Editing IPCC:AR6/SRCCL/Chapter-2 (section)

==== 2.3.1.2 Separation of the total net land flux into AFOLU fluxes and the land sink ====

<div id="section-2-3-1-2-separation-of-the-total-net-land-flux-into-afolu-fluxes-and-the-land-sink-block-1"></div>

The total net flux of carbon between land and the atmosphere can be divided into fluxes due to direct human activities (i.e., AFOLU) and fluxes due to indirect anthropogenic and natural effects (i.e., the land sink) (Table 2.3). These two components are less certain than their sums, the total net flux of CO <sub>2</sub> between atmosphere and land. The land sink, estimated with DGVMs, is least certain (Figure 2.5).

''Fluxes attributed to AFOLU''

The modelled AFOLU flux was a net emission of 5.2 ± 2.6 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''likely range'' ) for 2007–2016, approximately 13% of total anthropogenic CO <sub>2</sub> emissions (Le Quéré et al. 2018 <sup>[[#fn:r505|505]]</sup> ) (Table 2.3). This net flux was due to direct anthropogenic activities, predominately tropical deforestation, but also afforestation/reforestation, and fluxes due to forest management (e.g., wood harvest) and other types of land management, as well as peatland drainage and burning. The AFOLU flux is the mean of two estimates from bookkeeping models (Hansis et al. 2015 <sup>[[#fn:r506|506]]</sup> ; Houghton and Nassikas 2017 <sup>[[#fn:r507|507]]</sup> ), and this estimated mean is consistent with the mean obtained from an assemblage of DGVMs (Le Quéré et al. 2018 <sup>[[#fn:r508|508]]</sup> ) (Box 2.2 and Figure 2.5), although not all individual DGMVs include the same types of land use. Net CO <sub>2</sub> emissions from AFOLU have been relatively constant since 1900. AFOLU emissions were the dominant anthropogenic emissions until around the middle of the last century when fossil fuel emissions became dominant (Le Quéré et al. 2018 <sup>[[#fn:r509|509]]</sup> ). AFOLU activities have resulted in emissions of CO <sub>2</sub> over recent decades ( ''robust evidence, high agreement'' ) although there is a wide range of estimates from different methods and approaches (Smith et al. 2014 <sup>[[#fn:r510|510]]</sup> ; Houghton et al. 2012 <sup>[[#fn:r511|511]]</sup> ; Gasser and Ciais 2013 <sup>[[#fn:r512|512]]</sup> ; Pongratz et al. 2014 <sup>[[#fn:r513|513]]</sup> ; Tubiello et al. 2015 <sup>[[#fn:r514|514]]</sup> ; Grassi et al. 2018 <sup>[[#fn:r515|515]]</sup> ) (Box 2.2, Figure 2.5 and Figure 2.7).

DGVMs and one bookkeeping model (Hansis et al. 2015 <sup>[[#fn:r516|516]]</sup> ) used spatially explicit, harmonised land-use change data (LUH2) (Hurtt et al. 2017 <sup>[[#fn:r517|517]]</sup> ) based on HYDE 3.2. The HYDE data, in turn, are based on changes in the areas of croplands and pastures. In contrast, the Houghton bookkeeping approach (Houghton and Nassikas 2017 <sup>[[#fn:r518|518]]</sup> ) used primarily changes in forest area from the FAO Forest Resource Assessment (FAO 2015 <sup>[[#fn:r519|519]]</sup> ) and FAOSTAT to determine changes in land use. To the extent that forests are cleared for land uses other than crops and pastures, estimates from Houghton and Nassikas (2017 <sup>[[#fn:r520|520]]</sup> , 2018 <sup>[[#fn:r521|521]]</sup> ) are higher than estimates from DGMVs. In addition, both bookkeeping models (Hansis et al. 2015 <sup>[[#fn:r522|522]]</sup> ; Houghton and Nassikas 2017 <sup>[[#fn:r523|523]]</sup> ) included estimates of carbon emissions in Southeast Asia from peat burning from GFED4s (Randerson et al. 2015 <sup>[[#fn:r524|524]]</sup> ) and from peat drainage (Hooijer et al. 2010 <sup>[[#fn:r525|525]]</sup> ).

