Estimate Greenhouse Gas Emissions Research Articles

Bridging the Gap: A comprehensive review and cross-check analysis for China's Non-CO2 greenhouse gas emission estimates.

Accounting for methane from natural gas infrastructure in United States greenhouse gas emission estimates

Estimations of GHG emissions from drained peatlands: Accountability in the trans-border Neman River basin.

Ecologically unequal exchange theory highlights the asymmetric transfer of environmental pressures and economic benefits between core and peripheral countries. This study provides empirical evidence of these dynamics in Argentina’s trade with Brazil, China, and the European Union from 2000 to 2016. Using an environmentally extended multiregional input-output model, it estimates greenhouse gas emissions, natural resources, and value added embodied in trade flows. The findings reveal significant disparities: Argentina, as an exporter, bears environmental costs while receiving limited economic compensation in trade with core countries, whereas the latter avoid emissions but capture greater economic benefits. These results underscore ecologically unequal exchange as an aspect of trade between core and peripheral countries. JEL Classification : D57, F14, F18, Q56

The carbon-intensive petrochemical sector plays a critical role in numerous industrial supply chains. However, existing databases lack comprehensive carbon footprint profiles for petroleum products, hindering reliable assessments of greenhouse gas (GHG) emissions associated with derived chemicals and various downstream products. This study developed a quantification method that converts the material flows of oil refineries into input-output coefficient matrices using actual plant data, which further refined process-specific GHG emission estimates and identified key sources of variability across the full spectrum of petroleum products. The results show that inconsistent allocation methods lead to 40%-80% of the variation in emission estimates, while data uncertainty has a moderate influence. Configuration heterogeneity also affects GHG assessments by altering the process mix and upstream processes─specifically, a lower proportion of straight-run oil and deeper processing of heavier fractions result in higher emissions. Additionally, the aggregation of oil-based intermediates as chemical feedstocks introduces an uncertainty range of -19% to +44% in assessing the GHG emissions of major chemicals. This underscores the importance of comprehensive GHG emission profiles and variability analysis of petroleum products in advancing reliable emission assessments and decarbonization strategies for the petrochemical industry.

Inhalers are the primary treatment modality for asthma and chronic obstructive pulmonary disease (COPD). Metered-dose inhalers contain hydrofluoroalkane propellants that contribute to substantial greenhouse gas emissions. The US federal government is facilitating a phasedown of hydrofluorocarbons over the next decade under international treaty obligations, yet current understanding of the scope and trajectory of inhaler-related emissions in the US remains incomplete. To quantify the magnitude, sources, and social costs of inhaler-related emissions in the US from 2014 to 2024. This serial cross-sectional analysis estimated emissions from all inhalers approved for asthma or COPD using aggregated dispensing data across the US outpatient pharmaceutical market linked to estimated greenhouse gas emissions by inhaler. All products were characterized based on their active pharmaceutical ingredients, device type, propellant type, therapeutic class, branded status, manufacturer, payer, and pharmacy benefit manager. Key end points included product utilization, greenhouse gas emissions (measured in carbon dioxide equivalents [CO2e] based on previously validated studies), and the social costs of emissions, which account for the net harms to society from greenhouse gases. A total of 1.6 billion inhalers were dispensed in the US from 2014 to 2024, generating an estimated 24.9 million metric tons of CO2e (mtCO2e). Annual emissions increased by 24% from 1.9 million mtCO2e in 2014 to 2.3 million mtCO2e in 2024. Metered-dose inhalers were responsible for 98% of all emissions during the study period, and emissions were heavily concentrated among short-acting β-agonist, inhaled corticosteroid-long-acting β-agonist, and inhaled corticosteroid classes. Albuterol, budesonide-formoterol, and fluticasone propionate inhalers accounted for 87% of total emissions. The estimated social costs of emissions were $5.7 billion (lower bound, $3.5 billion; upper bound, $10.0 billion). Inhaler-related emissions in the US have increased over the past decade. Policymakers and regulators seeking to reduce emissions should identify targeted solutions aimed at shifting utilization to currently marketed dry powder and soft mist inhalers while facilitating the entry of newer, affordable metered-dose products containing propellants with low global warming potential.

