Estimate Greenhouse Gas Emissions Research Articles

Exploring the carbon sequestration of an asphalt base course mixture containing novel cold-bonded biochar-rich lightweight aggregates

An emerging strategy to compensate for the greenhouse gas emissions of products is to incorporate carbonaceous materials obtained from removed atmospheric carbon dioxide, mainly obtained through biomass conversion. This approach can turn asphalt pavements into a functional carbon sink. In particular, biochar has been used as a bitumen modifier. However, due to performance limitations, carbonaceous materials were only added in small quantities to asphalt mixtures. An alternative approach is to produce lightweight aggregates to substitute a part of the mineral aggregates of the asphalt mixture. To this end, biochar is pelletised with a hydraulic binder and water in a cold-bonding process, forming spherical pellets labelled as carbon-rich lightweight aggregates (C-LWA). Like other lightweight aggregates, C-LWA showed a reduced mechanical strength compared to conventional mineral aggregates, adversely affecting the asphalt mixture performance. Cracking and rutting resistance almost linearly decreased with C-LWA content. The direct addition of biochar had a similar adverse influence on the mixture performance. Despite a reduced performance, adding biochar and C-LWA reduces the greenhouse gas emissions of asphalt mixtures. Net-zero emissions were estimated for the produced asphalt mixture by adding 5.5 ± 0.4% C-LWA or 3.0 ± 0.2% biochar obtained from the pyrolysis of landscape management wood. A wider range of C-LWA addition (1% to 35.1%) was estimated considering the greenhouse gas emission estimation variability of both asphalt and biochar production.

Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N2O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N2O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R2 value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability.

Aboveground carbon stocks for different forest types in eastern Amazonia

Abstract Protected areas worldwide have been recognized as crucial entities in preserving and conserving forest ecosystem services. These areas play an important role in mitigating climate change due to their carbon stocks. In this study, we characterized the aboveground carbon stocks (AGC) across different forest types for the Protected Area Carajás National Forest in the Eastern Amazon, based on data from 387 forest inventory plots and two distinct AGC maps from remote sensing and LiDAR (Light Detection and Ranging) data fusion—GEDI and EBA. The estimates of AGC from field data showed higher amounts of AGC in the open forest formations (187.20 ± 46.83 MgC ha−1), followed by dense (105.90 ± 42.56 ha−1 MgC ha−1) and transitional forests (48.86 ± 31.72 MgC ha−1) with lower amounts under seasonal forests (38.65 ± 9.48 MgC ha−1) and secondary forest areas (>23 MgCha−1). However, the dense, seasonal, and transitional forests exhibited higher carbon density than the open and secondary areas in both AGC maps analyzed. We emphasize the significant importance of monitoring open and seasonal forest areas, due to the gap in field data for these forest types in Amazonia. Here, we underscore the importance of more standardized forest field data and better plot distribution in non-disturbed areas to reduce the uncertainty of AGC estimates and improve the estimation of greenhouse gas emissions from deforestation and forest fires.

A review-based estimation of GHG emissions of China's wastewater management system.

A review-based estimation of GHG emissions of China's wastewater management system.

Emission Factor Recommendation for Life Cycle Assessments with Generative AI.

Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product's entire lifecycle, from raw material extraction to end-of-life. Measuring the emissions outside a product owner's control is challenging, and practitioners rely on emission factors (EFs)─estimations of GHG emissions per unit of activity─to model and estimate indirect impacts. However, the current practice of manually selecting appropriate EFs from databases is time-consuming and error-prone and requires expertise. We present an AI-assisted method leveraging natural language processing and machine learning to automatically recommend EFs with human-interpretable justifications. Our algorithm can assist experts by providing a ranked list of EFs or operating in a fully automated manner, where the top recommendation is selected as final. Benchmarks across multiple real-world data sets show our method recommends the correct EF with an average precision of 86.9% in the fully automated case and shows the correct EF in the top 10 recommendations with an average precision of 93.1%. By streamlining EF selection, our approach enables scalable and accurate quantification of GHG emissions, supporting organizations' sustainability initiatives and progress toward net-zero emissions targets across industries.