Satellite-based estimates of CO <sub>2</sub> emissions from losses of tropical forests during 2000–2010 corroborate the modelled emissions but are quite variable; 4.8 GtCO <sub>2</sub> yr <sup>–1</sup> (Tyukavina et al. 2015 <sup>[[#fn:r526|526]]</sup> ), 3.0 GtCO <sub>2</sub> yr <sup>–1</sup> (Harris et al. 2015 <sup>[[#fn:r527|527]]</sup> ), 3.2 GtCO <sub>2</sub> yr <sup>–1</sup> (Achard et al. 2014 <sup>[[#fn:r528|528]]</sup> ) and 1.6 GtCO <sub>2</sub> yr <sup>–1</sup> (Baccini et al. 2017 <sup>[[#fn:r529|529]]</sup> ). Differences in estimates can be explained to a large extent by the different approaches used. For example, the analysis by Tyukavina et al. (2015 <sup>[[#fn:r530|530]]</sup> ) led to a higher estimate because they used a finer spatial resolution. Three of the estimates considered losses in forest area and ignored degradation and regrowth of forests. Baccini et al. (2017 <sup>[[#fn:r531|531]]</sup> ) in contrast, included both losses and gains in forest area and losses and gains of carbon within forests (i.e., forest degradation and growth). The four remote sensing studies cited above also reported committed emissions; in essence, all of the carbon lost from deforestation was assumed to be released to the atmosphere in the year of deforestation. In reality, only some of the carbon in trees is released immediately to the atmosphere at the time of deforestation. The unburned portion is transferred to woody debris and wood products. Both bookkeeping models and DGVMs account for the delayed emissions in growth and decomposition. Finally, the satellite-based estimates do not include changes in soil carbon.

In addition to differences in land-cover data sets between models and satellites, there are many other methodological reasons for differences (Houghton et al. 2012 <sup>[[#fn:r532|532]]</sup> ; Gasser and Ciais 2013 <sup>[[#fn:r533|533]]</sup> ; Pongratz et al. 2014 <sup>[[#fn:r534|534]]</sup> ; Tubiello et al. 2015 <sup>[[#fn:r535|535]]</sup> ) (Box 2.2). There are different definitions of land-cover type, including forest (e.g., FAO uses a tree cover threshold for forests of 10%, Tyukavina et al. (2017 <sup>[[#fn:r536|536]]</sup> ) used 25%), different estimates of biomass and soil carbon density (MgC ha–1), different approaches to tracking emissions through time (legacy effects) and different types of activity included (e.g., forest harvest, peatland drainage and fires). Most DGVMS only recently (since AR5) included forest management processes, such as tree harvesting and land clearing for shifting cultivation, leading to larger estimates of CO <sub>2</sub> emissions than when these processes are not considered (Arneth et al. 2017 <sup>[[#fn:r537|537]]</sup> ; Erb et al. 2018 <sup>[[#fn:r538|538]]</sup> ). Grazing management has likewise been found to have large effects (Sanderman et al. 2017 <sup>[[#fn:r539|539]]</sup> ), and is not included in most DGVMs (Pugh et al. 2015 <sup>[[#fn:r540|540]]</sup> ; Pongratz et al., 2018 <sup>[[#fn:r541|541]]</sup> ).

