in fCO₂ products to reconstruction biases, particularly in the Southern Ocean, indicating an overestimation of the ensemble mean trend. However, we also find compensatory positive biases in the ensemble, so the ensemble mean bias is smaller than the bias from some individual fCO₂ products.

## RECCAP-2 regions

Aligning with the RECCAP-2 initiative (Ciais et al., 2022; Poulter et al., 2022; DeVries et al., 2023), we provide a breakdown of this GCB paper estimate of the $E_{LUC}$, $S_{LAND}$, net land ($S_{LAND} - E_{LUC}$), and $S_{OCEAN}$ fluxes over the 10 land and 5 ocean RECCAP-2 regions, averaged over the period 2014–2023 (Fig. 15). The DGVMs and inversions suggest a positive net land sink in all regions, except for South America and Africa, where the inversions indicate a small net source of $-0.1 [-0.8, 0.3] \text{ GtC yr}^{-1}$ and $-0.3 [-0.7, -0.1] \text{ GtC yr}^{-1}$, respectively, compared to a small sink of $0.1 \pm 0.3 \text{ GtC yr}^{-1}$ and $0.3 \pm 0.1 \text{ GtC yr}^{-1}$ for the DGVMs. However, for South America, there is substantial uncertainty in both products (ensembles span zero). For the DGVMs, this is driven by uncertainty in both $S_{LAND}$ ($0.5 \pm 0.4 \text{ GtC yr}^{-1}$) and $E_{LUC}$ ($0.4 \pm 0.2 \text{ GtC yr}^{-1}$). The bookkeeping models also suggest an $E_{LUC}$ source of around $0.4 \text{ GtC yr}^{-1}$ in South America and Africa, in line with the DGVM estimates. Bookkeeping models and DGVMs similarly estimate a source of $0.3-0.4 \text{ GtC yr}^{-1}$ in Southeast Asia, with DGVMs suggesting a small net land sink ($0.1 \pm 0.1 \text{ GtC yr}^{-1}$). This is similar to the inversion mean estimate of a $0.1 [-0.3, 0.8] \text{ GtC yr}^{-1}$ sink, although the inversion spread is substantial. The inversions suggest the largest net land sinks are located in North America ($0.5 [-0.1, 1.0] \text{ GtC yr}^{-1}$), Russia ($0.6 [0.1, 0.9] \text{ GtC yr}^{-1}$), and East Asia ($0.4 [-0.2, 1.3] \text{ GtC yr}^{-1}$). This agrees well with the DGVMs in North America ($0.4 \pm 0.1 \text{ GtC yr}^{-1}$), which indicate a large natural land sink ($S_{LAND}$) of $0.6 \pm 0.2 \text{ GtC yr}^{-1}$, being slightly reduced by land-use-related carbon losses ($0.2 \pm 0.1 \text{ GtC yr}^{-1}$). The DGVMs suggest a smaller net land sink in Russia compared to inversions ($0.3 \pm 0.2 \text{ GtC yr}^{-1}$) and a similar net sink in East Asia ($0.2 \pm 0.1 \text{ GtC yr}^{-1}$).

There is generally a higher level of agreement in the estimates of regional $S_{OCEAN}$ between the different data streams (GOBMs, fCO₂ products, and atmospheric inversions) on a decadal scale compared to the agreement between the different land flux estimates. All data streams agree that the largest contribution to $S_{OCEAN}$ stems from the Southern Ocean due to a combination of high flux density and large surface area, but with important contributions also from the Atlantic (high flux density) and Pacific (large area) basins. In the Southern Ocean, GOBMs suggest a sink of $1.0 \pm 0.3 \text{ GtC yr}^{-1}$, in line with the fCO₂ products ($1.0 [0.8, 1.3] \text{ GtC yr}^{-1}$) and atmospheric inversions ($1.0 [0.7, 1.4] \text{ GtC yr}^{-1}$). There is similar agreement in the Pacific Ocean, with GOBMs, fCO₂ products, and atmospheric inversions indicating a sink of $0.6 \pm 0.2 \text{ GtC yr}^{-1}$, $0.7 [0.6, 1.0] \text{ GtC yr}^{-1}$, and $0.6 [0.1, 1.0] \text{ GtC yr}^{-1}$, respectively.

However, in the Atlantic Ocean, GOBMs simulate a sink of $0.5 \pm 0.1 \text{ GtC yr}^{-1}$, noticeably lower than both the fCO₂ products ($0.8 [0.7, 1.0] \text{ GtC yr}^{-1}$) and atmospheric inversions ($0.7 [0.4, 1.1] \text{ GtC yr}^{-1}$). It is important to note the fCO₂ products and atmospheric inversions have a substantial and uncertain river flux adjustment in the Atlantic Ocean ($0.3 \text{ GtC yr}^{-1}$) that also leads to a mean offset between GOBMs and fCO₂ products and inversions in the latitude band of the tropics (Fig. 14). The Indian Ocean, due to its smaller size, and the Arctic Ocean, due to its size and sea-ice cover that prevents air-sea gas exchange, are responsible for smaller but non-negligible $S_{OCEAN}$ fluxes (Indian Ocean: $0.3 [0.2, 0.3] \text{ GtC yr}^{-1}$, $0.3 [0.3, 0.4] \text{ GtC yr}^{-1}$, and $0.4 [0.3, 0.6] \text{ GtC yr}^{-1}$ for GOBMs, fCO₂ products, and atmospheric inversions, respectively; Arctic Ocean: $0.1 [0.1, 0.1] \text{ GtC yr}^{-1}$, $0.2 [0.1, 0.2] \text{ GtC yr}^{-1}$, and $0.1 [0.1, 0.2] \text{ GtC yr}^{-1}$ for GOBMs, fCO₂ products, and atmospheric inversions, respectively). Note that the $S_{OCEAN}$ numbers presented here deviate from numbers reported in RECCAP-2 where the net air-sea CO₂ flux is reported (i.e. without river flux adjustment for fCO₂ products and inversions and with river flux adjustment subtracted from GOBMs in most chapters or comparing unadjusted datasets with discussion of uncertain regional riverine fluxes as major uncertainty, e.g. Sarma et al., 2023; DeVries et al., 2023).

## Tropical vs. northern land uptake

A continuing conundrum is the partitioning of the global atmosphere–land flux between the Northern Hemisphere land and the tropical land (Stephens et al., 2007; Pan et al., 2011; Gaubert et al., 2019). It is of importance because each region has its own history of land-use change, climate drivers, and the impact of increasing atmospheric CO₂ and nitrogen deposition. Quantifying the magnitude of each sink is a prerequisite to understanding how each individual driver impacts the tropical and mid- to high-latitude carbon balance.

We define the north–south (N–S) difference as net atmosphere–land flux north of 30°N minus the net atmosphere–land flux south of 30°N. For the inversions, the N–S difference is 1.50 [0.05, 3.0] GtC yr⁻¹ across this year’s inversion ensemble. An apparent clustering of six satellite-driven solutions towards a common NH land sink noted in GCB2023 is no longer clear.

In the ensemble of DGVMs the N–S difference is $0.4 \pm 0.5 \text{ GtC yr}^{-1}$, a much narrower range than the one from atmospheric inversions. Only 3 out of 20 DGVMs have a N–S difference larger than $1.0 \text{ GtC yr}^{-1}$, compared to half of the inversion systems simulating a difference at least this large. The smaller spread across DGVMs compared to across inversions is to be expected as there is no correlation between northern and tropical land sinks in the DGVMs as opposed to the inversions where the sum of the two regions being well constrained by atmospheric observations leads to an anti-correlation between these two regions. This atmospheric N–
<figure><img src="image_22.png" /><figcaption>**Figure 15.** Decadal mean (a) land and (b) ocean fluxes for RECCAP-2 regions over 2014–2023. For land fluxes, $S_{LAND}$ is estimated by the DGVMs (green bars), with the error bar as ±1σ spread among models. A positive $S_{LAND}$ is a net transfer of carbon from the atmosphere to the land. $E_{LUC}$ fluxes are shown for both DGVMs (green) and bookkeeping models (orange), again with the uncertainty calculated as the ±1σ spread. Note that a positive $E_{LUC}$ flux indicates a loss of carbon from the land. The net land flux is shown for both DGVMs (green) and atmospheric inversions (purple), including the full model spread for inversions. The net ocean sink ($S_{OCEAN}$) is estimated by GOBMs (royal blue), $f\,CO_2$ products (cyan), and atmospheric inversions (purple). Uncertainty is estimated as the ±1σ spread for GOBMs and as the full model spread for the other two datasets. The dotted lines show the $f\,CO_2$ products and inversion results without river flux adjustment. Positive values are $CO_2$ sinks.</figcaption></figure>
S gradient could be used as an additional way to evaluate
tropical and NH uptake in DGVMs if their fluxes were com-
bined with multiple transport models. Vice versa, the much
smaller spread in the N–S difference between the DGVMs
could help in scrutinizing the inverse systems further. For ex-
ample, a large northern land sink and a tropical land source in
an inversion would suggest large sensitivity to CO₂ fertiliza-
tion (the dominant factor driving the land sinks) for northern
ecosystems, which would not be mirrored by tropical ecosys-
tems. Such a combination could be hard to reconcile with
the process understanding gained from the DGVM ensem-
bles and independent measurements (e.g. free-air CO₂ en-
richment (FACE) experiments).

