Editing IPCC:AR6/WGIII/Chapter-7 (section)

== 7.6 Assessment of Economic, Social and Policy Responses ==

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=== 7.6.1 Retrospective in Policy Efforts and Achieved Mitigation Within AFOLU ===

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Since the establishment of the UNFCCC, international agencies, countries, sub-national units and NGO’s have developed policies to facilitate and encourage GHG mitigation within AFOLU (Figure 7.18). Early guidance and policies focused on developing GHG inventory methodology with some emphasis on afforestation and reforestation projects, but the Clean Development Mechanism (CDM) in the Kyoto Protocol focused attention on emission reduction projects, mostly outside of AFOLU. As successive IPCC AR6 WGIII reports illustrated large potential for AFOLU mitigation, methods to quantify and verify carbon emission reductions emerged within several projects in the early 2000s, through both voluntary (e.g., the Chicago Climate Exchange (CCX)) and regulated (e.g., New South Wales and California) markets. The CDM dedicated large attention to LULUCF, including dedicated methodologies and bodies. The reasons for limited uptake of CDM afforestation/reforestation projects were multiple and not limited to the regulatory constraints, but also due to the low abatement potential (poor cost/performance ratio) compared to other mitigation opportunities.

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[[File:c94f057659f739b39f4030376d7f3bab IPCC_AR6_WGIII_Figure_7_18.png]]

'''Figure 7.18 | Milestones in policy development for AFOLU measures.''' ‘ag.’ and ‘agri.’ = agriculture.

Following COP 13 in Bali, effort shifted to advancing policies to reduce deforestation and forest degradation (REDD+) in developing countries. According to [[#Simonet--2019|Simonet et al. (2019)]] , nearly 65 Mha have been enrolled in REDD+ type programmes or projects funded through a variety of sources, including United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD), the World Bank Forest Carbon Partnership Facility, and bi-lateral agreements between countries with Norway being the largest donor. While there has been considerable focus on forest and agricultural project-based mitigation actions, national governments were encouraged to incorporate project-based approaches with other sectoral strategies in their Nationally Appropriate Mitigation Actions (NAMAs) after 2012. NAMAs reflect the country’s proposed strategy to reduce net emissions across various sectors within their economy (e.g., forests or agriculture). More recently, Nationally Determined Contributions (NDCs) indicate whether individual countries plan to use forestry and agricultural policies or related projects amongst a set of measures in other sectors, to reduce their net emissions as part of the Paris Agreement (e.g., [[#Forsell--2016|Forsell et al. 2016]] ; [[#Fyson--2019|Fyson and Jeffery 2019]] ).

The many protocols now available can be used to quantify the potential mitigation to date resulting from various projects or programs. For instance, carbon registries issue credits using protocols that typically account for additionality, permanence and leakage, thus providing evidence that the projects are a net carbon benefit to the atmosphere. Protocol development engages the scientific community, project developers, and the public over a multi-year period. Some protocols have been revised multiple times, such as the USA State of California’s forest carbon protocol, which is in its fifth revision, with the latest in 2019 (see http://www.climateactionreserve.org/how/protocols/forest/ ). Credits from carbon registries feed into regulatory programs, such as the cap and trade programme in California, or voluntary offset markets ( [[#Hamrick--2017a|Hamrick and Gallant 2017a]] ). Although AFOLU measures have been deployed across a range of projects and programmes globally to reduce net carbon emissions, debate about the net carbon benefits of some projects continues (e.g., [[#Krug--2018|Krug 2018]] ).

A new assessment of projects over the last two decades finds emission reductions or offsets of at least 7.9 GtCO 2 -eq (using GWP100 and a mix of IPCC values for CH 4 and N 2 O) over the last 12 years due to agricultural and forestry activities (Table 7.4). More than 80% of these emission reductions or offsets have been generated by forest-based activities. The total amounts to 0.66 GtCO 2 yr –1 for the period 2010–2019, which is 1.2% of total global, and 5.5% of AFOLU emissions reported in Table 7.1, over the same time period ( ''hi'' ''gh confidence'' ).

The array of activities in Table 7.4 includes the Clean Development Mechanism, REDD+ activities reported in technical annexes of country biennial update reports to the UNFCCC, voluntary market transactions, and carbon stored as a result of carbon markets in Australia, New Zealand and California in the USA. Although other countries and sub-national units have developed programmes and policies, these three regions are presented due to their focus on forest and agricultural carbon mitigation, their use of generally accepted protocols or measures and the availability of data to quantify outcomes.

The largest share of emission reductions or carbon offsets in Table 7.4 has been from slowing deforestation and REDD+, specifically from efforts in Brazil (86% of total), which substantially reduced deforestation rates between 2004 and 2012 ( [[#Nepstad--2014|Nepstad et al. 2014]] ), as well as other countries in Latin America. With the exception of [[#Roopsind--2019|Roopsind et al. (2019)]] , estimated reductions in carbon emissions from REDD+ in Table 7.4 are measured relative to a historical baseline. As noted in Brazil’s Third Biennial Update Report ( [[#Ministry%20of%20Foreign%20Affairs--2019|Ministry of Foreign Affairs 2019]] ), estimates are made in accordance with established methodologies to determine the benefits of results-based REDD+ payments to Brazil. REDD+ estimates from other countries also have been derived from biennial update reports.

Regulatory markets provide the next largest share of carbon removal to date. Data from the Australia Emissions Reduction Fund are carbon credits issued in for agricultural, and vegetation and savanna burning projects. In the case of California, offset credits from forest and agricultural activities, using methods approved by a third-party certification authority (Climate Action Reserve), have been allowed as part of their state-wide cap and trade system. Transaction prices for forest and agricultural credits in California were around USD13 tCO 2 –1 in 2018, and represented 7.4% of total market compliance. By the end of 2018, 80 MtCO 2 had been used for compliance purposes.

For New Zealand, the carbon reduction in Table 7.4 represents forest removals that were surrendered from post-1989 forests between 2008 and the 2020. Unlike offsets in voluntary markets or in California, where permanence involves long-term contracts or insurance pools, forests in the New Zealand market liable for emissions when harvested or following land-use change. This means sellers account for future emission risks related to harvesting when they enter forests into carbon contracts. Offset prices were around USD13 tCO 2 –1 in 2016 but have risen to more than USD20 tCO 2 –1 in 2020.

The voluntary market data in Table 7.4 are offsets developed under the major standard-setting organisations, and issued from 2008–2018 (e.g., [[#Hamrick--2018|Hamrick and Gallant 2018]] ). Note that there is some potential for double counting of voluntary offsets that may have been transacted in the California compliance market, however this would only have applied to transactions of US-issued offsets, and the largest share of annual transactions of voluntary AFOLU credits occurs with credits generated in Latin America, followed by Africa, Asia and North America. Europe and Oceania have few voluntary carbon market transactions. Within forestry and agriculture, most of the voluntary offsets were generated by forestry projects. Using historical transaction data from various ''Forest Trends'' reports, the offsets generated were valued at USD46.9 million yr –1 . Prices for voluntary offset transactions in the period 2014–2016 ranged from USD4.90 to USD5.40 tCO 2 –1 ( [[#Hamrick--2017a|Hamrick and Gallant 2017a]] ).

Voluntary finance has amounted to USD0.5 billion over a 10-year period for development of forest and agricultural credits. The three regulatory markets quantified amount to USD2.7 billion in funding from 2010 to 2019. For the most part, this funding has focused on forest projects and programs, with agricultural projects accounting for 5–10% of the total. In total, reported funding for AFOLU projects and programmes has been USD4.4 billion over the past decade, or about USD569 million yr –1 ( ''low confidence'' ). The largest share of the total carbon includes efforts in the Amazon by Brazil. Government expenditures on regulatory programmes and business expenditures on voluntary programmes in Brazil (e.g., the soy or cattle moratoriums) were not included in financing estimates due to difficulties obtaining that data. If Brazil and CDM (for which we have no cost estimates) are left out of the calculation, average cost per ton has been USD3.20 tCO 2 –1 .

The large number of policy approaches described in Table 7.4 combined with efforts by other international actors, such as the Global Environmental Facility (GEF), as well as non-state actors (e.g., eco-labelling programmes and corporate social responsibility initiatives) illustrate significant policy experimentation over the last several decades. Despite widespread effort, AFOLU measures have thus far failed to achieve the large potential for climate mitigation described in earlier IPCC WG III reports ( ''high confidence'' ). The limited gains from AFOLU to date appear largely to result from lack of investment and other institutional and social barriers, rather than methodological concerns ( ''hi'' ''gh confidence'' ).

'''Table 7.4 | Estimates of achieved emission offsets or reductions in AFOLU through 2018.''' Data include CDM, voluntary carbon standards, compliance markets, and reduced deforestation from official UNFCCC reports. Carbon sequestration due to other government policies not included.

{| class="wikitable"
|-
! '''Fund/mechanism'''

! '''Total emission reductions or offsets (MtCO''' 2 '''-eq)'''

! '''Time frame'''

! '''MtCO''' 2 '''-eq y''' '''r''' –1

! '''Financing'''

'''(million USD y''' '''r''' –1 ''')'''

|-
| CDM-forest a

| 11.3

| 2007–2015

| 1.3

| –

|-
| CDM-agriculture a

| 21.8

| 2007–2015

| 2.4

| –

|-
| REDD+ (Guyana) b

| 12.8

| 2010–2015

| 2.1

| 33.0

|-
| Reduced Deforestation/REDD+ Brazil c

| 6894.5

| 2006–2017

| 574.5

| 49.2

|-
| REDD+ Indonesia c

| 244.9

| 2013–2017

| 49.0

| 13.4

|-
| REDD+ Argentina c

| 165.2

| 2014–2015

| 55.1

| 1.4

|-
| REDD+ Others c

| 211.8

| 2010–2017

| 26.5

| 46.0

|-
| Voluntary Market d

| 95.3

| 2009–2018

| 9.5

| 46.9

|-
| Australia ERF e

| 42.7

| 2012—2019 h

| 6.1

| 53.6

|-
| California f

| 122.2

| 2013–2018

| 20.4

| 227.1

|-
| New Zealand carbon trading g

| 83.9

| 2010–2019

| 8.4

| 101.7

|-
| '''Total'''

| '''7,897.4'''

| '''2007–2018'''

| '''658.1 h'''

| '''569.1'''

|}

a Clean Development Mechanism Registry: [https://cdm.unfccc.int/Registry/index.html] [https://cdm.unfccc.int/Registry/index.html] (accessed 22 June 2021).

b [[#Roopsind--2019|Roopsind et al. 2019]] .

c UNFCCC REDD+ Web Platform ( https://redd.unfccc.int/submissions.html ) and UNFCCC Biennial Update Report database ( https://unfccc.int/BURs ).

d ( [[#Hamrick--2017a|Hamrick and Gallant 2017a]] ). State of Forest Carbon Finance. Forest Trends Ecosystem Marketplace. Washington, DC, USA.

e Data for Australia carbon credit units (ACCUs) from Australia Emissions Reduction Fund Registry for agricultural and vegetation and savanna burning projects through FY2018/19 (downloaded on 24/10/2019): ( http://www.cleanenergyregulator.gov.au/ERF/project-and-contracts-registers/project-register ) and from Emissions Reduction Fund auction results to December 2018: ( [http://www.cleanenergyregulator.gov.au/ERF/auctions-results/december-2018 http://www.cleanenergyregulator.gov.au/ERF/auctions-results/d ecember-2018] ).

f Data from the California Air Resources Board Offset Issuance registry ( https://ww2.arb.ca.gov/our-work/programs/compliance-offset-program ) for forestry and agricultural early action and compliance credits.

g Surrendered forest carbon credits from post-1989 forests in New Zealand. Obtained from New Zealand Environmental Protection Authority. ETS Unit Movement interactive report (Excel based). [https://www.epa.govt.nz/industry-areas/emissions-trading-scheme/ets-reports/unit-movement/ https://www.epa.govt.nz/industry-areas/emissions-trading-scheme/ets-reports/u nit-movement/] .

h Obtained 13/08/2020. All non-CO 2 gases are converted to CO 2 -eq using IPCC GWP100 values recommended at the time the project achieved approval by the relevant organisation or agency.

