Industrial ecology rests on a deceptively simple premise: human industry, viewed from a sufficient distance, behaves like a biological system. Factories consume inputs and excrete wastes; cities metabolize food, fuel, and construction materials; and the entire global economy can be described as a network of energy and matter flows. The term entered widespread use after a 1989 Scientific American article by Robert Frosch and Nicholas Gallopoulos, who asked why industrial systems could not behave more like natural ecosystems, where the waste of one organism becomes the food of another. Three decades later, the question has evolved from provocation to policy imperative, as governments grapple with supply chain vulnerabilities, critical mineral shortages, and the staggering scale of global waste streams.

Kennedy, a professor at the University of Toronto known for his work on urban metabolism and the material flows of cities, is unusually well positioned to write an advanced introduction. His research has quantified everything from the carbon footprints of major cities to the biophysical requirements of urban infrastructure, and his textbook inherits that empirical sensibility. According to Kahhat’s review, the book synthesizes the field’s intellectual history with its contemporary methods, covering material flow analysis, life cycle assessment, and the emerging science of circular economy transitions. The reviewer situates the volume alongside the foundational texts of the discipline, including the influential textbook by Thomas Graedel and Braden Allenby, which for years served as the field’s standard reference.

The technical core of industrial ecology lies in material flow analysis, a bookkeeping method that tracks substances through an economy the way an accountant tracks money. Analysts quantify the extraction of raw materials, their transformation into products, their residence times in the built environment, and their eventual fate in landfills, recycling streams, or the atmosphere. The approach has produced some of the most striking findings in sustainability science: the realization that the total mass of human-made materials, including concrete, steel, and plastics, now exceeds the total biomass of the Earth, and that anthropogenic mass is accumulating at a rate of tens of gigatonnes per year. These numbers, first rigorously compiled by researchers using industrial ecology methods, have become touchstones in debates about the Anthropocene.

Life cycle assessment, the field’s other methodological pillar, extends the accounting from materials to environmental impacts. An LCA traces a product from raw material extraction through manufacturing, use, and disposal, quantifying energy demand, greenhouse gas emissions, water consumption, and toxicity at each stage. The method has exposed counterintuitive truths that reshape policy: that the largest share of a smartphone’s lifetime emissions occurs before the device is ever switched on, embedded in mining and manufacturing, or that electric vehicles only deliver climate benefits when the electricity charging them is itself decarbonized. Kennedy’s treatment of these methods, Kahhat suggests, equips readers to engage with such analyses critically rather than accepting them at face value.

The circular economy, perhaps the field’s most visible contemporary expression, receives substantial attention. The concept, which envisions economies in which waste is designed out and products circulate through reuse, repair, remanufacturing, and recycling, has been embraced by the European Union, China, and dozens of national governments. Yet industrial ecologists have been among the most rigorous critics of circular economy rhetoric. A landmark 2017 special issue of the Journal of Industrial Ecology, co-edited by Nancy Bocken, Elsa Olivetti, and Jonathan Cullen, called for taking circularity to the next level by grounding circular economy claims in quantitative material flow evidence. The field’s practitioners have repeatedly shown that recycling rates for many critical materials remain dismally low, and that thermodynamic and economic constraints limit how circular any real economy can become.

Kahhat’s review also reflects on the field’s expansion beyond its engineering origins. Industrial ecology began largely among chemical engineers and environmental scientists, but it now draws on urban planning, economics, sociology, and data science. The study of industrial symbiosis, in which the waste streams of one facility become feedstocks for another, famously exemplified in the Danish town of Kalundborg, has evolved into a research area spanning network theory and regional development policy. Urban metabolism, Kennedy’s own specialty, applies ecological concepts to cities, measuring the inflows of water, energy, food, and materials and the outflows of waste and emissions. As urbanization accelerates globally, these analyses have become central to climate mitigation planning and infrastructure design.

The timing of the book’s publication carries its own significance. The world is entering what many analysts describe as a materials transition, comparable in scale to the energy transition. Decarbonizing electricity, transport, and industry will require unprecedented quantities of lithium, cobalt, copper, and rare earth elements, and the extraction and processing of these materials carries its own environmental and social burdens. Industrial ecology provides the analytical tools to assess whether the green transition is genuinely sustainable when its full material footprint is accounted for. Questions about the recyclability of solar panels, the lifespan of wind turbine blades, and the supply risks of battery minerals are all, at their core, industrial ecology questions.

For Kahhat, writing from Peru, a country whose economy is deeply tied to mineral extraction, the stakes of the field are concrete rather than abstract. His review acknowledges the support of the Spanish Cooperation Agency through a project on circular economy actions in Peruvian enterprises, reflecting how industrial ecology methods are being applied in developing economies where formal waste management systems are still emerging. The global south’s experience with informal recycling sectors, resource extraction, and rapid urbanization presents challenges and opportunities that the field’s largely northern origins have historically underweighted, and reviews like this one signal a discipline becoming genuinely global in its outlook.

An advanced introduction, by definition, is written for readers who already possess foundational knowledge, and Kennedy’s book appears designed for graduate students, researchers, and practitioners who need a rigorous synthesis rather than a primer. The review suggests the volume succeeds in connecting the field’s conceptual foundations to its current research frontiers, from the digitalization of material flow data to the integration of industrial ecology with climate scenario modeling. As the pressure to decarbonize and dematerialize the global economy intensifies, the discipline that taught us to see industry as an ecosystem is no longer an academic curiosity. It has become an indispensable lens for navigating the material limits of a finite planet, and Kennedy’s synthesis arrives as both a milestone and a guide for the work ahead.

Subject of Research: A peer-reviewed book review of Christopher Kennedy's Advanced Introduction to Industrial Ecology assessing the field's methods, history, and circular economy relevance.

