The study, published in the journal Environmental Geochemistry and Health, tackled a problem that has long concerned environmental microbiologists. When reclaimed water percolates through the vadose zone, the unsaturated layer of soil between the surface and the water table, it can ferry contaminants into rivers and aquifers. Among the most worrying of these hitchhikers are antibiotic resistance genes, or ARGs, which exist in two distinct forms. Intracellular ARGs travel inside living bacteria, while extracellular ARGs circulate as naked DNA released from dead cells. Both forms can spread resistance through the environment, and both have proven notoriously difficult to intercept simultaneously.

To test whether a bentonite seepage-control layer could do just that, the researchers built large-scale soil column simulators measuring three meters in length—an unusually realistic setup for laboratory infiltration studies. Over a 40-day experiment, they continuously fed reclaimed water through columns containing a bentonite barrier layer and compared the results against columns of native soil alone. The scale matters: smaller laboratory columns often fail to capture the hydraulic complexity of real field conditions, so the three-meter design lends the findings considerable practical weight.

The analytical toolkit was correspondingly thorough. The team combined hydraulic characterization with chemical analysis by high-performance liquid chromatography to track antibiotic concentrations, and they used quantitative real-time polymerase chain reaction to measure how many resistance genes made it through the system. To understand the ecological consequences, they sequenced the 16S rRNA gene, a standard molecular marker that reveals which microbial species are present and in what proportions. This combination allowed the researchers to quantify not just what was intercepted, but how the underground microbial community responded to the barrier’s presence.

The results were striking. The bentonite seepage-control layer enhanced the apparent attenuation of antibiotics by 48.51 percent compared with untreated soil, and it substantially reduced the migration of resistance genes through the profile. According to the authors, the barrier works through a dual mechanism: hydraulic retardation and sorptive retention. Bentonite, a swelling clay rich in montmorillonite, dramatically lowers the permeability of the layer it occupies, forcing water to move more slowly and giving physical, chemical, and biological processes more time to degrade or immobilize contaminants. At the same time, the clay’s negatively charged surfaces and layered structure provide abundant binding sites for antibiotic molecules and for DNA itself.

Independent batch adsorption experiments confirmed the sorptive side of the story. Bentonite showed a substantially higher capacity for retaining plasmid DNA—the circular DNA molecules that often carry resistance genes and can transfer them between bacteria—than the native soil did. This matters because plasmid-mediated gene transfer is one of the main routes by which resistance spreads through microbial communities, a process known as horizontal gene transfer. By binding plasmid DNA before it can reach downstream environments or encounter new bacterial hosts, the clay layer effectively cuts a key transmission pathway.

One consistent finding across both column systems was that extracellular ARGs remained the dominant fraction of the resistance gene pool throughout the experiment, a result the researchers report as statistically significant at p < 0.001. This observation aligns with a growing body of evidence that free DNA released from dead cells persists in soils and sediments far longer than once assumed, and that it can be taken up by competent bacteria in the environment. Extracellular DNA has been described in the literature as a neglected reservoir of resistance genes in aquatic systems, and its persistence in river sediments has been shown to facilitate the propagation of resistance. Any barrier technology that hopes to control the environmental spread of resistance must therefore handle the extracellular fraction, not just living bacteria—and the bentonite layer appears to do exactly that.

Crucially, the barrier achieved its interception without wreaking havoc on the underground ecosystem. The researchers found that microbial alpha-diversity, a measure of species richness and evenness within the community, was preserved in the presence of the bentonite layer, and the indigenous community structure remained stable over the course of the infiltration experiment. This is a non-trivial point. Some remediation approaches, such as chemical amendments or aggressive disinfection, can reshape microbial communities in ways that disrupt nutrient cycling or even select for hardier resistant strains. A passive barrier that filters contaminants while leaving the resident ecology essentially undisturbed represents a genuinely different kind of intervention—one that works with the soil rather than against it.

The practical implications are considerable. Bentonite is abundant, cheap, and already widely used in geotechnical engineering, most notably in geosynthetic clay liners that seal landfills and containment ponds. Adapting this off-the-shelf material for reclaimed water recharge basins, constructed wetland bottoms, or riverbank filtration systems would require no exotic chemistry and no ongoing energy input. The researchers describe the approach as robust, cost-effective, and passive—a strategy that works silently underground for years once installed. In an era when wastewater treatment plants are recognized as hotspots for the release of antibiotics and resistant bacteria, and when even very low concentrations of antibiotics in the environment can select for resistant strains, such a low-tech complement to high-tech treatment could be valuable.

Questions remain before bentonite barriers become standard practice. The study ran for 40 days, and long-term performance—including how the layer behaves through repeated wet-dry cycles, which are known to affect the swelling and hydraulic conductivity of bentonite liners—will need field-scale verification. The fate of captured antibiotics and genes after they bind to the clay, and whether saturated surfaces eventually release their cargo, also deserves attention. Still, the core message of the research is clear and timely: as reclaimed water becomes a pillar of urban water management, the ground beneath our feet can be engineered to serve as a last line of defense against one of public health’s most insidious threats. A layer of humble clay, it turns out, may be one of the simplest and most elegant tools in that fight.

Subject of Research: Bentonite seepage-control layers for intercepting antibiotics and antibiotic resistance genes during reclaimed water infiltration into soil and groundwater

Article Title: Bentonite seepage-control layers mitigate the transport of antibiotics and antibiotic resistance genes during reclaimed water infiltration

Article References: Zhang, Z., Chen, Z., Jiang, X., Han, Y., Zheng, H., Zhao, X., Xie, E., & Li, Y. (2026). Bentonite seepage-control layers mitigate the transport of antibiotics and antibiotic resistance genes during reclaimed water infiltration. Environmental Geochemistry and Health, 48(16), Article 626. https://doi.org/10.1007/s10653-026-03522-z

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03522-z

Keywords: antibiotic resistance genes, bentonite, reclaimed water, groundwater recharge, extracellular DNA, water reuse, soil contamination, clay minerals, vadose zone, qPCR, microbial ecology, seepage-control layer

The research, led by Ingo Richter of the Japan Agency for Marine-Earth Science and Technology together with colleagues from institutions in Japan, the United States, Norway, and Brazil, rests on an unusually broad foundation of evidence. The team compared simulations of a pre-industrial climate with projections of a high-emissions future, known as scenario ssp585, drawn from 23 state-of-the-art climate models participating in the Coupled Model Intercomparison Project Phase 6, the same modeling framework that underpins the assessments of the Intergovernmental Panel on Climate Change. By contrasting these two worlds, one without human interference and one heated by sustained emissions, the researchers could isolate how the machinery of Atlantic variability itself changes when the planet warms.

The headline finding confirms a trend that earlier studies had already hinted at: the variability of sea-surface temperatures along the equatorial Atlantic, the signature of the Atlantic Zonal Mode, weakens under global warming. In plain terms, the Atlantic Niño becomes less intense, swinging less dramatically between warm and cold phases. Off the equator, by contrast, the picture is different, with sea-surface temperature variability tending to increase slightly in the flanking regions of the tropical Atlantic. That split result matters, because the equatorial mode is the one most tightly linked to the seasonal rains that farmers and water managers from Senegal to Angola depend upon, and its weakening has been flagged in previous work as a potential source of forecast uncertainty.

What makes the new analysis distinctive is its dissection of why the weakening occurs. The Atlantic Niño, like its Pacific counterpart, is sustained by the Bjerknes feedback, a self-reinforcing loop in which a warm anomaly in the eastern equatorial ocean weakens the prevailing easterly trade winds, which in turn suppresses the upwelling of cold subsurface water, further warming the surface. The study finds that this feedback is losing its grip. Contrary to some earlier projections, the team found that the mean-state equatorial Atlantic thermocline, the sharp vertical boundary between warm surface water and cold deep water, actually shoals slightly under radiative forcing, a change that should, on its own, strengthen the mode by making the surface more sensitive to subsurface dynamics. The decline of the Atlantic Niño therefore cannot be blamed on a deepening thermocline. Instead, the culprits are a weakening of the mean upwelling that supplies cold water to the surface and a reduced sensitivity of surface winds to sea-surface temperature anomalies, both of which blunt the Bjerknes feedback at its most critical links.

