Estimate Greenhouse Gas Emissions Research Articles

Estimated greenhouse gas emissions in the Mbalmayo thermal power plant between 2016-2020 using the genetic-Gaussian algorithm coupling.

The greenhouse gas (GHG) emissions inventories in our context result from the production of electricity from fuel oil at the Mbalmayo thermal power plant between 2016 and 2020. Our study area is located in the Central Cameroon region. The empirical method of the second level of industrialisation was applied to estimate GHG emissions and the application of the genetic algorithm-Gaussian (GA-Gaussian) coupling method was used to optimise the estimation of GHG emissions. Our work is of an experimental nature and aims to estimate the quantities of GHG produced by the Mbalmayo thermal power plant during its operation. The search for the best objective function using genetic algorithms is designed to bring us closer to the best concentration, and the Gaussian model is used to estimate the concentration level. The results obtained show that the average monthly emissions in kilograms (kg) of GHGs from the Mbalmayo thermal power plant are: 526 kg for carbon dioxide (CO2), 971.41 kg for methane (CH4) and 309.41 kg for nitrous oxide (N2O), for an average monthly production of 6058.12 kWh of energy. Evaluation of the stack height shows that increasing the stack height helps to reduce local GHG concentrations. According to the Cameroonian standards published in 2021, the limit concentrations of GHGs remain below 30 mg/m3 for CO2 and 200 μg/m3 for N2O, while for CH4 we reach the limit value of 60 μg/m3. These results will enable the authorities to take appropriate measures to reduce GHG concentrations.

Manure temperature prediction for slurry storage in Sweden: Model validation including effects of shading, snow cover and mixing

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions.

Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data

In recent decades, Vietnam has gradually become a critical global rice producer. During that production process, residual straw becomes an environmental pollutant due to open burning, raising greenhouse gas emissions. This study combines the optical images of the Sentinel-2 satellite and the radar images of the Sentinel-1 satellite to estimate the dry biomass of rice and to determine gas emissions due to rice straw burning over the fields in Quoc Oai district, Hanoi city for urban environmental management purposes. Sentinel-2 images have been classified into the land covers, thereby identifying the areas of ​​rice cultivation and the areas of ​​burned straw. Meanwhile, the Sentinel-1 radar image has been used to calculate the dry biomass of rice due to its ability to penetrate clouds, an obstacle to optical images in tropical regions. Furthermore, a field trip during harvesting season allows us to measure aboveground dry biomass. Then, the analysis shows a high correlation between the backscatter V.V. and V.H. of the radar image and the in-situ dry biomass (R=0.923 and R2=0.852), with a relatively low average error (RMSE = 6.58 kg/100 m2). By linear regression method, the study found the total rice dry biomass of 28728.5 tons, which was obtained after the Summer rice crop 2020 for the whole Quoc Oai district, of which 2037.91 tons of rice straw have been burned, releasing a large amount of greenhouse gas emission with 2398.6 tons of CO2, 189.5 tons of CO, 18.8673 tons of PM10 dust, 17.2087 tons of PM2.5 dust and some other gases. The identical procedure has also been applied to the western region of Hanoi city center to estimate the amount of gas emissions. This study has proven the effectiveness of an approach and contributed to supporting urban managers in proposing appropriate policies to monitor and protect the environment.

Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods

AbstractPeatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed by peat extraction, afforestation and agriculture. This has an overall negative impact on the functioning of peatlands, shifting them from a moderate C sink to a large C source. Recognizing the importance of these ecosystems, efforts are underway for conservation (rewetting and rehabilitation), while accurately accounting for C stock and greenhouse gas (GHG) emissions. However, the implementation of these efforts requires accurate identification and mapping of artificial drainage ditches. This study utilized very high‐resolution (25 cm) aerial imagery, and a deep learning (U‐Net) approach to map the visible artificial drainage (unobstructed by vegetation or infill) in raised bogs at a national scale. The results show that artificial drainage is widespread, with ~20 000 km of drains mapped. The overall accuracy of the model was 80% on an independent testing dataset. The data were also used to derive the Fracditch which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Fracditch and can aid in IPCC Tier 2 reporting for Ireland. This is the first study to map drains with diverse sizes and patterns on Irish‐raised bogs using optical aerial imagery and deep learning methods. The map will serve as an important baseline dataset for evaluating the artificial drainage ditch conditions. It will prove useful for sustainable management, conservation and refined estimations of GHG emissions. The model's capacity for generalization implies its potential in mapping artificial drains in peatlands at a regional and global scale, thereby enhancing the comprehension of the global effects of artificial drainage ditches on peatlands.

