Energy and greenhouse gas balances of cassava-based ethanol

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Global warming potential and energy analysis of second generation ethanol production from rice straw in India

Dairy farming is a major source of greenhouse gas (GHG) emissions in agriculture. There are numerous scientific studies analysing GHG flows and testing GHG reduction methods in dairy farming, yet very few scientific papers cover all the relevant GHG flows. GHG flows that are difficult to quantify, such as C sequestration in soils, the effects of land-use change (LUC) or the energy input used to produce capital equipment, are not always considered.This paper describes the development and application of a model for energy and GHG accounting in dairy farming. This new model enables all relevant nutrient, energy and GHG flows to be modelled at farm level. This then forms the basis for system analysis and derivation of GHG mitigation strategies. The model was used on 18 organic and 18 con-ventional farms in Germany. Calculated CO2-eq emissions per kg of Energy Corrected Milk (ECM) were 995 g on average for organic farms (org) and 1,048 g on average for conventional farms (con). The largest contribution (55 % (org) and 43 % (con)) to total GHG emissions came from enteric methane emissions (549 g CO2-eq (kg ECM)-1 (org) and 449 g CO2-eq (kg ECM)-1 (con)). On the organic dairy farms, there was an increase in soil humus and therefore carbon storage and sequestration in soils, whereas the GHG emissions for the conventional farms included CO2 emissions from LUC due to soybean usage. The significantly higher energy input in the conventional systems resulted from the production of energy-intensive concentrates, mineral fertilisers and pesticides, and transportation (imported feed).This study shows that there are many factors that influence GHG emissions in dairy farming, and that these factors often interact with each other. An increase in productivity is one of several optimisation strategies; however, it must not be at the expense of productive lifetime or require an extremely high amount of concentrates. GHG reduction in dairy farming requires farm-specific optimisation approaches due to the heterogeneity of production systems.

BackgroundThe availability of feedstock options is a key to meeting the volumetric requirement of 136.3 billion liters of renewable fuels per year beginning in 2022, as required in the US 2007 Energy Independence and Security Act. Life-cycle greenhouse gas (GHG) emissions of sorghum-based ethanol need to be assessed for sorghum to play a role in meeting that requirement.ResultsMultiple sorghum-based ethanol production pathways show diverse well-to-wheels (WTW) energy use and GHG emissions due to differences in energy use and fertilizer use intensity associated with sorghum growth and differences in the ethanol conversion processes. All sorghum-based ethanol pathways can achieve significant fossil energy savings. Relative to GHG emissions from conventional gasoline, grain sorghum-based ethanol can reduce WTW GHG emissions by 35% or 23%, respectively, when wet or dried distillers grains with solubles (DGS) is the co-product and fossil natural gas (FNG) is consumed as the process fuel. The reduction increased to 56% or 55%, respectively, for wet or dried DGS co-production when renewable natural gas (RNG) from anaerobic digestion of animal waste is used as the process fuel. These results do not include land-use change (LUC) GHG emissions, which we take as negligible. If LUC GHG emissions for grain sorghum ethanol as estimated by the US Environmental Protection Agency (EPA) are included (26 g CO2e/MJ), these reductions when wet DGS is co-produced decrease to 7% or 29% when FNG or RNG is used as the process fuel. Sweet sorghum-based ethanol can reduce GHG emissions by 71% or 72% without or with use of co-produced vinasse as farm fertilizer, respectively, in ethanol plants using only sugar juice to produce ethanol. If both sugar and cellulosic bagasse were used in the future for ethanol production, an ethanol plant with a combined heat and power (CHP) system that supplies all process energy can achieve a GHG emission reduction of 70% or 72%, respectively, without or with vinasse fertigation. Forage sorghum-based ethanol can achieve a 49% WTW GHG emission reduction when ethanol plants meet process energy demands with CHP. In the case of forage sorghum and an integrated sweet sorghum pathway, the use of a portion of feedstock to fuel CHP systems significantly reduces fossil fuel consumption and GHG emissions.ConclusionsThis study provides new insight into life-cycle energy use and GHG emissions of multiple sorghum-based ethanol production pathways in the US. Our results show that adding sorghum feedstocks to the existing options for ethanol production could help in meeting the requirements for volumes of renewable, advanced and cellulosic bioethanol production in the US required by the EPA’s Renewable Fuel Standard program.

