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

The focus of research over the years has been mostly on industrial greenhouse gas emissions. While there has been an extensive analysis of the drivers of aggregate CO2 emissions from fossil fuel combustion and cement production, analysis of the drivers of greenhouse gases emissions from agriculture, forestry, and other land uses which are also known as non-industrial emissions are limited (Sanchez and Stern in Ecol Econ 124:17–24, 2016). Agriculture, forestry and other land use (AFOLU) represents 20–24% of the global GHG emissions, the largest emitting sector next to energy. In Asia, the AFOLU sector is important and accounts for the largest proportion of global AFOLU emissions. India is the world’s fourth largest economy and fifth largest global GHG emitter. The net AFOLU emissions in India were 146.7 million tCO2e, accounting for about 11% of its net national emissions. The agricultural emissions were 355.6 million tCO2e, accounting for 23% of gross national emissions and 96% of gross AFOLU emissions in the same year. AFOLU is not the largest emitter in India. The forestry and other land use (FOLU) is, on the other hand, an important sink with net removals of 236 million tCO2e as reported in 2000. Cities and towns have been found to be settled after clearing large areas under forest cover in Assam. The present study examines the non-Industrial (AFOLU) emissions in the city of Guwahati. This study analyses 100 years trend (1911–2015) of deforestation and conversion of forests to settlements, wetlands and agricultural land and fuelwood burning within the city limits. The area values of AFOLU sectors were computed from maps and satellite images. Emission factor (EF) values were obtained from available literature to study the AFOLU emissions in the city. The findings indicate that the share of deforestation in CO2 emission increased from 49% in 1911 to 85% in 2011, and contributed almost 0.91 tCO2 per capita to the total emissions. Past 100 years average AFOLU emission per capita for Guwahati was found to be 1.81 tCO2 against 1.03 tCO2 for the last 50-year average. The results would be useful for policymakers given the fact that the city of Guwahati is one of the 100 cities in India that has been taken up for the smart city project presently underway in the country. Moreover, the results of the study would also be useful for further research and decision-making for achieving the SDG 11.

Abstract. The Agriculture, Forestry and Other Land Use (AFOLU) sector contributes with ca. 20–25 % of global anthropogenic emissions (2010), making it a key component of any climate change mitigation strategy. AFOLU estimates, however, remain highly uncertain, jeopardizing the mitigation effectiveness of this sector. Comparisons of global AFOLU emissions have shown divergences of up to 25 %, urging for improved understanding of the reasons behind these differences. Here we compare a variety of AFOLU emission datasets and estimates given in the Fifth Assessment Report for the tropics (2000–2005) to identify plausible explanations for the differences in (i) aggregated gross AFOLU emissions, and (ii) disaggregated emissions by sources and gases (CO2, CH4, N2O). We also aim to (iii) identify countries with low agreement among AFOLU datasets to navigate research efforts. The datasets are FAOSTAT (Food and Agriculture Organization of the United Nations, Statistics Division), EDGAR (Emissions Database for Global Atmospheric Research), the newly developed AFOLU “Hotspots”, “Houghton”, “Baccini”, and EPA (US Environmental Protection Agency) datasets. Aggregated gross emissions were similar for all databases for the AFOLU sector: 8.2 (5.5–12.2), 8.4, and 8.0 Pg CO2 eq. yr−1 (for Hotspots, FAOSTAT, and EDGAR respectively), forests reached 6.0 (3.8–10), 5.9, 5.9, and 5.4 Pg CO2 eq. yr−1 (Hotspots, FAOSTAT, EDGAR, and Houghton), and agricultural sectors were with 1.9 (1.5–2.5), 2.5, 2.1, and 2.0 Pg CO2 eq. yr−1 (Hotspots, FAOSTAT, EDGAR, and EPA). However, this agreement was lost when disaggregating the emissions by sources, continents, and gases, particularly for the forest sector, with fire leading the differences. Agricultural emissions were more homogeneous, especially from livestock, while those from croplands were the most diverse. CO2 showed the largest differences among the datasets. Cropland soils and enteric fermentation led to the smaller N2O and CH4 differences. Disagreements are explained by differences in conceptual frameworks (carbon-only vs. multi-gas assessments, definitions, land use vs. land cover, etc.), in methods (tiers, scales, compliance with Intergovernmental Panel on Climate Change (IPCC) guidelines, legacies, etc.) and in assumptions (carbon neutrality of certain emissions, instantaneous emissions release, etc.) which call for more complete and transparent documentation for all the available datasets. An enhanced dialogue between the carbon (CO2) and the AFOLU (multi-gas) communities is needed to reduce discrepancies of land use estimates.

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. 

