Energy policies avoiding a tipping point in the climate system

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.

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.

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.

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

Latitudinal heat transport in the ocean and atmosphere represents a fundamental process of the Earth's climate system. The ocean component of heat transport is effected by the thermohaline circulation. Changes in this circulation, and hence latitudinal heat transport, would have a significant effect on global climate. Paleoclimate evidence from the Greenland ice cores and deep sea sediment cores suggests that during much of glacial time the climate system oscillated between two different states. Bimodal equilibrium states of the thermohaline circulation have been demonstrated in climate models. We address the question of the role of the atmospheric hydrological cycle on the global thermohaline circulation and the feedback to the climate system through changes in the ocean's latitudinal heat transport, with a simple coupled ocean-atmosphere energy-salt balance model. Two components of the atmospheric hydrological cycle, i.e., latitudinal water vapor transport and the net flux of water vapor from the Atlantic to the Pacific Ocean appear to play separate roles. If the inter-basin transport is sufficiently large, small changes in water vapor transport over the North Atlantic can effect bifurcation or a rapid transition between two different equilibria in the global thermohaline circulation; maximum difference between the modes occurs in the North Atlantic. If the inter-basin transport is from the Pacific to the Atlantic and sufficiently large, latitudinal vapor transport in the North Pacific controls the bifurcations, with maximum changes occurring in the North Pacific. For intermediate values of inter-basin transport, no rapid transitions occur in either basin. In the regime with vapor flux from the Atlantic to the Pacific, the ‘on’ mode has strong production of deep water in the North Atlantic and a large flux of heat to the atmosphere from the high latitude North Atlantic. The ‘off’ mode has strong deep water production in the Southern Ocean and weak production in the North Pacific. Heat transport into the high latitude North Atlantic by the ocean is reduced to about 20% of the ‘on’ mode value. For estimated values of water vapor transport for the present climate the model asserts that while water vapor transport from the Atlantic to the Pacific Ocean is sufficiently large to make the North Atlantic the dominant region for deep water production, latitudinal water vapor transport is sufficiently low that the thermohaline circulation appears stable, i.e., far from a bifurcation point. This conclusion is supported to some extent by the fact that the high latitude temperature of the atmosphere as recorded in the Greenland ice cores has changed little over the last 9000 years.

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.

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.

A diachronic study of greenhouse gas emissions of French dairy farms according to adaptation pathways

Summary Thermal insulation is a strategic product for reducing energy consumption and related greenhouse gas (GHG) emissions from the building sector. This study examines from a life cycle perspective the changes in GHG emissions resulting from the use of two rigid thermal insulation products manufactured and installed from 1971 to 2025. GHG emissions related to insulation production and fugitive releases of blowing agents are modeled and compared with GHG savings from reduced heating loads in North America, Europe, and Asia. Implementation of alternative blowing agents has greatly improved the carbon dioxide 100-year equivalent (CO2-eq) emission performance of thermal insulation. The net average CO2-eq savings to emissions ratio for current extruded polystyrene (XPS) and polyisocyanurate (PIR) insulation studied was 48:1, with a broad range from 3 to 1,800. Older insulation products manufactured with chlorofluorocarbons (CFCs) can result in net cumulative GHG emissions. Reduction of CO2-eq emissions from buildings is governed by complex interactions between insulation thickness and placement, climate, fuel type, and heating system efficiencies. A series of charts mapping both emissions payback and net savings demonstrate the interactions between these factors and provide a basis for specific policy recommendations to guide effective insulation investments and placement.

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.