Estimate Greenhouse Gas Emissions Research Articles

A Study on the Development of Destruction or Removal Efficiency (DRE) Considering the Characteristics of Greenhouse Gas Abatement Technology Used in the Semiconductor and Display Industries in South Korea

In this study, the Destruction or Removal Efficiency (DRE) of 10 types of F-gases used in the semiconductor and display industries in South Korea was measured. These industries use a large volume of F-gases with high Global Warming Potential (GWP), significantly contributing to national greenhouse gas emissions. Therefore, accurately calculating the greenhouse gas emissions from these industries and establishing appropriate mitigation plans is crucial. The current IPCC guidelines provide parameters for estimating greenhouse gas emissions for each gas, including DRE values. However, they present only a single coefficient for each gas, without considering the diverse abatement technologies that are commercially applied in practice. As a result, there is a potential for overestimating South Korea’s national greenhouse gas emissions, as these guidelines do not reflect the advanced abatement technologies used in each country’s semiconductor and display industries. To address this, the DREs of Combustion-type and Plasma-type abatement technologies, which are widely used in South Korea, were measured based on the Korean KS guidelines, developed from the U.S. EPA’s reduction efficiency measurement guidelines. The results showed that Plasma-type technologies, which are generally known to have better reduction efficiency, achieved higher DRE values compared with Combustion-type technologies. Furthermore, statistical analysis was conducted using SPSS 26 to assess whether it is significant to develop separate DRE values for different technologies. The analysis confirmed that developing distinct DREs for each technology is statistically significant. The findings of this study provide practical guidance for selecting optimal abatement technologies in South Korea’s semiconductor and display industries and serve as fundamental data for contributing to the achievement of sustainable carbon neutrality goals through more accurate greenhouse gas inventories in countries involved in semiconductor and display production.

Global Land Use Change and Its Impact on Greenhouse Gas Emissions.

Anthropogenic activities have altered approximately two-thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO2eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO2eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO2eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.

A First Assessment of Greenhouse Gas Emissions From Agricultural Peatlands in Canada: Evaluation of Climate Change Mitigation Potential

ABSTRACTCanada has a quarter of the world's peatlands accounting for an estimated 150 Gt of stored carbon. While over 98% of Canadian peatlands are intact, agriculture has been estimated as accounting for the greatest peatland disturbance by area. Greenhouse gas (GHG) emissions from peatland agriculture can contribute a large proportion of national anthropogenic emissions for some countries. In Canada, estimates of GHG emissions from cultivated peat soils are incomplete. Improved accounting of these GHG emissions is required to inform decisions about where to deploy ecological restoration projects and where to allow future agricultural expansion as climate warms. Compiled studies that measured GHG fluxes from agricultural peat fields in Canada resulted in mean emissions factors of 5.1 t CO2e ha−1 year−1, −0.12 kg CH4 ha−1 year−1, and 14.3 kg N2O‐N ha−1 year−1 for carbon dioxide, methane, and nitrous oxide, respectively. Combining these values with a compilation of estimates of agricultural peatland disturbance area in Canada, GHG emissions estimates in Canada arising from peatland converted to agriculture remain highly uncertain, ranging from 1.4 to 35 Mt CO2e year−1, with a median value near 18 Mt CO2e year−1. The largest contributor to this wide range of estimates is uncertainty peatland area affected, indicating an urgent need to improving mapping of organic soils under agriculture in Canada. To help guide decision‐making in Canada, we recommend a network of research stations across a range of agricultural management intensities and climate regions for monitoring hydrological conditions and GHG exchange on organic soils affected by agriculture.

