Editing IPCC:AR6/WGIII/TS (section)

=== TS.5.6 Agriculture, Forestry, Other Land Uses, and Food Systems ===

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==== TS.5.6.1 Agricultre, Forestry, and Other Land Use (AFOLU) ====

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'''The agriculture, forestry and other land use (AFOLU)''' [[#footnote-006|27]] '''sector encompasses managed ecosystems and offers significant mitigation opportunities while providing food, wood and other renewable resources as well as biodiversity conservation, provided the sector adapts to climate change.''' Land-based mitigation measures can reduce GHG emissions within the AFOLU sector, deliver CDR and provide biomass thereby enabling emission reductions in other sectors. [[#footnote-005|28]] The rapid deployment of AFOLU measures features in all pathways that limit global warming to 1.5°C. Where carefully and appropriately implemented, AFOLU mitigation measures are positioned to deliver substantial co-benefits and help address many of the wider challenges associated with land management. If AFOLU measures are deployed badly, when taken together with the increasing need to produce sufficient food, feed, fuel and wood, they may exacerbate trade-offs with the conservation of habitats, adaptation, biodiversity and other services. At the same time the capacity of the land to support these functions may be threatened by climate change ( ''high confidence'' ). {AR6 WGI Figure SPM.7; AR6 WGII, 7.1, 7.6}

'''The AFOLU sector, on average, accounted for 13–21% of global total anthropogenic GHG emissions in the period 2010–2019. At the same time managed and natural terrestrial ecosystems were a carbon sink, absorbing around one third of anthropogenic CO''' 2 '''emissions (''' '''''medium confidence''''' ''').''' Estimated anthropogenic net CO 2 emissions from AFOLU (based on bookkeeping models) result in a net source of +5.9 ± 4.1 GtCO 2 yr –1 between 2010 and 2019 with an unclear trend. Based on FAOSTAT or national GHG inventories, the net CO 2 emissions from AFOLU were 0.0 to +0.8 GtCO 2 yr –1 over the same period. There is a discrepancy in the reported CO 2 AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used {7.2.2} . If the responses of all managed and natural land to both anthropogenic environmental change and natural climate variability, estimated to be a gross sink of –12.5 ± 3.2 GtCO 2 yr –1 for the period 2010–2019, are added to land-use emissions, then land overall constituted a net sink of –6.6 ± 5.2 GtCO 2 yr –1 in terms of CO 2 emissions ( ''medium confidence'' ). (Table TS.4) {7.2, Table 7.1}

Table TS.4 | Net anthropogenic emissions (annual averages for 2010–201 9 '''''a''''' ) from agriculture, forestry and other land use (AFOLU). For context, the net flux due to the natural response of land to climate and environmental change is also shown for CO 2 in column E. Positive values represent emissions, negative values represent removals. Due to different approaches to estimate anthropogenic fluxes, AFOLU CO 2 estimates in the table below are not directly comparable to LULUCF in national greenhouse gas inventories (NGHGIs).

{| class="wikitable"
|-
| colspan="6"| '''Anthropogenic'''

| '''Natural response'''

| '''Natural and anthropogenic'''

|-
| rowspan="2"| '''Gas'''

| rowspan="2"| '''Units'''

| '''AFOLU net anthropogenic emissions'''

| '''Non-AFOLU anthropogenic GHG emissions'''

| '''Total net anthropogenic emissions (AFOLU and non-AFOLU) by gas'''

| '''AFOLU as a % of total net anthropogenic emissions by gas'''

| '''Natural land sinks including natural response of land to anthropogenic environmental change and climate variability'''

| '''Net-land atmosphere CO''' 2 '''flux (i.e., anthropogenic AFOLU and natural fluxes across entire land surface)'''

|-
| '''A'''

| '''B'''

| '''C = A + B'''

| '''D = (A/C) *100'''

| '''E'''

| '''F = A + E'''

|-
| '''CO''' 2

| GtCO 2 -eq yr –1

| 5.9 ± 4.1 (bookkeeping

models, managed

soils and pasture).