Which carbon footprint for my ICU? Benchmark, hot spots and perspectives

BackgroundThe purpose of this study was to identify the main greenhouse gas (GHG) emitting activities or products among the medical devices (MD) and medicines used in a polyvalent Intensive Care Unit (ICU).MethodsA pragmatic eco-audit was conducted in a 21-beds polyvalent ICU, in Saint-Brieuc, Bretagne, France. It consisted of estimating GHG emissions of products or activities, considering process-based life cycle analysis (LCA), economic input–output analysis (EIO) and hybrid-LCA. Results were expressed as Carbon Dioxide Equivalent (CO2e) emissions per patient-day considering each medication and MD (including personal protective equipment).ResultsWith remaining uncertainty, GHG emissions were estimated at 61.1 kgCO2e per patient-day. Two hundred and two individual MD were used per patient-day, equivalent to 5.1 kgCO2e per patient-day (process-based LCA). Gloves accounted for the main part of kgCO2e emissions (representing 1.8 kgCO2e per patient-day). Then, syringes (1.1 kgCO2e per patient-day), perfusion tubings (1.0 per patient-day) and gauze pads (0.4 kgCO2e per patient-day) were the most important sources of MD related GHG emissions. Forty-seven individual medicines were used per patient-day. Most consumed medications were sterile water for injection, propofol, and sodium chlorure. The GHG emissions of medications were estimated with EIO-LCA at 21.5 kgCO2e per patient-day, mostly due to injectable medicines (15.3 kgCO2e per patient-day).ConclusionUpcoming studies focusing on actions on these particular hot spots would be of interest in order to significantly decrease GHG emissions but also to increase resilience of critical care.

The Characteristics and Estimation of Greenhouse Gas Emissions from Urban Sewer Systems in Southern China

Carbon emission fluxes in urban sewer systems and the microbial community structure in sewer sediments remain unclear. In this study, a sewer system located in southern China was utilized to investigate the water quality characteristics. The results showed that the chemical oxygen demand loss rates in the branch pipe and sub-main pipe were 27.1% and 14.1%, respectively. The estimated carbon emission flux was estimated by the carbon emission factor method. The results revealed that the total carbon emission flux from the sewer system was 1.39 kg CO2-eq/m3 and the emission fluxes of methane and carbon dioxide were 0.87 kg CO2-eq/m3 and 0.51 kg CO2-eq/m3, accounting for 62% and 36.4%. The microbial community structure was analyzed by 16S rRNA. The results indicated that the methanogenic archaea in the sediments of the branch pipe and sub-main pipe were Methanobacterium, Methanosaeta, and Methanobrevibacter. The methanogenic activity of the sewer sediments was further assessed. This study further confirmed that the branch pipe and sub-main pipe were the main sources of carbon emissions and methane and carbon dioxide are the main greenhouse gases in the sewer system. This study furnishes novel insights for the control of carbon emissions in municipal sewage systems.

Enhancing MRT purple line offset project through forecasting ridership using the direct demand ridership model

The Mass Rapid Transit (MRT) Purple Line project is part of the Thai government’s energy- and transportation-related greenhouse gas reduction plan. The number of passengers estimated during the feasibility study period was used to calculate the greenhouse gas reduction effect of project implementation. Most of the estimated numbers exceed the actual number of passengers, resulting in errors in estimating greenhouse gas emissions. This study employed a direct demand ridership model (DDRM) to accurately predict MRT Purple Line ridership. The variables affecting the number of passengers were the population in the vicinity of stations, offices, and shopping malls, the number of bus lines that serve the area, and the length of the road. The DDRM accurately predicted the number of passengers within 10% of the observed change and, therefore, the project can help reduce greenhouse gas emissions by 1289 tCO2 in 2023 and 2059 tCO2 in 2030.

Improved methodology for the calculated monitoring of greenhouse gas emissions from the activities of road and off-road transport in the Russian Federation

A methodology is proposed to obtain consistent and coherent estimates of greenhouse gas and precursor gas emissions from road transport and off-road vehicles and to quantify their accuracy and uncertainty. Accuracy increase of the estimates is achieved by detailing the initial data, reflecting the specific features of the national vehicle fleet structure and transport activity indicators, for which a wide range of statistical, probabilistic and expert methods are used. Using the methodology implemented in the “Transport Model” software product, it is possible to reconstruct time series of emissions for previous periods with an assessment of their accuracy and uncertainties, as well as to perform scenario modelling in order to forecast future emissions. The methodology was used to obtain quantitative estimates of three types of greenhouse gases emissions and three precursor gases from road transport and off-road vehicles for the period of 2010 to 2022, to compare the results with National Inventory data, to produce emission estimates for four categories of off-road vehicles for the first time, and to estimate fuel consumption and changes in the number of vehicles of all categories for the period of 1990 to 2022. An information array containing official statistical data on indirect indicators of transport activity by vehicle category for the period of 1990 to 2022 was compiled, the results of the emissions calculations were compared with the indirect indicators using multiple regression methods, and the accuracy and uncertainties of these results were quantitatively assessed. The estimated greenhouse gas emissions were considered as random values for which a confidence interval was calculated as an indicator of uncertainty and a median correction as an indicator of accuracy. The high quality of the results of the calculations based on the methodology considered was confirmed.