3.8.3 Fire emissions in 2024

Fire emissions so far in 2024 have been above the average
of recent decades, chiefly due to synchronous large emis-
sions fluxes from North America and South America. Fig-
ure S9 shows global and regional emissions estimates for
the period of 1 January–30 September in each year 2003–
2024. Estimates are derived from two global fire emissions
products: the Global Fire Emissions Database (GFED, ver-
sion 4.1s; van der Werf et al., 2017) and the Global Fire As-
similation System (GFAS, operated by the Copernicus At-
mosphere Monitoring Service; Kaiser et al., 2012). The two
products estimate that global emissions from fires were 1.6–
2.2 GtC yr⁻¹ during January–September 2024. These esti-
mates are 11 %–32 % above the 2014–2023 average for the
same months (1.5–1.7 GtC yr⁻¹). In the GFED4.1s product,
the year-to-date emissions in 2024 were at their highest since
2003, exceeding even the large emissions estimate of 2023,
whereas the GFAS product showed lower emissions in 2024
than in 2023 and in six other years since 2003.

The pattern of high fire emissions from Canada in 2023,
which were record-breaking (Jones et al., 2024b; Byrne et al.,
2024a), continued into 2024. In January–September 2024,
emissions from Canada (0.2–0.3 GtC yr⁻¹) were half as great
as in the same months of 2023 (0.5–0.8 GtC yr⁻¹) but still
2.1–2.3 times the average of January–September periods in
2014–2023 (and 4–6 times greater than the average of those
months in 2003–2022 (excluding the record-breaking year in
2023); Fig. S9). The continued anomaly in Canada propa-
gated to the Northern Hemisphere, where emissions of 0.5–
0.6 GtC yr⁻¹ were 26 %–44 % above the average of 2014–
2023.

In January–September 2024, fire emissions from South
America (0.4–0.6 GtC yr⁻¹) were 94 %–164 % above the
average of the January–September periods in 2014–2023,
marking 2024 out as a year with synchronous high fire
emissions across the Americas. Emissions from Brazil in
January–September 2024 (0.2–0.3 GtC yr⁻¹) were 91 %–
118 % above the average of January–September periods of
2014–2023 and were at a level not seen since the major
drought year of 2010 (Fig. S9; Aragão et al., 2018; Silva Ju-

nior et al., 2019). In 2023, deforestation fire activity in the
Brazilian Amazon was below the average levels recorded in
national recording systems and is attributed to renewed envi-
ronmental policy implementation; however the fall in Ama-
zon deforestation fire activity was largely offset by above-
average wildfires related to historic drought (Mataveli et al.,
2024). According to the National Center for Monitoring and
Early Warning of Natural Disasters (CEMADEN), drought
conditions continued into 2024 and the current drought is
the most intense and widespread that Brazil has experienced
since records began in 1950 (CEMADEN, 2024), prompting
large wildfire anomalies across the Amazon, Cerrado, and
Pantanal regions (INPE, 2024).

Emissions anomalies in Africa strongly influence global
totals because the continent typically contributed 41%-47% of global fire emissions during 2014–2023 (average of January–September periods). GFAS suggests that fire emissions in Africa through September 2024 (0.6 GtC yr⁻¹) were slightly below the average of 2014–2023, whereas GFED4.1s suggests that fire emissions through September 2024 were slightly above the average of 2014–2023 (0.8 GtC yr⁻¹).

Tropical fire emissions through September 2024 (1.1–1.6 GtC yr⁻¹) accounted for 69%–74% of the global total emissions, which is close to the average of the 2014–2023 period (1.1–1.2 GtC yr⁻¹; 72%–75%). This marks a return to a more typical distribution of fire emissions between the tropics and extra-tropics after the tropical contribution fell to just 55%–59% during January–September 2023 (Fig. S9).

We caution that the fire emissions fluxes presented here
should not be compared directly with other fluxes of the bud-
get (e.g. S<sub>LAND</sub> or E<sub>LUC</sub>) due to incompatibilities between
the observable fire emissions fluxes and what is quantified
in the S<sub>LAND</sub> and E<sub>LUC</sub> components of the budget. The fire
emissions estimates from global fire products relate to all
fire types that can be observed in Earth observations (Giglio
et al., 2018; Randerson et al., 2012; Kaiser et al., 2012),
including (i) fires occurring as part of natural disturbance–
recovery cycles that would also have occurred in the pre-
industrial period (Yue et al., 2016; Keeley and Pausas, 2019;
Zou et al., 2019); (ii) fires occurring above and beyond nat-
ural disturbance–recovery cycle due to changes in climate,
CO<sub>2</sub>, and N fertilization and to an increased frequency of ex-
treme drought and heatwave events (Abatzoglou et al., 2019;
Jones et al., 2022; Zheng et al., 2021; Burton et al., 2024);
and (iii) fires occurring in relation to land use and land-
use change, such as deforestation fires and agricultural fires
(van der Werf et al., 2010; Magi et al., 2012). In the context
of the global carbon budget, only the portion of fire emis-
sions associated with (ii) should be included in the S<sub>LAND</sub>
component, and fire emissions associated with (iii) should
already be accounted for in the E<sub>LUC</sub> component. Emissions
associated with (i) should not be included in the global car-
bon budget. It is not currently possible to derive specific esti-
mates for fluxes (i), (ii), and (iii) using global fire emissions
products such as GFED or GFAS. In addition, the fire emis-
sions estimates from global fire emissions products represent a gross flux of carbon to the atmosphere, whereas the $S_{LAND}$ component of the budget is a net flux that should also include post-fire recovery fluxes. Even if emissions from fires of type (ii) could be separated from those of type (i), these fluxes may be partially or wholly offset in subsequent years by post-fire fluxes as vegetation recovers, sequestering carbon from the atmosphere to the terrestrial biosphere (Yue et al., 2016; Jones et al., 2024c). Increases in forest fire emissions and severity (emissions per unit area) globally during the past 2 decades have highlighted the increasing potential for fire emissions fluxes to outweigh post-fire recovery fluxes, though long-term monitoring of vegetation recovery is required to quantify the net effect on terrestrial C storage (Jones et al., 2024c).

## 3.9 Closing the global carbon cycle

### 3.9.1 Partitioning of cumulative emissions and sink fluxes

Emissions during the period 1850–2023 amounted to 710 ± 70 GtC and were partitioned among the atmosphere (285 ± 5 GtC; 40 %), ocean (185 ± 35 GtC; 26 %), and land (220 ± 60 GtC; 32 %). The cumulative land sink is almost equal to the cumulative land-use emissions (225 ± 65 GtC), making the global land nearly neutral over the whole 1850–2023 period (Fig. 3).

The use of nearly independent estimates for the individual terms of the global carbon budget shows a cumulative budget imbalance of 25 GtC (3 % of total emissions) during 1850–2023 (Fig. 3, Table 8), which, if correct, suggests that emissions could be slightly too high by the same proportion or that the combined land and ocean sinks are slightly underestimated (by about 6 %), although these are well within the uncertainty range of each component of the budget. Nevertheless, part of the imbalance could originate from the estimation of significant increase in $E_{FOS}$ and $E_{LUC}$ between the mid-1920s and the mid-1960s which is unmatched by a similar growth in atmospheric $CO_2$ concentration as recorded in ice cores (Fig. 3). However, the known loss of additional sink capacity of 30–40 GtC (over the 1850–2020 period) due to reduced forest cover has not been accounted for in our method and would exacerbate the budget imbalance (see Sects. 2.10 and S6.4).