Gas Pathing: Improved Greenhouse Gas Emission Estimates of Liquefied Natural Gas Exports through Enhanced Supply Chain Resolution

Gas Pathing: Improved Greenhouse Gas Emission Estimates of Liquefied Natural Gas Exports through Enhanced Supply Chain Resolution

Diagnosis of GHG Emissions in an Offshore Oil and Gas Production Facility

This work presents a diagnosis of greenhouse gas (GHG) emissions for floating production storage and offloading (FPSO) platforms for oil and gas production offshore, using calculation methodologies from the American Petroleum Institute (API) and U.S. Environmental Protection Agency (EPA). To carry out this analysis, design data of an FPSO platform is used for the GHG emissions estimation, considering operations under steady conditions and oil and gas processing system simulations in the Aspen HYSYS® software. The main direct emission sources of GHG are identified, including the main combustion processes (gas turbines for electric generation and gas turbine-driven CO2 compressors), flaring and venting, as well as fugitive emissions. The study assesses a high CO2 content in molar composition of the associated gas, an important factor that is considered in estimating fugitive emissions during the processes of primary separation and main gas compression. The resulting information indicates that, on average, 95% of total emissions are produced by combustion sources. In the latest production stages of the oil and gas field, it consumes 2 times more energy and emits 2.3 times CO2 in terms of produced hydrocarbons. This diagnosis provides a baseline and starting point for the implementation of energy efficiency measures and/or carbon capture and storage (CCS) technologies on the FPSO in order to reduce CO2 and CH4 emissions, as well as identify the major sources of emissions in the production process.

Carbon emission reduction associated with dapagliflozin compared to standard of care in the UK: translating the DAPA-HF and DELIVER trial findings to an environmental context

Abstract Background Healthcare systems account for 4–5% of UK national greenhouse gas emissions with delivery of care a major component.1 Preventing events that use healthcare resources should contribute to a reduction in emissions, in addition to the primary humanitarian aim of improving patient outcomes. The Dapagliflozin And Prevention of Adverse-outcomes in Heart Failure (DAPA-HF) and the Dapagliflozin Evaluation to Improve the Lives of Patient with Preserved Ejection Fraction Heart Failure (DELIVER) trials demonstrated that dapagliflozin reduced the frequency of hospitalizations for heart failure (hHF), urgent heart failure visits (UHFV) and cardiovascular (CV) death for patients with heart failure (HF).2,3 Purpose The study objective was to translate predicted differences in clinical events associated with dapagliflozin use in HF to a predicted carbon emission reduction from a UK perspective. Methods A previously published model4 based on pooled DAPA-HF and DELIVER event rates was used to predict hHF, UHFV and CV deaths in a cohort of 1,000 HF patients over a 3-year time horizon. Composite greenhouse gas (GHG) emissions (as kg CO2 equivalent) were calculated from components sourced from the Sustainable Healthcare Coalition’s Care Pathway Calculator according to assumed clinical pathways and were applied to predicted incidence of hHF, UHFV and CV death events. Average length of stay was sourced from the National Heart Failure Audit.5 CV deaths occurring outside hospital were assumed to incur no additional carbon emission except that associated with cremation. Proportion of funerals by cremation and associated GHG emissions were sourced from literature.6,7 Future emissions were not discounted, representing the NHS’s aim to approach ‘net zero’. Results Over a 3-year time horizon, treatment with dapagliflozin was projected to prevent 74 hHF events, 9 UHFV events and 20 CV deaths per 1,000 patients treated versus placebo. Total estimated GHG emissions associated with hHF (233.2 kg CO2e), UHFV (177.4 kg CO2e), and CV death (251.4 kg CO2e) were 85,660 kg CO2e per 1,000 patients treated with dapagliflozin versus 109,618 kg CO2e per 1,000 patients treated with placebo. The projected total reduction associated with dapagliflozin treatment was 23,958 kg CO2e over 3 years, a 22% reduction versus placebo. Per population (~690,000 HF patients), total emissions prevented by dapagliflozin over 3 years were projected to be equivalent to driving 42 million miles in an average gasoline-powered passenger vehicle. Conclusion While the main aim of any therapeutic intervention is to save lives and improve patient outcomes, to capture the full spectrum of benefit to stakeholders the environmental consequences of new therapies should be considered in addition to their clinical and economic effects. Our analysis suggests treating patients with dapagliflozin may contribute to reducing the environmental burden of HF in addition to improving patient outcomes.