The second major discovery concerns the changing character of the mode’s forcing. In the pre-industrial simulations, the Atlantic Zonal Mode is driven primarily by dynamic forcing, meaning anomalies in surface wind stress that stir the ocean and rearrange its heat content. In the high-emissions future, the composites of Atlantic Niño events show that this dynamic pathway diminishes, while thermodynamic forcing takes on a more prominent role. Thermodynamic forcing operates through the exchange of heat at the ocean surface, particularly through latent heat flux, the energy carried away by evaporation, and through shortwave radiation, the sunlight that warms the upper ocean. In a warmer world, the Atlantic Niño becomes less a story of winds pushing water around and more a story of clouds, evaporation, and radiant heat reshaping the sea surface.

That shift in mechanism is tied to a striking change in the Atlantic’s relationship with the Pacific. The analysis reveals that the influence of the El Niño-Southern Oscillation on the Atlantic Zonal Mode strengthens under global warming, to the point that a positive correlation emerges between the two phenomena, with Pacific events preceding their Atlantic counterparts by roughly half a year. In today’s climate, the connection between El Niño and the Atlantic Niño has long been described as inconsistent and fragile, appearing in some decades and vanishing in others, a puzzle that has occupied tropical climate scientists for years. The new projections suggest that the greenhouse-warmed atmosphere will knit the two basins together more tightly, transmitting Pacific signals across Central America and the tropical atmosphere with greater reliability.

Two processes appear to explain this tightening bond. First, El Niño itself grows stronger in the high-emissions simulations, and a more powerful Pacific oscillator naturally broadcasts a louder signal into neighboring basins. Second, and perhaps more intriguingly, the weakening of the coupled air-sea feedbacks within the equatorial Atlantic leaves that ocean more susceptible to external forcing. A system whose internal feedbacks have gone quiet is one that listens more attentively to remote voices. When the Atlantic’s own Bjerknes feedback can no longer dominate its variability, the Pacific’s influence, arriving through atmospheric bridges such as shifts in the Walker circulation and changes in tropical tropospheric temperature, finds less resistance and leaves a clearer imprint on Atlantic sea-surface temperatures.

Paradoxically, this foreign domination may carry a silver lining for forecasters. A simple linear analysis performed by the team indicates that, in some of the models, the association of the Atlantic Zonal Mode with El Niño makes the Atlantic mode more predictable, even as its amplitude fades. The logic is straightforward: if a large fraction of Atlantic variability can be traced back to a Pacific precursor that emerges about six months in advance, then forecast systems that skillfully predict El Niño gain, for free, a measure of skill in predicting the Atlantic Niño. Seasonal prediction centers in Africa, Europe, and the Americas could eventually exploit this teleconnection to extend the useful lead time of rainfall outlooks for the Sahel, the Guinea Coast, and northeastern Brazil, regions where the Atlantic mode’s influence on the West African monsoon and coastal precipitation is well documented.

The study also underscores how much remains uncertain. The 23 models do not speak with one voice; the emergence of the ENSO-Atlantic correlation is clear in the ensemble but varies in strength from model to model, and tropical Atlantic simulations remain haunted by persistent mean-state biases that have plagued coupled models for decades. The researchers cross-checked their projections against observational and reanalysis products, including the ERA5 atmospheric reanalysis, the ORAS5 ocean reanalysis, and the HadISST sea-surface temperature dataset, all of which are publicly available, but the fundamental limits of simulating a basin as small and as seasonally locked as the equatorial Atlantic still apply. Whether the real ocean will follow the models’ script is a question that only the coming decades of observation can answer.

Still, the implications are hard to ignore. A tropical Atlantic that dances increasingly to the Pacific’s tune would alter the risk landscape for coastal fisheries, which are disrupted when the cold tongue fails to deliver its usual nutrient-rich upwelling, and for agricultural planners who rely on the statistical rhythms of Atlantic variability. It would also complicate attribution studies, since an Atlantic event that once looked like a local fluke may in the future be the distant echo of an El Niño half a world away. The research, published in Climate Dynamics as volume 64, article 450, offers both a warning and a tool: the Atlantic Niño of the future may be weaker and less self-reliant, but by borrowing predictability from the Pacific, it may also become a signal that humanity can see coming with more confidence than ever before.

Subject of Research: Projected changes in tropical Atlantic sea-surface temperature variability and its coupling to ENSO under global warming

Article Title: Strengthened tropical Pacific influence on tropical Atlantic variability in global warming projections

Article References: Richter, I., Chang, P., Kataoka, T., Kido, S., Keenlyside, N., Kosaka, Y., Okumura, Y., Tokinaga, H., Tozuka, T., & Vilela, I. (2026). Strengthened tropical Pacific influence on tropical Atlantic variability in global warming projections. Climate Dynamics, 64(11), Article 450. https://doi.org/10.1007/s00382-026-08390-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08390-y

Keywords: Atlantic Niño, Atlantic Zonal Mode, ENSO, CMIP6, Bjerknes feedback, tropical Atlantic, sea-surface temperature, climate change, thermocline, seasonal prediction, Walker circulation, Climate Dynamics

The scale of the stakes is enormous. Food production occupies roughly 40 percent of all habitable land on Earth, drives about 80 percent of deforestation, and accounts for some 70 percent of terrestrial biodiversity loss. Livestock production sits at the center of this crisis, consuming one-third of the global cereal harvest, requiring about 40 percent of global arable land, and claiming all of the world’s pastures. Cattle production is particularly damaging because it combines direct on-farm emissions from enteric fermentation and manure with indirect emissions from feed production and land-use change. At the same time, the food system contributes roughly a third of total global greenhouse gas emissions, and climate change in turn accelerates biodiversity loss by altering habitats, disrupting migration patterns, and intensifying extreme weather events.

Previous life cycle assessments have generally concluded that meat analogues outperform beef across environmental indicators, but most of those studies relied on fixed or simplified supply chains and global-average environmental intensity data. That approach overlooks a crucial reality: the same ingredient can carry wildly different biodiversity consequences depending on the country and ecosystem it comes from. The new study addresses this gap by combining environmentally extended multi-regional input-output models with the GLAM life cycle impact assessment framework, which provides spatially and taxonomically explicit characterization factors for both land use and climate change. The GLAM land-use factors jointly account for land-use intensity and habitat fragmentation within a species-habitat relationship framework, while its climate factors model pixel-based temperature niche limits for individual species.

The technical machinery behind the analysis is considerable. The team employed a tiered hybrid model called FABEXIO, which integrates the Food and Agriculture Biomass Input-Output database, covering 191 countries and 123 food commodities, with EXIOBASE to capture non-biomass inputs such as fuels, fertilizers, and energy that conventional biomass models miss. The functional unit was a single raw burger patty of 113 grams, with recipes drawn from earlier experimental work on meat substitutes. Four alternatives were compared against beef: patties based on pea protein, soy protein, mealworm insects, and mycoprotein, a fungal protein grown on sugar-beet molasses. The ten consuming countries, Belgium, the Czech Republic, France, Germany, Greece, Italy, the Netherlands, Poland, Romania, and Spain, jointly represent about 80 percent of EU beef consumption and 82 percent of the associated biodiversity impacts.

