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Authors: Bianca R. Schell, Ann-Katrin Emmerich, Arooba Nazneen, Marc Widenmeyer, Anke Weidenkaff, Nico Bruns

In the face of the sustainability crisis, scientific laboratories, as resource-intensive units, must conduct research more sustainably. They are estimated to produce 5.5 million tonnes of plastic waste per year. Clearly, there is a need for greater circularity: Reduce-and-reuse frameworks for laboratory plastics exist to some extent, but none for recycling of disposed single-use articles. Biologically contaminated plastics from life science labs are disinfected by autoclaving after use, transforming them into conventional residual waste that is mostly incinerated, resulting in high CO2 eq. emissions. However, this waste stream consists of high-quality polymers, for which (thermo-)mechanical recycling is technically possible. Therefore, we performed a waste assessment of lab plastics and found in a one-week assessment that the majority of the residual waste consisted of recyclable plastics. However, autoclaving causes these mixed fractions to clump together. Thus, we created a separate collection scheme for the two most abundant plastic types, polystyrene and polypropylene, enabling us to retrieve high-quality monostream polymer fractions for (thermo-)mechanical recycling. This study is the first to present a transferable blueprint with detailed instructions for other institutions on how to implement the separate collection scheme. We obtained user feedback on the feasibility and demonstrated 85% user acceptance. Further, this study provides the first quantitative assessment of emission-saving potential associated with substituting incineration with (thermo-)mechanical recycling for laboratory single-use plastics. For two end-of-life scenarios – an institution-specific scenario at the University of Konstanz (waste incinerated in Weinfelden, Switzerland) and a national average (Germany) scenario – we calculated the average annual emission-saving potentials for the extrapolated amount of arising waste to be up to 10.6 t CO2 eq. and 7.2 t CO2 eq., respectively. Therefore, a separate collection of lab plastic waste offers an easy-to-implement measure to reduce incinerated plastic waste, thereby lowering emissions and increasing the sustainability of laboratory research.

Source: PLOS Sustainability and Transformation. DOI: 10.1371/journal.pstr.0000225. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Emma L. Yule, Joana Avi-Lorie, Pamela Candea, James Porter, Francesca Vantaggiato

Clear adaptation outcomes are essential for driving climate action and assessing progress. However, developing a shared vision of desirable adaptation outcomes and the steps to achieve them requires a shared understanding of what constitutes a well-adapted place and what resources are needed to enable communities to create and implement such visions. The third Scottish National Adaptation Plan (SNAP3) has “communities creating climate resilient places” as a key outcome. Our research draws upon a collaborative workshop that used storytelling techniques to co-produce adaptation visions: developing a shared narrative and identifying ‘turning points’ to put it into practice. Participants included community groups, public sector, and government officials. Key turning points included shifts in governance structures toward local communities inclusion in developing adaptation visions, consistent knowledge-sharing across scales via dedicated platforms/procedures, and enhanced communication and engagement around adaptation narratives at the local level. Unless these measures are implemented, communities will struggle to create the climate-resilient places envisioned in SNAP3. Our findings indicate that local adaptation targets are perceived as potentially useful drivers of adaptation action, but with limitations to their effectiveness: i.e., adaptation targets risk becoming siloed and failing to integrate broader resilience or community priorities. Our research highlights the positive and negative narratives around local adaptation targets, underscoring the need for continuous monitoring and a cautious, place-based approach to their implementation.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0001056. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Ntule Shimwela, Noah M. Pauline, Catherine A. Masao

Climate action disclosure among companies has gained significant attention in today’s competitive business arena in ensuring business entities effectively and appropriately reduce their greenhouse gas (GHGs) emissions. However, there are inadequate studies that have investigated the extent of corporate climate action disclosure and implications of legal frameworks and corporate attributes in achieving Net Zero Emissions (NZE) targets. To bridge this gap, this study intended to analyse the extent of climate action disclosure among Tanzanian listed companies. Data collection was conducted through document review of 78 sustainability reports, annual reports, and integrated reports published between 2022 and 2024. Further, interviews were conducted with six relevant experts from the listed private corporations and government regulatory authorities. Based on the metrics and targets recommended by the Task on Climate–related Financial Disclosure (TCFD), the climate change disclosure index was developed and used to analyse the extent of climate action disclosure. The distribution of the total mean content score was found to be 18.9%, indicating that the overall extent of climate action disclosure was low. The pattern of distribution was also low across TCFD areas. The low extent of climate action disclosure was attributed to inadequate implementation of relevant frameworks, lack of baseline data and inadequate capacity of companies and experts to perform scenario analysis and define clear climate change Key Performance Indicators (KPIs). Therefore, the findings pose important implications for governments, policymakers, and companies to strengthen climate action disclosure.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000951. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Danna Lesley Cruz-Reyes, Daniel Leonardo Ramírez Orozco