For the more recent 1960–2023 period where direct atmospheric $CO_2$ measurements are available, total emissions ($E_{FOS} + E_{LUC}$) amounted to 500 ± 50 GtC, of which 410 ± 20 GtC (82 %) was caused by fossil $CO_2$ emissions and 90 ± 45 GtC (18 %) by land-use change (Table 8). The total emissions were partitioned among the atmosphere (220 ± 5 GtC; 45 %), ocean (130 ± 26 GtC; 25 %), and land (150 ± 40 GtC; 30 %), with a near-zero (< 1 GtC) unattributed budget imbalance. All components except land-use change emissions have significantly grown since 1960,

# Anthropogenic carbon flows

<figure><img src="image_23.png" /><figcaption>**Figure 16.** Cumulative changes over the 1850–2023 period (a) and average fluxes over the 2014–2023 period (b) for the anthropogenic perturbation of the global carbon cycle. See the caption of Fig. 3 for key information and the Methods section in the text for full details. This figure was produced by Nigel Hawtin.</figcaption></figure>

with important interannual variability in the growth rate in atmospheric $CO_2$ concentration and in the land $CO_2$ sink (Fig. 4) and some decadal variability in all terms (Table 7). Differences with previous budget releases are documented in Fig. S6.

The global carbon budget averaged over the last decade (2014–2023) is shown in Figs. 2 and 16 (right panel) and Table 7. For this period, 90 % of the total emissions ($E_{FOS} + E_{LUC}$) were from fossil $CO_2$ emissions ($E_{FOS}$) and 10 % from land-use change ($E_{LUC}$). The total emissions were partitioned among the atmosphere (48 %), ocean (26 %), and land (30 %), with a small negative budget imbalance (~4 %, 0.4 GtC yr$^{-1}$). For single years, the budget imbalance can be larger (Fig. 4). For 2023, the combination of our estimated sources (11.1 ± 0.9 GtC yr$^{-1}$) and sinks (11.1 ± 0.9 GtC yr$^{-1}$) leads to a $B_{IM}$ of −0.02 GtC, suggesting a near-perfect closure of the global carbon budget.

### 3.9.2 Trend and variability in the carbon budget imbalance

The carbon budget imbalance ($B_{IM}$; Eq. 1, Fig. 4) quantifies the mismatch between the estimated total emissions and the estimated changes in the atmosphere, land, and ocean reservoirs. The budget imbalance from 1960 to 2023 is very small
(0.5 GtC over the period, i.e. < 0.01 GtC yr⁻¹ on average) and shows no trend over the full time series (Fig. 4e). The process models (GOBMs and DGVMs) and fCO₂ products have been selected to match observational constraints in the 1990s, but no further constraints have been applied to their representation of trend and variability. Therefore, the near-zero mean of and trend in the budget imbalance are seen as evidence of a coherent community understanding of the emissions and their partitioning on those timescales (Fig. 4). However, the budget imbalance shows substantial variability on the order of ±1 GtC yr⁻¹, particularly over semi-decadal timescales, although most of the variability is within the uncertainty in the estimates. The positive carbon imbalance during the 1960s and early 1990s indicates that either the emissions were overestimated or the sinks were underestimated during these periods. The reverse is true for the 1970s and to a lesser extent for the 1980s and 2014–2023 period (Fig. 4, Table 7).

We cannot attribute the cause of the variability in the budget imbalance with our analysis; we only note that the budget imbalance is unlikely to be explained by errors or biases in the emissions alone because of its component of large semi-decadal variability, a variability that is atypical of emissions and has not changed in the past 60 years despite a near tripling in emissions (Fig. 4). Errors in $S_{LAND}$ and $S_{OCEAN}$ are more likely to be the main cause for the budget imbalance, especially on interannual to semi-decadal timescales. For example, underestimation of the $S_{LAND}$ by DGVMs has been reported following the eruption of Mount Pinatubo in 1991, possibly due to missing responses to changes in diffuse radiation (Mercado et al., 2009). Although since GCB2021 we have accounted for aerosol effects on solar radiation quantity and quality (diffuse vs. direct), most DGVMs only used the former as input (i.e. total solar radiation) (Table S1). Thus, the ensemble mean may not capture the full effects of volcanic eruptions, i.e. those associated with high-light-scattering sulfate aerosols, on the land carbon sink (O’Sullivan et al., 2021). DGVMs are suspected to overestimate the land sink in response to the wet decade of the 1970s (Sitch et al., 2008). Quasi-decadal variability in the ocean sink has also been reported, with all methods agreeing on a smaller-than-expected ocean CO₂ sink in the 1990s and a larger-than-expected sink in the 2000s (Fig. 11; Landschützer et al., 2016; DeVries et al., 2019; Hauck et al., 2020; McKinley et al., 2020; Gruber et al., 2023), and the climate-driven variability could be substantial but is not well constrained (DeVries et al., 2023; Müller et al., 2023). Errors in sink estimates could also be driven by errors in the climatic forcing data, particularly precipitation for $S_{LAND}$ and wind for $S_{OCEAN}$. Also, the $B_{IM}$ shows substantial departure from zero on yearly timescales (Fig. 4e), highlighting unresolved variability in the carbon cycle, likely in the land sink ($S_{LAND}$), given its large year-to-year variability (Figs. 4d and 9).

Both the budget imbalance ($B_{IM}$, Table 7) and the residual land sink from the global budget ($E_{FOS} + E_{LUC} - G_{ATM} - S_{OCEAN}$, Table 5) include an error term due to the inconsistencies that arise from combining $E_{LUC}$ from bookkeeping models with $S_{LAND}$ from DGVMs, most notably the loss of additional sink capacity (see Sects. 2.10 and S6.4). Other differences include better accounting of land-use change practices and processes in bookkeeping models than in DGVMs and of the bookkeeping model error of having present-day observed carbon densities fixed in the past. That the budget imbalance shows no clear trend towards larger values over time is an indication that these inconsistencies probably play a minor role compared to other errors in $S_{LAND}$ or $S_{OCEAN}$.

Although the budget imbalance is near zero for recent decades, this could be due to a compensation of errors. We cannot exclude the possibility of an overestimation of CO₂ emissions, particularly from land-use change, given their large uncertainty, as has been suggested elsewhere (Piao et al., 2018), and/or an underestimate of the sinks. A larger DGVM estimate of the atmosphere–land CO₂ flux ($S_{LAND} - E_{LUC}$) over the extra-tropics would reconcile model results with inversion estimates for fluxes in the total land during the past decade (Fig. 14, Table 5). Likewise, a larger $S_{OCEAN}$ is also possible given the higher estimates from the fCO₂ products, inversions, and oxygen-based estimates (see Sect. 3.6.2, Figs. 11 and 14); the underestimation of interior-ocean anthropogenic carbon accumulation in the GOBMs (Sect. 3.6.5; Müller et al., 2023); known biases of ocean models (e.g. Terhaar et al., 2022, 2024); the role of potential temperature bias and skin effects in fCO₂ products (Watson et al., 2020; Dong et al., 2022; Bellenger et al., 2023; Fig. 11); and regionally larger estimates based, for example, on eddy covariance measurements and aircraft data (Dong et al., 2024a; Long et al., 2021; Jin et al., 2024). More integrated use of observations in the global carbon budget, either on their own or for further constraining model results, should help resolve some of the budget imbalance (Peters et al., 2017a).

## 4 Tracking progress towards mitigation targets

The average growth in global fossil CO₂ emissions peaked at nearly +3 % yr⁻¹ during the 2000s, driven by the rapid growth in emissions in China. In the last decade, however, the global growth rate has slowly declined, reaching a low of +0.6 % yr⁻¹ over 2014–2023. While this slowdown in global fossil CO₂ emissions growth is welcome, global fossil CO₂ emissions continue to grow, and we are far from the rapid emission decreases needed to be consistent with the temperature goals of the Paris Agreement.

