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

== 7.3 Drivers ==

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Since AR5 several global assessments ( [[#IPBES--2018a|IPBES 2018a]] ; [[#NYDF%20Assessment%20Partners--2019|NYDF Assessment Partners 2019]] ; [[#UNEP--2019|UNEP 2019]] ; [[#IPCC--2019|IPCC 2019]] ) and studies (e.g., [[#Tubiello--2019|Tubiello 2019]] ; [[#Tian--2020|Tian et al. 2020]] ) have reported on drivers (natural and anthropogenic factors that affect emissions and sinks of the land-use sector) behind AFOLU emissions trends, and associated projections for the coming decades. The following analysis aligns with the drivers typology used by [[#IPBES--2019b|IPBES (2019b)]] and the Global Environmental Outlook ( [[#UNEP--2019|UNEP 2019]] ). Drivers are divided into direct drivers resulting from human decisions and actions concerning land use and land-use change, and indirect drivers that operate by altering the level or rate of change of one or more direct drivers. Although drivers of emissions in agriculture and FOLU are presented separately, they are interlinked, operating in many complex ways at different temporal and spatial scales, with outcomes depending on their interactions. For example, deforestation in tropical forests is a significant component of sectorial emissions. A review of deforestation drivers’ studies published between 1996 and 2013, indicated a wide range of factors associated with deforestation rates across many analyses and studies, covering different regions ( [[#Busch--2017|Busch and Ferretti-Gallon 2017]] ) (Figure 7.9). Higher agricultural prices were identified as a key driver of deforestation, while law enforcement, area protection, and ecosystem services payments were found to be important drivers of reduced deforestation, while timber activity did not show a consistent impact.

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[[File:c88d1809d645ea0e319b28ad77c3d985 IPCC_AR6_WGIII_Figure_7_9.png]]

'''Figure 7.9 | Association of driver variables with more or less deforestation.''' Source: reproduced with permission from [[#Busch--2017|Busch and Ferretti-Gallon (2017)]] .

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=== 7.3.1 Anthropogenic Direct Drivers: Deforestation, Conversion of Other Ecosystems, and Land Degradation ===

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The global forest area in 2020 is estimated at 4.1 billion ha, representing 31% of the total land area ( [[#FAO--2020a|FAO 2020a]] ). Most forests are situated in the tropics (45%), followed by boreal (27%), temperate (16%) and subtropical (11%) domains. Considering regional distribution of global forest area, Europe and the Russian Federation accounts for 25%, followed by South America (21%), North and Central America (19%), Africa (16%), Asia (15%) and Oceania (5%). However, a significant share (54%) of the world’s forest area concerns five countries – The Russian Federation, Brazil, Canada, the USA and China ( [[#FAO--2020a|FAO 2020a]] ). Forest loss rates differ among regions though the global trend is towards a net forest loss ( [[#UNEP--2019|UNEP 2019]] ). The global forest area declined by about 178 Mha in the 30 years from 1990 to 2020 ( [[#FAO--2020a|FAO 2020a]] ). The rate of net forest loss has decreased since 1990, a result of reduced deforestation in some countries and forest gains in others. The annual net loss of forest area declined from 7.8 Mha in 1990–2000, to 5.2 Mha in 2000–2010, to 4.7 Mha in 2010–2020, while the total growing stock in global forests increased ( [[#FAO--2020a|FAO 2020a]] ). The rate of decline in net forest loss during the last decade was due mainly to an increase in the rate of forest gain (i.e., afforestation and the natural expansion of forests).

Globally, the area of the more open, other wooded land is also of significant importance, with almost 1 billion hectares ( [[#FAO--2020a|FAO 2020a]] ). The area of other wooded land decreased by 30.6 Mha between 1990 and 2020 with larger declines between 1990–2000 ( [[#FAO--2020a|FAO 2020a]] ). There are still significant challenges in monitoring the area of other wooded land, largely associated with difficulties in measuring tree-canopy cover in the range of 5–10%.The global area of mangroves, one of the most productive terrestrial ecosystems ( [[#Neogi--2020a|Neogi 2020a]] ), has also experienced a significant decline ( [[#Thomas--2017|Thomas et al. 2017]] ; [[#Neogi--2020b|Neogi 2020b]] ), with a decrease of 1.0 Mha between 1990 and 2020 ( [[#FAO--2020a|FAO 2020a]] ) due to agriculture and aquaculture ( [[#Bhattarai--2011|Bhattarai 2011]] ; [[#Ajonina--2014|Ajonina et al. 2014]] ; [[#Webb--2014|Webb et al. 2014]] ; [[#Giri--2015|Giri et al. 2015]] ; [[#Thomas--2017|Thomas et al. 2017]] ; Fauzi et al. 2019). Some relevant direct drivers affecting emissions and removal in forests and other ecosystems are discussed in proceeding sections.