Article Title: Book review for advanced introduction to industrial ecology by Christopher Kennedy

Article References: Kahhat, R. (2026). Book review for advanced introduction to industrial ecology by Christopher Kennedy. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00199-6

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00199-6

Keywords: industrial ecology, Christopher Kennedy, Ramzy Kahhat, material flow analysis, life cycle assessment, circular economy, urban metabolism, industrial symbiosis, Journal of Industrial Ecology, sustainability science, resource extraction, book review

The paper examines two recent, high-profile contributions led by Julian Kirchherr, one of the most cited scholars in the field: an essay asking whether the circular economy is a failing sustainability paradigm, and a broader systematic defence of the concept against its critics. Both texts concede a remarkable amount. They admit that the circular economy is embedded in existing economic systems, that mainstream policy uptake favours marginal tinkering and easily measured victories, and that the appeal of the concept lies partly in its win-win optimism. Yet Marjanović’s central claim is that these concessions are folded back into a defence of feasibility, patience and incrementalism, so that the paradigm’s structural limitations are acknowledged but never allowed to destabilise it.

The first and deepest problem, the critique argues, is that the defences reproduce an eco-modernist vision of sustainability in which the goal is to sustain economic growth while reducing environmental harm through better design, efficiency and circular business models. In this framing, firms and markets sit at the centre of the transition: companies are asked to cooperate across industries, adapt their business models and build circular infrastructure, while governments mainly enable these processes and consumers supply market signals through their purchases. Questions of redistribution, labour, ownership, sufficiency and democratic control are pushed to the margins. When critics point out that the circular economy aligns with corporate interests, the defenders respond that the field is now increasingly dominated by academics. Marjanović counters that this conflates the production of knowledge with the distribution of power, especially since academia itself is entangled with industry through funding, advisory roles and pressure to produce commercially useful research.

A second, politically charged issue is what the critique calls depoliticisation. Rather than refuting specific criticisms, the defences recast critical scholarship itself as a potential hazard, warning that overly critical perspectives can undermine legitimacy, deter investment and ultimately entrench the linear economy. Critique is tolerated only insofar as it remains grounded and proportionate. Marjanović argues this gets the relationship between criticism and progress exactly backwards: because the circular economy is a contested political discourse that shapes which actors are empowered and which trade-offs are obscured, systematic scrutiny is not a threat to momentum but one of its essential preconditions. Recent work on so-called agonistic circularity, which shows that contestation can produce more plural and robust forms of governance, suggests that disagreement is not external to circular transition but constitutive of it.

The treatment of systemic change illustrates the pattern. When critics argue that the circular economy ignores deep structural transformation, the defenders reply that systemic concepts underpin the field and that circularity has long been conceived as a whole-of-economy model. But, Marjanović notes, a concept can be systemic in scope without being transformative in substance: addressing the whole economy is not the same as challenging the property regimes, growth imperatives and unequal power structures through which that economy is organised. Similarly, the argument that the circular economy is wholly predicated on incrementalism effectively redefines the terms of judgment, so that modest, institutionally convenient and highly measurable shifts count as success while more radical demands are pushed outside the frame of what the concept is supposed to deliver. The bar is lowered, and then the paradigm is declared successful on its own reduced terms.

The critique also takes aim at the evidential basis of the defences. One paper is explicitly framed as a perspective piece with no original research data, while the other claims to offer a systematic synthesis of critiques, yet both make expansive claims about the field’s maturity, coherence and progress on the basis of selective literature synthesis, definitional reviews and chosen case examples. The handling of degrowth is a case in point: the mere existence of degrowth-related discussion is presented as proof that the circular economy is no longer fundamentally growth-oriented, even though by the defenders’ own figures, degrowth and economic sufficiency appeared in only around eleven to sixteen per cent of circular economy definitions in 2023. The presence of a minority post-growth strand, Marjanović argues, does not show that the field has overcome its growth attachment; if anything, the minority status of degrowth reinforces the critique that radical interpretations remain peripheral to the dominant, business-centred logic.

The response to criticism of the circle metaphor follows a similar pattern. The defenders argue that the metaphor is not misleading because serious scholars do not aim for perfect circularity and influential conceptualisations already acknowledge leakage and trade-offs. But this shifts the discussion from the effects of the metaphor to the intentions of scholars. The original criticism concerns what the circle does in public and policy imaginaries: it can naturalise the image of closure, encourage the fantasy of neat loops and obscure the social and spatial displacements involved in material flows. The defenders themselves briefly concede that the seductive simplicity of the closed loop can obscure questions of justice and power, yet they do not engage that concern directly. A metaphor can be politically powerful, the critique insists, even when its users do not take it literally.

Beyond the circular economy itself, the paper turns a sharper lens on academic culture. The defenders criticise the field for metric-driven opportunism, repetitive reviews and low-quality scholarship, yet Marjanović observes that their own output leans heavily on opinion pieces, definitional reviews and meta-level commentary with little original data, complicating sweeping dismissals of other forms of academic production. They also promote a pracademic ideal, celebrating scholars who move between universities, consultancy and public debate, and praising applied reports by organisations such as the UN for their clarity. Marjanović does not deny the value of practitioner engagement, but argues that this model projects a narrow, elitist hierarchy of scholarly worth in which visibility, applicability and policy influence define value, while slower, less performative and more adversarial forms of critique are cast as excessive or marginal. Teaching, mentoring, methodological rigour and conceptual clarification, he notes, are also central academic contributions, and not all impact is publicly visible.

Perhaps the most pointed section concerns self-positioning. The two defences cite roughly twenty-six publications authored or co-authored by Kirchherr, mobilising his own prior work to define the field, establish its maturity, diagnose its problems and defend its significance. The recommendation that scholars publish in practitioner-oriented venues is illustrated with the Journal of Circular Economy, of which Kirchherr is Editor-in-Chief and co-author Kris Hartley an editorial board member. Marjanović is careful to note that this does not amount to a formal conflict of interest or prove bad faith, but he argues it reflects a deeper epistemic self-positioning: the authors are not merely reviewing the field but shaping the canon through which it is understood, so that the circular economy is defended partly through texts that define what counts as meaningful progress within it.