Since the 1990s, the average growth rate of fossil CO₂ emissions has continuously declined across the group of developed countries of the Organisation for Economic Cooperation and Development (OECD), with emissions peaking
ing in around 2005 and declining at 1.4% yr⁻¹ in the decade 2014–2023, compared to a decline of 0.9% yr⁻¹ during the 2004–2013 period (Table 9). In the decade 2014–2023, territorial fossil CO₂ emissions decreased significantly (at the 95% confidence level) in 23 countries/economies whose economies grew significantly (also at the 95% confidence level): Belgium, Czechia, Denmark, Estonia, Finland, France, Gabon, Germany, Jordan, Luxembourg, the Netherlands, Aotearoa/New Zealand, Norway, Portugal, South Korea, Romania, Slovenia, Somalia, Spain, Sweden, Switzerland, the United Kingdom, and the USA (updated from Le Quéré et al., 2019). Altogether, these 23 countries emitted 2.2 GtC yr⁻¹ (8.2 GtCO₂ yr⁻¹) on average over the last decade, about 23% of world CO₂ fossil emissions. For comparison, 17 countries showed a significant decrease in territorial fossil CO₂ emissions over the previous decade (2004–2013).

Decomposing emission changes into the components of growth, a Kaya decomposition, helps give an initial understanding of the drivers of the changes (Peters et al., 2017b). The reduction in growth in global fossil CO₂ emissions in the last decade is due to slightly weaker economic growth, accelerating declines in CO₂ emissions per unit energy, and sustained declines in energy per unit GDP (Fig. 17). These trends are a supposition of the trends at the national level. Fossil CO₂ emission declines in the USA and EU27 are primarily driven by slightly weaker economic growth since the global financial crisis (GFC) in 2008–2009, sustained declines in energy per GDP, and sustained declines in CO₂ emissions per unit energy with a slight acceleration in the USA in the last decade. In contrast, fossil CO₂ emissions continue to grow in non-OECD countries, although the growth rate has slowed from 4.9% yr⁻¹ during the 2004–2013 decade to 1.8% yr⁻¹ in the last decade (Table 9). Representing 47% of non-OECD emissions in 2023, a large part of this slowdown is due to China, which has seen emissions growth decline from 7.5% yr⁻¹ in the 2004–2013 decade to 1.9% yr⁻¹ in the last decade. Excluding China, non-OECD emissions grew at 3% yr⁻¹ in the 2004–2013 decade compared to 1.7% yr⁻¹ in the last decade. China had weaker economic growth in the 2000s compared to the 2010s, and the rate of reduction in the energy intensity of economic production has weakened significantly since 2015, with accelerating declines in CO₂ emissions per unit energy (Fig. 17). India has had strong economic growth that has not been offset by declines in energy per GDP or declines in CO₂ emissions per unit energy, driving up fossil CO₂ emissions. Despite the high deployment of renewables in some countries (e.g. China, India), fossil energy sources continue to grow to meet growing energy demand (Le Quéré et al., 2019). In the rest of the world, economic growth has slowed considerably in the last decade but has only been partly offset by declines in energy or carbon intensity, leading to growing emissions.

Globally, fossil CO₂ emissions growth is slowing, and this is due in part to the emergence of climate policy (Eskander

and Fankhauser, 2020; Le Quéré et al., 2019) and technological change, which are leading to a shift from coal to gas and growth in renewable energies and reduced expansion of coal capacity. At the aggregated global level, decarbonization has shown a strong and growing signal in the last decade, with smaller contributions from lower economic growth and declines in energy per GDP (Fig. 17). Altogether, global fossil CO₂ emissions are still growing (average of 0.6% yr⁻¹ over the 2014–2023 decade), and they are far from the reductions needed to meet the ambitious climate goals of the UNFCCC Paris Agreement.

Lastly, we update the remaining carbon budget (RCB) based on two studies, the IPCC AR6 (Canadell et al., 2021) and the revision of the IPCC AR6 estimates (Forster et al., 2024; Lamboll et al., 2023). We update the RCB assessed by the IPCC AR6 (Canadell et al., 2021), accounting for the 2020–2024 estimated emissions from fossil fuel combustion (E fos) and land-use changes (E luc). From January 2025, the IPCC AR6 RCB (50% likelihood) for limiting global warming to 1.5, 1.7, and 2°C is estimated to amount to 85, 180, and 315 GtC (305, 655, 1155 GtCO₂). The Forster et al. (2024) study proposed a significantly lower RCB than IPCC AR6, with the largest reduction being due to an update of the climate emulator (MAGICC) used to estimate the warming contribution of non-CO₂ agents and due to the warming (i.e. emissions) that occurred over the 2020–2023 period. We update the Forster et al. (2024) budget, accounting for the 2024 estimated emissions from fossil fuel combustion (E fos) and land-use changes (E luc). From January 2025, the Forster et al. (2024) RCB (50% likelihood) for limiting global warming to 1.5, 1.7, and 2°C is estimated to amount to 45, 140, and 290 GtC (160, 510, 1060 GtCO₂), significantly smaller than the updated IPCC AR6 estimate. Both the original IPCC AR6 and the Forster et al. (2024) estimates include the Earth system uncertainty in the climate response to cumulative CO₂ emissions, which is reflected through the percent likelihood of exceeding the given temperature threshold, an additional uncertainty of ±220 GtCO₂ due to alternative non-CO₂ emission scenarios, and other sources of uncertainties (see Canadell et al., 2021). The two sets of estimates overlap when considering all uncertainties.

Here, we take the average of our 2024 update of both the IPCC AR6 and the Forster et al. (2024) estimates, giving a remaining carbon budget (50% likelihood) for limiting global warming to 1.5, 1.7, and 2°C of 65, 160, and 305 GtC (235, 585, 1110 GtCO₂), respectively, starting from January 2025. We emphasize the large uncertainties, particularly when close to the global warming limit of 1.5°C. These 1.5, 1.7, and 2°C remaining carbon budgets correspond to about 6, 14, and 27 years, respectively, from the beginning of 2025, at the 2024 level of total anthropogenic CO₂ emissions. Reaching net-zero CO₂ emissions by 2050 entails cutting total anthropogenic CO₂ emissions by about 0.4 GtC (1.6 GtCO₂), 3.9% of 2024 emissions, each year on average, comparable to the decrease in E fos observed in 2020 during
<table><caption>**Table 9.** Average annual growth rate in fossil CO₂ emissions over the most recent decade (2014–2023) and the previous decade (2004–2013). The data for the world include the cement carbonation sink. IAS denotes emissions from international aviation and shipping. The rest of the world is the world minus China, the USA, EU27, India, and IAS.</caption><thead><tr><th></th><th>World</th><th>China</th><th>USA</th><th>EU27</th><th>India</th><th>OECD</th><th>Non-OECD</th><th>IAS</th><th>Rest of the world</th></tr></thead><tbody><tr><td>2004–2013</td><td>2.4%</td><td>7.5%</td><td>-1.4%</td><td>-1.8%</td><td>6.4%</td><td>-0.9%</td><td>4.9%</td><td>2.6%</td><td>1.9%</td></tr><tr><td>2014–2023</td><td>0.6%</td><td>1.9%</td><td>-1.2%</td><td>-2.1%</td><td>3.6%</td><td>-1.4%</td><td>1.8%</td><td>-1.6%</td><td>0.4%</td></tr></tbody></table>

<figure><img src="image_24.png" /><figcaption>**Figure 17.** Kaya decomposition of the main drivers of fossil CO₂ emissions, considering population, GDP per person, energy per GDP, and CO₂ emissions per energy unit, for China (a), the USA (b), EU27 (c), India (d), the rest of the world (e), and world (f). Black dots are the annual fossil CO₂ emissions growth rate; coloured bars are the contributions from the different drivers to this growth rate. A general trend is that population and GDP growth put upward pressure on emissions (positive values), while energy per GDP and, more recently, CO₂ emissions per energy unit put downward pressure on emissions (negative values). Both the COVID-19-induced drop during 2020 and the recovery in 2021 led to a stark contrast to previous years, with different drivers in each region.</figcaption></figure>
the COVID-19 pandemic. However, this would lead to cumu-
lative emissions over 2025–2050 of 145 GtC (530 GtCO2),
well above the remaining carbon budget of 65 GtC to limit
global warming to 1.5 °C but still within the remaining bud-
get of 160 GtC to limit warming to 1.7 °C (in phase with the
“well below 2 °C” ambition of the Paris Agreement). Even
reaching net-zero CO2 globally by 2040, which would re-
quire annual emissions cuts of 0.7 GtC (2.5 GtCO2) on av-
erage, would still exceed the remaining carbon budget for
1.5 °C, with 90 GtC (325 GtCO2) of cumulative emissions
over 2025–2040, unless the global emissions trajectory be-
comes net negative (i.e. more anthropogenic CO2 sinks than
emissions) after 2040.

