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Pre- and post-production processes along supply chains increasingly dominate GHG emissions from agri-food systems globally and in most countries

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Abstract

This is earth analysis at global, regional and country level based on the new FAOSTAT Emissions shares database. Data cover all IPCC sectors and in particular quantify emissions from agri-food systems within the famr gate, due to land use change and along supply chains, domestic use and waste.
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Pre- and post-production processes along supply chains
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increasingly dominate GHG emissions from agri-food systems
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globally and in most countries
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Francesco N. Tubiello1, Kevin Karl1,2, Alessandro Flammini1,3, Johannes Gütschow4, Griffiths
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Obli-Laryea1, Giulia Conchedda1, Xueyao Pan1, Sally Yue Qi2, Hörn Halldórudóttir
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Heiðarsdóttir1, Nathan Wanner1, Roberta Quadrelli5, Leonardo Rocha Souza6, Philippe Benoit2,
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Matthew Hayek7, David Sandalow2, Erik Mencos-Contrera8,9, Cynthia Rosenzweig9,8, Jose’
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Rosero Moncayo1, Piero Conforti1 and Maximo Torero1
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1 Food and Agriculture Organization, Rome, Italy
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2Center on Global Energy Policy, Columbia University, New York, USA
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3United Nations Industrial Development Organization, Department of Environment, Vienna, Austria
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4Potsdam Institute for Global Climate Research, Potsdam, Germany
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5International Energy Agency, Paris, France
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6United Nations Statistics Division, New York, USA
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7Department of Environmental Science, New York University, New York, USA
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8Center of Global Climate Research, Columbia University, New York, USA
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9NASA Goddard Institute for Space Studies, New York, USA
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*Corresponding author: Francesco N. Tubiello, francesco.tubiello@fao.org
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Abstract. We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries
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and territories and over the period 1990-2019. We find that in 2019, world-total food systems emissions were 16.5
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billion metric tonnes (Gt CO2eq yr-1), corresponding to 31% of total anthropogenic emissions. Of the agri-food
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systems total, global emissions within the farm gate from crop and livestock production processes including on-
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farm energy usewere 7.2 Gt CO2eq yr-1; emissions from land use change, due to deforestation and peatland
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degradation, were 3.5 Gt CO2eq yr-1; and emissions from pre- and post-production processes manufacturing of
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fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposalwere
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5.8 Gt CO2eq yr-1. Over the study period 1990-2019, agri-food systems emissions increased in total by 17%, largely
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driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that
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since 1990 land use emissions decreased by 25%, while emissions within the farm gate increased only 9%. In
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2019, in terms of single GHG, pre- and post- production processes emitted the most CO2 (3.9 Gt CO2 yr-1),
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preceding land use change (3.3 Gt CO2 yr-1) and farm-gate (1.2 Gt CO2 yr-1) emissions. Conversely, farm-gate
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activities were by far the major emitter of methane (140 Mt CH4 yr-1) and of nitrous oxide (7.8 Mt N2O yr-1). Pre-
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and post- processes were also significant emitters of methane (49 Mt CH4 yr-1), mostly generated from the decay
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of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990
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highlighted by our analysis is the increasingly important role of food-related emissions generated outside of
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agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional
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and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate
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processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq
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yr-1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr-1), becoming larger than emissions
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from land-use change. By 2019 food supply chains had become the largest agri-food system component in China
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(1100 Mt CO2eq yr-1); USA (700 Mt CO2eq yr-1) and EU-27 (600 Mt CO2eq yr-1). This has important repercussions
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for food-relevant national mitigation strategies, considering that until recently these have focused mainly on
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reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information
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used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also
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available to users via the FAOSTAT database (FAO, 2021a), with annual updates.
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Keywords: Agri-food systems, GHG emissions, farm gate, land use change, supply chains
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1. Introduction
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Agriculture is a significant contributor to climate change as well as the economic sectors most at risk from it.
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Greenhouse gas (GHG) emissions generated within the farm gate by crop and livestock production and related
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land use change contribute about one-fifth to one-quarter of total emissions from all human activities, when
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measured in CO2 equivalents (Mbow et al., 2019; Smith et al., 2014; Vermeulen et al., 2012). In terms of single
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gases, impacts are even starker. Agriculture contribute nearly 50% of world total anthropogenic methane (CH4)
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and 75% of the total nitrous oxide (N2O) emissions (FAO, 2021b; Gütschow et al., 2021; Saunois, et al., 2020).
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Once pre- and post-production activities along agri-food systems supply chains are included, food and agriculture
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activities generate up to one-third of all anthropogenic emissions globally (Rosenzweig et al., 2020; Tubiello et
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al., 2021a). This larger food systems perspective expands the potential for designing GHG mitigation strategies
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that can address options in food and agriculture across the entire food system, i.e., over and above the more
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traditional focus on agricultural production and land use management within countries’ Nationally Determined
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Contributions (FAO, 2019).
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Significant progress has recently resulted in the development of novel databases with global coverage of country-
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level data on agri-food systems emissions (Crippa et al., 2021a,b; Tubiello et al., 2021a). Tubiello et al. (2021a) in
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particular provided a mapping of emission categories of the Intergovernmental Panel on Climate Change (IPCC),
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used for climate reporting by countries of national GHG inventories (NGHGI), to more relevant food and
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agriculture concepts that, developed by FAO and used to disseminate food and agriculture statistics in FAOSTAT,
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are more easily understood by farmers and planners in Ministries of Agriculture. Such mapping allows to more
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adequately capture important aspects of food and agriculture activities within existing climate reporting. Firstly, it
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expands the IPCC “agriculture” definition to include, in addition to non-CO2 emissions from the farm, also the
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CO2 generated in drained peatlands on agricultural land (Conchedda and Tubiello, 2020; Drösler et al., 2014) and
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through energy use in farm operations (FAO, 2020b; Flammini et al., 2021; Sims and Flammini, 2014). Secondly,
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it usefully disaggregates the ‘Land Use, land use change and forestry’ (LULUCF) of IPCC (2003) used in NGHGI,
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by separating out carbon sinks from land-based emissions sources that are more directly linked to food and
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agriculture, such as those generated by deforestation (Curtis et al., 2020; Tubiello et al., 2021c) and peat fires
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(Prosperi et al., 2020).