Taking Stock of Strategies on Climate Change and the Way Forward: A Strategic Climate Change Framework for Australia

Bioenergy from perennial grasses mitigates climate change via displacing fossil fuels and storing atmospheric CO2 belowground as soil carbon. Here, we conduct a critical review to examine whether increasing plant diversity in bioenergy grassland systems can further increase their climate change mitigation potential. We find that compared with highly productive monocultures, diverse mixtures tend to produce as great or greater yields. In particular, there is strong evidence that legume addition improves yield, in some cases equivalent to mineral nitrogen fertilization at 33–150 kg per ha. Plant diversity can also promote soil carbon storage in the long term, reduce soil N2O emissions by 30%–40%, and suppress weed invasion, hence reducing herbicide use. These potential benefits of plant diversity translate to 50%–65% greater life-cycle greenhouse gas savings for biofuels from more diverse grassland biomass grown on degraded soils. In addition, there is growing evidence that plant diversity can accelerate land restoration.

A systematic review and meta-analysis were used to assess the current state of knowledge and quantify the effects of land use change (LUC) to second generation (2G), non-food bioenergy crops on soil organic carbon (SOC) and greenhouse gas (GHG) emissions of relevance to temperate zone agriculture. Following analysis from 138 original studies, transitions from arable to short rotation coppice (SRC, poplar or willow) or perennial grasses (mostly Miscanthus or switchgrass) resulted in increased SOC (+5.0 ± 7.8% and +25.7 ± 6.7% respectively). Transitions from grassland to SRC were broadly neutral (+3.7 ± 14.6%), whilst grassland to perennial grass transitions and forest to SRC both showed a decrease in SOC (−10.9 ± 4.3% and −11.4 ± 23.4% respectively). There were insufficient paired data to conduct a strict meta-analysis for GHG emissions but summary figures of general trends in GHGs from 188 original studies revealed increased and decreased soil CO2 emissions following transition from forests and arable to perennial grasses. We demonstrate that significant knowledge gaps exist surrounding the effects of land use change to bioenergy on greenhouse gas balance, particularly for CH4. There is also large uncertainty in quantifying transitions from grasslands and transitions to short rotation forestry. A striking finding of this review is the lack of empirical studies that are available to validate modelled data. Given that models are extensively use in the development of bioenergy LCA and sustainability criteria, this is an area where further long-term data sets are required.

The Earth's surface temperature is steadily increasing due to the accumulation of greenhouse gases, a phenomenon known as global warming. Human activities are the root cause of this significant global issue. Reducing greenhouse gas (GHG) emissions is one of the most critical actions in climate change mitigation. Organizations can engage in activities that promote change and reduce greenhouse gases by acknowledging the significance of addressing climate change. By reducing GHG emissions and promoting the use of renewable energy, organizations can begin to address environmental issues. Therefore, the purpose of this investigation is to assess the reduction of GHG emissions in an educational institution by substituting electricity consumption from the electrical grid with renewable energy in the form of a solar PV rooftop on-grid system. The School of Renewable Energy's GHG emissions were assessed, covering three scopes of GHG emissions activities: direct emissions, indirect emissions, and other indirect emissions. The organization's activity data were collected over a 12-month period. Without installing a solar panel system, the organization reported total GHG emissions of 310.40 tCO2e, relying solely on imported electricity for internal use. The highest GHG emissions were from Scope 2, amounting to 239.38 tCO2e, primarily due to electricity importation. Scope 3 had the second highest GHG emissions, totaling 65.76 tCO2e, resulting from employee commuting and the use of purchased goods such as paper and tap water. Scope 1 had the lowest GHG emissions at 5.26 tCO2e, produced by the combustion of diesel and gasoline in both stationary and mobile sources, as well as CH4 emissions from the septic tank. The percentage of GHG emissions from Scope 2 activities was 77.12%, which was considered to have a significant environmental impact and contribute to global warming. This was because 478,851 kWh of electricity were imported. The installation of on-grid solar cells for power generation reduced imported electricity to 113,120 kWh. Consequently, GHG emissions from Scope 2 decreased to 56.55 tCO2e, leading to an overall reduction in the organization's GHG emissions to 127.57 tCO2e. The organization's GHG emissions decreased by 182.83 tCO2e as a result of using alternative energy to generate electricity. This assessment can serve as a database for educational institutions and prepare the government to report greenhouse gas emissions. Furthermore, it can serve as carbon credits for trading and exchanging carbon with other organizations to offset GHG emissions from various activities. In addition, it endorses the government's goal of achieving carbon neutrality and net zero emissions in the future.