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==== 7.3.1.1 Conversion of Natural Ecosystems to Agriculture ====

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Previous IPCC reports identify land-use change as an important driver of emissions and agriculture as a key driver of land-use change, causing both deforestation and wetland drainage (P. [[#Smith--2019|Smith et al. 2019]] a). The AR5 reported a trend of declining global agricultural land area since 2000 (Smith et al. 2014). The latest data ( [[#FAO--2021b|FAO 2021b]] ) indicate a 2% reduction in the global agricultural area between 2000 and 2019 (Figure 7.10). This area includes (though is not limited to) land under permanent and temporary crops or pasture, temporary fallow and natural meadows and pasture utilised for grazing or agricultural purposes ( [[#FAO--2021b|FAO 2021b]] ), although the extent of land used for grazing may not be fully captured ( [[#Fetzel--2017|Fetzel et al. 2017]] ). Data indicate changes in how agricultural land is used. Between 2000 and 2019, the area classified as permanent meadow and pasture decreased (–6%) while cropland area (under arable production and temporary crops) increased (+2%). A key driver of this change has been a general trend of intensification, including in livestock production (Barger et al. 2018; [[#OECD/FAO--2019|OECD/FAO 2019]] ; [[#UNEP--2019|UNEP 2019]] ), whereby less grazing land is supporting increasing livestock numbers in conjunction with greater use of crops as livestock feed (Barger et al. 2018). The share of feed crops, such as maize and soybean, of global crop production is projected to grow as the demand for animal feed increases with further intensification of livestock production ( [[#OECD/FAO--2019|OECD/FAO 2019]] ). Despite increased demand for food, feed, fuel and fibre from a growing human population ( [[#FAO--2019b|FAO 2019b]] ), global agricultural land area is projected to remain relatively stable during the next decade, with increases in production expected to result from agricultural intensification ( [[#OECD/FAO--2019|OECD/FAO 2019]] ).

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[[File:b0434d0760f149058329c3f76c6ebb5e IPCC_AR6_WGIII_Figure_7_10.png]]

'''Figure 7.10 | Trends in average global and regional land area under specific land uses ( [[#FAO--2021b|FAO 2021b]] ), inorganic nitrogen fertiliser use ( [[#FAO--2021e|FAO 2021e]] ) (top) and number of livestock ( [[#FAO--2021c|FAO 2021c]] ) (bottom) for three decades.''' For land use classification ‘cropland’ represents the FAOSTAT category ‘arable land’ which includes land under temporary crops, meadow, pasture and fallow. ‘Forest’ and ‘permanent meadow and pasture’ follow FAOSTAT categories.

Despite a decline in global agricultural area, the latest data document some regional expansion between 2000 and 2019, specifically in Africa (+3%) and Asia and the Pacific (+1%). Agricultural area declined in all other regions, notably in developed countries (–9%), due to multiple factors including among others, urbanisation (see [[#7.3.1.2|Section 7.3.1.2]] ).

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==== 7.3.1.2 Infrastructure Development and Urbanisation ====

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Although built-up areas (defined as cities, towns, villages and human infrastructure) occupy a relatively small fraction of land (around 1% of global land), since 1975 urban clusters (i.e., urban centres as well as surrounding suburbs) have expanded approximately 2.5 times ( [[#UNEP--2019|UNEP 2019]] ; Chapter 8, this report). Regional differences are striking. Between 1975 and 2015, built-up areas doubled in size in Europe while urban population remained relatively constant. In Africa built-up areas grew approximately fourfold, while urban population tripled ( [[#UNEP--2019|UNEP 2019]] ). Trends indicate that rural-to-urban migration will continue and accelerate in developing countries increasing environmental pressure in spite of measures to mitigate some of the impacts (e.g., by preserving or enhancing natural systems within cities, for example, lakes or natural and urban green infrastructures ( [[#UNEP--2019|UNEP 2019]] ). If current population densities within cities remain stable, the extent of built-up areas in developed countries is expected to increase by 30% and triple in developing countries between 2000 and 2050 (Barger et al. 2018).

Urban expansion leads to landscape fragmentation and urban sprawl with effects on forest resources and land use ( [[#Ünal--2019|Ünal et al. 2019]] ) while interacting with other drives. For example, in the Brazilian Amazon, the most rapid urban growth occurs within cities that are located near rural areas that produce commodities (minerals or crops) and are connected to export corridors ( [[#Richards--2015|Richards and VanWey 2015]] ). Urbanisation, coastal development and industrialisation also play crucial roles in the significant loss of mangrove forests (Hirales-Cota 2010; [[#Richards--2016|Richards and Friess 2016]] ; [[#Rivera-Monroy--2017|Rivera-Monroy et al. 2017]] ). Among infrastructural developments, roads are one of the most consistent and most considerable factors in deforestation, particularly in tropical frontiers ( [[#Pfaff--2007|Pfaff et al. 2007]] ; [[#Rudel--2009|Rudel et al. 2009]] ; [[#Ferretti-Gallon--2014|Ferretti-Gallon and Busch 2014]] ). The development of roads may also bring subsequent impacts on further development intensity due to increasing economic activities (see Chapter 8) mostly in the tropics and subtropics, where the expansion of road networks increases access to remote forests that act as refuges for biodiversity ( [[#Campbell--2017|Campbell et al. 2017]] ) (Box 7.1). Logging is one of the main drivers of road construction in tropical forests ( [[#Kleinschroth--2017|Kleinschroth and Healey 2017]] ) which leads to more severe long-term impacts that include increased fire incidence, soil erosion, landslides, and sediment accumulation in streams, biological invasions, wildlife poaching, illicit land colonisation, illegal logging and mining, land grabbing and land speculation ( [[#Laurance--2009|Laurance et al. 2009]] ; [[#Alamgir--2017|Alamgir et al. 2017]] ).

