Greenhouse Gas Emission Inventories Research Articles

Three years of CO2, CH4 and N2O fluxes from different sheepfolds in a semiarid steppe region, China

To better assess greenhouse gas (GHG) emissions from livestock folds in semi-arid steppe zones and reduce uncertainties in regional and national GHG emission inventories, we measured the fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from sheepfolds under contrasting management regimes (i.e., summer sheepfolds under continuous and rotational grazing strategies and the winter sheepfold) for 3 consecutive years. Our results showed that these GHG fluxes had high intra-annual and interannual variations, emphasizing the importance of multi-year measurement for achieving temporally representative annual budgets. Sheep presence and temperature appeared to be the key factors driving CH4, CO2 and N2O fluxes from sheepfolds, e.g., higher GHG emissions usually occurred in seasons with sheep presence. However, the sheepfold type exerted a distinct influence on the temperature sensitivity of GHG fluxes, i.e., the Q10 values for GHG fluxes were generally higher in summer sheepfolds than in winter sheepfold. The annual CH4, CO2 and N2O emissions for the 3 sheepfolds were estimated to be 1.5–16.5 kg C ha−1 yr−1 (or 1.9–2.6 g C yr−1sheep−1), 8.6–16.0 t C ha−1 yr−1 (or 5.1–6.6 kg C yr−1sheep−1) and 28.3–41.9 kg N ha−1 yr−1 (or 19.0–26.8 g N yr−1sheep−1), respectively. Averaging across the 3 years, the annual net GHG emissions (CH4 + CO2 + N2O) for all sheepfolds ranged from 47 to 71 t CO2-eq ha−1 yr−1 (or 27–36 kg CO2-eq yr−1 sheep−1), of which CO2 and N2O emissions contributed the most; moreover, the annual net GHG emissions had no significant differences between sheepfold types or grazing strategies. Given that local steppe soils have a lower magnitude of soil respiration (CO2) and N2O emissions and are also net sink for atmospheric CH4, the sheepfold sites in this region are undoubtedly one of the significant hotspots for GHG emissions and could be key areas to focus mitigation action.

Downstream natural gas composition across U.S. and Canada: implications for indoor methane leaks and hazardous air pollutant exposures

Previous research has shown that natural gas (NG) leaks from residential appliances are common, affecting greenhouse gas emission inventories and indoor air quality. To study these implications, we collected and analyzed 587 unburned NG samples from 481 residences over 17 North American cities for hydrocarbons, hazardous air pollutants, and organosulfur odorants. Nearly all (97% of) gas samples contained benzene (between-city mean: 2335 ppbv [95% CI: 2104, 2607]) with substantial variability between cities. Vancouver, Los Angeles, Calgary, and Denver had at least 2x higher mean benzene concentrations than other cities sampled, with Vancouver exhibiting a nearly 50x greater mean benzene level than the lowest-concentration city (Boston). We estimate that current U.S. and Canadian emissions inventories are missing an additional 25 000 [95% CI: 19 000, 34 000] and 4000 [95% CI: 3700, 5200] lbs benzene yr−1 through downstream NG leakage, respectively. Concentrations of odorants added for leak detection varied substantially across cities, indicating a lack of standardization. Houston, for instance, had 5x higher mean tert-butyl mercaptan levels than Toronto. Using these odorant measurements, we found that methane emissions as high as 0.0080–0.28 g h−1 and indoor benzene enhancements 0.0096–0.11 ppbv could go undetected by persons with an average sense of smell, with large uncertainties driven by smelling sensitivity, gas composition, and household conditions. We also observed larger leaks (>10 ppm ambient methane) in ∼4% of surveyed homes, confirming that indoor leakage occurs at varying degrees despite the presence of odorants. Overall, our results illustrate the importance of downstream NG composition to understand potential emissions, exposures, and odor-mediated leak detection levels. Given methane’s global warming potency, benzene’s toxicity, and wide variation in smelling abilities, our findings highlight the deficiencies regarding the sole reliance on odorization to alert and protect all occupants from indoor leaks.

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.