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We present and discuss results from the first emissions database in FAOSTAT of food and agriculture emissions.
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The new database covers, as in previous versions (Tubiello et al., 2013) agriculture production activities within
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the farm gate and associated land use and land use change emissions on agricultural land. Importantly, it also
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includes estimates of emissions from pre- and post-production processes along food supply chains, including:
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energy use within the farm gate, food processing, domestic and international food transport, retail, packaging,
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household consumption and food waste disposal. The new FAOSTAT database provides information of emissions
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of the four main GHG gases CO2, CH4, N2O and F-gases, as well as their combined CO2eq levels, by country, over
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the period 1990-2019. We examine new results and discuss how they can inform national mitigation strategies that
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are relevant to food and agriculture in countries, regionally and globally.
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2. Materials and methods
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Recent work (Rosenzweig et al., 2021; Tubiello et al., 2021a) helped characterize agri-food systems emissions into
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three components: 1) Farm Gate; 2) Land Use Change; and 3) Pre- and Post-Production. Emissions estimates from
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the first twogenerated by crop and livestock production activities within the farm gate and by the conversion of
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natural ecosystems to agriculture, such as deforestation and peatland degradationhave been long established and
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data are regularly disseminated in FAOSTAT (FAO, 2021; Tubiello, 2019). This paper adds emission along food
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supply chains outside of agricultural land, including those generated from energy use in fertilizer manufacturing;
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food processing; packaging; transport; retail; household consumption; and waste disposal.
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2.1 Mapping Agri-food Systems Components
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Emissions data are organized in IPCC emissions categories: Energy; Industrial Processes and Product Use (IPPU,
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henceforth referred to as Industry); Waste; Agriculture; Land Use, Land Use Change and Forestry (LULUCF);
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and Other. IPCC sectors and sub-sectors are mapped to FAO categories relevant to food and agriculture, in line
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with recent work (Tubiello, 2021a), with extensions made to cover all IPCC sectors with relevant food systems
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activities (Fig. 1). The methods applied herein cover a large component of food supply chain processes. It does not
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cover by design embedded energy in machinery and upstream emissions associated with oil and gas supply chains.
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2.2 Emissions Estimates
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We provide here the basic estimation methods used for this work, while referring the interested reader to a series
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of technical papers that document the underlying methodologies in full, detailing all coefficients and data sources
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used to estimate emissions from energy use in fertilizers manufacturing, food processing, transport, retail,
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household consumption, waste disposal (Tubiello et al., 2021b; Karl and Tubiello, 2021a, b); as well as energy use
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on the farm (Flammini et al., 2021). More generally, a step-wise approach was followed for the estimation of agri-
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food systems emissions: Step 1 identified, for each food systems component, the relevant international statistics
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needed to characterize country-level activity data (AD). Step 2 determined the food-related shares of the activity
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data (ADfood) and assigns relevant GHG emission factors (EF) to each activity. Step 3 implemented the generic
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IPCC method for estimating GHG emissions (Efood), using inputs of activity data and emission factors from the
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first two steps, as follows:
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Efood = EF*ADfood (1)
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Finally, Step 4 imputed any missing agri-food systems emissions data by component, using as input PRIMAP, a
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complete dataset of emissions estimates for all IPCC sectors, by country, covering the period 1990-2019
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(Gütschow et al., 2021). The PRIMAP data compile all available information on GHG emissions by country,
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including from official reporting. They were used internationally as the basis for an early, first-order estimate of
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agri-food systems shares in total GHG emissions (IPCC, 2019). Additionally, they were recently used in a
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UNFCCC Synthesis Report (UNFCCC, 2021) to assess world GHG emissions from all sectors in preparation of a
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stock take exercise that will be undertaken in 2022-203 to assess countries’ performance against their mitigation
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commitments under the Paris Agreement.
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2.3 Data uncertainty and limitations
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2.3.1 Boundaries
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Uncertainties in farm gate and land use change emissions estimates have been characterized elsewhere, ranging
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3070% across many processes (Tubiello, 2019). The uncertainties in the estimates of pre- and post-production
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activities described herein are less documented. On the one hand, uncertainties in underlying energy activity data
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and emissions factors are likely lower than for the other two components. On the other, the relative novelty in
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estimating food system shares for a range of activity data makes our estimates more uncertain, with heavy reliance
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on literature results from a subset of countries or regions that are necessarily extended to the rest of the world (Karl
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and Tubiello, 2021a). In addition, it should be noted that the processes covered herein do not span all processes
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attributable to agri-food systems. In particular, the scope of this work does not include, by design, upstream GHG
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emissions in the fuel chain, such as petroleum refining, as well as a methane leaks during extraction processes and
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piping. These are expected to be not negligible if considered. Conversely, processes such as F-gas emissions from
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household refrigeration and from climate-controlled transportation were not included for lack of available country-
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level data and estimation methods. Emissions from pesticide manufacturing were also not included due to the
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paucity of information and methodologies for their estimation, in contrast to advanced work in fertilizers
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manufacturing (Brentrup et al., 2016; Brentrup et al., 2018; IFS, 2019)
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2.3.2 Uncertainty
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Significant errors may be introduced by the use of sub-regional and regional coefficients, given the diversity in
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food system typology and their dependence on physical geography and national socio-economic drivers. These
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limitations nonetheless reflect the paucity of activity data available to describe agri-food systems components and
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their trends, globally and regionally. While knowledge and data exist for regions and countries such as the EU,
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USA China, and India, much remains to be done in terms of regional and country specific coverage.
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Uncertainties also exist in estimating GHG emission factors. These are typically related to difficulties in derive
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generic coefficients in the face of natural spatial and temporal variability characterizing the underlying bio-physical
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processes. More detailed information on uncertainties associated with emission factors and activity data can be
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found in the IPCC guidelines (2006).
