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

The research team, led by Aliaa Gar Alalm, Hani Mahanna, Mohamed Mossad, and Hamdy Awad, conducted their assessment in two regions of Dakahliya governorate in the Nile Delta. In the first region, the decentralized option was set against a centralized extended aeration plant, a widely used activated sludge configuration that relies on prolonged aeration to degrade organic matter. In the second region, the comparison pitted distributed membrane bioreactors against an advanced sequencing batch reactor, a centralized system that performs aeration, settling, and decanting in a single tank through timed operational cycles. Both centralized technologies are common in rural Egypt, making the comparison directly relevant to infrastructure decisions now being made across the country and throughout the developing world.

The methodological backbone of the study is life cycle assessment, a technique standardized under ISO 14040 and ISO 14044 that quantifies the environmental burdens of a product or system from construction through operation to decommissioning. The researchers defined their functional unit as one cubic meter of treated wastewater, ensuring a fair comparison between systems of different scales and designs. The system boundaries encompassed the construction phase, including the manufacture and installation of pipes, tanks, pumping stations, and membrane modules, as well as the operation phase, covering electricity consumption, emissions to water and air, sludge management, and infrastructure maintenance. Impacts were quantified using the CML-IA baseline version 3.10 method and the ReCiPe Midpoint and Endpoint methods, providing both midpoint categories such as global warming potential and endpoint indicators of damage to human health and ecosystems.

The results from region one were striking. Compared with the centralized extended aeration system, the decentralized membrane bioreactors reduced abiotic depletion of fossil fuels by 16.4 percent, human toxicity potential by 45.3 percent, freshwater aquatic ecotoxicity by 38.6 percent, terrestrial ecotoxicity by 49.5 percent, photochemical oxidation by 7.28 percent, acidification by 7.78 percent, and eutrophication by 26.4 percent. These are not marginal gains. Toxicity-related categories, which track the release of harmful substances to air, water, and soil, showed reductions approaching or exceeding half of the centralized baseline. For rural communities living near discharge points, such differences translate directly into lowered exposure to pollutants that can accumulate in fisheries, agricultural soils, and drinking water sources.

Region two told an even more compelling story. Against the centralized advanced sequencing batch reactor, the distributed membrane systems cut abiotic depletion of fossil fuels by 23.4 percent, global warming potential by 24.9 percent, human toxicity by a remarkable 60 percent, freshwater aquatic ecotoxicity by 52.9 percent, terrestrial ecotoxicity by 63.5 percent, photochemical oxidation by 14.9 percent, acidification by 16.7 percent, and eutrophication by 2.66 percent. The decentralized plants proved more environmentally friendly across nearly every impact category examined. The scale of the climate benefit is particularly noteworthy for Egypt, a country acutely vulnerable to sea level rise in the very Delta region where the study was conducted, and one that has committed to reducing greenhouse gas emissions under its national climate strategy.

Why do smaller, distributed plants perform so much better? The answer lies largely in energy. The analysis revealed that centralized systems impose their greatest environmental burden during the operation stage, driven overwhelmingly by electricity demand. Extended aeration processes are notoriously energy hungry, requiring continuous oxygen supply to large aeration basins, while the long force mains and pumping stations needed to transport sewage from scattered homes to a single central plant add further power consumption and embodied infrastructure. Decentralized membrane bioreactors, by contrast, treat wastewater at or near the point of generation, eliminating much of the collection network and its associated pumping energy. Although membrane filtration demands its own electricity for permeate suction and aeration, the superior treatment performance of membranes means less recirculation, fewer return streams, and cleaner effluent requiring less downstream polishing.

The study also uncovered a nuanced shift in where impacts occur. In the centralized scenarios, the operation phase dominated the environmental profile. In the decentralized membrane scenarios, however, the limited service life of membrane modules, which must be replaced periodically as fouling and wear degrade their performance, moved a greater share of impacts into the construction and materials phase. Manufacturing polymeric membranes, typically made of materials such as polyvinylidene difluoride, carries its own footprint in terms of fossil fuel extraction and chemical processing. Yet even accounting for these periodic replacements, the overall life cycle balance remained firmly in favor of the distributed systems. The finding underscores a critical point for technology developers: extending membrane lifespan through better fouling control and more durable materials could further amplify the environmental advantages of decentralized treatment.

On the economic side, the picture is more balanced but still favorable to the membrane approach when viewed holistically. The researchers found that centralized systems enjoy lower annual amortization costs, reflecting the distributed capital expense of mature, conventional technologies over long service lives, but they carry higher operating costs due chiefly to their insatiable appetite for electricity. Decentralized membrane bioreactors invert this pattern, demanding higher amortization costs because of expensive membrane modules and specialized equipment, while benefiting from lower operating expenses. In region one, the total cost was 0.86 Egyptian pounds per cubic meter for the centralized extended aeration system versus 0.96 for the decentralized membrane plants. In region two, the figures were 0.76 and 0.79 Egyptian pounds per cubic meter for the centralized and decentralized options respectively. The premium for the membrane systems amounted to roughly 0.03 to 0.10 Egyptian pounds per cubic meter, a difference the authors judged negligible when weighed against the substantial environmental gains.

The implications extend well beyond the villages of Dakahliya. Roughly half of humanity still lacks safely managed sanitation, and the gap is widest in rural areas of low- and middle-income countries where extending sewer networks to scattered households is prohibitively expensive. Conventional wisdom has often held that decentralization sacrifices treatment quality and professional oversight for the sake of convenience, and poorly maintained septic systems and pit latrines have reinforced that perception. This study complicates that narrative by showing that modern decentralized technology, when built around high-performance membrane bioreactors and assessed rigorously across the full life cycle, can outperform centralized plants environmentally while costing nearly the same. The finding aligns with a growing body of international research suggesting that hybrid and distributed infrastructure can outperform purely centralized paradigms in specific geographic and demographic contexts.

For policymakers in Egypt and comparable settings, the message is that the functional unit matters: judged per cubic meter of treated water, distributed membrane systems offer a genuinely sustainable pathway for rural sanitation, one that curtails toxicity, greenhouse gases, and nutrient pollution at almost no additional cost. For engineers, the study highlights membrane service life as the key lever for future improvement. And for the residents of rural residential complexes, it suggests that the small plant down the road may be quietly doing a better job of protecting their river, their soil, and their air than any distant centralized facility ever could. As water scarcity intensifies and climate pressures mount across the Middle East and North Africa, decisions informed by life cycle thinking rather than habit may determine whether the next generation of sanitation infrastructure becomes part of the problem or part of the solution.

Subject of Research: Comparative environmental and cost life cycle assessment of decentralized membrane bioreactors versus centralized wastewater treatment systems in rural residential complexes in Egypt.

Article Title: An environmental and cost assessment of decentralized membrane bioreactors versus centralized systems in rural residential complexes

Article References: An environmental and cost assessment of decentralized membrane bioreactors versus centralized systems in rural residential complexes. (n.d.). https://doi.org/10.1007/s10098-026-03609-2

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03609-2

Keywords: membrane bioreactor, decentralized wastewater treatment, centralized wastewater systems, life cycle assessment, rural sanitation, Egypt, Dakahliya, extended aeration, advanced sequencing batch reactor, water treatment, sustainability, clean technologies