Greenhouse gas emissions and net energy production of dark fermentation from food waste followed by anaerobic digestion

There are diverse estimations regarding the carbon reduction effects of alternative hydrogen production technologies. This study assessed the greenhouse gas (GHG) emissions and energy balance of a two-stage process combining dark fermentative hydrogen production and anaerobic digestion from food waste using the cradle-to-gate life cycle assessment (LCA). The system boundary included collection and transportation, pretreatment and feedstock storage, dark fermentation, H2 purification, anaerobic digestion and heat and power generation and the estimated GHG emission was compared with a single anaerobic digestion of food waste. GHG emission of the biohydrogen production was estimated as 2.48 kg CO₂-eq per kg H₂ without considering avoided emissions from heat and power generation. The environmental impacts were majorly influenced by electricity use. The net energy ratio of the two-stage process was calculated to be 8.18, confirming a net energy gain and the potential GHG emission avoidance for electricity and heat use. Given that the single-stage anaerobic digestion of 16 tons of food waste is replaced by the two-stage dark fermentation process, 76.6 kg CO₂-eq would be avoided. Sensitivity analysis revealed that energy-saving strategies are the most sensitive factors for achieving positive net energy production and low global warming potential.

A Novel Approach to Estimating Greenhouse Gas Emissions from Federal Highway Construction Projects in Brazil

A Novel Approach to Estimating Greenhouse Gas Emissions from Federal Highway Construction Projects in Brazil

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

AbstractTidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO2) and methane (CH4) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO2 and CH4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO2 and CH4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (Reco) modules explained, on average, 73% of the variation in CO2 exchange with low model error (normalized root mean square error (nRMSE) &lt;1). The CH4 module also explained the majority of variance in CH4 emissions in validation sites (R2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on Reco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH4 emissions, and (d) CH4 responses to non‐periodic changes in salinity.

Groundwater driven carbon fluxes in a restored coastal saltmarsh wetland: Implications for coastal wetland restoration

Coastal wetlands play a crucial role in the global carbon cycle, yet they have been extensively degraded over the past decade. Though restoration efforts are underway worldwide, there is limited understanding of the role groundwater plays in transporting dissolved carbon within restored wetlands. Here, we address this knowledge gap by investigating water and carbon fluxes in the restored Tomago Wetlands in coastal NSW, Australia. We aim to 1) quantify surface water exports of the main carbon components, 2) estimate greenhouse gas emissions from the aquatic parts of the wetland, and 3) determine the contribution of groundwater discharge to the surface water carbon export and greenhouse gas emissions from the wetland. Sampling took place following a wet period and a freshening event. A radon mass balance model estimated groundwater discharge contributing 10 % to the surface water flow in the restored wetland. The wetland was a source of four of the five main carbon components including DOC, DIC, alkalinity, and CO2, with most of the exported carbon being in the form of alkalinity. Surface water DOC, DIC, alkalinity, and CO2 exports were, 1.3 ± 0.5, 35.5 ± 13.1, 39.4 ± 14.6 and 6.5 ± 2.1 mmol/m2/d, respectively. The average water to atmosphere flux of CO2 and CH4 were 104 ± 209 (mmol/m2/d) and 21 ± 42 (µmol/m2/d). Groundwater-driven carbon fluxes contributed 100 % of the surface water DOC, 30 % of the DIC, 15 % of the alkalinity exports, and 5 % of the overall CO2 losses (lateral aqueous export + atmospheric emissions) from the wetland, with minor contributions to CH4. Carbon exports from both the wetland and groundwater observed in this restored wetland were found to be lower than those reported in the literature for natural or mature wetlands. This would have important implications when developing carbon accounting methods. Overall, our findings highlight the importance of groundwater in carbon transport within restored wetlands and emphasise the need for inclusion of shallow groundwater dynamics in carbon budgets and inventories of coastal wetlands that have or will undergo restoration.