Estimating Greenhouse Gas Emissions from Household Activities: A Case in Bogor Indonesia

Climate change is a result of global warming. Global warming is caused by an increase in the concentration of greenhouse gases (GHGs) in the atmosphere. One of the largest GHG-contributing sectors is the energy sector, which includes households with various activities. Household activities include using LPG, electricity, gasoline, waste generation, and farming and livestock activities to meet the household's needs. The level of GHG emissions produced in households is influenced by income, years of study, and knowledge of emissions. The objectives of this study are to estimate the amount of GHG emissions generated from household activities, analyze the factors that influence GHG emissions, and estimate the value of the carbon economy. The analytical methods used are quantitative descriptive analysis, IPCC (2006) method, multiple linear regression, and economic value analysis. The results showed that: (1) CO2-eq emissions generated in Sinarsari Village for 100 respondents amounted to 32,011.68 kg CO2-eq/month, and the amount of GHG emissions in one village scope amounted to 853,431.36 kg CO2-eq/month. (2) Income, length of study, and emission knowledge dummy influence GHG emissions. 3) Carbon economic value based on carbon pricing is Rp287,415,199.70/month.

Development of Activity Data for Greenhouse Gas Inventory in Settlements in South Korea

In South Korea, Agriculture, Forestry, and Other Land Use (AFOLU) collates greenhouse gas (GHG) inventories. However, the settlement category lacks a clear definition of land use and activity data. This study proposed a method for examining the settlement spatial extent and constructing activity data to estimate GHG emissions and absorption as a pilot calculation, as well as to provide data for land use classification. Utilizing cadastral maps (CDMs), settlement spatial extents were determined, with settlements occupying approximately 11% of the total land area in 2019, or 9% excluding overlaps. Activity data for settlements were established through a sampling method and analysis of aerial orthoimages from 2000 and 2019. After removing overlaps with digital forest type maps and smart farm maps, settlement activity data covered approximately 18.47% based on CDMs, or 12.66% excluding overlaps. In 2019, CO2 emissions and absorptions were estimated at 622.16 ktCO2yr−1 based on CDMs and 242.16 ktCO2yr−1, excluding overlaps. To enhance GHG inventory calculation consistency and compliance with TACCC principles, clear spatial extents for settlements must be established. This entails constructing activity data and assessing GHG inventories accordingly. GHG inventory statistics should also inform future nationally determined contributions.

Evaluating Development of Empirical Estimates Using Two Top-Down Methods at Midstream Natural Gas Facilities

To align with climate initiatives, multiple reporting programs are transitioning from generic activity-based emission factors to site-specific measured emissions data to estimate greenhouse gas emissions at oil and gas facilities. This study contemporaneously deployed two top-down (TD) aerial methods across 14 midstream facilities, building upon previous research in the field. The methods produced multiple whole-facility estimates at each facility, resulting in 773 individual paired estimates (same facility, same day), and robust mean estimates for each facility. Mean estimates for each facility, aggregated across all facilities, differed by nearly 2:1 (49% [32% to 69%]). At 6 of 14 facilities, the methods produced mean estimates that differed by more than a factor of two. These data suggest that one or both methods did not produce accurate facility-level estimates at a majority of facilities and in aggregate across all facilities. The overall results are augmented with two case studies where TD estimates at two pre-selected facilities were coupled with comprehensive onsite measurements to understand the factors driving the divergence between TD and bottom-up (BU) emissions estimates. In 3 of 4 paired comparisons between the intensive onsite estimates and one of the TD methods, the intensive onsite surveys did not conclusively diagnose the difference in estimates. In these cases, our work suggests that the TD methods mis-estimate emissions an unknown fraction of the time, for unknown reasons. While two methods were selected for this study, it is unlikely that the issues identified here are confined to these two methods; similar issues may exist for other similar whole-facility methods on midstream and/or other facility types. These findings have important implications for the construction of voluntary and regulatory reporting programs that rely on emission estimates for reporting fees or penalties, or for studies using whole-facility estimates to aggregate TD emissions to basin or regional estimates.