5 Discussion

Each year when the global carbon budget is published, each
flux component is updated for all previous years to consider
corrections that are the result of further scrutiny and verifi-
cation of the underlying data in the primary input datasets.
Annual estimates may be updated with improvements in data
quality and timeliness (e.g. to eliminate the need for extrap-
olation of forcing data such as land-use data). Of all terms
in the global budget, only the fossil CO₂ emissions and the
growth rate in atmospheric CO₂ concentration are based pri-
marily on empirical inputs supporting annual estimates in
this carbon budget. The carbon budget imbalance, still an
imperfect measure, provides a strong indication of the lim-
itations in observations, in understanding and representing
processes in models, and/or in the integration of the carbon
budget components.

The persistent unexplained variability in the carbon bud-
genvironmental change from countries' managed forest areas
(part of S<sub>LAND</sub> in this budget) to the budget E<sub>LUC</sub> estimate,
we successfully reconciled the large gap between our E<sub>LUC</sub>
estimate and the land-use flux from NGHGIs using the ap-
proach described in Grassi et al. (2021) for future scenar-
ios and in Grassi et al. (2023) using data from the Global
Carbon Budget 2021. The updated data presented here can
be used for potential adjustments in the policy context, e.g.
to help assess the collective countries' progress towards the
goal of the Paris Agreement and to avoid double account-
ing for the sink in managed forests. In the absence of this
adjustment, collective progress would hence appear better
than it is (Grassi et al., 2021). The application of this adjust-
ment is also recommended in the UNFCCC Synthesis Report
for the first Global Stocktake (UNFCCC, 2022) whenever a
comparison between LULUCF fluxes reported by countries
and the global emissions estimates of the IPCC is conducted.
However, this adjustment should be seen as a short-term and
pragmatic fix based on existing data rather than as a defini-
tive solution to bridge the differences between global mod-
els and national inventories. Additional steps are needed to

lite XCO₂ retrievals. The limit of the resolution of the car-
bon budget imbalance is still unclear but most certainly has
not yet been reached given the possibilities for improvements
that lie ahead.

Estimates of global fossil CO₂ emissions from different
datasets are in relatively good agreement when the different
system boundaries of these datasets are considered (Andrew,
2020a). But while estimates of E<sub>FOS</sub> are derived from re-
ported activity data requiring much fewer complex transfor-
mations than some other components of the budget, uncer-
tainties remain, and one reason for the apparently low vari-
ation between datasets is precisely the reliance on the same
underlying reported energy data. The budget excludes some
sources of fossil CO₂ emissions, which available evidence
suggests are relatively small (< 1 %). We have added emis-
sions from lime production in China and the USA, but these
are still absent in most other non-Annex I countries and be-
fore 1990 in other Annex I countries.

Estimates of E_LUC suffer from a range of intertwined issues, including the poor quality of historical land-cover and land-use change maps, the rudimentary representation of management processes in most models, and the confusion in methodologies and boundary conditions used across methods (e.g. Arneth et al., 2017; Pongratz et al., 2014; see also the Supplement, Sect. S6.4, on the loss of sink capacity; Bastos et al., 2021). Uncertainties in current and historical carbon stocks in soils and vegetation also add uncertainty to the E_LUC estimates. Unless a major effort to resolve these issues is made, little progress is expected in the resolution of E_LUC. This is particularly concerning given the growing importance of E_LUC for climate mitigation strategies and the large issues in the quantification of the cumulative emissions over the historical period that arise from large uncertainties in E_LUC.

By adding the DGVM estimates of CO₂ fluxes due to en-
vironmental change from countries' managed forest areas
(part of S<sub>LAND</sub> in this budget) to the budget E<sub>LUC</sub> estimate,
we successfully reconciled the large gap between our E<sub>LUC</sub>
estimate and the land-use flux from NGHGIs using the ap-
proach described in Grassi et al. (2021) for future scenar-
ios and in Grassi et al. (2023) using data from the Global
Carbon Budget 2021. The updated data presented here can
be used for potential adjustments in the policy context, e.g.
to help assess the collective countries' progress towards the
goal of the Paris Agreement and to avoid double account-
ing for the sink in managed forests. In the absence of this
adjustment, collective progress would hence appear better
than it is (Grassi et al., 2021). The application of this adjust-
ment is also recommended in the UNFCCC Synthesis Report
for the first Global Stocktake (UNFCCC, 2022) whenever a
comparison between LULUCF fluxes reported by countries
and the global emissions estimates of the IPCC is conducted.
However, this adjustment should be seen as a short-term and
pragmatic fix based on existing data rather than as a defini-
tive solution to bridge the differences between global mod-
els and national inventories. Additional steps are needed to
<table><caption>**Table 10.** Major known sources of uncertainties in each component of the global carbon budget, defined as input data or processes that have a demonstrated effect of at least ±0.3 GtC yr⁻¹.</caption><thead><tr><th>Source of uncertainty</th><th>Timescale (years)</th><th>Location</th><th>Evidence</th></tr></thead><tbody><tr><td colspan="4">Fossil CO<sub>2</sub> emissions (E<sub>FOS</sub>; Sect. 2.1)</td></tr><tr><td>Energy statistics</td><td>annual to decadal</td><td>global but mainly China and major developing countries</td><td>Korsbakken et al. (2016), Guan et al. (2012)</td></tr><tr><td>Carbon content of coal</td><td>annual to decadal</td><td>global but mainly China and major developing countries</td><td>Liu et al. (2015)</td></tr><tr><td>System boundary</td><td>annual to decadal</td><td>all countries</td><td>Andrew (2020a)</td></tr><tr><td colspan="4">Net land-use change flux (E<sub>LUC</sub>; Sect. 2.2)</td></tr><tr><td>Land-cover and land-use change statistics</td><td>continuous</td><td>global, in particular the tropics</td><td>Houghton et al. (2012), Gasser et al. (2020), Ganzenmüller et al. (2022), Yu et al. (2022)</td></tr><tr><td>Sub-grid-scale transitions</td><td>annual to decadal</td><td>global</td><td>Wilkenskjeld et al. (2014), Bastos et al. (2021)</td></tr><tr><td>Vegetation biomass</td><td>annual to decadal</td><td>global, in particular the tropics</td><td>Houghton et al. (2012), Bastos et al. (2021)</td></tr><tr><td>Forest degradation (fire, selective logging)</td><td>annual to decadal</td><td>the tropics, Amazon</td><td>Aragão et al. (2018), Qin et al. (2021), Lapola et al. (2023)</td></tr><tr><td>Wood and crop harvest</td><td>annual to decadal</td><td>global, SE Asia</td><td>Arneth et al. (2017), Erb et al. (2018)</td></tr><tr><td>Peat burning</td><td>multi-decadal trend</td><td>global</td><td>van der Werf et al. (2010, 2017)</td></tr><tr><td>Loss of additional sink capacity</td><td>multi-decadal trend</td><td>global</td><td>Pongratz et al. (2014), Gasser et al. (2020), Obermeier et al. (2021), Dorgeist et al. (2024)</td></tr><tr><td>Environmental effects</td><td>multi-decadal trend</td><td>global</td><td>Gasser et al. (2020), Dorgeist et al. (2024)</td></tr><tr><td colspan="4">Atmospheric growth rate (G<sub>ATM</sub>; Sect. 2.4) – no demonstrated uncertainties larger than ±0.3 GtC yr<sup>-1</sup>; the uncertainties in G<sub>ATM</sub> estimated as ±0.2 GtC yr<sup>-1</sup>, although the conversion of the growth rate into a global annual flux assuming instantaneous mixing throughout the atmosphere introduces additional errors that have not yet been quantified</td></tr><tr><td colspan="4">Ocean sink (S<sub>OCEAN</sub>; Sect. 2.5)</td></tr><tr><td>Sparsity in surface fCO<sub>2</sub> observations</td><td>mean, decadal variability and trend</td><td>global, in particular the Southern Hemisphere</td><td>Gloege et al. (2021), Denvil-Sommer et al. (2021), Hauck et al. (2023a), Dong et al. (2024b)</td></tr><tr><td>Riverine carbon outgassing and its anthropogenic perturbation</td><td>annual to decadal</td><td>global, in particular partitioning between the tropics and the south</td><td>Aumont et al. (2001), Lacroix et al. (2020), Crisp et al. (2022)</td></tr><tr><td>Models underestimate interior-ocean anthropogenic carbon storage</td><td>annual to decadal</td><td>global</td><td>Friedlingstein et al. (2022a), this study, DeVries et al. (2023), Müller et al. (2023)</td></tr><tr><td>Near-surface temperature and salinity gradients</td><td>mean on all timescales</td><td>global</td><td>Watson et al. (2020), Dong et al. (2022), Bellenger et al. (2023), Dong et al. (2024a)</td></tr></tbody></table>
<table><caption>**Table 10. Continued.**</caption><thead><tr><th>Source of uncertainty</th><th>Timescale (years)</th><th>Location</th><th>Evidence</th></tr></thead><tbody><tr><td>Land sink (S<sub>LAND</sub>; Sect. 2.6)</td><td></td><td></td><td></td></tr><tr><td>Strength of CO<sub>2</sub> fertilization</td><td>multi-decadal trend</td><td>global</td><td>Wenzel et al. (2016), Walker et al. (2021)</td></tr><tr><td>Response to variability in temperature and rainfall</td><td>annual to decadal</td><td>global, in particular the tropics</td><td>Cox et al. (2013), Jung et al. (2017), Humphrey et al. (2018, 2021)</td></tr><tr><td>Nutrient limitation and supply</td><td>annual to decadal</td><td>global</td><td>Zaehle et al. (2014)</td></tr><tr><td>Carbon allocation and tissue turnover rates</td><td>annual to decadal</td><td>global</td><td>De Kauwe et al. (2014), O'Sullivan et al. (2022)</td></tr><tr><td>Tree mortality</td><td>annual</td><td>global, in particular the tropics</td><td>Hubau et al. (2020), Brienen et al. (2020)</td></tr><tr><td>Response to diffuse radiation</td><td>annual</td><td>global</td><td>Mercado et al. (2009), O'Sullivan et al. (2021)</td></tr><tr><td>Estimation under constant pre-industrial land cover</td><td>multi-decadal trend</td><td>global</td><td>Gasser et al. (2020), Dorgeist et al. (2024)</td></tr></tbody></table>

