Advancing agricultural greenhouse gas quantification*

Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions — Application of system dynamics modeling for the case of Latvia

Emissions of methane (CH4) and nitrous oxide (N2O) from agricultural activities currently comprise 10-12% of the world’s total anthropogenic greenhouse gas (GHG) emissions. They are also forecast to rise by 30% above current levels by 2050. At the Conference of the United Nations Framework Convention on Climate Change (UNFCCC) held in Paris in December 2015, more than 100 countries indicated that they would reduce agricultural GHG emissions as part of the global effort to keep warming to a maximum of 2°C. Emissions from ruminant livestock present a particular challenge as enteric CH4 emissions alone comprise ~40% of total agricultural emissions. Estimating emissions from animal agriculture can be done through simple estimates, generically available data on animal populations and regional-level fixed emission factors per animal. But these estimates are subject to very large uncertainties and their appropriateness for estimating emissions at the country level is questionable. More appropriate country-specific methods can be developed using local data and expert opinion in the first instance, even in the absence of country-specific emission factors. Reducing GHG emissions from ruminant livestock is challenging technically even if livestock production is constant, and is particularly challenging if the sector is increasing in size. Internationally the quantity of GHG produced per unit of product has been declining consistently and for both cattle meat and milk than 50 years ago. This decline is largely due to increased efficiency of production. Increasing efficiency is therefore a key component of agricultural GHG mitigation. Increasing efficiency,while essential, may not be enough on its own. New technologies are therefore needed and for ruminant livestock there are some promising products; compounds that inhibit enteric CH4, vaccines, low emitting sheep have been successfully bred and, a variety of low emitting feeds, and feed additives.

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

Agriculture plays a central role in maintaining food security and achieving sustainable development for human society. It is a challenge for the agricultural sector to mitigate greenhouse gas (GHG) emissions and maintain agricultural production. However, dual-level uncertainties exist in the processes of agricultural GHG accounting and emission reduction management. In this research, an integrated approach for identifying adaptation strategies in agricultural GHG emission reduction management was developed through incorporating life cycle analysis (LCA) of agricultural production into a general mathematical programming model. This approach strengthened the applicability of LCA and the comprehensiveness of programming models in generating agricultural adaptive actions under different GHG emission restriction targets. Also, dual-level uncertainties of LCA and adaptation management can be effectively addressed. A case study was proposed to illustrate application of the approach in Dalian City, China. The results indicated that farming patterns in eight districts would change significantly. The total area of maize fields would account for the primary proportion (i.e., 40–45 %) in its agricultural sector. Rice, peanut and cabbage fields would be the minor contributors in terms of GHG emissions. In addition to effective rainfall (i.e., [156, 259] mm/ha), a certain amount of water would be supplied for agricultural irrigation to maximize the city’s agricultural yields. Compared with other agricultural crops, rice fields would need the largest amount of irrigation water (i.e., [153.72, 277.98] Mt) to meet the requirements of local government plans.

Nexus of embodied land use and greenhouse gas emissions in global agricultural trade: A quasi-input–output analysis

Abstract. To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

This study explores the interactions of groundwater extraction, quality, and greenhouse-gas (GHG) emissions within a productive agricultural region. Two conceptual models are proposed. In the first, GHG emissions are managed at the local level, and an efficient level of abatement is solved for endogenously to the system. Here, regional management of GHG emissions offers an alternative policy tool for managing quantity/quality by internalizing the costs of a common externality associated with both groundwater extraction and nitrogen fertilizer application. A simple numerical simulation is used to illustrate the potential groundwater co-benefits of managing agricultural GHG emissions within the system. The second model reflects the reality that GHG mitigation efforts will occur at the national or international level; agricultural markets and production will respond according to the scope of the policy mechanism and the anticipated effect on agricultural markets and input costs. For this scenario, the impacts of GHG mitigation on regional groundwater supplies are ambiguous. A set of scenarios is derived in which groundwater co-benefits or co-costs can be expected within a region. Groundwater managers should be cognizant of the indirect market pressures created by agricultural GHG mitigation and bioenergy development, and should adapt conservation and quality protection measures accordingly.

