Estimating Greenhouse Gas Emissions in the Pacific Island Countries

South Africa is a signatory to the United Nations Framework Convention on Climate Change (UNFCCC) and as such is required to report on Greenhouse gas (GHG) emissions from the Energy, Transport, Waste and the Agriculture, Forestry and Other Land Use (AFOLU) sectors every two years in national inventories. The AFOLU sector is unique in that it comprises both sources and sinks for GHGs. Emissions from the AFOLU sector are estimated to contribute a quarter of the total global greenhouse gas emissions. GHG emissions sources from agriculture include enteric fermentation; manure management; manure deposits on pastures, and soil fertilization. Emissions sources from Forestry and Other Land Use (FOLU) include anthropogenic land use activities such as: management of croplands, forests and grasslands and changes in land use cover (the conversion of one land use to another). South Africa has improved the quantification of AFOLU emissions and the understanding of the dynamic relationship between sinks and sources over the past decade through projects such as the 2010 GHG Inventory, the Mitigation Potential Analysis (MPA), and the National Terrestrial Carbon Sinks Assessment (NTCSA). These projects highlight key mitigation opportunities in South Africa and discuss their potentials. The problem remains that South Africa does not have an emissions baseline for the AFOLU sector against which the mitigation potentials can be measured. The AFOLU sector as a result is often excluded from future emission projections, giving an incomplete picture of South Africa’s mitigation potential. The purpose of this project was to develop a robust GHG emissions baseline for the AFOLU sector which will enable South Africa to project emissions into the future and demonstrate its contribution towards the global goal of reducing emissions.

The Paris Agreement commits 197 countries to stabilizing global average surface temperatures at less than 2 °C above pre-industrial levels. Many industrialized nations, including Italy, aim for climate neutrality by 2050 through “net zero” greenhouse gas (GHG) emissions policies, aimed at decarbonizing all the energy intensive sector. In this context, the role of agriculture, forestry, and other land use (AFOLU) sector play an ambiguous role. Challenges include balancing GHG mitigation with food security, addressing synergies with the energy sector (e.g., bio commodities), and leveraging AFOLU as a net sink to offset emissions from other sectors.Energy system optimization models (ESOMs), as widely used to design cost-optimal decarbonization policies, can be used to determine effective AFOLU management strategies at a national level. Nevertheless, their focus on energy-intensive processes had previously limited detailed AFOLU representation, despite its prominent role in emission mitigation. ESOMs often lack the integration of natural capital constraints, such as land and water availability, as well as the ability to model specific AFOLU commodities like crops, livestock, and forest products. To address this gap, we introduce a novel AFOLU module designed to couple with ESOMs, enabling the formulation of national decarbonization scenarios incorporating a technology-explicit AFOLU representation, biophysical constraints and the possibility to evaluate climate change impacts on the sector.The AFOLU module tracks GHG emissions from livestock, crops, and bioenergy production while optimizing sectoral contributions to national decarbonization goals. Additionally, it projects the evolution of AFOLU commodities, including shifts in crop types, livestock production, and forest management strategies in response to climate and policy drivers. Finally, it can account for biophysical constraints such as land use limitations, crop yield sensitivity to fertilizer and climate change, and forest absorption potential. The module is designed to be directly fed by the Global Agro-Ecological Zones (GAEZ) database from FAO, allowing for the automatized creation of national instances based on up-to-date geospatial datasets.To demonstrate the utility of the module, we integrate it with the open-source energy system optimization model TEMOA, which has been validated in Italian case studies and shown coherence with established models like TIMES, and similar in structure to other ESOMs like MESSAGE, and OSeMOSYS. The integrated model evaluates Italy’s national climate mitigation plans, focusing on the interplay between energy and AFOLU sectors, including land competition for bio crop production.Key outputs of the model include detailed accounting and optimization of AFOLU emissions, land and water use, and cost-effective decarbonization pathways for all the energy intensive sectors. For instance, scenarios explore the potential of organic farming to reduce crop-related emissions, the role of manure management in mitigating livestock emissions, and the benefits of afforestation for carbon sequestration. Preliminary results from the Italian case study reveal critical trade-offs and synergies, such as the tension between bioenergy production and food security, while identifying least-cost pathways to achieve climate neutrality.This research bridges a critical gap in decarbonization modeling by integrating a flexible AFOLU module with energy systems, offering a reproducible framework for other national applications. 

