The team built a unified cross-basin framework around a three-stage super-efficiency Slack-Based Measure model. In the first stage, an input-oriented SE-SBM model incorporating undesirable outputs such as industrial wastewater, sulfur dioxide, and smoke and dust emissions estimated each province’s efficiency. Unlike radial DEA approaches, the slack-based formulation accounts for input redundancy and pollution as an inevitable cost of economic activity, with the three pollutants weighted objectively through the entropy weight method into a comprehensive pollution index. Inputs spanned labor, capital, urban built-up land, energy consumption, and, innovatively, general public budget expenditures on agriculture, forestry, and water conservancy as a policy input reflecting basin governance. Desirable outputs included real GDP and local fiscal revenue, capturing both economic scale and development quality, with all monetary indicators deflated to 2010 prices.

The second stage is where the measurement gains its rigor. Input slack variables from the first stage were regressed through a Stochastic Frontier Approach against a Technology-Economy-Institution framework of environmental controls: R&D investment intensity, per capita GDP, and the share of government expenditure in regional GDP. The SFA decomposes each slack into managerial inefficiency, random disturbance, and external environmental factors. The likelihood ratio tests for all five input slack variables passed at the one percent significance level, and gamma values close to one confirmed that managerial inefficiency overwhelmingly dominated the mixed error term. This meant the raw first-stage scores were distorted: the Yellow River Basin’s initial average of 1.225 was partly an artifact of favorable conditions, while the Yangtze belt’s 1.025 was suppressed by unfavorable ones.

After adjusting inputs to a common external environment and eliminating random disturbances, the third stage re-ran the SE-SBM model. The adjusted averages settled at 1.153 for the Yellow River Basin and 1.029 for the Yangtze Economic Belt. The spatial fingerprints diverged sharply. The Yellow River Basin followed a stepwise gradient of downstream greater than midstream greater than upstream, with Shandong retaining the top position at 1.704 even after adjustment and Gansu at the bottom near 0.895. The Yangtze belt displayed a concave pattern with higher levels at both ends and a pronounced collapse in the middle: Shanghai led at 2.063 while Hubei sank to 0.565, and nearly half of its provinces never reached the efficiency frontier. Shanghai and Inner Mongolia saw their scores decline after adjustment, revealing efficiency supported by economic foundation or resource endowment, whereas Gansu and Sichuan improved, suggesting external constraints had previously masked their real management performance.

Because static scores cannot capture momentum, the researchers applied the Global Malmquist-Luenberger index, decomposing it into technical efficiency change and technical progress. The Yellow River Basin averaged 1.027, an annual improvement of 2.7 percent, against 1.017, or 1.7 percent, for the Yangtze belt, and its curve was steadier. Technical progress was the primary contributor in both basins, but the decomposition exposed a fragile, single-engine growth pattern. In several provinces, including Inner Mongolia in the north and Zhejiang, Hubei, Sichuan, and Yunnan in the Yangtze belt, the efficiency change component fell below one, meaning technology introduction masked declining management efficiency. Midstream regions of both basins posted the highest GML values, a catch-up effect driven mainly by green technology adoption rather than improved resource allocation, with Gansu, Shaanxi, Shanxi, and Yunnan achieving average annual growth of 6.8, 4.5, 4.2, and 3.4 percent respectively.

Kernel density estimation and the Dagum Gini coefficient then traced how the distributions evolved. The Yellow River Basin’s curves showed rising peaks and narrowing width, with its gravity center shifting from low-value to high-value concentration, a dual optimization of spatial balance and efficiency improvement moving toward convergence. The Yangtze belt instead settled into a prominent main peak with a long right tail, and long displayed double or even multi-peak structures in which high-efficiency cores coexisted with solidified low-efficiency sinks, a polarization the authors liken to a Matthew effect of the efficient growing more efficient. The Gini decomposition delivered the decisive structural verdict: inter-regional disparities contributed over seventy percent of the Yellow River Basin’s imbalance in every year except 2019, and between forty-two and sixty-one percent in the Yangtze belt, making gaps between upstream, midstream, and downstream regions the primary root of spatial inequality in both watersheds.

The methodological centerpiece is the Geographically Weighted Random Forest, which builds a local random forest at each provincial location using neighboring samples weighted by a Gaussian kernel of great-circle distances. Bandwidth selection employed leave-one-province-out spatial cross-validation across thirteen candidate bandwidths from 150 to 1,500 kilometers; the 200-kilometer fixed bandwidth achieved the lowest cross-validated RMSE of 0.3095 and MAE of 0.2133. Each of the nineteen local forests contained 500 regression trees trained on bootstrap samples, with two candidate variables per split and a minimum leaf size of five. Under a common benchmark, GWRF held a coefficient of determination at or above 0.94 and outperformed OLS, Spatial Regression Tree, Random Forest, and GWR models, confirming its comparative strength in capturing nonlinear, spatially heterogeneous relationships in high-dimensional panel data.