Studying the relationship between CO2 emissions and transport infrastructure development indicators

Subject. The article addresses the problem of increasing the volume of pollutant emissions into the atmosphere, environmental aspects of transport industry development in the Ural, Siberian, and Far Eastern Federal District. Objectives. We focus on the development of a tool to estimate greenhouse gas emissions from transport infrastructure. Methods. The study draws on methods of logical, statistical and econometric analysis. Results. We tested a regression model built on the basis of data reflecting the development of transport infrastructure in the regions of the Ural, Siberian, and Far Eastern Federal District in 2017–2021. The paper found that a particular impact on the volume of carbon dioxide emissions is exerted by the degree of depreciation of fixed assets of transport enterprises. An increase in the cost of fuel also has a negative effect on carbon dioxide emissions. Conclusions. The findings can be used in the reorganization process of transport systems to improve environmental performance in the regions.

69 The Holos model for estimating greenhouse gases and soil carbon: Characterizing regionalized beef farm model systems

Abstract The objective of this work is to characterize regionally representative beef farm systems that represent dominant or typical surveyed management practices for 11 beef-producing regions across Canada. This work fulfills two further purposes 1) to improve and expand the Holos model interface; and 2) to facilitate the estimation of greenhouse gas (GHG) emissions and soil carbon (C) changes on beef farms in different regions of Canada. Holos version 4 is the whole-farm model of Agriculture and Agri-Food Canada’s to estimate GHG emissions and changes in soil C on Canadian farms in response to shifts in management practices. Holos can be implemented in all 10 Canadian provinces and accounts for GHG emissions from crop and livestock production [enteric and manure methane (CH4), manure and soil N2O emissions], farm machines and infrastructure [on-farm energy carbon dioxide (CO2) emissions], as well as from the upstream production of some farm inputs (synthetic fertilizer and pesticides). The model is designed to utilize data readily available on the farm to answer, ‘What if?’ scenarios, whereby the user can test the effect of changing management practices on their whole-farm GHG budget. To reduce the data input burden on the user, Holos V4 has built-in model livestock systems for beef, dairy, swine and poultry production that characterize the dominant features of these operations in Canada at the national scale based on relevant literature/data and expert opinion. Regarding beef production, we have characterized regionally specific model beef farms for incorporation into Holos, one for each of 11 Canadian beef-producing regions. General characteristics and management practices for each farm were based on the 2011 Beef Farm Survey (Sheppard et al., 2015), which summarizes management information from 1,009 Canadian beef farms, combined with data from the Canadian Cow-Calf Cost of Production Network (Canfax 2023). Each regional farm includes cow-calf, backgrounding in confinement, backgrounding on pasture and finishing components, and considers all the specific feed (e.g., forage, grains, by-products) required for each stage of the beef cycle. These 11 model farms are simulated within the current Holos V4 model to explore the impacts of variation in beef management practices on farm GHG emissions across Canada and on soil C stocks on lands used to produce feed and graze cattle. An overview of the national-level dairy, swine and poultry components in Holos will be presented along with a more detailed perspective of whole-farm GHG budget and multi-decadal soil C dynamics in regionalized beef farms. The impact of management and environmental factors that lead to differences in GHG emissions and soil C stocks in beef farms will also be explored.

