The denim industry, a major contributor to global greenhouse gas emissions and resource consumption, is under increasing pressure to adopt more sustainable practices. While life cycle assessment (LCA) has been applied to denim products at the product level, large-scale, process-specific environmental assessments of conventional industrial indigo rope dyeing remain limited. The purpose of this study is therefore to provide a detailed environmental evaluation of that process, identify key environmental hotspots, and support targeted mitigation strategies at the process level. To this end, a gate-to-gate environmental LCA was conducted for conventional indigo rope dyeing IRD, using recent (2025) primary data collected from a large-scale Tunisian denim dyeing facility and complemented with relevant secondary datasets. The results indicate that the production of 1kg of indigo-dyed rope generates 2.34kg CO₂ eq. Contribution analysis shows that electricity and steam consumption mainly supplied by fossil-based cogeneration are the primary contributors to global warming potential (GWP) and abiotic depletion, while sodium dithionite is the main contributor to acidification. In addition, the consumption of steam and synthetic indigo dye is identified as a major contributor to terrestrial and marine aquatic ecotoxicity impacts. Scenario-based sensitivity analysis demonstrates that replacing fossil-based energy with renewable alternatives can substantially reduce the environmental burden of the dyeing process, particularly with respect to climate change and resource depletion. These findings highlight the critical role of energy transition and chemical substitution in improving the sustainability of denim dyeing.

Organic fertilizer substitution combined with Bacillus velezensis SQR9 inoculation reduces carbon footprint and enhances net eco-economic benefits in tropical vegetable fields.

ABSTRACTAgricultural production is a significant contributor to global greenhouse gas emissions, highlighting the need for region‐specific assessments of climate impacts across food systems. This study evaluates the carbon footprint of open‐field strawberry production in the Silifke district (Mersin, Türkiye) using a process‐based Life Cycle Assessment approach with a cradle‐to‐farm‐gate system boundary and a functional unit of 1 kg of strawberries. Life cycle modeling was performed in SimaPro using background data from Ecoinvent 3.10, and impacts were quantified using the IPCC 2021 Global Warming Potential method. The results indicate a total carbon footprint of 0.25052 kg CO2‐eq kg−1, placing the system within the lower range reported for Mediterranean strawberry production. The cultivation stage was the dominant contributor (77.56%), followed by raw material supply (22.44%). Fertilizer use, particularly nitrogen, phosphorus, and potassium (NPK) fertilizers, was identified as the primary emission hotspot. Scenario analysis showed that a 20% reduction in fertilizer application decreases the carbon footprint by 9.83%, while solar‐powered irrigation results in only a minor reduction (0.56%). Sensitivity analysis confirmed fertilizer inputs as the dominant emission driver (S ≈ 0.49), while electricity consumption has negligible influence. Yield sensitivity further showed that reduced productivity significantly increases carbon footprint, highlighting fertilizer optimization and yield stability as key mitigation strategies.

Food systems account for approximately one-third of global greenhouse gas emissions (GHGE), and many foods with high GHGE pose risks to human health. Environmental impact (eg, climate) labels on restaurant menus may promote lower GHGE and higher nutritional quality food choices. To compare the efficacy of climate labels on the GHGE and nutritional quality of food choices. This online randomized clinical trial was conducted using a menu based on a US fast food chain between December 2024 and February 2025. Participants included US adults aged 18 years or older. Analyses were conducted between February and April 2025. Participants were randomized to 1 of 6 label conditions: (1) control, (2) environmental cost (GHGE represented as a monetary cost), (3) climate grade (GHGE represented as a grade [eg, A to F, with A representing the lowest GHGE]), (4) climate grade plus scale (GHGE represented as a grade accompanied by a scale showing all possible grades), (5) high climate impact on items with high GHGE, and (6) warning high climate impact on items with high GHGE. Participants ordered from a fast food menu displaying their assigned label next to applicable items. Primary outcomes include the selection of an item with high climate impact and GHGE of menu orders. Secondary outcomes include Nutrient Profile Index (NPI) score and nutrients of menu orders and participant perceptions. This study included 10 342 participants (mean [SD] age: 46.4 [16.9] years; 53% women). Participants in the climate grade (825 of 1721 [48%]; P = .02), climate grade plus scale (787 of 1719 [46%]; P < .001), high climate impact (777 of 1724 [45%]; P < .001), and warning high climate impact (817 of 1725 [47%]; P = .01) conditions were significantly less likely to select an item with a high climate impact than those in the control condition (920 of 1728 [53%]). Participants in the climate grade plus scale (mean GHGE, 16.3 [95% CI, 15.5-17.2] kg CO2 equivalent, adjusted P < .001) and high climate impact (mean GHGE, 16.9 [95% CI, 16.0-17.8] kg CO2 equivalent, adjusted P = .04) conditions selected items with lower GHGE than those in the control condition (mean GHGE, 18.8 [95% CI, 17.9-19.6] kg CO2 equivalent). NPI scores did not differ among label conditions, but participants in the climate grade plus scale condition selected items with less saturated fat than those in the control condition (11.8 g vs 12.6 g; adjusted P = .04). Although there were no significant differences between the environmental cost and control labels for primary or secondary nutrition outcomes, when viewing labels in isolation, participants perceived the environmental cost label as the most discouraging of selecting high climate impact items (mean score, 3.0 [95% CI, 2.9-3.1]; P < .001). This randomized clinical trial's findings suggest that using climate labels on restaurant menus, particularly climate grade plus scale designs, may decrease climate impact and improve nutrition of restaurant orders. Future research is needed on environmental cost label designs and climate label use in real restaurant settings. ClinicalTrials.gov Identifier: NCT06651060.