Finland has set a legally binding goal of achieving “carbon neutrality” by 2035 and net-negative greenhouse gas emissions thereafter. The scientific background for this goal is based on an interpretation of what a nationally fair share of global carbon budget compatible with 1.5 °C warming is. This national carbon budget includes also non-CO2 greenhouse gas (GHG) emissions and is thus stricter than the original, global CO2-only carbon budget. Finland’s pathway to carbon neutrality relies not only on emission reductions but also on carbon sinks in the land use, land-use change and forestry (LULUCF) sector. The net sink in the LULUCF sector, as estimated in the national greenhouse gas inventory, is interpreted as negative emissions. This assumption is problematic, as part of the LULUCF sink is considered natural sink in the conceptual framework behind the global carbon budget estimates and assuming it entirely anthropogenic leads to underestimation of the net CO2 emissions.   Here we present an analysis and revision of the Finnish net greenhouse gas emission pathway with two major improvements. First, we extend the carbon budget framework to nationally allowed warming to be able to account explicitly also for national non-CO2 GHG emissions. The global allowed warming until 2050 from future GHG emissions is calculated as the sum of the remaining warming to 1.5 °C, the global decreasing warming impact of past non-CO2 GHG emissions by 2050, and future warming due to reduced aerosols by 2050. We use the fair share used previously for allocating national carbon budget to calculate national allowed warming contribution until 2050 and subtract non-CO2 GHG contribution based on a Finnish carbon neutrality scenario and simulations with a simple climate model FaIR 2.1. The remaining allowed warming is then used to calculate the national CO2-only carbon budget.   The second improvement is to consider the recent advancements in disentangling natural and anthropogenic carbon fluxes in the LULUCF sector. National results from a recent global study indicate that the Finnish LULUCF sector has been a carbon sink due to the natural sink induced by CO2 fertilization and climate change. The large natural sink is expected to decrease especially in the most stringent global emission reduction scenarios.    The preliminary results indicate that Finland’s currently planned pathway is not compatible with its national fair share of allowed warming compatible with the 1.5-degree target, and more stringent emission reductions coupled with strengthening of the land sink and other forms of negative emissions are very likely needed.

Forest carbon accounting methods and the consequences of forest bioenergy for national greenhouse gas emissions inventories

Abstract. The Paris Agreement commits 197 countries to achieve climate stabilisation at a global average surface temperature less than 2 ∘C above pre-industrial times using nationally determined contributions (NDCs) to demonstrate progress. Numerous industrialised economies have targets to achieve territorial climate neutrality by 2050, primarily in the form of “net zero” greenhouse gas (GHG) emissions. However, particular uncertainty remains over the role of countries' agriculture, forestry, and other land use (AFOLU) sectors for reasons including the potential trade-offs between GHG mitigation and food security, a non-zero emission target for methane as a short-lived GHG, and the requirement for AFOLU to act as a net sink to offset residual emissions from other sectors. These issues are represented at a coarse level in integrated assessment models (IAMs) that indicate the role of AFOLU in global pathways towards climate stabilisation. However, there is an urgent need to determine appropriate AFOLU management strategies at a national level within NDCs. Here, we present a new model designed to evaluate detailed AFOLU scenarios at national scale using the example of Ireland, where approximately 40 % of national GHG emissions originate from AFOLU. GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) is designed to run randomised scenarios of agricultural activities and land use combinations within biophysical constraints (e.g. available land area, livestock productivities, fertiliser-driven grass yields, and forest growth rates). Using AFOLU emission factors from national GHG inventory reporting, GOBLIN calculates annual GHG emissions out to the selected target year for each scenario (2050 in this case). The long-term dynamics of forestry are represented up to 2120 so that scenarios can also be evaluated against the Paris Agreement commitment to achieve a balance between emissions and removals over the second half of the 21st century. Filtering randomised scenarios according to compliance with specific biophysical definitions (GHG time series) of climate neutrality will provide scientific boundaries for appropriate long-term actions within NDCs. We outline the rationale and methodology behind the development of GOBLIN, with an emphasis on biophysical linkages across food production, GHG emissions, and carbon sinks at a national level. We then demonstrate how GOBLIN can be applied to evaluate different scenarios in relation to a few possible simple definitions of “climate neutrality”, discussing opportunities and limitations.

Stabilizing greenhouse gas (GHG) emissions from croplands as agricultural demand grows is a critical climate change mitigation strategy. Depending on management, the Agriculture, Forestry, and Other Land Use (AFOLU) sector can be both a source as well as a net sink for carbon. Currently, it contributes 25% of the global anthropogenic carbon emissions. Although India’s emissions from this sector are around 8% of the total national GHG emissions, it can contribute significantly to the country’s aspirations of reaching net-zero emissions by 2070. In this review, we explain the carbon footprints of the AFOLU sector in India, focusing on enteric fermentation, fertilizer and manure management, rice paddies, burning of crop residues, forest fires, shifting cultivation, and food wastage. Furthermore, using the standard autoregressive integrated moving average method, we project India’s AFOLU sector emission routes for 2070 under four scenarios: business as usual (BAU) and three emission reduction levels, viz., 10%, 20%, and 40% below BAU. The article focuses on how the AFOLU sector can be leveraged proactively to reach the net-zero emission goals. Increasing forest cover, agroforestry, and other tree-based land-use systems; improving soil health through soil management, better crop residue, and livestock feed management; emission avoidance from rice ecosystems; and reducing food waste are all important strategies for lowering India’s AFOLU sector carbon footprints.