0 to 0.8 (NGHGI/

FAOSTAT data)

| 36.2 ± 2.9

| 42.0 ± 29.0

| 14%

| –12.5 ± 3.2

| –6.6 ± 4.6

|-
| rowspan="2"| '''CH''' 4

| MtCH 4 yr – 1

| 157.0 ± 47.1

| 207.5 ± 62.2

| 364.4 ± 109.3

|
|-
| GtCO 2 -eq yr –1

| 4.2 ± 1.3

| 5.9 ± 1.8

| 10.2 ± 3.0

| 41%

|
|-
| rowspan="2"| '''N''' 2 '''O'''

| MtN 2 O yr –1

| 6.6 ± 4.0

| 2.8 ± 1.7

| 9.4 ± 5.6

|
|-
| GtCO 2 -eq yr –1

| 1.8 ± 1.1

| 0.8 ± 0.5

| 2.6 ± 1.5

| 69%

|
|-
| '''Total'''

| GtCO 2 -eq yr –1

| 11.9 ± 4.4

(CO 2 component considers bookkeeping models only)

| 44 ± 3.4

| 55.9 ± 6.1

| 21%

|
|}

a Estimates are given for 2019 as this is the latest date when data are available for all gases, consistent with [https://www.ipcc.ch/chapters/chapter-2 Chapter 2] of this report. Positive fluxes are emission from land to the atmosphere. Negative fluxes are removals. For all Table footnotes see Table 7.1. {Table 7.1}

'''Land-use change drives net AFOLU CO''' 2 '''emission fluxes. The rate of deforestation, which accounts for 45% of total AFOLU emissions, has generally declined, while global tree cover and global forest-growing stock levels are''' '''''likely''''' '''increasing (''' '''''medium confidence''''' ''').''' There are substantial regional differences, with losses of carbon generally observed in tropical regions and gains in temperate and boreal regions. Agricultural CH 4 and N 2 O emissions are estimated to average 157 ± 47.1 MtCH 4 yr –1 and 6.6 ± 4.0 MtN 2 O yr –1 or 4.2 ± 1.3 and 1.8 ± 1.1 GtCO 2 -eq yr –1 (using IPCC AR6 GWP100 values for CH 4 and N 2 O) respectively between 2010 and 2019 {7.2.1, 7.2.3} . AFOLU CH 4 emissions continue to increase ''',''' the main source of which is enteric fermentation from ruminant animals. Similarly, AFOLU N 2 O emissions are increasing, dominated by agriculture, notably from manure application, nitrogen deposition, and nitrogen fertiliser use ( ''high confidence'' ). In addition to being a net carbon sink and source of GHG emissions, land plays an important role in climate through albedo effects, evapotranspiration, and aerosol loading through emissions of volatile organic compounds (VOCs). The combined role of CH ''4'' , N 2 O and aerosols in total climate forcing, however, is unclear and varies strongly with bioclimatic region and management practice. {2.4.2.5, 7.2, 7.3}

'''The AFOLU sector offers significant near-term mitigation potential at relatively low cost and can provide 20–30% of the 2050 emissions reduction described in scenarios that limit warming to 2°C (&gt;67%) or lower (''' '''''high evidence''''' ''''', medium agreement''''' ''').''' The AFOLU sector can provide 20–30% (interquartile range) of the global mitigation needed for a 1.5°C or 2°C pathway towards 2050, though there are highly variable mitigation strategies for how AFOLU potential can be deployed for achieving climate targets {Illustrative Mitigation Pathways in 7.5} . The estimated economic (&lt;USD100 tCO 2 -eq –1 ) AFOLU sector mitigation potential is 8 to 14 GtCO 2 -eq yr –1 between 2020–2050, with the bottom end of this range representing the mean from IAMs and the upper end representing the mean estimate from global sectoral studies. The economic potential is about half of the technical potential from AFOLU, and about 30–50% could be achieved under USD20 tCO ''2'' -eq ''–'' 1 {7.4} . The implementation of robust measurement, reporting and verification processes is paramount to improving the transparency of changes in land carbon stocks and this can help prevent misleading assumptions or claims on mitigation. {7.1, 7.4, 7.5}

