Estimate Greenhouse Gas Emissions Research Articles

Estimating Greenhouse Gas Emissions from a Seafood Processing Facility

Estimating Greenhouse Gas Emissions from a Seafood Processing Facility

Reducing the Environmental Impact of Health Care Conferences: A Study of Emissions and Practical Solutions.

We aimed to examine the impact of different conference formats (in-person, virtual, and hybrid) of the ASCO conference on greenhouse gas (GHG) emissions and to recommend sustainable options for future conferences. This study used data on the number of attendees, their departure locations, and the type of attendance (in-person v virtual) provided by ASCO between 2019 and 2022. The GHG emissions resulting from air and ground travel, remote connectivity, conference space utilization, hotel stays, distributed conference materials, and electricity use were estimated for each year. Emissions were stratified by attendee country of origin, type of attendance, and year. Simulations were conducted to evaluate how changes in conference size, location, and format impact emissions, as well as estimate the resulting mitigations from adopting the proposed changes. The highest estimated GHG emissions, calculated in carbon dioxide equivalents (CO2e), were associated with the 2019 in-person conference (37,251 metric tons of CO2e). Although international attendees had the largest contribution to emissions in all years (>50%), location optimization models, which selected conference locations that most minimized GHG emissions, yielded only minimal reductions (approximately 3%). Simulations examining changes to the conference format, location, and attendance percentage suggested that hub-and-spoke, where multiple conference locations are selected by global region, or hybrid models, with both in-person and virtual components, are likely to cause the largest drops in emissions (up to 86%). Using historical conference data, this study identifies key aspects that can be modified to reduce emissions and consequently promote more sustainable and equitable conference attendance. Hybrid conferences may be the best solution to maintain the networking opportunities provided by conferences while balancing out their environmental footprint.

Temporary stratification promotes large greenhouse gas emissions in a shallow eutrophic lake

Abstract. Shallow lakes and ponds undergo frequent temporary thermal stratification. How this affects greenhouse gas (GHG) emissions is moot, with both increased and reduced GHG emissions hypothesised. Here, weekly estimations of GHG emissions, over the growing season from May to September, were combined with temperature and oxygen profiles of an 11 ha temperate shallow lake to investigate how thermal stratification shapes GHG emissions. There were three main stratification periods with profound anoxia occurring in the bottom waters upon isolation from the atmosphere. Average diffusive emissions of methane (CH4) and nitrous oxide (N2O) were larger and more variable in the stratified phase, whereas carbon dioxide (CO2) was on average lower, though these differences were not statistically significant. In contrast, there was a significant order of magnitude increase in CH4 ebullition in the stratified phase. Furthermore, at the end of the period of stratification, there was a large efflux of CH4 and CO2 as the lake mixed. Two relatively isolated turnover events were estimated to have released the majority of the CH4 emitted between May and September. These results demonstrate how stratification patterns can shape GHG emissions and highlight the role of turnover emissions and the need for high-frequency measurements of GHG emissions, which are required to accurately characterise emissions, particularly from temporarily stratifying lakes.

GREENHOUSE GAS EMISSIONS FROM MUNICIPAL WASTE DISPOSAL SITES AND MEASURES FOR ITS REDUCTION

Disposal of household waste at landfills and dumps leads to greenhouse gas emissions. Usually emissions are estimated based on the IPCC methodology, which takes into account the amount and composition of household waste as well as the climate conditions of the region. However, greenhouse gas emissions are significantly influenced by the technical and operational parameters of landfills and dumps. The Ukrainian landfill gas model, which is adapted to local conditions, allows these parameters to be taken into account. The objective of this study was the estimation of greenhouse gas emissions from certain classes of landfills and dumps, taking into account their specific parameters based on the Ukrainian landfill gas model, as well as the development of measures for its reduction. For this, landfills and dumps were classified by area, which indirectly characterizes their capacity and place of location. Based on the results of the classification, it was determined that out of more than 7,000 landfills and dumps in Ukraine, about 320 are classified as large sites. These sites occupy more than a third of the total area of landfills and dumps in Ukraine and contain up to 84% of disposed household waste. Large landfills and dumps are a priority for greenhouse gas emissions study. According to the results of greenhouse gas emissions study, their total amount is estimated to be 5,833.5 thousand tСО2. These amount 32.9% less than similar estimates according to the IPCC methodology, which is explained by the higher accuracy of the Ukrainian landfill gas model and its parameters. Of the total amount of greenhouse emissions, up to 40% can potentially be reduced by the continued development of landfill gas collection systems, which actually reduced about 10% greenhouse gas emission in 2021. This potential can be increased by half and the reduction of greenhouse gas emissions can potentially reach up to 60% of their total emissions from landfills and dumps. For this purpose, the rehabilitation of the landfills and dumps should be conducted along with the development of landfill gas collection systems. However, rehabilitation is a complex and expensive measure, and the rationale for its application may be the subject of further research.