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=== Box 7.1 | Case Study: Reducing the Impacts of Roads on Deforestation ===

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'''Summary'''

Rapidly expanding roads, particularly in tropical regions, are linked to forest loss, degradation, and fragmentation because the land becomes more generally accessible. Increase of land values of areas adjacent to roads also drives speculation and deforestation related to land tenure ( [[#Fearnside--2015|Fearnside 2015]] ). If poorly planned, infrastructure can facilitate fires, illegal mining, and wildlife poaching with consequences for GHG emissions and biodiversity conservation. However, some initiatives are providing new approaches for better planning and then limit environmental and societal impacts '''.'''

'''Background'''

Although the number and extent of protected areas has increased markedly in recent decades ( [[#Watson--2014|Watson et al. 2014]] ), many other indicators reveal that nature is in broad retreat. For example, the total area of intact wilderness is declining rapidly worldwide ( [[#Watson--2016|Watson et al. 2016]] ), 70% of the world’s forests are now less than 1 km from a forest edge ( [[#Haddad--2015|Haddad et al. 2015]] ), the extent of tropical forest fragmentation is accelerating exponentially ( [[#Taubert--2018|Taubert et al. 2018]] ). One of the most direct and immediate driver of deforestation and biodiversity decline is the dramatic expansion of roads and other transportation infrastructure ( [[#Laurance--2014a|Laurance et al. 2014a]] ; [[#Laurance--2017|Laurance and Arrea 2017]] ; [[#Alamgir--2017|Alamgir et al. 2017]] ).

'''Case description'''

From 2010 to 2050, the total length of paved roads is projected to increase by 25 million km ( [[#Dulac--2013|Dulac 2013]] ) including large infrastructure-expansion schemes in Asia ( [[#Laurance--2017|Laurance and Arrea 2017]] ; [[#Lechner--2018|Lechner et al. 2018]] ) and in South America ( [[#Laurance--2001|Laurance et al. 2001]] ; [[#Killeen--2007|Killeen 2007]] ), as well as widespread illegal or unplanned road building ( [[#Laurance--2009|Laurance et al. 2009]] ; [[#Barber--2014|Barber et al. 2014]] ). For example, in the Amazon, 95% of all deforestation occurs within 5.5 km of a road, and for every km of legal road there are nearly three km of illegal roads ( [[#Barber--2014|Barber et al. 2014]] ).

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

More than any other proximate factor, the dramatic expansion of roads is determining the pace and patterns of habitat disruption and its impacts on biodiversity ( [[#Laurance--2009|Laurance et al. 2009]] ; [[#Laurance--2017|Laurance and Arrea 2017]] ). Much road expansion is poorly planned. Environmental Impact Assessments (EIAs) for roads and other infrastructure are typically too short term and superficial to detect rare species or assess long-term or indirect impacts of projects ( [[#Flyvbjerg--2009|Flyvbjerg 2009]] ; [[#Laurance--2017|Laurance and Arrea 2017]] ). Another limitation is the consideration of each project in isolation from other existing or planned developments ( [[#Laurance--2014b|Laurance et al. 2014b]] ). Hence, EIAs alone are inadequate for planning infrastructure projects and assessing their broader environmental, social, and financial impacts and risks ( [[#Laurance--2015a|Laurance et al. 2015a]] ; [[#Alamgir--2017|Alamgir et al. 2017]] , 2018).

'''Lessons'''

The large-scale, proactive land-use planning is an option for managing the development of modern infrastructure. Approaches such as the ‘Global Roadmap’ scheme ( [[#Laurance--2013|Laurance and Balmford 2013]] ; [[#Laurance--2014a|Laurance et al. 2014a]] ) Strategic Environmental Assessments ( [[#Fischer--2007|Fischer 2007]] ) can be used to evaluate the relative costs and benefits of infrastructure projects, and to spatially prioritise land uses to optimise human benefits while limited new infrastructure in areas of intact or critical habitats. For example, the Global Roadmap strategy has been used in parts of South-East Asia ( [[#Sloan--2018|Sloan et al. 2018]] ), Indochina ( [[#Balmford--2016|Balmford et al. 2016]] ), and sub-Saharan Africa ( [[#Laurance--2015b|Laurance et al. 2015b]] ) to devise land-use zoning that can help optimise the many risks and rewards of planned infrastructure projects.

