Towards farm-level net-zero greenhouse gas emissions: Contributions of climate mitigation actions - A study of four European crop and dairy farms.

This study presents a methodological refinement of calculating greenhouse gas emissions from agricultural land management in the Czech Republic according to IPCC requirements. The authors utilize the SoilClim model providing input data in a 500 × 500 m grid for finer division of the Czech territory into climate zones according to IPCC. The research analyses the application of nitrogen fertilizers at the district level for the period 2015–2023 instead of national data. Results show significant dynamics of climate zones, where the extent of “dry” areas fluctuated in individual years from 40% to more than 80%. Implementation of the refined model led to a reduction in reported nitrous oxide emissions by an average of more than 9% in the reported period 1990–2023, representing savings of 20 Mt CO2 equivalent. The model-based use of district-level data on fertilizer application led to an additional reduction of 1.2 Mt CO₂ equivalent for the years 2015–2023. The study demonstrates that methodological refinement and the use of region-specific activity data can significantly influence the national greenhouse gas inventory. It highlights the need for continuous updates of calculation methods and activity data to improve the quality of national reporting and provides essential insights for a more effective emission reduction strategy in the agricultural sector of the Czech Republic.

Lakes, ponds, and reservoirs (hereafter: "lakes") are important sources of the greenhouse gases carbon dioxide (CO2) and methane (CH4). Emissions of CO2 and CH4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO2 and CH4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.

Data scarcity makes subnational input-output accounts inaccurate. Builders of such accounts must resort to allocating output, value added, imports, and exports to regions using readily available industry-wise data, like shares of national jobs by industry. Meanwhile, population shares are typically used to allocate other final demand components. Lately, however, some statistical agencies have been releasing more subnational data. Surprisingly, builders of subnational input-output accounts do not appear to use them. This is probably due to uncertain trade-offs between the costs and benefits of deploying such data. We, therefore, explore the degree to which using some value-added and household consumption data can improve subnational multiregional input-output accounts. We find that integrating either household-consumption or some value-added data improves account accuracy little. Using both datasets in combination, however, does improve estimated accounts somewhat. Plus, together they portray rather accurate estimates of interregional income multipliers and consumption-based greenhouse gas emissions.

Understanding greenhouse gas (GHG) emissions from natural gas systems is essential for transitioning to a low-carbon economy. This work estimates well-through-transmission GHG emissions of the US natural gas from one million wells covering 91% production in 2023. A high-resolution US oil and gas production area map is developed to harmonize spatial and tabular data from the oil and natural gas (O&NG) supply chain. We systematically integrate latest aerial campaign measurement into natural gas life cycle GHG emission estimates, capturing methane fugitives with better characterization of superemitter events. More than ten public and commercial data sets are integrated with an engineering-based unit process life cycle assessment (LCA) model. The estimated total GHG emissions from the US gas sector are 719 MMT CO2eq, more than twice the estimates of the US Environmental Protection Agency. The average well-through-transmission carbon intensity (CI) for US natural gas is 15.99 [15.14, 16.90] gCO2eq/MJ, with an upstream (exploration through processing) CI of 12.27 [11.84, 12.68] gCO2eq/MJ and a midstream (transmission) CI of 3.72 [3.30, 4.22] gCO2eq/MJ (bracketed values indicate uncertainty ranges). Methane fugitive and venting account for 61% and 21% of the upstream CI, an order of magnitude higher than flaring contributions (2.1%). Reducing methane fugitive and venting loss rates by 75% would reduce the upstream CI by half.

