Greenhouse gas analysis at global scale using the CAMS EGG4 product

The shift from straw incorporation to biofuel production entails emissions from production, changes in soil organic carbon (SOC) and through the provision of (co‐)products and entailed displacement effects. This paper analyses changes in greenhouse gas (GHG) emissions arising from the shift from straw incorporation to biomethane and bioethanol production. The biomethane concept comprises comminution, anaerobic digestion and amine washing. It additionally provides an organic fertilizer. Bioethanol production comprises energetic use of lignin, steam explosion, enzymatic hydrolysis and co‐fermentation. Additionally, feed is provided. A detailed consequential GHG balance with in‐depth focus on the time dependency of emissions is conducted: (a) the change in the atmospheric load of emissions arising from the change in the temporal occurrence of emissions comparing two steady states (before the shift and once a new steady state has established); and (b) the annual change in overall emissions over time starting from the shift are assessed. The shift from straw incorporation to biomethane production results in net changes in GHG emissions of (a) −979 (−436 to −1,654) and (b) −955 (−220 to −1,623) kg CO2‐eq. per tdry matter straw converted to biomethane (minimum and maximum). The shift to bioethanol production results in net changes of (a) −409 (−107 to −610) and (b) −361 (57 to −603) kg CO2‐eq. per tdry matter straw converted to bioethanol. If the atmospheric load of emissions arising from different timing of emissions is neglected in case (a), the change in GHG emissions differs by up to 54%. Case (b) reveals carbon payback times of 0 (0–49) and 19 (1–100) years in case of biomethane and bioethanol production, respectively. These results demonstrate that the detailed inclusion of temporal aspects into GHG balances is required to get a comprehensive understanding of changes in GHG emissions induced by the introduction of advanced biofuels from agricultural residues.

To mitigate greenhouse gas (GHG) emissions from the wastewater treatment industry, it is crucial to explore GHG emission patterns and propose useful measures. In this study, we use the Kaya model and LMDI decomposition method to analyze the changes in GHG emissions from urban domestic wastewater treatment at the provincial level and further explore the distribution characteristics and driving factors of urban domestic wastewater treatment GHG emissions across various years and regions. The results indicate the following: (1) In the temporal dimension, urban domestic wastewater treatment GHG emissions are increasing, from 21.0 MtCO2 in 2011 to 27.1 MtCO2 in 2020, with an average annual growth rate of 2.88%. The spatial distribution is high in the southeast and low in the northwest. There is variability in the spatial evolution trend of GHG emissions by province, with the growth rate becoming slower or even negative in Jiangsu, Zhejiang, and North China, while the average annual growth rate exceeds 25% in Inner Mongolia and Xinjiang. (2) According to the decomposition results of driving factors, economic scale is the dominant positive driver, and the positive contributions of TI and the population effect are limited. The sludge disposal structure is the main negative driver, and the EEI and technology have restricted negative contributions. (3) Based on the decomposition results, for major coastal GHG emitters, such as Guangdong and Shandong, it is necessary to invest capital and technology to continuously upgrade the wastewater treatment process and reduce non-CO2 emissions. Along with adopting circular economy schemes, local governments in the northwestern region should transform the traditional sludge disposal structure and optimize the power supply structure to increase carbon offset and reduce CO2 emissions. The findings suggest a low-carbon transformation path to support the industry's dual carbon goals.

Biochar has been extensively utilized to amend soil and mitigate greenhouse gas (GHG) emissions from croplands. However, the effectiveness of biochar application in reducing cropland GHG emissions remains uncertain due to variations in soil properties and environmental conditions across regions. In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. The findings suggest that the functional groups play an essential role in determining biochar surface activity and the soil’s capacity for adsorbing GHGs. ML was an effective method in predicting the changes in soil GHG emissions and crop yield following biochar application. Moreover, poor-fertility soils exhibited greater changes in GHG emissions compared to fertile soil. Implementing an optimized global strategy for biochar application may result in a substantial reduction of 684.25 Tg year−1 CO2 equivalent (equivalent to 7.87% of global cropland GHG emissions) while simultaneously improving crop yields. This study improves our understanding of the interaction between biochar surface properties and soil GHG, confirming the potential of global biochar application strategies in mitigating cropland GHG emissions and addressing global climate degradation. Further research efforts are required to optimize such strategies.Graphical