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==== 7.3.1.3 Extractive Industry Development ====

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The extent and scale of mining is growing due to increased global demand ( [[#UNEP--2019|UNEP 2019]] ). Due to declining ore grades, more ore needs to be processed to meet demand, with extensive use of open cast mining. A low-carbon future may be more mineral intensive with, for example, clean energy technologies requiring greater inputs in comparison to fossil-fuel-based technologies ( [[#Hund--2020|Hund et al. 2020]] ). Mining presents cumulative environmental impacts, especially in intensively mined regions ( [[#UNEP--2019|UNEP 2019]] ). The impact of mining on deforestation varies considerably across minerals and countries. Mining causes significant changes to the environment, for example, through mining infrastructure establishment, soil erosion, urban expansion to support a growing workforce and development of mineral commodity supply chains ( [[#Sonter--2015|Sonter et al. 2015]] ). The increasing consumption of gold in developing countries, increased prices, and uncertainty in financial markets is identified as driving gold mining and associated deforestation in the Amazon region ( [[#Alvarez-Berrios--2015|Alvarez-Berrios and Mitchell Aide 2015]] ; [[#Dezécache--2017|Dezécache et al. 2017]] ; Asner and Tupayachi 2017; [[#Espejo--2018|Espejo et al. 2018]] ). The total estimated area of gold mining throughout the region increased by about 40% between 2012 and 2016 (Asner and Tupayachi 2017). In the Brazilian Amazon, mining significantly increased forest loss up to 70 km beyond mining lease boundaries, causing 11,670 km 2 of deforestation between 2005 and 2015, representing 9% of all Amazon forest loss during this time ( [[#Sonter--2015|Sonter et al. 2015]] ).

Mining is also an important driver of deforestation in African and Asian countries. In the Democratic Republic of Congo, where the second-largest area of tropical forest in the world occurs, mining-related deforestation exacerbated by violent conflict ( [[#Butsic--2015|Butsic et al. 2015]] ). In India, mining has contributed to deforestation at a district level, with coal, iron and limestone having had the most adverse impact on forest area loss ( [[#Ranjan--2019|Ranjan 2019]] ). Gold mining is also identified as a driver of deforestation in Myanmar ( [[#Papworth--2017|Papworth et al. 2017]] ).

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==== 7.3.1.4 Fire Regime Changes ====

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Wildland fires account for approximately 70% of the global biomass burned annually ( [[#van%20der%20Werf--2017|van der Werf et al. 2017]] ) and constitute a large global source of atmospheric trace gases and aerosols ( [[#Gunsch--2018|Gunsch et al. 2018]] ) (IPCC WGI AR6). Although fires are part of the natural system, the frequency of fires has increased in many areas, exacerbated by decreases in precipitation, including in many regions with humid and temperate forests that rarely experience large-scale fires naturally. Natural and human-ignited fires affect all major biomes, from peatlands through shrublands to tropical and boreal forests, altering ecosystem structure and functioning ( [[#Argañaraz--2015|Argañaraz et al. 2015]] ; [[#Nunes--2016|Nunes et al. 2016]] ; [[#Remy--2017|Remy et al. 2017]] ; [[#Mancini--2018|Mancini et al. 2018]] ; [[#Aragão--2018|Aragão et al. 2018]] ; [[#Engel--2019|Engel et al. 2019]] ; [[#Rodríguez%20Vásquez--2021|Rodríguez Vásquez et al. 2021]] ). However, the degree of incidence and regional trends are quite different and a study over 14 years indicated, on average, the largest fires in Australia, boreal North America and Northern Hemisphere Africa ( [[#Andela--2019|Andela et al. 2019]] ). More than half of the terrestrial surface of the Earth has fire regimes outside the range of natural variability, with changes in fire frequency and intensity posing major challenges for land restoration and recovery (Barger et al. 2018). In some ecosystems, fire prevention might lead to accumulation of large fuel loads that enable wildfires ( [[#Moreira--2020a|Moreira et al. 2020a]] ).