The agriculture and forestry sector accounts for approximately 24% of total greenhouse gas (GHG) emissions in Australia. Over the years researchers have produced new knowledge about agricultural GHG emissions and energy use patterns and opportunities to decrease them, albeit in small-scale studies or large-scale ones with coarse resolution. Linking the multiple, diverse and rich datasets around agricultural production in Australia into one dataset that allows for an estimate of GHG emissions and energy use related to agriculture at a national scale and high resolution has not been done before. The approach we describe here is based upon a link between operational data sourced from gross margin (GM) handbooks and life cycle assessment (LCA) process data. We have collected and processed these datasets to produce a comprehensive database of typical agricultural operations covering 72 commodities grown in 42 regions across Australia. We have also created a system that estimates the GHG emission and energy use patterns of the aforementioned commodities using the best available LCA process data. To capture GHG emissions of non-domestically produced fertiliser, we queried the United Nations Commodity Trade Statistics Database (COMTRADE) to analyse the fertiliser and pesticide import patterns for Australia between 2000-2010. This analysis determined the average country energy-mix for fertiliser and pesticide manufacturing and allowed linking the associated GHG emissions to Australian agricultural production. Finally we spatialised agricultural operational data, emissions and energy use at the national scale using the latest Australian Land Use Map (2005/06). Our findings suggest that in 2005/06 greenhouse gas emissions related to Australian agricultural production equate to a total of 95.8 Mt CO-e using 75.7 GWh of energy. According to our results 29.4% of these emissions come from sources that were categorised as non-agricultural (e.g. industrial processes or energy use) in the Australian National Greenhouse Gas Inventory (NGGI) 2006. We find that the provision of transparently modeled GHG emissions and having them linked to a spatially explicit component helps identifying new opportunities for emission reduction and facilitates an assessment of their effects. For example, our findings suggest production of ethanol from corn stover and sugarcane bagasse could have avoided 4.37 Mt CO-e emissions (4.56% of total) without affecting food production.

The growing concern about climate change led governments around the world to agree on forming the United Nations Framework Convention on Climate Change (UNFCCC) during the 1992 Rio summit. This Convention entered into force in March 1994 and by May 1997, the UNFCCC had over 130 signatory countries. The UNFCCC works for the “stabilization of carbon dioxide (CO 2 ) concentrations at a level that would prevent dangerous anthropogenic interference with the climate system”. The Convention requires the cooperation of national administrations in providing a framework of regulations and policies to implement its various provisions. The Kyoto Protocol to the UNFCCC was adopted in December 1997, according to which the developed country parties agree to reduce their Greenhouse Gases (GHGs) emissions by a global average of 5.2 per cent below the 1990 levels by the period 2008–2012. The Protocol came into effect in February 16, 2005 without its ratification by USA and Australia. India is not required to adopt any GHG reduction targets though she has to follow the general commitments under the UNFCCC. It is well known that the USA considers the Kyoto Protocol to be a “totally flawed” and “unsound international treaty”. The Kyoto Protocol created flexible cooperative mechanisms, such as GHG Emissions Trading and Clean Development Mechanism (CDM), allowing member countries to meet their commitments with respect to reduction in emissions. Through CDM, developing countries that did not accept binding emissions reductions at Kyoto can be involved in GHG mitigation.

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The European Union (EU) has proposed legislative revisions to achieve climate neutrality in EU by 2050. The Land Use, Land-Use Change and Forestry (LULUCF) Regulation, adopted in 2018, is being revised to ensure that accounted greenhouse gas (GHG) emissions from LULUCF are balanced by equivalent accounted removals of carbon dioxide (CO2) from the atmosphere. This study focuses on the impact of targeted tree introduction in agricultural land in Latvia, specifically afforestation of drained organic soil and implementation of agroforestry systems (riparian buffer strips), on national GHG reduction targets for the LULUCF sector. The potential contributions of selected measures were evaluated using evaluation methods including GHG emissions factors based on the Intergovernmental Panel on Climate Change (IPCC) guidelines and recent scientific studies. The study differentiated between different land use categories by GHG emissions from soil and CO2 removals in living biomass, dead wood, litter, mineral soil, and organic soil. Basic scenarios were compared with additional scenarios that included afforestation of drained organic soils and implementation of agroforestry systems. The study analysed the possibilities of achieving LULUCF sector goals for 2030, 2035, and 2050 with the selected scenarios. According to the basic scenarios, the LULUCF sector has been a continuous source of GHG emissions since 2019, partly compensated by forest management by 2040, but after 2040 forest management becomes a source of GHG emissions as well. The study shows that afforestation of organic soils currently used for agricultural production can reduce GHG emissions and ensure the achievement of national LULUCF targets for 2021-2025, with a significant decrease in GHG emissions by 3.9 million t CO2 eq. during the 2021-2025 period if compared to the basic scenario. However, the study finds that national target of net GHG removals is not achieved for 2026-2030 according to both basic and afforestation scenarios if no additional measures, e.g., establishment of the shelter belts, are implemented.