''Nationally reported greenhouse gas inventories versus global model estimates''

There are large differences globally between estimates of net anthropogenic land-atmosphere fluxes of CO <sub>2</sub> from national GHGIs and from global models, and the same is true in many regions (Figure 2.5). Fluxes reported to the UNFCCC through country GHGIs were noted as about 4.3 GtCO <sub>2</sub> yr <sup>–1</sup> lower (Grassi et al. 2018 <sup>[[#fn:r542|542]]</sup> ) than estimates from the bookkeeping model (Houghton et al. 2012) used in the carbon budget for AR5 (Ciais et al. 2013a <sup>[[#fn:r543|543]]</sup> ). The anthropogenic emissions of CO <sub>2</sub> from AFOLU reported in countries’ GHG inventories were 0.1 ± 1.0 GtCO <sub>2</sub> yr <sup>–1</sup> globally during 2005–2014 (Grassi et al. 2018 <sup>[[#fn:r544|544]]</sup> ) much lower than emission estimates from the two global bookkeeping models of 5.1 ± 2.6 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''likely range'' ) over the same time period (Le Quéré et al. 2018 <sup>[[#fn:r545|545]]</sup> ). Transparency and comparability in estimates can support measuring, reporting and verifying GHG fluxes under the UNFCCC, and also the global stocktake, which will assess globally the progress towards achieving the long-term goals of the Paris Agreement. These differences can be reconciled largely by taking account of the different approaches to defining ‘anthropogenic’ in terms of different areas of land and treatment of indirect environmental change (Grassi et al. 2018 <sup>[[#fn:r546|546]]</sup> ).

To date there has been one study that quantitatively reconciles the global model estimates with GHGIs (Grassi et al. 2018 <sup>[[#fn:r547|547]]</sup> ). The separation of anthropogenic from non-anthropogenic effects is impossible with direct observation (IPCC 2010 <sup>[[#fn:r548|548]]</sup> ). The different approaches of models and GHGIs to estimating anthropogenic emissions and removals are shown in (Figure 2.6). The difficulty is that ''indirect'' effects of environmental changes (e.g., climate change and rising atmospheric CO <sub>2</sub> ) affect both manged and unmanaged lands, and some approaches treat these as anthropogenic while others do not. Bookkeeping models (e.g., Houghton and Nassikas 2017 <sup>[[#fn:r549|549]]</sup> ) attempt to estimate the fluxes of CO <sub>2</sub> driven by direct anthropogenic effects alone. DGVMs model the ''indirect'' environmental effects of climate and CO <sub>2</sub> . If the indirect effects happen on land experiencing anthropogenic land cover change or management (harvest and regrowth), DGVMs treat this as anthropogenic. Country GHGIs separately report fluxes due to land conversion (e.g., forests to croplands) and fluxes due to land management (e.g., forest land remaining forest land). The ‘managed land proxy’ is used as a pragmatic approach to estimate anthropogenic fluxes on managed lands, whereby countries define the areas they consider managed and include all of the emissions and removals that occur on those lands. Emissions and removals are caused simultaneously by direct, indirect and natural drivers and are captured in the reporting, which often relies on inventories.

Grassi et al. (2018 <sup>[[#fn:r550|550]]</sup> ) demonstrated that estimates of CO <sub>2</sub> emissions from global models and from nationally reported GHGIs were similar for deforestation and afforestation, but different for managed forests. Countries generally reported larger areas of managed forests than the models and the carbon removals by these managed forests were also larger. The flux due to indirect effects on managed lands was quantified using post-processing of results from DGVMs, looking at the indirect effects of CO <sub>2</sub> and climate change on secondary forest areas. The derived DGVM indirect managed forest flux was found to account for most of the difference between the bookkeeping models and the inventories.