About 98 Mha of forest and savannahs are estimated to have been affected by fire in 2015 ( [[#FAO%20and%20UNEP--2020|FAO and]] [[#UNEP--2020|UNEP 2020]] ). Fire is a prevalent forest disturbance in the tropics where about 4% of the total forest and savannah area in that year was burned and more than two-thirds of the total area affected was in Africa and South America; mostly open savanna types ( [[#FAO%20and%20UNEP--2020|FAO and]] [[#UNEP--2020|UNEP 2020]] ). Fires have many different causes, with land clearing for agriculture the primary driver in tropical regions, for example, clearance for industrial oil-palm and paper-pulp plantations in Indonesia ( [[#Chisholm--2016|Chisholm et al. 2016]] ), or for pastures in the Amazon ( [[#Barlow--2020|Barlow et al. 2020]] ). Other socio-economic factors are also associated with wildfire regimes such as land-use conflict and socio-demographic aspects ( [[#Nunes--2016|Nunes et al. 2016]] ; [[#Mancini--2018|Mancini et al. 2018]] ). Wildfire regimes are also changing by the influence of climate change, with wildfire seasons becoming longer, wildfire average size increases in many areas and wildfires occurring in areas where they did not occur before ( [[#Jolly--2015|Jolly et al. 2015]] ; [[#Artés--2019|Artés et al. 2019]] ). Human influence has likely increased fire weather in some regions of all inhabited continents (IPCC AR6 WGI Technical Summary) and, in the last years, fire seasons of unprecedented magnitude occurred in diverse regions as California ( [[#Goss--2020|Goss et al. 2020]] ), the Mediterranean basin ( [[#Ruffault--2020|Ruffault et al. 2020]] ), Canada ( [[#Kirchmeier‐Young--2019|Kirchmeier‐Young et al. 2019]] ) with unprecedented fires in British Columbia in 2021, the Arctic and Siberia ( [[#McCarty--2020|McCarty et al. 2020]] ), Brazilian Amazon ( [[#Silva--2021|Silva et al. 2021]] ) and Pantanal ( [[#Leal%20Filho--2021|Leal Filho et al. 2021]] ), Chile ( [[#Bowman--2019|Bowman et al. 2019]] ) and Australia ( [[#Ward--2020|Ward et al. 2020]] ; [[#Gallagher--2021|Gallagher et al. 2021]] ). Lightning plays an important role in the ignition of wildfires, with the incidence of lightning igniting wildfires predicted to increase with rises in global average air temperature ( [[#Worden--2017|Worden et al. 2017]] ).

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==== 7.3.1.5 Logging and Fuelwood Harvest ====

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The area of forest designated for production has been relatively stable since 1990. Considering forest uses, about 30% (1.2 billion ha) of all forests is used primarily for production (wood and non-wood forest products), about 10% (424 Mha) is designated for biodiversity conservation, 398 Mha for the protection of soil and water, and 186 Mha is allocated for social services (recreation, tourism, education research and the conservation of cultural and spiritual sites) ( [[#FAO%20and%20UNEP--2020|FAO and]] [[#UNEP--2020|UNEP 2020]] ). While the rate of increase in the area of forest allocated primarily for biodiversity conservation has slowed in the last ten years, the rate of increase in the area of forest allocated for soil and water protection has grown since 1990, and notably in the last ten years. Global wood harvest (including from forests, other wooded land and trees outside forests) was estimated to be almost 4.0 billion m 3 in 2018 (considering both industrial roundwood and fuelwood) (FAO, 2019). Overall, wood removals are increasing globally as demand for, and the consumption of wood products grows annually by 1% in line with growing populations and incomes with this trend expected to continue in coming decades. When done in a sustainable way, more regrowth will occur and is stimulated by management, resulting in a net sink. However illegal and unsustainable logging (i.e., harvesting of timber in contravention of the laws and regulations of the country of harvest) is a global problem with significant negative economic (e.g., lost revenue), environmental (e.g., deforestation, forest degradation, GHG emissions and biodiversity losses) and social impact (e.g., conflicts over land and resources, disempowerment of local and indigenous communities) ( [[#World%20Bank--2019|World Bank 2019]] ). Many countries around the world have introduced regulations for the international trade of forest products to reduce illegal logging, with significant and positive impacts ( [[#Guan--2018|Guan et al. 2018]] ).

Over-extraction of wood for timber and fuelwood is identified as an important driver of mangrove deforestation and degradation ( [[#Bhattarai--2011|Bhattarai 2011]] ; [[#Ajonina--2014|Ajonina et al. 2014]] ; [[#Webb--2014|Webb et al. 2014]] ; [[#Giri--2015|Giri et al. 2015]] ; [[#Thomas--2017|Thomas et al. 2017]] ; Fauzi et al. 2019). Unsustainable selective logging and over-extraction of wood is a substantial form of forest and mangrove degradation in many tropical and developing countries, with emissions associated with the extracted wood, incidental damage to the surrounding forest and from logging infrastructure ( [[#Bhattarai--2011|Bhattarai 2011]] ; [[#Ajonina--2014|Ajonina et al. 2014]] ; [[#Webb--2014|Webb et al. 2014]] ; [[#Pearson--2014|Pearson et al. 2014]] , [[#Giri--2015|Giri et al. 2015]] ; [[#Thomas--2017|Thomas et al. 2017]] ; Fauzi et al. 2019). Traditional fuelwood and charcoal continue to represent a dominant share of total wood consumption in low-income countries (Barger et al. 2018). Regionally, the percentage of total wood harvested used as fuelwood varies from 90% in Africa, 62% in Asia, 50% in South America to less than 20% in Europe, North America and Oceania. Under current projections, efforts to intensify wood production in plantation forests, together with increases in fuel-use efficiency and electrification, are suggested to only partly alleviate the pressure on native forests (Barger et al. 2018). Nevertheless, the area of forest under management plans has increased in all regions since 2000 by 233 Mha ( [[#FAO--2020e|FAO 2020e]] ). In regions representing the majority of industrial wood production, forests certified under sustainable forest management programmes accounted for 51% of total managed forest area in 2017, an increase from 11% in 2000 ( [[#ICFPA--2021|ICFPA 2021]] ).