Evaluating machine learning models for greenhouse gas emissions prediction in diversified semi‐arid cropping systems

AbstractClimate‐smart agriculture emphasizes improving crop production while mitigating greenhouse gas (GHG) emissions. In recent years, machine learning (ML) models have been increasingly used to unlock complex problems in agriculture, such as an enhanced understanding of drivers regulating GHG emissions. However, these models have not been evaluated widely, specifically quantifying GHG emissions in arid and semi‐arid cropping systems. We evaluated soil GHG emissions in irrigated crop rotations with and without cover cropping using field‐based measurements and five ML models, that is, decision tree, random forest (RF), gradient boosting (GB), bagging regressor, and XGB booster. The ML model was trained and evaluated using >1600 data points of daily carbon dioxide (CO2), and nitrous oxide (N2O) measurements from four cover cropping practices (no‐cover crop and grass–brassica, grass–legume, and grass–brassica–legume cover crops) along with environmental (air temperature, precipitation), soil (moisture, temperature), and crop and management (cover crop carbon content, irrigation) variables and CO2 and N2O emissions was predicted. The RF model predicted CO2 emissions (R2 up to 0.68) more accurately, and N2O emissions were predicted better by the RF and GB models than other models with R2 up to 0.56 and 0.55, respectively. Irrigation was the most critical driver of CO2 emissions, and air temperature was the main driver of N2O emissions. ML models can effectively estimate GHG emissions using simple predictors, such as field management and environmental parameters, and help in designing climate‐smart management in arid and semi‐arid regions.

A stochastic LCA model for estimating greenhouse gas emissions from federal highway construction projects in Brazil

A stochastic LCA model for estimating greenhouse gas emissions from federal highway construction projects in Brazil

Estimation of Greenhouse Gas Emissions and Analysis of Driving Factors in Jiangxi Province’s Livestock Industry from a Life Cycle Perspective

As a significant source of greenhouse gas emissions, objectively understanding the quantity of emissions from the livestock industry and their spatiotemporal evolution is crucial for advancing low-carbon and green development in regional livestock industries. Therefore, based on the Life Cycle Assessment (LCA) method, this study estimated greenhouse gas emissions from the livestock industry across 11 municipal regions in Jiangxi Province from 2002 to 2022, revealing the spatiotemporal characteristics of these emissions. The study also employed the Logarithmic Mean Divisia Index (LMDI) model to analyze the influencing factors. The results showed that (1) between 2002 and 2022, total greenhouse gas emissions from Jiangxi Province’s livestock industry exhibited an upward trend, increasing from 13.52 million tons to 21.27 million tons, with an average annual growth rate of 2.36%. (2) During the study period, the spatial patterns of super-high-emission and light-emission zones remained relatively stable, while medium and heavy emission zones showed dynamic evolution. (3) Intensity effects, agricultural structural effects, and rural population size played a suppressive role in livestock greenhouse gas emissions, while regional development levels and urbanization levels were key drivers of increased emissions. Therefore, governments should accelerate the implementation of clean production models, enhance technological innovation, promote pollution reduction at the source, and develop differentiated livestock development strategies based on regional resource endowments and demographic–economic characteristics.