The power sector is a major contributor to global energy use and greenhouse gas emissions. In 2020, China’s power sector accounted for over 40% of national carbon emissions, underscoring the importance of its decarbonization for achieving global climate targets. Effective emission-reduction policies require detailed and reliable carbon data. Given China’s “top-down” target-setting system, accurate regional data are essential for designing differentiated local policies and evaluating their effectiveness. In this study, we systematically estimate CO2 emissions from the power sector in 352 Chinese cities from 2000 to 2019, providing broader spatial and temporal coverage than existing datasets. Using a particle swarm optimization–back propagation algorithm, we established relationships among provincial and prefectural power generation, socioeconomic indicators, and operating revenue for the electricity and heat production and supply sectors. City-level power generation was derived based on provincial training results, combined with the 2021 electricity CO2 emissions factor published by the Ministry of Ecology and Environment and the National Bureau of Statistics, providing a valuable foundation for regional low-carbon research and policymaking.

Climate change is affecting ecosystems, communities, and human health worldwide. These changes pose risks to global energy systems so there is a dire need to combat climate change and limit global warming to 1.5°C. This study undertake global energy systems and forecasted total energy consumption, production and greenhouse gas (GHG) emissions worldwide for the study period 2021–2050 by taking the input data from 1970 to 2020 using the four algorithm’s namely, Holt Winter (HW), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) implemented in Python. It is found that HW and ES have same forecast results globally with energy consumption of 236,285 TWh which can easily meet by 475,980 TWh generation until 2050. Renewables and fossil fuels contributed to 250,106 TWh units and 225,874 TWh units with 48 billion metric tons of GHG emissions until 2050. The global forecast of ARIMA model suggested that 232,878 TWh energy consumption is noticed which can easily meet by 446,126 TWh generation with 213,052 TWh share of renewables and 233,074 TWh of fossil fuels with 49 billion metric tons of GHG emissions produced until 2050. SARIMA model forecast is very much valuable for limiting global mean temperature to 1.5 °C. The global energy consumption is forecasted to be 231,022 TWh which easily meet by 350,054 TWh green energy generation potential with almost zero emissions until 2050 and it is found that SARIMA model has 98% of accuracy. • Python based algorithms are used to achieve clean energy targets globally (1970–2050). • Forecasted global energy consumption, production, and GHG emissions using holt winter, exponential smoothing, ARIMA and SARIMA. • By 2040, renewable energy is projected to 100% in global power mix under SARIMA model. • Global CO 2 emissions must decrease by 60% in 2035 and 100% in 2040 under SARIMA model. • The transition to sustainable energy system is expected to boost global GDP by 15% until 2040.