Abstract. MAMAP2D-Light is an airborne passive remote sensing imaging push-broom spectrometer developed at the Institute for Environmental Physics at the University of Bremen to determine atmospheric methane (CH4) and carbon dioxide (CO2) column anomalies in the 1.6 µm-band to quantify point-source emissions. In its initial version, as flown in 2022 in Canada, a significant stray light level of 5.6 % of the measured signal has been observed post-campaign, causing apparent error patterns in the retrieved CO2 and CH4 column anomalies. Measurement data collected during an airborne campaign in 2022 in Canada offer the unique opportunity to investigate the end-to-end impact of stray light and its correction on the retrieved CO2 and CH4 column anomalies, as well as the derived emission rates. We successfully developed and applied a stray light correction to the instrument and investigated its impact on the CH4/CO2 proxy method, the CH4 column, and derived point-source emissions. In nearly all cases, applying the CH4/CO2 proxy method reduced the stray-light-related column errors below the CH4 column noise. The derived emission rates for the proxy-retrieval with and without stray light corrected spectra are comparable, proving the ability of the CH4/CO2 proxy method to correct stray-light-related artifacts. In this paper, we additionally investigate the impact on the CH4 total column retrieval for a high contrast scene condition under which the correction by applying the proxy method is no longer sufficient. Following the initial campaign in 2022, the post-campaign stray light characterization and analysis revealed that a significant fraction of stray light was attributed to reflective surfaces in the object plane of the spectrometer. Based on these findings, the total stray light was reduced by ∼ 63 % by implementing a hardware modification from 2023 onward.

The accurate and transparent estimation of greenhouse gas emissions is essential for corporate sustainability reporting and machine learning applications. Existing emission-factor datasets have restrictive licenses, insufficient spatiotemporal granularity, or outdated information, limiting their reproducibility and utility across disciplines. We present ExioML, an open-source dataset derived from Exiobase 3.8.2. It integrates environmentally extended multi-regional input-output tables with a graphics processing unit (GPU)-accelerated computational toolkit, facilitating compatibility with and extensibility to other datasets. ExioML encompasses sector-level emission factor data for 49 regions and 28 years from 1995 to 2022, structured into two aggregation schemes: a product-by-product format covering 200 categories, and an industry-by-industry format covering 163 categories. To validate dataset usability and establish a reproducible baseline, we define a regression task for predicting sectoral greenhouse gas emissions. The task is evaluated using tree-based and neural-network-based models, with mean squared error as the evaluation metric. ExioML provides openly accessible emission-factor tables and a reproducible baseline intended to support reuse and benchmarking across sustainability and machine-learning studies.

Abstract Warming bottom waters on the Arctic shelf are thawing subsea permafrost, unlocking large amounts of old organic carbon. This thaw is expected to accelerate with continued sea‐ice loss and ocean warming. However, the rate at which thawed subsea permafrost organic matter (OM) is degraded into CO 2 and CH 4 remains uncertain. Here, we use data from 156 subsea and terrestrial permafrost incubation experiments, combined with a reactive continuum model, to estimate permafrost OM reactivity (i.e. parameters a , ν ) and quantify OM degradation rates after thaw. Our results show that the reactivity of subsea permafrost OM is similar to terrestrial permafrost OM degraded under anoxic conditions, underscoring that the terrestrial data set provides a strong empirical basis for constraining subsea permafrost OM reactivity. (Subsea) permafrost OM is, on average, less reactive ( a mean = 7.39 × 10 −4 yr; ν mean = 1.85 × 10 −3 ) than terrestrial or marine OM but retains a small, highly reactive fraction driving high initial degradation rates. These initially high rates decline rapidly over years to decades and most OM degrades slowly under anoxic conditions. Using a 1,000‐member ensemble of thaw and degradation scenarios, we estimate cumulative subsea permafrost OC loss of up to 96 Pg C (18–126 Pg C) over 300 years, with mean annual degradation rates of ∼350 Tg C yr −1 (60–450 Tg C yr −1 ) under moderate thawing. If fully converted by methanogens, CH 4 production could exceed current global ocean CH 4 emissions by tenfold. This study provides the first quantitative framework for informing subsea permafrost degradation models over long timescales and can help improve estimates of greenhouse gas emissions and their uncertainties under future warming scenarios.