'''Between 2020 and 2050, mitigation measures in forests and other natural ecosystems provide the largest share of the AFOLU mitigation potential (up to USD100 tCO''' 2 '''-eq''' –1 '''), followed by agriculture and demand-side measures (''' '''''high confidence''''' ''').''' In the global sectoral studies, the protection, improved management, and restoration of forests, peatlands, coastal wetlands, savannas and grasslands have the potential to reduce emissions and/or sequester 7.3 mean (3.9–13.1) GtCO 2 -eq yr –1 . Agriculture provides the second largest share of the mitigation potential, with 4.1 (1.7–6.7) GtCO ''2'' -eq yr ''–'' 1 (up to USD100 tCO 2 -eq –1 ) from cropland and grassland soil carbon management, agroforestry, use of biochar, improved rice cultivation, and livestock and nutrient management. Demand-side measures including shifting to sustainable healthy diets, reducing food waste, building with wood, biochemicals, and bio-textiles, have a mitigation potential of 2.2 (1.1–3.6) GtCO ''2'' -eq yr –1 . Most mitigation options are available and ready to deploy. Emissions reductions can be achieved relatively quickly, whereas CDR needs upfront investment. Sustainable intensification in agriculture, shifting diets, and reducing food waste could enhance efficiencies and reduce agricultural land needs, and are therefore critical for enabling supply-side measures such as reforestation, restoration, as well as decreasing CH 4 and N 2 O emissions from agricultural production. In addition, emerging technologies (e.g., vaccines or CH 4 inhibitors) have the potential to substantially increase the CH 4 mitigation potential beyond current estimates. AFOLU mitigation is not only relevant in countries with large land areas. Many smaller countries and regions, particularly with wetlands, have disproportionately high levels of AFOLU mitigation potential density. {7.4, 7.5}

'''The economic and political feasibility of implementing AFOLU mitigation measures is hampered by persistent barriers. Assisting countries to overcome barriers will help to achieve significant short-term mitigation (''' '''''medium confidence''''' ''').''' Finance forms a critical barrier to achieving these gains as currently mitigation efforts rely principally on government sources and funding mechanisms which do not provide sufficient resources to enable the economic potential to be realised. Differences in cultural values, governance, accountability and institutional capacity are also important barriers. Climate change itself could reduce the mitigation potential from the AFOLU sector, although an increase in the capacity of natural sinks could occur despite changes in climate ( ''medium'' ''confidence'' ) {AR6 WGI Figure SPM.7 and Sections 7.4 and 7.6} . The continued loss of biodiversity makes ecosystems less resilient to climate change extremes and this may further jeopardise the achievement of the AFOLU mitigation potentials indicated in this chapter ( ''high confidence'' ). (Box TS.15) {7.6}

'''The provision of biomass for bioenergy (with/without BECCS) and other bio-based products represents an important share of the total mitigation potential associated with the AFOLU sector, though these mitigation effects accrue to other sectors (''' '''''high confidence''''' ''').''' Recent estimates of the technical bioenergy potential, when constrained by food security and environmental considerations, are within the ranges 5–50 and 50–250 EJ yr –1 by 2050 for residues and dedicated biomass production systems, respectively. [[#footnote-004|29]] (TS.5.7) {7.4, 12.3}

'''Bioenergy is the most land-intensive energy option, but total land occupation of other renewable energy options can also become significant in high deployment scenarios. While not as closely connected to the AFOLU sector as bioenergy, other renewable energy options can influence AFOLU activities in both synergistic and detrimental ways (''' '''''high confidence''''' ''').''' The character of land occupation, and associated impacts, vary considerably among mitigation options and also for the same option depending on geographic location, scale, system design and deployment strategy. Land occupation can be large uniform areas, for example, reservoir hydropower dams and tree plantations, and more distributed occupation that is integrated with other land uses, for example, wind turbines and agroforestry in agriculture landscapes. Deployment can be partly decoupled from additional land use, for example, use of organic waste and residues and integration of solar PV into buildings and other infrastructure ( ''high confidence'' ). Wind and solar power can coexist with agriculture in beneficial ways ( ''medium confidence'' ). Indirect land occupation includes new agriculture areas following displacement of food production with bioenergy plantations and expansion of mining activities providing minerals required for manufacture of EV batteries, PV, and wind power. {7.4, 12.5}