Partial dependence plots extracted from the model revealed thresholds that average-effect models would blur. Urbanization boosted eco-efficiency once the rate exceeded roughly 0.2 in the Yellow River Basin, but the Yangtze belt needed to surpass 0.4 before its urban infrastructure fully leveraged agglomeration advantages. Environmental regulation showed a turning point near 0.2 in both basins, with predicted efficiency rising rapidly with regulatory intensity before the gains became gradual. Industrial structure and financial development traced U-shaped trajectories, declining first as provinces absorbed the adjustment costs of eliminating backward capacity or expanding finance, then recovering beyond turning regions near 0.5 for industrial structure in the Yangtze belt and 0.2 for financial development in the Yellow River Basin. Openness carried divergent attributes: the Yangtze belt successfully imported advanced foreign environmental technologies during its highly open period, while the Yellow River Basin saw efficiency decline during a relatively higher openness phase, consistent with absorbing some high-pollution foreign enterprises.

Local feature-importance maps for 2010, 2014, 2018, and 2022 exposed two distinct spatial architectures. The Yellow River Basin showed a regulation-oriented constraint pattern: educational development held high local importance in upstream provinces such as Inner Mongolia, Gansu, and Qinghai, while environmental regulation dominated the midstream and downstream heavy-industry regions, where strict policy constraints are needed to break through carbon lock-in. The Yangtze Economic Belt presented a market-oriented radiation pattern, with openness and financial development most important in downstream coastal areas like Shanghai and Jiangsu, and urbanization most important in the midstream, where urban cluster agglomeration reduced pollution control costs. Notably, the high-importance areas of industrial structure did not stay fixed but rotated spatially among midstream provinces, indicating that the focus of China’s green industrial transformation is shifting from coastal pioneers toward inland hinterlands. The authors caution that these results are model-based associations rather than causal effects, given the observational panel design, and that prefecture-scale data and emerging factors such as digital transformation and carbon trading remain outside the current framework.

Subject of Research: Comparison and spatial heterogeneity of eco-efficiency in two major river basins of China: nonlinear mechanism analysis based on spatial machine learning

Article Title: Comparison and spatial heterogeneity of eco-efficiency in two major river basins of China: nonlinear mechanism analysis based on spatial machine learning

Article References: Liu, P., Dong, D., Zhao, F., Meng, J., & Deng, G. (2026). Comparison and spatial heterogeneity of eco-efficiency in two major river basins of China: nonlinear mechanism analysis based on spatial machine learning. Frontiers in Environmental Science, 14, Article 1937303. https://doi.org/10.3389/fenvs.2026.1937303

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1937303

Keywords: Comparison, spatial, heterogeneity, eco-efficiency, major, river, basins, China, nonlinear, mechanism, analysis, based

The research, led by Amin Esmaeili with Osmani Albernas and Lin Li, introduces the Eco-Efficiency Value Stream Mapping framework, or EE-VSM. Its intellectual ancestry lies in lean manufacturing, the discipline born of the Toyota Production System, which taught engineers to visualize every step of production and strip out activities that absorb resources without creating value. Traditional value stream mapping is superb at exposing waiting time, excess inventory and unnecessary motion, but it is famously blind to environmental performance. Over the past decade researchers have patched this gap with variants that add energy flows, carbon emissions, water use and even social indicators to the familiar lean diagrams. The problem, the authors contend, is that these variants rarely allow one factory to compare itself meaningfully against another, which is precisely the benchmarking function that small manufacturers need before committing scarce capital.

To build a benchmarking foundation, the team turned to one of the richest publicly available datasets in industrial efficiency: the database of the Industrial Assessment Centers, a program funded by the United States Department of Energy since 1976 that pairs engineering schools with local manufacturers. Across its history the program has identified more than 287 quadrillion British thermal units of potential energy savings, worth over 2.6 billion dollars and roughly 37 million tons of carbon dioxide equivalent, through more than 22,500 assessments conducted at 37 universities. The researchers filtered this trove down to 11,624 manufacturing assessments performed between 2001 and 2025, then applied percentile-based filters on plant area, employment and energy consumption to isolate a representative population of small and medium-sized facilities. The final dataset comprised 8,707 assessment records.

The scale of the data allowed the team to quantify something rarely visible at this granularity: how much money each type of improvement actually saves relative to a facility’s energy bill. They defined a new metric, the Saving Magnitude Ratio, calculated by dividing a recommendation’s estimated annual cost savings by the plant’s annual energy expenditure. Because both numbers are validated by assessment teams and peer-reviewed at Rutgers University, which developed the reporting standard, the ratio rests on unusually reliable ground. The analysis revealed that 93.4 percent of the 65,651 recommendations in the dataset concerned energy management, with waste minimization accounting for 3.9 percent and productivity enhancement for 2.7 percent. Compressed air improvements, targeting what engineers call the industrial fourth utility, were by far the most frequent recommendation, appearing 14,418 times, followed by electric motor upgrades at 6,474 instances and lighting at 13,591.