Cattle, carcass, economic, and estimated emission impacts of feeding finishing steers lubabegron or ractopamine hydrochloride.

Lubabegron (Experior; Elanco, Greenfield, IN, USA) is the first U.S. Food and Drug Administration approved feed additive for reducing gas emissions from feedlot animals or their waste; it does not have live or carcass performance claims. Our primary objective was to determine the effect of lubabegron on feedlot performance and carcass traits in finishing beef steers compared to ractopamine hydrochloride (Optaflexx; Elanco, Greenfield, IN, USA). A commercial feedlot trial using cross-bred beef steers (n = 2,117; 373 ± 15kg initial body weight [BW]) was completed with a randomized complete block design. Treatments consisted of two feed additives: (1) OPT targeted to deliver 300mg/animal/d of ractopamine hydrochloride for 28 ± 7 d out from harvest and (2) EXP targeted to deliver 36mg/animal/d of lubabegron 56 ± 7 d out from harvest and a 4-d preslaughter withdrawal period. Twenty 70 to 142 hd pens with 10 pens per treatment were used. Cattle were weighed at arrival processing and at harvest and fed for an average of 167 d. Data were used to calculate production metrics, partial budgets, and estimated greenhouse gas emissions using published methods, and were analyzed using linear mixed models with pen as the experimental unit and block as a random intercept. A statistical significance threshold of α = 0.05 was determined a priori. There was no evidence for statistically significant differences between treatments for initial BW (P = 0.70), health-related outcomes (P values ≥ 0.43), or mobility scores (P = 0.09). Cattle-fed EXP had increased final BW, ADG, G:F, and decreased dry matter intake (P values ≤ 0.01) compared to OPT. Carcasses were 11 ± 1.76kg (hot carcass weight) heavier in EXP group (P < 0.01), and differed between treatments for both yield grades (YG) and quality grades distributions (P values ≤ 0.01). Cattle-fed EXP had a shift toward more YG 1 and 2, select and sub-select carcasses compared to OPT, which had as shift toward more YG 3, 4, 5, prime and choice carcasses. With increased beef production and efficiency compared to OPT, the estimated CO2 equivalent emissions from production were reduced by 6.2% per unit of carcass weight for EXP (P ≤ 0.01). Estimated net returns/animal shipped were $56.61 ± 9.37 more for EXP than OPT (P ≤ 0.01). In conclusion, when cattle were fed for the same total number of days, feeding EXP compared to OPT increased net returns, feedlot performance, and efficiency, but resulted in carcass yield and quality characteristics that may impact marketing programs.

Strategies to Mitigate Greenhouse Gas (GHG) Emissions from the Solid Waste Management Sector: A Case Study of Vavuniya, Sri Lanka.

The waste sector is a substantial source of GHG emissions worldwide. Open dumping and internal combustion (IC) waste collection vehicles are significant sources of GHG emissions in Vavuniya. This research aims to estimate GHG emissions and recommend strategies to reduce emissions from the solid waste management sector. The IPCC methodology, considering Tier 1 estimation values based on default activity data, was used to estimate CH4 emissions from solid waste disposal sites. GHG emissions from collection vehicles were calculated based on IPCC mobile combustion recommendations. Three recommended strategies were considered based on demand, economic, and environmental feasibility and are expected to commence in 2025. According to current practices, open dumping generated 29.217 Gg of CO2 equivalent up to 2023, projected to rise by 37.8% by 2040. There will be a 57% decrease in open dumping-related GHG emissions by 2040 if composting is made mandatory for biodegradable waste, even though it produces emissions. Solar panels will be used to charge electric vehicles that will replace IC ones to cut emissions and fuel expenditures by 2025. The carbon sink reforestation program at the district level would need to begin in 2025 with an area of 161 hectares to sequester cumulative GHG emissions from composting dumpsites and fuel vehicles to achieve carbon neutrality by 2040. Investments from Vavuniya Carbon Sink Bonds (VCSB) on additional solar panels will ensure financial feasibility, having an internal rate of return of 23.18%. It paves the path to reducing GHG emissions, which is highly emphasized in the Nationally Determined Contributions, National Climate Change Policy, and waste management policy of Sri Lanka.