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=== 7.6.2 Review of Observed Policies and Policy Instruments ===

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==== 7.6.2.1 Economic Incentives ====

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'''Emissions Trading/Carbon Taxes.''' While emissions trading programmes have been developed across the globe, forest and agriculture have not been included as part of the cap in any of the existing systems. However, offsets from forestry and agriculture have been included in several of the trading programs. New Zealand has a hybrid programme where carbon storage in forests can be voluntarily entered into the carbon trading program, but once entered, forests are counted both as a sink for carbon if net gains are positive, and a source when harvesting occurs. New Zealand is considering rules to include agricultural GHG emissions under a future cap ( [[#Henderson--2020|Henderson et al. 2020]] ; see: [https://www.agmatters.nz/topics/he-waka-eke-noa/ https://www.agmatters.nz/topics/he- waka-eke-noa/] ).

The state of California has developed a formal cap and trade programme that allows a limited number of forest and agricultural offset credits to be used under the cap. All offsets must meet protocols to account for additionality, permanence and leakage. Forest projects used as offsets in California currently are located in the USA, but the California Air Resources Board adopted a tropical forest carbon standard, allowing for avoided deforestation projects from outside the USA to enter the California market ( [[#CARB--2019|CARB 2019]] ).

Canadian provinces have developed a range of policy options that can include carbon offsets. Quebec has an emissions trading programme that plans to allow forest and agricultural offsets generated within the province to be utilised. Alberta also allows offsets to be utilised by regulated sectors while British Columbia allows offsets to be utilised by the government for its carbon neutrality goals ( [[#Government%20of%20Alberta--2021|Government of Alberta, 2021]] ). Over 20 countries and regions have adopted explicit carbon taxes on carbon emission sources and fossil fuels, however, the charges have not been applied to non-CO 2 agricultural emissions ( [[#OECD--2021a|OECD 2021a]] ). California may implement regulations on methane emissions from cattle, however, regulations if approved, will not go into effect until 2024. Institutional and trade-related barriers (e.g., leakage) likely will limit widespread implementation of taxes on emissions in the food sector globally. Many countries exempt purchases of fuels used in agricultural or fishery production from fuel or carbon taxes, thus lowering the effective tax rate imposed on those sectors ( [[#OECD--2021a|OECD 2021a]] ). Furthermore, bioenergy, produced from agricultural products, agricultural waste, and wood is often exempted from explicit carbon taxes. Colombia recently implemented a carbon tax on liquid fuels but allowed domestically produced forestry credits to offset the tax. Colombia also is in the process of developing an emissions trading scheme ( [[#OECD--2021a|OECD 2021a]] ).

'''REDD+/Payment for Ecosystem Services (PES).''' PES programmes for a variety of ecosystem services have long been utilised for conservation (e.g., [[#Wunder--2007|Wunder 2007]] ) and may now be as large as USD42 billion yr –1 ( [[#Salzman--2018|Salzman et al. 2018]] ). REDD+ emerged in the early 2000s and is a widely recognised example of PES programme focused on conservation of tropical forests (Table 7.4). However, our summation of actually paid funds in Table 7.4 is much smaller than what is portrayed by [[#Salzman--2018|Salzman et al. (2018)]] . REDD+ may operate at the country level, or for specific programmes or forests within a country. As with other PES programs, REDD+ has evolved towards a results-based programme that involves payments that are conditioned on meeting certain successes or milestones, such as rates of deforestation ( [[#Angelsen--2017|Angelsen 2017]] ).

A large literature has investigated whether PES programmes have successfully protected habitats. Studies in the USA found limited additionality for programmes that encouraged conservation tillage practices, but stronger additionality for programmes that encouraged set-asides for grasslands or forests ( [[#Woodward--2016|Woodward et al. 2016]] ; [[#Claassen--2018|Claassen et al. 2018]] ), although the set-asides led to estimated leakage of 20 up to 100% ( [[#Pfaff--2017|Pfaff and Robalino 2017]] ; Kallio et al. 2018; [[#Wu--2000|Wu 2000]] ). Evidence from the EU similarly suggests that payments for some agroenvironmental practices may be additional, while others are not ( [[#Chabé-Ferret--2013|Chabé-Ferret and Subervie 2013]] ). Other studies, in particular in Latin America where many PES programmes have been implemented, have found a wide range of estimates of effectiveness (e.g., Honey-Rosés et al. 2011; [[#Robalino--2013|Robalino and Pfaff 2013]] ; [[#Alix-Garcia--2015|Alix-Garcia et al. 2015]] ; [[#Robalino--2015|Robalino et al. 2015]] ; [[#Mohebalian--2016|Mohebalian and Aguilar 2016]] ; [[#Jayachandran--2017|Jayachandran et al. 2017]] ; [[#Börner--2017|Börner et al. 2017]] ; [[#Burivalova--2019|Burivalova et al. 2019]] ). Despite concerns, the many lessons learned from PES programme implementation provide critical information that will help policymakers refine future PES programmes to increase their effectiveness ( ''medi'' ''um confidence'' ).

While expectations that carbon-centred REDD+ would be a simple and efficient mechanism for climate mitigation have not been met ( [[#Turnhout--2017|Turnhout et al. 2017]] ; [[#Arts--2019|Arts et al. 2019]] ), progress has nonetheless occurred. Measuring, monitoring and verification systems have been developed and deployed, REDD readiness programmes have improved capacity to implement REDD+ on the ground in over 50 countries, and a number of countries now have received results-based payments.

Empirical evidence that REDD+ funding has slowed deforestation is starting to emerge. [[#Simonet--2019|Simonet et al. (2019)]] showed that a REDD+ project in Brazil reduced deforestation certainly until 2018, while [[#Roopsind--2019|Roopsind et al. (2019)]] showed that country-level REDD+ payments to Guyana encouraged reduced deforestation and increased carbon storage. Although more impact evaluation (IE) analysis needs to be conducted on REDD+ payments, these studies support the country-level estimates of carbon benefits from REDD+ shown in Table 7.4. Nearly all of the analysis of PES and REDD+ to date has focused on the presence or absence of forest cover, with little to no analysis having been conducted on forest degradation, conservation, or enhancement of forest stocks.

'''Agroenvironmental Subsidy Programs/PES.''' Climate policy for agriculture has developed more slowly than in other sectors due to concerns with food security and livelihoods, political interests, and difficulties in coordinating diffuse and diverse activities and stakeholders (e.g., nutritional health, rural development, and biodiversity conservation) ( [[#Leahy--2020|Leahy et al. 2020]] ). However, a review of the National Adaptation Programme of Action (NAPAs), National Adaptation Plans (NAPs), NAMAs, and NDCs in the Paris Agreement suggest an increasing focus of policy makers on agriculture and food security. The vast majority of parties to the Paris Agreement recognise the significant role of agriculture in supporting a secure sustainable development pathway ( [[#Richards--2015|Richards and VanWey 2015]] ) with the inclusion of agriculture mitigation in 103 NDCs from a total of 160 NDC submissions. Livestock is the most frequently cited specific agricultural sub-sector, with mitigation activities generally focusing on increasing efficiency and productivity.

Agriculture is one of the most subsidised industries globally, especially in the European Union and the USA. While subsidy payments over the last 20 years have shifted modestly to programmes designed to reduce the environmental impact of the agricultural sector, only 15–20% of the more than USD700 billion spent globally on subsidies are green payments ( [[#OECD--2021b|OECD 2021b]] ). Under the Common Agricultural Policy in the EU, up to 30% of the direct payments to farmers (Pillar 1) have been green payments ( [[#Henderson--2020|Henderson et al. 2020]] ), including some actions that could increase carbon storage or reduce emissions. Similarly, at least 30% of the rural development payments (Pillar 2) are used for measures that reduce environmental impact, including reduction of GHG emissions and carbon storage. There is limited evidence that these policies contributed to the 20% reduction in GHG emissions from the agricultural sector in the EU between 1990 and 2018 ( [[#Baudrier--2015|Baudrier et al. 2015]] ; [[#Eurostat--2020|Eurostat 2020]] ).

The USA spends USD4 billion yr –1 on conservation programs, or 12% of net farm income ( [[#Department%20of%20Agriculture--2020|Department of Agriculture 2020]] ). In real terms, this expenditure has remained constant for 15 years, supporting 12 Mha of permanent grass or woodland cover in the Conservation Reserve Program (CRP), which has increased soil carbon sequestration by 3 tCO 2 ha –1 yr –1 ( [[#Conant--2017|Conant et al. 2017]] ; [[#Paustian--2019|Paustian et al. 2019]] ), as well as other practices that could lower net emissions. Gross GHG emissions from the agricultural sector in the US, however, have increased since 1990 ( [[#USEPA--2020|USEPA 2020]] ) due to reductions in the area of land in the US CRP programme and changes in crop rotations, both of which caused soil carbon stocks to decline ( [[#USEPA--2020|USEPA 2020]] ). When combined with increased non-CO 2 gas emissions, the emission intensity of US agriculture increased from 1.5 to 1.7 tCO 2 ha –1 between 2005 and 2018 ( ''hi'' ''gh confidence'' ).

China has implemented large conservation programmes that have influenced carbon stocks. For example, the Sloping Land Conversion Program, combined with other programs, has increased forest cover and carbon stocks, reduced erosion and increased other ecosystem services in China in recent years ( [[#Ouyang--2016|Ouyang et al. 2016]] ). As part of Brazil’s national strategy, numerous practices to reduce GHG emissions from agriculture, and in particular from the animal agriculture industry, have been subsidised. Estimates by Manzatto et al. (2020) suggest that the programme may have reduced agricultural emissions by 169 MtCO 2 between 2010 and 2020. Given the large technical and economic potential for agroforestry to be deployed in Africa, subsidy approaches could be deployed along with other polices to enhance carbon through innovative practices such as regreening (Box 7.10).

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<span id="regulatory-approaches"></span>
==== 7.6.2.2 Regulatory Approaches ====

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'''Regulations''' on land use include direct controls on how land is used, zoning, or legally set limits on converting land from one use to another. Since the early 2000s, Brazil has deployed various regulatory measures to slow deforestation, including enforcement of regulations on land-use change in the legal Amazon area. Enforcement of these regulations, among other approaches is credited with encouraging the large-scale reduction in deforestation and associated carbon emissions after 2004 ( [[#Nepstad--2014|Nepstad et al. 2014]] ). Empirical evidence has found that regulations reduced deforestation in Brazil ( [[#Arima--2014|Arima et al. 2014]] ) but over time, reversals occurred when enforcement was not consistent ( [[#Azevedo--2017|Azevedo et al. 2017]] ) (Box 7.9).

Many OECD countries have strong legal frameworks that influence agricultural and forest management on both public and private land. These include for example, legal requirements to protect endangered species, implement conservation tillage, protect riparian areas, replant forests after harvest, maintain historical species composition, forest certification, and other approaches. Increasingly, laws support more widespread implementation of nature-based solutions for a range of environmental issues (e.g., see [[#European%20Commission-EU--2021|European Commission-EU 2021]] ). The extent to which the combined influence of these regulations has enhanced carbon storage in ecosystems is not quantified although they are likely to explain some of the persistent carbon sink that has emerged in temperate forests of OECD countries ( ''high confidence'' ). In the least developed and developing countries, regulatory approaches face challenges in part because environmental issues are a lower priority than many other socio-economic issues (e.g., poverty, opportunity, essential services), and weak governance ( [[#Mayer%20Pelicice--2019|Mayer Pelicice 2019]] ; [[#Walker--2020|Walker et al. 2020]] ) (Box 7.2).

'''Set asides and protected areas''' have been a widely utilised approach for conservation, and according to ( [[#FAO--2020d|FAO 2020d]] ), 726 Mha (18%) of forests are in protected areas globally. A review of land sparing and land sharing policies in developing countries indicated that most of them follow land sparing models, sometimes in combination with land sharing approaches. However, there is still no clear evidence of which policy provides the best results for ecosystem services provision, conservation, and livelihoods ( [[#Mertz--2017|Mertz and Mertens 2017]] ). The literature contains a wide range of results on the effectiveness of protected areas to reduce deforestation ( [[#Burivalova--2019|Burivalova et al. 2019]] ), with studies suggesting that protected areas provide significant protection of forests (e.g., [[#Blackman--2015|Blackman 2015]] ), modest protection ( [[#Andam--2008|Andam et al. 2008]] ), as well as increases in deforestation ( [[#Blackman--2015|Blackman 2015]] ) and possible leakage of harvesting to elsewhere (Kallio et al. 2018). An estimate of the contributions of protected areas to mitigation between 2000 and 2012, showed that in the tropics, PAs reduced carbon emissions from deforestation by 4.88 PgC, or around 29%, when compared to the expected rates of deforestation ( [[#Bebber--2017|Bebber and Butt 2017]] ). In that study, the tropical Americas (368.8 TgC yr −1 ) had the largest contribution, followed by Asia (25.0 TgC yr −1 ) and Africa (12.7 TgC yr −1 ). The authors concluded that local factors had an important influence on the effectiveness of protected areas. For example, in the Brazilian Amazon, protected area effectiveness is affected by the government agency controlling the land (federal indigenous lands, federal PAs, and state PAs) ( [[#Herrera--2019|Herrera et al. 2019]] ). Because protected areas limit not just land-use change, but also logging or harvesting non-timber forest products, they may be relatively costly approaches for forest conservation ( ''medi'' ''um confidence'' ).