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=== 7.3.2 Anthropogenic Direct Drivers – Agriculture ===

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==== 7.3.2.1 Livestock Populations and Management ====

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Enteric fermentation dominates agricultural CH 4 emissions ( [[#7.2.3|Section 7.2.3]] ) with emissions being a function of both ruminant animal numbers and productivity (output per animal). In addition to enteric fermentation, both CH 4 and N 2 O emissions from manure management (i.e., manure storage and application) and deposition on pasture, make livestock the main agricultural emissions source ( [[#Tubiello--2019|Tubiello 2019]] ). The AR5 reported increases in populations of all major livestock categories between the 1970s and 2000s, including ruminants, with increasing numbers directly linked with increasing CH 4 emissions (Smith et al. 2014). The SRCCL identified managed pastures as a disproportionately high N 2 O emissions source within grazing lands, with ''medium confidence'' that increased manure production and deposition was a key driver ( [[#Jia--2019|Jia et al. 2019]] ). The latest data ( [[#FAO--2021c|FAO 2021c]] ) indicate continued global livestock population growth between 1990 and 2019 (Figure 7.10), including increases of 18% in cattle and buffalo numbers, and 30% in sheep and goat numbers, corresponding with CH 4 emission trends. Data also indicate increased productivity per animal for example, average increases of 16% in beef, 17% in pig meat and 70% in whole (cow) milk per respective animal between 1990 and 2019 ( [[#FAO--2021c|FAO 2021c]] ). Despite these advances leading to reduced emissions per unit of product (calories, meat and milk) ( [[#FAO--2016|FAO 2016]] ; [[#Tubiello--2019|Tubiello 2019]] ), increased individual animal productivity generally requires increased inputs (e.g., feed) and this generates increased emissions ( [[#Beauchemin--2020|Beauchemin et al. 2020]] ). Manipulation of livestock diets, or improvements in animal genetics or health may counteract some of this. In addition, the production of inputs to facilitate increased animal productivity, may indirectly drive further absolute GHG emissions along the feed supply chain.

Although there are several potential drivers ( [[#McDermott--2010|McDermott et al. 2010]] ; [[#Alary--2015|Alary et al. 2015]] ), increased livestock production is principally in response to growth in demand for animal-sourced food, driven by a growing human population (FAO, 2019) and increased consumption resulting from changes in affluence, notably in middle-income countries ( [[#Godfray--2018|Godfray et al. 2018]] ). Available data document increases in total meat and milk consumption by 24 and 22% respectively between 1990 and 2013, as indicated by average annual per capita supply ( [[#FAO--2017a|FAO 2017a]] ). Updated data indicate that trends of increasing consumption continued between 2014 and 2018 ( [[#FAO--2021d|FAO 2021d]] ). Sustained demand for animal-sourced food is expected to drive further livestock sector growth, with global production projected to expand by 14% by 2029, facilitated by maintained product prices and lower feed prices ( [[#OECD/FAO--2019|OECD/FAO 2019]] ).

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==== 7.3.2.2 Rice Cultivation ====

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In addition to livestock, both AR5 and the SRCCL identified paddy rice cultivation as an important emissions source (Smith et al. 2014), with ''medium evidence'' and ''high agreement'' that its expansion is a key driver of growing trends in atmospheric CH 4 concentration ( [[#Jia--2019|Jia et al. 2019]] ). The latest data indicate the global harvested area of rice to have grown by 11% between 1990 and 2019, with total paddy production increasing by 46%, from 519 Mt to 755 Mt ( [[#FAO--2021c|FAO 2021c]] ). Global rice production is projected to increase by 13% by 2028 compared to 2019 levels ( [[#OECD/FAO--2019|OECD/FAO 2019]] ). However, yield increases are expected to limit cultivated area expansion, while dietary shifts from rice to protein as a result of increasing per capita income, is expected to reduce demand in certain regions, with a slight decline in related emissions projected to 2030 ( [[#USEPA--2019|USEPA 2019]] ).

Between 1990 and 2019, Africa recorded the greatest increase (+160%) in area under rice cultivation, followed by Asia and the Pacific (+6%), with area reductions evident in all other regions ( [[#FAO--2021c|FAO 2021c]] ) broadly corresponding with related regional CH 4 emission (Figures 7.3 and 7.8). Data indicate the greatest growth in consumption (average annual supply per capita) between 1990 and 2013 to have occurred in Eastern Europe and West Central Asia (+42%) followed by Africa (+25%), with little change (+1%) observed in Asia and the Pacific ( [[#FAO--2017a|FAO 2017a]] ). Most of the projected increase in global rice consumption is in Africa and Asia ( [[#OECD/FAO--2019|OECD/FAO 2019]] ).