A framework for assessing the effects of shock events on livestock and environment in sub-Saharan Africa: The COVID-19 pandemic in Northern Kenya

Abstract. This paper summarizes currently available data on greenhouse gas (GHG) emissions from African natural ecosystems and agricultural lands. The available data are used to synthesize current understanding of the drivers of change in GHG emissions, outline the knowledge gaps, and suggest future directions and strategies for GHG emission research. GHG emission data were collected from 75 studies conducted in 22 countries (n = 244) in sub-Saharan Africa (SSA). Carbon dioxide (CO2) emissions were by far the largest contributor to GHG emissions and global warming potential (GWP) in SSA natural terrestrial systems. CO2 emissions ranged from 3.3 to 57.0 Mg CO2 ha−1 yr−1, methane (CH4) emissions ranged from −4.8 to 3.5 kg ha−1 yr−1 (−0.16 to 0.12 Mg CO2 equivalent (eq.) ha−1 yr−1), and nitrous oxide (N2O) emissions ranged from −0.1 to 13.7 kg ha−1 yr−1 (−0.03 to 4.1 Mg CO2 eq. ha−1 yr−1). Soil physical and chemical properties, rewetting, vegetation type, forest management, and land-use changes were all found to be important factors affecting soil GHG emissions from natural terrestrial systems. In aquatic systems, CO2 was the largest contributor to total GHG emissions, ranging from 5.7 to 232.0 Mg CO2 ha−1 yr−1, followed by −26.3 to 2741.9 kg CH4 ha−1 yr−1 (−0.89 to 93.2 Mg CO2 eq. ha−1 yr−1) and 0.2 to 3.5 kg N2O ha−1 yr−1 (0.06 to 1.0 Mg CO2 eq. ha−1 yr−1). Rates of all GHG emissions from aquatic systems were affected by type, location, hydrological characteristics, and water quality. In croplands, soil GHG emissions were also dominated by CO2, ranging from 1.7 to 141.2 Mg CO2 ha−1 yr−1, with −1.3 to 66.7 kg CH4 ha−1 yr−1 (−0.04 to 2.3 Mg CO2 eq. ha−1 yr−1) and 0.05 to 112.0 kg N2O ha−1 yr−1 (0.015 to 33.4 Mg CO2 eq. ha−1 yr−1). N2O emission factors (EFs) ranged from 0.01 to 4.1 %. Incorporation of crop residues or manure with inorganic fertilizers invariably resulted in significant changes in GHG emissions, but results were inconsistent as the magnitude and direction of changes were differed by gas. Soil GHG emissions from vegetable gardens ranged from 73.3 to 132.0 Mg CO2 ha−1 yr−1 and 53.4 to 177.6 kg N2O ha−1 yr−1 (15.9 to 52.9 Mg CO2 eq. ha−1 yr−1) and N2O EFs ranged from 3 to 4 %. Soil CO2 and N2O emissions from agroforestry were 38.6 Mg CO2 ha−1 yr−1 and 0.2 to 26.7 kg N2O ha−1 yr−1 (0.06 to 8.0 Mg CO2 eq. ha−1 yr−1), respectively. Improving fallow with nitrogen (N)-fixing trees led to increased CO2 and N2O emissions compared to conventional croplands. The type and quality of plant residue in the fallow is an important control on how CO2 and N2O emissions are affected. Throughout agricultural lands, N2O emissions slowly increased with N inputs below 150 kg N ha−1 yr−1 and increased exponentially with N application rates up to 300 kg N ha−1 yr−1. The lowest yield-scaled N2O emissions were reported with N application rates ranging between 100 and 150 kg N ha−1. Overall, total CO2 eq. emissions from SSA natural ecosystems and agricultural lands were 56.9 ± 12.7 × 109 Mg CO2 eq. yr−1 with natural ecosystems and agricultural lands contributing 76.3 and 23.7 %, respectively. Additional GHG emission measurements are urgently required to reduce uncertainty on annual GHG emissions from the different land uses and identify major control factors and mitigation options for low-emission development. A common strategy for addressing this data gap may include identifying priorities for data acquisition, utilizing appropriate technologies, and involving international networks and collaboration.