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2.3.3 Areas for Advancement
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Work towards estimating agri-food systems emissions at the country level can be advanced in several ways. The
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present approach could be expanded on by including other country- and region-specific studies that estimate trends
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in energy consumption across a range of similar activities as proxies whether or not they are distinctly related
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to food. Furthermore, other data sources could help explain and estimate variations in agri-food systems between
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countries, such as: GDP per capita, urbanization levels, proxies for infrastructure and industrial development, and
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geographic and climate considerations. The development of a methodology to estimate emissions from pesticides
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could be explored, as it would help complement the understanding of emissions associated with chemical use in
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agriculture, in addition to fertilizers. Emissions from machinery manufacturing and from upstream GHG emissions
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in the fuel chain could also be added to further refine the analysis. This work could be further expanded by focusing
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on specific food commodities requiring an additional focus on international trade and on supply and demand
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patterns (Dalin and Rodríguez-Iturbe, 2016). Such analysis would ultimately enable consumers to understand the
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full carbon footprint of particular commodities across global supply chains, which can facilitate GHG mitigation
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actions taken at the consumer level (Poore and Nemecek, 2018). Furthermore, it would be also useful to further
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investigate the increasing role of bioenergy and renewables as important mitigation opportunities in the food sector
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(Clark et al., 2020, JRC, 2015; Pablo-Romero et al., 2017; Wang, 2014).
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Data availability
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The GHG emission data presented herein cover the period 1990-2019, at the country level, with regional and global
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aggregates. They are available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d) and via
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the FAOSTAT (FAO, 2021a) database.
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3 Results
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3.1 Global trends
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In 2019 world-total agri-food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr-1), corresponding to
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31% of total anthropogenic emissions (Tab. 1). Of the food systems total, global emissions within the farm gate
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from crop and livestock production processes including on-farm energy usewere 7.2 Gt CO2eq yr-1; emissions
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from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr-1; and emissions from
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pre- and post-production processes manufacturing of fertilizers, food processing, packaging, transport, retail,
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household consumption and food waste disposalwere 5.8 Gt CO2eq yr-1. Over the study period 1990-2019, agri-
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food systems emissions increased in total by 17%, though they have remained rather constant since about 2006
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(Fig. 2). These trends were largely driven by a doubling of emissions from pre- and post-production processes,
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while land use emissions decreased by 25% and farm gate increased only 9%. In terms of single GHG, pre- and
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post- production processes emitted the most CO2 (3.9 Gt CO2 yr-1) in 2019, preceding land use change (3.3 Gt CO2
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yr-1) and farm-gate (1.2 Gt CO2 yr-1) emissions. Conversely, farm-gate activities were by far the major emitter of
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methane (140 Mt CH4 yr-1) and of nitrous oxide (7.8 Mt N2O yr-1). Pre-and post- processes were also significant
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emitters of methane (49 Mt CH4 yr-1), mostly generated from the decay of solid food waste in landfills and open-
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dumps.
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Emissions from within the farm gate and those due to related land use processes, including details of their sub-
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components, have been discussed in Tubiello et al. (2021a) and are regularly presented within FAOSTAT statistical
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briefs (e.g., FAO, 2020a). Here we provide a detailed discussion of the components of agri-food systems emissions
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from pre- and post-production activities along supply chains and their relative contribution to the food system
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totals (Fig. 3). Our data show that in 2019 emissions from deforestation were the single largest emission
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component of agri-food systems, at 3,058 Mt CO2 yr-1, having decreased 30% since 1990. The second most
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important component were non-CO2 emissions from enteric fermentation (2,823 Mt CO2eq yr-1), with increases of
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13%. These were followed by emissions from livestock manure (1,315 Mt CO2eq yr-1) and several pre- and post-
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production emissions, including CO2 from household consumption (1,309 Mt CO2eq yr-1), CH4 from food waste
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disposal (1,278 Mt CO2eq yr-1), mostly CO2 from fossil-fuel combustion for on-farm energy use (1,021 Mt CO2eq
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yr-1), and CO2 and F-gases emissions from food retail (932 Mt CO2eq yr-1). Importantly, our data show that growth
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in pre- and post-production components was particularly strong, with emissions from retail increasing from 1990
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to 2019 by more than seven-fold, while emissions from household consumption more than doubled over the same
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period.
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3.2 Regional Trends
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Our results indicate significant regional variation in terms of the composition of agri-food systems emissions by
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component (Fig. 4). Specifically, in terms of total agri-food systems emissions in 2019, Asia had the largest
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contribution, at 7 Gt CO2eq yr-1, followed by Africa (2.7 Gt CO2eq yr-1), South America (2.4 Gt CO2eq yr-1) and
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Europe (2.1 Gt CO2eq yr-1). North America (1.5 Gt CO2eq yr-1) and Oceania (0.3 Gt CO2eq yr-1) were the smallest
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emitters among regions (Fig. 4). Focusing on GHG emissions beyond agricultural land, pre- and post-production
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emissions in 2019 were largest in Asia (2.9 Gt CO2eq yr-1), followed by Europe and North America (0.8-1.1 Gt
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CO2eq yr-1). Regions also varied in terms of how agri-food systems components contributed to the total (Tab. 2).
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In 2019, pre- and post- production emissions were the largest food systems contributor in Europe (55%), North
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America (52%) and Asia (42%). Conversely, they were smallest in Oceania (23%), Africa (14%) and South
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America (12%). Additionally, the contribution of pre- and post-production processes along food supply chains
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significantly increased since 1990, when in no region they were the dominant emissions component. Since then,
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they doubled in all regions except in Africawhere it remained below 15%.
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Finally, the data show which pre- and post-production process was most important by region (Tab. 2). In 2019,
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food household consumption was the dominant process outside of agricultural land emissions in Asia (0.9 Gt
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CO2eq yr-1) and Africa (0.2 Gt CO2eq yr-1). Conversely, Europe, Oceania and North America pre- and post-
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production processes were led by emissions from food retail (0.3-0.4 Gt CO2eq yr-1), while South America was
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dominated by emissions from food waste disposal (0.2 Gt CO2eq yr-1).
