The research, published in the American Journal of Criminal Justice by Jason R. Silva of William Paterson University and Adam Lankford of the University of Alabama, is the most comprehensive assessment of its kind to date. The authors examined every United States mass shooting from 2000 to 2023 in which four or more victims were killed, a total of 483 incidents. Drawing primarily on a National Institute of Justice funded mass shooting database compiled by Jillian Turanovic and colleagues and updated through 2023, the team verified and supplemented the records using seven other open-source datasets, The Violence Project database, official documents such as court records and government reports, and extensive news coverage. Official records were located for 49 percent of cases, and information was corroborated across multiple sources wherever possible, with official documentation given precedence when accounts differed. Three researchers independently reviewed every case for evidence of pre-attack communication, and the final coding decisions were unanimous after discussion and third-party review of any contested cases.

The central distinction the researchers drew is conceptually important. Leakage refers to violent intent communicated to a third party, such as when person A tells person B about an interest in harming person C. A threat refers to violent intent communicated to the potential victim directly. The team also coded whether perpetrators revealed a specific interest in committing a mass killing, or only a general interest in violence such as homicide, along with the mode of communication, the type of recipient, whether the public reported the information, and whether the criminal justice system responded. In total, 197 of the 483 mass shootings, or 41 percent, were preceded by the perpetrator revealing violent intent through leakage or threats.

The type of mass shooting mattered enormously. Public mass shootings, those occurring in populated locations with victims chosen at random or for symbolic value, showed by far the highest rates of pre-attack communication. Leakage or threats preceded 71 percent of public mass shootings, with leakage occurring in 56 percent of cases and threats in 42 percent. By contrast, leakage and threats were less common among family mass shootings and least common among felony mass shootings, those connected to underlying criminal activity such as robbery, gang violence, or drug deals. In fact, family mass shooters were the group most likely to have never leaked, threatened, or discussed their violent intent with anyone, a pattern found in 61 percent of those cases. Notably, the researchers’ hypothesis that family and felony shooters would favor direct threats over leakage was not supported. Public shooters exceeded the other subtypes on every measure of pre-attack communication, largely because they were far more likely to reveal a specific interest in committing a mass killing, which occurred before 35 percent of public shootings but only 8 percent of family shootings and 2 percent of felony shootings.

Why would perpetrators deliberately sabotage the secrecy their attacks require? The study tested three theories using logistic regression models that controlled for the incident year, the volume of news coverage, and the availability of official records. The first is criminal propensity, rooted in Gottfredson and Hirschi’s general theory of crime, which holds that some individuals are so prone to risk-taking that the long-term consequences of talking about violence seem trivial. The second is the cry for help theory, adapted from suicidality research, which suggests that some perpetrators consciously or unconsciously wish to be stopped or rescued before committing acts that will almost certainly end in their death or imprisonment. The third is attention-seeking, grounded in Merton’s anomie theory, which proposes that individuals who lack legitimate means to attain valued goals such as recognition may resort to extreme or provocative statements.

All three theories received strong empirical support. When leakage and threats were analyzed as a composite measure, prior arrest records and histories of domestic violence, markers of criminal propensity, mental health problems and suicidal ideation, markers of a cry for help, and fame-seeking and ideological motives, markers of attention-seeking, were all significantly associated with pre-attack communication. When the behaviors were disaggregated, an instructive pattern emerged. Mental health problems, fame-seeking, and ideological motives predicted leakage specifically, while prior arrests, domestic violence history, mental health problems, and suicidal ideation predicted direct threats. The authors suggest this split makes intuitive sense: attention-seeking individuals may recognize that leaking to a third party garners attention without the added risk of making a threat that is more likely to be reported, whereas individuals with criminal propensity may care less about risks and may be accustomed to using threats coercively from past behavior.

The study also traced precisely where prevention efforts break down, and the results point more toward the public than the system. Of the 197 shootings involving leakage or threats, 54 percent were reported by the public before the attack, meaning nearly half went unreported. Yet when reports were made, 83 percent produced a formal criminal justice response, usually involving law enforcement being dispatched or the eventual perpetrator being interviewed or investigated. The attrition varied sharply by subtype. Public mass shootings were reported most often, at 61 percent of cases with leakage or threats, but those reports were least likely to trigger a response, at 70 percent. Felony mass shootings were reported only 20 percent of the time, though when they were reported, the criminal justice system responded in every single case. Family mass shootings followed a similar pattern, with reports producing a response 98 percent of the time. Even when responses occurred, escalation to arrest happened in only 21 percent of cases with leakage or threats, and incarceration in only 6 percent.