<p>The agriculture sector is one of the largest users of water and a significant source of greenhouse gas (GHG) emissions. The development of low-GHG-emission and water-conserving agriculture will inevitably be the trend in the future. Because of the physiological differences among crops and their response efficiency to external changes, changes in planting structure, climate and input of production factors will have an impact on regional agricultural water use and GHG emissions. This paper systematically analyzed the spatial-temporal evolution characteristics of crop planting structure, climate, and production factor inputs in Heilongjiang Province, the main grain-producing region of China, from 2000 to 2015, and quantified the regional agricultural water use and GHG emissions characteristics under different scenarios by using the Penman-Monteith formula and the Denitrification-Decomposition (DNDC) model. The results showed that the global warming potential (GWP) increased by 15% due to the change in planting structure. A large increase in the proportion of rice and corn sown was the main reason. During the study period, regional climate change had a positive impact on the water- saving and emission reduction of the agricultural industry. The annual water demand per unit area decreased by 19%, and the GWP decreased by 12% compared with that in 2000. The input of fertilizer and other means of production will have a significant impact on GHG emissions from farmlands. The increase in N fertilizer input significantly increased N<sub>2</sub>O emissions, with a 5% increase in GWP. Agricultural water consumption and carbon emissions are affected by changes in climate, input of means of production, and planting structure. Therefore, multiple regulatory measures should be taken in combination with regional characteristics to realize a new layout of planting structure with low emissions, water conservation, and sustainability.</p>

Since agriculture is a major source of greenhouse gas emissions, accurately calculating these emissions is essential for simultaneously addressing climate change and food security challenges. This paper explores the critical role of trade in transferring agricultural greenhouse gas (AGHG) emissions throughout global agricultural supply chains. We develop a detailed AGHG emission inventory with comprehensive coverage across a wide range of countries and emission sources at first. Utilizing this inventory, the multi-regional input-output analysis is integrated with the complex network analysis to specifically reveal the characteristics of global AGHG flow networks. Finally, scenario analyses reflecting current trends and policy changes in global agriculture are conducted to investigate the emission reduction potential of different networks. The results show that the community division and characteristics of different AGHG networks vary, with more communities in the rice-CH4 and livestock-CH4, N2O networks, and fewer in the cropland-N2O network. Production-side technology improvements (reducing direct carbon emission intensities) and consumption-side livestock products substitution can contribute to the reduction of global AGHG emissions. At the same time, these impacts may propagate to other countries through AGHG networks. In contrast, localization substitution has minimal impact on AGHG emissions and may even result in slight negative effects. It is suggested that enhancing cooperation between countries from a network perspective, such as strengthening the transfer of advanced production technologies within communities, could help reconceptualize global agricultural decarbonization.

The land use, land-use change and forestry (LULUCF) sector plays and will continue to play a crucial role in international efforts to mitigate climate change. Developing a greenhouse gas (GHG) inventory for the LULUCF sector is inherently associated with complexity and methodological difficulties. The ability to appropriately identify and classify land use and land-use changes is a key prerequisite for developing an inventory in line with the principles of the Intergovernmental Panel on Climate Change (IPCC). Recently, the Food and Agriculture Organization (FAO) released the Open Foris Collect Earth (CE) software to assist countries in collecting, managing and analyzing land-cover and land-use information. However, limited guidance is available to assist users in the design of the land categories and the collection of the necessary data for land representation in line with the IPCC principles. The different classification protocols that are currently followed, in parallel with the subjectivity in users’ interpretation, risk multiple users classifying the same land area under different land-use categories. This article proposes a methodological framework for using CE to prepare land area data that are complete, consistent, accurate, transparent and comparable, in accordance with the IPCC Guidelines.

Abstract. Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.