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3.3 Country Trends
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Our estimates show a marked variation among countries in terms of total emissions as well as the composition of
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contributions across farm gate, land use change and pre- and post-processing components (Fig. 5). China had the
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most emissions (1.9 Gt CO2eq yr-1), followed by India, Brazil, Indonesia and the USA (1.2-1.3 Gt CO2eq yr-1).
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Democratic Republic of Congo (DRC) and Russian Federation followed with 0.5-0.6 Gt CO2eq yr-1, followed by
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Pakistan, Canada and Mexico with 0.2-0.3 Gt CO2eq yr-1. The contribution of the three main agri-food systems
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components to the national total differed among countries significantly (Fig. 5). For instance, China and India had
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virtually no contribution from land use change to agri-food systems emissions. The land use contribution was also
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minor in the USA, Russian Federation and Pakistan. Conversely, the latter was the dominant emissions component
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in Brazil, Indonesia and the DRC. Additionally, the new database allowed for an in-depth analysis by country of
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pre- and post-production emissions along the agri-food chain, highlighting a significant variety in most relevant
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sub-process contribution (Tab. 3). For the year 2019, pre- and post-production emissions were dominated in China
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by food household consumption processes (463 Mt CO2eq yr-1), whereas food waste disposal was the dominant
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pathway in Brazil, Indonesia (77 Mt CO2eq yr-1), DRC (8 Mt CO2eq yr-1), Pakistan (33 Mt CO2eq yr-1) and Mexico,
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(56 Mt CO2eq yr-1). Emissions from food retail dominated the pre- and post-production component in the USA
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(292 Mt CO2eq yr-1), Russian Federation (177 Mt CO2eq yr-1) and Canada (20 Mt CO2eq yr-1). Finally, on-farm
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energy use was the largest pre- and post-production component in India (205 Mt CO2eq yr-1).
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4 Discussion
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The most important trend over the 30-year period since 1990 to present that emerges from our analysis is the
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increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-
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production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019.
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Our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG
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component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr-1). While farm gate emissions still
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dominated food-systems processes in non-Annex I parties, emissions from pre- and post-production were closing
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the gap in 2019, surpassing land use change having doubled since 1990 to 3.5 Gt CO2eq yr-1. By 2019 food supply
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chains had become the largest agri-food system component in China (1,100 Mt CO2eq yr-1); USA (700 Mt CO2eq
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yr-1) and EU-27 (600 Mt CO2eq yr-1). This has important repercussions for food-relevant national mitigation
2
strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the
3
farm gate and on CO2 mitigation from land use change.
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Importantly, the FAOSTAT database presented here allows for an estimation of the percentage share contribution
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of food systems emissions in total anthropogenic emissions, by country as well as at regional and global levels,
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over the period 1990-2019. The FAOSTAT-PRIMAP database covering all sectors which underlies this study
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estimates total anthropogenic emissions at about 52 Gt CO2eq yr-1 without land use, land use change and forestry
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emissions (LULCUF), and about 54 Gt CO2eq yr-1 with LULUCFconsistently with recent estimates (IPCC,
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2019). We use the latter figure to compute emissions shares. A number of important issues can be highlighted to
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this end (Tab. 4 and Fig. 6). First, in terms of CO2eq, the share of world total agri-food systems emissions
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decreased from 40% in 1990 to 31%. Thus while it is important to note that one-third of all GHG emissions today
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are generated by agri-food systems, their shares in total emissions may continue decreasing in the near future. This
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decreasing trend was driven by trends in large regions with ongoing transformations in their agri-food systems and
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land use change patterns. For instance, in South America, the region with the highest food systems share over the
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entire study period (Fig. 6), food shares went from 96% to 72% in 2019. In Africa, from 67% to 57%, in Asia from
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49% to 24% and in Oceania from 57% to 39%. In contrast to these trends however, in regions dominated by modern
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agri-food systems such as Europe and North America, our data suggest that the overall share of agri-food systems
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emissions increased from 1990 to 2019, specifically from 24% to 31% in Europe and from 17% to 21% in North
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America. Such increases in these two regions were due to a disproportionate increase in emission from pre- and
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post-production activities, as noted earlier, resulting in addition to doubling absolute emission also doubled their
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underlying shares (Tab. 4). It is also worth noting that in all regions absolute emissions form pre- and post-
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production activities increased from 1990 to 2019, and that such increased in all regions but Africa were
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accompanied by larger relative shares of this food system component in 2019 compared to 1990.
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A final analysis on agri-food systems impacts on total GHG emissions would not be complete without a focus on
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component gases in addition to quantities expressed in CO2eq. The FAOSTAT data confirm the trends form 1990
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to 2019 seen for total CO2eq emissions, with important features (Tab. 5). First, the impact of agri-food systems on
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world total CO2 emissions was 21% in 2019 (down from 31% in 1990), a respectable share considering the
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importance of carbon dioxide in any effective long-term mitigation strategy. While most regions had contributions
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around this value, ranging 13%-23% for North America, Oceania, Europe and Asia, the CO2 contribution of agri-
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food systems was higher in Africa (52%) and South America (70%), largely in relation to the land use change
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emissions that are still significant therein. Additionally, Europe and North America were the only regions where
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the CO2 shares actually increased from 1990 to 2019, confirming the growing weight of pre- and post-production
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processes, which typically involve fossil-fuel energy use. Second, the data highlight the significant contribution of
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agri-food systems to 2019 world total emissions of CH4 (53%) and N2O (78%), also confirmed at regional levels
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(Tab. 5), linked to farm gate production processes (Tubiello, 2019). Finally, the data highlight a very large increase
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in agri-food systems contributions of F-gas emissions, which went from near zero in 1990 to more than one-quarter
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of the world total in 2019 with larger contributions in many regions. At least with respect to the underlying
38
assumptions made in our methods, such a marked increase was entirely due to strong growth of refrigeration in the
39
food retail sector (Hart et al., 2020; IIR, 2021; Tubiello et al., 2021b).