'''The deployment of land-based mitigation measures can provide co-benefits, but there are also risks and trade-offs from inappropriate land management (''' '''''high confidence''''' '''). Such risks can best be managed if AFOLU mitigation is pursued in response to the needs and perspectives of multiple stakeholders to achieve outcomes that maximise synergies while limiting trade-offs (''' '''''medium confidence''''' ''').''' The results of implementing AFOLU measures are often variable and highly context-specific. Depending on local conditions (e.g., ecosystem, climate, food system, land ownership) and management strategies (e.g., scale, method), mitigation measures can positively or negatively affect biodiversity, ecosystem functioning, air quality, water availability and quality, soil productivity, rights infringements, food security, and human well-being. The agriculture and forestry sectors can devise management approaches that enable biomass production and use for energy in conjunction with the production of food and timber, thereby reducing the conversion pressure on natural ecosystems ( ''medium confidence'' ). Mitigation measures addressing GHGs may also affect other climate forcers such as albedo and evapotranspiration. Integrated responses that contribute to mitigation, adaptation, and other land challenges will have greater likelihood of being successful ( ''high confidence'' ); measures which provide additional benefits to biodiversity and human well-being are sometimes described as ‘Nature-based Solutions’. {7.1, 7.4, 7.6, 12.4, 12.5}

'''AFOLU mitigation measures have been well understood for decades but deployment remains slow, and emissions trends indicate unsatisfactory progress despite beneficial contributions to global emissions reduction from forest-related options (''' '''''high confidence''''' ''').''' Globally, the AFOLU sector has so far contributed modestly to net mitigation, as past policies have delivered about 0.65 GtCO 2 yr –1 of mitigation during 2010–2019 or 1.4% of global gross emissions. The majority (&gt;80%) of emission reduction resulted from forestry measures. Although the mitigation potential of AFOLU measures is large from a biophysical and ecological perspective, its feasibility is hampered by lack of institutional support, uncertainty over long-term additionality and trade-offs, weak governance, fragmented land ownership, and uncertain permanence effects. Despite these impediments to change, AFOLU mitigation options are demonstrably effective and with appropriate support can enable rapid emission reductions in most countries. {7.4, 7.6}

'''Concerted, rapid and sustained effort by all stakeholders, from policymakers and investors to land owners and managers is a pre-requisite for achieving high levels of mitigation in the AFOLU sector (''' '''''high confidence''''' ''').''' To date USD0.7 billion yr ''–1'' is estimated to have been spent on AFOLU mitigation. This is well short of the more than USD400 billion yr ''–1'' that is estimated to be necessary to deliver the up to 30% of global mitigation effort envisaged in deep mitigation scenarios ( ''medium confidence'' ). This estimate of the global funding requirement is smaller than current subsidies provided to agriculture and forestry. A gradual redirection of existing agriculture and forestry subsidies would greatly advance mitigation. Effective policy interventions and national (investment) plans as part of NDCs, specific to local circumstances and needs, are urgently needed to accelerate the deployment of AFOLU mitigation options. These interventions are effective when they include funding schemes and long-term consistent support for implementation with governments taking the initiative together with private funders and non-state actors. {7.6}

'''Realising the mitigation potential of the AFOLU sector depends strongly on policies that directly address emissions and drive the deployment of land-based mitigation options, consistent with carbon prices in deep mitigation scenarios (''' '''''high confidence''''' ''').''' Examples of successful policies and measures include establishing and respecting tenure rights and community forestry, improved agricultural management and sustainable intensification, biodiversity conservation, payments for ecosystem services, improved forest management and wood-chain usage, bioenergy, voluntary supply chain management efforts, consumer behaviour campaigns, private funding and joint regulatory efforts to avoid, for example, leakage. The efficacy of different policies, however, will depend on numerous region-specific factors. In addition to funding, these factors include governance, institutions, long-term consistent execution of measures, and the specific policy setting. While the governance of land-based mitigation can draw on lessons from previous experience with regulating biofuels and forest carbon, integrating these insights requires governance that goes beyond project-level approaches emphasising integrated land-use planning and management within the frame of the Sustainable Development Goals. {7.4, Box 7.2, 7.6}