'''Community forest management (CFM)''' allows less intensive use of forest resources, while at the same time providing carbon benefits by protecting forest cover. Community forest management provides property rights to communities to manage resources in exchange for their efforts to protect those resources. In many cases, the local communities are indigenous people who otherwise would have insecure tenure due to an advancing agricultural frontier or mining activity. Other examples are forest owner associations like those discussed in Box 7.8. According to the [[#Rights%20and%20Resources%20Initiative--2018|Rights and Resources Initiative (2018)]] , the area of forests under community management increased globally by 152 Mha from 2002 to 2017, with over 500 Mha under community management in 2017. Studies have now shown that improved property rights with community forest management can reduce deforestation and increase carbon storage ( [[#Deininger--2002|Deininger and Minten 2002]] ; [[#Alix-Garcia--2005|Alix-Garcia et al. 2005]] ; [[#Alix-Garcia--2007|Alix-Garcia 2007]] ; [[#Bowler--2012|Bowler et al. 2012]] ; [[#Blackman--2015|Blackman 2015]] ; [[#Fortmann--2017|Fortmann et al. 2017]] ; [[#Burivalova--2019|Burivalova et al. 2019]] ). Efforts to expand property rights, especially community forest management, have reduced carbon emissions from deforestation in tropical forests in the last two decades ( ''high confidence'' ), although the extent of carbon savings has not been quantified globally.

'''Bioenergy targets.''' Multiple policies have been enacted at national and supra-national levels to promote the use of bioenergy in the transport sector, and for bioelectricity production. Existing policies mandate or subsidise the production and use of bioenergy. In the past few years, policies have been proposed, put in place or updated in Australia (Renewable Energy Target), Brazil (RenovaBio, Nationally Determined Contribution), Canada (Clean Fuel Standard), China (Biodiesel Industrial Development Policy, Biodiesel Fuel Blend Standard), the European Union (Renewable Energy Directive II), the USA (Renewable Fuel Standards), Japan (FY2030), Russia (Energy Strategy Bill 2035), India (Revised National Policy on Biofuels), and South Africa (Biofuels Regulatory Framework).

While current policies focus on bioenergy to decarbonise the energy system, some also contain provisions to minimise the potential environmental and social trade-offs from bioenergy production. For instance, the EU Renewable Energy Directive (EU-RED II) and US Renewable Energy Standard (US-RFS) assign caps on the use of biofuels, which are associated with indirect land-use change and food-security concerns. The Netherlands has a stringent set of 36 sustainability criteria to which the certified biomass needs to comply. The EU-RED II also sets a timeline for the complete phase-out of high-risk biofuels ( [[#7.4.4|Section 7.4.4]] ).

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<span id="voluntary-actions-and-agreements"></span>
==== 7.6.2.3 Voluntary Actions and Agreements ====

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'''Forest certification programs''' , such as Forest Sustainability Council (FSC) or Programme for the Endorsement of Forest Certification (PEFC), are consumer driven, voluntary programmes that influence timber harvesting practices, and may reduce emissions from forest degradation with reduced impact logging and other approaches ( ''medium confidence'' ). Forest certification has expanded globally to over 440 Mha ( [[#Kraxner--2017|Kraxner et al. 2017]] ). As the area of land devoted to certification has increased, the amount of timber produced from certified land has increased. In 2018, FSC accounted for harvests of 427 million m 3 and jointly FSC and PEFC accounted for 689 million m 3 in 2016 or around 40% of total industrial wood production ( [[#FAO--2018c|FAO 2018c]] ). There is evidence that reduced impact logging can reduce carbon losses in tropical regions ( [[#Pearson--2014|Pearson et al. 2014]] ; [[#Ellis--2019|Ellis et al. 2019]] ). However, there is conflicting evidence about whether forest certification reduces deforestation (e.g., [[#Blackman--2018|Blackman et al. 2018]] ; [[#Tritsch--2020|Tritsch et al. 2020]] ).

'''Supply chain management''' in the food sector encourages more widespread use of conservation measures in agriculture ( ''high confidence'' ). The number of private commitments to reduce deforestation from supply chains has greatly increased in recent years, with at least 865 public commitments by 447 producers, processors, traders, manufacturers and retailers as of December, 2020 ( [[#New%20York%20Declaration%20on%20Forests--2021|New York Declaration on Forests 2021]] ). Industry partnerships with NGOs, such as the Roundtable on Sustainable Palm Oil (RSPO), have become more widespread and visible in agricultural production. For example, RSPO certifies members all along the supply chain for palm oil and claims around 19% of total production. Similar sustainability efforts exist for many of the world’s major agricultural products, including soybeans, rice, sugar cane, and cattle.

There is evidence that the Amazon Soy Moratorium (ASM), an industry-NGO effort whereby large industry consumers agreed voluntarily not to purchase soybeans grown on land deforested after 2006, reduced deforestation in the legal Amazon ( [[#Nepstad--2014|Nepstad et al. 2014]] ; [[#Gibbs--2015|Gibbs et al. 2015]] ). However, recent studies have shown that some deforestation from the Amazon was displaced to the Cerrado (Brazilian savannas) region ( [[#Moffette--2021|Moffette and Gibbs 2021]] ), which is a global hotspot for biodiversity, and has significant carbon stocks. These results illustrate the importance of broadening the scope of supply chain management to minimise or eliminate displacement ( [[#Lima--2019|Lima et al. 2019]] ). In addition, while voluntary efforts may improve environmental outcomes for a time, it is not clear that they are sufficient to deliver long-term reductions in deforestation, given the increases in deforestation that have occurred in the Amazon in recent years (Box 7.9). Voluntary efforts would be more effective at slowing deforestation if they present stronger linkages to regulatory or other approaches ( [[#Lambin--2018|Lambin et al. 2018]] ).

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<span id="box-7.8-management-of-native-forests-by-the-menominee-people-in-north-america-and-lessons-from-forest-own-er-associations"></span>
=== Box 7.8 | Management of Native Forests by the Menominee people in North America and Lessons From Forest Owner Associations ===

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'''Summary of the case.''' Indigenous peoples include more than 5000 different peoples, with over 370 million people, in 70 countries on five continents ( [[#UNIPP--2012|UNIPP 2012]] ). For example, in Latin America and Caribbean, forests cover more than 80% of the area occupied by indigenous peoples (330 million hectares) ( [[#FAO%20and%20FILAC--2021|FAO and FILAC, 2021]] ) which points to their critical role for forest governance ( [[#Garnett--2018|Garnett et al. 2018]] ; [[#Fa--2020|Fa et al. 2020]] ). The Menominee people (Wisconsin, USA) practice sustainable forestry on their reservation according to a land ethic integral to the tribal identity. The Tribe calls themselves ‘The Forest Keepers’, recognising that the connection of their future to the sustainable management of the forest that allowed the forest volume standing today to be higher than when timber harvesting began more than 160 years ago. Management practices are based on continuous forest inventories ( [[#Mausel--2017|Mausel et al. 2017]] ).

'''Introduction to the case.''' Forest management and timber harvesting operations began shortly after the Menominee Indian Reservation was created by treaty in 1854. The Menominee reservation sits on about 95,000 ha of land in Wisconsin that spans multiple forest types and is more diverse than adjacent forests. The collectively maintained reservation has 87% of its land under sustained yield forestry.

'''Case description.''' The Tribe, in the 19th century, had already mastered vegetation manipulation with fire, sustainable forestry, multiple-use, ecosystem, and adaptive management. The centrepiece of the Tribe’s economy has been its forest product industry, Menominee Tribal Enterprises (MTE) (Pecore 1992). A balance between growth and removals and continuous forest inventories (CFI) are central for forest management for the past 160 years, aiming not at very large volumes, but at very high-quality trees. During this same period, more than 2.3 billion board feet have been harvested from the same area, equivalent to 0.3 m 3 ha –1 yr –1 .

'''Interactions and limitations.''' In 2013, the Menominee Tribe started a collaboration with the US Forest Service to implement climate adaptation measures. The Tribe actively works to reduce the risk of forest damage and decided to further promote diversity by planting tree seedlings adapted to a warming climate ( https://toolkit.climate.gov/case-studies/and-trees-will-last-forever ). However, new challenges are related to increasing pressures on forest ecosystems such as non-native insects, pathogens, weed invasions, and the costs for continuous forest inventories to support long-term forest management.

'''Identified lessons.''' The elements of sustainability are intertwined with Menominee history, culture, spirituality, and ethics. The balance between the environment, community, and economy for the short term as well as future generations is an example of protecting the entire environment as the Menominee land is a non-fragmented remnant of the prehistoric Lake States forest which has been dramatically reduced all around the reserve ( [[#Schabel--1997|Schabel and Pecore 1997]] ). These and other types of community forest owner associations exist all over the world. Examples are Södra in Sweden (with 52,000 forest owners) ( [[#Södra--2021|Södra, 2021]] ) or Waldbauernverband in North-Rhine Westphalia (with 150,000 forest owners and covering 585,000 ha) (AGDW-The Forest Owners, 2021). These are ways for small forest owners to educate, jointly put wood on the market, employ better forest management, use machinery together, and apply certification jointly. In this manner and with all their diversity of goals, they manage to maintain carbon sinks and stocks, while preserving biodiversity and producing wood.

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<span id="box-7.9-case-study-deforestation-control-in-the-b-razilian-amazon"></span>

=== Box 7.9 | Case Study: Deforestation Control in the Brazilian Amazon ===

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'''Summary'''

Between 2000 and 2004, deforestation rates in the Brazilian Legal Amazon (is a socio-geographic division containing all nine Brazilian states in the Amazon basin) increased from 18,226 to 27,772 km 2 yr –1 2008 ( [[#INPE--2021|INPE, 2021]] ). A set of public policies designed in participatory process involving federal government, states, municipalities, and civil society successfully reduced deforestation rates until 2012. However, deforestation rates increased after 2013, and particularly between 2019 and 2020. Successful deforestation control policies are being negatively affected by changes in environmental governance, weak law enforcement, and polarisation of the national politics.

Box 7.9 (Continued)

'''Background'''

In 2004, the Brazilian federal government started the Action Plan for Prevention and Control of Deforestation in the Legal Amazon (PPCDAm) (Ministry of Environment, Government of Brazil, 2018).

The PPCDAm was a benchmark for the articulation of forest conservation policies that included central and state governments, prosecutor offices, and the civil society. The decline in deforestation after 2008 is mostly attributed to these policy options. In 2012, deforestation rates decreased to 4,571 km 2 yr –1 .

'''Case description'''

Combating deforestation was a theme in several programs, government plans, and projects not being more restricted to the environmental agenda. This broader inclusion resulted from a long process of insertion and articulation in the government dating back to 2003 while elaborating on the Sustainable Amazon Plan. In May 2003, a historic meeting took place in an Amazonian city, with the president of the Republic, state governors, ministers, and various business leaders, civil institutions, and social movements. It was presented and approved the document entitled ‘Sustainable Amazonia – Guidelines and Priorities of the Ministry of Environment for the Sustainable Development of the Amazon Brazilian’, containing several guidelines for conservation and sustainable use in the region. At the meeting, the Union and some states signed a Cooperation Agreement aiming to elaborate a plan for the Amazon, to be widely discussed with the various sectors of the regional and national society (Ministerio do Meio Ambiente, MMA, 2013).