The critique ends on a conciliatory but firm note. The defenders are right that parts of the critical literature can be schematic and repetitive, that circular economy scholarship has grown more internally plural, and that institutional uptake matters politically. Yet internal plurality does not erase the dominance of growth-compatible interpretations, and the weaknesses of some critical work do not invalidate the need for critique as such. A genuinely convincing defence, Marjanović concludes, would need to show that the circular economy can engage seriously with power, labour, ownership, redistribution, wellbeing and sufficiency, rather than treating them as secondary matters of implementation and design. Until then, the debate over the circular economy is best understood not as a technical dispute about recycling rates, but as a struggle over who gets to define the concept, which critiques count as legitimate, and what kind of academic authority is permitted to shape its future.

Subject of Research: Critical analysis of normative bias, academic elitism and self-positioning in circular economy scholarship

Article Title: What defenders of the circular economy get right, and what they don’t: normative bias, academic elitism, and self-positioning in circular economy scholarship

Article References: Marjanović, M. (2026). What defenders of the circular economy get right, and what they don’t: normative bias, academic elitism, and self-positioning in circular economy scholarship. Journal of Industrial Ecology, 30(4), 2089-2099. https://doi.org/10.1007/s44498-026-00141-w

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00141-w

Keywords: circular economy, sustainability, ecological modernisation, academic critique, degrowth, green growth, scholarly debate, Journal of Industrial Ecology, positionality, environmental policy, self-citation, pracademics

The team focused on two of the most widely used data sources in the discipline, which approach the economy from fundamentally different angles. Ecoinvent is a process-based database, built bottom-up from detailed measurements of individual industrial activities, supply chains traced link by link. Exiobase, by contrast, is a multiregional input-output database, built top-down from national economic accounts and environmental statistics, capturing the entire global economy in one enormous matrix of monetary flows between sectors and countries. Process data are typically more precise for the activities they cover but risk truncation, missing distant upstream links; input-output data are complete by construction but highly aggregated, lumping many heterogeneous activities into single sectors. Analysts have long suspected that these structural differences translate into different levels of uncertainty in the final results, but rigorous, like-for-like comparisons have been scarce.

To make such a comparison possible, the researchers first had to solve a thorny problem: exiobase, unlike ecoinvent, does not ship with ready-made uncertainty estimates for its thousands of data points. They therefore tested two approaches to estimating data uncertainty in exiobase. The first assumed a uniform level of uncertainty across the entire database, a simple but crude benchmark. The second applied the pedigree matrix method, a well-established scheme in life cycle assessment that scores each data point on qualitative characteristics such as reliability, completeness, temporal correlation, geographical correlation and further technological correlation, and converts those scores into quantitative uncertainty factors. The pedigree approach, originally developed for ecoinvent, allowed the team to assign differentiated, defensible uncertainty distributions to exiobase entries rather than painting the whole database with one brush.

A second layer of uncertainty arises when a specific product system, the so-called foreground, is connected to the background database. Matching the activities of a novel technology to representative background datasets involves judgment calls, and each mismatch introduces model uncertainty. The researchers estimated this matching uncertainty too, again using the pedigree matrix, so that both the data quality of the background numbers and the quality of the coupling between foreground and background entered the final error budget. This dual treatment is one of the study’s technical contributions, because most previous comparisons of the two database families considered data uncertainty alone and ignored the additional noise injected by the matching step.

The case study chosen to stress-test the framework was an emerging wastewater treatment technology developed under the European ToSynFuel project, which turns sewage sludge into biofuels and hydrogen. Emerging technologies are exactly where uncertainty matters most: there are no mature inventories for them, foreground data are provisional, and the choice of background database can swing the headline climate impact. The team built the technology’s life cycle model and then ran stochastic error propagation, using Monte Carlo simulation in the open-source Brightway Python framework, to compute climate impact scores as probability distributions rather than single numbers. Ad hoc simulations were designed for both databases, drawing thousands of random samples from the uncertainty distributions of the underlying inventory data and propagating them through the full linear model each time.

The results are striking in their symmetry. When uncertainty in exiobase was estimated with the pedigree matrix, the analysis confirmed a proportional relationship between input and output uncertainty: uncertainty in the database entries propagates through the input-output calculations in a predictable, roughly proportional fashion. That finding matters because it suggests the aggregation embedded in input-output tables does not wildly amplify or dampen error in the way some analysts feared. The error structure of a highly aggregated global database turns out to be more tractable than its sprawling matrix appearance might suggest.

Even more consequential is the head-to-head comparison of the final climate scores. Calculated through exiobase, the wastewater treatment technology’s climate impact came out with a median of 0.146 million tonnes of carbon dioxide equivalent, with a 5th to 95th percentile range of 0.140 to 0.150 million tonnes. Calculated through ecoinvent, the median was 0.148 million tonnes of carbon dioxide equivalent, with a range of 0.143 to 0.152 million tonnes. The two distributions overlap almost completely. The medians differ by less than two percent, and the uncertainty bands are nearly identical in width. On that basis the authors ruled out the hypothesis that, when pedigree-based uncertainty estimates are used, one database systematically yields more uncertain results than the other, at least for this technology and this impact category.

For practitioners, the practical message is one of reassurance with caveats. Analysts who prefer ecoinvent for its process-level detail and those who turn to exiobase for its economy-wide completeness can now point to evidence that, once uncertainty is handled consistently with the pedigree approach, the two routes deliver climate impact estimates of comparable reliability. The choice between them can be driven by scope, coverage and the question at hand, rather than by fear that one path is statistically shakier. The study also supplies the tools for others to repeat the exercise: the scripts used in the work are archived on Zenodo, and the team released a general-purpose library, hosted on GitHub, that performs the same type of computation on other cases, potentially lowering the barrier for uncertainty analysis across the field.