In this study, we apply fully Bayesian spatio-temporal models to locality-level water consumption in Bogotá, Colombia, from 2019 through 2024. An additive model identifies spatial differences after adjusting for common temporal dynamics and city-wide climatic and socioeconomic time series. Because these covariates are shared across all localities, they provide temporal adjustment but cannot explain between-locality spatial variation. A formal sensitivity analysis adding a structured space–time interaction substantially improves model fit (WAIC: 604.0 versus 2502.7; DIC: 333.9 versus 2500.5), showing that locality deviations are not adequately represented as constant throughout the study period. Student-t likelihoods indicate heavy residual tails (posterior mean degrees of freedom: 3.02 in the additive model and 2.60 in the interaction model), but the Gaussian interaction model retains substantially better information criteria than its Student-t counterpart. The principal spatial main effects remain highly correlated across likelihoods (r = 0.975). The results therefore support spatially heterogeneous temporal dynamics and show that the main spatial ordering is robust to heavy-tailed errors.

Source: PLOS Water. DOI: 10.1371/journal.pwat.0000512. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Lydia Otoo Amponsah, David Nartey Obemah, Belinda Lee Quayson, Albert Banunle, Daniel Kwame Oware Asamoah, Mwema Felix Mwema

This study evaluated the effectiveness of a decentralized, on-farm wastewater treatment system using locally generated rice husk biochar for safe agricultural irrigation reuse in Diewuoso-Obuasi, Ghana. The system, a two-stage downflow filtration system, treated raw municipal wastewater over 10 days. Results showed significant removal of turbidity (66.92%), total dissolved solids (38.19%), and salinity (22.03%), meeting FAO irrigation limits. However, electrical conductivity increased (26.58%) due to ash-mineral leaching. Microbial analysis indicated substantial log-scale removal efficiencies for E. coli (90.42%), fecal coliforms (98.39%), and total coliforms (99.99%), but final effluent concentrations exceeded WHO and Ghana EPA thresholds for unrestricted irrigation. The study concludes that while the rice husk biochar filter is an efficient pre-treatment component, it requires secondary post-treatment disinfection to eliminate public health risks, offering a scalable model for circular resource recovery in rural farming communities.

Source: PLOS Water. DOI: 10.1371/journal.pwat.0000612. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Moritz Egger, Michael Curran, Alice Aubert, Norman Kearney, Diana Ventura, Guillermo Andrés Zambrano Mohauad, Adriana Patricia Santos Ordóñez, Johanna Jacobi

Sustainability transitions in smallholder agriculture depend on how local communities understand and prioritise different futures, yet most assessment tools rely on externally defined criteria and expert-led evaluations. This study examines how deliberation shapes cocoa farmers’ evaluations of sustainability pathways. In two-day workshops with eight farmer groups (n = 36) in two regions of Ecuador (Amazon and Coast), farmers evaluated four scenarios using intuitive ranking, urgency ratings, and swing-weighting, based on sustainability criteria they had generated in a prior Photovoice process. We compared intuitive and criteria-based rankings of scenarios and calculated indices of individual deliberative reasoning and intersubjective consensus before and after deliberation. In the Amazon, where intuitive scenario preferences diverged, deliberation led all groups to converge on the same scenario rankings and to reweight criteria towards long-term environmental and social concerns. Importance-consensus and individual reasoning indices increased modestly, indicating tighter alignment between what farmers valued and the futures they preferred. In the Coastal region, groups already shared a strong preference for an environmentally restorative scenario. Deliberation primarily reinforced this consensus, substantially increasing agreement on the prioritisation of sustainability criteria while leaving overall scenario rankings unchanged. Across regions, some groups showed closer alignment between intuitive and criteria-based rankings after deliberation, while others diverged, suggesting that deliberation can both stabilise and reconfigure preferences as trade-offs are recognised. The results show that deliberative multicriteria evaluation, when grounded in community-generated criteria and narratives, can articulate and consolidate shared sustainability visions even where prior engagement has already fostered high agreement. The deliberation indices proposed here provide a transferable framework for tracking learning, reasoning, and consensus in participatory planning, supporting the design of more context-sensitive sustainability interventions.

Source: PLOS Sustainability and Transformation. DOI: 10.1371/journal.pstr.0000279. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Mark Ferguson, Joanne K. Garrett, Barbara Tschirren, Benedict W. Wheeler, Alexander G. C. Vaux, Cornelia Guell, Jolyon M. Medlock, Camille Bonneaud, Timothy J. Taylor, Gesche M. Huebner

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0001016. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Franck Hervé Akaffou, Salomon Obahoundje, Arona Diedhiou, Kouakou Lazare Kouassi, Dior Diallo, Ernest Amoussou, Daniel Yamegueu, Eric Antwi Ofosu