Quantifying the greenhouse gas emissions of New Zealand households’ food purchases: An analysis by sociodemographic variables

New Zealand has committed to a 50% reduction in greenhouse gas emissions (GHGEs) from 2005 levels by 2030. Dietary changes within New Zealand could simultaneously improve population health and contribute towards the nation’s emissions reduction target, as agricultural emissions are estimated to account for half of New Zealand’s GHGEs(1). This research aimed to quantify the GHGEs associated with household purchases of major food groups in New Zealand and identify the sociodemographic characteristics that are associated with per capita household dietary emissions. Household dietary emissions were estimated using the NielsenIQ Homescan(R) consumer panel — a large sample of households within New Zealand who report purchasing data of take-home food and beverages. The sample is nationally representative in terms of broad geographical regions and selected key demographic characteristics. Carbon emission estimates were assigned to 1,908,485 total food and beverage purchases from 1,775 households over one year (2019) using a process-based life cycle assessment (LCA) dataset initially constructed in the United Kingdom (UK) and adapted for New Zealand(2). This LCA dataset contains estimates of greenhouse gas emissions generated over the life cycle of the production of food products from the following stages: farming and processing, transit packaging, consumer packaging, transport, warehouse and distribution, refrigeration, and overheads. Greenhouse gas emissions are expressed in kg of carbon dioxide equivalents per kg of food product over a 100-year time horizon. Total emissions from purchases of major food groups were then estimated. Multiple linear regression was used to examine the relationships between household variables and per capita dietary emissions. Overall purchases of red and processed meat (35%) and dairy products (19%) were responsible for the greatest proportion of emissions. The age group of the primary household shopper as well as household size were predictors of per capita dietary emissions — households with primary shoppers > 65 years had, on average, 33% (95% CI: 20% to 49%) higher per capita dietary emissions, compared to households with primary shoppers 34 years; and every additional household member was associated with, on average, 11% (95% CI: 9% to 13%) lower per capita dietary emissions. We have shown in this large representative sample of New Zealand households that purchases of just two food groups — red and processed meat, and dairy — were responsible for approximately half of dietary greenhouse gas emissions. Larger households had lower per capita dietary greenhouse gas emissions, and older shoppers had relatively higher greenhouse gas emissions. Whilst similar associations have been reported elsewhere more research is needed to confirm these latter findings. With enhanced understanding of the observed association between age of a household’s primary shopper and per capita dietary emissions, interventions may be devised that encourage shoppers to purchase lower-emitting foods, particularly less meat and dairy.

Increasing the proportion of plant to animal protein in hospital patient menus: what do stakeholders think?

Replacing dietary animal protein with plant protein has a positive impact on greenhouse gas emissions(1) and preventing death from chronic disease(2). Despite being ideally situated to re-design menus, foodservices in hospitals have not focused on changing protein sources(3). Implementation in hospitals requires cooperation from stakeholders across the foodservice system e.g., managers, dietitians, menu planners, purchasers, cooks. A qualitative descriptive study design using semi-structured interviews explored perspectives of hospital foodservice stakeholders on increasing the proportion of plant to animal protein in hospital patient menus and outlined actions required to do this. Interviews were based on participatory backcasting with a “desirable future” defined as hospital patient menus containing, on average, more plant-based protein foods (PBPF) (i.e., legumes, nuts, plant-based meat alternatives) than animal-based protein foods (ABPF) (i.e., beef, lamb, pork, poultry, fish, eggs, dairy) by the year 2050. Analysis was completed using a general inductive approach. Thirty-five stakeholders participated (foodservice dietitians n = 10; foodservice managers, n = 6; dietetic professional leads n = 4; chef/cooks n = 4; information technology n = 4; manager [contracts] n = 4; manager [other] n = 3). Most (n = 25) supported patient menu changes to increase the proportion of plant to animal protein foods, though all described barriers. Common concerns were that patients wouldn’t eat the meals (n = 32), that menu re-design would have a negative impact on protein intake and malnutrition rates (n = 30), and that cost of PBPF was too high making the change unfeasible (n = 25). Benefits were an improvement in the nutrition profile of the menu and subsequent improvement to health (n = 16), lower cost of legumes compared to meat (n = 10), improvements in greenhouse gas emissions (n = 10) and opportunity to use the menu as an education tool (n = 8). We developed a model describing the required actions which began with a trigger for change followed by a cyclical design process, preparation, implementation and monitoring. The cyclical design process included stakeholder consultation, setting a target, choosing a strategy, developing a menu and recipes, finding product, planning the system and operation, and checking it worked. Participants valued gradual changes and maintaining choice during the change process. When prompted about specific strategies, stakeholders were most supportive of replacing ABPF with PBPFs in familiar recipes or replacing entire menu items (n = 21), adding PBPF options (n = 25), and moving PBPFs before ABPF-based items on the menu (n = 22). Hospital foodservices and policy makers wishing to increase the proportion of plant to animal protein in hospital patient menus can use the process and actions identified to plan pathways and communicate these to stakeholders. Future research should explore strategies for increasing the proportion of plant to animal protein while maintaining some ABPF on hospital menus, and evaluating effects of changes uptake, cost, estimated greenhouse gas emissions, satisfaction, dietary intake and health outcomes.