The headline result is that land occupation dominates terrestrial biodiversity impacts, accounting for more than 96 percent of the total across all patty types and all countries, with climate change contributing less than 4 percent. This finding aligns with the IPBES global assessment, which identifies land-use change as the leading cause of terrestrial biodiversity loss, but it contradicts earlier life cycle studies that had identified climate change as the dominant driver using older impact assessment methods. Beef patties generally showed the highest land-driven biodiversity impacts, driven jointly by pastures and cropland, with pastures dominating in Spain, Greece, and Italy. On climate-driven impacts, beef also ranked highest everywhere, primarily due to methane emissions from the cattle themselves.

But the most surprising result concerns the pea patty. In Belgium, the Czech Republic, Germany, and Poland, the pea-based burger actually exceeded beef in terrestrial biodiversity impact. The culprit is coconut oil. Although it makes up only 5.7 grams of a pea patty, coconut oil sourced largely from the Philippines, Indonesia, and Vanuatu dominates the biodiversity impacts of pea and mycoprotein patties, contributing roughly 75 percent and 58 percent of their totals respectively. Tropical ecosystems carry extremely high biodiversity sensitivity in the GLAM framework because of high global extinction probabilities, small ecoregion sizes, and fragmented land-use patterns, with cropland characterization factors in the Philippines and Indonesia reaching up to 2.2 times ten to the minus fourteen PDF per square meter. By contrast, beef supply chains in the countries where pea patties performed worst are highly diversified, relying primarily on domestic production with modest imports from Australia, India, and Italy, all of which have comparatively low biodiversity sensitivity.

The decoupling between land-use footprints and biodiversity impacts is one of the study’s most consequential insights. Pea protein, the main ingredient at 20 grams per patty, dominates the pea patty’s land-use footprint at about 46 percent but contributes only around 14 percent of its biodiversity impact, because it is sourced mostly within the EU. Similarly, molasses, the main substrate for mycoprotein patties at 339 grams per patty, accounts for roughly 40 percent of the land footprint but only about 23 percent of biodiversity impacts on average. Conventional mass-based life cycle assessment methods, and land-use footprints more broadly, systematically fail to capture these spatial differences in biodiversity sensitivity, meaning high-impact, low-mass ingredients like coconut oil can slip through the environmental accounting unnoticed.

Not all alternatives fared badly. Soy patties consistently delivered the lowest biodiversity impacts across all ten countries, with their impacts driven mainly by soy protein and rape oil. Replacing beef patties with soy patties in food service, assumed to represent about 5 percent of national beef consumption, could reduce land-driven biodiversity impacts by 71 to 97 percent per patty, corresponding to an estimated 3.6 to 4.9 percent reduction in national biodiversity loss associated with beef. The largest gains would come in Spain, Greece, and Italy, where beef’s impact is highest at around 96 to 97 percent reduction, while Belgium would see the smallest benefit at 71 percent. Even here, geography matters: rape oil sourced from Australia, with a cropland factor of 9.1 times ten to the minus fifteen PDF per square meter, drove up impacts in Belgium, France, Germany, Italy, and the Netherlands, while domestic sourcing kept impacts lower in the Czech Republic, Poland, and Romania.

The sensitivity analyses tested the robustness of these conclusions against alternative allocation ratios, protein-based and energy-based functional units, and beef patty composition. The best- and worst-performing patties remained largely unchanged under most scenarios, though under an energy-based functional unit soy patties lost their top position in Belgium, Germany, and Poland due to their relatively low energy content of 191 kilocalories per patty. The most dramatic shift came when coconut oil was replaced with rape oil on a mass-equivalent basis: biodiversity impacts fell by approximately 74 percent for pea patties and 62 percent for mycoprotein patties, transforming the rankings. Under that scenario, beef patties consistently exhibited the highest biodiversity impacts across all countries, demonstrating that a single reformulation decision can outweigh the choice of protein source itself.

The authors frame their findings as a warning against biodiversity leakage, the displacement of harmful activities from one region to another through trade and consumption shifts. Local environmental gains in Europe may be offset, or more than offset, by impacts displaced to more biodiversity-sensitive tropical regions. They also caution that avoided biodiversity impact means preventing further habitat occupation, not restoring degraded ecosystems, since spared land can be reoccupied for urban expansion, alternative agriculture, or biofuel production. Their recommendations are concrete: manufacturers should adopt biodiversity-aware sourcing and reformulate away from high-impact ingredients, while policymakers should embed biodiversity metrics into sustainability standards, public procurement, and labelling schemes for alternative-protein products. The message for consumers is nuanced but clear: the plant-based burger is not automatically the biodiversity-friendly choice, and the geography of the supply chain matters as much as the patty on the plate.

Subject of Research: Terrestrial biodiversity impacts of replacing beef patties with alternative-protein burgers across ten EU countries

Article Title: Terrestrial biodiversity impacts of replacing beef with alternative-protein burger patties across ten European Union Countries

Article References: Zhang, Y., Rasul, K., Dorber, M., Stadler, K., Hertwich, E. G., & Verones, F. (2026). Terrestrial biodiversity impacts of replacing beef with alternative-protein burger patties across ten European Union Countries. Journal of Industrial Ecology, 30(4), 1761-1777. https://doi.org/10.1007/s44498-026-00120-1

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00120-1

Keywords: biodiversity, alternative protein, beef, plant-based burgers, life cycle assessment, land use, coconut oil, soy protein, mycoprotein, supply chains, European Union, food system

Graphitic carbon nitride, known chemically as g-C3N4, is a polymeric semiconductor composed of carbon and nitrogen arranged in layered sheets reminiscent of graphite. It has attracted intense scientific interest because it is chemically stable, non-toxic, inexpensive, and remarkably easy to synthesize from common nitrogen-rich precursors. Crucially, it absorbs visible light, meaning it can in principle be driven by ordinary sunlight rather than expensive ultraviolet lamps. When photons strike the material, they excite electrons from the valence band to the conduction band, leaving behind positively charged holes. These photogenerated charge carriers can then react with water, oxygen, and dissolved species to produce highly reactive oxidizing agents capable of shredding organic pollutant molecules into harmless fragments.

Yet pristine g-C3N4 suffers from well-documented weaknesses that have kept it out of real-world deployment. Photogenerated electrons and holes recombine rapidly, releasing energy as heat before they can do useful chemical work. The material’s surface is comparatively inert, limiting its ability to adsorb and react with target pollutants, and its visible-light absorption, while real, does not extend deeply enough into the solar spectrum. The Hanoi review systematically examines how researchers have overcome these limitations by constructing hybrid photocatalysts, or CN-HPs, in which g-C3N4 is coupled with other materials to form sophisticated junctions that separate charges, extend light harvesting, and boost surface reactivity.

The strategies catalogued in the review are strikingly diverse. Heterojunctions pair g-C3N4 with metal oxides such as ZnO, TiO2, WO3, and BiVO4, or with more exotic partners including MXenes, layered double hydroxides, perovskites, and metal-organic frameworks. Z-scheme and S-scheme architectures are particularly elegant: they mimic natural photosynthesis by selectively retaining the most energetic electrons and holes on different components while allowing the useless carriers to recombine, thereby preserving strong oxidation and reduction power simultaneously. Doping with elements such as sulfur or phosphorus tunes the electronic structure, while carbon quantum dots and noble metal nanoparticles act as electron sinks and co-catalysts. Magnetic components such as Fe3O4 even allow the catalyst particles to be retrieved from treated water with a simple magnet, addressing one of the classic headaches of nanoparticle-based treatment.

The performance figures reported across the literature are impressive, at least under laboratory conditions. CN-HPs have achieved high removal efficiencies for a wide range of pharmaceutical pollutants, including tetracycline, ciprofloxacin, levofloxacin, oxytetracycline, amoxicillin, sulfamethoxazole, ibuprofen, naproxen, acetaminophen, and the anticancer drug 5-fluorouracil. Many systems operate under visible light or even natural sunlight, and some have been tested in continuous-flow configurations and real hospital or mariculture wastewater matrices. The review emphasizes that these materials also retain their activity over repeated catalytic cycles, a critical requirement for any technology hoping to move from the bench to a treatment plant.