Perhaps the most counterintuitive finding concerns what motivates people to report. The researchers had hypothesized that fear of retribution would suppress reporting when shooters had prior arrests or histories of domestic violence, and that perceived attention-seeking behavior would likewise dampen reports. Both predictions failed. Shooters who directly threatened their victims were reported 71 percent of the time, compared with 48 percent for those who merely leaked, and 80 percent when both occurred. Perpetrators with prior arrest records, domestic violence histories, mental health problems, or pre-attack suicidal ideation were all reported at or above average rates. Fame-seeking shooters were reported 74 percent of the time, well above the 54 percent baseline. The authors conclude that public uncertainty about whether someone is genuinely dangerous, rather than fear of the dangerous person, appears to be the greatest barrier to reporting. Clearer signals, specific interest in mass violence, communication through multiple modes or to multiple recipients, and the expression of both leakage and threats, all raised reporting rates significantly, sometimes to 73 or 78 percent.

Only one hypothesis about reporting was confirmed in the unexpected direction: ideologically motivated shooters were reported less often than average, at 46 percent. The researchers suggest that members of the public may struggle to differentiate between people who intend to commit ideologically driven attacks and others who hold extremist views but are not violent, a challenge that even professional threat assessors face, since radicalized beliefs are themselves a weak predictor of attacks. Overall, the pattern aligns closely with the classic bystander effect described by Latané and Darley, in which diffusion of responsibility, pluralistic ignorance, and evaluation apprehension lead people to assume someone else will act or that the situation is not truly an emergency. The authors recommend that law enforcement agencies educate the public about evidence-based warning signs, encourage reporting even when people are uncertain, provide easy and anonymous reporting channels, and publicize success stories showing how community members’ reports helped avert violence. The findings suggest that while it may be impossible to prevent all mass shootings, a substantial share of them broadcast their intentions in advance, and the greatest room for improvement lies in convincing ordinary bystanders that what they are hearing is worth taking seriously.

Subject of Research: Pre-attack leakage, threats, public reporting, and criminal justice responses in U.S. mass shootings

Article Title: A Comprehensive Assessment of Leakage, Threats, Public Reporting, and Responses to Reports among Public, Family, and Felony Mass Shootings

Article References: A Comprehensive Assessment of Leakage, Threats, Public Reporting, and Responses to Reports among Public, Family, and Felony Mass Shootings. (n.d.). https://doi.org/10.1007/s12103-026-09946-8

Image Credits: AI Generated

DOI: 10.1007/s12103-026-09946-8

Keywords: mass shootings, leakage, threats, violence prevention, threat assessment, criminal justice, bystander effect, public reporting, criminology, fame-seeking, suicidal ideation, open-source research

The idea of a food emissions tax is deceptively simple. Just as carbon pricing has been applied to fossil fuels, a food tax would charge producers or consumers in proportion to the climate pollution associated with growing, raising, and transporting what ends up on the plate. Beef and lamb, which carry the heaviest emissions footprints per kilogram because of methane from ruminants and the land required for pasture and feed, would face the steepest charges. Plant-based staples such as grains, legumes, and most fruits and vegetables, with far smaller footprints, would see little or no penalty. The economic logic is that when the price of high-emission foods rises, consumers substitute toward cheaper, cleaner alternatives, and producers shift resources accordingly.

The central worry, however, has always been leakage. If one country or region taxes emissions-intensive foods while its neighbours do not, production of those foods may simply migrate, moving to untaxed jurisdictions where the climate advantage of the policy is eroded. Worse, some feared, a tax could push consumption patterns in perverse directions, with consumers swapping taxed beef for untaxed but equally polluting alternatives, or with domestic production cuts being offset by imports raised under laxer conditions. Critics have pointed to the history of unilateral climate policies, where carbon-intensive industries sometimes relocated abroad, leaving global emissions unchanged or even higher, to argue that food taxes would be similarly undermined.