'''Addressing the many knowledge gaps in the development and testing of AFOLU mitigation options can rapidly advance the likelihood of achieving sustained mitigation (''' '''''high''''' '''''confidence''''' ''').''' Research priorities include improved quantification of anthropogenic and natural GHG fluxes and emissions modelling, better understanding of the impacts of climate change on the mitigation potential, permanence and additionality of estimated mitigation actions, and improved (real-time and cheap) measurement, reporting and verification. There is a need to include a greater suite of mitigation measures in IAMs, informed by more realistic assessments that take into account local circumstances and socio-economic factors and cross-sector synergies and trade-offs. Finally, there is a critical need for more targeted research to develop appropriate country-level, locally specific, policy and land-management response options. These options could support more specific NDCs with AFOLU measures that enable mitigation while also contributing to biodiversity conservation, ecosystem functioning, livelihoods for millions of farmers and foresters, and many other SDGs. {7.7, Figure 17.1}

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==== TS.5.6.2 Food Systems ====

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'''Realising the full mitigation potential from the food system requires change at all stages from producer to consumer and waste management, which can be facilitated through integrated policy packages (''' '''''high confidence''''' ''').''' Food systems are associated with 23–42% of global GHG emissions, while there is still widespread food insecurity and malnutrition. Absolute GHG emissions from food systems increased from 14 to 17 GtCO 2 -eq yr –1 in the period 1990–2018. Both supply- and demand-side measures are important to reduce the GHG intensity of food systems. Integrated food policy packages based on a combination of market-based, administrative, informative, and behavioural policies can reduce cost compared to uncoordinated interventions, address multiple sustainability goals, and increase acceptance across stakeholders and civil society ( ''limited evidence'' , ''medium agreement'' ) ''.'' Food systems governance may be pioneered through local food policy initiatives complemented by national and international initiatives, but governance on the national level tends to be fragmented, and thus has limited capacity to address structural issues like inequities in access. (Figure TS.18, Table TS.5, Table TS.6) {7.2, 7.4, 12.4}

[[File:a1089d0ce8d599427cec711ba4074362 IPCC_AR6_WGIII_Figure_TS_18.png]]
'''Figure TS.18''' '''|''' '''Food-system GHG emissions from the agriculture, and land use, land-use change and forestry (LULUCF), waste, and energy and industry sectors.''' {Figure 12.5}

'''Table TS''' '''.5 |''' '''Food system mitigation opportunities.'''

{| class="wikitable"
|-
| colspan="2"| '''Food system mitigation options (I: incremental; T: transformative)'''

| '''Direct and indirect effect on GHG mitigation (+/0/–)''' a

| '''Co-benefits/adverse effects''' b

|-
| rowspan="5"| Food from agriculture, aquaculture and fisheries

| (I) Dietary shift, in particular increased share of plant-based protein sources

| D+ ↓ GHG footprint

| A+ Animal welfare

L+ Land sparing

H+ Good nutritional properties, potentially ↓ risk from zoonotic diseases, pesticides and antibiotics

|-
| (I/T) Digital agriculture

| D+ ↑ logistics

| L+ Land sparing

R+ ↑ resource-use efficiencies

|-
| (T) Gene technology

| D+ ↑ productivity or efficiency

| H+ ↑ nutritional quality

E0 ↓ use of agrochemicals; ↑ probability of off-target impacts

|-
| (I) Sustainable intensification Land-use optimisation

| D+ ↓ GHG footprint

E0 Mixed effects

| L+ Land sparing

R– Might ↑ pollution/biodiversity loss

|-
| (I) Agroecology

| D+ ↓ GHG/area, positive micro-climatic effects

E+ ↓ energy, possibly ↓ transport FL+ Circular approaches

| E+ Focus on co-benefits/ecosystem services

R+ Circular, ↑ nutrient and water use efficiencies

|-
| Controlled environment agriculture

| (T) Soil-less agriculture

| D+ ↑ productivity, weather independent

FL+ Harvest on demand

E– Currently ↑ energy demand, but ↓ transport, building spaces can be used for renewable energy

| R+ Controlled loops ↑ nutrient- and water-use efficiency

L+ Land sparing

H+ Crop breeding can be optimised for taste and/or nutritional quality

|-
| rowspan="4"| Emerging food production technologies

| (T) Insects

| D0 Good feed conversion efficiency

FW+ Can be fed on food waste

| H0 Good nutritional qualities but attention to allergies and food safety issues required