Beneath the headline efficiency numbers lies a more nuanced chemical story. The review details how degradation proceeds through the concerted action of three principal reactive species: superoxide radicals, hydroxyl radicals, and photogenerated holes. Each attacks drug molecules at characteristic bonds, cleaving rings and side chains to produce a cascade of smaller intermediate compounds. Ideally, these intermediates are then progressively mineralized into carbon dioxide, water, and inorganic ions. However, the authors highlight an important caveat that recurs throughout the literature: total organic carbon reduction and complete mineralization typically require substantially more time than the initial disappearance of the parent pollutant. In other words, a water sample may test clean for the original drug while still carrying a cocktail of partially oxidized fragments.

That gap between degradation and detoxification is where the review delivers its most consequential warning. Toxicity assessments show that the by-products formed during photocatalytic degradation can carry toxicity profiles different from, and in some cases potentially greater than, those of the original compounds. High removal efficiency alone, the authors argue, does not demonstrate environmental safety. A treatment system that rapidly converts an antibiotic into unidentified fragments without verifying their ecological impact may simply be trading one hazard for another. The review therefore calls for toxicity evaluation to become a standard, non-negotiable component of photocatalysis research, rather than an optional add-on performed in only a minority of studies.

The authors are equally candid about the obstacles standing between laboratory success and field deployment. Most published experiments use purified water, single pollutants at concentrations far above environmental levels, and idealized light sources. Real wastewater presents a hostile environment: competing organic matter scavenges radicals, suspended solids scatter light, mixtures of drugs interact unpredictably, and pH fluctuates continuously. Long-term environmental safety of the nanomaterials themselves, including potential release of catalyst components into treated water, remains insufficiently characterized. Energy efficiency and the engineering challenge of scaling thin-film or slurry reactors to the volumes handled by municipal plants are also unresolved. The review frames these not as reasons for pessimism but as a well-defined research agenda.

One of the most forward-looking threads in the review concerns artificial intelligence. The authors highlight the potential of combining experimental photocatalysis data with machine learning models to optimize treatment conditions and predict how CN-HP systems will perform against specific target pollutants in the field. Given the enormous design space of possible hybrid materials, dopants, junction types, and operating parameters, data-driven approaches could dramatically accelerate the search for formulations that balance efficiency, stability, cost, and safety. Similar AI-assisted strategies are already gaining traction across membrane design and pollution monitoring, and photocatalysis appears poised to follow the same trajectory.

What emerges from the Hanoi analysis is a technology at an inflection point. The fundamental chemistry works: metal-free, sunlight-driven catalysts can genuinely dismantle the pharmaceutical residues that conventional treatment leaves behind, and they can do so repeatedly and, in many configurations, using abundant and benign raw materials. The remaining task is to prove that this chemistry survives contact with the messy, variable, and economically constrained reality of full-scale water treatment, and that the end products of degradation are demonstrably safe for the ecosystems that receive them. If researchers can close the gap between breaking molecules and proving detoxification, graphitic carbon nitride hybrids may become a cornerstone of the next generation of water purification infrastructure.

Subject of Research: g-C3N4-based hybrid photocatalysts for photocatalytic degradation of emerging pharmaceutical pollutants in water and wastewater

Article Title: g-C3N4-based hybrid photocatalysts for removal of emerging pharmaceutical pollutants from water: recent advances, degradation pathways, toxicity assessment,and future perspectives

Article References: Hanh, D. T. M., & Hoai, P. T. T. (2026). g-C3N4-based hybrid photocatalysts for removal of emerging pharmaceutical pollutants from water: recent advances, degradation pathways, toxicity assessment,and future perspectives. Environmental Geochemistry and Health, 48(16), Article 625. https://doi.org/10.1007/s10653-026-03530-z

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03530-z

Keywords: graphitic carbon nitride, photocatalysis, pharmaceutical pollutants, water treatment, heterojunctions, advanced oxidation processes, degradation pathways, toxicity assessment, antibiotics, mineralization, artificial intelligence, wastewater remediation

The team focused on what forecasters call the subseasonal range, roughly two weeks to a month or more ahead, a notoriously awkward window that sits beyond the reach of weather forecasts but before the timescales where seasonal climate outlooks shine. To probe this gap, the researchers turned to the European Centre for Medium-Range Weather Forecasts Subseasonal to Seasonal, or S2S, ensemble reforecast dataset, a treasure trove of hindcast simulations produced by one of the world’s leading prediction systems. They compared these forecasts against high-quality sea surface temperature observations from the National Oceanic and Atmospheric Administration’s Optimum Interpolation Sea Surface Temperature product, evaluating performance across the tropical Indian Ocean for the twenty-year period from 2001 to 2020.

The headline finding is a hard limit on predictability: the effective sea surface temperature forecast lead time over the tropical Indian Ocean is about 31 days. Beyond roughly a month, the model’s temperature forecasts lose the skill needed to meaningfully constrain marine heatwave prediction. That number is more than a technical footnote. It defines the practical horizon within which fisheries managers, aquaculture operators, and conservation agencies could realistically act on a forecast before an oceanic heat event arrives, and it sets the benchmark against which future improvements to prediction systems will be measured.

But the study’s most striking results concern not just how far ahead forecasts work, but where and how they go wrong. When the researchers broke down the deterministic forecasts, meaning forecasts that give a single best estimate of future conditions, they found a sharp geographic split in the nature of the errors. In the near-equatorial region, the model systematically overestimated both the number of marine heatwave days and the cumulative intensity of events. The culprit was an excess of false positives, or false alarms, cases where the model predicted heatwave conditions that never actually materialized in the observations.

Off the equator, the story flipped. In these off-equatorial regions, the forecasts underestimated marine heatwave days and cumulative intensity, primarily because of false negatives, meaning misses where real heatwave events occurred but the model failed to flag them. These biases were not static; they grew steadily worse as the forecast lead time increased. At the maximum effective lead of 31 days, the overestimation of marine heatwave days in the near-equatorial region reached up to 82 percent, with cumulative intensity overestimated by as much as 62 percent. In the off-equatorial zones, the underestimation of days climbed to 47 percent and of cumulative intensity to 56 percent at the same lead time.

Why does the equator behave so differently from its surroundings in the model’s eyes? The researchers traced these regional differences closely to the spatial distribution of sea surface temperature anomaly forecast biases. In other words, where the forecast model systematically runs too warm or too cold relative to reality, the marine heatwave diagnostics inherit those errors. A warm bias near the equator pushes the model over the heatwave threshold too often, manufacturing spurious events, while cold biases elsewhere suppress the model’s ability to detect genuine warming episodes. This connection between background temperature bias and extreme event detection is a crucial insight, because it suggests that improving the mean state of the forecast should directly improve heatwave prediction.

There was one metric, however, where the errors were more uniform. The mean intensity of marine heatwaves, as opposed to their cumulative intensity or duration, was underestimated across the entire tropical Indian Ocean, regardless of latitude. This underestimation grew from about 10 percent at a one-day lead time to roughly 15 percent at 31 days. The implication is sobering: even when the model correctly identifies that a heatwave is underway, it tends to paint the event as milder than it truly is. For ecosystems sitting near their thermal tolerance limits, such as coral reefs where a single extra degree of sustained warmth can trigger mass bleaching, an intensity underestimate could translate into dangerously complacent risk assessments.