The new study puts those fears to a rigorous quantitative test. Using an economic modelling framework that couples global agricultural production with international trade and consumer demand, the researchers simulated emissions-based food taxes across multiple scenarios, ranging from unilateral action by a single large economy to coordinated pricing implemented by major emitters simultaneously. In each scenario, they tracked not only emissions from agriculture and land use but also changes in what people actually eat, and from those dietary shifts they estimated consequences for chronic disease and premature mortality.

The results are striking on both fronts. On the health side, the tax produces meaningful reductions in consumption of red and processed meats in the regions where it applies, and because red and processed meat are associated with elevated risks of cardiovascular disease, type 2 diabetes, and colorectal cancer, the dietary shift translates into fewer premature deaths. The health benefits are not confined to wealthy countries. Because food prices ripple through international markets, the analysis finds dietary improvements and associated mortality reductions spreading well beyond the taxing regions, as trade adjustments alter the relative prices of meats and plant proteins on global markets.

On the climate side, the tax directly reduces agricultural emissions by shrinking production of the most polluting commodities, and it produces additional gains by easing pressure on land. When demand for ruminant meat falls, less pasture and feed cropland is needed, creating space for forests and other carbon-rich ecosystems to recover or remain intact. Land-use change is one of the largest sources of food-system emissions, so even modest reductions in grazing and feed demand can compound into significant carbon savings. The modelling shows these effects are robust across scenarios, though naturally largest where the tax covers the most countries and the most emissions-intensive products.

Perhaps the most consequential finding concerns leakage itself. Across the scenarios examined, the analysis finds that emissions leakage from food taxes is limited rather than pervasive. Some production does shift to untaxed regions, but the scale of that shift is small relative to the overall emission reductions achieved, so the net global effect remains strongly positive. The study attributes this to several structural features of agricultural markets. Food production is tied to land, climate, and infrastructure in ways that industrial manufacturing is not, making relocation slower and costlier. Consumer tastes are also sticky, so demand in taxing regions falls rather than simply redirecting to imports. And because the health benefits arise from reduced consumption of harmful foods, they accrue regardless of exactly where the avoided production would otherwise have taken place.

Still, the study is careful to identify where policy design matters. Leakage is lowest when major producing regions act in concert, which argues for coordinated or trade-linked approaches rather than isolated unilateral taxes. The design of the tax base is also critical: pricing that covers emissions from land-use change, in addition to direct production emissions, captures more of the true climate cost of food and reduces the incentive to shift production toward commodities whose footprints are understated when deforestation is ignored. The analysis also points to the distributional question that any food tax must confront, since food spending consumes a larger share of income for poorer households. The modelling suggests that the health benefits themselves are progressive, falling disproportionately on lower-income groups that suffer the highest burdens of diet-related disease, but the authors note that pairing a tax with rebates or targeted support could cushion any regressive price effects.

The findings arrive at a moment when the food system accounts for roughly a third of global greenhouse gas emissions and shows few signs of decarbonizing on its own. Agricultural emissions are notoriously difficult to address through technology alone: methane from cattle lacks an inexpensive abatement fix at scale, and demand growth for meat in emerging economies continues to outpace efficiency gains. Command-and-control approaches to diet are politically fraught, and voluntary dietary guidance has moved consumption little. Pricing instruments occupy the middle ground, working through the same market mechanisms that have driven demand growth, but in the opposite direction. The evidence that such instruments can operate with limited leakage strengthens the case for incorporating food into carbon pricing frameworks that to date have focused almost entirely on energy and industry.

The research also adds a dimension that pure climate accounting has missed. Because the same foods that drive emissions also drive disease, a single instrument simultaneously addresses two of the largest policy challenges of the century, and the health co-benefits alone may be large enough to justify the policy even before climate damages are counted. For governments weighing the political cost of taxing food, that combination matters: it widens the coalition of beneficiaries, from climate ministries to health systems straining under the cost of diet-related illness, and it offers a narrative in which the policy is not merely a burden but an investment whose returns arrive in fewer hospitalizations and longer lives. As negotiations over how to bring agriculture into climate policy continue, this analysis suggests that the leakage problem, long invoked as a reason to delay, is a manageable design challenge rather than a fatal flaw, and that well-crafted food emissions taxes can be a powerful, double-duty instrument for both planetary and human health.

Subject of Research: Modeling the health and climate effects of food emissions taxes, including production leakage, across global agricultural trade scenarios.