|-
| (I/T) Algae and bivalves

| D+ ↓ GHG footprints

| A+ Animal welfare

L+ Land sparing

H+ Good nutritional qualities; risk of heavy-metal and pathogen contamination

R+ Biofiltration of nutrient-polluted waters

|-
| (I/T) Plant-based alternatives to animal-based food products

| D+ No emissions from animals, ↓ inputs for feed

| A+ Animal welfare

L+ Land sparing

H+ Potentially ↓ risk from zoonotic diseases, pesticides and antibiotics; but ↑ processing demand

|-
| (T) Cellular agriculture (including cultured meat, microbial protein)

| D+ No emissions from animals, high protein conversion efficiency

E– ↑ energy need

FLW+ ↓ food loss and waste

| A+ Animal welfare

R+ ↓ emissions of reactive nitrogen or other pollutants

H0 Potentially ↓ risk from zoonotic diseases, pesticides and antibiotics; ↑ research on safety aspects needed

|-
| rowspan="4"| Food processing and packaging

| (I) Valorisation of by-products, FLW logistics and management

| M+ Substitution of bio-based materials

FL+ ↓ of food losses

|
|-
| (I) Food conservation

| FW+ ↓ of food waste

E0 ↑ energy demand but also energy savings possible (e.g., refrigeration, transport)

|
|-
| (I) Smart packaging and other technologies

| FW+ ↓ of food waste

M0 ↑ material demand and ↑ material efficiency

E0 ↑ energy demand; energy savings possible

| H+ Possibly ↑ freshness/reduced food safety risks

|-
| (I) Energy efficiency

| E+ ↓ energy

|
|-
| rowspan="5"| Storage and distribution

| (I) Improved logistics

| D+ ↓ transport emissions

FL+ ↓ losses in transport

FW– Easier access to food could ↑ food waste

|
|-
| (I) Specific measures to reduce food waste in retail and food catering

| FW+ ↓ of food waste

E+ ↓ downstream energy demand

M+ ↓ downstream material demand

|
|-
| (I) Alternative fuels/transport modes

| D+ ↓ emissions from transport

|
|-
| (I) Energy efficiency

| E+ ↓ energy in refrigeration, lightening, climatisation

|
|-
| (I) Replacing refrigerants

| D+ ↓ emissions from the cold chain

|
|}

a Direct and indirect GHG effects: D – direct emissions except emissions from energy use, E – energy demand, M – material demand, FL – food losses, FW – food waste; direction of effect on GHG mitigation: (+) increased mitigation, (0) neutral, (–) decreased mitigation.

b Co-benefits/adverse effects: H – health aspects, A – animal welfare, R – resource use, L – land demand, E – ecosystem services; (+) co-benefits, (–) adverse effects. {Table 12.8}

'''Table TS.''' '''6 |''' '''Assessment of food system policies targeting (post-farm gate) food-chain actors and consumers.'''

{| class="wikitable"
|-
!
! ''Level G: global/multinational; N: national; L: local''

! ''Transformative potential''

! ''Environmental effectiveness''

! ''Feasibility''

! ''Distributional effects''

! ''Cost''

! ''Co-benefits'' a ''and adverse side effect''

! ''Implications for coordination, coherence and consistency in policy package'' b

|-
| '''Integrated food policy packages'''

| '''NL'''

|
| ''can be controlled''

| ''cost efficient''

| '''+ balanced, addresses multiple sustainability goals'''

| Reduces cost of uncoordinated interventions; increases acceptance across stakeholders and civil society ( ''robust evidence'' , ''high agreement'' )

|-
| Taxes on food products

| GN

|
| ''regressive''

| low # 1

| ''– unintended substitution effects''

| High enforcing effect on other food policies; higher acceptance if compensation or hypothecated taxes ( ''medium evidence'' , ''high agreement'' )

|-
| rowspan="2"| GHG taxes on food

| rowspan="2"| GN

| rowspan="2"|
| rowspan="2"| ''regressive''

| rowspan="2"| low # 2

| ''– unintended substitution effects''

| rowspan="2"| Supportive, enabling effect on other food policies, agricultural/fishery policies; requires changes in power distribution and trade agreements ( ''medium evidence'' , ''medium agreement'' )

|-
| + high spillover effect

|-
| rowspan="2"| Trade policies

| rowspan="2"| G

| rowspan="2"|
| rowspan="2"| impacts global distribution

| rowspan="2"| complex effects

| + counters leakage effects

| rowspan="2"| Requires changes in existing trade agreements ( ''medium'' ''evidence'' , ''high agreement'' )