Deterministic forecasts tell only half the story, and the study’s probabilistic analysis offers a more encouraging picture. Rather than asking whether the model predicts a heatwave or not, probabilistic verification asks how well the forecast probabilities discriminate between events and non-events. Here the researchers used two standard tools: the Brier Skill Score, which measures whether a probabilistic forecast beats a naive reference forecast, and the Area Under the Curve, or AUC, which quantifies discrimination skill. The results showed that marine heatwave forecasting exhibits relatively high skill at lead times of one to seven days, with predominantly positive Brier Skill Scores and AUC values approaching 0.80, a level indicating strong ability to separate heatwave conditions from ordinary ones.

The practical stakes of this work are considerable. Marine heatwaves have been linked to devastating ecological outcomes worldwide, including coral bleaching, shifts in species distributions, and cascading effects on biodiversity and the ecosystem services that oceans provide. Their socioeconomic footprint is equally real, touching fisheries yields, aquaculture operations, and coastal livelihoods. In the Indian Ocean specifically, these events interact with the monsoon system and with climate modes such as the Indian Ocean Dipole and the Madden-Julian Oscillation, making the region both a hotspot for oceanic extremes and a critical piece of the global climate puzzle. A reliable early warning capability, even one limited to a few weeks of lead time, could allow fishers to adjust operations, aquaculture farms to deploy mitigation measures, and reef managers to prioritize interventions before the thermal stress peaks.

The study also maps out a clear agenda for improvement. Because the heatwave forecast errors track the underlying sea surface temperature anomaly biases, efforts to reduce those biases in subseasonal prediction models, whether through better ocean-atmosphere coupling, improved initialization, or higher resolution, should pay dividends for extreme event prediction. The finding that probabilistic skill remains respectable in the first week, while deterministic biases compound with lead time, suggests that forecast products for the tropical Indian Ocean may be most useful when they communicate uncertainty explicitly rather than offering a single deterministic answer. As global warming continues to push ocean temperatures upward and marine heatwaves grow longer, more frequent, and more intense, knowing precisely where our forecasting window ends, and where the blind spots lie within it, is an essential first step toward seeing the ocean’s next fever coming in time to act.

Subject of Research: Subseasonal forecast skill of marine heatwaves in the tropical Indian Ocean

Article Title: Evaluation of the subseasonal forecast skill of marine heatwaves in the tropical Indian ocean

Article References: Meng, L., Li, X., & Yan, Y. (2026). Evaluation of the subseasonal forecast skill of marine heatwaves in the tropical Indian ocean. Climate Dynamics, 64(11), Article 449. https://doi.org/10.1007/s00382-026-08374-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08374-y

Keywords: marine heatwaves, tropical Indian Ocean, subseasonal forecasting, sea surface temperature, ECMWF S2S, forecast verification, Brier Skill Score, SST anomaly bias, climate dynamics, coral bleaching, ocean warming, probabilistic forecasting

The term ‘magic concept’ was introduced by scholars Christopher Pollitt and Peter Hupe in 2011 to describe ideas that dominate policy debates not because they solve problems, but because of how they function in language. Importantly, the word ‘magic’ here refers to discursive flexibility rather than deception or superstition. Pollitt and Hupe identified four defining characteristics of such concepts: a high degree of abstraction, a strongly positive normative charge, a seeming ability to dissolve previous dilemmas and binary oppositions, and mobility across domains. In the language adopted by Kirchherr and Hartley, these become broadness and discursive hybridity, normative attractiveness, implication of consensus, and universal marketability. Magic concepts typically emerge by overtaking older or anachronistic ideas, and they thrive during periods of rapid change when legacy frameworks seem inadequate and novelty carries its own mandate. Their core ingredients, however, are rarely new—collaboration, trust, and experimentation have all been repackaged into magic concepts at various moments.

The circular economy’s own history illustrates this pattern. While the practices it describes—reusing, recycling, and recovering materials—are arguably as old as material use itself, the modern concept traces its intellectual lineage to Kenneth Boulding’s 1966 notion of ‘Spaceship Earth,’ which framed the planet as a closed system with finite resources. Other scholars trace the term’s formal origins to China in the 1990s, where it was embedded in national policy before spreading globally. The concept’s academic breakthrough, however, owes much to the Ellen MacArthur Foundation, whose influential reports from 2013 onward gave the idea both intellectual structure and celebrity endorsement. The foundation’s creation was motivated in part by the founder’s experience sailing solo around the world, where resource finitude and the necessity of circulation became matters of survival rather than abstraction. By 2017, research had moved beyond incidental case studies toward conceptual and systemic questions, and the circular economy is now recognized as a distinct, maturing field with shared concepts, policy commitments, and a growing epistemic community.

So how does the circular economy measure up against the four criteria of a magic concept? The first is broadness. A comprehensive analysis of 221 definitions by Kirchherr and colleagues yielded a meta-definition describing a regenerative economic system requiring a paradigm shift to replace the ‘end of life’ concept with reducing, reusing, recycling, and recovering materials across supply chains. But the sheer variety of those 221 definitions is itself the point. ‘Going circular’ can mean anything from a single firm adopting one recycling activity to a fundamental overhaul of entire economic systems. Research by D’Angelo and colleagues quantifies this breadth by tallying the diverse activities firms undertake under the circular banner, from reduced water and energy use to product redesign. This ambiguity is not a flaw from the magic concept perspective—it is the engine of the concept’s survival, allowing it to serve as both a detailed operational blueprint and a vague narrative flexible enough to support high-level political objectives.

The second characteristic, normative attractiveness, is equally evident. Early advocacy, particularly by the Ellen MacArthur Foundation, promised that circularity could reconcile economic growth with environmental health—a proposition with obvious moral and political appeal. Some scholars have gone further, framing the circular economy as a societal ‘ought’ that retains validity even when violated in practice. Yet the promises remain contested. Researchers have documented the phenomenon of ‘circular rebound,’ whereby efficiency gains from circularity indirectly undermine sustainability by stimulating additional consumption. Others, such as Figge and colleagues, insist the concept should refer strictly and only to material flows, resisting the expansion of its normative ambitions. The tension between these positions reflects how collective optimism about an emergent idea tends to graft moral expectations onto it, transforming a technical framework into a quasi-ethical project.

The third characteristic, implication of consensus, may be the most consequential. Magic concepts appear to stand above ideology, presenting themselves as neutral and plausibly undebatable. The circular economy has largely achieved this status: it is embraced by businesses, policymakers, and many scholars as a unifying construct that dissolves the traditional conflict between economic and environmental interests. Ziegler and colleagues observe that the dominant conception presents the circular transition as a politically neutral, technology-driven process of efficiency improvement. But critical scholars challenge this neutrality. Researchers aligned with degrowth and political economy perspectives argue that the circular economy is a politically motivated agenda driven by neoliberal ideology and business interests, protecting the status quo economic system while delivering only incremental or performative progress. Giampietro and Funtowicz put it starkly, describing the term’s success as an example of socially constructed ignorance in which folk tales of zero emissions and rapid decarbonization depoliticize the sustainability debate. Consensus, in this reading, is manufactured through multilateral institutionalization—when circularity is folded into global policy agendas, it borrows their scientific and political credibility and marginalizes alternative framings.

The fourth characteristic, universal marketability, explains the concept’s reach across otherwise disconnected audiences. For businesses, circularity offers profitability through streamlined production, waste mitigation, and sustainability credentials recognized by certification bodies. For governments, it provides rhetorical advantages, allowing states to present themselves as simultaneously pro-business and pro-environment on an issue—waste management—that touches public infrastructure. In an era of trade retrenchment and geopolitical tension, circularity has acquired an additional selling point: resource autonomy and sovereignty through reduced dependence on global supply chains. For academics, the concept has become a thematic compass spanning industrial engineering, public policy, futures studies, and business management. Consumers, by contrast, remain the least engaged stakeholder group, largely because circular transition makes relatively low demands on them beyond end-of-life recycling and the higher prices of circular products—a tentative claim the authors note requires empirical validation across sectors and regions.