Carbon Emission Analysis and Reporting in Urban Emissions: An Analysis of the Greenhouse Gas Inventories and Climate Action Plans in Sarıçam Municipality

The urban carbon footprint (UCF) is an important tool for assessing an organization’s ecological impacts and in guiding sustainability efforts. This calculation is usually measured in tons of carbon dioxide equivalent (CO2-eq). Calculations provide important data to determine strategies to reduce the carbon footprint and establish sustainability targets. Various standards and protocols guide UCF calculation, and many organizations aim to make these data transparent to their stakeholders and the public. This study aims to calculate the UCF of Sarıçam Municipality (SM) in the Adana Province of Türkiye. This study includes the greenhouse gas emission inventories resulting from all activities of the SM main service building, guest house, construction site service building, Cultural Center service building, and additional service buildings between 1 January 2022 and 31 December 2022. The calculations include generator fuel consumption, electricity consumption, the refrigerant gas leaks and refills resulting from these activities, the fuel consumed in vehicles owned by the company or whose fuel consumption is under company control, emissions originating from personal travel, emissions originating from customers and visitors, emissions originating from business travel, purchases, etc. Emissions from products purchased and emissions from waste transportation are included. The findings show that, in 2022, the total UCF of SM was equal to 10,862.46 tons of CO2-eq. The Paris Agreement aims to reduce the per capita emissions to approximately two tons of CO2-eq by 2030. The carbon footprint per employee within the municipality was calculated at 12.43 tons of CO2-eq, as derived from the analyzed data. The results reveal the importance of implementing sustainable practices and strategies within SM, such as energy efficiency measures, waste reduction, and the adoption of renewable energy sources, to mitigate its carbon footprint. This study plans to provide a basis for SM’s reduction efforts by keeping greenhouse gas emissions under control.

Characterizing Spatial and Temporal Variations in N2O Emissions from Dairy Manure Management in China Based on IPCC Methodology

The emission factor method (Tier 1) recommended by the Intergovernmental Panel on Climate Change (IPCC) is commonly used to estimate greenhouse gas (GHG) emissions from livestock and poultry farms. However, the estimation accuracy may vary due to practical differences in manure management across China. The objectives of this study were to estimate the direct and indirect nitrous oxide (N2O) emissions from dairy manure management between 1990 and 2021 in China and characterize its spatial and temporal variations following IPCC guideline Tier 2. The N2O emission factor (EF) of dairy cow manure management systems was determined at the national level and regional level as well. The results showed that the national cumulative N2O emission of manure management from 1990 to 2021 was 113.1million tons of CO2 equivalent, ranging from 90.3 to 135.9 million tons with an uncertainty of ±20.2%. The annual EF was 0.021 kg N2O-N (kg N)−1 for total emissions, while it was 0.014 kg N2O-N (kg N)−1 for direct emissions. The proportions of N2O emissions in North China, Northeast China, East China, Central and Southern China, Southwest China and Northwest China were 32.3%, 18.6%, 11.4%, 5.8%, 6.1% and 25.8%, respectively. In addition, N2O emissions varied among farms in different scales. The respective proportions of total N2O emissions from small-scale and large-scale farms were 64.8% and 35.2% in the past three decades. With the improvement in farm management and milk production efficiency, the N2O emissions per unit mass of milk decreased from 0.77 × 10−3 kg to 0.48 × 10−3 kg in 1990–2021. This study may provide important insights into compiling a GHG emission inventory and developing GHG emission reduction strategies for the dairy farming system in China.