understand and reconcile the remaining differences, some of which are relevant at the country level (Grassi et al., 2023; Schwingshackl et al., 2022).

The comparison of GOBMs, fCO₂ products, and inversions highlights a substantial discrepancy in the temporal evolution of S<sub>OCEAN</sub> in the Southern Ocean and northern high latitudes (Fig. 14; Hauck et al., 2023a) and in the mean S<sub>OCEAN</sub> in the tropics. A large part of the uncertainty in the mean fluxes stems from the regional distribution of the river flux adjustment term. The current distribution simulates the largest share of the outgassing occurring in the tropics (Lacroix et al., 2020). The long-standing sparse data coverage of fCO₂ observations in the Southern Hemisphere compared to the Northern Hemisphere (e.g. Takahashi et al., 2009) continues to exist (Bakker et al., 2016, 2024; Fig. S1) and to lead to substantially higher uncertainty in the S<sub>OCEAN</sub> estimate for the Southern Hemisphere (Watson et al., 2020; Gloege et al., 2021; Hauck et al., 2023a). This discrepancy, which also hampers model improvement, points to the need for increased high-quality fCO₂ observations especially in the Southern Ocean. At the same time, model uncertainty is illustrated by the large spread of individual GOBM estimates (indicated by shading in Fig. 14) and highlights the need for model improvement. The issue of diverging trends in S<sub>OCEAN</sub> from different methods is smaller this year because the trend in the fCO₂ products was revised downwards with the data available in this GCB release, but it remains a matter of concern. Recent and ongoing work suggests that the fCO₂ products may overestimate the trend (Hauck et al., 2023a; see the Supplement, Sect. S3.4), though the full fCO₂-product ensemble remains to be tested. A data-constrained model approach suggests that the GOBMs underestimate the ampli-

tude of decadal variability but that the fCO₂ products overestimate the trend (Mayot et al., 2024). At the same time, evidence is accumulating that GOBMs likely underestimate the mean flux (Sect. 3.6.2; Terhaar et al., 2022; DeVries et al., 2023; Müller et al., 2023; Dong et al., 2024a). The independent constraint from atmospheric oxygen measurements gives a larger sink for the past decade and a steeper trend. However, the estimate is consistent within uncertainties with S<sub>OCEAN</sub>, with the relatively large ocean sink in the fCO₂ products and some of the GOBMs. The assessment of the net land-atmosphere exchange from DGVMs and atmospheric inversions also shows substantial discrepancy, particularly for the estimate of the net land flux over the northern extra-tropics. This discrepancy highlights the difficulty of quantifying complex processes (CO₂ fertilization, nitrogen deposition and fertilizers, climate change and variability, land management, etc.) that collectively determine the net land CO₂ flux. Resolving the differences in the Northern Hemisphere land sink will require the consideration and inclusion of larger quantities of observations.

We provide metrics for the evaluation of the ocean and land models and the atmospheric inversions (Figs. S2 to S4, Table S11). These metrics expand the use of observations in the global carbon budget, helping (1) to support improvements in the ocean and land carbon models that produce the sink estimates and (2) to constrain the representation of key underlying processes in the models and to allocate the regional partitioning of the CO₂ fluxes. The introduction of process-based metrics targeted to evaluate the simulation of S<sub>OCEAN</sub> in the ocean biogeochemistry models is an important addition to the evaluation based on ocean carbon observations. This is an initial step towards the introduction of a
broader range of observations and more stringent model eval-
uation that we hope will support continued improvements in
the annual estimates of the global carbon budget.

We have previously established that a sustained decrease
of -1% in global emissions could be detected at the 66%
likelihood level after a decade only (Peters et al., 2017a).
Similarly, a change in behaviour of the land and/or ocean
carbon sink would take as long to detect and much longer
if it emerges more slowly. To continue reducing the carbon
imbalance on annual to decadal timescales, regionalizing the
carbon budget and integrating multiple variables are power-
ful ways to shorten the detection limit and ensure the research
community can rapidly identify issues of concern in the evo-
lution of the global carbon cycle under the current rapid and
unprecedented changing environmental conditions.

6 Conclusions

The estimation of global CO₂ emissions and sinks is a ma-
jor effort by the carbon cycle research community that re-
quires a careful compilation and synthesis of measurements,
statistical estimates, and model results. The delivery of an
annual carbon budget serves two purposes. First, there is a
large demand for up-to-date information on the state of the
anthropogenic perturbation of the climate system and its un-
derpinning causes. A broad stakeholder community relies on
the datasets associated with the annual carbon budget includ-
ing scientists, policymakers, businesses, journalists, and non-
governmental organizations engaged in adapting to and mit-
igating human-driven climate change. Second, over recent
decades we have seen unprecedented changes in human and
biophysical environments (e.g. changes in the growth of fos-
sil fuel emissions, impact of COVID-19 pandemic, Earth's
warming, and strength of the carbon sinks), which call for
frequent assessments of the state of the planet, a better quan-
tification of the causes of changes in the contemporary global
carbon cycle, and an improved capacity to anticipate the evo-
lution of the global carbon cycle in the future. Building this
scientific understanding to meet the extraordinary climate
mitigation challenge requires frequent, robust, transparent,
and traceable datasets and methods that can be scrutinized
and replicated. Via living data, this paper helps to keep track
of new budget updates.

7 Data availability

The data presented here are made available in the belief that
their wide dissemination will lead to greater understanding
and new scientific insights of how the carbon cycle works,
how humans are altering it, and how we can mitigate the
resulting human-driven climate change. Full contact details
and information on how to cite the data shown here are given
at the top of each page in the accompanying database and are
summarized in Table 2.

The accompanying database includes three Excel files or-
ganized in the following spreadsheets.

The file Global_Carbon_Budget_2024v1.0.xlsx includes
the following:

1. a summary,

2. the global carbon budget (1959–2023),

3. the historical global carbon budget (1750–2023),

4. global CO₂ emissions from fossil fuels and cement pro-
duction by fuel type and the per capita emissions (1850–
2023),

5. CO₂ emissions from land-use change from the individ-
ual bookkeeping models (1959–2023),

6. the ocean CO₂ sink from the individual global ocean biogeochemistry models and fCO₂ products (1959–2023),

7. the terrestrial CO₂ sink from the individual DGVMs (1959–2023),

8. the cement carbonation CO₂ sink (1959–2023).