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 greenhouse gas (GHG) emissions from agriculture, forestry, and other land use (AFOLU) account for more than 10% of the total GHG emissions in Iran. To reduce the environmental impact, assessments of Iran’s GHG emissions status are critical for identifying the national policies to achieve Sustainable Development Goals (SDGs) in the bio-based industry. However, there is no study exploring the dependency between AFOLU and GHG emissions in Iran by using the Vine Copula approach. Hence, the study aims to examine the causality direction and correlation structure among selected horticulture, farming crops, livestock, and poultry products and carbon dioxide (CO2), nitrogen dioxide (N2O), and methane emissions (CH4) in the Iranian agriculture sector over the period 1961–2019, to determine which crops or products are more responsible to deteriorate the environment. The empirical strategy used a C-Vine Copula model to measure the correlations together with the Granger causality (GC) test to analyze the causality links. According to the empirical findings, several crops and products are the sources of emissions. Rice and vegetable cultivations, as well as meat and milk products (Kendall’s τ values of 0.37, 0.33, 0.31, and 0.31, respectively), are the leading sources of CH4 emissions. Legumes, eggs, maize, rice, and milk enhance N2O emissions, while CO2 emissions are caused by apple, potato, and apricot crops (Kendall’s τ values of 0.22, 0.18, and 0.16, respectively). Finally, based on the findings, policy implications are offered.

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.

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.

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.

This study uses GLOBIOM ‒ the most detailed global economic model of agriculture, land use and greenhouse gas (GHG) emissions ‒ to assess the effectiveness of different policies in cutting net emissions from the Agriculture, Forestry and Other Land Use (AFOLU) sector, with a view to helping limit long-term global temperature increases to 1.5°C and 2°C. Trade-offs between emission reductions and impacts on food producers, consumers and government budgets are also evaluated for each policy package. A full complement of policy options is deployed globally across AFOLU, comprising emission taxes for emitting AFOLU activities and subsidies rewarding carbon sequestration. Using a carbon price consistent with the 2°C target (1.5°C target), this is projected to mitigate 8 GtCO2 eq/yr (12 GtCO2 eq/yr) in 2050, representing 89% (129%) reduction in net AFOLU emissions, and 12% (21%) of total anthropogenic GHG emissions. Nearly two-thirds of the net emission reductions are from the Land Use, Land-Use Change and Forestry (LULUCF) component of AFOLU, mostly from reduced deforestation. A global carbon tax on AFOLU is found to be twice as effective in lowering emissions as an equivalently priced emission abatement subsidy because the latter keeps high emitting producers in business. However, a tax has trade-offs in terms of lower agricultural production and food consumption, which a subsidy avoids. A shift to lower emission diets by consumers has a much smaller impact on reducing agricultural emissions than any of the policy packages involving taxes on emissions.

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.

In light of the urgency for policy action to address climate change, this report provides the first detailed global catalogue of targets and policies for mitigating greenhouse gas emissions in the Agriculture, Forestry and Other Land Use (AFOLU) sector. It covers 20 countries which collectively account for nearly half of the world’s AFOLU emissions. Most of these countries have recently set targets within their AFOLU sector as part of national climate mitigation strategies and commitments, although these targets are only legally-binding for two countries. However, policies to incentivise emission reductions and achieve these targets still need to be developed. Consequently, policy efforts will need to intensify for the AFOLU sector to contribute effectively to limiting global temperature increases to well below 2°C, and especially to meet the more ambitious 1.5°C target of the Paris Agreement.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

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).

Carbon prices and greenhouse gases abatement from agriculture, forestry and land use in Nepal

The food and land use sector is a major contributor to India’s total greenhouse gas (GHG) emissions. On one hand, India is committed to sustainability targets in the Agriculture, Forestry and Other Land Use (AFOLU) sectors, on the other, there is little clarity whether these objectives can align with national developmental priorities of food security and environmental protection. This study fills the gap by reviewing multiple corridors to sustain the AFOLU systems through an integrated assessment framework using partial equilibrium modeling. We create three pathways that combine the shared socio-economic pathways with alternative assumptions on diets and mitigation strategies. We analyze our results of the pathways on key indicators of land-use change, GHG emissions, food security, water withdrawals in agriculture, agricultural trade and production diversity. Our findings indicate that dietary shift, improved efficiency in livestock production systems, lower fertilizer use, and higher yield through sustainable intensification can reduce GHG emissions from the AFOLU sectors up to 80% by 2050. Dietary shifts could help meet EAT-Lancet recommended minimum calorie requirements alongside meeting mitigation ambitions. Further, water withdrawals in agriculture would reduce by half by 2050 in the presence of environmental flow protection and mitigation strategies. We conclude by pointing towards specific strategic policy design changes that would be essential to embark on such a sustainable pathway.