Article Title: Food emissions taxes can deliver health and environmental benefits with limited leakage

Article References: Bouyssou, C. G., Springmann, M., Clora, F., Jensen, J. D., & Yu, W. (2026). Food emissions taxes can deliver health and environmental benefits with limited leakage. Nature Food. https://doi.org/10.1038/s43016-026-01429-7

Image Credits: AI Generated

DOI: 10.1038/s43016-026-01429-7

Keywords: food emissions tax, carbon pricing, leakage, global health, red meat, dietary change, agricultural emissions, land use, climate policy, trade modelling, ruminant methane, mortality

The stakes are enormous. Aging water distribution networks generate critical economic, environmental, and public health challenges, from catastrophic main breaks that flood streets and cut supply to slow, invisible leaks that waste scarce freshwater and can contaminate drinking water. Historically, engineers tried to anticipate failures using heuristic rules, physical deterioration models, and classical statistics, including single-variate and multivariate regression, Weibull proportional hazard models, and non-homogeneous Poisson processes. These approaches captured broad patterns, such as the tendency of break rates to accelerate after a pipe’s first failure, but they were limited in their ability to handle the sheer complexity of factors that determine when and where a buried pipe will give way.

The review traces how the field transformed with the arrival of machine learning. Supervised learning techniques now dominate the literature, with tree-based ensembles such as random forests and gradient boosted decision trees, artificial neural networks, and support vector machines forming the core toolkit. Through quantitative synthesis of recent studies, the survey finds that tree-based ensembles consistently achieve the highest discriminative performance in classifying which pipes are likely to fail. Random Survival Forests, an extension of random forests adapted for time-to-event data, excel at a different and arguably more valuable task: predicting not just whether a pipe will break, but how long until it does. Hybrid frameworks that combine multiple methods have pushed accuracy beyond 0.889 in recent applications.

What makes these models powerful is their ability to digest heterogeneous inputs that classical methods struggled to combine. Studies reviewed in the survey show that pipe failures are influenced by engineering characteristics such as material, diameter, and age, alongside geological and soil conditions, climate and weather variations, and even socioeconomic factors. One Hong Kong case study used a hybrid machine learning model to predict water main failures under varying climatic conditions, while research in the Netherlands quantified how weather drives break patterns. Spatial clustering approaches, which group pipes by the geographic patterns of past breaks, have further improved predictions by capturing local effects that pipe-by-pipe models miss.

Yet the review is candid about the obstacles standing between promising research papers and reliable utility operations. Data scarcity remains the most fundamental problem: many utilities lack complete records of their network’s assets, let alone decades of georeferenced failure history. Class imbalance compounds the difficulty, because failures are rare events relative to the total number of pipes, which can fool models into simply predicting that nothing will break. Geographic heterogeneity means a model trained in one city may perform poorly in another with different soils, materials, and climate. There is also a persistent trade-off between accuracy and interpretability, since the deepest models are often the hardest for engineers to explain and trust.

To address interpretability, the review highlights the growing use of tools such as SHAP, which quantify how much each input feature contributes to an individual prediction. Combined with optimization techniques in hybrid frameworks, these interpretability tools allow utilities to understand why a model flags a particular pipe as high risk, turning a black-box output into actionable engineering insight. Unsupervised clustering and semi-supervised approaches have also been proposed to cope with imbalanced and incomplete datasets, while survival analysis provides a statistically principled way to handle pipes that have been repaired or replaced and therefore lack complete failure records.

The paper’s most practically valuable contribution may be its stepped roadmap for utilities seeking to transition to machine learning-driven predictive maintenance. The roadmap begins with data infrastructure, since no model can outperform the quality of the records feeding it, and then moves through careful pilot selection on well-documented network segments, deliberate feature engineering that incorporates soil, climate, and operational variables, and workforce training so that engineers can use and maintain the models. Continuous retraining is emphasized as essential, because water networks are dynamic systems where each repair, replacement, and seasonal shift changes the underlying patterns.

Looking forward, the review identifies emerging paradigms that could transform the field further. Physics-informed neural networks embed physical laws and system knowledge directly into the learning process, potentially allowing accurate prediction even where data is sparse. Digital twins, virtual replicas of physical networks continuously updated with real-time monitoring data, promise to integrate predictive models with live operational decisions. Graph neural networks, which treat the pipe network as a mathematical graph, are also attracting attention because they naturally capture how failures and pressure changes propagate through interconnected systems. Deep reinforcement learning is being explored for leak management, learning control strategies that adapt over time.