High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills

Abstract. We quantify 2019 annual mean methane emissions in the contiguous US (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a−1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find emissions for livestock of 10.4 (10.0–10.7) Tg a−1, for oil and gas of 10.4 (10.1–10.7) Tg a−1, for coal of 1.5 (1.2–1.9) Tg a−1, for landfills of 6.9 (6.4–7.5) Tg a−1, for wastewater of 0.6 (0.5–0.7), and for other anthropogenic sources of 1.1 (1.0–1.2) Tg a−1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 70 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI's new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 27 % larger than the GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a−1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Characteristics and key driving factors of nitrous oxide emissions from a full-scale landfill leachate treatment system

The characteristics of N2O emission from a full-scale landfill leachate treatment system were investigated by in-situ monitoring over 1.4 years and driving factors responsible for these emissions were identified by statistical analysis of multidimensional environmental variables. The results showed that the maximum N2O emission flux of 2.21 × 107 mg N·h−1 occurred in the nitrification tanks, where 98.5 % of the total N2O was released, with only 1.5 % of the total N2O emitted from the denitrification tanks. Limited oxygen in nitrification tank was responsible for N2O hotspot. The N2O emissions from the parallel lines A and B (both comprising the primary biochemical system) accounted for 52.6 % and 46.6 %, respectively, while the secondary biochemical system contributed only 0.8 % to the total emissions. Higher nitrite concentration in line A and lower nitrogen loading in the secondary biochemical system caused these discrepancies. We found that during the steady state of leachate treatment, intensive N2O emissions of 253.4–1270.5 kg N·d−1 were measured. The corresponding N2O emission factor (EF) ranged from 8.86 to 49.6 %, much higher than those of municipal wastewater treatment. But N2O EF was inconceivably as low as 0.42 % averagely after system maintenance. Influent with low salinity was the key reason, followed by the high MLSS and varying microbial community after maintenance. The dominant genus shifted from Lentimicrobium and Thauera to Norank-F-Anaerolineaceae and Unclassified-F-Rhodocyclaceae. This study underscores the significance of landfill leachate treatment in urban nitrogen management and provides valuable insights into the characteristics and driving factors of N2O emissions from such systems. The findings offer important references for greenhouse gas emission inventories and strategies for N2O control in full-scale wastewater treatment plants.

Back in the Driver’s Seat: How New EU Greenhouse-Gas Reporting Schemes Challenge Corporate Accounting

Greenhouse-gas (GHG) reporting schemes for companies are increasingly part of climate-mitigation policies worldwide. Notably, the European Green Deal (2019) boosts new public regulations that oblige companies to compile GHG emission inventories, i.e., account for their emissions in a given system boundary. Along with this boost, the workload for companies increases; at the same time, the quality of reporting is questioned. Given the overarching goal to improve companies’ climate-mitigation performance, the quality of reporting is inseparably connected to the quality of the respective accounting. However, the literature discusses carbon accounting as a universal umbrella term focusing on managerial issues, thus disregarding the crucial role of accounting methodologies in the sense of calculation approaches. In this publication, we apply an analytical approach introducing a clear differentiation between the task of quantitatively accounting for GHG inventories and the task of reporting results from calculated inventories in response to stakeholder or policy expectations. We use this approach to investigate European GHG reporting schemes and related GHG accounting methodologies in detail. Our findings indicate that the current phase of the European Green Deal depicts a quantitative growth in reporting schemes and a significant qualitative change by shifting from formerly voluntary to mandatory reporting schemes, along with the application of accounting methodologies originally not intended for politically compulsory purposes. We analyze the consequences of this shift, which poses new challenges for companies and policymakers, i.e., data-management concepts and refined methodological frameworks.

Characterizing urban GHG emissions based on land-use change—A case of Airport New City

Measuring greenhouse gas (GHG) emissions across different types of land is essential for urban planning during the transition to low-carbon cities. Urban land identification, based on remote sensing images and points of interest (POIs), has the potential to narrow down the statistical unit, helping achieve spatial visualization of GHG emissions below the city level and distinguish emission characteristics of different land-use types. Considering land type as a classification standard for GHG emissions, we constructed the modified STRIPAT-PLS model by identifying land-use types, reconstructing land-use GHG emissions (LUGEs) inventory, and extracting driving indexes, which calculated driving force and LUGEs that were missed in historical years. It deepened the depth and precision of GHG emissions research and provided a reference for LUGE reduction and land management below the city level where missing historical data on GHG inventories. The results showed that: 1) the modified STRIPAT-PLS model achieved a simulation accuracy of 94.9%, demonstrating the effectiveness of our research approach in depicting GHG emissions across different land-use types. 2) Land scale, socio-economic development, and industrial development were key factors that impacted agricultural land, residential land, and airport respectively. 3) Airport was the most significant carbon contributor, while industrial witnessed the highest growth in emission intensity; 4) the spatial analysis indicated decreasing differences in GHG emissions and an overall increase in emission intensity. In addition, the research proposed emission reduction priorities and strategies for each land type to promote low-carbon goals in airport cities.