Colonoscopy significantly impacts healthcare's carbon footprint, and although cold snare polypectomy (CSP) offers a safer, more efficient method for small polyp removal, its environmental impact remains unclear. This study compares carbon footprints between the forceps plus hot snare polypectomy (HSP) versus the universal CSP strategy for subcentimetric polyps. This retrospective analysis compared two distinct polypectomy strategies: forceps plus HSP, involving biopsy removal for diminutive adenomas and HSP for small adenomas, and universal CSP for adenomas smaller than 10 mm. A life cycle assessment evaluated the environmental impacts of endoscopy procedures, with parameters obtained from our previous pragmatic trial and empirical hospital data in 2022. Sensitivity analyses were conducted to assess the robustness of greenhouse gas (GHG) emissions estimates. The universal CSP strategy generated 22.08 kg of carbon dioxide equivalents (CO2e) per colonoscopy, a 5.30% (95% CI 4.71%-5.89%) reduction compared with 23.32 kg CO2e for the forceps plus HSP strategy. Based on 15,177 colonoscopies performed in 2022, including 5599 polypectomies, transitioning to universal CSP would reduce an institution's annual GHG emissions by an estimated 6915 kg CO2e. Adopting a universal CSP strategy for subcentimetric polyps offers a significant environmental benefit alongside established clinical advantages. This single transition could cut procedural emissions by over 5% and substantially reduce the annual carbon footprint of endoscopy units, equivalent to the emissions from over 19,700 miles of passenger car travel. Our findings establish CSP as a key strategy for promoting sustainable healthcare.

Introduction The agricultural sector faces increasing pressure to contribute to the Sustainable Development Goals (SDGs) while simultaneously acting as a driver of global climate change. In response, agroecology amongst other approaches is gaining recognition as a holistic framework to transform food systems towards greater sustainability and resilience. Methods To facilitate standardized assessment of agroecological performance, the Tool for Agroecology Performance Evaluation (TAPE) was developed by the UN Food and Agriculture Organization (FAO). While a greenhouse gas (GHG) emission score was originally envisioned within the TAPE framework, to date, no standardized implementation exists. Results This study presents a first step toward operationalizing such a score by estimating livestock GHG emissions, specifically from enteric fermentation and manure management, using TAPE data collected from smallholder farms in Kenya's Nandi and Bomet counties. Emission factors (EFs) derived from local studies were applied to improve accuracy of the GHG emission score, achieving a level of specificity comparable to Intergovernmental Panel on Climate Change (IPCC) Tier II methods. TAPE results showed correlations between total livestock emissions as well as milk-based emission intensity (EI) of cattle and the agroecological indicators: Efficiency, Recycling, and Resilience. Discussion The correlations varied in direction and magnitude, with the strongest relation found for Recycling and EI. Our findings demonstrate (1) the feasibility of integrating a GHG emission estimation into TAPE, and (2) highlight its potential as a tool for identifying synergies and tradeoffs between agroecological performance, emissions and emission intensities.

This cross-sectional study evaluated the environmental impact of athletes' dietary patterns by estimating greenhouse gas emissions (GHGE) and water footprint (WF). It also assessed knowledge, attitudes, and behaviors related to sustainable nutrition using a structured questionnaire aligned with the FAO definition of sustainable diets. Conducted between January and August 2023 in Ankara, the study included 100 elite athletes (mean age: 21.0 ± 3.3years; 65% female) from various sports disciplines. Dietary intake was assessed using a semi-quantitative food frequency questionnaire, and GHGE and WF values were calculated based on life cycle assessment data. The mean GHGE and WF values of athletes' diets were 3017.1 ± 1877.4g CO₂-eq/day and 5.4 ± 3.2mL/g, respectively. Male athletes and strength/power athletes exhibited significantly higher dietary environmental impacts than female and team sports athletes (p < 0.001 and p < 0.05, respectively). Red meat and animal protein consumption were strongly associated with higher GHGE and WF values (p < 0.001). Despite the increasing importance of sustainability, more than 90% of participants lacked accurate knowledge of sustainable nutrition. Moreover, a discrepancy was observed between athletes' stated willingness to support environmental values and their actual food choices, indicating an intention-behavior gap. This study contributes to the limited literature by quantifying the environmental impact of athletes' diets while concurrently examining sustainability-related behavioral factors. The findings underscore the importance of integrating sustainability principles into sports nutrition planning and education to reduce environmental impacts while maintaining performance goals.