By bridging academic research and operational implementation, the survey provides actionable guidance for researchers, policymakers, and utility managers working toward sustainable and resilient water infrastructure. The message to utilities is ultimately encouraging: the algorithms are mature, the accuracy benchmarks are proven, and the path from reactive firefighting to proactive prediction is mapped. What remains is the institutional work of building the data foundations, piloting the technology thoughtfully, and training the people who will run it, before the next aging main bursts.

Subject of Research: Systematic review of machine learning methods for predicting water pipeline failures in water distribution networks

Article Title: Employing Machine Learning Approaches for Predicting Pipeline Failures in Water Systems: A Survey of Challenges and Opportunities

Article References: Asadi, Y. (2026). Employing Machine Learning Approaches for Predicting Pipeline Failures in Water Systems: A Survey of Challenges and Opportunities. Water Resources Management, 40(11), Article 516. https://doi.org/10.1007/s11269-026-04882-y

Image Credits: AI Generated

DOI: 10.1007/s11269-026-04882-y

Keywords: machine learning, water distribution networks, pipe failure prediction, predictive maintenance, random forest, survival analysis, SHAP, digital twins, physics-informed neural networks, water infrastructure, leakage, artificial neural networks

The evidence for failure at scale is sobering. Individual projects frequently succeed: a meta-analysis cited in the review found that conservation interventions have produced measurable positive outcomes for species and ecosystems, and local communities in many regions have benefited from collaborative approaches to managing wildlife. Yet the aggregate picture documented by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, or IPBES, shows continued decline across most taxa and biomes. The review summarises the arithmetic bluntly: the sum of biodiversity gains within conservation projects has been smaller than losses across the rest of the world’s surface. The Kunming-Montreal Global Biodiversity Framework responds by calling for restoring 30 percent of degraded ecosystems, conserving 30 percent of land and sea by 2030, halting extinctions, halving food waste and removing harmful incentives, all at an estimated cost of roughly US$200 billion per year. Thomas does not dispute the value of these targets but questions whether scaling up place-based protection can ever deliver a planetary turnaround.

The central technical problem the review identifies is leakage, the displacement of environmental pressure from protected locations to unprotected ones. When farmland is de-intensified, rewilded or abandoned for conservation, food production in that location falls, but demand for food does not. Production typically shifts elsewhere, often to regions with higher biodiversity or weaker environmental governance, and the review notes that leakage can in principle exceed 100 percent, producing a net global loss. The same dynamic applies to fisheries, where restrictions under one jurisdiction push fishing effort into other waters, other species or aquaculture; one cited study found that spatial restrictions inadvertently doubled the carbon footprint of Norway’s mackerel fleet. Because trade networks are diffuse and biodiversity is distributed unevenly, the magnitude of biodiversity leakage is difficult to quantify, but the mechanism itself undermines the assumption that protecting land locally equates to protecting nature globally.

Geography compounds the leakage problem. Under the Convention on Biological Diversity, commitments are devolved to nation states, so each signatory aims to conserve roughly 30 percent of its own territory. The globally efficient solution, Thomas argues, would look very different: considerably more than 30 percent of species-rich, endemic-rich countries such as Indonesia and Madagascar, and far less of most north-temperate nations. Studies of conservation prioritisation show that when nations plan independently they protect nationally rare species and sites that may not be threatened globally, so the total biodiversity secured is substantially lower than under globally coordinated prioritisation. Conservation prioritisation software and hotspot approaches championed by organisations such as Conservation International can identify where the maximum biodiversity can be conserved in the minimum area, and they have worked well in countries like Madagascar, but politics, not science, limits their global application.

Beneath these distributional problems lies the deeper causal hierarchy. IPBES identifies land and sea use change and direct exploitation of organisms as the top two direct drivers of biodiversity loss, but the review insists these are themselves consequences of indirect drivers, principally what and how much humanity eats. Global population is projected to rise by roughly a further quarter this century, while per capita intakes of calories, protein, fat and especially meat and dairy continue to climb. Around 30 percent of the Earth’s ice-free land surface is already devoted to meat and dairy production, including feed crops, against 9 percent for plants eaten directly by people. Human appropriation of the planet’s annual photosynthesis is forecast to reach between 27 and 44 percent by 2050 depending on agricultural trajectories. Since people must eat and that food must be produced somewhere, Thomas characterises food as the most intractable of the indirect drivers and therefore the proper first target of biodiversity mitigation.