The averages conceal a striking hierarchy of opportunity. Productivity enhancement measures carried the highest category average, a Saving Magnitude Ratio of 0.053, meaning such projects typically claw back more than five percent of a plant’s annual energy spending. Defective product minimization reached 0.074, warehouse optimization 0.085, and combined heat and power installations topped the energy category at 0.092. Waste minimization averaged 0.031, with liquid waste recycling at 0.039 and contaminant removal at 0.042. Energy management, though overwhelmingly dominant in volume, averaged only 0.018, because most of its recommendations, such as boiler tuning and insulation, deliver modest but reliable returns. The distributions were strongly right-skewed, with medians consistently below means, a statistical caution the authors flag for anyone using the benchmarks.

The second half of the paper demonstrates how a factory can translate these population-level statistics into a map of its own inefficiencies. The illustrative case is a metal fabrication plant producing chrome-plated components for refrigerated containers through three stages: die-casting of a zinc alloy melted at around 410 degrees Celsius, CNC machining and polishing, and electroplating. The EE-VSM diagram layers energy and material flows beneath the conventional lean timeline, organizing energy into four end-use groups, process heating, motors, compressed air and other uses, chosen because Department of Energy footprint data attribute nearly 90 percent of manufacturing energy consumption to the first three. Water, solvent and solid material flows are mapped alongside them.

Two new dimensionless metrics give the map its analytical teeth. The Value-Added Magnitude Ratio divides the estimated cost of genuinely productive resource use by the facility’s total annual energy cost, while the Muda Magnitude Ratio, named for the Toyota term for waste, captures the non-value-added remainder. Because both share a common denominator, they convert kilowatt-hours, gigajoules, liters and kilograms into a single comparable currency of wasted money. At the demonstration facility, motor systems emerged as the largest sink, with a cumulative Muda Magnitude Ratio of 0.145 driven largely by idle time at the machining and polishing stations, while the melting furnace’s 52 percent efficiency made heating the second-largest contributor at 0.074, and an assumed 15 percent efficiency for compressed air end-uses produced a ratio of 0.065.

The framework’s payoff comes when these internal ratios are read against the ITAC benchmarks. Matching the plant’s motor inefficiency against the database’s 6,474 motor improvement cases, which average a Saving Magnitude Ratio of 0.015, yields a projected annual saving of about 4,560 dollars, equivalent to roughly 49,600 kilowatt-hours. Benchmarking the furnace against 692 comparable cases suggests 5,776 dollars and 542 gigajoules per year, while water minimization strategies drawn from 829 assessments project 7,600 dollars and nearly 2,500 cubic meters of water annually. Compressed air leak repair and pressure reduction, benchmarked against 14,418 cases, translate to 3,344 dollars and more than 285,000 normal cubic meters of air. For a manager deciding between competing capital projects, these are the kinds of concrete, defensible numbers that sustainability proposals have historically lacked.

The authors are candid about the framework’s limits. The efficiency assumptions used to separate value-added from non-value-added consumption, such as the 52 percent furnace efficiency and the 38 percent value-added share of electroplating water, come from published literature rather than site-specific engineering analysis, and practitioners are advised to calibrate them with their own measurements. The benchmarks themselves represent averages across many industries, and the Saving Magnitude Ratio should be read as a relative indicator shaped by local energy prices and operating conditions rather than a universal performance standard. The team recommends that future work extend the framework with life cycle assessment, carbon footprint analysis, real-time sensor data and eventually social sustainability metrics such as absenteeism and physical workload, building on the three-pillar tradition of earlier sustainable mapping methods.

What makes the study resonate beyond its technical contribution is its timing. While headlines celebrate smart factories and industrial artificial intelligence, the majority of small manufacturers still run conventional processes little changed in decades, and those processes harbor exactly the kind of embedded, cost-effective inefficiencies that decarbonization strategies identify as the lowest-hanging fruit. By fusing a half-century of federal assessment data with a visualization tool that shop-floor engineers already understand, the EE-VSM framework offers a plausible route for sustainability to compete on equal footing with any other investment proposal on an executive’s desk. If it achieves even part of its promise, the quiet revolution it enables may unfold not in gleaming flagship plants but in the thousands of modest factories where most of industry’s emissions are actually made.

Subject of Research: An eco-efficiency value stream mapping framework for benchmarking sustainability and operational performance in small and medium-sized manufacturers

Article Title: Developing an Eco-Efficiency Value Stream Mapping (EE-VSM) Framework for Benchmarking Sustainability and Operational Performance

Article References: Esmaeili, A., Albernas, O., & Li, L. (2026). Developing an Eco-Efficiency Value Stream Mapping (EE-VSM) Framework for Benchmarking Sustainability and Operational Performance. Cleaner Engineering and Technology, Article 101336. https://doi.org/10.1016/j.clet.2026.101336

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101336

Keywords: eco-efficiency, value stream mapping, lean manufacturing, sustainability, SMEs, energy efficiency, benchmarking, industrial assessments, waste minimization, compressed air, carbon emissions, manufacturing

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