|-
| +/– effects on market structure and jobs

|-
| Investment into research and innovation

| GN

|
| none

| medium

| + high spillover effect + converging with digital society

| Can fill targeted gaps for coordinated policy packages (e.g., monitoring methods) ( ''robust evidence'' , ''high agreement'' )

|-
| Food and marketing regulations

| N

|
| low

|
| Can be supportive; might be supportive to realise innovation; voluntary standards might be less effective ( ''medium evidence'' , ''medium agreement'' )

|-
| Organisational-level procurement policies

| NL

|
| low

| + can address multiple sustainability goals

| Enabling effect on other food policies; reaches large share of population ( ''medium evidence'' , ''high agreement'' )

|-
| Sustainable food-based dietary guidelines

| GNL

|
| none

| low

| + can address multiple sustainability goals

| Little attention so far on environmental aspects; can serve as benchmark for other policies (labels, food formulation standards, etc.) ( ''medium evidence'' , ''medium agreement'' )

|-
| Food labels/ information

| GNL

|
| education level relevant

| low

| + empowers citizens

+ increases awareness

+ multiple objectives

| Effective mainly as part of a policy package; incorporation of other objectives (e.g., animal welfare, fair trade); higher effect if mandatory ( ''medium evidence'' , ''medium'' ''agreement'' )

|-
| Nudges

| NL

|
| none

| low

| + possibly counteracting information deficits in population subgroups

| High enabling effect on other food policies ( ''medium'' ''evidence'' , ''high agreement'' )

|}

Effect of measures:   negative   none/unclear   slightly positive   positive 

Notes: #1 Minimum level to be effective 20% price increase; #2 Minimum level to be effective USD50–80 tCO 2 -eq. a In addition, all interventions are assumed to address health and climate change mitigation. b Requires coordination between policy areas, participation of stakeholders, transparent methods and indicators to manage trade-offs and prioritisation between possibly conflicting objectives; and suitable indicators for monitoring and evaluation against objectives.

'''Diets high in plant protein and low in meat and dairy are associated with lower GHG emissions (''' '''''high confidence''''' ''').''' Ruminant meat shows the highest GHG intensity. Beef from dairy systems has lower emissions intensity than beef from beef herds (8–23 and 17–94 kgCO ''2'' -eq (100 g protein) ''–1'' , respectively) when some emissions are allocated to dairy products. The wide variation in emissions reflects differences in production systems, which range from intensive feedlots with stock raised largely on grains through to rangeland and transhumance production systems. Where appropriate, a shift to diets with a higher share of plant protein, moderate intake of animal-source foods and reduced intake of saturated fats could lead to substantial decreases in GHG emissions. Benefits would also include reduced land occupation and nutrient losses to the surrounding environment, while at the same time providing health benefits and reducing mortality from diet-related non-communicable diseases. (Figure TS.19) {7.4.5, 12.4}

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[[File:5cdd59f7745e0d8b1e84d32d8bfe52b7 IPCC_AR6_WGIII_Figure_TS_19.png]]

'''Figure TS.19''' '''|''' '''Regional differences in health outcome, territorial per-capita GHG emissions from national food systems, and share of food system GHG emission from energy use.''' GHG emissions are calculated according to the IPCC Tier 1 approach and are assigned to the country where they occur, not necessarily where the food is consumed. Health outcome is expressed as relative contribution of each of the following risk factors to their combined risk for deaths: Child and maternal malnutrition (red), Dietary risks (yellow) or High body-mass index (blue). {Figure 12.7}

'''Emerging food technologies such as cellular fermentation, cultured meat, plant-based alternatives to animal-based food products, and controlled environment agriculture, can bring substantial reduction in direct GHG emissions from food production (''' '''''limited evidence,''''' '''''high agreement''''' ''').''' These technologies have lower land, water, and nutrient footprints, and address concerns over animal welfare. Realising the full mitigation potential depends on access to low-carbon energy as some emerging technologies are relatively more energy intensive. This also holds for deployment of cold-chain and packaging technologies, which can help reduce food loss and waste, but increase energy and materials use in the food system. (Table TS.5) {11.4.1.3, 12.4}

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