The work also feeds a broader methodological debate about how uncertainty should be handled in comparative life cycle assessment. The authors engage with a literature that questions whether classical statistical testing and confidence intervals are appropriate at all when results come from Monte Carlo simulations of overlapping distributions, citing arguments that such tests can mislead when distributions are not independent. By reporting medians and percentile ranges rather than verdicts of statistical significance, the study models a reporting style that lets readers judge the overlap for themselves. It likewise builds on a lineage of work stretching from early error analyses of input-output inventories to recent estimates of greenhouse gas uncertainty in global multiregional models, positioning itself as the first, or among the first, to propagate uncertainty through both database families at different aggregation levels within a single consistent framework.

Limitations remain, and the authors are careful about scope. The comparison rests on one emerging technology and one impact category, climate change, so extending the conclusions to other technologies, to toxicity or water impacts, or to other database versions will require further work. The pedigree scores themselves embed expert judgment, and different scoring choices could shift the distributions. Yet the core result stands as an unusually clean empirical answer to a question the field has debated largely on theoretical grounds: at different levels of aggregation, from sector-level economic matrices to plant-level process chains, the uncertainty of a carbon footprint is less a property of the database you choose and more a property of how honestly you characterize the numbers inside it. For a discipline whose outputs increasingly steer billions of euros of investment, that is a finding worth propagating.

Subject of Research: Uncertainty propagation in life cycle assessment using input-output and process-based inventory databases

Article Title: Uncertainty propagation of input–output and process-based life cycle inventories at different aggregation levels

Article References: Pigni, E., An, N., Righi, S., Marazza, D., Balugani, E., & Pizzol, M. (2026). Uncertainty propagation of input–output and process-based life cycle inventories at different aggregation levels. Journal of Industrial Ecology, 30(4), 1671-1682. https://doi.org/10.1007/s44498-026-00113-0

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00113-0

Keywords: life cycle assessment, uncertainty propagation, exiobase, ecoinvent, Monte Carlo simulation, pedigree matrix, input-output analysis, carbon footprint, wastewater treatment, Brightway, climate impact, Journal of Industrial Ecology

A new study published in the Journal of Industrial Ecology offers one of the most detailed answers to date. Researchers Chuanqi Chen, Dongyang Pan, Yao Wang, and Zitong Zhang assembled large-scale micro-level data on green financial policy across all 31 provincial-level regions of mainland China, and for the first time constructed two distinct comprehensive indices: one measuring the intensity of green financial policy itself, and a second measuring the development of the green financial market that those policies are meant to cultivate. By separating the policy lever from the market mechanism it is supposed to activate, the team was able to test not just whether green finance works, but how it works—and, crucially, where the machinery breaks down.

The circular economy, the framework at the heart of the analysis, is more than a buzzword. It describes an economic system that decouples growth from resource consumption by emphasizing environmental quality, energy efficiency, and the conservation of natural resources—closing material loops rather than following the traditional take-make-dispose pathway. The researchers therefore evaluated green financial policy against these three dimensions simultaneously, asking whether provinces with stronger green finance mandates saw measurable improvements in air and environmental quality, in the efficiency with which they converted energy into economic output, and in the sustainability of their natural resource use.

The headline finding is encouraging for policymakers. Green financial policy, the study reports, exerts positive effects not only on the greenness of the financial market itself but also on all three pillars of the circular economy: environmental quality, energy efficiency, and natural resource conservation. In other words, provinces that pushed harder on green finance saw their environments improve, their energy productivity rise, and their resource stewardship strengthen. On the surface, this suggests that China’s financial regulators have, in fact, succeeded in promoting circular development—an outcome that would validate the growing global trend of assigning environmental objectives to institutions originally designed to safeguard monetary systems.

But the deeper mechanism analysis reveals a more complicated and arguably more important story. When the researchers traced how the policy effects flow through the economy, they found that the mediating role of the green financial market was significant only for environmental quality. For energy efficiency and natural resource conservation, the market channel was not statistically significant. This is a subtle but consequential distinction. It implies that green financial policy appears to operate primarily like a direct environmental policy—regulators pushing outcomes through mandates, incentives, and administrative pressure—rather than through the intended indirect route of mobilizing private capital markets to finance circular transitions.

This distinction matters because the entire theoretical appeal of green finance rests on the leverage effect. A direct environmental regulation, such as an emissions standard, changes behavior only where it is enforced. A financial policy, by contrast, is supposed to work by changing the price and availability of capital, so that banks, investors, and insurers independently reallocate trillions of dollars toward green projects, multiplying the impact of every regulatory intervention. If the market channel is not functioning, the multiplier never materializes. The policy works, but only as an extension of the regulator’s own reach—precisely the outcome green finance was designed to transcend.

The study does identify a condition under which the market mechanism comes alive. In further analysis, the researchers found that for the specific policy objectives where the green financial market does actively function as a transmission channel, the level of marketization—the degree to which the local economy operates through market institutions rather than administrative allocation—significantly reinforces the policy’s overall effectiveness. Provinces with more developed market institutions appear better able to convert regulatory signals into genuine financial market activity, which in turn amplifies the circular economy outcomes. This suggests that the missing link in China’s green finance architecture is not the ambition of the policies but the institutional environment in which they operate.

For China specifically, these findings arrive at a pivotal moment. The country has made the circular economy a national development strategy for nearly two decades, evolving from early pilot programs into comprehensive policy frameworks, while simultaneously building one of the world’s largest green bond and green credit markets. The new evidence confirms that these two agendas are connected—green finance is delivering real environmental and resource gains across the provinces—but it also exposes the gap between regulatory intent and market response. The authors argue that their results give financial regulators more targeted insights to refine policy design, pointing toward reforms that strengthen market institutions rather than simply escalating policy intensity.

The implications extend well beyond China’s borders. The European Union, the United Kingdom, and dozens of other jurisdictions are building their own sustainable finance taxonomies, disclosure rules, and climate risk frameworks, all premised on the idea that financial markets can be harnessed as engines of environmental transformation. The Chinese experience offers a cautionary benchmark: measurable progress is achievable through regulatory muscle alone, but the deeper prize—a self-sustaining market that prices environmental risk and funds circular innovation at scale—requires marketization, transparency, and institutional depth that regulation by itself cannot conjure. For emerging economies watching China’s experiment, the lesson is that building the market may matter as much as building the mandate.