''Regional differences''

Figure 2.7 shows regional differences in emissions due to AFOLU. Recent increases in deforestation rates in some tropical countries have been partially balanced by increases in forest area in India, China, the USA and Europe (FAO-FRA 2015 <sup>[[#fn:r551|551]]</sup> ). The trend in emissions from AFOLU since the 1990s is ''uncertain'' because some data suggest a declining rate of deforestation (FAO-FRA 2015 <sup>[[#fn:r552|552]]</sup> ), while data from satellites suggest an increasing rate (Kim 2014 <sup>[[#fn:r553|553]]</sup> ; Hansen et al. 2012 <sup>[[#fn:r554|554]]</sup> ). The disagreement results in part from differences in the definition of forest and approaches to estimating deforestation. The FAO defines deforestation as the conversion of forest to another land use (FAO-FRA 2015 <sup>[[#fn:r555|555]]</sup> ), while the measurement of forest loss by satellite may include wood harvests (forests remaining forests) and natural disturbances that are not directly caused by anthropogenic activity (e.g., forest mortality from droughts and fires). Trends in anthropogenic and natural disturbances may be in opposite directions. For example, recent drought-induced fires in the Amazon have increased the emissions from wildfires at the same time that emissions from anthropogenic deforestation have declined (Aragão et al. 2018 <sup>[[#fn:r556|556]]</sup> ). Furthermore, there have been advances since AR5 in estimating the GHG effects of different types of forest management (e.g., Valade et al. 2017 <sup>[[#fn:r557|557]]</sup> ). Overall, there is ''robust evidence and high agreement'' for a net loss of forest area and tree cover in the tropics and a net gain, mainly of secondary forests and sustainably managed forests, in the temperate and boreal zones (Chapter 1).

''Processes responsible for the land sink''

Just over half of total net anthropogenic CO <sub>2</sub> emissions (AFOLU and fossil fuels) were taken up by oceanic and land sinks ( ''robust evidence, high agreement'' ) (Table 2.3). The land sink was referred to in AR5 as the ‘residual terrestrial flux’, as it was not estimated directly, but calculated by difference from the other directly estimated fluxes in the budget (Table 2.3). In the 2018 budget (Le Quéré et al. 2018 <sup>[[#fn:r558|558]]</sup> ), the land sink term was instead estimated directly by DGVMs, leaving a budget imbalance of 2.2 GtCO <sub>2</sub> yr <sup>–1</sup> (sources overestimated or sinks underestimated). The budget imbalance may result from variations in oceanic uptake or from uncertainties in fossil fuel or AFOLU emissions, as well as from land processes not included in DGVMs.

The land sink is thought to be driven largely by the indirect effects of environmental change (e.g., climate change, increased atmospheric CO <sub>2</sub> concentration, nitrogen deposition) on unmanaged and managed lands ( ''robust evidence, high agreement'' ). The land sink has generally increased since 1900 and was a net sink of 11.7 ± 3.7 GtCO <sub>2</sub> yr <sup>–1</sup> during the period 2008–2017 (Table 2.3), absorbing 29% of global anthropogenic emissions of CO <sub>2</sub> . The land sink has slowed the rise in global land-surface air temperature by 0.09 ± 0.02°C since 1982 (medium confidence) (Zeng et al. 2017 <sup>[[#fn:r559|559]]</sup> ).

The rate of CO <sub>2</sub> removal by land accelerated from –0.026 ± 0.24 GtCO <sub>2</sub> yr <sup>–1</sup> during the warming period (1982–1998) to –0.436 ± 0.260 GtCO <sub>2</sub> yr <sup>–1</sup> during the warming hiatus (1998–2012). One explanation is that respiration rates were lower during the warming hiatus (Ballantyne et al. 2017 <sup>[[#fn:r560|560]]</sup> ). However, the lower rate of growth in atmospheric CO <sub>2</sub> during the warming hiatus may have resulted, not from lower rates of respiration, but from declining emissions from AFOLU (lower rates of tropical deforestation and increased forest growth in northern mid-latitudes) (Piao et al. 2018 <sup>[[#fn:r561|561]]</sup> ). Changes in the growth rate of atmospheric CO <sub>2</sub> , by themselves, do not identify the processes responsible and the cause of the variation is uncertain.