The file National_Fossil_Carbon_Emissions
_2024v1.0.xlsx includes the following:

1. summary,

2. territorial country CO₂ emissions from fossil fuels and cement production (1850–2023),

3. consumption country CO₂ emissions from fossil fuels and cement production and emissions transfer from the international trade of goods and services (1990–2020) using CDIAC/UNFCCC data as reference,

4. emissions transfers (consumption minus territorial emissions, 1990–2020),

5. country definitions.

The file National_LandUseChange_Carbon_Emissions
_2024v1.0.xlsx includes the following:

1. a summary,

2. territorial country CO₂ emissions from land-use change (1850–2023) from three bookkeeping models.

All three spreadsheets are published by the Integrated Car-
bon Observation System (ICOS) Carbon Portal and are avail-
able at https://doi.org/10.18160/GCP-2024 (Friedlingstein et
al., 2024). National emissions data are also available at
https://doi.org/10.5281/zenodo.13981696 (Andrew and Pe-
ters, 2024), from the Global Carbon Atlas (http://www.
globalcarbonatlas.org/, Global Carbon Project, 2024) and
from Our World in Data (2024, https://ourworldindata.org/
co2-emissions).
**Supplement.** The supplement related to this article is available online at https://doi.org/10.5194/essd-17-965-2025-supplement.

**Author contributions.** PF, MO, MWJ, RMA, JH, PL, CLQ, HL, ITL, AO, GPP, WP, JP, CS, and SSi designed the study, conducted the analysis, and wrote the paper with input from JGC, PCi, and RBJ. RMA, GPP, and JIK produced the fossil CO₂ emissions and their uncertainties and analysed the emissions data. MH and GMA provided fossil fuel emissions data. JP, TGa, ZQ, and CS provided the bookkeeping land-use change emissions with synthesis by JP and CS. SSm provided the estimates of non-vegetation CDR fluxes. LB, MC, ÖG, NG, TI, TJ, LR, JS, RS, and HTs provided an update of the global ocean biogeochemical models; LMD, ARF, DJF, MG, LG, YI, AJ, CR, AR, JZ, and PC provided an update of the ocean fCO₂ data products, with synthesis on both streams by JH, PL, and NMA. SRA, NRB, MB, CFB, HCB, KC, KE, WE, RAF, TGk, SKL, NL, NMe, NMM, SN, LO, TO, DP, AJS, ST, BT, CN, and RW provided ocean fCO₂ measurements for the year 2023, with synthesis by AO and TS. AA, VA, PCa, THC, JD, CDR, AF, JHe, AKJ, EK, JK, PCM, LM, TN, MO, QS, HTi, XYa, WY, XYu, and SZ provided an update of the dynamic global vegetation models, with synthesis by SSi and MO. HL, RSA, OT, and ET provided estimates of land and ocean sinks from Earth system models, as well as a projection of the atmospheric growth rate for 2024. NC, FC, ARJ, FJ, ZJ, JL, SM, YN, PIP, CR, DY, and NZ provided an updated atmospheric inversion. WP, FC, and ITL developed the protocol and produced the synthesis and evaluation of the atmospheric inversions. RMA provided projections of the 2024 fossil emissions and atmospheric CO₂ growth rate. PL provided the predictions of the 2024 ocean and land sinks. LPC, GCH, KKG, TMR, GRvdW, WX, and ZY provided forcing data for land-use change. FT and GG provided data for the land-use change GHGMI harmonization. RFK provided key atmospheric CO₂ data. EJM and RFK provided the atmospheric oxygen constraint on surface net carbon sinks. MWJ provided the historical atmospheric CO₂ concentration and growth rate. MO and NB produced the aerosol diffuse radiative forcing for the DGVMs. IH provided the climate forcing data for the DGVMs. PCM provided the evaluation of the DGVMs. MWJ provided the emissions prior for use in the inversion systems. XD provided seasonal emissions data for the most recent years for the emissions prior. PF, MO, and MWJ coordinated the effort and revised all figures, tables, text, and numbers to ensure the update was clear in relation to the 2023 edition and in line with https://globalcarbonatlas.org/ (last access: 21 January 2025).

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of *Earth System Science Data*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** The views expressed in this paper are the authors' only and do not necessarily reflect those of FAO.

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**Acknowledgements.** We thank all the people and institutions who provided the data used in the Global Carbon Budget 2024 and the Global Carbon Project members for their input throughout the development of this publication. We thank Nigel Hawtin for producing Figs. 2 and 16. We thank Alex Vermeulen and Hannah Ritchie for hosting the global carbon budget datasets on the ICOS portal and the Our World in Data website, respectively. We thank Ram Alkama, Ian G. C. Ashton, Dorothee Bakker, Raffaele Bernardello, Ida Bagus Mandhara Brasika, Sebastian Brune, Fatemeh Cheginig, Emeric Claudel, Jason Cole, Lushanya Dayathilake, Pengyue Du, Christian Ethé, Stefanie Falk, Kristina Frölich, Lonneke Goddijn-Murphy, Ian Harman, Thomas Holding, Drew Holzworth, Rajesh Janardanan, Daniel Kennedy, Erik Kluzek, Fabrice Lacroix, Vladimir Lapin, Peter Lawrence, Sam Levis, Yi Liu, Damian Loher, Zoé Lloret, Adrien Martinez, Hideyuki Nakano, Lorna Nayagam, Naiqing Pan, Shufen Pan, Tristan Quaife, Simone Rossi, Paridhi Rustogi, Jamie D. Shutler, Richard Sims, Victoria Spada, Sean Swenson, Phillip Townsend, Katsuya Toyama, Shogo L. Urakawa, Anthony P. Walker, Jing Wang, Andrew J. Watson, S. Lachlan Whyborn, David K. Woolf, and Yakun Zhu for their involvement in the development, use, and analysis of the models and data products used here. We thank Kim Currie, Siyabulela Hamnca, Boris Herrmann, Arne Körtzinger, C. Lo Monaco, Team Malizia, Pedro Monteiro, and Mutshutshu Tsanwani, who contributed to the provision of surface ocean CO₂ observations for the year 2023 (see Table S7). We also thank Stephen D. Jones of the Ocean Thematic Centre of the EU Integrated Carbon Observation System (ICOS) Research Infrastructure, Eugene Burger of NOAA's Pacific Marine Environmental Laboratory, and Alex Kozyr of NOAA's National Centers for Environmental Information for their contribution to surface ocean CO₂ data and metadata management. We thank the scientists, institutions, and funding agencies responsible for the collection and quality control of the data in SOCAT as well as the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean - Lower Atmosphere Study (SOLAS), and the Integrated Marine Biosphere Research (IMBeR) programme for their support. We thank Nadine Goris and Lavinia Patara for support in calculating observational ocean evaluation metrics. We thank Fortunat Joos, Samar Khatiwala, and Timothy DeVries for providing historical atmospheric and ocean data. We thank the data providers ObsPack GLOBALVIEWplus v9.0 and NRT v9.2 for atmospheric CO₂ observations. Ingrid T. Luijkx and Wouter Peters thank the CarbonTracker Europe team at Wageningen University, including Remco de Kok, Joram Hooghiem, Linda Kooijmans, and Auke van der Woude. Ian Harris thanks the Japan Meteorological Agency (JMA) for producing the Japanese 55-year Reanalysis (JRA-55). Reinel Sospedra-Alfonso thanks William J. Merryfield, Woosung Lee, Jason Cole, and Victoria Spada for their help in setting up and producing CanESM5 runs. Olivier Torres thanks Patricia Cadule, Juliette Mignot, Didier Swingedouw, and Laurent Bopp for contributions to the IPSL-CM6A-CO₂-LR simulations. Yosuke Niwa thanks CSIRO, EC, EMPA, FMI, IPEN, JMA, LSCE, NCAR, NIES, NILU, NIWA, NOAA, SIO, and TU/NIPR for providing data for NISMON-CO₂. Zhe Jin thanks Xiangjun Tian, Yi-
long Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, and Shilong Piao for their contributions to the GONGGA inversion system. Paul I. Palmer thanks Lian Fang and acknowledges ongoing support from the National Centre for Earth Observation. Ning Zeng thanks Zhiqiang Liu, Yun Liu, Eugenia Kalnay, and Gassem Asrar for their contributions to the COLA system. Fei Jiang acknowledges the High-Performance Computing Center (HPCC) of Nanjing University for performing the inversions on its blade cluster system and thanks Weimin Ju for updating a priori fluxes of the terrestrial ecosystems. Meike Becker and Are Olsen thank Sparebanken Vest/Agenda Vestlandet for their support for the observations on the *Statsraad Lehmkuhl*. Wiley Evans and Katie Campbell thank the Tula Foundation for funding support. Thanos Gkritzalis and the VLIZ ICOS team are thankful to the crew of the research vessel *Simon Stevin* for all the support and help they provide. Bronte Tilbrook and Craig Neill thank Australia's Integrated Marine Observing System (IMOS) for sourcing CO₂ data. FAOSTAT is funded by FAO member states through their contributions to the FAO Regular Programme; data contributions by national experts are greatly acknowledged. Finally, we thank all funders who have supported the individual and joint contributions to this work (see details below), as well as the two reviewers of this paper and the many researchers who have provided feedback.