Kirchherr and Hartley argue that the magic concept perspective outperforms two rival analytical frameworks often applied to the circular economy. The first is the ‘umbrella construct,’ defined by Hirsch and Levin as a broad concept used loosely to encompass diverse phenomena. While the circular economy certainly functions this way—spanning the ‘9R’ framework from recovering and recycling to rethinking and refusing—breadth alone does not explain its mobilizing power. The second is the ‘boundary object,’ a concept flexible enough to adapt to different social worlds yet structured enough to serve as a common reference point for collaboration. The circular economy operates as a boundary object in fields as varied as water management, urban planning, and the emerging notion of a ‘circular society.’ But neither framework captures the normative charge, the implied consensus, or the marketability that make the concept ‘sticky.’ As the authors put it, any magic concept is likely also a boundary object, but not every boundary object is a magic concept.

The practical implications cut in two directions. For advocates, the four dimensions offer a survival strategy: embrace broadness by welcoming both allied and opposing concepts, sustain normative attractiveness by pairing moral appeal with practical benefits, strengthen consensus through credible evidence rather than politically selected metrics, and promote universal marketability by framing circularity in terms that resonate from the firm to the city to the global economy. Yet the authors also warn that magic concepts can lose their magic. The thirst for conceptual novelty—driven partly by academics and consultancies peddling fresh frameworks, and partly by businesses and governments eager to appear innovative—means that today’s buzzword can be supplanted by tomorrow’s, much as ‘smart cities’ once swept through urban policy debates. If the circular economy loses prominence without a conceptual heir, the broader sustainability discourse could fragment further. Its narrative fate, the authors suggest, is arguably tied to that of sustainability itself: if commitment to sustainability principles wavers, the circular economy’s legitimacy will likely be undermined with it.

Perhaps the most sobering conclusion is a call for neutrality amid the optimism. Pollitt and Hupe observed that magic concepts excite discussion, but when the show is over many hard choices remain. The circular economy is, in the end, a label—a powerful one for political and corporate messaging, but not a guarantee of outcomes. The authors argue that production practices classifiable as circular should be supported insofar as they demonstrably advance broader societal goals, regardless of whether they fit the fleeting expectations of those promoting a particular buzzword. The tangible practices attributed to circularity, they contend, should be iteratively and independently tested for their impact and redesigned accordingly. Whether the concept now evolves toward greater flexibility and universality, or toward tighter definitions and stricter inclusion criteria—as framing concepts often do over time—remains an open question. What is clear is that its magic, however real, was never a substitute for the hard work of measuring whether circular practices actually deliver the sustainable future they promise.

Subject of Research: Discursive analysis of the circular economy as a 'magic concept' in sustainability policy and scholarship

Article Title: Circular economy as a magic concept: the rise and (possible) fall of a sustainability buzzword

Article References: Kirchherr, J., & Hartley, K. (2026). Circular economy as a magic concept: the rise and (possible) fall of a sustainability buzzword. Journal of Industrial Ecology, 30(4), 1715-1726. https://doi.org/10.1007/s44498-026-00116-x

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00116-x

Keywords: circular economy, magic concept, sustainability, discourse analysis, pollitt and hupe, ellen macArthur foundation, waste management, policy narratives, degrowth, industrial ecology, circularity gap, buzzwords

The research, led by Dhia Zalfa Zahira, Anjar Dimara Sakti, and Ketut Wikantika of Institut Teknologi Bandung, goes beyond simply counting lost trees. It tracks how much carbon those forests actually stored — both above ground in trunks, branches, and leaves, and below ground in root systems — and how that storage changed year by year. Most previous studies of the region offered static snapshots for a single year or focused only on aboveground biomass. By modelling both aboveground and belowground biomass carbon across five time points, the team has produced one of the most dynamic pictures yet of a tropical carbon reservoir in decline.

Defining what counts as an intact forest was itself a technical feat. The researchers started with the Hansen tree cover dataset derived from Landsat imagery, flagging any pixel with at least 30 percent canopy cover in 2000. They then filtered out oil-palm plantations using a global planting-year dataset and removed cropland using the MODIS land cover product. From that baseline, they iteratively subtracted three drivers of loss — stand-replacing deforestation, new cropland, and new oil-palm planting — at five-year intervals, generating intact forest maps for 2000, 2005, 2010, 2015, and 2020. This multi-layer filtering matters because oil-palm plantations, though tree-covered, are not ecologically intact forests; they typically replace natural forest and store far less carbon.

With the forest maps in hand, the team turned to a Random Forest regression model, an ensemble machine learning method that aggregates predictions from hundreds of decision trees. Thirteen environmental predictors fed the model, spanning vegetation indices such as NDVI and NIRv, productivity metrics including gross and net primary productivity, leaf area index, and the fraction of absorbed photosynthetically active radiation, along with climate variables like land surface temperature and precipitation, and topographic features including elevation, slope, and a topographic wetness index. All layers were processed at 300-metre resolution and aggregated to 1.5-by-1.5-kilometre grids to reduce noise and pixel misalignment.

A key design choice set this model apart: rather than training only on forest pixels, the researchers trained across the full gradient of Southeast Asian vegetation — forests, shrublands, grasslands, and cropland alike — using more than two million grid cells from 2010. This ensures the model learned the entire spectrum of carbon density, reducing bias at forest edges and recently disturbed areas. The data was split 80-20 into training and testing sets, with a Kolmogorov-Smirnov test confirming that both subsets covered the region geographically without spatial bias. Against the NASA reference biomass dataset, the model achieved a testing R² of 0.899 for aboveground carbon and 0.874 for belowground carbon, indicating strong predictive skill with limited overfitting.

Independent validation against the European Space Agency’s Climate Change Initiative Biomass product — built from radar observations by Sentinel-1, Envisat, and the ALOS satellites — showed moderate but consistent agreement, with R² values of 0.645 and 0.632 for the two carbon components. Among the thirteen predictors, the fraction of absorbed photosynthetically active radiation emerged as the single most influential variable, which makes physical sense: how much solar energy a canopy captures directly governs how much carbon it can fix into biomass. Land surface temperature and precipitation followed, while the classic vegetation indices contributed surprisingly little, apparently limited in their sensitivity to structural biomass in dense tropical canopies.

The carbon accounting that followed is sobering. In 2000, intact forests in the region held an estimated 26.18 megatonnes of aboveground biomass carbon and 6.61 megatonnes belowground, a combined total of roughly 32.8 megatonnes. By 2020, those figures had fallen to 15.96 and 3.96 megatonnes respectively — a drop of more than 39 percent in twenty years. The steepest losses occurred between 2010 and 2015, when forest area shrank by nearly 384,000 square kilometres and biomass carbon declined at a rate of about 1,496 megatonnes per year in total. The hardest-hit landscapes were Kalimantan and Sumatra in Indonesia, Sarawak and Sabah in Malaysia, and cropland-driven loss zones in northern Thailand, Myanmar, and parts of Papua.

To translate those ecological losses into terms policymakers cannot ignore, the team ran an indicative economic valuation under two carbon pricing scenarios. At the current average price across ASEAN+3 countries — a modest 6.45 US dollars per tonne of CO₂ equivalent — the region’s intact forest carbon was worth about 776 billion dollars in 2000, falling to roughly 471 billion by 2020. Under the globally recommended price of 75 dollars per tonne, aligned with social cost of carbon estimates, the value dropped from about 9.02 trillion dollars to 5.48 trillion. The authors are careful to stress these are illustrative, non-market figures, not credit-eligible carbon trading values, but they make the scale of the liability unmistakable: a loss of nearly 4 trillion dollars in indicative carbon assets in two decades.