Inferring trip purposes and mode substitution effect of rental e-scooters in London

The lack of information on trip purpose and alternative mode in micromobility service usage data remains a major analytical challenge. Conventional survey method is subject to significant sampling and stated preference biases. To overcome this challenge, this paper presents a new inference method through a case study of rental e-scooters in London. The inference method features a rule-based algorithm for matching observed rental e-scooter trips with filtered trip samples in the English National Travel Survey (NTS) series. Probability distribution of trip purposes and alternative modes are then retrieved from NTS. Inference results are validated using official data. Discrepancies, sources of biases and correction measures are investigated. Based on the inferred mode substitution pattern, we estimate greenhouse gas emissions reduction of selected rental e-scooter trips in London (36–103 g CO2e per mile). It is expected that the proposed method is applicable to a wide range of micromobility studies using service usage data.

A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale

To address and mitigate the environmental impacts of synthetic greenhouse gases it’s crucial to quantify their emissions to the atmosphere on different spatial scales. Atmospheric Inverse modelling is becoming a widely used method to provide observation-based estimates of greenhouse gas emissions with the potential to provide an independent verification tool for national emission inventories. A sensitivity study of the FLEXINVERT+ model for the optimisation of the spatial and temporal emissions of long-lived greenhouse gases at the regional-to-country scale is presented. A test compound HFC-134a, the most widely used refrigerant in mobile air conditioning systems, has been used to evaluate its European emissions in 2011 to be compared with a previous study. Sensitivity tests on driving factors like—observation selection criteria, prior data, background mixing ratios, and station selection—assessed the model’s performance in replicating measurements, reducing uncertainties, and estimating country-specific emissions. Across all experiments, good prior (0.5–0.8) and improved posterior (0.6–0.9) correlations were achieved, emphasizing the reduced sensitivity of the inversion setup to different a priori information and the determining role of observations in constraining the emissions.The posterior results were found to be very sensitive to background mixing ratios, with even slight increases in the baseline leading to significant decrease of emissions.

Integrating work into life helps reduce residential greenhouse gas emissions

Work from home (WFH) creates work–life integration by moving work into traditional life at home, but its influence on residential greenhouse gas (GHG) emissions remains unclear. In this study, an activity-based bottom-up model was developed to analyze the time-use patterns (activity durations and timeline of a typical day) of participants under WFH and traditional home life and to quantify their residential GHG emissions. Under WFH, participants generated an average of 9.03 kg CO2e/person/day, primarily attributed to space heating and cooling, cooking, grooming, work, and watching TV and movies. Notably, the GHG footprints varied across groups (8.08–9.93 kg CO2e/person/day) due to different work and household responsibilities and leisure time and varied with climate region (4.99–10.63 kg CO2e/person/day) because of emission factors of electricity, space heating and cooling, and cooking. Compared with traditional life at home (10.06 kg CO2e/person/day), WFH participants spent less time on almost all major activities (especially sleeping and watching TV and movies) to focus on work, enabling an 11.34% (1.02 kg CO2e/person/day) mitigation of GHG emissions. The reductions also varied by group and climate region, mainly associated with laundry, cooking, and watching TV and movies. Opportunities to reduce GHG emissions under WFH lie in targeting key activities, balancing the time spent on various activities, and developing group- and spatial-specific strategies. This study provides a systematic and high-resolution estimation of residential GHG emissions under WFH and traditional home life, with a complete system boundary, activity-specific considerations, and countrywide understanding. The findings reveal the environmental impact of work–life integration from the residential perspective and can aid residents and policymakers in utilizing decarbonization opportunities to advance low-carbon living under WFH.

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH4), remain challenging. We estimated LULUCF-related CH4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH4. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories.