40
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Another aspect of the dataset underlying this study is that it provides food and agriculture relevant information
1
across IPCC and FAO definitions and classifications. In terms of national GHGH inventories, it is worth pointing
2
out that while agri-food systems were found to be about one-third of total anthropogenic emissions, our data
3
indicated that emissions from land use, land use change and forestry (LULUCF) in 2019 only represented 3-4%,
4
while emissions from agriculture, forestry and other land use (AFOLU), were a mere 15% of the total
5
anthropogenic emissions.
6
5 Conclusions
7
This paper provided details of a new FAOSTAT domain characterizing GHG emissions along the entire agri-food
8
systems chain, including crop and livestock production processes on the farm, land use change activities from the
9
conversion of natural ecosystems to agricultural land, and processes along food supply chains, from input
10
manufacturing to food processing, transport and retail, including household consumption and waste disposal.
11
The data are provided in open access mode to users worldwide and are available by country over the time period
12
1990-2019. The major trends identified in this work help identify emissions hotspots across agri-food systems and
13
by country, helping to identify areas for effective mitigation actions in food and agriculture. This work adds to
14
knowledge well established in the literature but limited in terms of datasets to farm gate processes and land use
15
change, by adding a wide range of additional details on emissions from pre- and post-production processes. The
16
new data highlight the increasingly important role that these play in the overall emissions footprint of agri-food
17
systems, reflecting a pattern of development from traditional to modern agri-food systems and overall economic
18
growth. The granularity of the dataset allows, for the first time, to highlight specific processes of importance in
19
specific countries or group of countries with similar characteristics. The relevance of the information being
20
provided cuts across several national and international priorities, specifically those aiming at achieving more
21
productive and sustainable food systems, including in relation to climate change. To this end, the work presented
22
herein completes a mapping of IPCC categories, used by countries for reporting to the climate convention, to food
23
and agriculture categories that are more readily understandable by farmers and ministries of agriculture in
24
countries. This helps better identify agri-food systems entry points within existing and future national determined
25
contributions. Finally, the methodological work underlying these efforts complements and extends recent
26
pioneering efforts by FAO and other groups in characterizing technical coefficients to enable quantifying the
27
weight of agri-food systems within countries’ emissions profiles. The next steps in such efforts would need the
28
involvement of interested national and international experts in compiling a first set of coefficients for agri-food
29
systems as a pratical ‘agri-food systems annex’ to the existing guidelines of the Intergovernmental Panel on
30
Climate Change, providing guidance to countries on how to better characterize food and agriculture emissions
31
within their national GHG inventories.
32
6. Disclaimer
33
The views expressed in this paper are the authors’ only and do not necessarily reflect those of FAO, UNSD,
34
UNIDO and IEA.
35
7. Acknowledgements
36
FAOSTAT is supported by the FAO regular budget, funded by its member countries. We acknowledge the efforts
37
of national experts who provide the statistics on food and agriculture as well as on energy use that are at the basis
38
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of this effort. All authors contributed critically to the drafts and gave final approval for the publication. We are
1
grateful for overall support by the Food Climate Partnership at Columbia University.
2
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TABLES
1
2
3
Process
1990
2019
Change
Net Forest conversion
4,392
3,058
-30%
Enteric Fermentation
2,494
2,823
13%
Livestock Manure
1,101
1,315
19%
Household Consumption
541
1,309
142%
Waste Disposal
984
1,278
30%
On-farm energy use
757
1,021
35%
Retail
128
932
631%
Drained organic soils
736
833
13%
Rice Cultivation
621
674
9%
Fires
558
654
17%
Synthetic Fertilizers
422
601
42%
Transport
327
586
79%
Food Processing
421
510
21%
Fertilizers Manufacturing
152
408
168%
Packaging
166
310
87%
Crop Residues
161
226
40%
Forestland
-3,391
-2,571
-24%
4
Table 1. GHG emissions (Mt CO2eq) by agri-food systems component for all processes considered in this work.
5
Data on forestland removals are provided for completeness of land-based emissions available in FAOSTAT.
6
7
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14
1
2
Region
Farm
Gate
LUC
PPP
Total
%PPP
%PPP
(1990)
Highest
PPP
note
Asia
3.2
0.9
2.9
7.0
42%
24%
0.9
Household
Africa
1.1
1.2
0.4
2.7
14%
16%
0.2
Household
South America
1.0
1.1
0.3
2.4
12%
6%
0.1
Waste
Europe
0.9
0.1
1.1
2.1
55%
26%
0.4
Retail
Northern America
0.6
0.2
0.8
1.5
52%
35%
0.3
Retail
Oceania
0.2
0.0
0.1
0.3
23%
11%
0.0
Retail
3
4
Table 2. Regional GHG emissions (Gt CO2eq) by agri-food systems component, showing total food systems
5
emissions and percentage contribution of emissions form pre- and post-production processes. 1990 and 2019. The
6
last two columns show the largest sub-component of pre- and post-production emissions by region.
7
8
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1
2
3
4
Country
Farm-gate
LUC
PPP
Total
Max PPP
Note
China
792
0
1102
1894
469
Household
India
768
0
618
1386
205
On-farm
Brazil
553
663
144
1360
79
Food Waste
Indonesia
491
658
132
1281
76
Food Waste
United States of America
477
60
696
1232
292
Retail
DRC
28
624
9
660
8
Food Waste
Russian Federation
146
35
362
542
177
Retail
Pakistan
205
7
71
283
33
Food Waste
Canada
97
96
81
274
20
Retail
Mexico
115
15
116
246
56
Food Waste
5
Table 3. Top ten country GHG emissions (Gt CO2eq) by agri-food systems component and total food systems
6
emissions, 2019. The last two columns show the dominant sub-component of pre- and post-production processes.