The findings land at a delicate moment for climate policy. Southeast Asia harbours nearly 15 percent of the world’s tropical forests, and their continued erosion threatens not only regional biodiversity but global mitigation commitments under the Paris Agreement, REDD+ frameworks, and Sustainable Development Goal targets on sustainable forest management. There is also a real risk of a tipping dynamic: as intact forests degrade, they can flip from carbon sinks to net carbon sources, emitting more than they absorb and accelerating the very warming that stresses them. The spatially explicit maps produced by this study could help governments prioritise conservation zones where avoided deforestation yields the greatest climate benefit, and design community-based forest management schemes that recognise indigenous peoples as the primary stewards of these carbon reservoirs.

The study is candid about its limitations. The 2000 baseline may include some old plantations that the Hansen dataset cannot distinguish from natural forest; the model assumes predictor relationships stable across two decades; and the absence of explicit spatial block cross-validation means the reported accuracy figures may be somewhat optimistic. The economic valuation, by design, omits additionality, leakage, and monitoring requirements. Still, the core signal — a 59 percent collapse in intact forest area and a 39 percent loss of biomass carbon, tracked with low reported uncertainty — is difficult to dispute. Future work integrating radar-based biomass data and country-specific carbon accounting could sharpen the picture further. What this study makes clear is that Southeast Asia’s intact forests are not merely an ecological treasure; they are a quantifiable, and rapidly depreciating, planetary asset.

Subject of Research: Remote sensing and random forest modelling of intact forest biomass carbon dynamics and carbon economics in Southeast Asia from 2000 to 2020

Article Title: Intact forest carbon dynamics and indicative carbon economy in Southeast Asia: A remote sensing and random forest approach

Article References: Zahira, D. Z., Sakti, A. D., & Wikantika, K. (2026). Intact forest carbon dynamics and indicative carbon economy in Southeast Asia: A remote sensing and random forest approach. Environmental Advances, 26, Article 100758. https://doi.org/10.1016/j.envadv.2026.100758

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100758

Keywords: intact forests, Southeast Asia, biomass carbon, remote sensing, random forest, deforestation, oil palm, carbon pricing, REDD+, aboveground biomass, belowground biomass, machine learning

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 researchers ran a set of sensitivity experiments with RegCM4 version 4.5.0, driving the model with ERA-Interim reanalysis data at the lateral boundaries and NOAA Optimum Interpolation weekly sea surface temperatures at the lower boundary. They then evaluated the simulations against the ERA5 reanalysis for precipitation, two-meter air temperature, atmospheric circulation, and water vapor flux divergence, and against the CLARA-A2 satellite cloud products from the CM SAF archive for cloud properties. The baseline control experiment, labeled CTRL, used the model’s default configuration, while three alternative experiments paired different cloud microphysics schemes with different cloud fraction diagnostic schemes, including the Nogherotto–Tompkins multi-phase microphysics scheme and the Xu–Randall semiempirical cloud fraction formulation.

The verdict on the default configuration was unflattering. The CTRL experiment clearly overestimated both summer precipitation and near-surface air temperature over most of China, reproducing biases that have plagued regional modeling efforts over East Asia for years. These errors matter far beyond academic bookkeeping. Regional climate models such as RegCM4 are workhorses for dynamical downscaling of global projections, assessing extreme heat and rainfall risks, and informing adaptation planning. If a model systematically runs too hot and too wet across the world’s most populous country, every downstream projection inherits that distortion.

Among the four configurations tested, one stood out. The experiment the authors call SEXPMF, which couples the Nogherotto–Tompkins microphysics scheme with the Xu–Randall cloud fraction scheme, delivered the best simulation of near-surface air temperature and precipitation across most of China. The improvements were quantitatively substantial. Compared with CTRL, SEXPMF reduced the root mean square error of two-meter air temperature simulations by more than 28.4 percent across all sub-regions of China, and cut the precipitation root mean square error by more than 14.5 percent in the Tibetan Plateau, Northwest China, and Northeast China. In Southeast China, where the scheme actually pushed precipitation magnitudes further above observations, the spatial correlation of the simulated precipitation pattern still improved by 49.0 percent, meaning the model got the geography of rainfall considerably more right even as it exaggerated its intensity.

The mechanistic explanation is where the study becomes genuinely fascinating, because it reveals how a seemingly local change in cloud physics can reorganize an entire regional circulation. The SEXPMF configuration generates more high-level ice clouds than the default setup. These icy veils intercept incoming sunlight, reducing the downward shortwave radiation that reaches the surface. Less solar heating at the ground means the widespread warm bias in surface temperature is alleviated. But the story does not stop at the surface. The altered heating profile creates what the authors describe as an upper-warming and lower-cooling vertical thermal structure, which strengthens atmospheric stability in the column above.

Over the Tibetan Plateau, that stabilization proved decisive. The plateau acts as an elevated heat source in summer, and spurious model heating there has long driven artificial convection and exaggerated rainfall. With the new cloud scheme damping surface heating, the modified thermal field triggered deep anomalous subsidence and moisture divergence over the plateau. Sinking air and diverging moisture are precisely the opposite of what convective storms need, so the model’s spurious convection was fundamentally suppressed and the overestimated precipitation reined in. In other words, a change in how the model represents ice clouds cascaded downward through radiation, surface energy balance, vertical stability, and finally the large-scale dynamics that govern whether storms can form at all.

Southeast China told the opposite story, and it is a cautionary tale about compensating errors. There, the modified thermal field produced anomalous upper-tropospheric divergence paired with low-level moisture convergence, a combination that drives strong vertical ascent and creates favorable conditions for cloud formation. The model responded by generating abundant clouds and, consequently, excessive precipitation. The authors attribute this over-simulation to compensating errors: improvements in one part of the system unmasked or even amplified biases elsewhere. The precipitation pattern became more realistic in its spatial structure, yet the magnitude drifted further from observations. It is a vivid illustration that model skill cannot be judged by a single metric in a single region, because the climate system’s internal compensations can trade one error for another.

Why should cloud parameterization choices matter so much in the first place? Clouds occupy a peculiar position in climate models: they form at scales far smaller than a model grid cell, yet they control the planet’s energy budget by reflecting sunlight and trapping infrared radiation. Microphysics schemes govern how water vapor condenses into cloud droplets and ice crystals, how those particles grow, collide, and precipitate, and how long clouds persist. Cloud fraction schemes, sometimes called macrophysics, determine how much of a grid box is covered by cloud, which directly controls the radiative fluxes computed by the radiation code. The two must work in concert, and the study demonstrates that their interaction, not either scheme alone, determines the fidelity of the simulation. A better microphysics scheme paired with a mismatched cloud fraction diagnostic can still produce a worse climate.

The findings carry practical weight for the regional modeling community. RegCM4 is one of the most widely used regional climate models in the CORDEX framework, which coordinates downscaling experiments across the globe, and it has been applied extensively over East Asia for monsoon studies and climate projection work. The identification of the Nogherotto–Tompkins plus Xu–Randall combination as the top performer over China gives modeling groups a concrete, evidence-based configuration choice rather than a default inherited from historical precedent. More broadly, the study underscores that the critical ingredient in simulating complex regional climates is the coupling among clouds, radiation, and dynamics, not the isolated performance of any single parameterization. A scheme that produces the right cloud amounts for the wrong reasons, or the right clouds without the right dynamical response, will ultimately mislead.

There is also a larger lesson about uncertainty in climate science. Cloud feedbacks remain among the largest sources of spread in climate sensitivity estimates, and the Intergovernmental Panel on Climate Change has repeatedly flagged them as a key uncertainty in projections of future warming. Studies like this one, which trace exactly how a parameterization change propagates through radiation, stability, and circulation to reshape regional climate, provide the process-level understanding needed to narrow that uncertainty. For China, where summer monsoon rainfall sustains agriculture for over a billion people and heat extremes are intensifying, the difference between a model that runs 28 percent too hot and one that does not is far more than a technical footnote. It is the difference between projections that can be trusted and projections that must be corrected. The Nanjing team’s work shows that sometimes the path to better climate information runs not through bigger computers or finer grids, but through the quiet, meticulous business of choosing the right equations for the clouds.