'''Interactions''' '''and limitations'''

The PPCDAm had three main lines of action: (i) territorial management and land use; (ii) command and control; and (iii) promotion of sustainable practices. During the execution of the 1st and 2nd phases of the PPCDAm (2004–2011), important results in the territorial management and land-use component included, for example, the creation of 25 Mha of federal Protected Areas (PAs) located mainly in front of the expansion of deforestation, as well as the homologation of 10 Mha of Indigenous Lands. Also, states and municipalities created approximately 25 Mha, so that all spheres of government contributed to the expansion of PAs in the Brazilian Amazon. In the ‘command and control’ component, agencies performed hundreds of inspection operations against illegal activities (e.g., illegal logging) under strategic planning based on technical and territorial priorities. Besides, there was a significant improvement of the environmental monitoring systems, involving the analysis of satellite images to guide actions on the ground. Another policy was the restriction of public credit to enterprises linked to illegal deforestation following a resolution of the Brazilian Central Bank (2008) (Ministerio do Meio Ambiente, MMA, 2013). Also, in 2008, Brazil created the Amazon Fund, a REDD+ mechanism (Government of Brazil, n.d.).

However, the country’s political polarisation has gradually eroded environmental governance, especially after the Brazilian Forest Code changes in 2012 (major environmental law in Brazil), the presidential impeachment in 2016, presidential elections in 2018, and the start of the new federal administration in 2019. Successful deforestation control policies are being negatively affected by critical changes in the political context, and weakening the environmental rule of law, forest conservation, and sustainable development programmes (for example, changes in the Amazon Fund governance in disagreement with the main donors). In 2019, the annual deforestation rate reached 10,129 km 2 being the first time it surpassed 10,000 km 2 since 2008 ( [[#INPE--2021|INPE, 2021]] ). Besides, there has been no effective transition from the historical economic model to a sustainable one. The lack of clarity in the ownership of land is still a major unresolved issue in the Amazon.

'''Lessons'''

The reduction of deforestation in the Brazilian Amazon was possible due to effective political and institutional support for environmental conservation. The initiatives of the Action Plan included the expansion of the protected areas network (conservation unities and indigenous lands), improvement of deforestation monitoring to the enforcement of environmental laws, and the use of economic instruments, for example, by cutting off public credit for municipalities with higher deforestation rates ( [[#Ricketts--2010|Ricketts et al. 2010]] ; [[#Souza--2013|Souza et al. 2013]] ; [[#Nepstad--2014|Nepstad et al. 2014]] ; [[#Arima--2014|Arima et al. 2014]] ; Blackman and Veit 2018).

The array of public policies and social engagement was a historical and legal breakthrough in global protection. However, the broader political and institutional context and actions to reduce the representation and independent control of civil society movements in decision-making bodies weaken this structure with significant increases in deforestation rates, burnings, and forest fires.

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<span id="box-7.10-regreening-the-sahel-northern-africa"></span>

=== Box 7.10 | Regreening the Sahel, Northern Africa ===

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'''Case description'''

More than 200 million trees have regenerated on more than 5 Mha in the Sahel ( [[#Sendzimir--2011|Sendzimir et al. 2011]] ). The Maradi/Zinder region of Niger is the epicentre of experimentation and scale up. This vast geographic extent generates significant mitigation potential despite the relatively modest per unit area increase in carbon of about 0.4 MgC ha –1 a –1 ( [[#Luedeling--2012|Luedeling and Neufeldt 2012]] ). In addition to the carbon benefits, these agroforestry systems decrease erosion, provide animal fodder, recharge groundwater, generate nutrition and income benefits and act as safety nets for vulnerable rural households during climate and other shocks ( [[#Bayala--2014|Bayala et al. 2014]] , 2015; [[#Binam--2015|Binam et al. 2015]] ; [[#Sinare--2015|Sinare and Gordon 2015]] ; [[#Ilstedt--2016|Ilstedt et al. 2016]] ).

'''Lessons'''

A mélange of factors contributed to regreening in the Sahel. Increased precipitation, migration, community development, economic volatility and local policy reform have all likely played a role ( [[#Haglund--2011|Haglund et al. 2011]] ; [[#Sendzimir--2011|Sendzimir et al. 2011]] ; [[#Brandt--2019a|Brandt et al. 2019a]] ; Garrity and Bayala 2019); the easing of forestry regulations has been particularly critical in giving farmers greater control over the management and use of trees on their land ( [[#Garrity--2010|Garrity et al. 2010]] ). This policy shift was catalysed by greater regional autonomy resulting from economic decline and coincided with successful pilots and NGO-led experimentation, cash-for-work, and training efforts to support changes in land management ( [[#Sendzimir--2011|Sendzimir et al. 2011]] ). Participation of farmers in planning and implementation helped align actions with local knowledge and goals as well as market opportunities.

Regreening takes place when dormant seed or tree stumps sprout and are cultivated through the technique, called Farmer Managed Natural Regeneration (FMNR). Without planting new trees, FMNR is presumed to be cheaper than other approaches to restoration, though comparative economic analysis has yet to be conducted ( [[#Chomba--2020|Chomba et al. 2020]] ). Relatively lower investment costs are believed to have contributed to the replication across the landscape. These factors worked together to contribute to a groundswell of action that affected rights, access, and use of local resources ( [[#Tougiani--2009|Tougiani et al. 2009]] ).

Regreening in the Sahel and the consequent transformation of the landscape has resulted from the actions of hundreds of thousands of individuals responding to social and biophysical signals ( [[#Hanan--2018|Hanan 2018]] ). This is an example for climate change mitigation, where eliminating regulations – versus increasing them – has led to carbon dioxide removal.

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<span id="mitigation-effectiveness-additionality-permanence-and-leakage"></span>
==== 7.6.2.4 Mitigation Effectiveness: Additionality, Permanence and Leakage ====

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Additionality, permanence and leakage have been widely discussed in the forestry and agricultural mitigation literature ( [[#Murray--2007|Murray et al. 2007]] ), including in AR5 ( [[IPCC:Wg3:Chapter:Chapter-11#11.3.2|Section 11.3.2]] of the AR5WGIII report) and earlier assessment reports. Since the earlier assessment reports, new studies have emerged to provide new insights on the effect of these issues on the credibility of forest and agricultural mitigation. This assessment also provides additional context not considered in earlier assessments.

Typically, carbon registries will require that project developers show additionality by illustrating that the project is not undertaken as a result of a legal requirement, and that the project achieves carbon reductions above and beyond a business as usual. The protocols developed by the California Air Resources Board to ensure permanence and additionality are strong standards and may even limit participation (e.g., [[#Ruseva--2017|Ruseva et al. 2017]] ). The business as usual is defined as past management actions by the same entity that can be verified. Additionality can thus be observed in the future as a difference from historical actions. This approach has been used by several countries in their UNFCCC Biennial Update reports to establish reductions in carbon emissions from avoided deforestation (e.g., Brazil and Indonesia).

However, alternative statistical approaches have been deployed in the literature to assess additionality with a quasi-experimental method that rely on developing a counterfactual (e.g., [[#Andam--2008|Andam et al. 2008]] ; [[#Blackman--2015|Blackman 2015]] ; [[#Sills--2015|Sills et al. 2015]] ; [[#Fortmann--2017|Fortmann et al. 2017]] ; [[#Roopsind--2019|Roopsind et al. 2019]] ). In several studies, additionality in avoided deforestation was established after the project had been developed by comparing land-use change in treated plots where the policy or programme was in effect with land-use change in similar untreated plot. Alternatively, synthetic matching statistically compares trends in a treated region (i.e., a region with a policy) to trends in a region without the policy, and has been applied in a region in Brazil (e.g., [[#Sills--2015|Sills et al. 2015]] ), and at the country level in Guyana ( [[#Roopsind--2019|Roopsind et al. 2019]] ). While these analyses establish that many projects to reduce deforestation have overcome hurdles related to additionality ( ''high confidence'' ), there has not been a systematic assessment of the elements of project or programme design that lead to high levels of additionality. Such assessment could help developers design projects to better meet additionality criteria.

The same experimental methods have been applied to analyse additionality of the adoption of soil conservation and nutrient management practices in agriculture. [[#Claassen--2018|Claassen et al. (2018)]] find that programmes to promote soil conservation are around 50% additional across the USA (i.e., 50% of the land enrolled in soil conservation programmes would not have been enrolled if not for the programme), while [[#Woodward--2016|Woodward et al. (2016)]] find that adoption of conservation tillage is rarely additional. [[#Claassen--2018|Claassen et al. (2018)]] find that payments for nutrient management plans are nearly 100% additional, although there is little evidence that farmers reduce nutrient inputs when they adopt plans. It is not clear if the same policy approaches would lead to additionality in other regions.

Permanence focuses on the potential for carbon sequestered in offsets to be released in the future due to natural or anthropogenic disturbances. Most offset registries have strong permanence requirements, although they vary in their specific requirements. The Verified Carbon Standard (VCS) from the Verra programme requires a pool of additional carbon credits that provides a buffer against inadvertent losses. The Climate Action Reserve (CAR) protocol for forests requires carbon to remain on the site for 100 years. The carbon on the site will be verified at pre-determined intervals over the life of the project. If carbon is diminished on a given site, the credits for the site have to be relinquished and the project developer has to use credits from their reserve fund (either other projects or purchased credits) to make up for the loss. Estimates of leakage in forestry projects in AR5 suggest that it can range from 10% to over 90% in the USA ( [[#Murray--2004|Murray et al. 2004]] ), and 20–50% in the tropics (Sohngen and Brown 2004) for forest set-asides and reduced harvesting. Carbon offset protocols have made a variety of assumptions. The Climate Action Reserve (CAR) assumes it is 20% in the USA. One of the voluntary protocols (Verra) uses specific information about the location of the project to calculate a location specific leakage factor.

More recent literature has developed explicit estimates of leakage based on statistical analysis of carbon projects or programs. The literature suggests that there are two economic pathways for leakage (e.g., ( [[#Roopsind--2019|Roopsind et al. 2019]] ), either through a shift in output price that occurs when outputs are affected by the policy or programme implementation, as described in ( [[#Wear--2004|Wear and Murray 2004]] ; [[#Murray--2004|Murray et al. 2004]] ; Sohngen and Brown 2004; [[#Gan--2007|Gan and McCarl 2007]] ), or through a shift in input prices and markets, such as for labour or capital, as analysed in ( [[#Andam--2008|Andam et al. 2008]] ; [[#Alix-Garcia--2012|Alix-Garcia et al. 2012]] ; Honey-Rosés et al. 2011; [[#Fortmann--2017|Fortmann et al. 2017]] ). Estimates of leakage through product markets (e.g., timber prices) have suggested leakage of up to 90% (Sohngen and Brown 2004; [[#Murray--2004|Murray et al. 2004]] ; [[#Gan--2007|Gan and McCarl 2007]] ; Kallio et al. 2018), while studies that consider shifts in input markets are considerably smaller. The analysis of leakage for the Guyana programme by [[#Roopsind--2019|Roopsind et al. (2019)]] revealed no statistically significant leakage in Suriname. A key design feature for any programme to reduce leakage is to increase incentives for complementary mitigation policies to be implemented in areas where leakage may occur. Efforts to continue to draw more forests into carbon policy initiatives will reduce leakage over time [[#Roopsind--2019|Roopsind et al. (2019)]] , suggesting that if NDCs continue to encompass a broader selection of policies, measures and forests over time, leakage will decline.

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<span id="assessment-of-current-policies-and-potential-future-approaches"></span>

=== 7.6.3 Assessment of Current Policies and Potential Future Approaches ===

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The Paris Agreement encourages a wide range of policy approaches, including REDD+, sustainable management of forests, joint mitigation and adaptation, and emphasises the importance of non-carbon benefits and equity for sustainable development ( [[#Martius--2016|Martius et al. 2016]] ). Around USD0.7 billion yr –1 has been invested in land-based carbon offsets (Table 7.4), but as noted in [[#Streck--2012|Streck (2012)]] , there is a large funding gap between these efforts and the scale of efforts necessary to meet 1.5 or 2.0°C targets outlined in SR1.5. As Box 7.12 discusses, forestry actions could achieve up to 5.8 GtCO 2 yr –1 with costs rising from USD178 billion yr –1 to USD400 billion yr –1 by 2050. Over half of this investment is expected to occur in Latin America, with 13% in SE Asia and 17% in sub-Saharan Africa ( [[#Austin--2020|Austin et al. 2020]] ). Other studies have suggested that similar sized programmes are possible, although they do not quantify total costs (e.g., [[#Griscom--2017|Griscom et al. 2017]] ''';''' [[#Busch--2019|Busch et al. 2019]] ). The currently quantified efforts to reduce net emissions with forests and agricultural actions are helpful, but society will need to quickly ramp up investments to achieve carbon sequestration levels consistent with high levels of mitigation. Only 2.5% of climate mitigation funding goes to land-based mitigation options, an order of magnitude below the potential proportional contribution ( [[#Buchner--2015|Buchner et al. 2015]] ).