7
8
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16
1
2
3
4
Farm gate
Land Use Change
Supply Chains
Food Systems
1990
2019
1990
2019
1990
2019
1990
2019
Africa
705
1139
1017
1220
323
388
2045
2747
23%
24%
33%
26%
11%
8%
67%
57%
Asia
2595
3250
1273
865
1223
2930
5091
7044
25%
11%
12%
3%
12%
10%
49%
24%
Europe
1603
854
88
83
589
1140
2280
2077
16%
13%
1%
1%
6%
17%
23%
31%
North America
538
574
175
156
376
777
1089
1507
8%
8%
3%
2%
6%
11%
17%
21%
South America
728
982
1974
1106
176
281
2878
2369
23%
30%
64%
34%
6%
9%
93%
72%
Oceania
267
223
65
16
42
71
374
309
40%
28%
10%
2%
6%
9%
57%
39%
World
6604
7214
4676
3503
2886
5827
14165
16544
19%
13%
13%
6%
8%
11%
40%
31%
5
6
Table 4. Regional GHG emissions (Gt CO2eq) by agri-food systems component and total food systems emissions,
7
2019. The last two columns show the dominant sub-component of pre- and post-production processes.
8
9
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17
1
2
3
1990
2019
1990
2019
1990
2019
1990
2019
1990
2019
CO2eq
CO2
CH4
N2O
F-gases
World
40
31
31
21
60
53
79
78
0
27
Africa
67
57
65
52
63
58
90
87
0
20
Northern America
17
21
11
13
36
42
60
70
0
56
South America
93
72
97
70
82
75
94
92
0
6
Asia
49
24
38
16
66
49
84
80
0
9
Europe
23
31
13
23
46
47
70
74
0
28
Oceania
57
39
38
22
76
64
93
77
0
63
4
5
Table 5. World total and regional GHG food systems emissions shares, 2019-2019, for all single GHG and in
6
CO2eq.
7
8
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FIGURE LEGENDS
1
2
Figure 1. Mapping of emissions across agri-food systems. Left-hand panel: IPCC sectors and processes used in
3
national GHG emissions inventories. Right-hand panel: food and agriculture sectors and categories aligned to
4
FAO’s definitions.
5
6
Figure 2. World-total GHG emissions from agri-food systems, 1990-2019. Color bars show contributions by
7
emissions within the farm gate (yellow); land use change (green) and pre- and post- production along food supply
8
chains (blue). Source: FAOSTAT (FAO, 2021). Also shown are emissions per capita (authors’ own calculations).
9
10
Figure 3. World total 2019 GHG emission from agri-food systems, showing contributions on agricultural land
11
(left panel) and from pre- and post- production along food supply chains (right panel). Net removals on forest land
12
are also shown, for completeness. The sum of emissions from agricultural land and forest land correspond to the
13
IPCC AFOLU category. Source: FAOSTAT (FAO, 2021).
14
15
Figure 4. Total GHG emission from agri-food systems by FAO regions, 2019. Color bars show contributions by
16
emissions within the farm gate (yellow); land use change (green) and pre- and post- production along food supply
17
chains (blue). Source: FAOSTAT (FAO, 2021).
18
19
Figure 5. Total GHG emission from agri-food systems by country, top ten emitters, 2019. Color bars show
20
contributions by emissions within the farm gate (yellow); land use change (green) and pre- and post- production
21
along food supply chains (blue). Source: FAOSTAT (FAO, 2021).
22
23
Figure 6. Top panel: Agri-food sytems emissions (GtCO2eq yr-1); Bottom panel: shares of agri-food systems in
24
total anthropogenic emissions (%). Data shown by region, 1990-2019. Color bars show contributions component:
25
farm gate (yellow); land use change (green) and pre- and post- production along food supply chains (blue). Source:
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FAOSTAT (FAO, 2021).
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Figure 1. Mapping of emissions across agri-food systems. Left-hand panel: IPCC sectors and processes used in
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national GHG emissions inventories. Right-hand panel: food and agriculture sectors and categories aligned to FAO’s
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definitions
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Figure 2. World-total GHG emissions from agri-food systems, 1990-2019. Color bars show contributions by emissions
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within the farm gate (yellow); land use change (green) and pre- and post- production along food supply chains (blue).
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Source: FAOSTAT (FAO, 2021). Also shown are emissions per capita (authors’ own calculations).
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Figure 3. World total 2019 GHG emission from agri-food systems, showing contributions on agricultural land (left
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panel) and from pre- and post- production along food supply chains (right panel). Net removals on forest land are also
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shown, for completeness. The sum of emissions from agricultural land and forest land correspond to the IPCC AFOLU
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category. Source: FAOSTAT (FAO, 2021).
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Figure 4. Total GHG emission from agri-food systems by FAO regions, 2019. Color bars show contributions by
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emissions within the farm gate (yellow); land use change (green) and pre- and post- production along food supply chains
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(blue). Source: FAOSTAT (FAO, 2021).
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Figure 5. Total GHG emission from agri-food systems by country, top ten emitters, 2019. Color bars show contributions
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by emissions within the farm gate (yellow); land use change (green) and pre- and post- production along food supply
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chains (blue). Source: FAOSTAT (FAO, 2021).
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Figure 6. Top panel: Agri-food systems emissions (GtCO2eq yr-1); Bottom panel: shares of agri-food systems in total
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anthropogenic emissions (%). Data shown by region, 1990-2019. Color bars show contributions component: farm gate
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(yellow); land use change (green) and pre- and post- production along food supply chains (blue). Source: FAOSTAT
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(FAO, 2021).
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... Previous and ongoing research leaves little doubt that dietary patterns are inherently related to greenhouse (GHG) emissions, and thus a driver of climate change, via agricultural practices and food production systems (Biesbroek et al., 2018;Hallstrom et al., 2015;Mertens et al., 2020;Springmann et al., 2018). In 2019, global anthropogenic emissions equated to 54 billion metric tonnes of CO 2 equivalents, of which 31% (16.5 billion metric tonnes) derived from agri-food systems (Tubiello et al., 2021). Moreover, livestock production, a large component of the agricultural sector, is associated with approximately 14.5%-18% of all anthropogenic GHG emissions (Chaudhary and Tremorin, 2020;Farchi et al., 2017;Mogensen et al., 2020;Ridoutt et al., 2021;Seves et al., 2017). ...