This study presents a novel five-step ensemble machine learning approach to improve predictive accuracy in assessing climate change impacts on inflow patterns and hydropower generation across seven dam basins in West Africa. The methodology integrates precipitation and temperature using the multi-lag approach. An initial pool of fifteen machine learning models were evaluated, and top-performing models were selected for further refinement through iterative ensemble stacking and weak learner elimination. Historical analysis (1983–2014) utilized CHIRPS and CHIRTS datasets. Future projections employed twelve bias-adjusted CMIP6 models and their ensemble mean (EnsMean) under SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the near (2036–2067) and far (2068–2099) futures. Results showed notable improvements in model accuracy and efficiency across layers, with R² and NSE exceeding 0.6 for all inflow simulations and for selected energy simulations (Bagre, Nangbeto, and Taabo). Projections indicated warming up to 4.5°C and spatially heterogeneous precipitation changes across basins and scenarios, with SSP5-8.5 projecting the most pronounced shifts. Inflow reductions are projected to reach up to 24% at Buyo and 13% at Nangbeto, while hydropower output may decline by up to 19% at Nangbeto and 58% at Taabo in the future. Conversely, Manantali and Taabo are projected to experience inflow increases, and Bagre may see energy gains of up to 42% under SSP5-8.5. These findings highlight heightened vulnerability, as well as contrasting opportunities across the region, underscoring the urgent need for adaptive management strategies, such as enhancing hydropower system resilience, diversifying energy portfolios, and integrating renewable sources to mitigate climate risks. Hydropower managers and policymakers must prioritize proactive measures to ensure energy security and sustainable resource management amid changing climatic conditions.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000986. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Stephanie Jean Tsang, Dandan Wang

Public support for climate action hinges on the integrity of information environments. In an online experiment, U.S. adults used ChatGPT to evaluate climate-related claims. We first conducted computational text analysis of GPT conversation logs, assessing valence, formality, bias, recency, authority cues, and semantic richness of ChatGPT’s responses using language-model based classifiers and link-level metadata. Concurrently, we measured credibility judgments for both the claims and ChatGPT’s responses through participant self-reports. Credibility perceptions were driven primarily by individual differences; greater acceptance of the scientific consensus, attention to climate change, prior familiarity with ChatGPT, and younger age predicted higher perceived credibility. Once these factors were accounted for, authority cues were associated with slightly less extreme climate attitudes, while positive valence and semantic richness correlated positively with perceived credibility. These results support transparent, unbiased, audience-tailored engagement as a pathway to strengthen responsible climate communication and help bridge the gap between scientific consensus and public understanding.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000930. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Guanyu Yang, Amy Rodger, Elif Naz Çoker, David Shipworth

Energy, transport, and food sectors contribute substantially to climate change and are key areas where behaviour change can have the greatest impact. International policy learning and transfer are central to government approaches in these sectors. However, policies often show limited effectiveness when applied in varied contexts, likely due to assumptions of population homogeneity and over-reliance on socio-demographic characteristics. Using Multilevel Latent Class Analyses (MLCA) on over 17,000 respondents across 61 regions in nine OECD countries, we found that less sustainable behavioural patterns were prevalent across the three sectors, though with notable heterogeneity in the energy sector, and that socio-demographic characteristics failed to predict segment membership consistently. Individuals engaging in sustainable behaviours in one sector were more likely to do so in others, and unsustainable behaviours had similar positive associations across sectors, suggesting coordinated cross-sector interventions are crucial. Regional distribution of population segments was typically grouped geographically by country, underscoring the importance of contextual influences. However, we identified significant within-country variations in regional distributions, with some regions showing substantially different proportions of people in sustainable segments than neighbouring regions under identical national policies, highlighting the importance of regional context. Moving beyond traditional ad-hoc policy transfer approaches, we proposed a systematic framework for identifying evidence-based transfer opportunities, showing policymakers not only who needs interventions but where successful patterns emerge and which contexts may enable sustainable behaviours. These findings provide actionable insights for the international policy community seeking to accelerate behavioural change at the scale and speed required for climate mitigation.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0001039. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Ashley Avila, Nicole Iroz-Elardo, Kristina M. Currans, Ladd Keith

Understanding the causes of heat in various microclimates in cities is vital to improving human thermal comfort and health in outdoor spaces. This pilot study uses an experimental design to evaluate microclimate heat risk – including ambient air temperature (TA) and wet bulb globe temperature (WBGT) – and approximate personal heat exposure of people 1.5-3.0 meters (5–10 feet) away from idling vehicles. These measurements were taken at a University of Arizona covered parking garage in June 2022 to investigate personal heat exposure attributable to vehicular waste heat while also minimizing the effect of solar radiant heat and wind. We used Kestrel 5400 devices to document the waste heat effects of a fleet of six identical gasoline-powered vehicles on the surrounding microclimate by collecting TA, wind velocity, and WBGT. When compared to the control, as well as comparing a period when engines were not idling, we found a strong correlation between vehicle presence, TA, and WBGT. Specifically, ordinary least-mean square (OLS) modeling shows an additional TA per minute increase of 0.006°C (a 25% increase) per minute when the vehicles are on and idling versus 0.024°C per minute increase that occurs from expected background morning temperatures when the vehicle effect is removed. Until we transition to an electric fleet and increase use of alternative modes of transportation, these findings can help inform how transportation professionals design the built environment and manage traffic and transit during summer months to prevent excessive heat exposure for pedestrians, cyclists, transit riders, and other individuals near idling vehicles.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000824. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.