To date, there has been significantly less investment in agricultural projects than forestry projects to reduce net carbon emissions (Table 7.4). For example, the economic potential (available up to USD100 tCO 2 –1 ) for soil carbon sequestration in croplands is 1.9 (0.4–6.8) GtCO 2 yr –1 ( [[#7.4.3.1|Section 7.4.3.1]] ), however, less than 2% of the carbon in Table 7.4 is derived from soil carbon sequestration projects. While reductions in CH 4 emissions due to enteric fermentation constitute a large share of potential agricultural mitigation reported in [[#7.4|Section 7.4]] , agricultural CH 4 emission reductions so far have been relatively modest compared to forestry sequestration. The protocols to quantify emission reductions in the agricultural sector are available and have been tested, and the main limitation appears to be the lack of available financing or the unwillingness to re-direct current subsidies ( ''medi'' ''um confidence'' ).

Although quantified emission reductions in agricultural projects are limited to date, a number of OECD and economy in transition parties [https://agresearchnz-my.sharepoint.com/personal/jeremy_emmet-booth_nzagrc_org_nz/Documents/Downloads/Section%207.6%20revised%20(Nov%2016).docx#_msocom_2] have reduced their net emissions through carbon storage in cropland soils since 2000. These reductions in emissions have typically resulted from policy innovations outside of the climate space, or market trends. For example, in the USA, there has been widespread adoption of conservation tillage in the last 30 years as a labour-saving crop management technique. In Europe, agricultural N 2 O and CH 4 emissions have declined due to reductions in nutrient inputs and cattle numbers ( [[#Henderson--2020|Henderson et al. 2020]] ). These reductions may be attributed to mechanism within the Common Agricultural Policy ( [[#7.6.2.1|Section 7.6.2.1]] ), but could also be linked to higher nutrient prices in the 2000–2014 period. Other environmental policies could play a role, for example, efforts to reduce water pollution from phosphorus in The Netherlands, may ultimately reduce cattle numbers, also lowering CH 4 emissions.

Numerous developing countries have established policy efforts to abate agricultural emissions or increase carbon storage. Brazil, for instance, developed a subsidy programme in 2010 to promote sustainable development in agriculture, and practices that would reduce GHG emissions. [[#Henderson--2020|Henderson et al. (2020)]] report that this programme reduced GHG emission in agricultural by up to 170 MtCO 2 between 2010 and 2018. However, the investments in low-carbon agriculture in Brazil amounted only 2% of the total funds for conventional agriculture in 2019. Programmes on deforestation in Brazil had successes and failures, as described in Box 7.9. Indonesia has engaged in a wide range of programmes in the REDD+ space, including a moratorium implemented in 2011 to prevent the conversion of primary forests and peatlands to oil palm and logging concessions ( [[#Wijaya--2017|Wijaya et al. 2017]] ; [[#Tacconi--2019|Tacconi and Muttaqin 2019]] ; [[#Henderson--2020|Henderson et al. 2020]] ). Efforts to restore peatlands and forests have also been undertaken. Indonesia reports that results-based REDD+ programmes have been successful and have led to lower rates of deforestation (Table 7.4).

Existing policies focused on GHG management in agriculture and forestry is less advanced in Africa than in Latin American and Asia, however, [[#Henderson--2020|Henderson et al. (2020)]] report on 10 countries in sub-Saharan Africa that have included explicit policy proposals for reducing AFOLU GHG emissions through their NDCs. These include efforts to reduce N 2 O emission, increase implementation of conservation agriculture, improve livestock management, and implement forestry and grassland practices, including agroforestry (Box 7.10). Within several of the NDCs, countries have explicitly suggested intensification as an approach to reduce emission in the livestock sector. However, it is important to note caveats associated with pursuing mitigation via intensification (Box 7.11).

The agricultural sector throughout the world is influenced by many policies that affect production practices, crop choices and land use. It is difficult to quantify the effect of these policies on reference level GHG emissions from the sector, as well as the cost estimates presented in Sections 7.4 and 7.5. The presence of significant subsidy programmes intended to improve farmer welfare and rural livelihoods makes it more difficult to implement regulatory programmes aimed at reducing net emissions in agriculture, however, it may increase the potential to implement new subsidy programmes that encourage practices aimed at reducing net emissions ( ''medium confidence'' ). For instance, in the USA, crop insurance can influence both crop choices and land use ( [[#Miao--2016|Miao et al. 2016]] ; [[#Claassen--2017|Claassen et al. 2017]] ), both of which will affect emission trends. Regulations to limit nutrient applications have not been widely considered, however, federal subsidy programmes have been implemented to encourage farmers to conduct nutrient management planning.

A factor that will influence future carbon storage in so-called land-based reservoirs involves considering short- and long-term climate benefits, as well as interactions among various natural climate solution options. The benefits of various natural climate solutions depend on a variety of spatially dependent issues as well as institutional factors, including their management status (managed or unmanaged systems), their productivity, opportunity costs, technical difficulty of implementation, local willingness to consider, property rights and institutions, among other factors. Biomass energy, as described elsewhere in this chapter and in (Cross-Working Group Box 3 in Chapter 12), is a potential example of an option with trade-offs that emerge when policies favour one type of mitigation strategy over another. Bioenergy production needs safeguards to limit negative impacts on carbon stocks on the land base as is already in place in the EU Renewable Energy Directive and several national schemes in Netherlands, UK and Denmark ( [[#Buchholz--2016|Buchholz et al. 2016]] ; [[#Khanna--2017|Khanna et al. 2017]] ; [[#DeCicco--2018|DeCicco and Schlesinger 2018]] ; [[#Favero--2020|Favero et al. 2020]] ). It is argued that a carbon tax on only fossil fuel derived emissions, may lead to massive deployment of bioenergy, although the effects of such a policy can be mitigated when combined with policies that encourage sustainable forest management and protection of forest carbon stocks as well as forest management certification ( ''high confidence'' ) ( [[#Nabuurs--2017|Nabuurs et al. 2017]] , [[#Baker--2019|Baker et al. 2019]] and [[#Favero--2020|Favero et al. 2020]] ).

If biomass energy production expands and shifts to carbon capture and storage (e.g., BECCS) during the century, there could be a significant increase in the area of crop and forestland used for biomass energy production (Sections 7.4 and 7.5). BECCS is not projected to be widely implemented for several decades, but in the meantime, policy efforts to advance land-based measures including reforestation and restoration activities ( [[#Strassburg--2020|Strassburg et al. 2020]] ) combined with sustainable management and provision of agricultural and wood products are widely expected to increase the terrestrial pool of carbon (Cross-Working Group Box 3 in Chapter 12). Carbon sequestration policies, sustainable land management (forest and agriculture), and biomass energy policies can be complementary ( [[#Favero--2017|Favero et al. 2017]] ; [[#Baker--2019|Baker et al. 2019]] ). However, if private markets emerge for biomass and BECCS on the scale suggested in the SR1.5, policy efforts must ramp up to substantially value, encourage, and protect terrestrial carbon stocks and ecosystems to avoid outcomes inconsistent with many SDGs ( ''high confidence'' ).

<div id="box-7.12" class="h2-container box-container"></div>

<span id="box-7.12-financing-afolu-mitigation-what-are-the-cost-s-and-who-pays"></span>
=== Box 7.12 | Financing AFOLU Mitigation; What Are the Costs and Who Pays? ===

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Achieving the large contribution to mitigation that the AFOLU sector can make requires public and private investment. [[#Austin--2020|Austin et al. (2020)]] estimate that in forestry, USD178 billion yr –1 is needed over the next decade to achieve 5 GtCO 2 yr –1 , and investments need to ramp up to USD400 billion yr –1 by 2050 to expand effort to 6 GtCO 2 yr –1 . Other land-based options, such as mangrove protection, peatland restoration, and agricultural options would increase this total cost estimate, but have smaller to negligible opportunity costs.

Financing needs in AFOLU, and in particular in forestry, include both the direct effects of any changes in activities – costs of planting or managing trees, net revenues from harvesting, costs of thinning, costs of fire management, and so on – as well as the opportunity costs associated with land-use change. Opportunity costs are a critical component of AFOLU finance, and must be included in any estimate of the funds necessary to carry out projects. They are largest, as share of total costs, in forestry because they play a prominent role in achieving high levels of afforestation, avoided deforestation, and improved forest management. In case of increasing soil carbon in croplands through reduced tillage, there are often cost savings associated with increased residues because there is less effort tilling, but the carbon effects per hectare are also modest. There could, however, be small opportunity costs in cases where residues may otherwise be marketed to a biorefinery. The effect of reduced tillage on yields varies considerably across sites and crop types, but tends to enhance yields modestly in the longer-run.

Opportunity costs are a direct financing costs for activities that require land uses to change. For instance a government can encourage planting forests on agricultural land by (a) requiring it, (b) setting up a market or market-based incentives, or (c) buying the land and doing it themselves. In each case, the required investment is the same – the planting cost plus the net foregone returns of agricultural rents – even though a different entity pays the cost. Private entities that pay for carbon credits will also bear the direct costs of planting plus the opportunity costs. In the case of avoided deforestation, opportunity costs similarly must be paid to individual actors to avoid the deforestation.

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<span id="box-7.11-sustainable-intensification-within-agriculture-evide-nce-and-caveats"></span>

=== Box 7.11 | Sustainable Intensification Within Agriculture: Evidence and Caveats ===

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'''Introduction'''

Sustainable intensification (SI) has received considerable attention as a suggested means of pursuing increased overall production, reducing associated environmental externalities, and potentially releasing agricultural land for alternative uses, such as forestry or rewilding (Godfray and [[#Garnett--2014|Garnett 2014]] ; [[#Pretty--2018|Pretty 2018]] ). The concept was explored within the SRCCL (SRCCL ( [[#Mbow--2019|Mbow et al. 2019]] ), [[IPCC:Wg3:Chapter:Chapter-5#5.6.4|Section 5.6.4]] .4 and Cross-Chapter Box 6 in Chapter 5). SI is context specific and dynamic, with no universally prescribed methodology ( [[#HLPE--2019|HLPE 2019]] ). Equal importance is given to enhancing sustainability as to achieving agricultural intensification. The former aspect is often challenging to realise, measure and maintain.

'''The extent of sustainable''' '''intensification'''

Total global agricultural land area has remained relatively stable while overall production has increased in recent decades ( [[#7.3|Section 7.3]] ), indicating that agricultural intensification, as judged by production per unit of land ( [[#Petersen--2015|Petersen and Snapp 2015]] ; OECD and FAO 2019) has taken place. However, changes in agricultural land use and degradation of natural resources ( [[#UNEP--2019|UNEP 2019]] ; [[#IPBES--2019b|IPBES 2019b]] ) suggests that not all of this intensification is sustainable. Although agricultural intensification has led to less GHG emissions compared to a scenario where that intensification had not taken place ( [[#Burney--2010|Burney et al. 2010]] ), absolute agriculture related emissions have continued to increase ( [[#7.2|Section 7.2]] ). Active pursuit of SI was found to be expanding, with implementation on an increasing area, notably in developing countries ( [[#Pretty--2018|Pretty et al. 2018]] ), yet regional agricultural area expansion at the expense of native habitat also continues in such regions ( [[#7.3|Section 7.3]] ). Although there are specific examples of SI (Box 7.13) global progress in achieving SI is acknowledged as slow ( [[#Cassman--2020|Cassman and Grassini 2020]] ) with potentially multiple, context specific geophysical and socio-economic barriers to implementation ( [[#Firbank--2018|Firbank et al. 2018]] ; [[#da%20Silva--2021|da]] [[#Silva--2021|Silva et al. 2021]] ).

'''Preconditions to ensure sustainable''' '''intensification'''

'''Increasing the total amount of product produced by improving production efficiency (output per unit of input) does not guarantee SI.''' It will only be successful if increased production efficiency translates into reduced environmental and social impacts as well as increased production. For example, AR5 highlighted a growing emphasis on reducing GHG emissions per unit of product via increasing production efficiency (Smith et al. 2014), but reductions in absolute GHG emissions will only occur when production efficiency increases at a greater rate than the rate at which production increases ( [[#Clark--2005|Clark et al. 2005]] ).