Estimation of greenhouse gas emissions using linear and logarithmic models: A scenario-based approach for Turkiye's 2030 vision

Turkiye pledged to considerably reduce its greenhouse gas (GHG) emissions by the year 2030 as a part of its commitment under the Paris Agreement. The problem with analyzing mitigation of greenhouse gas emissions requires generation of accurate, reliable and consistent emission forecasts. This study aimed to accurately forecast Turkiye's total CO2 emissions per capita and per capita CO2 emissions from energy industries, industrial processes and agricultural sectors till 2030 using linear and logarithmic models based on increasing and decreasing scenarios. Turkiye's total CO2 emissions per capita in 2030 could reach to 7.6 and 7.7 tons of CO2, with total emissions of about 635 and 643 million tons (Mt) based on linear and logarithmic models, respectively. Linear modeling results showed that per capita CO2 emissions from Turkiye's energy industries, industrial processes and agricultural sectors could reach to 5.3, 0.9 and 0.9 tons in 2030, respectively, while logarithmic modeling results yielded that per capita CO2 emissions from Turkiye's energy industries, industrial processes and agricultural sectors could be 5.5, 1.1 and 0.9 tons in 2030, respectively. The accuracy of fit for linear and logarithmic models was assessed by calculating root mean square error (RMSE < 0.2036) and mean absolute percentage error (MAPE < 12.3347) values which showed that the models fitted well with the timeline data. In conclusion, Turkiye's greenhouse gas emissions can be reduced if utilization of renewable energy sources in the country's energy portfolio is increased, and the shares of energy intensive processes in the industrial and agricultural sectors are reduced.

Effect of a Fortified Biostimulant Extract on Tomato Plant Productivity, Physiology, and Growing Media Properties.

The pursuit of sustainable and productive agriculture demands the exploration of innovative approaches to improve plant productivity and soil health. The utilization of natural agricultural biostimulants, such as extracts from seaweed, fish, and humus, has gained prominence as an ecological strategy to achieve this goal. In this study we investigated the effectiveness of a fortified biostimulant extract (FBE), composed of extracts from seaweed, fish, and humus, on tomato plant physiology, productivity, and growing media properties, and estimated carbon emissions associated with tomato production. The FBE was applied to the growing media of tomato plants produced in a greenhouse, in experiments over two growing seasons. The productivity assessments demonstrated that the application of FBE significantly increased tomato fruit yield by 20% and relative marketable fruit yield by 27%, and reduced estimated greenhouse gas (GHG) emissions associated with production by 29%. FBE treatment improved plant shoot and root biomass, accelerated flower and fruit set initiation, and increased chlorophyll content in leaves, resulting in enhanced plant physiology and advanced development. FBE treatment positively influenced the availability of crucial nutrients such as nitrogen, phosphorus, and iron in the growing media. FBE promoted the growth of total active microbes in the growing media, particularly the fungal population, which plays an important role in nutrient cycling and health. These findings highlight the beneficial effects of the FBE due to enhanced plant productivity and growth, improved fertility, the promotion of beneficial plant and growing media interactions, and the reduction in estimated GHG emissions.

Data Wormholes and Speculative Rice Fields: An Infrastructural Politics of Anticipating Greenhouse Gas Emissions

The 2015 Paris declaration obligated international development organizations to assess the climate compatibility of their projects. For irrigation projects, like those negotiated between the Agence Française de Développement, and the Cambodian government in the early 2020s, calculations of estimated greenhouse gas emissions have become important requirements. But how to estimate emissions from future rice fields and the effects of irrigation infrastructures that do not exist? To address this issue, emissions calculators have been developed as a means to bridge climate science and development knowledge infrastructures, so that data and forms of calculation from climate science can easily enter the world of development. However, by engaging in an infrastructural inversion, we argue that this understanding is flawed. Drawing on a case study of an irrigation project in Cambodia, we show that heterogeneous data concatenations are continuously transformed in the movement across infrastructures until referentiality breaks down. Emission calculators operate as a data wormhole, emitting extremely uncertain numbers that contribute to a problematic and speculative politics of anticipation. In contrast with the dominant politics of anticipation, which depends on futile efforts to neutralize uncertainty, infrastructural inversion makes it possible to envision a decentered politics attentive to distributed agency.