Salinity gradient power (or blue energy) is a renewable energy source mentioned in the literature since the 1950s. It refers to the production of electricity by mixing of two solutions with different salt concentrations, for example river and sea water. The global potential of salinity power has been estimated in the 1970s as substantial, but the state of membrane technology at that time – crucial for energy recovery – did not permit the practical use of this resource. More recently, the interest in salinity power has been growing because of the need for carbon neutral, renewable sources of electricity. This study aims to assess the potential of salinity-gradient power for reducing emissions of CO2 and non-CO2 greenhouse gases. First, we discuss the global technical potential for blue energy, i.e. the maximum amount of energy that could be retrieved at the current state of technology. We focus on rivers as source of fresh water and seas as source of saline water. The analysis is based on global datasets of annual river discharges for more than 5000 world rivers. The resulting estimates of global and regional potentials for salinity gradient power are used to estimate the potential for reducing greenhouse gases, assuming that salinity power would reduce the need for fossil fuels. The results are shown for global totals, regional totals and selected rivers.

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Abstract. Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in the European Union (EU281). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.3662371 (Petrescu et al., 2020).

BackgroundThe Agriculture, Forestry and Other Land Use (AFOLU) sector is responsible for almost a quarter of the global Greenhouse gases (GHG) emissions. The emissions associated with AFOLU activities are projected to increase in the future. The agriculture sector in Thailand accounted for 21.9% of the country’s net GHG emissions in 2013. This study aims to estimate the GHG emissions in the AFOLU sector and mitigation potential at various carbon prices during 2015–2050. This study uses an AFOLU bottom-up (AFOLUB) model to estimate GHG emissions in a business-as-usual (BAU) scenario, and then identifies no-regret options, i.e. countermeasures that are cost-effective without any additional costs. In addition, the study also identifies countermeasure options and mitigation potential at various carbon prices.ResultsResults show that emissions from the agriculture sector in the BAU will increase from 45.3 MtCO2eq in 2015 to 63.6 MtCO2eq in 2050, whereas net emission from the AFOLU will be 8.3 MtCO2eq in 2015 and 24.6 MtCO2eq in 2050. No-regret options would reduce emissions by 6.1 and 6.8 MtCO2eq in 2030 and 2050, respectively. The carbon price above $10 per tCO2eq will not be effective to achieve significant additional mitigation/sequestration.ConclusionsIn 2050, no-regret options could reduce total AFOLU emissions by 27.5%. Increasing carbon price above $10/tCO2eq does not increase the mitigation potential significantly. Net sequestration (i.e., higher carbon sequestration than GHG emissions) in AFOLU sector would be possible with the carbon price. In 2050, net sequestration would be 1.2 MtCO2eq at carbon price of $5 per tCO2eq, 21.4 at $10 per tCO2eq and 26.8MtCO2eq at $500 per tCO2eq.

Agriculture, Forestry, and Other Land Use (AFOLU) is one of the most important sectors for the food and livelihood security, as well as being among the leading greenhouse gases (GHG) emitters, especially from the developing countries. (AFOLU is responsible for about a quarter of human-induced GHG emissions.) Among the different AFOLU activities, deforestation and agriculture are leading drivers of growing emission. In order to reduce GHG from the AFOLU sector, it is necessary to develop cost-effective mitigation strategies and adaptation measures via investment for adequate land and environment management. Investments should be made in food security efforts, boosting carbon sinks, modernizing old technologies, and introducing new technical innovation in order to minimize AFOLU emissions. The AFOLU mitigation measure can also give a co-benefit in the form of ecosystem service, but the adverse effects of the mitigation strategies, implementation problems and barriers should not be overlooked. Nevertheless, there are ample potential and perspectives to minimize GHG emission from the AFOLU sector. Therefore, in this chapter, the different sub-sectors of AFOLU are explored in terms of their emission status along with proper land and environment management including cost-effective mitigation measures, challenges and opportunities for making the AFOLU sector net zero or negative emitter of GHG.