'''Defined indicators are required.''' Measurement of SI requires multiple indicators and metrics. It can be assessed at farm, regional or global scales and temporal aspect must be considered. SI may warrant whole system redesign or land reallocation ( [[#Garnett--2013|Garnett et al. 2013]] ; [[#Pretty--2018|Pretty et al. 2018]] ). Accordingly, there is ''high agreement'' concerning the need to consider multiple environmental and social outcomes

at wider spatial scales, such as catchments or regions ( [[#Weltin--2018|Weltin et al. 2018]] ; [[#Bengochea%20Paz--2020|Bengochea Paz et al. 2020]] ; [[#Cassman--2020|Cassman and Grassini 2020]] ). Impacts may be considered in relative terms (per area or product unit), with relationships potentially antagonistic. Both area- and product unit-based metrics are valid, relevant under different contexts and useful in approaching SI, but do not capture overall impacts and trade-offs ( [[#Garnett--2014|Garnett 2014]] ). To reduce the risk of unsustainable intensification, quantitative data and selection of appropriate metrics to identify and guide strategies are paramount ( [[#Garnett--2013|Garnett et al. 2013]] ; [[#Gunton--2016|Gunton et al. 2016]] ; [[#Cassman--2020|Cassman and Grassini 2020]] ).

'''Avoiding unsustainable''' '''intensification'''

It is critical that intensification does not drive expansion of unsustainable practices. Increased productivity with associated economic reward could incentivise and reward agricultural land expansion, or environmentally and socially damaging practices on existing and former agricultural land ( [[#Ceddia--2013|Ceddia et al. 2013]] ; [[#Phalan--2018|Phalan 2018]] ). Accordingly, coordinated policies are crucial to ensuring desired outcomes (Godfray and [[#Garnett--2014|Garnett 2014]] ; [[#Reddy--2020|Reddy et al. 2020]] ; Kassam and [[#Kassam--2020|Kassam 2020]] ). [[#Barretto--2013|Barretto et al. (2013)]] found that in agriculturally consolidated areas, land-use intensification coincided with either a contraction of both cropland and pasture areas, or cropland expansion at the expense of pastures, both resulting in a stable farmed area. In contrast, in agricultural frontier areas, land-use intensification coincided with expansion of agricultural lands.

In conclusion, SI within agriculture is needed given the rising global population and the need to address multiple environmental and social externalities associated with agricultural activities. However, implementation requires strong stakeholder engagement, appropriate regulations, rigorous monitoring and verification and comprehensive outreach and knowledge exchange programmes.

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=== 7.6.4 Barriers and Opportunities for AFOLU Mitigation ===

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The AR5 and other assessments have acknowledged many barriers and opportunities to effective implementation of AFOLU measures. Many of these barriers and opportunities focus on the context in developing countries, where a significant portion of the world’s cost-effective mitigation exists, but where domestic financing for implementation is likely to be limited. The SSPs capture some of this context, and as a result, IAMs ( [[#7.5|Section 7.5]] ) exhibit a wide range of land-use outcomes, as well as mitigation potential. Potential mitigation, however, will be influenced by barriers and opportunities that are not considered by IAMs or by bottom-up studies reviewed here. For example, more efficient food production systems, or sustainable intensification within agriculture, and globalised trade could enhance the extent of natural ecosystems leading to lower GHG emissions from the land system and lower food prices ( [[#Popp--2017|Popp et al. 2017]] ), but this (or any) pathway will create new barriers to implementation and encourage new opportunities, negating potential benefits (Box 7.11). It is critically important to consider the current context in any country.

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==== 7.6.4.1 Socio-economic Barriers and Opportunities ====

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'''Design and coverage of financing mechanisms.''' The lack of resources thus far committed to implementing AFOLU mitigation, income and access to alternative sources of income in rural households that rely on agriculture or forests for their livelihoods remains a considerable barrier to adoption of AFOLU ( ''high confidence'' ). [[#7.6.1|Section 7.6.1]] illustrates that to date only USD0.7 billion yr –1 has been spent on AFOLU mitigation, well short of the more than USD400 billion yr –1 that would be needed to achieve the economic potential described in [[#7.4|Section 7.4]] . Despite long-term recognition that AFOLU can play an important role in mitigation, the ''economic incentives'' necessary to achieve AFOLU aspirations as part of the Paris Agreement or to maintain temperatures below 2.0°C have not emerged. Without quickly ramping up spending, the lack of funding to implement projects remains a substantial barrier ( ''high confidence'' ). Investments are critically important in the livestock sector, which has the highest emissions reduction potential among options because actions in the sector influence agriculture specific activities, such as enteric fermentation, as well as deforestation ( [[#Mayberry--2019|Mayberry et al. 2019]] ). In many countries with export-oriented livestock industries, livestock farmers control large swaths of forests or re-forestable areas. Incentive mechanisms and funding can encourage adoption of mitigation strategies, but funding is currently too low to make consistent progress.

'''Scale and accessibility of financing.''' The largest share of funding to date has been for REDD+, and many of the commitments to date suggest that there will be significant funding in this area for the foreseeable future. Funding for conservation programmes in OECD countries and China affects carbon, but has been driven by other objectives such as water quality and species protection. Considerably less funding has been available for agricultural projects aimed at reducing carbon emissions, and outside of voluntary markets, there do not appear to be large sources of funding emerging either through international organisations, or national programs. In the agricultural sector, funding for carbon must be obtained by redirecting existing resources from non-GHG conservation to GHG measures, or by developing new funding streams ( [[#Henderson--2020|Henderson et al. 2020]] ).

'''Risk and uncertainty.''' Most approaches to reduce emissions, especially in agriculture, require new or different technologies that involve significant time or financial investments by the implementing landholders. Adoption rates are often slow due to risk aversion among agricultural operators. Many AFOLU measures require carbon to be compensated to generate positive returns, reducing the likelihood of implementation without clear financial incentives. Research to show costs and benefits is lacking in most parts of the world.

'''Poverty.''' Mitigation and adaptation can have important implications for vulnerable people and communities, for example, mitigation activities consistent with scenarios examined in the SR1.5 could raise food and fiber prices globally (Section. 7.5). In the NDCs, 82 Parties included references to social issues (e.g., poverty, inequality, human well-being, marginalisation), with poverty the most cited factor (70 Parties). The number of hungry and food insecure people in the world is growing, reaching 821 million in 2017, or one in every nine people ( [[#FAO--2018b|FAO 2018b]] ), and two-thirds live in rural areas (Laborde Debucquet et al. 2020). Consideration of rural poverty and food insecurity is central in AFOLU mitigation because there are a large number of farms in the world (about 570 million), and most are smaller than 2 hectares. It is important to better understand how different mitigation policies affect the poor.

'''Cultural values and social acceptance.''' Barriers to adoption of AFOLU mitigation will be strongest where historical practices represent long-standing traditions ( ''high confidence'' ). Adoption of new mitigation practices, however, may proceed quickly if the technologies can be shown to improve crop yields, reduce costs, or otherwise improve livelihoods ( [[#Ranjan--2019|Ranjan 2019]] ). AR6 presents new estimates of the mitigation potential for shifts in diets and reductions in food waste, but given long-standing dietary traditions within most cultures, some of the strongest barriers exist for efforts to change diets ( ''medium confidence'' ). Furthermore, the large number of undernourished who may benefit from increased calories and meat will complicate efforts to change diets. Regulatory or tax approaches will face strong resistance, while efforts to use educational approaches and voluntary measures have limited potential to slow changes in consumption patterns due to free-riders, rebound effects, and other limitations. Food loss and waste occurs across the supply chain, creating significant challenges to reduce it ( [[#FAO--2019c|FAO 2019c]] ). Where food loss occurs in the production stage, in other words, in fields at harvest, there may be opportunities to align reductions in food waste with improved production efficiency, however, adoption of new production methods often requires new investments or changes in labour practices, both of which are barriers.

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==== 7.6.4.2 Institutional Barriers and Opportunities ====

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'''Transparent and accountable governance.''' Good governance and accountability are crucial for implementation of forest and agriculture mitigation. Effective nature-based mitigation will require large-scale estimation, modelling, monitoring, reporting and verification of GHG inventories, mitigation actions, as well as their implications for sustainable development goals and their interactions with climate change impacts and adaptation. Efforts must be made to integrate the accounting from projects to the country level. While global datasets have emerged to measure forest loss, at least temporarily (e.g., [[#Hansen--2013|Hansen et al. 2013]] ), similar datasets do not yet exist for forest degradation and agricultural carbon stocks or fluxes. Most developing countries have insufficient capacity to address research needs, modelling, monitoring, reporting and data requirements ( [[#Ravindranath--2017|Ravindranath et al. 2017]] ), compromising transparency, accuracy, completeness, consistency and comparability.

Opportunity for political participation of local stakeholders is barrier in most places where forest ownership rights are not sufficiently documented ( [[#Essl--2018|Essl et al. 2018]] ). Since incentives for self-enforcement can have an important influence on deforestation rates ( [[#Fortmann--2017|Fortmann et al. 2017]] ), weak governance and insecure property rights are significant barriers to introduction of forest carbon offset projects in developing countries, where many of the low-cost options for such projects exist (Gren and Zeleke 2016). Governance challenges exist at all levels of government, with poor coordination, insufficient information sharing, and concerns over accountability playing a prominent role within REDD+ projects and programmes ( [[#Ravikumar--2015|Ravikumar et al. 2015]] ). In some cases, governments are increasingly centralising REDD+ governance and limiting the distribution of governance functions between state and non-state actors ( [[#Zelli--2017|Zelli et al. 2017]] ; [[#Phelps--2010|Phelps et al. 2010]] ). Overlap and duplication in Forest Law Enforcement, Governance and Trade (FLEGT) and REDD+ also limits governance effectiveness ( [[#Gupta--2016|Gupta et al. 2016]] ).

'''Clear land tenure and land-use rights.''' Unclear property rights and tenure insecurity undermine the incentives to improve forest and agricultural productivity, lead to food insecurity, undermine REDD+ objectives, discourage adoption of farm conservation practices, discourage tree planting and forest management, and exacerbate conflict between different land users ( [[#Antwi-Agyei--2015|Antwi-Agyei et al. 2015]] ; [[#Felker--2017|Felker et al. 2017]] ; [[#Sunderlin--2018|Sunderlin et al. 2018]] ; [[#Borras--2018|Borras and Franco 2018]] ; [[#Riggs--2018|Riggs et al. 2018]] ; [[#Kansanga--2019|Kansanga and Luginaah 2019]] ). Some positive signs exist as over 500 million hectares of forests have been converted to community management with clear property rights in the past two decades ( [[#Rights%20and%20Resources%20Initiative--2018|Rights and Resources Initiative 2018]] ), but adoption of forest and agricultural mitigation practices will be limited in large remaining areas with unclear property rights ( [[#Gupta--2016|Gupta et al. 2016]] ).

'''Lack of institutional capacity.''' Institutional complexity, or lack thereof, represents a major challenge when implementing large and complex mitigation programmes (e.g., REDD+) in agriculture, forest and other land uses ( [[#Bäckstrand--2017|Bäckstrand et al. 2017]] ). Without sufficient capacity, many synergies between agricultural and forest programs, or mitigation and adaptation opportunities, may be missed ( [[#Duguma--2014|Duguma et al. 2014]] ). Another aspect of institutional complexity is the different biophysical and socio-economic circumstances as well as the public and private financial means involved in the architecture and implementation of REDD+ and other initiatives ( [[#Zelli--2017|Zelli et al. 2017]] ).

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==== 7.6.4.3 Ecological Barriers and Opportunities ====

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'''Availability of land and water.''' Climate mitigation scenarios in the two recent special reports (SR1.5 and SRCCL) that aim to limit global temperature increase to 2°C or less involve carbon dioxide (CO 2 ) removal from the atmosphere. To support large-scale CDR, these scenarios involve significant land-use change, due to afforestation/reforestation, avoided deforestation, and deployment of biomass energy with carbon capture and storage (BECCS). While a considerable amount of land is certainly available for new forests or new bioenergy crops, that land has current uses that will affect not only the costs, but also the willingness of current users or owners, to shift uses. Regions with private property rights and a history of market-based transactions may be the most feasible for land-use change or land management change to occur. Areas with less secure tenure or a land market with fewer transactions in general will likely face important hurdles that limit the feasibility of implementing novel nature-based solutions.