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Dietary patterns are inherently related to greenhouse (GHG) emissions via agricultural practices and foodproduction systems. As the global population is predicted to increase from 8 billion (current) to 9.6 billion by2050 added pressure will be placed on existing agricultural systems, resulting in increased GHG emissions thusexacerbating climate change. Therefore, there is an urgent need to understand present-day dietary patterns toshift to sustainable and healthy diets to mitigate GHG emissions and meet future climate targets. However, noreview or pooled analyses of dietary pattern emissions from a farm-to-fork perspective has been undertaken todate. The current study sought to i) identify the current dietary habits within high-income regions from 2009to 2020 and ii) quantify the GHG emissions associated with these dietary patterns via a global systematisedreview and pooled analysis. Twenty-three peer-reviewed studies were identified through online bibliographicdatabases. Dietary patterns are being examined based on fixed inclusion/exclusion criteria. Five dietarypatterns were identified in the review with their mean GHG emissions: high-protein diets (5.71 CO2eq kgperson−1 day−1), omnivorous diet (4.83 CO2eq kg person−1 day−1), lacto-ovo-vegetarian/pescatarian diet (3.86CO2eq kg person−1 day−1), recommended diet (3.68 CO2eq kg person−1 day−1), and the vegan diet (2.34CO2eq kg person−1 day−1). The lacto-ovo-vegetarian/pescatarian diet was associated with significantly loweremissions than both the omnivorous and high-protein dietary patterns, with -22% and -41% GHG emissions,respectively. The high-protein dietary pattern exhibited significantly higher GHG emissions than other dietarypatterns. Geographically, significant statistical differences (p = 0.001) were only reported for the omnivorousdiet between North America and Europe. Findings reveal that GHG emissions vary based on dietary patternsand have the potential to be reduced by shifting dietary patterns, which benefits the environment by lesseningone of the drivers of climate change.
... Food production and consumption have been accounted to contribute one third of the household's environmental impact in the Western world ( Tubiello et al., 2021 ). There is consensus that a shift from Western diets high in meat to a more plant-based diet can relieve environmental pressure . ...
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Pulses support sustainable production and consumption. Their culinary versatility creates a wide range of possibilities for new products, bridging consumers’ preparation barriers. However, this potential is often intangible for consumers who have little knowledge about plant-based foods. Based on an online survey in Denmark, Germany, Poland, Spain, and the United Kingdom (N = 4,226), this study aimed to investigate consumer utilization and perception of pulses as a versatile, low-carbon food relative to objective life cycle assessment (LCA) measures of 12 pulse types. The most popular pulse types, with specific preferences across countries, were lentils, kidney beans, and chickpeas, typically consumed at home and purchased in dried or canned form. Respondents associated pulses with being healthy and natural, but sustainability was not an essential attribute related to the perception of pulses. LCA revealed a low environmental impact caused by pulse production and consumption, with marginal variations between types and produce. Respondents were unaware of the nuances in the environmental impact of different pulse types, generally perceiving uncommon pulses to be relatively more sustainable than others. In conclusion, a low consumption combined with a misconception of pulses’ environmental impact may demand different promotional strategies including clear communication to inform consumers.
... La importancia de la agroecología en esta otra civilización a la que debemos avanzar no es menor. Debemos recordar, por una parte, que casi un tercio de las emisiones globales que producen cambio climático se le responsabilizan al actual sistema agroalimentario dominado por las corporaciones (Tubiello et al., 2021); y transforman las huellas antrópicas de la modernidad capitalista; paisajes en los que pueblos verdean la tierra, deciden poner techos tecnológicos y van en una dirección que simplifica el modo de vivir, tornándolo más frugal, más convivial, más autónomo, menos suntuoso. Esto no significa "regresar" a una idílica comunidad de aldeas al modo de los jardineros agroforestales amazónicos. ...
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Multitudes agroecológicas busca nutrir la imaginación política y la creatividad sociológica a fin de seguir pensando el difícil pero urgente proyecto de abrir las condiciones para las transiciones civilizatorias y las transformaciones poscapitalistas, en un contexto de inminente colapso del sistema hegemónicamente instituido. La obra muestra de qué manera una multitud de procesos agroecológicos hacen surgir lo inédito y de ese modo hilvanan la emancipación, en un escenario en el que parece imposible hacerlo. Millones de personas organizadas alrededor del mundo, en el campo y la ciudad, están, de manera intuitiva y creativa, desmontando paulatinamente el sistema que nos oprime, mientras traen al mundo de la vida muchos otros sistemas sustitutivos. Esas experiencias están dando las nuevas pistas de la revolución, y enseñando que es posible un cambio cualitativo en el que los antiguos fines de crecimiento, urbanización, modernización, industrialización, se reemplacen por otros distintos, como la compatibilización con los ciclos de la vida, la creación de lo común, la autonomía territorial, la relocalización, la artesanalización y el florecimiento de la potencia de los pueblos.
... Meat consumption has developed a solid position in our diet, social and cultural life, whereby current meat production contributes significantly to climate change. Food and agricultural production contribute 31% of the total anthropogenic emissions (Tubiello et al., 2021) with 14.5% resulting from livestock supply chains (AGA, 2017). Thereby, greenhouse gas emissions (GHG) related to beef production can become up to approximately 50 times higher than the production of pulses, referring to the protein content of each food (Ritchi & Roser, 2020). ...
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The growing world population and increased meat consumption pose a challenge for current food production systems. While pulses present a promising position in terms of low impacts in primary production and high nutritional quality, it is unclear whether consumers are willing to consume pulses instead of meat. Based on an online survey answered by 4,322 respondents across five European countries, this study examined consumers’ willingness to utilize pulses as a plant-based alternative to animal-based products. More than a third of pulse consumers (42%) were, to some extent, already using pulses as an alternative to animal-based foods. Beef was noted as the most frequently replaced type of food, mainly driven by arguments relating to health, environment, and sustainability, especially relevant for German and Danish consumers. Respondents who did not indicate a current replacement of animal-based foods stated a relatively low willingness to change in the future (40%). German pulse consumers were likely to be part of the low willingness segment. In contrast, Polish consumers possessed a relatively higher incidence of using pulses instead of meat, especially pork and poultry. Respondents with a low replacement willingness indicated a high importance of future pulse-based products to be natural, while respondents already using pulses instead of animal-based foods expected convenient and minimally processed foods. Respondents, who already replaced meat with pulses or expressed a low future willingness, stated to prefer plain pulses over processed and meat-resembling pulse-based products alternatively to meat. These preferences and expectations should be considered for future product development, especially if aiming to attract unwilling consumers to shift to pulse-based foods.