Comparison of greenhouse gas emission estimates from six hydropower reservoirs using modeling versus field surveys

As with most aquatic ecosystems, reservoirs play an important role in the global carbon (C) cycle and emit greenhouse gases (GHG) as carbon dioxide (CO2) and methane (CH4). However, GHG emissions from reservoirs are poorly quantified, especially in temperate systems, resulting in high uncertainty. We compared reservoir C emission estimates and uncertainty of diffusive, ebullitive, and degassing pathways in six hydropower reservoirs in the southeastern United States among four data sources: two field-based surveys and two models (including the GHG Reservoir “G-res” Tool). We found that CH4 diffusion was most similar across data sources (modeled minus observed, bias = − 21 g CO2-eq m−2 y−1) and had low relative uncertainty (coefficient of variation, CV = 0.98). On the other hand, CO2 diffusion was least consistent across data sources (bias = − 518 g CO2-eq m−2 y−1). Both field surveys indicated strong negative CO2 diffusion (i.e., CO2 uptake) at all reservoirs, while G-res estimated positive CO2 diffusion. By extension, total C emissions showed similar discrepancies, leading to high uncertainty in upscaling and interpreting reservoir source-sink dynamics. Finally, CH4 ebullition had the highest relative uncertainty (CV = 2.77) due to high variability across sites. We discuss limitations of field surveys and these models, including temperature-based annualization methods, varying definitions of ebullition zones, low sampling resolution, and lack of dynamism. Future field efforts focused on capturing variability in CO2 diffusion and CH4 ebullition will be especially valuable in reducing uncertainty and improving models to advance our understanding reservoir GHG emissions.

Estimating GHG emissions from cloud computing: sources of inaccuracy, opportunities and challenges in location-based and use-based approaches

Estimating GHG emissions from cloud computing: sources of inaccuracy, opportunities and challenges in location-based and use-based approaches

China's greenhouse gas budget during 2000–2023

ABSTRACT National greenhouse gas (GHG) budget, including CO2, CH4 and N2O has increasingly become a topic of concern in international climate governance. China is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate change. Accurate estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in China. This study used comprehensive methods, including emission factor methods, process-based models, atmospheric inversions, and data-driven models, to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000–2023, and produced an up-to-date China GHG Budget dataset (CNGHG). The total gross emissions of the three GHGs show a 3-fold increase from 5.0 (95% CI: 4.9–5.1) Gt CO2-eq yr−1 (in 2000) to 14.3 (95% CI: 13.8–14.8) Gt CO2-eq yr−1 (in 2023). CO2 emissions represented 81.8% of the GHG emissions in 2023, while 12.7% and 5.5% were for CH4 and N2O, respectively. As the largest CO2 source, the energy sector contributed 87.4% CO2 emissions. In contrast, the agriculture, forestry and other land use sector was the largest sector of CH4 and N2O, representing 50.1% and 66.3% emissions, respectively. Moreover, China's terrestrial ecosystems serve as a net CO2 sink (1.0 Gt CO2 yr−1, 95% CI: 0.2–1.9 Gt CO2 yr−1) during 2012 to 2021, equivalent to an average of 14.3% of fossil CO2 emissions. Our GHG emission estimates showed a general consistency with national GHG inventories, with gridded and sector-specific estimates of GHG fluxes over China, providing the basis for curtailing GHG emissions for each region and sector.

Estimation of carbon footprint in nuclear medicine: illustration of a french department.

In order to limit climate changes, we need to reduce the carbon footprint of human activities, including those due to health systems. We performed an estimation of the carbon footprint of our nuclear medicine department using a methodology developed with the help of a specialized consulting firm. The estimate of greenhouse gas (GHG) emissions comprises direct and indirect emissions. Direct emissions are due to fuels consumption (by the hospital and by hospital's vehicles), refrigerant leaks and impact of buildings on biomass (land use change). Indirect emissions include upstream and downstream emissions. Upstream emissions are linked to electricity and heating consumption, transport of merchandises, transport of patients and employees, business travels, purchases, and fixed assets. Downstream emissions are due to usage and disposal of manufactured products created by the hospital. Different GHGs (CO2, CH4, N2O…) each have a different global warming potential. To aggregate all GHG emissions, the results were expressed in carbon dioxide equivalent (CO2e). In 2022, 13,303 diagnostic and therapeutic procedures were performed in our department, for an estimated carbon footprint reaching 772 tons of CO2 equivalent. Transport of people accounts for 67% of total emissions. Purchases are responsible for 14% of total emissions, of which 11.8% are due to radiotracers supply. Energy consumption accounts for 6.9% of total emissions. Imaging devices (2 PET/CT, 2 SPECT/CT and 1 cardiac imaging dedicated CZT camera) account for 5.5% of emissions. Our emissions are mainly due to indirect emission which is a common result in tertiary sector.