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.

Civil aviation emissions in China in 2019: Characteristics and abatement potential

Civil aviation is one of the industries facing the greatest challenge in reaching carbon neutrality by the middle of this century, and this sector also contributes to adverse impacts on the regional air quality and human health. China exhibits the second highest air passenger turnover worldwide. Our understanding of civil aviation emissions must be urgently enhanced, and the mitigation potential should be explored. In this study, on the basis of real domestic flight information for 2019, we built a greenhouse gas and air pollution emission inventory for the civil aviation sector in China with the fuel flow method based on the cruise and other phases. We thoroughly analyzed emissions by region, aircraft and engine types, and aircraft age, based on which we designed four measures to evaluate the abatement potential. We found that the hydrocarbon (HC), CO, NOx, SO2, particulate matter (PM) and CO2 emissions in 2019 reached 79.9 kt (95% CI [51.6–114.5]), 176.3 kt (95% CI [114.5–248.2]), 304.2 kt (95% CI [203.4–420.7]), 23.2 kt (95% CI [14.2–33.7]), 1.0 kt (95% CI [0.61–1.44]) and 87.0 Mt (95% CI [57.4–119.6]), respectively. The cruise phase was the major emission phase, accounting for 67%-87% of the total pollutant emissions. If four measures were jointly implemented, the HC, CO, NOx, SO2, PM and CO2 emissions could be reduced by 61%, 54%, 55%, 45%, 32% and 38%, respectively. Utilizing lower-emission aircraft and switching travel modes could substantially reduce civil aviation emissions in China.

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.

THE PERFORMANCE OF KUALA LUMPUR’S CARBON EMISSIONS IN THE CONTEXT OF URBAN PLANNING

Cities are responsible for 70% of greenhouse gas (GHG) emissions on a global scale, and cities play an important role in reducing GHG emissions. It is essential for Kuala Lumpur to consider reducing the city's GHG emissions. The city's GHG emission inventory can track and monitor the effectiveness of the climate action plans that has been implemented. The aim of this study is to identify the performance level of GHG emissions in Kuala Lumpur between 2010 and 2019. It is also to identify the performance of Kuala Lumpur's GHG emissions in 2019 in comparison to the global and Malaysian level. Data is calculated using the Global Protocol for Community-Scale Greenhouse Gas Emissions Inventory (GPC), which is recognised and utilised globally. Secondary data for the years 2010 and 2019 was analysed as well as the performance of the Kuala Lumpur GHG emission profile in 2019. With three (3) identified sources of emissions, Kuala Lumpur managed to reduce its GHG emission intensity from 2010 by 74.07% in 2019. The city's GHG emission was recorded at 15,675 ktCO2eq in 2019. The stationary energy sector contributes higher GHG emission than other sector, with 12,043 ktCO2eq (76.83%), followed by the transportation sector with 3,180 ktCO2eq (20.29%) and the waste sector with 452 ktCO2eq (2.88%). As of 2019, Kuala Lumpur's absolute carbon contribution to the global average is 0.03%, whereas Malaysia's absolute carbon contribution is 4.74%. Additionally, the city contributes just 0.07 kgCO2eq/RM (30.17%) to Malaysia's total GHG emission intensity.