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==== 7.3.2.3 Synthetic Fertiliser Use ====

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Both AR5 and the SRCCL described considerable increases in global use of synthetic nitrogen fertilisers since the 1970s, which was identified to be a major driver of increasing N 2 O emissions ( [[#Jia--2019|Jia et al. 2019]] ). The latest data document a 41% increase in global nitrogen fertiliser use between 1990 and 2019 ( [[#FAO--2021e|FAO 2021e]] ) corresponding with associated increased N 2 O emissions (Figure 7.3). Increased fertiliser use has been driven by pursuit of increased crop yields, with for example, a 61% increase in average global cereal yield per hectare observed during the same period ( [[#FAO--2021c|FAO 2021c]] ), achieved through both increased fertiliser use and varietal improvements. Increased yields are in response to increased demand for food, feed, fuel and fibre crops which in turn has been driven by a growing human population (FAO, 2019), increased demand for animal-sourced food and bioenergy policy ( [[#OECD/FAO--2019|OECD/FAO 2019]] ). Global crop production is projected to increase by almost 15% over the next decade, with low income and emerging regions with greater availability of land and labour resources expected to experience the strongest growth, and account for about 50% of global output growth ( [[#OECD/FAO--2019|OECD/FAO 2019]] ). Increases in global nitrogen fertiliser use are also projected, notably in low income and emerging regions ( [[#USEPA--2019|USEPA 2019]] ).

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=== 7.3.3 Indirect Drivers ===

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The indirect drivers behind how humans both use and impact natural resources are outlined in Table 7.2. Specifically; demographic, economic and cultural, scientific and technological, and institutional and governance drivers. These indirect drivers not only interact with each other at different temporal and spatial scales but are also subject to impacts and feedbacks from the direct drivers (Barger et al. 2018).

'''Table 7.2''' | '''Indirect drivers of anthropogenic land and natural resourc''' '''e use patterns.'''

{| class="wikitable"
|-
| '''Demography'''

| '''Global and regional trends in population growth:''' There was a 43% increase in global population between 1990 and 2018. The greatest growth was observed in Africa and the Middle East (+104%) and least growth in Eastern Europe and West-Central Asia (+7%) ( [[#FAO--2019b|FAO 2019b]] ).

'''Global and regional projections:''' Population is projected to increase by 28% between 2018 and 2050 reaching 9.7 billion (FAO 2019). The world’s population is expected to become older, more urbanised and live in smaller households ( [[#UNEP--2019|UNEP 2019]] ).

'''Human migration:''' Growing mobility and population are linked to human migration, a powerful driver of changes in land and resource use patterns at decadal time scales, with the dominant flow of people being from rural areas to urban settlements over the past few decades, notably in the developing world ( [[#Adger--2015|Adger et al. 2015]] ; Barger et al. 2018).

|-
| '''Economic development and cultural factors'''

| Changes in land use and management come from individual and social responses to economic opportunities (e.g., demand for a particular commodity or improved market access), mediated by institutions and policies (e.g., agricultural subsidies and low-interest credit or government-led infrastructure projects) (Barger et al. 2018).

'''Projections on consumption:''' If the future global population adopts a per capita consumption rate similar to that of the developed world, the global capacity to provide land-based resources will be exceeded (Barger et al. 2018). Economic growth in the developing world is projected to double the global consumption of forest and wood products by 2030, with demand likely to exceed production in many developing and emerging economies in Asia and Africa within the next decade (Barger et al. 2018).

'''Global trade:''' Market distorting agricultural subsidies and globalisation increases pressure on land systems and functions, with global trade and capital flow influencing land use, notably in developing countries ( [[#Furumo--2017|Furumo and Aide 2017]] ; [[#Yao--2018|Yao et al. 2018]] ; [[#Pendrill--2019a|Pendrill et al. 2019a]] ; [[#UNEP--2019|UNEP 2019]] , [[#OECD/FAO--2019|OECD/FAO 2019]] ). Estimates suggest that between 29 and 39% of emissions from deforestation in the tropics resulted from the international trade of agricultural commodities ( [[#Pendrill--2019a|Pendrill et al. 2019a]] ).

|-
| '''Science and technology'''

| Technological factors operates in conjunction with economic drivers of land use and management, whether through intensified farming techniques and biotechnology, high-input approaches to rehabilitating degraded land (e.g., [[#Lin--2017|Lin et al. 2017]] ; [[#Guo--2020|Guo et al. 2020]] ) or through new forms of data collection and monitoring (e.g., [[#Song--2018|Song et al. 2018]] ; [[#Thyagharajan--2019|Thyagharajan and Vignesh 2019]] ; [[#Arévalo--2020|Arévalo et al. 2020]] ).