Carbon footprints of tea production in smallholder plantations: A case study of Fenghuang Dancong tea in China

The high fertilizer application in tea planting and the large energy consumption in tea processing have raised concerns about greenhouse gas (GHG) emissions of tea production. China is the world's largest tea production country, but its tea production is dominated by smallholder farmers. Therefore, when it comes to GHG emission reduction in tea production, specific measures need to be developed for the smallholder mode. The purpose of this study is to reveal the characteristics and influencing factors of GHG emissions of smallholder tea plantations, as well as to develop GHG emission reduction measures suitable for the smallholder mode. Here, we focus on Fenghuang Dancong tea and two types of smallholder tea plantations, the high-altitude tea plantation (HTP11HTP: the high-altitude tea plantation; MLP: the mid-low-altitude tea plantation; CF: carbon footprint; GHG: greenhouse gas; CO2-eq: CO2 emission equivalent; LCA: life cycle assessment.) and the mid-low-altitude tea plantation (MLP), in Fenghuang Town, Chaozhou City, China. The carbon footprint (CF) method is employed, which is a widely used method for estimating GHG emissions and expressed in CO2 emission equivalent (CO2-eq). The assessment on the CF of the Fenghuang Dancong tea production and a comparative analysis between the HTP and the MLP have been made. Results show that (1) at the tea planting stage, fertilizer input is the main contributor to the CFplanting; electricity input is the primary component of the CFprocessing at the tea processing stage; (2) considering the two stages of tea planting and processing, the CFtea of the MLP is more than twice that of the HTP; (3) the production and management measures adopted by tea farmers have a significant impact on the CFtea, which are mainly driven by economic interests. In view of these findings, we emphasize the importance of balancing GHG emission reduction with economic benefits and propose GHG emission reduction measures such as formulating subsidy policies for organic fertilizers, promoting green and organic product certification, improving the energy input structure and strengthening scientific guidance. We submit that this study will not only help local managers formulate efficient GHG emission reduction measures, but also provide references for other studies on GHG emissions of tea production in the smallholder mode.

Greenhouse gas emissions and carbon footprint of maize-based feed products for animal farming in Thailand.

Due to the growing demand for livestock products both within the country and in foreign markets, there is a need to boost the production of maize-based animal feed in Thailand. However, greenhouse gas (GHG) emissions and the potential for reducing these emissions through the production of various types of animal feed remain ambiguous. Thus, this study is aimed at estimating GHG emissions from broiler, layer, and swine feed production in Thailand and identifying economic advantages of alternative methods to mitigate those emissions. Field surveys were carried out to quantify the input and output of energy and materials in 10 commercial feed mills so as to determine greenhouse gas emissions using proper emission factors. The scope of this study is based on the cradle-to-gate approach. The functional unit used for greenhouse gas evaluation was kg CO2-eq/t of feed. Total greenhouse gas emissions from broiler, layer, and swine feed production were found to be 650 ± 20, 706 ± 20, and 466 ± 20 kg CO2-eq/t of feed, respectively. Layer feed production created the highest greenhouse gas emissions, 1.09 and 1.52 times that of broiler and swine feed production, respectively. This is because layer feed required intensive fish meal (FM) as protein sources for improving egg quality. In broiler and swine feed production, the most significant emissions are attributed to the use of maize grain (MG) and soybean meal (SBM) as sources of carbohydrate and protein in those feeds. However, animal feed production operation at the existing condition still emits CO2 to the atmosphere as CO2 fixation efficiencies of 69.3, 67.5, and 75.9% for broiler, layer, and swine feed, respectively. From the sustainable resource consumption scenarios in broiler, layer, and swine feed production, approximately 39.6, 49.6, and 43.3% reduced carbon emissions could be achieved by using MG rotated with SB in the maize plantation phase and substituting FM, wheat grain and fossil fuel needed in the manufacturing process with SBM, locally-produced tapioca chips and biomass energy. Consequently, the potential cost savings of such replacements were determined to be 54.0, 62.5, and 29.7 USD/t of feed, respectively.