Methodologically, the study also marks a step forward for the field. By constructing separate indices for policy intensity and market development from micro-level data spanning all 31 provinces, the researchers created the analytical resolution needed to distinguish between a policy that works and a policy that works for the right reasons. That distinction will shape how economists evaluate green finance everywhere. The verdict from China is ultimately two-sided: financial regulators can indeed drive the circular economy, and they are already doing so—but the financial markets that were supposed to carry the load are still learning to pull. Until they do, the world’s most ambitious green finance experiment remains, at its core, a very effective piece of environmental policy wearing the clothes of finance.

Subject of Research: The effects and transmission mechanisms of green financial policy on the circular economy across China's 31 provincial-level regions

Article Title: Can financial regulators truly drive circular economy? Novel evidence from 31 provinces in China

Article References: Chen, C., Pan, D., Wang, Y., & Zhang, Z. (2026). Can financial regulators truly drive circular economy? Novel evidence from 31 provinces in China. Journal of Industrial Ecology, 30(4), 2119-2130. https://doi.org/10.1007/s44498-026-00143-8

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00143-8

Keywords: green finance, circular economy, financial regulation, China, environmental quality, energy efficiency, natural resource conservation, green financial market, marketization, policy evaluation, Journal of Industrial Ecology, sustainable development

The circular economy, in broad terms, seeks to reduce waste and resource extraction by preserving the value of products and materials over time. Yet the authors emphasise that there is no single agreed definition. Frameworks such as slow, close and narrow loops, designing out waste, keeping products and materials in use, and regenerating natural systems all circulate simultaneously, and even core concepts like regeneration remain open to interpretation. This interpretive openness is precisely where the trouble begins. An LLM cannot reproduce the entire field in a single answer. It must select and organise material, bringing some ideas into view and leaving others out. In a discipline whose meanings and priorities are still unsettled, that selection determines which interpretations users encounter most readily, and which fade from sight.

The first of the paper’s three analytical dimensions, the Content dimension, examines how AI constructs and curates circular economy knowledge. The mechanics matter. LLMs learn from vast training corpora spanning websites, books, academic papers, policy documents, corporate reports and media content, adjusting their internal parameters to capture recurring relationships between tokens and their contexts. When answering a prompt, they predict what is likely to follow, one token at a time. Crucially, the answer is generated from statistical regularities in the source material, not retrieved as a settled account or independently assessed for the strength of its claims. A formulation that appears frequently in the corpus may be reproduced more readily than a better-supported but less visible alternative. The authors call the resulting convergence a data-driven consensus: agreement around the patterns most strongly represented in the available material, rather than through debate or critical assessment.

The consequences for circularity are concrete. Recycling, for example, remains one of the most extensively studied and adopted circular economy strategies and dominates implementation narratives, even though many materials can only be recycled a limited number of times, meaning recycling may prolong rather than replace linear production and consumption. Higher-value strategies such as refuse, reuse, maintenance and upgrading, or newer territory like nature regeneration, may receive less attention simply because they are less prevalent in the historical record. The authors illustrate the tendency with an exploratory exercise, reported in the paper’s supplementary information, in which six widely used public-facing LLMs were asked the same open question, can you define the circular economy, across three collection rounds in 2025. The exercise was explicitly illustrative rather than a benchmark, but it shows how models may carry forward the imbalances of their training data and amplify them in the accounts presented to users.

The knowledge base itself compounds the problem. AI scholarship has long flagged uncertain data provenance and opacity around how training material is selected and processed. For the circular economy, this is aggravated by the explosive growth and mixed composition of the literature since the mid-2010s, which blends peer-reviewed research with consultancy reports, policy documents and unvetted grey material. English dominates the field, so perspectives developed in other linguistic contexts are likely underrepresented. Meanwhile, the publish-or-perish pressures of academic life can prioritise quantity over robustness, and even the ISO standard for the circular economy struggles to harmonise competing interpretations. LLMs draw on this heterogeneous material without reliably distinguishing robust, contested or weak knowledge. Concepts may also suffer what the authors describe as knowledge decay, erosion or dilution: the 9R framework presented as the circular economy itself, circularity stretched to encompass almost any corporate initiative, or the term used as a generic label in policy and business. When such formulations become widely reproduced, models carry them into new answers without recognising that their original meaning has been narrowed, stretched or detached from its foundations.

The second dimension, the Transformative dimension, asks what this mediated knowledge does to innovation and entrepreneurship, the twin engines of the circular transition. Here the authors draw a sharp distinction between optimisation and systemic change. AI systems excel at answering specific questions: optimising routing, maintenance and fleet management, or designing more circular single-use packaging. But a meaningful circular shift in the automobile sector, for instance, requires redefining the car as a service rather than a product, alongside changes in consumer behaviour, municipal policy and manufacturer strategy. If AI is used only to make the linear model of private car ownership more efficient, it will not drive systemic reconfiguration. LLMs cannot independently assess socio-technical systems or determine that long-term sustainability goals should take precedence over short-term data patterns, which limits their capacity to propose the bold redesigns a genuine transformation demands.

Entrepreneurship presents a similar tension. Circular entrepreneurs often rely on inspiration, contextual insight and unconventional thinking, and grassroots circular entrepreneurs are frequently driven by non-economic motives such as passion for the environment and concern for social issues, prioritising long-term environmental and social benefits over immediate commercial returns. LLMs can simulate aspects of entrepreneurial thinking and assist with generating business ideas, crafting marketing strategies or analysing trends, but they lack the courage, emotions and willingness to defy prevailing odds that characterise many founders. More pointedly, because circular entrepreneurs often pursue ideas that challenge established ways of thinking, while LLMs tend to reproduce familiar and well-represented approaches, AI assistance may favour conventional solutions precisely where more radical innovation is needed. Innovation itself also depends on collective sense-making, trust-building and learning alliances through which actors negotiate standards and share risks; these are human, socially attuned processes that models cannot replicate.

