The research team, led by Adrian Sobral-Lores of the University of Santiago de Compostela together with colleagues in Spain and Chile, examined twenty operational offshore wind farms in the United Kingdom. Their approach wove together life cycle assessment, or LCA, the standard method for tracing environmental impacts from raw material extraction through manufacturing, installation, operation and end of life, with data envelopment analysis, or DEA, a mathematical benchmarking technique that compares each facility against the best performers in the sample. The twist that sets this work apart is a contribution-analysis step inserted between the two: before any benchmarking takes place, the LCA identifies precisely which material flows dominate the environmental profile, and only those materials are fed into the efficiency model.

The results of that hotspot analysis are striking in their clarity. Low-alloy steel, the workhorse metal of towers, foundations and hubs, accounted for 49.37 percent of the average environmental contribution across eighteen impact categories. Copper, the metal of choice for cables and electrical equipment, contributed a further 21.99 percent. Adding chromium steel, glass fibre and aluminium brought the five selected materials to 78.65 percent of the total average environmental contribution. In other words, less than a handful of substances effectively determine whether an offshore wind farm is environmentally lean or heavy, a finding that dramatically narrows where engineers and policymakers should focus their attention.

With these five materials established as the environmentally decisive inputs, the team built a Slack-Based Measure DEA model under variable returns to scale, using lifetime electricity generation as the single output. The choice of model matters: unlike simpler radial approaches, the slack-based formulation captures non-proportional excesses in individual inputs and remains valid when inputs are measured in different physical units. Because the sample contained only twenty facilities, the researchers also respected a classical adequacy rule from the DEA literature, which limits the number of input and output variables relative to the number of units being compared, and they tested the robustness of their model by repeating the analysis with four and then three inputs.

The benchmarking verdict was split. Nine of the twenty wind farms achieved a perfect efficiency score of 1.00, defining the empirical frontier against which their peers were measured. The remaining eleven fell short, with scores ranging from 0.584 for the least efficient facility to 0.795 for the closest of the laggards. Crucially, the model did not merely rank the farms; it produced material-specific reduction targets for each inefficient one, quantifying how much less low-alloy steel, copper, chromium steel, glass fibre or aluminium each facility would need to generate the same lifetime electricity. The largest single gap involved copper at one wind farm, where a reduction of 78.66 percent was projected, while the least efficient farm showed substantial excesses across all five materials simultaneously.

Here the study makes its most methodologically important move. Rather than assuming that material savings translate automatically into environmental gains, the researchers transferred the DEA-derived reduction targets back into the life cycle inventories and ran a second, full LCA on the adjusted inventories, holding electricity generation and all other inventory flows constant. This verification step proved essential, because the environmental consequences of material savings vary enormously depending on the impact category. Median improvements across the eleven inefficient farms ranged from 8.85 percent for fossil resource scarcity to 30.32 percent for terrestrial and freshwater ecotoxicity, and the maximum reduction reached 59.18 percent for both ecotoxicity indicators at a single facility.

The pattern of these gains tells a coherent story about cause and effect. Toxicity, ecotoxicity, acidification and land-use categories, all of which are strongly influenced by copper and other metal inputs, showed the largest improvements, while global warming potential, with a median reduction of 10.62 percent, responded more modestly because the dominant driver, low-alloy steel, was subject to comparatively smaller projected cuts. The sensitivity analysis reinforced confidence in the overall picture: when aluminium and then glass fibre were excluded from the model, all eleven inefficient farms remained inefficient, classification agreement between scenarios ranged from 95 to 100 percent, and the material-reduction targets for the shared inputs were unchanged to the precision reported by the software.

The study also explored what distinguishes efficient farms from inefficient ones, and the correlations it uncovered are intriguing, if exploratory. Efficiency showed a statistically significant positive association with distance from shore in both Pearson and Spearman analyses, and a significant monotonic relationship with the number of turbines. Positive but non-significant trends linked efficiency to installed capacity and estimated electricity generation, hinting that larger farms may achieve higher material eco-efficiency. By contrast, efficiency was essentially unrelated to individual turbine capacity, rolling load factor or year of commissioning. With only twenty observations, the authors are careful to frame these correlations as hypothesis-generating rather than causal, and they acknowledge that because site-specific conditions such as wind resource quality were not embedded as control variables, part of the measured efficiency variation may reflect location and design circumstances rather than pure resource management.

The authors are equally careful about what the reduction targets do and do not mean. The DEA projections represent comparative benchmarks derived from peer farms in the sample, not engineering prescriptions that could be applied directly to a real project. Structural, geotechnical and site-specific constraints, from water depth and seabed characteristics to fatigue and installation limits, determine how much steel a foundation genuinely requires, and the optimal quantity differs for every site. Supporting engineering literature cited in the study suggests real headroom exists: integrated structural optimisation has been shown to cut the mass of a turbine support structure by nearly twenty percent, and alternative foundation designs have delivered life-cycle environmental scores roughly eighteen percent below conventional monopiles. The benchmark targets, in this light, mark the territory where such optimisation efforts are most worth pursuing.