Globally, agriculture is directly responsible for 14% of annual greenhouse gas(GHG) emissions and induces an additional 17% through land use change, mostlyin developing countries (Vermeulen et al 2012). Agricultural intensification andexpansion in these regions is expected to catalyze the most significant relativeincreases in agricultural GHG emissions over the next decade (Smith et al 2008,Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa andAsia are predominately managed by smallholders, with 80% of land holdingssmaller than ten hectares (FAO 2012). One can therefore posit that smallholderfarming significantly impacts the GHG balance of these regions today and willcontinue to do so in the near future.However, our understanding of the effect smallholder farming has on theEarth’s climate system is remarkably limited. Data quantifying existing andreduced GHG emissions and removals of smallholder production systems areavailable for only a handful of crops, livestock, and agroecosystems (Herrero et al2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteenstudies of nitrous oxide emissions from soils have taken place in sub-SaharanAfrica, leaving the rate of emissions virtually undocumented. Due to a scarcity ofdata on GHG sources and sinks, most developing countries currently quantifyagricultural emissions and reductions using IPCC Tier 1 emissions factors.However, current Tier 1 emissions factors are either calibrated to data primarilyderived from developed countries, where agricultural production conditions aredissimilar to that in which the majority of smallholders operate, or from data thatare sparse or of mixed quality in developing countries (IPCC 2006). For the mostpart, there are insufficient emissions data characterizing smallholder agricultureto evaluate the level of accuracy or inaccuracy of current emissions estimates.Consequentially, there is no reliable information on the agricultural GHG budgetsfor developing economies. This dearth of information constrains the capacity totransition to low-carbon agricultural development, opportunities for smallholdersto capitalize on carbon markets, and the negotiating position of developingcountries in global climate policy discourse.Concerns over the poor state of information, in terms of data availability andrepresentation, have fueled appeals for new approaches to quantifying GHGemissions and removals from smallholder agriculture, for both existing conditionsand mitigation interventions (Berry and Ryan 2013, Olander et al 2013).Considering the dependence of quantification approaches on data and the currentdata deficit for smallholder systems, it is clear that in situ measurements must bea core part of initial and future strategies to improve GHG inventories and