U.S. hospitals generate considerable food waste, contributing to environmental degradation strategies. This study evaluated the feasibility, impact, and perception of a novel composting program implemented at Rhode Island Hospital over six months beginning in December 2024. Compostable waste bins were installed in the cafeteria with educational signage. Surveys assessing composting knowledge, attitudes, and roles in waste management were distributed to staff, patients, and administrators. Collected food waste was transported to Bootstrap Compost, which provided daily weight data used to estimate greenhouse gas emissions reductions, compare composting with landfill disposal costs, and project annual outcomes. Over the study period, 490.6 kg of food waste were diverted from landfills, corresponding to a reduction of 0.35 metric tons of CO2-equivalent emissions. While composting was more expensive than landfill disposal ($6.45/kg vs. $0.24/kg), cost neutrality could be achieved with diversion rates at or above 116 kg per day. Surveys revealed strong support for composting but limited awareness of its relevance to healthcare's environmental footprint. Respondents suggested improvements in education, signage, and infrastructure. This program demonstrated how hospital-based composting initiatives align with healthcare institutions' environmental stewardship goals while highlighting financial and logistical challenges relevant for pilot-scale efforts.

Abstract The oil and gas industry produces diverse hazardous and toxic wastes (HTW) classified into dominant wastes, including chemicals residues, while non‐dominant wastes, such as contaminated materials, which pose greenhouse gas (GHG) emissions risks. Therefore, this study provides a detailed estimation of its total GHG emissions on the period from 2020 to 2024 and its transportation in the oil and gas industry. The Tier 1 approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines was applied for estimating, covering three major GHG: carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O). Results indicate a substantial 83.99% reduction in total emissions from hazardous waste from 705,093 tCO 2 eq in 2020 to 112,888 tCO 2 eq in 2024 reflecting improved waste handling practices. Emissions from dominant waste types declined by 42.46%, while non‐dominant waste types showed a 99.67% reduction. Its transportation‐related emissions of marine transport were 14,292 tCO 2 eq, while land transport of 0.001742069 tCO 2 eq from CH 4 and N 2 O conversion value which due to avoid double counting, CO 2 emissions were treated as part of the energy sector. The study contributes to policy formulation by highlighting the importance of incorporating both treatment and transportation emissions in waste management strategies to achieve low‐carbon and sustainable development goals.

This study estimates and projects livestock-related greenhouse gas (GHG) emissions in Nepal between 2002 and 2022, providing crucial insights for climate change mitigation strategies. The latest secondary data on livestock population was collected from the Ministry of Agriculture and Livestock Development (MoALD) Nepal. The IPCC tier 1 methods were used to estimate emissions and forecast future trends. Studies reveal that Nepal livestock-related GHG emissions reached 28,603Gg CO2 e /year in 2022,with buffalo accounting for 39 % of the total emissions followed by cattle and goats. In 2022, the primary sources of emissions were direct nitrous oxide (48.2%), enteric methane (44.7%), manure methane (4.5%), and indirect nitrous oxide (0.6%). Future projections indicate a potential increase in total GHG emissions by 3.06 % and 3.56% up to 2050, suggesting a growing environmental impact if current practices continue. The provincial (regional) analysis identified Koshi province as the highest emitter in 2022. This research underscores the need for effective management strategies to mitigate emissions from the livestock sector in Nepal. Further, it recommends transitioning to the IPCC Tier 2 approach when sufficient national-level data becomes available to enhance the accuracy of future inventories

The climate and health impact of U.S. radiation therapy: estimating greenhouse gas emissions, DALYs, and potential of hypofractionation.