Application of the domain adaptation method using a phenological classification framework for the land-cover classification of North Korea

Efforts to achieve carbon neutrality are global, encompassing a wide range of factors. For the estimation of greenhouse gas emissions from the agriculture, forestry, and other land use (AFOLU) sector, the Intergovernmental Panel on Climate Change has proposed an advanced method that requires Approach 3, the highest level of suggested method, of activity data. Accordingly, we propose a phenological classification framework (PCF) that can perform land-cover classification by training the climatic repeatability of the annual cycle using a U-Net deep learning model. Additionally, the domain adaptation (DA) method can be applied to classify areas with insufficient data. We applied these methods to classify North Korea (i.e., using South Korean data), with an accuracy of 81.31%; overall this effort culminated in the simultaneous classification of the Korean Peninsula. Domain distribution comparison showed that the results for the two regions were similar. The PCF and DA methods proposed in this study allow for annual production of a land-cover map and change matrix, regardless of the presence or absence of data. The application of these methods is expected to provide a scientific basis for policy decisions that can facilitate the global attainment of carbon neutrality.

Assessing uncertainties and discrepancies in agricultural greenhouse gas emissions estimation in China: A comprehensive review

Due to diverse methodologies and data sources, China's agricultural greenhouse gas (AGHG) emissions estimates demonstrate significant variability. A quantitative review was conducted on the prevalence and application of various accounting methods to clarify and reduce discrepancies in AGHG emissions estimation techniques and data application. Using a systematic PRISMA framework, 4257 global studies were analyzed, yielding 681 AGHG emission records from 57 papers and four databases explicitly focusing on China. The analysis reveals similarities between Chinese and global research trends, concentrating on environmental science, soil science, and ecology. Furthermore, the spatial scale and methodologies for China's AGHG emissions estimation are relatively centralized, with 53.21% of the research targeting site-specific scales and 74.12% employing inventory methods and statistical analysis. Regarding the variability in China's AGHG emission estimates between 1996 and 2020, estimates ranged from 100 to 800 Mt. C-eq, with the highest estimate being approximately 25 times the minimum within the same period. Notably, compared to the average AGHG emissions calculated for 1996–2020 (284.7 to 548.7 Mt. C-eq), estimates from the FAO and Our World in Data were higher by an average of 114.7% and 81.9%, respectively. These findings underscore the need to enhance remote sensing and survey data application and standardize AGHG emissions estimation methodologies and boundaries to ensure comparability across various accounting methods and datasets. This study conducts a quantitative analysis to identify and bridge the gaps in AGHG emission evaluation techniques, laying the groundwork for establishing a more comprehensive and resilient framework for AGHG emissions assessments.

Reducing uncertainties in greenhouse gas emissions from chemical production

AbstractUncertainties in greenhouse gas emissions estimates for petrochemical production have lacked quantification globally, impacting emissions reporting and decarbonization policymaking. Here we analyze cradle-to-gate emissions of 81 chemicals at 37,000 facilities worldwide, assessing 6 uncertainty sources. The results estimate a 34% uncertainty in total global emissions of 1.9 ± 0.6 Gt of CO2-equivalent emissions for 2020, and 15–40% uncertainties across most petrochemicals analyzed. The largest uncertainties stem from the inability to assign specific production processes to facilities owing to data limitations. Uncertain data on feedstock production and off-site energy generation contribute substantially, while on-site fuel combustion and chemical reactions have smaller roles. Allocation method choices for co-products are generally insignificant. Prioritizing facility-level process specification in data collection for just 20% of facilities could reduce global uncertainty by 80%. This underscores the necessity of quantifying uncertainty in petrochemical greenhouse gas emissions globally and outlines priorities for improved reporting. The dataset generated offers independent emissions factor estimates based on facility-specific information for 81 chemicals, supporting future analyses.

