation, and with AI capabilities advancing faster than the governance structures meant to steer them, the window for deliberate, coordinated deployment is narrowing. The authors’ unifying message is that artificial intelligence should be positioned not as a silver bullet but as an enabler of an equitable, resilient, and sustainable clean energy future, one whose benefits depend on responsible engineering choices made now. By consolidating more than 450 studies into a single cross-sectoral synthesis, and by pairing its catalog of technical advances with a sober accounting of data, interpretability, bias, security, and footprint challenges, the review offers researchers, industry, and governments something the fragmented literature could not: a shared map of where AI in clean technologies stands, and a concrete account of what it will take to get where it needs to go.

Subject of Research: Cross-sectoral applications of artificial intelligence in clean energy technologies and sustainability

Article Title: Artificial intelligence for clean technologies: a cross-sectoral review of sustainable, data-driven, and policy-aware systems

Article References: Ejiyi, C. J., Wei, L., Eze, T. F., Nnani, A. O., Gu, Y. H., Al-antari, M. A., Bamisile, O. O., & Cai, D. (2026). Artificial intelligence for clean technologies: a cross-sectoral review of sustainable, data-driven, and policy-aware systems. Journal of Big Data. https://doi.org/10.1186/s40537-026-01555-w

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01555-w

Keywords: artificial intelligence, clean technologies, renewable energy forecasting, carbon capture, life cycle assessment, techno-economic analysis, explainable AI, circular economy, smart grids, energy storage, hydrogen, AI governance