Estimation of Greenhouse Gas (GHG) emissions in waste management in Pekanbaru City, Indonesia

Estimation of Greenhouse Gas (GHG) emissions in waste management in Pekanbaru City, Indonesia

Neural networks based-simple estimated model for greenhouse gas emission from irrigated paddy fields

<span lang="EN-US">The current study aims to develop a simple model for estimating greenhouse gas emissions originating from paddy fields, utilizing backpropagation neural networks. The model integrated three input parameters: soil moisture, soil temperature, and soil electrical conductivity (EC), while generating estimations for two output parameters: methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O) emissions. The model was put into practice across three different irrigation systems, i.e., continuous flooded (FL), wet (WT), and dry (DR) regimes. For model training and validation, the input parameters were measured by a single 5-TE sensor. Concurrently, CH<sub>4</sub> and N<sub>2</sub>O emissions were determined utilizing a closed chamber, and gas samples were subjected to laboratory analysis. Findings unveiled that the developed model accurately estimated CH<sub>4 </sub>and N<sub>2</sub>O emissions, demonstrating commendable coefficient of determination (R<sup>2</sup>) values ranging from 0.60 to 0.97 for validation process. Notably, the WT irrigation system exhibited the highest precision, boasting R<sup>2</sup> values of 0.97 for CH<sub>4</sub> and 0.73 for N<sub>2</sub>O estimation, respectively. Conversely, the FL irrigation system has the lowest accuracy with R<sup>2</sup> values of 0.66 and 0.60. Despite variances in accuracy across irrigation systems, the overall performance remained deemed acceptable, warranting the model's applicability for estimating greenhouse gas emissions under diverse irrigation scenarios.</span>

Greenhouse gas emission calculation and energy impact of TPS3R flamboyan using waste reduction model (WARM) V.15: Implications for disaster risk reduction

Background: Human activities contribute to increased greenhouse gas concentrations, such as CO₂ and CH₄, which intensify the greenhouse effect and elevate Earth's temperature. TPS3R Flamboyan aims to reduce plastic waste through recycling, composting, and landfilling at TPA Cipeucang. While these processes help reduce waste, they can also produce CO₂ emissions. This study evaluates the CO₂ emissions from the baseline waste management scenario and compares it with an alternative scenario using the Waste Reduction Model (WARM) to assess GHG emissions and energy use. Methods: Data was collected in June 2020 from TPS3R Flamboyan and TPA Cipeucang in South Tangerang, analyzing waste types and GHG emissions using the WARM software. The study utilized baseline and alternative waste management scenarios to assess CO2 emissions and energy use, with input data on various waste types such as food waste and plastics. WARM compared the emissions and energy use for each scenario, providing insights on GHG reductions and energy efficiency in waste management practices. Findings: Total GHG emissions from baseline MSW generation and management (MTCO2E) is -2,23 and total GHG emissions from alternative MSW Generation ad management (MTCO2E) is -4,46. Total Energy use from baseline MSW Generation and Management (million BTU) is -33,98 and total Energy use from alternative MSW generation and Management (million BTU) is -92,22. Conclusion: Both scenarios indicate that the alternative scenario results in a higher reduction of emissions compared to the baseline management. This demonstrates the effectiveness of the alternative waste management practices in reducing greenhouse gas emissions. Novelty/Originality of this article: This research provides a novel approach by using the Waste Reduction Model (WARM) application, developed by the U.S. Environmental Protection Agency (EPA), to estimate greenhouse gas emissions and energy use in municipal solid waste management scenarios. This application offers high-level estimates for emissions reduction and energy efficiency, providing valuable insights for waste management practices.

Estimating greenhouse gas emissions from the health sector in Canada: Mind the gap.

Healthcare is a surprisingly large contributor to climate change, responsible for a significant quantity of global Greenhouse Gas (GHG) emissions. Global commitments to achieve "net zero" health systems, including by the federal government in Canada, suggest a growing need to understand and mobilize capacity for GHG emissions estimation across Canada's health sector. Our analysis highlights efforts by public sector healthcare organizations in Canada to estimate an increasingly broad scope of GHG emissions, building on longstanding efforts to report or reduce energy-related emissions from facilities. It also identifies why such efforts will not be sufficient. Developing capacity for routine system-wide greenhouse gas emissions estimation can help Canada's health systems to better understand their progress, including through international comparison. Yet emissions estimation is itself an investment, one that should not displace efforts to reduce the full scope of pollutants from the healthcare enterprise, and to build a truly sustainable health system.