Global warming, driven by greenhouse gas (GHG) emissions, is accelerating globally and highlights the need for effective mitigation strategies. This study assesses the economic feasibility of rainbow trout aquaculture by incorporating GHG emissions into its analysis, thereby contributing to mitigation efforts in the fisheries sector. Focusing on two farming systems—recirculating aquaculture systems (RAS) and flow-through systems (FTS)—we estimated GHG emissions and conducted an economic evaluation using data collected through field surveys. The average GHG emission was 7.14 kg CO2 eq per kilogram of trout produced, with RAS showing lower emissions than FTS. Electricity and feed were identified as the primary emission sources. The economic analysis revealed an average net present value (NPV) of USD 987,609 and an internal rate of return (IRR) of 18%, with RAS outperforming FTS in profitability. A sensitivity analysis under carbon pricing showed that economic feasibility was maintained, but the NPV declined by about 24% under the carbon tax scenario. Overall, these findings underscore the importance of balancing profitability and emission reduction for sustainable aquaculture management.

Low-carbon competitiveness of cities in solid waste disposal systems: Spatial and temporal variations in greenhouse gas emissions in the Yangtze River Delta.

An Estimate of Greenhouse Gas Emissions from Light Duty Vehicles Resulting from Traffic Calming Stops in the U.S. Suburbs

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

BackgroundThe planetary health diet (PHD) has been proposed as a dietary index with potential co-benefits for human and planetary health. Evidence is limited on its association with type 2 diabetes (T2D) incidence and greenhouse gas (GHG) emissions. Our objective was to assess the associations of adherence to the PHD with incident T2D and GHG emissions.Methods and findingsWe analysed data from 23,722 participants (55% female), with a mean (standard deviation, SD) age of 59.1 (9.3) in the UK-based EPIC-Norfolk prospective cohort study. Dietary intake was assessed across three time points (1993–1997, 1998–2000 and 2004–2011) using a food frequency questionnaire. We assessed adherence to the PHD (theoretical score range 0–140 points) based on the consumption of 13 food groups and two nutrients. Cox proportional hazards regression models, which accounted for time-varying covariates, were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for T2D. Linear regression models were used to analyse the association between the PHD and estimated GHG emissions. During a mean follow-up period of 19.4 (SD 6.8) years, 3,496 cases of incident T2D were recorded over 461,086 person-years. Greater adherence to the PHD was associated with lower T2D incidence; comparing the highest PHD quintile (85.7–117.8 points) to the lowest (33.9–68.4 points), the HR (95% CI) was 0.68 (0.61, 0.76) in the most adjusted model including socio-demographic, behavioural factors, energy intake, adiposity, and prevalent cardiovascular disease or cancer. The estimated population attributable fraction (PAF) for incident T2D due to adherence below the 80th percentile (85.7 points) was 12.3% (95% CI: 9.2%, 15.3%). Those in the highest quintile of the PHD had approximately 18% lower GHG emissions compared to those in the lowest (β5th/1st −18.4% (95% CI: −19.3%, −17.5%)). The main limitation of this research is the possibility of residual confounding due to the observational design of this study.ConclusionsOur findings of a lower incidence of T2D and reduced GHG emissions among those with higher adherence to the PHD support the promotion of this diet for the population-level prevention of T2D and for planetary sustainability.

Impact of GHG mitigation measures in sanitation service chains: A focus on septic tanks and sewers.