**Financial support.** This research was supported by the following sources of funding: the Argentinian-Uruguayan Joint Technical Commission of the Maritime Front (Comisión Técnica Mixta del Frente Marítimo, CTMFM) (Argentina); the Instituto Nacional de Investigación y Desarrollo Pesquero (Argentina); Australia's Integrated Marine Observing System (IMOS), which is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS) (Australia); Australian Community Climate and Earth System Simulator National Research Infrastructure (ACCESS-NRI) (Australia); the Australian National Environmental Science Program, Climate Systems Hub (Australia); the Research Foundation - Flanders (ICOS Flanders, grant no. I001821N) (Belgium); the Tula Foundation (Canada); the National Key Research and Development Program (grant nos. 2021YFD2200405, 2023YFF0805400, and 2023YFB3907404) (China); the Jiangsu Provincial Science Fund for Distinguished Young Scholars (grant no. BK20231530) (China); the National Natural Science Foundation (grant no. 42141020) (China); the Carbon Neutrality and Energy System Transformation (CNEST) programme led by Tsinghua University, granted by the National Key Research and Development Program of China (grant no. 2023YFE0113000) (China); the Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2022QZKK0101) (China); the CAS Project for Young Scientists in Basic Research (grant no. YSBR-037) (China); the Copernicus Atmosphere Monitoring Service, implemented by ECMWF (grant no. CAMS2 55) (European Commission); the Copernicus Marine Environment Monitoring Service, implemented by MOI (grant no. CMEMS-TAC-MOB) (European Commission); Horizon 2020 4C (grant no. 821003) (European Commission); Horizon 2020 ESM2025 - Earth System Models for the Future (grant no. 101003536) (European Commission); Horizon 2020 GEORGE (grant no. 101094716) (European Commission); Horizon 2020 EYE-CLIMA (grant no. 101081395) (European Commission); ERC-2022-STG OceanPeak (grant no. 101077209) (European Commission); Horizon 2020 OceanICU Improving Carbon Understanding (grant no. 101083922) (European Commission); Horizon 2020 RESCUE project (grant no. 101056939) (European Commission); Horizon 2020 COMFORT project (grant no. 820989) (European Commission); Climate Space RECCAP-2 (European Space Agency); Ocean Carbon for Climate (European Space Agency); EO-LINCS (European Space Agency); the OceanSODA project (grant no. 4000112091/14/I-LG) (European Space Agency); Institut National des Sciences de l'Univers (INSU) (France); Institut Polaire français, Paul-Émile Victor (IPEV) (France); Observatoire des sciences de l'univers Ecce Terra (OSU at Sorbonne Université) (France); Institut de recherché français sur les ressources marines (IFREMER) (France); the French Oceanographic Fleet (FOF) (France); ICOS France (France); Institut de Recherche pour le Développement (IRD) (France); Agence Nationale de la Recherche - France 2030 (PEPR TRACCS programme under grant no. ANR-22-EXTR-0009) (France); Institut de l'Océan and the Institut des Sciences du Calcul et des Données de Sorbonne Université (IDEX SUPER 11-IDEX-0004, project team FORMAL) (France); the Federal Ministry of Education and Research, collaborative project C-SCOPE (project no. 03F0877A) (Germany); the Helmholtz Association ATMO programme (Germany); the Initiative and Networking Fund of the Helmholtz Association (grant no. VH-NG-19-33) (Germany); ICOS Germany (Germany); the Federal Ministry of Education and Research (BMBF), project STEPSEC (grant no. 01LS2102A) (Germany); Helmholtz Young Investigator Group Marine Carbon and Ecosystem Feedbacks in the Earth System (MarESys, grant no. VH-NG-1301) (Germany); Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy - EXC 2037 "Climate, Climatic Change, and Society" (project no. 390683824) (Germany); the Arctic Challenge for Sustainability II project (ArCS II; grant no. JP-MXD1420318865) (Japan); the Environment Research and Technology Development Fund (grants no. JPMEERF24S12205, JPMEERF24S12206, and JPMEERF24S12200) (Japan); CREST, Japan Science and Technology Agency (grant no. JPMJCR23J4) (Japan); the Global Environmental Research Coordination System, Ministry of the Environment (grant no. E2252) (Japan); the Meteorological Agency (Japan); the Ministry of Education, Culture, Sports, Science and Technology, MEXT Program for The Advanced Studies of Climate Change Projection (SENTAN) (grant nos. JPMXD0722680395 and JPMXD0722681344) (Japan); the NIES GOSAT project (Japan); the Research Council of Norway (N-ICOS-2, grant no. 296012) (Norway); the Research Council of Norway (grant no. 270061) (Norway); the Swiss National Science Foundation (grant no. 200020-200511) (Switzerland); the Natural Environmental Research Council, National Centre for Earth Observation (grant no. NE/R016518/1) (UK); the Natural Environment Research Council, UK EO Climate Information Service (grant no. NE/X019071/1) (UK); the Natural Environment Research Council (grant nos. NE/V01417X/1, NE/Y005260/1, and NE/V013106/1) (UK); the Natural Environment Research Council, National Centre for Atmospheric Science (UK); the Royal Society (grant no. RP\R1\191063) (UK); the National Center for Atmospheric Research (NSF cooperative agreement no. 1852977) (USA); the NOAA Ocean Acidification Program (grant no. 100018228) (USA); the NOAA Global Ocean Monitoring and Observing Program (grant no. 100018302) (USA); NOAA (cooperative agreement nos. NA22OAR4320151 and NA20OAR4310340
and grant no. 1305M322PNRMJ0338) (USA); NASA (grant nos. 80NSSC22K0150 and 80NM0018D0004); the National Science Foundation (grant nos. NSF-2019625, NSF-831361857, NSF-1903722, and NSF-1852977) (USA); the NASA Terrestrial Ecology Program (USA); NASA Carbon Monitoring System programme (80NSSC21K1059) (USA); the NASA Land Cover and Land Use Change Program (80NSSC24K0920) (USA); the US Department of Energy, Office of Science, Office of Biological and Environmental Research (USA); the Department for Education SciDAC (grant no. DESC0012972) (USA); IDS (grant no. 80NSSC17K0348) (USA); and Schmidt Sciences, LLC (USA).

We were also supported by the following computing facilities: the Australian Community Climate and Earth System Simulator National Research Infrastructure (ACCESS-NRI) (Australia); the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bm0891 (Germany); HPC cluster Aether at the University of Bremen, financed by DFG within the scope of the Excellence Initiative (Germany); HPC resources of TGCC under the allocation A0150102201 awarded by GENCI and of CCRT under grant no. CCRT2024-p24cheva awarded by CEA/DRF (France); HPC resources of Météo-France (France); JAMSTEC's ES4 supercomputer system (Japan); the NIES supercomputer system (Japan); UNINETT Sigma2, national infrastructure for high-performance computing and data storage in Norway (NN2980K and NS2980K) (Norway); the UK CEDA JASMIN supercomputer (UK); the UEA (University of East Anglia) high-performance computing cluster (UK); the Derecho supercomputer (https://doi.org/10.5065/D6RX99HX), provided by the Computational and Information Systems Laboratory (CISL) at NCAR (USA); the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory (USA); and, for ISAM simulations, Cheyenne, NCAR HPC resources managed by CISL (https://doi.org/10.5065/D6RX99HX) (USA).

**Review statement.** This paper was edited by Kirsten Elger and reviewed by H. Damon Matthews and Andrew Lenton.

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