Food systems contribute 23–42% of global greenhouse gas emissions. Reducing food system emissions is an essential component of climate change mitigation, and a system-wide approach, including production, processing, trade and demand-side transformations, will be needed. Long-term analysis of greenhouse gas (GHG) emissions of food supply is crucial for informing this transformation, and understanding the processes contributing to existing trends can reveal opportunities for future mitigation strategies. To address these needs we used data on food supply, trade and emission intensity to quantify changes in GHG emissions between 1986 and 2017 resulting from food supply in the United Kingdom (UK). Uniquely, the relative contributions of supply-side and demand-side changes to historical trends in food emissions were assessed, and the gap between current UK food consumption and EAT-Lancet recommended diets was used to estimate the additional GHG emission reductions that could be achieved by shifting to the Planetary Health Diet (PHD). It was estimated that in the UK, per-capita GHG emissions from food fell by 32% (from 4.6 tCO2eq/capita to 3.1 tCO2eq/capita) between 1986 and 2017. Of this 32% reduction, 21% was due to supply-side changes (a fall in emission intensity per unit of production due to increased efficiency of farming practices), 10% was due to demand-side changes (including dietary change and waste reduction), and 2% was due to changing trade patterns. Relative to the PHD, however, the average UK citizen still greatly over-consumes beef, lamb and pork, tubers and starchy vegetables and dairy products, and under-consumes vegetables, nuts, and legumes. It was estimated that by adopting the PHD, UK per capita food emissions could be reduced by a further 42% to 1.8 tCO2eq/capita. These results expose the historic contributions of both supply- and demand-side changes to reductions in GHG emissions from food, and highlight the underutilised potential of dietary change in contributing to mitigation of GHG emissions from food.

ABSTRACTThe electric power industry has often been considered one of the key sectors for energy-saving and emission reduction. It is important to explore the main factors driving the greenhouse gas (GHG) emission changes of this industry. This study applies the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze disparities in the driving forces from national and regional perspectives. In addition to the factors related to the generation sector, this study puts forward the concepts of power self-sufficiency ratio and available-to-consumed ratio to indicate the influence of factors related to the transmission and distribution sector. The results show that economic activity was the main factor promoting the growth of GHG emissions; the power intensity of gross domestic product (GDP) and population change had smaller promotional effects at the national level; the generation structure and the energy intensity of thermal power were two main contributors to inhibiting the growth of GHG emissions of the national power industry, but they had promotional effects in some provinces; and the energy mix of thermal power and the power self-sufficiency ratio contributed slightly to decreases in the GHG emissions of the power industry, despite their promotional effects in some provinces.

Factors affecting changes of greenhouse gas emissions in Belt and Road countries

Refrigeration transforms developing food systems, changing the dynamics of production and consumption. This study models the introduction of an integrated refrigerated supply chain, or "cold chain," into sub-Saharan Africa and estimates changes in preretail greenhouse gas (GHG) emissions if the cold chain develops similarly to North America or Europe. Refrigeration presents an important and understudied trade-off: the ability to reduce food losses and their associated environmental impacts, but increasing energy use and creating GHG emissions. It is estimated that postharvest emissions added from cold chain operation are larger than food loss emissions avoided, by 10% in the North American scenario and 2% in the European scenario. The cold chain also enables changes in agricultural production and diets. Connected agricultural production changes decrease emissions, while dietary shifts facilitated by refrigeration may increase emissions. These system-wide changes brought about by the cold chain may increase the embodied emissions of food supplied to retail by 10% or decrease them by 15%, depending on the scenario.