Comparative analysis of greenhouse gas emission inventory for Pakistan: Part II agriculture, forestry and other land use and waste

Transparent, accurate, comparable, and complete estimates of greenhouse gas emissions and removals are needed to support mitigation goals and performance assessments under the Paris Agreement. Here, we present a comparative analysis of the agriculture forestry and other land use (AFOLU) emission estimates from different datasets, including National Greenhouse Gas Inventories (NGHGIs), FAOSTAT, the BLUE, OSCAR, and Houghton (here after updated H&N2017) bookkeeping models; Emissions Database for Global Atmospheric Research (EDGAR); and the US Environmental Protection Agency (EPA). We disaggregate the fluxes for the forestry and other land use (FOLU) sector into forest land, deforestation, and other land uses (including non-forest land uses), while agricultural emissions are disaggregated according to the sources (i.e., livestock, croplands, rice cultivation, and agricultural fires). Considering different time periods (1990–1999, 2000–2010, and 2011–2018), we analyse the trend of the fluxes with a key focus on the tropical regions (i.e., Latin America, sub-Saharan Africa, and South and Southeast Asia). Three of the five data sources indicated a decline in the net emissions over the tropics over the period 1990–2018. The net FOLU emissions for the tropics varied with values of 5.47, 5.22, 4.28, 3.21, and 1.17 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs, respectively) over the recent period (2011–2018). Gross deforestation emissions over the same period were 5.87, 7.16, 5.48, 3.96, and 3.74 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). The net forestland sink was −1.97, −3.08, −2.09, −0.53, and −3.00 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). Continental analysis indicated that the differences between the data sources are much large in sub-Saharan Africa and South and Southeast Asia than in Latin America. Disagreements in the FOLU emission estimates are mainly explained by differences in the managed land areas and the processes considered (i.e., direct vs indirect effects of land use change, and gross vs net accounting for deforestation). Net agricultural emissions from cropland, livestock, and rice cultivation were more homogenous across the FAOSTAT, EDGAR, and EPA datasets, with all the data sources indicating an increase in the emissions over the tropics. However, there were notable differences in the emission from agricultural fires. This study highlights the importance of investing and improving data sources for key fluxes to achieve a more robust and transparent global stocktake.

In the Netherlands, several measures were implemented to curb the emissions of non-CO2 greenhouse (NCG) gases in the period 1990–2003. Without the implementation of these reduction measures, emissions of NCG gases in 2003 would have been 11 million tonnes of CO2-eq. higher. Policies which were already in place before specific NCG gas policies were introduced, are predominantly responsible for the reductions achieved so far. Roughly 80% of the achieved reductions in the Netherlands can be attributed to these policies, and 20% to a specific Dutch Reduction Plan on NCG gases that was introduced in 2000. Our analysis shows that the policies in place in the period 1990–2003 were very economical from the government point of view: almost half of the emission reductions can be achieved against costs below 5 euro per tonne CO2-eq. Whereas, for example, the cost-effectiveness of government CO2 reduction programmes in the housing sector ranged from 4 to over 300 euro per tonne of CO2-eq. in the period 1995–2002.

Mitigating global warming problems initially involves reducing greenhouse gas (GHG) emissions, in which the uncertainty of GHG emission estimates is assessed concisely. Although the uncertainty of GHG emission estimates is generally evaluated using classical confidence intervals, quantifying the uncertainty based on non-normal GHG emission estimates or small dataset may lead to a significant bias. Using bootstrap confidence intervals is an effective means of reducing such a bias. This study presents a procedure for constructing the four bootstrap confidence intervals to assess the uncertainty of GHG emission estimates for non-normal distributions. These bootstrap confidence intervals are standard bootstrap (SB) confidence interval, percentile bootstrap (PB) confidence interval, Bias-corrected percentiles bootstrap (BCPB) confidence interval and bias-corrected and accelerated (BCa) confidence interval. The sensitivity of bootstrap intervals for emission data is examined under various combinations of sample size and parameter values of normal and non-normal distributions by using three indices: coverage performance, interval mean, and interval standard deviation. Additionally, this study finds the minimum sample size when constructing bootstrap confidence intervals for emission estimates without considering the distribution of the emission data. Simulation results indicate that the bootstrap intervals are more applicable than the classical confidence interval for the non-normal dataset and small sample size. Moreover, when sample size n is less than 30, the bootstrap confidence interval has a smaller interval length with a smaller deviation than that of the classical 95% confidence interval regardless of whether the data distribution is normal or non-normal. This study recommends a sample size greater than or equal to 15 for estimating the uncertainty of emission estimates without checking the distribution of the data. When the sample size n exceeds 30, either the normality-based 95% confidence interval or bootstrap confidence intervals may be used regardless of whether the data distribution is normal or non-normal. Two case studies with emission data were utilized to demonstrate the effectiveness of the proposed procedure.