'''Changes in farming and forestry systems:''' Changes can have both positive and negative impacts regarding multiple factors, including GHG emission trends. Fast advancing technologies shape production and consumption, and drive land-use patterns and terrestrial ecosystems at various scales. Innovation is expected to help drive increases in global crop production during the next decade ( [[#OECD/FAO--2019|OECD/FAO 2019]] ). For example, emerging gene editing technologies, may advance crop breeding capabilities, though are subject to biosafety, public acceptance and regulatory approval ( [[#Jaganathan--2018|Jaganathan et al. 2018]] ; [[#Chen--2019|Chen et al. 2019]] ; [[#Schmidt--2020|Schmidt et al. 2020]] ). Technological changes were significant for the expansion of soybean in Brazil by adapting to different soils and photoperiods ( [[#Abrahão--2018|Abrahão and Costa 2018]] ). In Asia, technological development changed agriculture with significant improvements in production and adaptation to climate change ( [[#Thomson--2019|Thomson et al. 2019]] ; [[#Giller--2019|Giller and Ewert 2019]] ; [[#Anderson--2020|Anderson et al. 2020]] ; [[#Cassman--2020|Cassman and Grassini 2020]] ). Developments such as precision agriculture and drip irrigation have facilitated more efficient agrochemical and water use ( [[#UNEP--2019|UNEP 2019]] ).

Research and development are central to forest restoration strategies that have become increasingly important around the world as costs vary depending on methods used, from natural regeneration with native tree species to active restoration using site preparation and planting ( [[#Löf--2019|Löf et al. 2019]] ). In addition, climate change poses the challenge about tree species selection in the future. Innovations in the forest sector also form the basis of a bioeconomy associated with bioproducts and new processes ( [[#Verkerk--2020|Verkerk et al. 2020]] ) (Cross-Working Group Box 3 in Chapter 12).

'''Emerging mitigation technologies:''' Chemically synthesised methanogen inhibitors for ruminants are expected to be commercially available in some countries within the next two years and have considerable CH 4 mitigation potential ( [[#McGinn--2019|McGinn et al. 2019]] ; [[#Melgar--2020|Melgar et al. 2020]] ; [[#Beauchemin--2020|Beauchemin et al. 2020]] ; [[#Reisinger--2021|Reisinger et al. 2021]] ) ( [[#7.4.3|Section 7.4.3]] ). There is growing literature (in both academic and non-academic spheres) on the biological engineering of protein. Although in its infancy and subject to investment, technological development, regulatory approval and consumer acceptance, it is suggested to have the potential to disrupt current livestock production systems and land use ( [[#Stephens--2018|Stephens et al. 2018]] ; [[#Ben-Arye--2019|Ben-Arye and Levenberg 2019]] ; [[#RethinkX--2019|RethinkX 2019]] ; [[#Post--2020|Post et al. 2020]] ). The extent to which this is possible and the overall climate benefits are unclear ( [[#Lynch--2019|Lynch and Pierrehumbert 2019]] ; [[#Chriki--2020|Chriki and Hocquette 2020]] ).

|-
| '''Institutions and governance'''

| Institutional factors often moderate the relevance and impact of changes in economic and demographic variables related to resource exploitation and use. Institutions encompass the rule of law, legal frameworks and other social structures (e.g., civil society networks and movements) determining land management (e.g., formal and informal property rights, regimes and their enforcement); information and knowledge exchange systems; local and traditional knowledge and practice systems (Barger et al. 2018).

'''Land rights''' : Land tenure often allows communities to exercise traditional governance based on traditional ecological knowledge, devolved and dynamic access rights, judicious use, equitable distribution of benefits ( [[#Mantyka-Pringle--2017|Mantyka-Pringle et al. 2017]] ; [[#Wynberg--2017|Wynberg 2017]] ; [[#Thomas--2017|Thomas et al. 2017]] ), biodiversity ( [[#Contreras-Negrete--2014|Contreras-Negrete et al. 2014]] ) and fire and grazing management ( [[#Levang--2015|Levang et al. 2015]] ; [[#Varghese--2015|Varghese et al. 2015]] ).

'''Agreements and Finance:''' Since AR5, global agreements were reached on climate change, sustainable development goals, and the mobilisation of finance for development and climate action. Several countries adopted policies and commitments to restore degraded land (Barger et al. 2018). The UN Environment Programme (UNEP) and the Food and Agriculture Organization of the UN (FAO), launched the UN Decade on Ecosystem Restoration ( https://www.decadeonrestoration.org/ ).

Companies have also made pledges to reduce impacts on forests and on the rights of local communities as well as eliminating deforestation from their supply chains. The finance sector, a crucial driver behind action ( [[#7.6|Section 7.6]] , Box 7.12), has also started to make explicit commitments to avoiding environmental damage (Barger et al. 2018) and net zero targets ( [[#Forest%20Trends%20Ecosystem%20Marketplace--2021|Forest Trends Ecosystem Marketplace 2021]] ), though investment is sensitive to market outlook.

|}

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