High Resolution Emission Inventory of Greenhouse Gas and Its Characteristics in Guangdong，China

Based on the typical city survey data and statistics of Guangdong Province， a 2018-based 3 km×3 km gridded greenhouse gas emissions inventory was developed for Guangdong Province using the combination of top-down and bottom-up emission factor methods. The inventory covered the CO2， CH4， and N2O emissions from energy， industrial processes， agriculture， land use change and forest， waste management， and indirect sources. The results showed that estimates for CO2， CH4， and N2O in Guangdong Province for the year 2018 were 8.5×108， 1.9×106， and 1.1×105 t， respectively， and 8.5×108， 4.0×107， and 3.4×107 t by equivalent carbon dioxide， totaling 9.2×108 t. CO2 was the main greenhouse gas in Guangdong Province， accounting for 92.0% of the total emissions. Energy and indirect sources were the main emission sources， accounting for 77.9% and 7.6%， respectively， totaling 85.5%. Spatial distributions illustrated that most grids were greenhouse gas emissions， whereas some others were greenhouse gas sinks； the greenhouse gas emissions were distributed mainly in the Pearl River Delta region and had certain characteristics of distribution along the road network and channels. The greenhouse gas grids of high emission were mainly the locations of high energy-consuming enterprises such as large power plants， steel mills， and cement plants.

Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Navigating Sustainability through Greenhouse Gas Emission Inventory: ESG Practices and Energy Shift in Bangladesh’s Textile and Readymade Garment Industries

As the world's demand for textiles and clothing rapidly increases, this industry's greenhouse gas (GHG) emissions are becoming a major environmental concern. Bangladesh, a key player in the global textile supply chain and one of the top producers, contributes significantly to these emissions. However, accessible data on activity and GHG emissions, crucial for researchers, the private sector, and policymakers in decision-making, is scarce. To address this gap, this study combines a detailed field survey with expert interviews to establish a comprehensive emission inventory. This inventory aims to identify hotspots and facilitate the adoption of effective mitigation strategies. Focusing on a prominent industrial zone's textile and readymade garments (RMG) industries, the research employs a mix of top-down and bottom-up approaches and follows the IPCC guidelines to develop a GHG emission inventory for 2022. The study evaluates various emission sources, including scope 1 (onsite fuel combustions), scope 2 (grid electricity usage), and scope 3 (waste and wastewater treatment). In the total emissions (6043.5 Gg CO2eq.), textile and RMG industries contribute 67.8% and 32.2%, respectively, with scope 1 emissions dominating at 85%. Notably, scope 2 emissions exhibit significant uncertainty (−10.4% to +11.9%), largely due to variations in national grid emission factors. This study forecasts GHG emissions until 2030, considering current trends (26 thousand Gg CO2 eq.). It also explores various energy mix scenarios, factoring in the depletion of existing natural gas reserves (ranging from 8 thousand to 33 thousand Gg CO2 eq.). This study delves into the impact of the Environmental, Social, and Governance (ESG) system on industries' GHG emissions. Besides improving worldwide emission databases and identifying hotspots, this research aims to promote a sustainable transition in both Bangladesh and other developing textile manufacturing nations across the globe.

The spatial and source heterogeneity of agricultural emissions highlight necessity of tailored regional mitigation strategies

Agriculture contributes considerable greenhouse gas emissions while feed the constantly expanding world population. The challenge of balancing food security with emissions reduction to create a mutually beneficial situation is paramount. However, assessing targeted mitigation potential for agricultural emissions remains challenging, lacking comprehensive sub-national evaluations. Here, we have meticulously compiled the agricultural greenhouse gas emission inventories of China spanning the years 2000 to 2019, employing spatial analysis techniques to identify regional characteristics. We find that the peak of China's agricultural production emissions occurred in 2015 (1.03 × 109 tCO2 equivalent), followed by a valley in 2019 (0.94 tCO2 equivalent), largely attributed to shifts in livestock-related activities. Notably, methane emissions were the most dominant greenhouse gas, the Hunan province emerged as a prominent contributor, livestock raising stood out as a major activity, and enteric fermentation ranked as the primary emission source. There were substantial differences in the emission structure and sources among the provinces. Further spatial analysis showed geographical disparities in both total emissions and per capita emissions. The west-east blocked spatial characteristics of per capita emissions at the Hu Line sides emerged. We advocate that tailored mitigation strategy focusing on specific emission sources and regions can achieve substantial progress with minimal effort.