Application of environmentally extended input-output data to estimate greenhouse gas emissions attributable to packaged foods and beverages in Australia

Input-output tables for national economies are widely available and may provide an established mechanism for estimating environmental impacts of industry sectors. We used environmentally extended input-output (EEIO) analysis to estimate greenhouse gas emissions (GHGe) for Australian packaged foods and beverages and compared the findings to those derived from life cycle analysis (LCA). GHGe intensities in purchaser prices were used alongside corresponding median prices per kilogram from NielsenIQ Homescan, a nationally representative database with food prices. Applying GHGe estimates to a 2019 Australian packaged food database, we reported median and interquartile range (IQR) GHGe per kilogram, comparing them with LCA-derived estimates. EEIO-derived intensities were estimated for 23,550 products, and the median overall GHGe based on EEIO data was 6.87 kg CO2eq / kg (IQR 4.20 to 10.5). LCA-derived estimates were comparatively lower, showing a median overall GHGe of 2.42 kg CO2eq / kg (IQR 1.41 to 5.00) using Poore and Nemecek data and 2.35 kg CO2eq / kg (IQR 1.24 to 4.53) using FoodSwitch. Despite differences in magnitude, EEIO-derived data discriminated between the highest emitting categories in alignment with the LCA-based approaches. Future work is required to understand the discrepancies and make the EEIO method a compelling alternative to LCA-based approaches.

Greenhouse gas emissions from New Zealand deer farming systems: current and future options

ABSTRACT Deer production options were modelled in two contrasting environments, Otago and Hawkes Bay, New Zealand, to estimate greenhouse gas emissions, productivity and profitability. A standard New Zealand deer farm was compared to the deer production scenarios: (1) increasing hind longevity through improved reproductive performance, (2) improving animal health management to increase weaner growth, (3) pre-winter finishing using larger hinds and superior growth genetics sires or (4) shifting to a mixed velvet antler/venison enterprise. Production (kg product/ha) increased as scenarios 1–3 were applied, being greatest for scenario 3, in both environments (+21% and +2% in the Otago and Hawkes Bay environments respectively). Production increases in the Hawkes Bay environment were muted by the inclusion of beef and sheep enterprises. Production in Otago and Hawkes Bay was reduced in scenario 4 by 12 and 7% but achieved increased gross margin (33% and 20% respectively). Some small differences in the magnitude of response were seen between the environments. Changes in total GHG emissions per hectare ranged between +4.6% and −6.7%. GHG emission per $ gross margin was reduced by up to 15%–21%. Altering production options in the deer industry altered total GHG emissions per hectare and reduced GHG emissions per kg of product.

Estimation of Greenhouse Gas Emissions of Taxis and the Nonlinear Influence of Built Environment Considering Spatiotemporal Heterogeneity

The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas.

Leveraging the trend analysis for modeling of the greenhouse gas emissions associated with coal combustion

In this paper, it is aimed, for the first time, at deriving simple models, leveraging the trend analysis in order to estimate the future greenhouse gas emissions associated with coal combustion. Due to the expectations of becoming the center of global economic development in the future, BRICS-T (Brazil, the Russian Federation, India, China, South Africa, and Turkiye) countries are adopted as cases in the study. Following the models’ derivation, their statistical validations and estimating accuracies are also tested through various metrics. In addition, the future greenhouse gas emissions associated with coal combustion are estimated by the derived models. The results demonstrate that the derived models can be successfully used as a tool for estimating the greenhouse gas emissions associated with coal combustions with accuracy ranges from at least 90% to almost 98%. Moreover, the estimating results show that the total amount of greenhouse gas emissions associated with coal combustions in the relevant countries and in the world will increase to 14 BtCO2eq and 19 BtCO2eq by 2035, with an annual growth of 2.39% and 1.71%, respectively. In summary, the current study’s findings affirm the usefulness of trend analysis in deriving models to estimate greenhouse gas emissions associated with coal combustion.