Abstract Peatlands play a critical role in the global carbon cycle, acting as long-term carbon sinks but becoming significant greenhouse gas sources when drained or degraded. Accurate estimation of carbon dioxide and methane emissions from peatlands is essential for climate change mitigation and land management. Traditional field-based measurement techniques, such as eddy covariance and chamber systems, are spatially limited and resource intensive. Remote sensing provides a scalable and cost-effective alternative for monitoring surface and vegetation characteristics linked to carbon dynamics. This study presents a comprehensive review of remote-sensing-based methods for estimating greenhouse gas emissions from peatlands and evaluates the data readiness in Latvia for national-scale implementation. Two primary approaches are identified: land-cover-based estimation using emission factors and regression-based modelling of carbon fluxes derived from vegetation and temperature indices. Latvia possesses extensive open-access datasets, including Sentinel-2 satellite imagery, Light Detection and Ranging data, and national habitat inventories, which enable reproducible and transparent monitoring. The proposed conceptual framework integrates these resources into a unified, automated workflow for Greenhouse Gas emission estimation, establishing a foundation for operational peatland greenhouse gas monitoring and reporting.

Optimizing bioenergy biofuel harvest: a comparative analysis of stepwise and integrated methods for economic and environmental sustainability.

Sanitation service chains (SSCs) often consist of a complex mix of different components, frequently involving the coexistence of non-sewered sanitation (e.g., septic tanks) and sewered sanitation. Poorly-maintained components within these chains can lead to substantial, yet potentially avoidable greenhouse gas (GHG) emissions. In this study, we developed a model for estimating the impact of GHG mitigation measures along SSCs that feature overlapping and poorly maintained non-sewered and sewered sanitation, taking the interdependencies of the GHG emissions of these components into account. To this end, we employed mass balance, empirical emission equations, and a carbon footprint estimation model to estimate GHG emissions by component at baseline and under four mitigation scenarios using an example SSC in Hanoi. The results showed that the SSC is predominantly methane-emitting, with poorly-maintained septic tanks and sewers being the primary contributors to the GHG emissions. Annual septic tank emptying was also identified as an effective strategy for reducing GHG emissions and it accounted for a 31-38 % decline in total emissions relative to baseline emission level. Scenario comparison further showed that removing septic tanks and upgrading sewers, even though associated with a slight increase in N2O emissions from the wastewater treatment plant, offer the greatest long-term mitigation potential, yielding 15-24 % lower emissions than annual emptying septic tanks with sewer upgrades. Additionally, if septic tanks are not removed, they will remain the primary source of GHG emissions even after upgraded sewer and centralized treatment is established. However, in cases where septic tank removal poses social challenges, frequent emptying remained a robust and immediately applicable mitigation option. Overall, this study provides a framework for identifying and quantifying major GHG emission reduction strategies for complex SSCs. Additionally, the results obtained indicated that managing septic tanks and sewers are important climate action strategies for ensuring sustainable city-wide inclusive sanitation.

Abstract Background . Estimating environmental impacts has become an integral part of dietary assessments. These estimations rely on available life cycle assessment (LCA) data and databases of LCA information, which should be as recent and context specific as possible. Objectives . We aimed to update the existing New Zealand (NZ) dietary LCA database with recently available NZ data and provide documentation to streamline regular future updates. Following the update, we also aimed to investigate the impact of the updated database on emission estimates for dietary intakes in the population. Methods . A search was developed and conducted on November 18th, 2024. Recently published or identified LCA data for food items produced in NZ were integrated into the database. A comparison of the estimates of total greenhouse gas emissions due to dietary intake between the original database and updated database was conducted on existing NZ dietary intake data. Results and discussion . Updates in nine food items resulted in small net emissions decreases in fourteen food categories of the New Zealand Adult Nutrition Survey 2008/09. The estimate of average daily emissions due to dietary intake per adult in NZ decreased by approximately 1%, from 4.99 kgCO 2 eq to 4.91 kgCO 2 eq. This resulted in a decreased estimate of 350 tCO 2 eq per day for the entire population. Small changes in LCA data can accumulate to substantial effects over the entire population; however, a 1% decrease in production emissions over 15 years shows that efficiencies in status quo patterns of food production alone are vastly inadequate to meet Paris obligations. More transformative changes will rapidly be needed.

Development of a dynamic bottom-up model for city-scale residential building stock to estimate greenhouse gas emissions