Subject of Research: Evaluation of cloud microphysics and cloud fraction parameterization scheme combinations in the RegCM4 regional climate model for simulating summer temperature and precipitation over China

Article Title: Impact of different cloud parameterization scheme combinations in the RegCM4 on the summer climate simulations over China

Article References: Li, F., Huang, A., & Gu, C. (2026). Impact of different cloud parameterization scheme combinations in the RegCM4 on the summer climate simulations over China. Climate Dynamics, 64(11), Article 448. https://doi.org/10.1007/s00382-026-08408-5

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08408-5

Keywords: RegCM4, cloud parameterization, cloud microphysics, cloud fraction, regional climate modeling, China summer climate, Tibetan Plateau, precipitation bias, radiation-cloud coupling, Climate Dynamics, monsoon simulation, model evaluation

The study, published in Environmental Advances, drew on electronic health records from the Medical Research Council Unit The Gambia clinic in Keneba, a facility that has provided free healthcare to residents of the surrounding West Kiang region for decades. Between January 2010 and December 2022, the clinic recorded 11,331 visits for diarrheal diseases and 29,420 visits for skin infections. Roughly seven in ten diarrheal visits involved children under five, while skin infections were more evenly distributed across age groups. The researchers aggregated these visits into weekly counts and matched them against weekly rainfall and temperature data from the European Centre for Medium-Range Weather Forecasts ERA5-Land reanalysis dataset, extracted at roughly ten kilometre resolution for the grid cell containing the clinic.

Because the relationship between weather and disease is rarely linear, the team employed distributed lag non-linear models, a statistical framework that can capture both curved exposure-response relationships and effects that unfold over time. Weekly counts of clinic visits were highly overdispersed, so negative binomial regression was used, with natural cubic splines of time flexibly adjusting for long-term trends and seasonality. The models allowed rainfall effects to emerge across a zero-to-three-week lag window, chosen to encompass the incubation periods of the major pathogens involved as well as the time needed for environmental exposure, pathogen replication and the decision to seek care.

The headline finding is a threshold effect. For diarrheal disease, the cumulative risk of a clinic visit rose steadily as weekly rainfall increased, peaking somewhere between 35 and 40 millimetres of rain per week, after which the risk plateaued and drifted back toward null. A week with 20 millimetres of rainfall, roughly the 75th percentile of the full series, was associated with a 20 percent increase in diarrheal visits compared with dry weeks, after adjusting for temperature. Without that temperature adjustment, the apparent risk was 41 percent higher, a difference that proved to be one of the study’s most methodologically important observations.

Skin infections told a similar but not identical story. Risk climbed with rainfall up to a threshold of about 30 millimetres per week, then declined at heavier totals. Twenty millimetres of weekly rainfall was associated with a 39 percent increase in skin infection visits once temperature was accounted for, and the effect persisted across lags of one, two and three weeks, longer than the one-to-two-week window over which diarrheal risks appeared. The researchers suggest this may reflect different underlying mechanisms: diarrheal pathogens are typically ingested after relatively rapid contamination of water sources, while skin infections may follow slower routes of environmental exposure, crowding during wet weather and changes in hygiene practices when water becomes scarce or contaminated.

Stratifying the data by pathogen category revealed a striking asymmetry. Bacterial diarrheal visits carried the strongest rainfall association, with a 66 percent increase in risk at 20 millimetres of weekly rainfall after temperature adjustment, whereas viral diarrheal visits showed no clear evidence of a rainfall effect at all. A parallel pattern emerged for skin disease: bacterial skin infections were the most rainfall-sensitive category, though fungal and clinically classified local skin infections also showed elevated risks. This aligns with a growing body of evidence from other settings that rainfall and temperature shape the transmission of bacterial and viral enteric pathogens in fundamentally different ways, with wetter conditions generally favouring bacterial spread.

Age modified the risks in unexpected directions. For diarrheal disease, the rainfall association was stronger among patients aged five and over, with a 63 percent increase in risk at 20 millimetres of weekly rainfall, than among children under five, for whom no significant association was detected after temperature adjustment. The authors note that the vast majority of under-five diarrheal visits were classified as viral, which may partly explain the difference. For skin infections the pattern reversed: children under five bore the greater rainfall-associated burden, with a 77 percent increase in visits at 20 millimetres of rain, consistent with the idea that young children crawling and playing in contaminated standing water face heightened exposure. Sex-stratified analyses suggested slightly higher risks among females for both outcomes, though the evidence for modification by sex was weaker than for age.

Perhaps the most counterintuitive result concerns heavy rainfall events, defined as days receiving more than 21.05 millimetres of rain, the 95th percentile of rainy days between 2000 and 2022. In unadjusted models, weeks containing at least one such event were associated with substantially increased risks of both diarrheal and skin infection visits. But once maximum temperature was included in the models, those associations vanished entirely. And when total weekly rainfall was additionally controlled for, a heavy rainfall event was actually associated with a modest decrease in diarrheal risk. The authors propose that competing mechanisms may be at work: heavy rain can flush and dilute pathogens in water sources, while also improving water availability for hygiene, offsetting the contamination and infrastructure damage such events can cause.

The confounding role of temperature deserves emphasis, because many prior studies of rainfall and diarrheal disease have not adjusted for it at all. In The Gambia, temperature and rainfall are strongly inversely related, with the hottest weeks falling just before the onset of the rainy season. Failing to account for this coupling can therefore inflate or distort apparent rainfall effects. The sensitivity analyses were reassuring on other fronts: results held up under alternative methods of seasonal adjustment, different spline configurations, shorter and longer lag windows, and explicit adjustment for the sharp decline in diarrheal visits after rotavirus vaccine introduction in 2014. Crucially, rainfall showed no comparable association with clinic visits for routine child health checks, toothache or hypertension, outcomes that lie outside any plausible rainfall-transmission pathway, suggesting the findings are not simply an artefact of weather-driven changes in health-seeking behaviour.

The study has limitations the authors acknowledge. Clinic visits are a proxy for disease incidence rather than a direct measure, pathogen classifications rest on clinical assessment rather than laboratory confirmation, and reanalysis weather data averaged over ten-kilometre grid cells may smooth out the local extremes of rainfall that matter most. Even so, the work offers one of the most detailed pictures to date of how rainfall shapes water-related disease in West Africa, a region where such evidence has been sparse and where climate projections point toward greater rainfall variability and more intense wet-season events. As the authors conclude, the non-linear risks they document, together with the anticipated shifts in West African rainfall, argue for strengthening the climate resilience of water, sanitation and hygiene services, and for studying rainfall and temperature jointly rather than in isolation if early-warning systems for climate-sensitive diseases are to become a reality.

Subject of Research: The association between weekly rainfall, temperature and clinic visits for diarrheal diseases and skin infections in rural Gambia

Article Title: The impact of weekly rainfall on water-related diseases in The Gambia: a time-series analysis of health facility data

Article References: Bose, I., Part, C., Moirano, G., Murray, K. A., Green, R., Cerami, C., Mistry, M. N., & Kovats, S. (2026). The impact of weekly rainfall on water-related diseases in The Gambia: a time-series analysis of health facility data. Environmental Advances, 26, Article 100757. https://doi.org/10.1016/j.envadv.2026.100757

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100757

Keywords: rainfall, diarrheal disease, skin infections, The Gambia, time-series analysis, climate and health, heavy rainfall events, distributed lag non-linear models, water-related diseases, WASH, temperature confounding, West Africa