One caveat deserves emphasis for anyone tempted to extrapolate the numbers to tomorrow’s projects. The underlying inventory comes from an earlier generation of UK offshore wind farms, and the authors caution that the efficiency scores and material targets should not be directly applied to modern installations using turbines above fifteen megawatts or floating foundations, whose material requirements can differ substantially. Still, the framework itself, which links hotspot identification, efficiency benchmarking and environmental verification in a single loop, remains applicable wherever comparable inventories exist, and recent assessments of floating wind confirm that steel, cables and composite blades remain the dominant hotspots. As Europe pursues its target of 300 gigawatts of installed offshore wind capacity by 2050, the lesson of this study is that the greenest kilowatt-hour is not only the one generated without emissions, but the one generated with the least steel and copper behind it, and that knowing exactly which materials matter is the first step toward building a wind fleet that is clean from seabed to scrapyard.

Subject of Research: Material eco-efficiency assessment of offshore wind farms using integrated life cycle assessment and data envelopment analysis

Article Title: Assessing the eco-efficiency of offshore wind energy using a combined life cycle assessment and data envelopment analysis approach

Article References: Sobral-Lores, A., Rebolledo-Leiva, R., Feijoo, G., & Moreira, M. T. (2026). Assessing the eco-efficiency of offshore wind energy using a combined life cycle assessment and data envelopment analysis approach. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00191-0

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00191-0

Keywords: offshore wind, life cycle assessment, data envelopment analysis, eco-efficiency, low-alloy steel, copper, United Kingdom, renewable energy, environmental impacts, materials, sustainability, benchmarking

The research team, led by Sarah J. McLaren of Massey University alongside Graham A. McAuliffe, Thomas C. Ponsioen, Ratna C. Purwestri, Flaminia Ortenzi, and Ty Beal, addressed a persistent blind spot in nutritional life cycle assessment, or nLCA. Previous reviews had documented that most nLCA studies are concentrated in high-income countries, rely on nutrient indices developed from United States data, and suffer from a lack of consensus on how to define a nutritional functional unit. Indonesia, now the world’s sixteenth largest economy yet still home to a poverty rate of 9.4 percent unevenly spread across its islands, had virtually no local life cycle inventory data for its foods. Existing global meta-analyses leaned heavily on proxy data from other countries that poorly reflect Indonesian agroecological conditions, cooking practices, and waste treatment.

To close this gap, the researchers assessed 90 foods spanning five groups: animal-source foods; pulses, nuts, and seeds; starchy staples; vegetables; and fruits. The environmental side of the analysis covered seven impact categories from cradle to grave, meaning from primary production through post-harvest handling, processing, packaging, distribution, retail, home or street preparation, and finally waste treatment. The categories included climate change, scarcity-weighted water use, marine and freshwater eutrophication, terrestrial acidification, particulate matter formation, and land use. Crucially, the team modelled distinctly Indonesian realities: cooking on liquefied petroleum gas stoves, congee prepared as street food, and end-of-life treatment dominated by open dumping and burning.

The nutritional side employed the Nutritional Value Score, or NVS, a recently developed profiling system scaled from 1 to 100 that captures both essential nutrient density and protective factors against noncommunicable diseases. The NVS is the weighted mean of seven normalised dietary attributes: vitamins at 20 percent, minerals at 20 percent, protein at 12.5 percent, omega-3 fatty acids at 10 percent, fibre at 7.5 percent, calories at 7.5 percent, and nutrient ratios covering sodium to potassium, saturated to unsaturated fat, and carbohydrate to fibre at 22.5 percent, with a 25 percent penalty applied to ultraprocessed foods. Traditional Indonesian fermented foods such as tempeh and tofu are not classified as ultraprocessed and therefore escape the penalty. The functional unit was defined as 100 NVS, the highest nutritional value on the scale, of food as consumed, which required accounting for inedible fractions, cooking yield, and consumer waste when calculating the quantity of food that must be purchased.

The results expose enormous heterogeneity. Among animal-source foods, dairy products other than cheese achieved the highest enviro-nutritional efficiencies, while beef, lamb, rabbit, pork, and tilapia ranked lowest. Water use within this group ranged from 0.12 to 79.8 cubic metres per functional unit, and land use from 0.01 to 28.2 square-metre-years of crop equivalents. The drivers are well understood: enteric fermentation from ruminants generates methane, manure management produces nitrous oxide and contributes to eutrophication and acidification, and high feed conversion ratios inflate the footprint of monogastric meats. Economic allocation, which assigns most livestock system impacts to meat rather than to lower-value co-products, explains why chicken organs rank comparably to dairy products despite coming from the same birds.