Estimating soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N2O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N2O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N2O emissions (N2Om, kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N2O drivers. Significantly different N2Om were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N2O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N2O emissions reported based on area (N2Om), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N2O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N2Om using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

The environmental impact of lightweight body in white structures in vehicle manufacturing

The quest for sustainability and reduction of human impact on the planet has led to discussions about environmental efficiency in the manufacture and use of automotive vehicles. The advancement of environmental and government laws has encouraged the automotive industry to seek constant improvements in safety, fuel economy and reduction of greenhouse gas emissions. To achieve these goals, reducing the weight of vehicles has been considered a relevant strategy, with several research and development efforts in search of high-tech materials and innovative design. The assessment of the life cycle of vehicles, from the extraction of raw materials to the end of their useful life, is essential for estimating environmental impacts. In this context, this paper focuses on the life cycle analysis of vehicles with new generation steel structures (Body in White Solution) compared to current steels (Body in White Baseline), aiming at reducing greenhouse gas emissions by reducing of the thickness of the steel plates, maintaining the mechanical properties necessary to guarantee the safety of the vehicle. The methodology of this study was divided into the evaluation of Body in White (BIW) mass in current steels (Baseline) and in advanced high resistance steels (Solution); detailing the vehicular life cycle from the extraction of materials to its end of life; in the estimation of greenhouse gas emissions through simulation and in the comparison of vehicle emissions with BIW in current steels and in advanced high resistance steels. As a result, it was possible to observe that, in the comparison of the CO2 footprint, modeled in the Eco Audit, there is a 15% reduction in emissions in the CO2 footprint for the BIW solution, as well as a 7% reduction in the Ceq total, in the on-site test and a saving of 260.40 L in fuel consumption, that is, the reduction in vehicle weight plays a significant role in reducing CO2 emissions, providing a mitigation factor for global warming.

What is the environmental impact of a blood transfusion? A life cycle assessment of transfusion services across England.

Healthcare activities significantly contribute to greenhouse gas (GHG) emissions. Blood transfusions require complex, interlinked processes to collect, manufacture, and supply. Their contribution to healthcare emissions and avenues for mitigation is unknown. We performed a life cycle assessment (LCA) for red blood cell (RBC) transfusions across England where 1.36 million units are transfused annually. We defined the process flow with seven categories: donation, transportation, manufacturing, testing, stockholding, hospital transfusion, and disposal. We used direct measurements, manufacturer data, bioengineering databases, and surveys to assess electrical power usage, embodied carbon in disposable materials and reagents, and direct emissions through transportation, refrigerant leakage, and disposal. The central estimate of carbon footprint per unit of RBC transfused was 7.56 kg CO2 equivalent (CO2eq). The largest contribution was from transportation (2.8 kg CO2eq, 36% of total). The second largest was from hospital transfusion processes (1.9 kg CO2eq, 26%), driven mostly by refrigeration. The third largest was donation (1.3 kg CO2eq, 17%) due to the plastic blood packs. Total emissions from RBC transfusion are ~10.3 million kg CO2eq/year. This is the first study to estimate GHG emissions attributable to RBC transfusion, quantifying the contributions of each stage of the process. Primary areas for mitigation may include electric vehicles for the blood service fleet, improving the energy efficiency of refrigeration, using renewable sources of electricity, changing the plastic of blood packs, and using methods of disposal other than incineration.

Industrial waste heat potential and heat exploitation solutions

The industrial waste heat potential in EU countries and UK is assessed using the most recent data, based on an established methodology that allows to estimate this underexplored resource per country, temperature level and industrial branch. This methodology is applied here combining two previous studies using the most recent energy balances data from Eurostat and other indicators of 2021. The waste heat potential and heat demand are then estimated, in order to identify the progress towards reduction of waste energy. It has been revealed that this progress is significant in terms of energy intensity, leading to a reduction of waste heat from 2015 to 2021, but still this heat amount is large and equal to 221.32 TWh/year in 2021. The second part of this work concerns the assessment of three mainstream solutions to exploit this waste heat: direct heat recovery, heat-to-power conversion, and heat upgrading with high-temperature heat pumps. The heat and electricity production potential of these solutions is identified, followed by the estimation of greenhouse gas emissions savings introduced by each one. It has been shown that the direct heat recovery solution brings the highest savings of almost 50 million CO2-eq. tonnes/year, followed by the heat upgrading one.