Irrigation can be a major emitter of greenhouse gases, and can contribute to global climate change. Most studies dealing with the evaluation of irrigation projects worldwide have not taken this aspect of irrigation development into account. In this study, changes in greenhouse gas emissions from converting a given area from dryland into irrigated acreage in Alberta and Saskatchewan were estimated. A systems approach was used in which changes in crop production mix are considered in terms of induced livestock production, increased input demand, and other induced economic activities. A modified Canadian Economic and Emissions Model for Agriculture was employed to estimate the change in emissions. The net contribution of irrigation to emissions of three major greenhouse gases (GHG): carbon dioxide, methane, and nitrous oxide was estimated. Results suggest that each hectare of land converted into irrigation leads directly to an additional emission of major GHG of 1.68–2.61 t yr−1 (in carbon dioxide equivalents). When various indirect and induced sources of emissions were included with the direct emissions, irrigation’s net emission level is 3.35–3.65 t ha−1 yr−1 (in carbon dioxide equivalents). Additionally, storage reservoirs, developed for supplying water to these irrigation projects, could also be associated with emissions of GHGs. Considering all these sources, irrigation adds 3.7% and 6.5% of total (agriculture and agri-food sector level) GHG emissions in Saskatchewan and Alberta, respectively. This has implications for the assessment of future irrigation projects from an environmental perspective.

Is demand-side management environmentally beneficial? Analyzing the greenhouse gas emissions due to load shifting in electric power systems

Total uncertainty in greenhouse gas (GHG) emissions changes over time due to “learning” and structural changes in GHG emissions. Understanding the uncertainty in GHG emissions over time is very important to better communicate uncertainty and to improve the setting of emission targets in the future. This is a diagnostic study divided into two parts. The first part analyses the historical change in the total uncertainty of CO2 emissions from stationary sources that the member states estimate annually in their national inventory reports. The second part presents examples of changes in total uncertainty due to structural changes in GHG emissions considering the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) emissions scenarios that are consistent with the EU’s “20-20-20” targets. The estimates of total uncertainty for the year 2020 are made under assumptions that relative uncertainties of GHG emissions by sector do not change in time, and with possible future uncertainty reductions for non-CO2 emissions, which are characterized by high relative uncertainty. This diagnostic exercise shows that a driving factor of change in total uncertainty is increased knowledge of inventory processes in the past and prospective future. However, for individual countries and longer periods, structural changes in emissions could significantly influence the total uncertainty in relative terms.

Agriculture is an important contributor to greenhouse gas (GHG) emissions. While the development of agricultural GHG emissions on national and global scales is well studied for the last three to six decades, little is known about their trajectory and drivers over longer periods. In this article, we address this research gap by calculating and analyzing GHG emissions related to agriculture in Austria from 1830 to 2018. We calculate territorial emissions on an annual basis and include all GHG emissions from the processes directly involved in agricultural production. Based on this time series, we quantify the relative importance of major drivers of changes in GHG emissions across time and agricultural product categories, applying a structural decomposition analysis. We find that agricultural GHG emissions in Austria increased by 69 % over the total study period, from 4.6 Mt. CO2e/yr in 1830 to 7.7 Mt. CO2e/yr in 2018. While emissions increased only moderately from 1830 to 1945 (+22 % overall), with strong fluctuations between 1914 and 1945, they doubled from 1945 to 1985. In the most recent period from 1985 to 2018, emissions fell by one third, with decreases leveling off over time. Our decomposition analysis reveals that increases in agricultural production per capita most importantly contributed to the high growth in GHG emissions from 1945 to 1985. Conversely, decreasing emission intensities of products and a more climate friendly product mix were key drivers in the emissions reduction observed after 1985. We also contribute to the discussion around the global warming potential star (GWP*), by calculating GHG emissions based on this alternative metric, and contextualize our data within total socio-economic GHG emission trends. By providing insights into the historical trends and drivers of agricultural GHG emissions, our findings enhance the understanding of their long-term historical dynamics and adds to the knowledge base for future mitigation efforts.