... The contributions of industrial food processing (such as grinding, cutting, centrifuging and washing), storage, and transport from retail to home have previously been estimated to contribute up to 32% to the environmental impact for highly processed foods such as pizza, as mentioned by Mertens et al. [137]. Further, estimates have shown an increasingly important role of food-related emissions generated outside of agricultural land, in pre-and post-production processes along food supply chains (manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal), now accounting for 35% [153], suggesting that more attention should be paid to correct estimations in future LCA studies. Tubiello et al. conclude that this has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions in non-CO 2 gases at the farm and on CO 2 mitigation from land use change. ...
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Dietary transitions are important for combating many of the environmental challenges humanity is facing today and reducing the global burden of disease. Different dietary patterns are associated with substantially different carbon footprints (CFs). This study aims to estimate the potential CF reduction on a transition from the current Danish diet to a plant-rich diet consistent with the Danish food-based dietary guidelines (FBDG) and to compare results obtained from the use of two different CF databases. Dietary intake data for adults aged 18–64 y from the national dietary survey 2011–2013 were used to calculate the CF of the current diet, and this was compared with the estimated CF of the plant-rich diet modelled for the FBDG. Calculations were carried out using an attributional life cycle assessment (LCA) database (AU-DTU data) and compared to calculations using a top-down hybrid consequential LCA database (BCD data). The transition from the current diet to the plant-rich diet showed a substantial estimated CF reduction of 31% with AU-DTU data, and a greater reduction with BCD data (43%). Ruminant meat reduction was the largest contributor to this CF reduction, especially with the use of BCD data, and other animal-based foods also contribute considerably to the CF reduction, especially with AU-DTU data. These results indicate that the choice of LCA methodology and CF database is important in estimation of dietary CF and for the development of guidelines to promote dietary change.
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Food systems are important contributors to global emissions of air pollutants. Here, building on the EDGAR-FOOD database of greenhouse gas emissions, we estimate major air pollutant compounds emitted by different stages of the food system, at country level, during the past 50 years, resulting from food production, processing, packaging, transport, retail, consumption and disposal. Air pollutant estimates from food systems include total nitrogen and its components (N2O, NH3 and NOx), SO2, CO, non-methane volatile organic compounds (NMVOC) and particulate matter (PM10, PM2.5, black carbon and organic carbon). We show that 10% to 90% of air pollutant emissions come from food systems, resulting from steady increases over the past five decades. In 2018, more than half of total N (and 87% of ammonia) emissions come from food systems and up to 35% of particulate matter. Food system emissions are responsible for about 22.4% of global mortality due to poor air quality and 1.4% of global crop production losses.
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Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from Land Use, Land-Use Change and Forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC)-and of the differences with other 20 datasets based on country reported data-is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from National Greenhouse Gas Inventories (NGHGIs) communicated via a range of country reports to the 25 UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including National Communications, Biennial Update Reports, submissions to the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) framework and Nationally Determined Contributions. 30 The data are disaggregated into fluxes from forest land, deforestation, organic soils and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. 35 Results indicate a mean net global sink of-1.6 Gt CO2/yr over the period 2000-2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to FAO and 40 used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap-filled as in our study, results in a net global LULUCF sink of-5.4 Gt CO2/yr. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1. 3 of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI 45 database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a 50 fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global Stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several 55 report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC.
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The deterioration of planetary health—from threats such as climate change, environmental pollution, biodiversity loss, and ocean acidification—are a growing hazard to the foundation of health and the “healthspan.” For those with chronic conditions—a large and growing subset of the global population—the health dangers are even greater. Climate change is a threat to the very pillars of lifestyle medicine that we rely on to prevent and manage chronic disease. Already, the planetary crisis is limiting our ability to prescribe healthy nutrition, safe outdoor physical activity, stress management strategies, social connection, restorative sleep, and toxic substance avoidance. In this article, we discuss the proceedings of our workshop at the American College of Lifestyle Medicine (ACLM) annual conference LM2021, “Lifestyle Medicine for Personal and Planetary Health.” We examine how lifestyle medicine (LM) interventions are a prescription for individual, community, and planetary health. Our prescriptions work to not only restore the health of individuals and families, but also to bolster health equity while allowing us to mitigate and adapt to the health impacts of the planetary crises.
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Despite an increasing attention on the role of land in meeting countries’ climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from Land Use, Land-Use Change and Forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) – and of the differences with other datasets based on country reported data – is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from National Greenhouse Gas Inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including National Communications, Biennial Update Reports, submissions to the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) framework and Nationally Determined Contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of -1.6 Gt CO2/yr over the period 2000–2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to FAO and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap-filled as in our study, results in a net global LULUCF sink of -5.4 Gt CO2/yr. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2/yr. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global Stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al. 2022), at https://doi.org/10.5281/zenodo.6390739.
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Methods for estimating GHG emissions from food waste disposal, a component of food systems emissions. Processes include emissions from solid food waste disposal in landfills, incineration, and wastewaters.
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The global impacts of food production Food is produced and processed by millions of farmers and intermediaries globally, with substantial associated environmental costs. Given the heterogeneity of producers, what is the best way to reduce food's environmental impacts? Poore and Nemecek consolidated data on the multiple environmental impacts of ∼38,000 farms producing 40 different agricultural goods around the world in a meta-analysis comparing various types of food production systems. The environmental cost of producing the same goods can be highly variable. However, this heterogeneity creates opportunities to target the small numbers of producers that have the most impact. Science , this issue p. 987
Technical Report
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The amount of energy necessary to cultivate, process, pack and bring the food to European citizens’ tables accounts for 17 % of the EU's gross energy consumption, equivalent to about 26 % of the EU's final energy consumption in 2013. Challenges and solutions for decreasing energy consumption and increasing the use of renewable energy in the European food sector are presented and discussed.
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