While year-to-year variability in the indirect land sink is high in response to climate variability, DGVM fluxes are influenced far more on decadal timescales by CO <sub>2</sub> fertilisation. A DGVM intercomparison (Sitch et al. 2015 <sup>[[#fn:r562|562]]</sup> ) for 1990–2009 found that CO <sub>2</sub> fertilisation alone contributed a mean global removal of –10.54 ± 3.68 GtCO <sub>2</sub> yr <sup>–1</sup> (trend –0.444 ± 0.202 GtCO <sub>2</sub> yr <sup>–1</sup> ). Data from forest inventories around the world corroborate the modelled land sink (Pan et al. 2011). The geographic distribution of the non-AFOLU land sink is less certain. While it seems to be distributed globally, its distribution between the tropics and non-tropics is estimated to be between 1:1 (Pan et al. 2011 <sup>[[#fn:r563|563]]</sup> ) and 1:2 (Houghton et al. 2018 <sup>[[#fn:r564|564]]</sup> ).

As described in Box 2.3, rising CO <sub>2</sub> concentrations have a fertilising effect on land, while climate has mixed effects; for example, rising temperature increases respiration rates and may enhance or reduce photosynthesis depending on location and season, while longer growing seasons might allow for higher carbon uptake. However, these processes are not included in DGVMs, which may account for at least some of the land sink. For example, a decline in the global area burned by fires each year (Andela et al. 2017 <sup>[[#fn:r565|565]]</sup> ) accounts for an estimated net sink (and/or reduced emissions) of 0.5 GtCO <sub>2</sub> yr <sup>–1</sup> (limited evidence, medium agreement) (Arora and Melton 2018 <sup>[[#fn:r566|566]]</sup> ).

Boreal forests represent an exception to this decline (Kelly et al. 2013 <sup>[[#fn:r567|567]]</sup> ). The reduction in burning not only reduces emissions, but also allows more growth of recovering forests. There is also an estimated net carbon sink of about the same magnitude (0.5 GtCO <sub>2</sub> yr <sup>–1</sup> ) as a result of soil erosion from agricultural lands and redeposition in anaerobic environments where respiration is reduced (limited evidence, low agreement) (Wang et al. 2017d <sup>[[#fn:r568|568]]</sup> ). A recent study attributes an increase in land carbon to a longer-term (1860–2005) aerosol-induced cooling (Zhang et al. 2019 <sup>[[#fn:r569|569]]</sup> ). Recent evidence also suggests that DGVMs and ESMs underestimate the effects of drought on CO <sub>2</sub> emissions (Humphrey et al. 2018 <sup>[[#fn:r570|570]]</sup> ; Green et al. 2019 <sup>[[#fn:r571|571]]</sup> ; Kolus et al. 2019 <sup>[[#fn:r572|572]]</sup> ).

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<span id="table-2.3"></span>
<!-- START TABLE -->
'''Table 2.3'''

<span id="perturbation-of-the-global-carbon-cycle-caused-by-anthropogenic-activities-gtco-2-yr1"></span>
'''Perturbation of the global carbon cycle caused by anthropogenic activities (GtCO <sub>2</sub> yr–1)'''

Source: Le Quéré et al. (2018 <sup>[[#fn:r573|573]]</sup> ).

<!-- TABLE -->
{| class="wikitable"
|-

CO <sub>2</sub> flux (GtCO <sub>2</sub> y <sup>–1</sup> ), 10-year mean

|-
|

1960–1969

1970–1979

1980–1989

1990–1999

2000–2009

2008–2017

|-

Emissions

|-

Fossil CO <sub>2</sub> emissions

11.4 ± 0.7

17.2 ± 0.7

19.8 ± 1.1

23.1 ± 1.1

28.6 ± 1.5

34. ± 1.8

|-

AFOLU net emissions

5.5 ± 2.6

4.4 ± 2.6

4.4 ± 2.6

5.1 ± 2.6

4.8 ± 2.6

5.5 ± 2.6

|-

Partitioning

|-

Growth in atmosphere

6.2 ± 0.3

10.3 ± 0.3

12.5 ± 0.07

11.4 ± 0.07

14.7 ± 0.07

17.2 ± 0.07

|-

Ocean sink

3.7 ± 1.8

4.8 ± 1.8

6.2 ± 1.8

7.3 ± 1.8

7.7 ± 1.8

8.8 ± 1.8

|-

Land sink (non-AFOLU)