The good news, the review stresses, is that a portfolio of social and technological transformations capable of relieving that pressure already exists. Demand-side measures include dietary shifts toward plant-rich and alternative-protein diets, halving food waste, reforming economic norms that reward growth in consumption over wellbeing, improving equity so that consumption is distributed more fairly, and removing perverse subsidies and incentives. Supply-side measures include plant-based and precision-fermented meat and dairy alternatives, cultivated meat, microbial protein grown on food waste and agro-industrial by-products, and even emerging approaches that synthesise carbohydrates directly from carbon dioxide and energy. None of these alone is sufficient, and it is unclear which combinations will prevail, but the review argues that together they could progressively reduce pressure on land and seas during the second half of the twenty-first century and, if supported and scaled, virtually eliminate food-related drivers of biodiversity decline within a century, allowing long-term ecosystem recovery.

Critically, this technological and social transformation must precede any wholesale shift to extensive farming. Organic systems produce roughly 20 to 25 percent less food per hectare than intensive agriculture, and the review warns that expanding cropland and pasture by that margin to compensate would be catastrophically damaging to global biodiversity. Wildlife-friendly and regenerative approaches become globally viable only once total production pressure has fallen, at which point remaining farmland could be de-intensified, agrochemicals largely removed and pollutants and welfare concerns addressed. The review also cautions that land released from food production must not simply be converted to biomass monocultures, plantation forestry or urban expansion, which would cancel the gains; overarching policies are needed to ensure that wins in one sector are not offset by losses in another. Importantly, this mitigation framing does not apply to the existing mitigation hierarchy of avoid, minimise, restore and offset, which Thomas classifies as adaptation because it manages the consequences of consumption rather than consumption itself.

On the adaptation side, the review argues that conventional conservation’s fixation on restoring historical baselines sets itself up to fail. Atmospheric carbon dioxide is already higher than at any time in roughly three million years, altering plant growth, carbon-nitrogen stoichiometry and climate in ways that will persist for tens of thousands of years. Species compositions have already shifted in most communities and will continue to shift regardless of conservation action, even inside protected areas. Instead of equating adaptation with resistance, Thomas endorses flexible decision frameworks such as Resist-Accept-Direct, developed for US national parks, and its generalised Facilitate-Accept-Resist variant. Managers would explicitly choose, case by case, whether to facilitate adaptive change, for example by enabling range shifts and novel community combinations; to accept change without intervention; or to resist change, reserved for situations where whole species are endangered or an irreplaceable ecosystem service is at stake. Facilitation and acceptance should normally come first, with resistance deployed surgically rather than as default strategy.

The review’s institutional conclusion is that the Convention on Biological Diversity should reorganise itself into parallel mitigation and adaptation work streams, mirroring the relationship between the IPCC and UNFCCC in climate policy, and drawing expertise from the FAO, trade bodies and others who govern the indirect drivers. It points out that biodiversity credits, no net loss rules and biodiversity net gain schemes, however well intentioned, risk enabling continued consumption growth and generating further leakage unless the underlying drivers are constrained. Traditional protected-area conservation will remain necessary, but it cannot substitute for mitigation. Recent biodiversity trends, the review concludes, cannot be halted or reversed at planetary scale unless the production and consumption causes of environmental change are recognised, reduced and replaced, and that will not happen by chance: it requires deliberate institutional redesign and political will on a scale conservation has never yet mobilised.

Subject of Research: Mitigation and adaptation strategies for halting and reversing global biodiversity decline by addressing the human consumption drivers of environmental change

Article Title: Mitigation and adaptation strategies to reverse biodiversity decline

Article References: Thomas, C. D. (2026). Mitigation and adaptation strategies to reverse biodiversity decline. BMC Environmental Science, 3(1), Article 19. https://doi.org/10.1186/s44329-026-00059-5

Image Credits: AI Generated

DOI: 10.1186/s44329-026-00059-5

Keywords: biodiversity, conservation, mitigation, adaptation, food systems, leakage, Convention on Biological Diversity, IPBES, cellular agriculture, dietary change, protected areas, Kunming-Montreal