BackgroundThe carbon footprint of severe asthma and the impact of biologic therapy in this population is unknown.MethodsThis was a retrospective cohort study in adults with severe asthma, using data from the Northern Ireland Regional Severe Asthma Service (September 2015–November 2021). We calculated annual greenhouse gas (GHG) emissions (carbon dioxide equivalent) for asthma-related medications and healthcare resource utilisation, compared patient characteristics by GHG quartile, calculated GHG change post-biologic initiation, and explored the relationship between GHG change and clinical response.ResultsAmong 303 patients with severe asthma, mean±sd GHG emissions were 474±431 kg, largely driven by SABA use (50.7%) and emergency department (ED) visits/inpatient admissions (21.0%). Those with highest-quartile GHG emissions were more likely to have uncontrolled disease (Asthma Control Questionnaire-5 score 3.5 versus 2.5; p<0.001), be more deprived (46.1% versus 25.0%; p=0.029) and have depression/anxiety (35.5% versus 14.7%; p=0.002) versus those with lowest-quartile GHG emissions. Among patients who received a biologic (n=213), modest GHG reductions (−28.0±286 kg; p=0.15) were observed, largely driven by a reduction in ED/hospitalisation-related GHG emissions (−59.3±224 kg; p<0.001). SABA-related GHG emissions were relatively unchanged (−6.1±138 kg; p=0.518). Total GHG emissions were 72.4±352 kg (p=0.044) lower than baseline at 4 years post-biologic initiation. Although there was substantial clinical improvement post-biologic initiation, this was not associated with GHG reductions.ConclusionsSevere asthma is associated with substantial GHG emissions, primarily driven by SABA use and emergency care utilisation. Although GHG emissions were lower post-biologic, largely due to a reduction in emergency care, the changes in GHG emissions were modest and SABA use was relatively unchanged. An improved understanding of the factors driving elevated GHG emissions is required.

This article generalizes ITU-T Recommendation L.1480 ‘Enabling the Net Zero transition: Assessing how the use of information and communication technology solutions impact greenhouse gas emissions of other sectors’ (www.itu.int/rec/T-REC-L.1480-202212-I) by applying it to an action outside the Information and Communication Technology (ICT) field covered by this ITU-T Recommendation L.1480, namely the use of a photovoltaic solar power plant in Poland, including the transition to scale. The study quantifies this use by accounting all greenhouse gases (GHG) emissions consequences (incl. installation, operation and maintenance) over the duration of the action (i.e. the supply and operation of the photovoltaic panels, inverter and associated services), through the construction of a consequence tree and the effective observation of usage behaviors; it thus avoids pushing potentially negative effects outside the scope of the study, like rebound effects (ex.: increase of 10% in electricity consumption after photovoltaic panels commissioning) or the consequences of the use of financial gains (or losses). Three main innovations are shown: • a step-by-step implementation of Recommendation L.1480 and its supplement L Suppl. 54 to a non-ICT sector, • the effectiveness of the use of solar panels to reduce GHG emissions in Poland, through actual measurement of usage data, in particular data linked to rebound effects, • the possibility of generalizing this methodological framework to assess the changes in GHG emissions induced by any action already undertaken ( ex post ) or under consideration ( ex ante ). By assessing all GHG emissions consequences and defining the steps for carrying out this assessment, L.1480 methodology covers all effects on a global scale and reflects real changes in GHG emissions. It could thus be applied to assessing the GHG emissions consequences of actions and decisions of various kinds: public policies (like carbon storage), corporate investment or household behavior. Moreover, adding other categories of environmental impact (biodiversity, scarcity of natural resources (metals, water), waste and pollution, radiative effect, etc) would improve the exhaustiveness of environmental effects measurement.