Building cooling and heating account for around 25% of global greenhouse gas emissions, with a rising trend. While the growing cooling demand is largely met by chillers, decarbonization and electrification efforts are boosting the adoption of heat pumps. Both technologies rely on electricity-intensive vapor compression cycles, which contribute to environmental impacts and place stress on power grids. To mitigate these effects, previous research has suggested improving the performance of vapor compression systems (VCSs) through optimization and fault detection. In this regard, digital twins (DTs) offer a promising approach by enabling real-time simulation with minimal manual effort. However, their adoption in the building sector remains limited. This paper presents a systematic literature review of modeling methods for VCSs, discussing their strengths and limitations in the context of DT applications. Additionally, it introduces a set of evaluation criteria to analyze the performance of DTs. The findings highlight that (i) model selection is highly dependent on contextual factors such as load and weather variations, (ii) commonly used modeling methods often face challenges when applied to DT scenarios, and (iii) focusing only on error-based metrics may overlook other aspects such as generalizability, resiliency, scalability, and interpretability which might be crucial in final applications. These insights facilitate the development and validation of VCS DTs and supports their adaptation in the building sector. • A systematic review of existing modeling methods for vapor compression systems (VCSs) is conducted. • Strengths and limitations of VCS modeling methods are discussed in the context of digital twinning. • A structured set of evaluation criteria for DT-oriented modeling is introduced. • The importance and role of each evaluation criterion are discussed in relation to practical DT applications. • A case study comparing seven commonly used modeling methods in terms of accuracy, reliability, robustness, scalability, and interpretability is provided. • Results suggest that model selection should consider multiple aspects beyond accuracy alone.

Climate change and pediatric neurology: A call to action.

Agriculture contributes significantly to global greenhouse gas (GHG) emissions. With increasingly diverse rotations, interest is growing in break crops such as linseed ( Linum usitatissimum L.) to support soil health, pest and disease control, and reduce nitrogen inputs. However, the controls on GHG emissions (CO 2 , CH 4 , N 2 O) from linseed, particularly genotypic variation, remain poorly understood. This study investigated genotypic variations in GHG emissions among four elite linseed genotypes (Empress, Omegalin, Richess, VT50) at two UK field sites: Leicestershire (Ashby-de-la-Zouch) in 2024 and Derbyshire (near Elvaston) in 2025. Using closed static chambers connected to a Gasmet GT5000 portable FTIR analyser, weekly GHG fluxes, soil temperature, volumetric soil moisture, and crop height were measured in randomised plot designs under commercial fertiliser regimes. Cumulative emissions were calculated by trapezoidal integration of daily mean fluxes, and data were analysed with ANOVA and linear mixed-effects models. Significant site- and year-driven differences in overall GHG emissions were observed (P &amp;lt; 0.05), with markedly higher cumulative CO 2 , N 2 O and CH 4 fluxes in the warmer, wetter 2024 season. No significant genotypic differences were found in cumulative CO 2 or N 2 O emissions. However, genotype VT50 exhibited a significant CH 4 sink (–2.83 g ha -1 ) in 2025. Crop height varied significantly between sites (P &amp;lt; 0.001) and positively influenced CO 2 fluxes (P &amp;lt; 0.05), with interactive effects of height × soil temperature on CO 2 and height × volumetric moisture on CH 4 and N 2 O fluxes (P &amp;lt; 0.05). Environmental factors (site, climate, and management) dominated over genotype in determining GHG emissions from linseed under field conditions. VT50 showed strong potential for low-emission production, particularly as a CH 4 sink in certain years. These findings highlight the value of integrated genetic and environmental strategies, including growth-optimised genotypes, to enhance break crops like linseed for net-zero and sustainable agriculture.

HighlightsPublic health relevance—How does this work relate to a public health issue?Environmental degradation and climate change are major contributors to human morbidity and mortality.The healthcare sector is a major contributor to global greenhouse gas emissions. Quantifying and improving the carbon footprint of the healthcare sector could have a drastic impact on environmental health and public health.Public health significance—Why is this work of significance to public health?Reducing the healthcare sector’s carbon footprint will lead to improved environmental conditions and therefore improved health outcomes.Improvements in environmental conditions will have population-wide health impacts.Public health implications—What are the key implications or messages for practitioners, policymakers and/or researchers in public health?Data on the carbon footprint of specific clinical practices are limited. More granular emissions data are key to identifying and modifying processes that generate significant emissions.This analysis demonstrates the potential environmental benefits of a home hospital program for neonates, which showed particular reductions in carbon footprint through reduced travel distances and waste generation.The healthcare sector is a major contributor to global greenhouse gas emissions. Little is known about the impact of individual clinical practices on overall emissions; more granular healthcare emissions data are needed to identify opportunities for resource stewardship. Our objective was to deploy an interdisciplinary team to perform Life Cycle Assessments (LCAs) comparing carbon emissions attributable to a novel home-care program for premature infants to those attributable to routine care in the Neonatal Intensive Care Unit (NICU). We used LCA methodology to compare the carbon footprint of two weeks of traditional care of infants in our NICU to that of those enrolled in an institutional alternative care program known as “Hope Grows at Home,” which transitions eligible infants requiring nasogastric feeds to the home setting with ongoing NICU team support. Our analysis showed that in-home care produces 77 kg of CO2 emissions (kgCO2e) per infant over a 14-day period, as compared to in-hospital care, which produced 338 kgCO2e. Transportation to a healthcare facility accounted for the majority of emissions in both groups (292 kgCO2e for NICU care and 58 kgCO2e for home care). This finding is likely impacted by our facility’s rural location. Home care reduced solid waste emissions by approximately 94% relative to NICU care (1.74 vs. 26.97 kgCO2e per term), reflecting the home setting’s reuse of feeding syringes and bottles that are routinely single-use in the hospital. Prospective data collection strategies for infants enrolled in home care will further refine our results. Exploring additional interdisciplinary collaborations may facilitate similar analyses, offering more insight into environmental stewardship opportunities within healthcare.