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

Abstract Drained peatlands are a significant source of greenhouse gas (GHG) emissions; however, research has predominantly focused on terrestrial areas, with much less attention given to drainage ditches. Despite their small surface area, ditches can substantially affect the peatland GHG balance owing to conditions that favour GHG production. We conducted monthly field campaigns in five ditches across two agricultural polders in the Netherlands using floating chambers to measure daytime diffusive emissions of carbon dioxide (CO2), diffusive and ebullitive methane (CH4), and nitrous oxide (N2O), and bubble traps to measure CH4 ebullition. We compared ditch emissions to landscape-scale emissions obtained via eddy covariance (EC) and automated terrestrial chamber measurements during the same period, and explored key drivers and spatial variation in ditch GHG emissions. Mean ditch emission ranged from 8.9 to 22.8 g CO2-eq m−2 day−1 (based on a 100-year global warming potential). CH4 contributed 44–94% of total emissions, averaging 62%. Although ditches occupied only 8% and 19% of the landscape in polder Zegveld and Langeweide, respectively, they contributed disproportionally to the total landscape GHG emissions, accounting for 25% in Zegveld and 33% in Langeweide. We argue that this a conservative estimate as we lacked night-time CO2 emissions from the ditches, which tend to be higher. Our findings suggest that while manual chamber-based measurements and eddy covariance estimates of ditch emissions roughly align, significant discrepancies persist. A combined approach that uses both methods offers the most robust and representative estimates of GHG emissions from ditches.

Impacts of a minimum pricing policy on high fat, sugar and sodium (HFSS) foods: Environmental, dietary and welfare analysis.

Traffic flow data in Europe are collected locally by city authorities using different systems and standards, making it difficult to compare cities or evaluate large-scale maps, such as those used for emission inventories. To address this gap, we compiled and harmonized publicly available traffic data for 36 European cities, linking geolocalized information to road segments, spanning years from 2015 to 2024 depending on data availability. Annual Average Daily (or Weekday) Traffic is provided, and supplementary variables (e.g., truck flow percentages and speed metrics) are included where available. The data are georeferenced, with geometries corresponding to each measurement location. The dataset was enriched with additional attributes through map matching of traffic measurement locations to OpenStreetMap. Code and methodology for transforming raw data into a uniform structure are documented in Python Jupyter Notebooks, ensuring transparency and reproducibility. This dataset in a unified format facilitates cross-city comparisons and supports applications in environmental science, including the estimation of greenhouse gas and pollutant emissions, as well as urban planning and road transport management.

Uruguay's livestock methane is a major source of greenhouse gas (GHG) emissions, despite the country’s low overall emissions. A case study was conducted on Forestal Caja Bancaria (FCB), a commercial farm in Uruguay's central region, with an integrated production system covering 5,802 hectares. The study aimed to estimate GHG emissions from livestock, crops and forestry, CO2 captured by eucalypt and pine plantations, and soil organic carbon for all land uses. Results showed that cattle enteric fermentation accounted for 54 kg.ha-1.yr-1 of methane (CH4), and total emissions accounted for 1,746 kg.ha-1.yr-1 of CO2 equivalent. E. grandis, E. dunnii, and Pinus spp. captured 31, 38, and 17 Mg.ha-1.yr-1 CO2 equivalent during their pre-harvest growth cycles. According to GWP100 AR6, this capture rates could offset emissions from 17.6, 21.6 and 9.9 hectares of livestock production, respectively. Using a real system approach considering staggered sowing, harvesting, and subsequent resprouting or replanting, the estimated potential offsets are adjusted to 9.6, 11.8, and 5.1 hectares, respectively, until the first harvest. After this point, there is not further net biomass accumulation, and mitigation relies on the depletion of the remaining carbon stock in the forest area, which exceeds the carbon needed to offset livestock emissions from the previous phase. GTP100 AR6 and GWP* metrics indicated significantly lower CO2-eq emission values. This study aims to provide technical coefficients to quantify how forest plantations can offset livestock emissions, contributing to the goal of "carbon-neutral" meat. This information will help producers assess forest systems' mitigation potential and explore livestock-forestry combinations.