Greenhouse Gas Emissions Inventory from Roadway Construction: Case Study for the Washington State Department of Transportation

Recent emphasis on actions to reduce greenhouse gas (GHG) emissions has pushed many state departments of transportation (DOTs) to develop carbon accounting practices compatible with their current standard data collection and storage guidelines. Once accurate and reliable accounting of GHG emissions is established, strategies can be formed that could help mitigate the adverse environmental impacts of materials used by state DOTs. To date, the Washington State Department of Transportation (WSDOT) has not conducted comprehensive research on the embodied carbon within its construction material usage (i.e., upstream Scope 3 emissions inventory of procured materials) with most previous carbon accounting practices being focused on Scope 1 and Scope 2 emissions (i.e., the carbon footprint of direct and indirect energy usage). This paper summarizes the results of a life cycle assessment on the agency-wide material procurements and construction operations that emit GHGs at WSDOT as a case study. This study uses several data sources from WSDOT in conjunction with publicly available life cycle emissions factor data to estimate GHG emissions attributed to the materials used to build and maintain roadways under WSDOT’s jurisdiction. Results indicate that upstream Scope 3 emissions for WSDOT as an agency is a significant contributor to its overall GHG emissions inventory. Specifically, between 2017 and 2022, this paper estimates an average annual upstream Scope 3 emissions of 310,000 metric tons of CO2 equivalents, which translates to 56% of the total annual GHG emissions including Scope 1 and 2 emissions.

Greenhouse gas emissions benefits of the lightweight vehicle fleet in China: A dynamic fleet perspective

Lightweighting is believed to be a silver bullet to reduce greenhouse gas (GHG) emissions related to the road transportation. However, the fleet-scale and long-term GHG emissions estimation on the light-duty passenger vehicle (LDPV) fleet in China is still underexplored. A dynamic fleet-based life cycle assessment (LCA) model is developed to predict the annual GHG emissions attributable to the lightweight LDPV fleet in China at provincial level. With the most aggressive implementation of lightweighting, the cumulative mitigation potential of lightweight LDPV fleet from 2021 to 2050 is 0.72 Gt CO2 eq and 0.55 Gt CO2 eq using high-strength steel (AHSS) and aluminium (Al), respectively. Sooner implementation brings a higher cumulative reduction in GHG emissions. Delaying the implementation by five years would sacrifice 0.16 Gt CO2 eq ∼0.2 Gt CO2 eq cumulative reduction, and the loss would increase to 0.31 Gt CO2 eq ∼0.41 Gt CO2 eq when postpone the lightweighting by ten years.

Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

Carbon Loss from Earthquake Induced Landslides in Fiordland

Landslides triggered by two earthquakes in Fiordland, New Zealand, were mapped using Google Earth Pro retro satellite imagery (eye altitude of 1.5-2.5 km, enabling ± 50 m precision), then catalogued and classified in a GIS inventory. Ground acceleration during the 2003 Mw 7.2 Secretary Island Earthquake resulted in at least 1852 landslides, but the larger Mw 7.8 2009 Dusky Sound Earthquake had lower accelerations and produced only 313. Both events dislodged large swaths of native forest, grassland vegetation and soil from Fiordland’s steep slopes in similar proportions, some landing in rivers and fiords. The landslide maps and magnitude-frequency distributions show close similarity to a published Global Forest Loss dataset derived from 2001-2022 satellite imagery. Assuming forest loss in this wilderness is predominantly landslide-related, it enables the more-precise earthquake-induced landslide mapping to be placed in a longer-term context of other vegetation loss, mostly rainfall-induced landslides, during the past two decades. The total area of forest loss during the 2003 earthquake was anomalous, whereas during 2009 the earthquake losses were similar to areas of forest loss assumed to be rainfall-induced landslides throughout Fiordland each year. Carbon concentrations of landslide vegetation and soil were calculated by defining vegetation types from a published Land Cover Database, and soil organic carbon concentration from a 1 km resolution raster dataset. Total carbon loss from earthquake-induced landslides amounts to 2.05E+06 tonnes for the 2003 Mw7.2 and 2.17E+05 tonnes for the 2009 Mw 7.8. By way of comparison, New Zealand’s total annual carbon sequestration was 6.3E+06 tonnes in 2020. Earthquake-induced and rainfall-induced landslides are shown to account for significant changes in stored carbon and available carbon sequestration, especially in areas of densely vegetated land, such as Fiordland. Processes of landscape disturbance should be included in carbon accounting and estimates of national greenhouse gas emissions.
 
 Supervised by: Caroline Orchiston
 Scholarship Project Funded by: Centre for Sustainability