4.4 ± 1.8

7.7 ± 1.5

6.6 ± 2.2

8.8 ± 1.8

9.9 ± 2.6

11.7 ± 2.6

|-

Budget imbalance

2.2

–1.1

–1.1

0.7

0.7

1.8

|-

Total net land flux (AFOLU – land sink)

+1.1 ± 3.2

–3.3 ± 3.0

–2.2 ± 3.4

–3.7 ± 2.2

–5.1 ± 3.2

–6.2 ± 3.7

|}
<!-- END TABLE -->

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<span id="figure-2.5"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.5'''

<span id="global-net-co2-emissions-due-to-afolu-from-different-approaches-in-gtco2-yr1.brown-line-the-mean-and-individual-estimates-brown-shading-from-two-bookkeeping-models-houghton-and-nassikas-2017-hansis-et-al.-2015.-blue-line-the-mean-from-dgvms-run-with-the-same-driving-data-with-the-pale-blue-shading-showing-the-1-standard-deviation"></span>
<!-- IMG CAPTION -->
'''Global net CO2 emissions due to AFOLU from different approaches (in GtCO2 yr–1).Brown line: the mean and individual estimates (brown shading) from two bookkeeping models (Houghton and Nassikas 2017; Hansis et al. 2015). Blue line: the mean from DGVMs run with the same driving data with the pale blue shading showing the ±1 standard deviation […]'''
<!-- IMG FILE -->
[[File:d2d43ba6645535ca1b26c05b8836a7ed Figure-2.5-1024x431.jpg]]

Global net CO <sub>2</sub> emissions due to AFOLU from different approaches (in GtCO <sub>2</sub> yr <sup>–1</sup> ).Brown line: the mean and individual estimates (brown shading) from two bookkeeping models (Houghton and Nassikas 2017 <sup>[[#fn:r574|574]]</sup> ; Hansis et al. 2015 <sup>[[#fn:r575|575]]</sup> ). Blue line: the mean from DGVMs run with the same driving data with the pale blue shading showing the ±1 standard deviation range. Yellow line: data downloaded from FAOSTAT website (Tubiello et al. 2013 <sup>[[#fn:r576|576]]</sup> ); the dashed line is primarily forest-related emissions, while the solid yellow line also includes emissions from peat fires and peat draining. Orange line: Greenhouse Gas Inventories (GHGI) based on country reports to UNFCCC (Grassi et al. 2018 <sup>[[#fn:r577|577]]</sup> ), data are shown only from 2005 because reporting in many developing countries became more consistent/reliable after this date. For more details on methods see Box 2.2.

<!-- END IMG -->
<div id="section-2-3-1-2-separation-of-the-total-net-land-flux-into-afolu-fluxes-and-the-land-sink-block-4"></div>

<span id="figure-2.6"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.6'''

<span id="summary-of-the-main-conceptual-differences-between-ghg-inventories-and-global-models-in-considering-what-is-the-anthropogenic-land-co2-flux.-adapted-from-grassi-et-al.-2018-effects-of-key-processes-on-the-land-flux-as-defined-by-ipcc-2010-including-where-these-effects-occur-in-managed-andor-unmanaged-lands-and-how-these-effects-are"></span>
<!-- IMG CAPTION -->
'''Summary of the main conceptual differences between GHG Inventories and global models in considering what is the ‘anthropogenic land CO2 flux’. Adapted from Grassi et al. (2018), effects of key processes on the land flux as defined by IPCC (2010) including where these effects occur (in managed and/or unmanaged lands) and how these effects are […]'''
<!-- IMG FILE -->
[[File:5d2dee5ed5f22d7ddea96a7298575f3d Figure-2.6-1024x622.jpg]]