Current agricultural practices contribute significantly to greenhouse gas emissions. The majority of non-CO2 emissions of agriculture include methane (54%), nitrous oxide (28%) and carbon dioxide (18%), which collectively account for 12% of the world's yearly greenhouse gas (GHG) emissions (7.1 Gt CO2 equivalent). GHG emissions contribute to agricultural activity in direct and indirect activities, accounting for 30% of total global anthropogenic GHG emissions. Agriculture serves a major role in climate change. Agricultural practices lead to the emission of greenhouse gases. Moreover, conventional farming uses synthetic fertilisers. Deforestation and soil degradation are examples of inappropriate land use practices that lower the amount of organic matter in soil. The inappropriate carbon footprint of agriculture is a result of these activities as well as the wasteful use of inputs like water. carbon-neutral methods that reduce greenhouse gas emissions from the production of crops and livestock, and agricultural rice, enteric fermentation, and manure. Agriculture use the renewable energy irrigation source they help to reduce the GHG emissions. It also help to Sustainable development goal climate change.it is crucial role in the climate resilience. These include switching to alternative rice farming techniques, using technologies for managing nitrogen fertilisers, decarbonising on-farm energy use, and developing feeding and breeding strategies that lower enteric methane. When taken as a whole, these actions can cut agricultural GHG emissions by as much as 45%. However, to achieve net-zero agriculture, carbon dioxide removal technology offsets will be needed to balance residual emissions of 3.8 Gt CO2 equivalent per year. Bioenergy with improved carbon collection and storage. Greenhouse Gas emissions profound influence on their effects. Here an overview of inventions and technology was provided with the aim of lowering greenhouse gas emissions from agriculture. The study concluded that the rate and amount of SOC sequestration differ with soil types, depths, land use and land cover and vary from one region to another. Sequestration of carbon in soil can improve soil health, and improvement in soil health will help in improving input use efficiency in agriculture. Thus sequestering carbon in soil and biota can mitigate climate change.

Importance of reducing GHG emissions in power transmission and distribution systems

Assessment of renewable energy and energy efficiency plans in Thailand’s industrial sector

Terrestrial ecosystems play a crucial role in mitigating climate change by reducing greenhouse gas (GHG) emissions and sequestering significant amounts of atmospheric carbon dioxide (CO2). Wetlands, particularly coastal wetlands, are highly efficient carbon sinks but can also be large sources of methane (CH4). Natural and agricultural wetlands, such as rice paddies, contribute to 37 % of global CH4 emissions. Monitoring wetland-atmosphere carbon exchange is essential to evaluate the effectiveness of natural climate solutions (NCS), such as wetlands restoration and sustainable agricultural practices, in reducing GHG emissions and increasing soil carbon storage. Traditional methods for quantifying GHG emissions from wetlands include chamber flux measurements and eddy-covariance flux towers. These techniques provide valuable insights into carbon dynamics at the plot and ecosystem scale levels but fail to capture carbon fluxes at a regional scale, where policy decisions are often made. Recently, atmospheric composition observations have been used at regional scales and over urban areas to constrain the spatial and temporal distribution of GHG fluxes derived from land surface models. Applying similar methodologies to wetland regions, provided sufficient atmospheric observations are available, could enhance understanding of atmospheric carbon dynamics in these areas. The Ebre River Delta, a mixed natural-agricultural wetland system of international importance in terms of sustaining economic activities and biodiversity, offers a unique opportunity to investigate carbon sequestration and GHG emissions. This potential is enhanced by the availability of atmospheric GHG observations from in situ site tower and vehicle transects conducted across the regions.Here, we integrate advanced modelling techniques and observational data to refine our understanding of GHG fluxes in the Ebre Delta. Biogenic GHG emissions over the Delta are estimated using a high-resolution Vegetation Photosynthesis and Respiration Model (VPRM) adapted for wetland ecosystems for CO2, and the Kaplan model embedded in the Weather Research and Forecasting (WRF) Greenhouse Gas (WRF-GHG) model to estimate CH4 emissions.  A sensitivity analysis is performed to compare VPRM CO2 emissions from different model configurations, entailing a default and a wetland-adapted model versions, and two sources of input satellite-vegetation indices, MODIS and Sentinel-2, with contrasting  spatial resolutions. Then, modelled atmospheric CO2 and CH4 mixing ratios with WRF-GHG during growing season are compared with in situ observations from the site tower and vehicle transects to assess their accuracy. The framework developed in this study will provide the basis for investigating sequestration and emission hotspots over a mosaic of wetland land-uses and evaluate the region's potential for climate change mitigation and adaptation.