Abstract This study presents estimates of future years (2024–2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and mining of that fuel. Results from the coal production estimate indicate a decline in production from Federal leases; with known production of 240 million short tons (mtn) in 2023 and a projected decline to 34.0 mtn by 2051, which represents a reduction to 14.2% of the 2023 value. In parallel with this projection, total GHG emissions are estimated to decrease from 402.2 million metric tons of carbon dioxide equivalent (MMT CO2 eq.) in 2024 to 55.0 MMT CO2 eq. in 2051, a decline to 13.7% of 2024 emissions estimates. The reductions in coal production and emissions are mainly the result of planned coal combustion power plant closures, with major projected closures in 2037 and 2048. However, GHG emissions estimates for future years can be uncertain as they rely heavily on coal production estimates from operators' public business plans and other publicly available resources. Forward looking plans of this type are subject to significant changes if economic and political factors deviate from current information. Results suggest that average GHG emissions over the time series breakout to 95% end point combustion, 3.7% transportation combustion emissions, and 1.3% fugitive emissions, although there is uncertainty associated with these figures. Uncertainty stemming from production projections, sector distributions, and emissions factors on the future emissions estimates increases with time, ranging from −28% to +48% within the 2024–2051 timeframe.

Abstract Soil respiration (Rs) is defined as the emission of carbon dioxide from soil into the atmosphere, which represents a critical carbon flux within terrestrial ecosystems. Precipitation change significantly influences Rs, generating feedback mechanisms pertinent to global climate change. Nevertheless, the global distribution and environmental determinants of precipitation's effects on Rs remain uncertain. We compiled a database encompassing 570 Rs observations from field experiments that manipulated precipitation, derived from 221 published studies. Utilizing this comprehensive data set, we conducted a meta‐analysis to elucidate Rs responses to precipitation alterations. Subsequently, we employed a machine learning approach to provide a globally spatially explicit quantification of precipitation change effects on Rs under future climate scenarios. Our findings revealed that increased experimental precipitation markedly enhances Rs, while decreased precipitation inhibits it. Furthermore, Rs responses to precipitation change exhibited variability across ecosystems and climatic regions. This study also confirmed that the Rs responses vary based on the intensity and duration of precipitation change, with short‐term or heavy precipitation fluctuations exerting the strongest effects. Environmental conditions influenced the reaction of Rs to precipitation change, as factors such as soil type, vegetation, and climate worked together to mediate spatial differences. Projections based on bioclimatic predictors suggest that future climate scenarios significantly amplify Rs responses to precipitation change, potentially increasing uncertainties in greenhouse gas emissions estimates. Overall, our analysis emphasizes the significance of context dependencies and offers a spatially explicit assessment of precipitation change impacts on Rs on a global level, providing a comprehensive reference for comprehending ecosystem carbon dynamics.

ABSTRACTThis study develops a CO2 emission function to estimate greenhouse gas emissions from fuel consumption in mine haulage operations, particularly focusing on diesel trucks utilized in small‐scale mining (SSM) haulage with a limited technological level. The methodology, validated in a Brazilian gold mine, integrates real‐time monitoring with accurate emissions estimation, facilitating achievable reduction targets and promoting sustainable mining practices. The analysis indicated an estimation of 510 t CO2e based on a year of operational data, in contrast to the 523 t CO2e formally reported by the company, achieving an accuracy of 97% per the mine's Annual Sustainability Report. Through multivariate analysis, key variables impacting CO2 emissions were identified, and predictive modeling techniques, including partial least squares regression (PLSR) and random forest (RF), were utilized to assess the accuracy and robustness of the function. The PLSR model demonstrated appropriate performance under low‐ and high‐variability conditions, achieving a root mean square error (RMSE) of 0.02 and 0.07, a mean absolute percentage error (MAPE) of 0.24% and 0.47%, and calibration accuracy of 94.5% and 92.2%, respectively, in emissions prediction. The sensitivity analysis conducted on both models revealed that operator efficiency, fleet management, and material management are critical factors in achieving emission reductions of 15%–20%, 25%, and 30%, respectively. This method highlights its capacity for broad implementation, enabling SSM operations to play a crucial role in global efforts to combat climate change.