Summary of the main conceptual differences between GHG Inventories and global models in considering what is the ‘anthropogenic land CO <sub>2</sub> flux’. Adapted from Grassi et al. (2018) <sup>[[#fn:r578|578]]</sup> , effects of key processes on the land flux as defined by IPCC (2010) <sup>[[#fn:r579|579]]</sup> including where these effects occur (in managed and/or unmanaged lands) and how these effects are captured in (a) bookkeeping models that do not explicitly model the effects of environmental change (although some is implicitly captured in data on carbon densities and growth and decay rates), (b) DGVMs that include the effects of environmental change on all lands, and run the models with and without land use change to diagnose ‘land use change’. The ‘land sink’ is then conceptually assumed to be a natural response of land to the anthropogenic perturbation of environmental change, DGVMs include the effects of inter-annual climate variability, and some include fires but no other natural disturbances, and (c) GHG Inventories reported by countries to the UNFCCC that report all fluxes in areas the countries define as ‘managed land’ but do not report unmanaged land. This is the CO <sub>2</sub> flux due to Land Use Land Use Change and Forestry (LULUCF) which is a part of the overall AFOLU flux. The area of land considered as managed in the inventories is greater than that considered as subject to direct management activities (harvest and regrowth) in the models.

<!-- END IMG -->
<div id="section-2-3-1-2-separation-of-the-total-net-land-flux-into-afolu-fluxes-and-the-land-sink-block-5"></div>

<span id="figure-2.7"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.7'''

<span id="regional-trends-in-net-anthropogenic-land-atmosphere-co2-flux-from-a-range-of-different-approaches-in-gtco2-yr1.red-symbols-bookkeeping-models-hexagon-houghton-and-nassikas-2017-square-hansis-et-al.-2015.-blue-cross-the-mean-from-dgmvs-with-the-box-showing-the-1-standard-deviation-range.-green-triangles-downloaded-from-faostat-website-the-open-triangle-is"></span>
<!-- IMG CAPTION -->
'''Regional trends in net anthropogenic land-atmosphere CO2 flux from a range of different approaches (in GtCO2 yr–1).Red symbols: bookkeeping models (hexagon: Houghton and Nassikas 2017; square: Hansis et al. 2015). Blue cross: the mean from DGMVs with the box showing the 1 standard deviation range. Green triangles: downloaded from FAOSTAT website; the open triangle is […]'''
<!-- IMG FILE -->
[[File:34c8313cefcdc0cc91b0aebb3814647d Figure-2.7-970x1024.jpg]]

Regional trends in net anthropogenic land-atmosphere CO <sub>2</sub> flux from a range of different approaches (in GtCO <sub>2</sub> yr <sup>–1</sup> ).Red symbols: bookkeeping models (hexagon: Houghton and Nassikas 2017 <sup>[[#fn:r580|580]]</sup> ; square: Hansis et al. 2015 <sup>[[#fn:r581|581]]</sup> ). Blue cross: the mean from DGMVs with the box showing the 1 standard deviation range. Green triangles: downloaded from FAOSTAT website; the open triangle is primarily forest-related emissions, while the closed triangle includes emission from peat fires and peat drainage. Yellow inverted triangle: GHGI LULUCF flux based on country reports to UNFCCC (Grassi et al. 2018 <sup>[[#fn:r582|582]]</sup> ). Data for developing countries are only shown for 2006–2015 because reporting in many developing countries became more consistent/reliable after 2005. For more details on methods see Box 2.2.

<!-- END IMG -->
<div id="section-2-3-1-3-gross-emissions-and-removals-contributing-to-afolu-emissions"></div>

<span id="gross-emissions-and-removals-contributing-to-afolu-emissions"></span>