Implementation of nature-based solution may have local or regionally important consequences for other ecosystem services, some of which may be negative ( ''high confidence'' ). Land-use change has important implications for the hydrological cycle, and the large land-use shifts suggested for BECCS when not carried out in a carefully planned manner, are expected to increase water demands substantially across the globe ( [[#Stenzel--2019|Stenzel et al. 2019]] ; [[#Rosa--2020|Rosa et al. 2020]] ). Afforestation can have minor to severe consequences for surface water acidification, depending on site-specific factors and exposure to air pollution and sea-salts ( [[#Futter--2019|Futter et al. 2019]] ). The potential effects of coastal afforestation on sea-salt related acidification could lead to re-acidification and damage on aquatic biota.

'''Specific soil conditions, water availability, GHG emission-reduction potential as well as natural variability and resilience.''' Recent analysis by ( [[#Cook-Patton--2020|Cook-Patton et al. 2020]] ) illustrates large variability in potential rates of carbon accumulation for afforestation and reforestation options, both within biomes/ecozones and across them. Their results suggest that while there is large potential for afforestation and reforestation, the carbon uptake potential in land-based climate change mitigation efforts is highly dependent on the assumptions related to climate drivers, land use and land management, and soil carbon responses to land-use change. Less analysis has been conducted on bioenergy crop yields, however, bioenergy crop yields are also likely to be highly variable, suggesting that bioenergy supply could exceed or fall short of expectations in a given region, depending on site conditions.

The effects of climate change on ecosystems, including changes in crop yields, shifts in terrestrial ecosystem productivity, vegetation migration, wildfires, and other disturbances also will affect the potential for AFOLU mitigation. Climate is expected to reduce crop yields, increase crop and livestock prices, and increase pressure on undisturbed forest land for food production creating new barriers and increasing costs for implementation of many nature-based mitigation techniques ( ''medium confidence'' ) (IPCC AR6 WGII Chapter 5).

The observed increase in the terrestrial sink over the past half century can be linked to changes in the global environment, such as increased atmospheric CO 2 concentrations, N deposition, or changes in climate ( [[#Ballantyne--2012|Ballantyne et al. 2012]] ), though not always proven from ground-based information (Vandersleen et al. 2015). While the terrestrial sink relies on regrowth in secondary forests ( [[#Houghton--2017|Houghton and Nassikas 2017]] ), there is emerging evidence that the sink will slow in the Northern Hemisphere as forests age ( [[#Nabuurs--2013|Nabuurs et al. 2013]] ), although saturation may take decades ( [[#Zhu--2018|Zhu et al. 2018]] ). Forest management through replanting, variety selection, fertilisation, and other management techniques, has increased the terrestrial carbon sink over the last century ( [[#Mendelsohn--2019|Mendelsohn and Sohngen 2019]] ). Saturation of the sink in situ may not occur when, for example, substitution effects of timber usage are also considered.

Increasing concentrations of CO 2 are expected to increase carbon stocks globally, with the strongest effects in the tropics ( [[#Schimel--2015|Schimel et al. 2015]] ; [[#Kim--2017a|Kim et al. 2017a]] ) (IPCC AR6 WGII Chapter 5) and economic models suggest that future sink potential may be robust to the impacts of climate change ( [[#Tian--2018|Tian et al. 2018]] ). However, it is uncertain if this large terrestrial carbon sink will continue in the future ( [[#Aragão--2018|Aragão et al. 2018]] ), as it is increasingly recognised that gains due to CO 2 fertilisation are constrained by climate and increasing disturbances ( [[#Schurgers--2018|Schurgers et al. 2018]] ; [[#Duffy--2021|Duffy et al. 2021]] ) (IPCC AR6 WGII Chapter 5). Further, negative synergies between local impacts like deforestation and forest fires may interact with global drivers like climate change and lead to tipping points ( [[#Lovejoy--2018|Lovejoy and Nobre 2018]] ). Factors that reduce permanence or slow forest growth will drive up costs of forest mitigation measures, suggesting that climate change presents a formidable challenge to implementation of nature-based solutions beyond 2030 ( ''hi'' ''gh confidence'' ).

In addition to climate change, [[#Dooley--2018|Dooley and Kartha (2018)]] also note that technological and social factors could ultimately limit the feasibility of agricultural and forestry mitigation options, especially when deployed at large scale. Concern is greatest with widespread use of bioenergy crops, which could lead to forest losses ( [[#Harper--2018|Harper et al. 2018]] ). Deployment of BECCS and forest-based mitigation can be complementary ( [[#Favero--2017|Favero et al. 2017]] ; [[#Baker--2019|Baker et al. 2019]] ), but inefficient policy approaches could lead to net carbon emissions if BECCS replaces high-carbon content ecosystems with crops.

'''Adaptation benefits and biodiversity conservation.''' Biodiversity may improve resilience to climate change impacts as more-diverse systems could be more resilient to climate change impacts, thereby maintaining ecosystem function and preserving biodiversity ( [[#Hisano--2018|Hisano et al. 2018]] ). However, losses in ecosystem functions due species shifts or reductions in diversity may impair the positive effects of biodiversity on ecosystems. Forest management strategies based on biodiversity and ecosystems functioning interactions can augment the effectiveness of forests in reducing climate change impacts on ecosystem functioning ( ''high confidence'' ). In spite of the many synergies between climate policy instruments and biodiversity conservation, however, current policies often fall short of realising this potential ( [[#Essl--2018|Essl et al. 2018]] ).

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==== 7.6.4.4 Technological Barriers and Opportunities ====

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'''Monitoring, reporting, and verification.''' Development of satellite technologies to assess potential deforestation has grown in recent years with the release of 30 m data by [[#Hansen--2013|Hansen et al. (2013)]] , however, this data only captures tree cover loss, and increasing accuracy over time may limit its use for trend analysis ( [[#Ceccherini--2020|Ceccherini et al. 2020]] ; [[#Palahí--2021|Palahí et al. 2021]] ). Datasets on forest losses are less well developed for reforestation and afforestation. As [[#Mitchell--2017|Mitchell et al. (2017)]] point out, there has been significant improvement in the ability to measure changes in tree and carbon density on sites using satellite data, but these techniques are still evolving and improving and they are not yet available for widespread use.

Ground-based forest inventory measurements have been developed in many countries, most prominently in the Northern Hemisphere, but more and more countries are starting to develop and collect national forest inventories. Training and capacity building is going on in many developing countries under UNREDD and FAO programmes. Additional efforts to harmonise data collection methods and to make forest inventory data available to the scientific community would improve confidence in forest statistics, and changes in forest statistics over time. To some extent the Global Forest Biodiversity Initiative fills in this data gap ( [https://gfbi.udl.cat/ https://g fbi.udl.cat/] ).

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=== 7.6.5 Linkages to Ecosystem Services, Human Well-being and Adaptation (including SDGs) ===

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The linkage between biodiversity, ecosystem services, human well-being and sustainable development is widely acknowledged (Millenium Ecosystem Assesment 2005; [[#UNEP--2019|UNEP 2019]] ). Loss of biodiversity and ecosystem services will have an adverse impact on quality of life, human well-being and sustainable development (IPBES 2019a). Such losses will not only affect current economic growth but also impede the capacity for future economic growth.

Population growth, economic development, urbanisation, technology, climate change, global trade and consumption, policy and governance are key drivers of global environmental change over recent decades ( [[#Kram--2014|Kram et al. 2014]] ; [[#UNEP--2019|UNEP 2019]] ; [[#WWF--2020|WWF 2020]] ). Changes in biodiversity and ecosystem services are mainly driven by habitat loss, climate change, invasive species, over-exploitation of natural resources, and pollution (Millenium Ecosystem Assesment 2005). The relative importance of these drivers varies across biomes, regions, and countries. Climate change is expected to be a major driver of biodiversity loss in the coming decades, followed by commercial forestry and bioenergy production ( [[#OECD--2012|OECD 2012]] ; [[#UNEP--2019|UNEP 2019]] ). Population growth along with rising incomes and changes in consumption and dietary patterns, will exert immense pressure on land and other natural resources ( [[#IPCC--2019|IPCC 2019]] ). Current estimates suggest that 75% of the land surface has been significantly anthropogenically altered, with 66% of the ocean area experiencing increasing cumulative impacts and over 85% of wetland area lost (IPBES 2019a). As discussed, in [[#7.3|Section 7.3]] , land-use change is driven amongst others by agriculture, forestry (logging and fuelwood harvesting), infrastructural development and urbanisation, all of which may also generate localised air, water, and soil pollution (IPBES 2019a). Over a third of the world’s land surface and nearly three-quarters of available freshwater resources are devoted to crop or livestock production (IPBES 2019a). Despite a slight reduction in global agricultural area since 2000, regional agricultural area expansion has occurred in Latin America and the Caribbean, Africa and the Middle East ( [[#FAO--2019c|FAO 2019c]] ; OECD and FAO 2019). The degradation of tropical forests and biodiversity hotspots, endangers habitat for many threatened and endemic species, and reduces valuable ecosystem services. However, trends vary considerably by region. As noted in [[#7.3|Section 7.3]] , global forest area declined by roughly 178 Mha between 1990 and 2020 ( [[#FAO--2020a|FAO 2020a]] ), though the rate of net forest loss has decreased over the period, due to reduced deforestation in some countries and forest gains in others. Between 1990 to 2015, forest cover fell by almost 13% in South-East Asia, largely due to an increase in timber extraction, large-scale biofuel plantations and expansion of intensive agriculture and shrimp farms, whereas in North-East Asia and South Asia it increased by 23% and 6% respectively, through policy instruments such as joint forest management, payment for ecosystem services, and restoration of degraded forests (IPBES 2018b). It is lamenting that the area under natural forests which are rich in biodiversity and provide diverse ecosystem services decreased by 301 Mha between 1990 and 2020, decreasing in most regions except Europe and Oceania with largest losses reported in sub-Saharan Africa ( [[#FAO--2020a|FAO 2020a]] ). The increasing trend of mining in forest and coastal areas, and in river basins for extracting has had significant negative impacts on biodiversity, air and water quality, water distribution, and on human health ( [[#7.3|Section 7.3]] ). Freshwater ecosystems equally face a series of combined threats including from land-use change, water extraction, exploitation, pollution, climate change and invasive species (IPBES 2019a).

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==== 7.6.5.1 Ecosystem Services ====

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An evaluation of eighteen ecosystem services over the past five decades (1970–2019) found only four (agricultural production, fish harvest, bioenergy production and harvest of materials) to demonstrate increased performance, while the remaining fourteen, mostly concerning regulating and non-material contributions, were found to be in decline (IPBES 2019a). The value of global agricultural output (over USD3.54 trillion in 2018) had increased approximately threefold since 1970, and roundwood production (industrial roundwood and fuelwood) by 27%, between 1980 to 2018, reaching some 4 billion m 3 in 2018. However, the positive trends in these four ecosystem services does not indicate long-term sustainability. If increases in agricultural production are realised through forest clearance or through increasing energy-intensive inputs, gains are likely to be unsustainable in the long run. Similarly, an increase in fish production may involve overfishing, leading to local species declines which also impacts fish prices, fishing revenues, and the well-being of coastal and fishing communities ( [[#Sumaila--2020|Sumaila and Lam 2020]] ). Climate change and other drivers are likely to affect future fish catch potential, although impacts will differ across regions ( [[#Sumaila--2017|Sumaila et al. 2017]] ; [[#Domke--2019|Domke et al. 2019]] ).

The increasing trend in aquaculture production especially in South and South-East Asia through intensive methods affects existing food production and ecosystems by diverting rice fields or mangroves ( [[#Bhattacharya--2011|Bhattacharya and Ninan 2011]] ). Although extensive traditional fish farming of carp in central Europe can contribute to landscape management, enhance biodiversity and provide ecosystem services, there are several barriers to scale up production due to strict EU environmental regulations, vulnerability to extreme weather events, and to avian predators that are protected by EU laws, and disadvantages faced by small-scale enterprises that dominate the sector (European-Commission 2021). Bioenergy production may have high opportunity costs in land-scarce areas and compete with land used for food production which threatens food security and affects the poor and vulnerable. But these impacts will differ across scale, contexts and other factors.