Reduction of Greenhouse Gas Emissions through Community-Scale Pyrolysis Technology in Bogor City and its Financial Feasibility

Population growth affects the generation of plastic waste and could potentially increase greenhouse gas (GHG) emissions through the burning process. This has become a severe problem as it contributes to global warming. Therefore, plastic waste management is required, for instance, by using pyrolysis technology on a community scale. Such a project will reduce plastic waste and GHG emissions by processing plastic into valuable products. This study aims (1) to estimate potential GHG emissions before the project implementation, (2) to estimate potential GHG emissions reduction after the project implementation, and (3) to assess both potential revenue and profit of pyrolysis products. This study employs SNI 19-3694-1994 method to estimate household waste generated, the clean development mechanism (CDM) method to estimate GHGs emissions reduction, and the profit comparison method (PCM) to asses both revenue and profit of pyrolysis products. The results show that GHG emissions before the project will be 3.69 t CO2e in 2021 and could increase to 4.61 t CO2e in 2030. Potential GHG emissions reduction depends on the fuel types to heat the reactor. Only electric pyrolysis will reduce GHG emissions by up to 0.46 t CO2e (13%) annually. This project is not financially feasible because operational costs (15,772,779 IDR) exceed the annual revenue (1,014,000 IDR).

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

Future of Blockchain: Data Storage, Carbon Calculation, Accounting, and Emissions Trading

The paper explores various aspects of data storage technologies, including Solid-State Storage, Cloud Computing, Edge Computing, and Blockchain, in the context of their implications, advantages, and limitations. Each technology's impact on data storage, performance, security, scalability, and environmental considerations is examined. Carbon Footprint (CF) is introduced as a measure of the environmental impact of data storage methods. The paper then delves into carbon calculation approaches, emphasizing the importance of accurate measurement in a computing environment. The integration of Carbon Footprint and Blockchain technology is discussed, presenting a framework for managing carbon emissions in data storage. Carbon accounting methodologies, including spend-based, activity-based, and hybrid methods, are detailed along with their applications in estimating greenhouse gas emissions. Finally, the paper explores the intersection of Carbon Emission Trading and Blockchain, highlighting how Blockchain's transparency and security attributes can address challenges within carbon trading systems.

Comprehensive analysis of refrigerant R134a: Implications for estimating and managing greenhouse gas emissions

This study introduces a comprehensive analytical method for assessing refrigerant purity, offering various insights into accurate and precise estimations of greenhouse gas (GHG) emissions and the reclamation of refrigerants. Samples of three different states of CH2FCF3 (R134a), namely recovered R134a, reclaimed R134a, and new R134a, were prepared and analyzed for all impurities. Non-condensable gases (O2, N2, CH4, CO, and H2) were analyzed using gas chromatography (GC) with a thermal conductivity detector (TCD) and a pulsed discharge detector (PDD), and unidentified complex compound impurities containing C, H, Cl, and F were investigated using GC with mass spectrometry (MS) and an atomic emission detector (AED). The purities of the three samples were determined by calculating the total quantity of impurities and their uncertainties. The calculated values for the purities of recovered R134a, reclaimed R134a, and new R134a were 953,125.38 µmol/mol (1,272.45 µmol/mol, k = 2), 998,529.67 µmol/mol (31.58 µmol/mol, k = 2), and 998,803.56 µmol/mol (32.50 µmol/mol, k = 2), respectively. The majority of impurities detected were GHGs with high global warming potentials (GWPs) up to 11,200. Certain impurities were found at concentrations exceeding 1,000 µmol/mol. The annual GHG emissions resulting only from R134a impurities in South Korea were approximately 0.01 MtCO2eq. Because of the large number of GHGs in refrigerants, a comprehensive examination of refrigerant impurities is necessary for refrigerant monitoring and GHG inventory management.

Connecting catch profiles with efficiency: An application to the beam trawl fishery in the Northern Adriatic Sea

Estimation of technical efficiency in fisheries is most commonly finalized at comparing the conversion from inputs (e.g. time at sea, fuel consumption) to production (i.e. landed fish) among vessels or gears. In this paper we adopt a novel approach focusing on the efficiency of fishing trips characterized by different catch composition within the same fleet. In particular, the case study under analysis is represented by the beam trawl (“rapido”) fishery of Chioggia (Adriatic Sea, GSA 17), which has been subject to increasing technical and effort limitations in recent years due to its severe impacts on the marine ecosystem. The methodological strategy first involves a cluster analysis of catch profiles to identify homogeneous groups of fishing trips, which are subsequently compared in terms of economic performance based on the efficiency scores from stochastic frontier analysis. By including also an estimation of Greenhouse Gas emissions per trip, the results from our study can be used to inform future management interventions that aim at improving the overall efficiency of the fishery while reducing its carbon footprint and the pressure on the most relevant target species.