The third dimension, the Moral dimension, confronts the ethics of relying on such systems. A central concern is what the authors term operational indifference to truth: LLMs optimise statistical likelihood rather than evaluate reality, prioritising plausibility over verification and occasionally producing hallucinations, plausible-sounding but fabricated content. In the circular economy context this can mean references to non-existent research, exaggerated claims about circular initiatives, or unsupported performance figures. Compounding the risk is AI assertiveness, the tendency to present responses with high confidence regardless of underlying uncertainty, and compliance bias, whereby models affirm or accommodate incorrect or misleading user statements because they are designed to generate cooperative, engaging responses. Repeated interactions with accommodating systems may create an echo chamber that reinforces conformist thinking, reduces critical scrutiny of circular practices and narrows the range of options considered legitimate. There is also the question of who bears the costs when decisions are poorly informed: biased outputs may direct resources towards established actors and familiar technologies while neglecting locally appropriate alternatives in emerging economies or local communities.

Underlying all of this is the absence of moral agency. LLMs may reproduce the language of ethical deliberation, but they do not act for moral reasons or understand the consequences of what they recommend, and they cannot assume responsibility for decisions informed by their outputs. The authors argue that responsible judgement, understood in the virtue-ethics tradition as integrity, fairness and consistency in weighing competing environmental, social and economic claims, must remain with human actors. Without it, circularity risks becoming cosmetic: products promoted as recycled or recyclable suggesting that consumption may continue largely unchanged, diverting attention from the need to reduce material throughput while leaving the structures of the linear economy untouched. Uncritical reliance on LLMs could even foster intellectual passivity, weakening the very reflection needed to scrutinise their assumptions.

The paper closes with governance priorities rather than despair. Model developers and organisations deploying LLMs should make the sources and assumptions behind their responses explicit, opening them to expert scrutiny by interdisciplinary teams of data scientists, environmental experts, social scientists and policymakers. Training techniques such as reinforcement learning from human feedback could encourage models to signal uncertainty and acknowledge contested claims, supported by standardised benchmarks for epistemic humility, the principle that AI outputs should not claim more certainty than the evidence supports. The human-in-the-loop principle offers a practical safeguard for ethically sensitive choices, and education should equip users to treat LLMs as a critical collaborator rather than an authoritative oracle. Used critically and responsibly, the authors conclude, LLMs can widen access to circular economy knowledge and support more informed deliberation. Used without sufficient scrutiny, they may reinforce prevailing interpretations and lend legitimacy to circularity in name only. The framework also opens a research agenda: comparing LLM responses across models, prompts, languages and user contexts, and testing model recommendations against expert judgement where environmental and social goals conflict with economic priorities. The stakes, the authors suggest, could hardly be higher, because whether AI helps challenge linearity or quietly preserves it will depend on the judgement of the humans who use it.

Subject of Research: How large language models mediate and shape knowledge of the circular economy

Article Title: The circular economy, explained by AI

Article References: Morseletto, P., & Bocken, N. (2026). The circular economy, explained by AI. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00192-z

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00192-z

Keywords: circular economy, large language models, artificial intelligence, epistemic intermediaries, sustainability, recycling, innovation, entrepreneurship, AI ethics, hallucination, governance, Journal of Industrial Ecology

The research, led by Pippa Edwards and Rupert J. Myers of Imperial College London together with Wilson Ricardo Leal da Silva of Fuller Technologies and Paul Fennell, also of Imperial College, addresses a long-standing blind spot in life cycle assessment, or LCA, the standard method for quantifying the environmental impacts of products across their entire life cycle. Although clinker substitution currently displaces about 25 percent of clinker in Portland cements on average worldwide, and could reduce global cement emissions by up to 1.3 gigatonnes per year according to earlier work, the emissions associated with processing the substitute materials themselves have often been ignored, estimated crudely, or hidden inside pre-aggregated emission factors. The new study argues that this omission can overestimate the climate benefits of composite cements and obscure impacts in other categories, such as particulate matter pollution, that matter for human health and ecosystems.

To close this gap, the team conducted a systematic Scopus literature search and screened 188 articles out of 539 results, ultimately assembling 36 sets of inventory data that they disaggregated into 57 distinct unit process datasets. The materials covered include granulated blast furnace slag from ironmaking, coal fly ash from power stations, natural pozzolans, silica fume, calcined clays, limestone, steel slag, bauxite residue, biomass ash, mine tailings, and recycled concrete fines, in both uncarbonated and carbonated forms. For each material, the researchers traced every processing stage between generation or collection and its point of use in cement, capturing inputs and outputs of materials and energy, and converting everything into consistent SI units. Crucially, the data are presented as unit process diagrams, allowing analysts to swap fuel sources, test grid decarbonisation scenarios, or model new technologies, something impossible with the single aggregated numbers offered by many existing databases.

Data quality was assessed using the pedigree matrix approach, which scores reliability, completeness, temporal correlation, geographical correlation and technological correlation on a scale from 1, the best, to 5, the worst. The results reveal sobering weaknesses. Completeness was the lowest-scoring category, and data collection duration or the number of sites surveyed was not even specified in 19 of the 36 inventory datasets, often because the figures were derived from models rather than operational measurements. Roughly 55 percent of the data were more than 15 years old, meaning many studies of common substitutes rely on figures too outdated to meet current environmental product declaration requirements, which demand generic data less than a decade old. Most life cycle assessments of granulated blast furnace slag, for instance, still draw on a single 2003 survey of United States producers, underscoring how thin the evidence base for the world’s most important clinker substitutes really is.