Currently, land degradation is estimated to have reduced productivity in 23% of the global terrestrial area, and between USD235 billion and USD577 billion in annual global crop output is at risk because of pollinator loss (IPBES 2019a). The global trends reviewed above are based on data from 2000 studies. It is not clear whether the assessment included a quality control check of the studies evaluated and suffer from aggregation bias. For instance, a recent meta-analysis of global forest valuation studies noted that many studies reviewed had shortcomings such as failing to clearly mention the methodology and prices used to value the forest ecosystem services, double counting, data errors, and so on ( [[#Ninan--2013|Ninan and Inoue 2013]] ). Furthermore, the criticisms against the paper by ( [[#Costanza--1997|Costanza et al. 1997]] ), such as ignoring ecological feedbacks and non-linearities that are central to the processes that link all species to each other and their habitats, ignoring substitution effects may also apply to the global assessment ( [[#Smith--1997|Smith 1997]] ; [[#Bockstael--2000|Bockstael et al. 2000]] ; [[#Loomis--2000|Loomis et al. 2000]] ). Land degradation has had a pronounced impact on ecosystem functions worldwide (IPBES 2018e). Net primary productivity of ecosystem biomass and of agriculture is presently lower than it would have been under a natural state on 23% of the global terrestrial area, amounting to a 5% reduction in total global net primary productivity (IPBES 2018e). Over the past two centuries, soil organic carbon, an indicator of soil health, has seen an estimated 8% loss globally (176 GtC) from land conversion and unsustainable land management practices (IPBES 2018e). Projections to 2050 predict further losses of 36 GtC from soils, particularly in sub-Saharan Africa. These losses are projected to come from the expansion of agricultural land into natural areas (16 GtC), degradation due to inappropriate land management (11 GtC) and the draining and burning of peatlands (9 GtC) and melting of permafrost (IPBES 2018e). Trends in biodiversity measured by the global living planet index between 1970 to 2016 indicate a 68% decline in monitored population of mammals, birds, amphibians, reptiles, and fish ( [[#WWF--2020|WWF 2020]] ). FAO’s recent report on the state of the world’s biodiversity for food and agriculture points to an alarming decline in biodiversity for food and agriculture including associated biodiversity such as pollination services, microorganisms which are essential for production systems ( [[#FAO--2019d|FAO 2019d]] ). These suggest that overall ecosystem health is consistently declining with adverse consequences for good quality of life, human well-being, and sustainable development.

Although numerous studies have estimated the value of ecosystem services for different sites, ecosystems, and regions, these studies mostly evaluate ecosystem services at a single point in time ( [[#Costanza--1997|Costanza et al. 1997]] ; [[#Xue--2001|Xue and Tisdell 2001]] ; [[#Nahuelhual--2007|Nahuelhual et al. 2007]] ; [[#de%20Groot--2012|de Groot et al. 2012]] ; [[#Ninan--2016|Ninan and Kontoleon 2016]] ). The few studies that have assessed the trends in the value of ecosystem services provided by different ecosystems across regions and countries indicate a declining trend ( [[#Costanza--2014|Costanza et al. 2014]] ; [[#Kubiszewski--2017|Kubiszewski et al. 2017]] ). Land-use change is a major driver behind loss of biodiversity and ecosystem services in most regions (IPBES 2018b; IPBES 2018c, IPBES 2018d, [[#Rice--2018|Rice et al. 2018]] ). Projected impacts of land-use change and climate change on biodiversity and ecosystem services (material and regulating services) between 2015 to 2050 were assessed to have relatively less negative impacts under global sustainability scenarios as compared to regional competition and economic optimism scenarios (IPBES 2019a). The projected impacts were based on a subset of Shared Socio-economic Pathway (SSP) scenarios and GHG emissions trajectories (RCP) developed in support of IPCC assessments. There are synergies, trade-offs and co-benefits between ecosystem services and mitigation options with impacts on ecosystem services differing by scale and contexts ( ''high confidence'' ). Measures such as conservation agriculture, agroforestry, soil and water conservation, afforestation, adoption of silvopastoral systems, can help minimise trade-offs between mitigations options and ecosystem services ( [[#Duguma--2014|Duguma et al. 2014]] ). Climate-smart agriculture (CSA) is being promoted to enable farmers to make agriculture more sustainable and adapt to climate change (Box 7.4). However, experience with CSA in Africa has not been encouraging. For instance, a study of climate-smart cocoa production in Ghana shows that due to lack of tenure (tree) rights, bureaucratic and legal hurdles in registering trees in cocoa farms, and other barriers small cocoa producers could not realise the project benefits (Box 7.13). Experience of CSA in some other sub-Saharan African countries and other countries such as Belize too has been constrained by weak extension systems and policy implementation, and other barriers ( [[#Arakelyan--2017|Arakelyan et al. 2017]] ; [[#Kongsager--2017|Kongsager 2017]] ).

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==== 7.6.5.2 Human Well-being and Sustainable Development Goals ====

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Conservation of biodiversity and ecosystem services is part of the larger objective of building climate resilience and promoting good quality of life, human well-being and sustainable development. While two of the 17 SDGs directly relate to nature (SDGs 14 and 15 covering marine and terrestrial ecosystems and biodiversity), most other SDGs relating to poverty, hunger, inequality, health and well-being, clean sanitation and water, energy, and so on, are directly or indirectly linked to nature ( [[#Blicharska--2019|Blicharska et al. 2019]] ). A survey among experts to assess how 16 ecosystem services could help in achieving the SDGs relating to good environment and human well-being suggested that ecosystem services could contribute to achieving about 41 targets across 12 SDGs ( [[#Wood--2018|Wood et al. 2018]] ). They also indicated cross-target interactions and synergetic outcomes across many SDGs. Conservation of biodiversity and ecosystem services is critical to sustaining the well-being and livelihoods of poor and marginalised people, and indigenous communities who depend on natural resources ( ''high confidence'' ). Nature provides a broad array of goods and services that are critical to good quality of life and human well-being. Healthy and diverse ecosystems can play an important role in reducing vulnerability and building resilience to disasters and extreme weather events ( [[#SCBD--2009|SCBD 2009]] ; [[#The%20Royal%20Society%20Science%20Policy%20Centre--2014|The Royal Society Science Policy Centre 2014]] ; [[#Ninan--2017|Ninan and Inoue 2017]] ).

Current negative trends in biodiversity and ecosystem services will undermine progress towards achieving 80% (35 out of 44) of the assessed targets of SDGs related to poverty, hunger, health, water, cities, climate, oceans and land (IPBES 2019a). However, [[#Reyers--2020|Reyers and Selig (2020)]] note that the assessment by (IPBES 2019a) could only assess the consequences of trends in biodiversity and ecosystem services for 35 out of the 169 SDG targets due to data and knowledge gaps, and lack of clarity about the relationship between biodiversity, ecosystem services and SDGs.

Progress in achieving the 20 Aichi Biodiversity targets which are critical for realising the SDGs has been poor with most of the targets not being achieved or only partially realised ( [[#SCBD--2020|SCBD 2020]] ). There could be synergies and trade-offs between ecosystem services and human well-being. For instance, a study notes that although policy interventions and incentives to enhance supply of provisioning services (e.g., agricultural production) have led to higher GDP, it may have an adverse effect on the regulatory services of ecosystems ( [[#Kirchner--2015|Kirchner et al. 2015]] ). However, we are aware of the inadequacies of traditional GDP as an indicator of well-being. In this context the Dasgupta Biodiversity Review argues for using the inclusive wealth approach to accurately measure social well-being by tracking the changes in produced, human and natural capital ( [[#Dasgupta--2021|Dasgupta 2021]] ). Targets for nature (biodiversity and ecosystem services) should be refined so as to fit in with the metrics tracked by the SDGs (IPBES 2016; [[#Rosa--2017|Rosa et al. 2017]] ).

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==== 7.6.5.3 Land-based Mitigation and Adaptation ====

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Combined mitigation and adaptation approaches have been highlighted throughout [[#7.4|Section 7.4]] regarding specific measures. Land-based mitigation and adaptation to the risks posed by climate change and extreme weather events can have several co-benefits as well as help promote development and conservation goals. Land-based mitigation and adaptation will not only help reduce GHG emissions in the AFOLU sector, but measures are required to closely link up with adaptation. In the central 2°C scenario, improved management of land and more efficient forest practices, a reduction in deforestation and an increase in afforestation, would account for 10% of the total mitigation effort over 2015–2050 ( [[#Keramidas--2018|Keramidas et al. 2018]] ). If managed and regulated appropriately, the Land sector could become carbon-neutral as early as 2030–2035, being a key sector for emissions reductions beyond 2025 ( [[#Keramidas--2018|Keramidas et al. 2018]] ). Nature-based solutions (NBS) with safeguards has immense potential for cost-effective adaptation to climate change; but their impacts will vary by scale and contexts ( ''high confidence'' ). [[#Griscom--2017|Griscom et al. 2017]] estimate this potential to provide 37% of cost-effective CO 2 mitigation until 2030 needed to meet 2°C goals with likely co-benefits for biodiversity. However, due to the time lag for technology deployment and natural carbon gain this mitigation potential of NBS by 2030 or 2050 can be delayed or much lower than the estimated potential ( [[#Qin--2021|Qin et al. 2021]] ).

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=== Box 7.13 | Case Study: Climate-smart Cocoa Production in Ghana ===

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'''Policy objectives'''

i. To promote sustainable intensification of cocoa production and enhance the adaptive capacity of small cocoa producers.

ii. To reduce cocoa-induced deforestation and GHG emissions.

iii. To improve productivity, incomes, and livelihoods of smallholder cocoa producers.

'''Policy mix'''

The climate-smart cocoa (CSC) production programme in Ghana involved distributing shade tree seedlings that can protect cocoa plants from heat and water stress, enhance soil organic matter and water holding capacity of soils, and provide other assistance with agroforestry, giving access to extension services such as agronomic information and agrochemical inputs. The shade tree seedlings were distributed by NGOs, government extension agencies, and the private sector free of charge or at subsidised prices and was expected to reduce pressure on forests for growing cocoa plants. The CSC programme was mainly targeted at small farmers who constitute about 80% of total farm holdings in Ghana. Although the government extension agency (Cocobod) undertook mass spraying or pruning of cocoa farms they found it difficult to access the 800,000 cocoa smallholders spread across the tropical south of the country. The project brought all stakeholders together, in other words, the government, private sector, local farmers and civil society or NGOs to facilitate the sustainable intensification of cocoa production in Ghana. Creation of a community-based governance structure was expected to promote benefit sharing, forest conservation, adaptation to climate change, and enhanced livelihood opportunities.

'''Governance context'''

''Cr'' ''itical enablers''

The role assigned to local government mechanisms such as Ghana’s Community Resource Management Area Mechanisms (CREMAs) was expected to give a voice to smallholders who are an important stakeholder in Ghana’s cocoa sector. CREMAs are inclusive because authority and ownership of natural resources are devolved to local communities who can thus have a voice in influencing CSC policy thereby ensuring equity and adapting CSC to local contexts. However, ensuring the long-term sustainability of CREMAs will help to make them a reliable mechanism for farmers to voice their concerns and aspirations, and ensure their independence as a legitimate governance structure in the long run. The private sector was assigned an important role to popularise climate-smart cocoa production in Ghana. However, whether this will work to the advantage of smallholder cocoa producers needs to be seen.

Critical barriers

The policy intervention overlooks the institutional constraints characteristic of the cocoa sector in Ghana where small farmers are dominant and have skewed access to resources and markets. Lack of secure tenure (tree rights) where the ownership of shade trees and timber vests with the state, bureaucratic and legal hurdles to register trees in their cocoa farms are major constraints that impede realisation of the expected benefits of the CSC programme. This is a great disincentive for small cocoa producers to implement CSC initiatives and nurture the shade tree seedlings and undertake land improvement measures. The state marketing board has the monopoly in buying and marketing of cocoa beans including exports which impeded CREMAs or farming communities from directly selling their produce to MNCs and traders. However, many MNCs have been involved in setting up of CREMA or similar structures, extending premium prices and non-monetary benefits (access to credit, shade tree seedlings, agrochemicals) thus indirectly securing their cocoa supply chains. A biased ecological discourse about the benefits of climate-smart agriculture and sustainable intensive narrative, complexities regarding the optimal shade levels for growing cocoa, and dependence on agrochemicals are issues that affect the success and sustainability of the project intervention. Dominance of private sector players especially MNCs in the sector may be detrimental to the interests of smallholder cocoa producers ( [[#Nasser--2020|Nasser et al. 2020]] ).

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