A bottom-up approach for greenhouse gas emission estimation at the community level: A case study in Japan

Greenhouse gas (GHG) emission mitigation and climate change adaptation in urban communities are urgent. There is enormous potential to improve the GHG inventory at the community level. This research aims to develop a community-scale GHG emission inventory and improve its accuracy and consistency through a bottom-up approach. This study covers direct and indirect emissions categorized into three scopes and selects the Honjo Waseda community in Japan as a case study community. Energy consumption from different sectors (the residential and commercial, transport, and waste management sectors) is evaluated and calculated through Residential Energy Estimation based on the Daily Activities (REEDA) database, National Census, and first-hand field data. The outcome revealed that the residential and commercial sector emissions rank highest in the total GHG emissions of the community, with a value of 39 %. Translated into the scope concept, the emissions under scopes 2 and 3 account for 39 % and 61 %, respectively. Furthermore, a sensitivity analysis of the implementation of green technologies to reduce GHG emissions is also conducted. Mechanical biological treatment (MBT) implementation could yield the largest emission reduction (18 %–22 %), followed by implementing electric vehicles, ultralightweight vehicles, photovoltaic systems, and ground heat utilization.

Estimating the dynamics of greenhouse gas emission during black soldier fly larvae growth under controlled environmental conditions

Edible insects are considered a sustainable alternative protein source. Several studies have indicated that greenhouse gas (GHG) emissions from insects are generally low. These data were usually recorded in closed static chambers, which did not reflect the actual rearing conditions. On the other hand, open respiration chambers need high amounts of substrate and insects, making the analyses time consuming and expensive. In the present study, a novel small-scale open gas exchange recording system for evaluation of GHG emissions from insects is presented. The device consists of four independent respiration chambers, each one equipped with a pump for supplying air, a flow meter and a septum for sampling the flowing air. A validation experiment was carried out using black soldier fly larvae as model organisms and moistened chicken feed (dry matter 40%) as rearing substrate. Three independent runs were conducted, each with two replicates, using 200 neonates larvae and 300 g of substrate. Moistened chicken feed (300 g) without larvae was used as control. Throughout the 12 days rearing period, gas sampling occurred every 12 h. Data interpolation considering a linear tendency between two successive sampling points was applied and overall amounts of gases were quantified as area under the curve. Irrespective of the run, CO2 emissions in the insect-substrate systems increased during the initial 7 d of experiment, consistently followed by a rapid decrease. Multiple cross correlation analysis showed high reproducibility (r ≥ 0.9), while significant differences in the CO2 emissions between insects-substrate system (range 7.76–11.88 g g−1 dry insects) and sole substrate (range 0.20–0.33 g g−1 initial dry substrate) were observed. Very limited N2O (≤1.13 mg g−1 dry insects) and CH4 (≤1.33 mg g−1 dry insects) emissions were detected in this experiment, indicating that the performed black soldier fly larvae rearing did not result in significant production of these GHGs. Overall, the experiment showed that the novel device may be particularly suited for quick and reliable estimation of GHG emissions from edible insects.

Carbon emissions from inland waters may be underestimated: Evidence from European river networks fragmented by drying

AbstractRiver networks contribute disproportionately to the global carbon cycle. However, global estimates of carbon emissions from inland waters are based on perennial rivers, even though more than half of the world's river length is prone to drying. We quantified CO2 and CH4 emissions from flowing water and dry riverbeds across six European drying river networks (DRNs, 120 reaches) and three seasons and identified drivers of emissions using local and regional variables. Drivers of emissions from flowing water differed between perennial and non‐perennial reaches, both CO2 and CH4 emissions were controlled partly by the annual drying severity, reflecting a drying legacy effect. Upscaled CO2 emissions for the six DRNs at the annual scale revealed that dry riverbeds contributed up to 77% of the annual emissions, calling for an urgent need to include non‐perennial rivers in global estimates of greenhouse gas emissions.

Estimation of Greenhouse Gas Emission from Municipal Solid Waste Management Techniques - A Case of Rampur Municipality, Palpa District, Nepal

Estimation of Greenhouse Gas Emission from Municipal Solid Waste Management Techniques - A Case of Rampur Municipality, Palpa District, Nepal