Causal direct drivers of greenhouse gas emissions in 40,722 wastewater treatment plants in China

The intraoperative carbon footprint of burn surgery: A prospective case-level analysis.

Strategies for decarbonizing printed circuit board supply chain.

From conflict to climate crisis: How wars shape the future environment.

The contribution of patient travel for phlebotomy to the carbon footprint of clinical laboratories.

The building sector is a major contributor to global energy consumption and greenhouse gas emissions, positioning zero-energy home (ZEH) strategies as a critical pathway toward sustainable development. While the technical feasibility of zero-energy buildings has been widely studied, their successful implementation depends largely on residents' awareness, acceptance, and willingness to adopt such strategies. However, empirical evidence examining residents' perceptions of ZEH in the Middle East, particularly in Saudi Arabia, remains limited. This study investigates Saudi residents' perceptions of zero-energy homes, their awareness of passive and active ZEH strategies, and the key barriers hindering their adoption. A structured three-part survey was distributed across Saudi Arabia, yielding 411 valid responses. Descriptive statistical analysis was used to examine awareness levels, behavioral intentions, and current practices, while thematic analysis was applied to identify perceived obstacles. The findings reveal a substantial knowledge gap, with 45% of respondents unfamiliar with the concept of zero-energy homes. Despite this limited awareness, most participants recognized the environmental benefits of ZEH and expressed a willingness to invest in such homes. Passive strategies, including insulation and natural ventilation, were more widely recognized and adopted than active strategies such as photovoltaic systems and advanced HVAC technologies. Four primary barriers to adoption were identified: high initial costs, lack of technical expertise, insufficient public awareness, and inadequate infrastructure for retrofitting existing homes. This study contributes user-centric empirical evidence to the body of knowledge on zero-energy homes in a hot-climate, oil-dependent context and highlights the need for targeted policy interventions, public awareness programs, and industry capacity building.

The environmental impact of diagnosis and therapy in obstructive sleep Apnea: A systematic review.

The cement industry is a major contributor to global energy consumption and greenhouse gas emissions, motivating the development of sustainable cementitious materials through partial cement substitution. This study investigates the combined mechanical, durability-related, and environmental performance of mortars incorporating a 20% replacement of Portland cement by volume with different natural and waste-derived mineral additions, including natural pozzolan, brick waste, glass powder, recycled concrete powder, and calcined clay as pozzolanic or potentially reactive supplementary materials, while silica sand was used as an inert mineral filler. Mechanical performance was evaluated through compressive strength, while durability-related behavior was assessed using water absorption by immersion at 28 days. In parallel, a Life Cycle Assessment (LCA) was conducted to quantify the environmental impacts associated with climate change, acidification, eutrophication, photochemical oxidant formation, material resource depletion, and non-renewable energy consumption. The results show that mortars incorporating natural pozzolan and brick waste achieved compressive strengths comparable to the reference mortar, while maintaining low water absorption values, indicating effective microstructural densification. Glass powder also provided acceptable mechanical and durability-related performances, whereas silica sand, recycled concrete powder, and calcined clay exhibited reduced strength and increased absorption due to dilution effects, inherited porosity, or delayed pozzolanic activity. From an environmental perspective, all cement-substituted mortars demonstrated significant reductions across all assessed LCA impact categories, with decreases typically ranging from 15% to 20% relative to the reference mix. The most pronounced environmental benefits were observed for mortars incorporating waste-derived materials, particularly brick waste. Overall, the combined mechanical and environmental assessment demonstrates that a 20% cement substitution using supplementary materials can substantially reduce the environmental footprint of mortars without compromising essential engineering properties.

Using Spirulina (Limnospira platensis) as an alternative feedstuff for poultry: Effects on ammonia and greenhouse gas emissions from excreta during storage.

Experimental evaluation of a R290 vapour compression refrigeration system hybridised with a thermoelectric subcooler