In the pulses, nuts, and seeds group, cashews stood out for all the wrong reasons, showing the lowest efficiencies across most environmental categories, driven largely by poor fertiliser use efficiency in cashew cultivation. Soybean products, by contrast, achieved the highest efficiencies, aided by leguminous nitrogen fixation that reduces fertiliser requirements. Excluding cashews, variability in this group was modest for several categories but reached 211 times the lowest result for marine eutrophication, water use, and land use, while nutritional scores varied little, from 46 to 65, meaning the efficiency differences were almost entirely attributable to supply-chain activities rather than nutritional composition.

Among starchy staples, congee, the study’s only multi-ingredient food, performed three to five times worse than the next least efficient item across every category, a consequence of its high salt content as street food, an unfavourable carbohydrate-to-fibre ratio, and low overall nutrient density. White rice and rice noodles also ranked poorly, while whole wheat pasta and sweet corn performed best. Vegetables told a striking story: the five dark green leafy vegetables studied, including pumpkin leaves, drumstick leaves, sweet potato leaves, tree fern, and water spinach, achieved the highest efficiencies in the group, partly because they are low-value co-products assigned zero primary production burdens. Among fruits, imports from China ranked among the least efficient, primarily due to high water use, and durian emerged as a cautionary example: its skin comprises 76 percent of the fruit and carries more than half of its climate change impact through transport and methane emissions at end of life.

Across the life cycle, primary production dominated, contributing at least 30 percent of impacts for all categories and typically more than 95 percent for land use. Yet the relationship between nutritional value and environmental performance proved far from universal. Overall, the correlation between NVS and the enviro-nutritional single score was weak, with an R-squared of just 0.03. It was strongest for starchy staples, where per-kilogram impacts are relatively homogeneous so nutritional score drives the ranking, at R-squared 0.65, moderate for fruits at 0.30, weak for vegetables at 0.15, and effectively absent for animal-source foods at R-squared 0.00, where both nutritional value and environmental impact vary substantially and independently. The practical implication is that nutritional quality alone cannot predict environmental performance, and vice versa, particularly for animal products.

For Indonesian policy, the findings point toward concrete opportunities. The authors identify dark green leafy vegetables, chayote, carrots, papaya, guava, cantaloupe, milk, yogurt, chicken organs, whole grains, and soy products as nutritious foods with comparatively low environmental footprints. Four items, beef and chicken organs, edamame, mussels, and tuna, scored at least 65 on the NVS while ranking in the top 20 percent least impactful foods for four or more impact categories, marking them as candidates for local prioritisation. Beyond food choice, the study highlights improvements in preparation and waste management, such as reducing salt in congee and removing durian skins before distribution, alternative sourcing of domestically produced fruits and nuts, and supply-chain gains in fertiliser and irrigation efficiency. The authors caution, however, that these attributional results should be complemented by consequential analysis before recommending large dietary shifts, and that the approach requires contextualisation rather than oversimplified recommendations.

The study also delivers a substantial data contribution: an open-access life cycle inventory for Indonesian foods, built from Agri-footprint 6.3, ecoinvent 3.9.1, Agribalyse 3.1, and the World Food LCA Database 3.5, adapted with country-specific water flows and electricity grid mixes and prioritising geographical and technological representativeness. Limitations remain, including reliance on the largest supplier country for each food, inability to capture sub-national heterogeneity from Java’s volcanic soils to mountainous farming systems, and uncertainty in water scarcity characterisation factors and toxicity categories, which contributed more than half of the single score for 14 of the 90 foods. Future work should extend the method to composite dishes such as nasi goreng and soto, localise nutrient profiling weightings, and invest in sub-nationally disaggregated data on livestock, transport, and food losses. As a proof of concept, the study demonstrates that nLCA can counteract what the authors call carbon tunnel vision, replacing single-issue metrics with a nutritional lens that spans the entire food supply chain and can be adapted to other low- and middle-income countries striving to feed growing populations within planetary boundaries.

Subject of Research: Integrated enviro-nutritional life cycle assessment of commonly consumed local foods in Indonesia

Article Title: Integrated environmental and nutritional efficiencies of local foods in Indonesia: a cradle-to-grave enviro-nutritional life cycle assessment

Article References: McLaren, S. J., McAuliffe, G. A., Ponsioen, T. C., Purwestri, R. C., Ortenzi, F., & Beal, T. (2026). Integrated environmental and nutritional efficiencies of local foods in Indonesia: a cradle-to-grave enviro-nutritional life cycle assessment. The Lancet Regional Health – Western Pacific, 74, Article 101977. https://doi.org/10.1016/j.lanwpc.2026.101977

Image Credits: AI Generated

DOI: 10.1016/j.lanwpc.2026.101977

Keywords: life cycle assessment, Indonesia, nutrition, food systems, Nutritional Value Score, sustainable diets, environmental impacts, greenhouse gas emissions, animal-source foods, leafy vegetables, food policy, Southeast Asia