Research on high-precision accounting methods for regional carbon emissions under the peak carbon target

In recent years, the level of digitalization and intelligence in the energy sector has profoundly changed the management mode of the energy sector. However, in the process of promoting the digital transformation of the energy sector, we also face some difficulties. One of the main manifestations is the lack of energy data sharing, and the difficulty of realizing the convergence and integration of various types of energy data. In addition, there is insufficient mastery of customer-side energy use data and a lag in the perception of customer demand, which requires the improvement and enhancement of energy efficiency diagnosis and energy saving services. At the same time, we have not yet established advanced digital technology monitoring means to grasp the user’s energy use and carbon emissions in real time. Therefore, this paper proposes the study of high-precision accounting methods for regional carbon emissions under the carbon peak goal. Based on the energy consumption and production activities of each region, a greenhouse gas emission inventory is compiled, so that carbon emissions can be accurately calculated. The high-precision accounting method of carbon emissions is analyzed, and by establishing a more complete database and model, and constructing a regional online monitoring system under the peak carbon target, we aim to improve the efficiency of data acquisition and processing, as well as how to combine carbon emission accounting with the peak carbon target, in order to provide support for the formulation of a more effective emission reduction policy. The testing experiments after flow and CO2 concentration measurements show that the accuracy of the experiments on regional carbon emissions is as high as 97.5%.

CARBON CREDIT MARKET IN THE MOBILITY SECTOR: OPPORTUNITIES IN THE BRAZILIAN CONTEXT

The transportation sector is one of the largest emitters of greenhouse gases, contributing to climate change. Numerous initiatives have been adopted worldwide to reduce the emission of these gases, including the carbon credit market. The objective of this study was to review and contextualize mobility and pathways for transitioning to a low-carbon economy. The research was conducted using national and international databases and organizations related to the carbon credit market. Studies, reports, and documents were selected based on the following criteria: Materials related to mobility, sustainability, and carbon credit themes; in English, Portuguese, or French up until November 2022. The analysis of the information identified that life cycle analysis calculation methodologies are predominantly focused on nature, with little published about the transportation sector. Companies still need to incorporate environmental performance management through greenhouse gas emission inventories and evolve towards life cycle analysis of their products and services. In the global scenario, road vehicles account for 77% of the global carbon emissions from transportation (34% being heavy vehicles). Heavy vehicles disproportionately contribute more to the warming potential due to particle emissions. In conclusion, there is no single solution for the decarbonization of the transportation sector in Brazil; a combination of fleet renewal, biofuels, and improvements in the efficiency of the existing fleet, such as the electrification of the second axle of heavy vehicles, are essential to achieve Brazilian goals.

Establish of air pollutants and greenhouse gases emission inventory and co-benefits of their reduction of transportation sector in Central China

Recently, the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth. Considering air pollutants and greenhouse gases come from the same emission sources, it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China, the most polluted region in China. The inventory includes on-road mobile, non-road mobile, oil storage and transportation, and covers 9 types of air pollutants and 3 types of greenhouse gases. Based on the Long-range Energy Alternatives Planning System (LEAP) model, the emissions of pollutants were predicted for the period from 2020 to 2035 in different scenarios. Results showed that in 2020, emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, BC, OC, CO2, CH4, and N2O in Henan Province were 27.5, 503.2, 878.6, 20.1, 17.4, 222.1, 21.5, 9.4, 2.9, 92,077.9, 6.0, and 10.4 kilotons, respectively. Energy demand and pollutant emissions in Henan Province are simulated under four scenarios (Baseline Scenario (BS), Pollution Abatement Scenario (PA), Green Transportation Scenario (GT), and Reinforcing Low Carbon Scenario (RLC)). The collaborative emission reduction effect is most significant in the RLC scenario, followed by the GT scenario. By 2035, under the RLC scenario, energy consumption and emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, CO2, CH4, and N2O are projected to decrease by 72.0%, 30.0%, 55.6%, 56.0%, 38.6%, 39.7%, 51.5%, 66.1%, 65.5%, 55.4%, and 52.8%, respectively. This study provides fundamental data support for subsequent numerical simulations.