The study also maps where new data are most urgently needed. For calcined clays, one of the most promising emerging substitutes, there are no inventory datasets based on verified facility operating data, despite 14 operational calcined clay plants existing globally as of 2023. Only a single dataset covers production of recycled concrete fines, and single datasets also describe carbonation of those fines and specialty slag processing steps such as metal recovery and slag carbonation. Furthermore, fewer than half of the unit process datasets included elementary flows in the foreground system, meaning direct releases to the environment such as particulate matter or heavy metals. No dataset reported heavy metal flows at all, even though materials like mine tailings, bauxite residue and raw clays are known to contain them, and no calcined clay dataset accounted for CO2 released from carbonates in the raw clay, a potentially significant omission given that some UK clays contain more than 55 percent carbonate by weight.

On the quantitative side, the researchers ran gate-to-gate and cradle-to-gate life cycle assessments using the IPCC 2021 global warming potential method with ecoinvent background data, comparing coal, natural gas and refuse-derived fuel as process heat sources. The results are striking in their clarity. Calcination, the thermal treatment used to activate clays, had the highest carbon footprint of any unit process, with a median of roughly 370 kilograms of CO2-equivalent per tonne of product, driven almost entirely by fuel combustion. Heat-fired drying followed at around 50 kilograms per tonne. Mechanical processes were far gentler on the climate: milling typically fell between 17 and 32 kilograms of CO2-equivalent per tonne using European electricity, while crushing and quarrying were lower still. The message is that grinding a material to boost its reactivity is a far lower-impact strategy than heating it, although the energy demand of size reduction rises steeply as particles get smaller.

When the team compared cradle-to-gate carbon footprints against reactivity measured with the R3 heat release test, calcined clays emerged as the most reactive substitutes but also among the highest in carbon footprint, a tension that fuel switching could ease. Carbonated recycled concrete fines were the only carbon-negative material in the analysis, thanks to the mineralisation of roughly 210 kilograms of CO2 per tonne of fines into stable calcium carbonates, although the researchers caution this represents a best-case scenario that ignores transport emissions and assumes optimistic mineralisation potentials. Calcined mine tailings, by contrast, showed only modest reactivity gains from calcination, suggesting that lower-impact mechanical processing or vitrification may make more environmental sense for these materials. Notably, the carbon footprint of a given unit process proved broadly similar across different materials, meaning the data can serve as reliable proxies for emerging substitutes processed by conventional means.

Perhaps the study’s most consequential finding concerns composite cements defined under the European standard EN 197. When the researchers calculated the carbon footprints of these cements, they found that the degree of clinker substitution, rather than the type of substitute used, was the dominant driver of emissions. Cements with the lowest clinker content, such as limestone calcined clay cements and very low-clinker slag cements, delivered the lowest footprints even when their substitutes carried relatively high processing emissions. For the roadmap targets of the Global Cement and Concrete Association, which envision average clinker contents of 58 percent by 2030 and 52 percent by 2050, clinker itself will remain the overwhelming contributor to cement emissions regardless of which substitute is chosen. The practical implication is that research and investment should prioritise maximising clinker substitution and clinker reactivity, including emerging strategies such as clinker micronisation, rather than agonising over marginal differences between substitute materials.

The study also confronts an uncomfortable supply problem. Slag cements cannot scale to meet global demand of roughly 4100 million tonnes per year because only 330 to 410 million tonnes of granulated blast furnace slag are available annually, a figure that may shrink further as the iron and steel sectors decarbonise and coal power stations close, threatening fly ash supplies too. Moreover, under economic allocation, the high market price of slag, up to 160 pounds per tonne, could add as much as 480 kilograms of CO2 per tonne in allocated upstream burden, a reminder that accounting choices can transform conclusions. By publishing their complete, disaggregated inventory openly, the authors provide a transparent baseline that can verify commercial data, improve the credibility of environmental product declarations, and guide where the industry should invest in data collection. As cement makers race toward 2050 climate targets, this work delivers something the field has quietly lacked for decades: a rigorous, flexible map of where the carbon really lies in the materials meant to save it.

Subject of Research: Process-based life cycle inventory data for Portland clinker substitute materials used in low-carbon composite cements.

Article Title: Toward process-based inventory data for Portland clinker substitutes

Article References: Edwards, P., Leal da Silva, W. R., Fennell, P., & Myers, R. J. (2026). Toward process-based inventory data for Portland clinker substitutes. Journal of Industrial Ecology, 30(5), 2661-2677. https://doi.org/10.1007/s44498-026-00186-x

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00186-x

Keywords: clinker substitutes, cement decarbonisation, life cycle assessment, inventory data, calcined clay, supplementary cementitious materials, recycled concrete fines, blast furnace slag, carbon footprint, EN 197, green cement, Journal of Industrial Ecology

The selection process is deliberately structured to balance technical excellence with accessibility. Papers are nominated by members of the journal’s Editorial Board or its Prize Committee, and each candidate is evaluated against three criteria: professional merit, contribution to the field, and presentation quality. The adjudication committee publishes its rules on the journal’s website, and past award announcements, stretching back to the first winners introduced in 2015, provide a record of how the discipline’s methodological frontier has shifted over the past decade, from early material flow accounting toward machine learning, Bayesian statistics, and high-resolution global spatial modeling.

Nine papers were nominated for the 2025 cycle, and their collective breadth offers a snapshot of where industrial ecology is heading. Four were research articles. Aleksandra Kim, Christopher Mutel, and Stefanie Hellweg applied machine learning to conduct a global sensitivity analysis of correlated uncertainties in life cycle assessment, addressing one of the most persistent statistical problems in environmental footprinting. Jasmine Chea and colleagues automated the mapping of chemicals through their conditions of use, a step toward faster and more scalable life cycle chemical assessment. Two further nominated papers involved high-resolution mapping of materials in building stocks: a comprehensive global analysis of material stocks in buildings by Helmut Haberl and colleagues, and a dynamic assessment of building stock turnover across Japan by Satoshi Nagata and colleagues, which traced material and flow patterns nationwide from 2003 to 2020.

The remaining five nominated papers were methods articles, reflecting the field’s intense current interest in better analytical machinery. Philipp Grimmel and colleagues demonstrated how decision-support algorithms and databases of existing material exchanges can identify optimal opportunities for industrial symbiosis in regional economies, essentially building a recommendation platform that learns from historical exchange patterns to connect urban factories. Karin Krych, Daniel Müller, and Johan Pettersen cleverly incorporated what they describe as the nature and nurture of product lifetimes into a hazard function for dynamic stock modeling, separating intrinsic durability from contextual factors that shorten or extend product use. Jing Liao and colleagues took a technically sophisticated Bayesian approach to managing uncertainty in material flow networks, using model selection to discriminate between competing network structures and to support risk-informed decisions.

Rounding out the methods nominees, Miguel Sierra-Montoya and colleagues presented WindTrace, an open-source parametric model for generating life cycle inventories of wind turbines and wind parks, allowing environmental impacts of alternative wind energy designs to be assessed before hardware is ever built. Hanspeter Wieland, Dominik Wiedenhofer, Nina Eisenmenger, Takuma Watari, and Stefan Giljum used a global physical input-output model to assess the footprint of global iron ore mining on ecosystems, linking the steel consumed in one part of the world to the land disturbed in another. Together, the nominated set demonstrates a discipline that is simultaneously becoming more computational, more spatially explicit, and more directly connected to decision-making in energy and materials policy.

The 2025 Junior Best Paper Prize was awarded to Aleksandra Kim, Christopher Mutel, and Stefanie Hellweg for their publication on global sensitivity analysis of correlated uncertainties in life cycle assessment. Kim and Mutel are affiliated with the Laboratory for Energy Systems Analysis at the Paul Scherrer Institute in Villigen, Switzerland, while Kim also works with Hellweg in the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. The paper confronts a subtle but consequential problem: life cycle assessment databases are riddled with data whose uncertainties are correlated, because the same background processes, emission factors, and measurement methods feed into many different inventory entries. Classical sensitivity analysis techniques assume independence among inputs, and when that assumption fails, they can wildly misidentify which parameters actually drive the variability in a result.

The prize committee judged the Kim paper to be highly novel, providing new insight into how correlations between data can be handled when performing a global sensitivity analysis in life cycle assessment. The committee emphasized that the work is of high scientific quality and important for the field precisely because the approach can be applied to any life cycle assessment dataset, helping analysts address the credibility and robustness of their results. In practical terms, this matters for anyone who has ever questioned whether an environmental footprint number can be trusted: the method gives analysts a principled way to rank the true sources of uncertainty even when inputs are statistically entangled. The committee also praised the paper’s organization and readability, noting that complex techniques were explained clearly and supported by effective diagrams, a reminder that even the most mathematically demanding work in this field is judged partly on how well it communicates.

The junior prize competition was described as particularly close this year, and the committee gave special mention to two runner-up papers. The first was the Bayesian model selection work by Liao and colleagues, which brings formal probabilistic reasoning to the problem of choosing among competing material flow network structures. The second was the product lifetime paper by Krych and colleagues, whose nature-and-nurture framing of survival analysis offers dynamic stock modelers a more realistic treatment of why some products endure and others fail prematurely. That two of the three most celebrated junior-authored papers are methods innovations underscores how methodological development currently defines the field’s intellectual edge.

The 2025 Senior Best Paper Prize went to Hanspeter Wieland, Dominik Wiedenhofer, Nina Eisenmenger, Takuma Watari, and Stefan Giljum for their study assessing mining-related land footprints of global steel use with a global physical input-output model. Wieland, Wiedenhofer, and Eisenmenger are based at the Institute of Social Ecology at the University of Natural Resources and Life Sciences in Vienna, Austria. Watari works in the Material Cycles Division of the National Institute for Environmental Studies in Tsukuba, Japan, and Giljum is at the Institute for Ecological Economics at the Vienna University of Economics and Business. The paper builds on the authors’ previous global input-output model for iron and steel, but adds a decisive new layer of ecological granularity by disaggregating material flows into the specific biomes where mining takes place and by modeling both embodied land footprints and the human appropriation of net primary production.

The committee recognized many novel features in the winning senior paper. While iron ore mining is globally dominated by China, Australia, and Brazil, the authors showed that Europe holds the largest share of ecologically vulnerable tropical biomes in its embodied iron ore imports, a finding that reframes the geography of responsibility for mining-driven ecosystem damage. Rather than measuring trade in tonnes alone, the model traces exactly which ecosystems are disturbed to supply which consuming economies, connecting consumption patterns in wealthy regions to habitat loss and productivity appropriation in biodiversity-rich mining frontiers. The committee noted that the text was well written, aside from a high use of acronyms, and supported by vibrant diagrams, again rewarding technical rigor delivered with clarity.

Taken together, the 2025 Graedel Prizes highlight a discipline in methodological ascent. The winning junior paper makes uncertainty quantification in life cycle assessment more trustworthy; the winning senior paper makes the ecological consequences of the global steel economy visible at the biome scale. Around them, the nominated field is building recommendation engines for industrial symbiosis, parametric inventories for wind energy, Bayesian tools for flow networks, and continent-scale maps of the materials locked in buildings. For a field founded on the idea that human industry should be understood as an integrated system within the biosphere, the current generation of prize-recognized work shows that vision becoming operational: quantified, spatialized, and increasingly ready to inform real environmental decisions.

Subject of Research: The 2025 Graedel Prizes recognizing the best junior and senior first-authored papers in the Journal of Industrial Ecology.

Article Title: Winners of the 2025 Graedel Prizes: The Journal of Industrial Ecology Best Paper Prizes

Article References: Kennedy, C., & Wood, R. (2026). Winners of the 2025 Graedel Prizes: The Journal of Industrial Ecology Best Paper Prizes. Journal of Industrial Ecology, 30(5), 2209-2211. https://doi.org/10.1007/s44498-026-00165-2

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00165-2

Keywords: Graedel Prizes, industrial ecology, life cycle assessment, sensitivity analysis, material flow analysis, steel land footprint, input-output model, industrial symbiosis, product lifetimes, building material stocks, wind energy, Journal of Industrial Ecology