# Challenges to achieve carbon neutrality of china by 2060: status and perspectives

Baozhang Chen, Fahu Chen, Philippe Ciais, Huifang Zhang, Honggang Lü, Tao Wang, Frederic Chevallier, Zhu Liu, Wenping Yuan, Wouter Peters

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Baozhang Chen, Fahu Chen, Philippe Ciais, Huifang Zhang, Honggang Lü, et al.. Challenges to achieve carbon neutrality of china by 2060: status and perspectives. Science Bulletin, 2022, 10.1016/j.scib.2022.08.025. hal-03775397

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1 Perspective

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3 Challenges to achieve carbon neutrality of China by 2060: status and perspectives
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Baozhang Chen<sup>1,2,3*</sup>, Fahu Chen<sup>4</sup>, Philippe Ciais<sup>5</sup>, Huifang Zhang<sup>2</sup>, Honggang Lü<sup>6</sup>,

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Tao Wang<sup>7,8</sup>, Frédéric Chevallier<sup>5</sup>, Zhu Liu<sup>9</sup>, Wenping Yuan<sup>10</sup>, Wouter Peters<sup>11,12</sup>

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<sup>1</sup>School of Remote Sensing and Geomatics Engineering, Nanjing University of
Information Science and Technology, Nanjing 210044, China

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<sup>2</sup>State Key Laboratory of Resource and Environmental Information System, Institute
of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China.

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<sup>3</sup> College of Resources and Environment, University of Chinese Academy of Sciences,
Beijing 100190, China.

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<sup>4</sup>Alpine Paleoecology and Human Adaption Group, State Key Laboratory of Tibetan
Plateau Earth System Science, Institute of Tibetan Plateau Research, Chinese
Academy of Sciences, Beijing 100101, China

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<sup>5</sup>Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France.

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<sup>6</sup>Key Laboratory of Marine Hazards Forecasting, National Marine Environmental
Forecasting Center, Ministry of Natural Resources of China, Beijing 100081, China.

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<sup>7</sup>Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese
Academy of Sciences, Beijing 100085, China

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<sup>8</sup>Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences,
Beijing 100085, China.

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<sup>9</sup>Department of Earth System Science, Tsinghua University, Beijing 100084, China

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<sup>10</sup>School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate
Change and Natural Disaster Studies, Zhuhai Key Laboratory of Dynamics Urban
Climate and Ecology, Sun Yat-sen University, Zhuhai 510245, China.

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<sup>11</sup>Department of Meteorology and Air Quality, Environmental Sciences Group,
Wageningen University (WU), Wageningen 6708 PB, the Netherlands.

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<sup>12</sup>Centre for Isotope Research, Energy and Sustainability Research Institute
Groningen, Groningen University, Groningen 729700 AB, the Netherlands.

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Corresponding author: Baozhang Chen (baozhang.chen@igsnrr.ac.cn)

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China's high ambitions to reach peak CO₂ emissions by 2030 and carbon neutrality by 2060 make carbon mitigation an urgent issue with widespread societal consequences. To develop an achievable roadmap and an effective portfolio of climate policies, it is essential that a clear picture of the magnitude and uncertainty of China's current carbon balance is available, at both national and regional levels.

The continued increase in atmospheric carbon dioxide (CO₂) concentration in the last half century is primarily due to anthropogenic burning of fossil fuels and land-use change. The resulting global warming is already affecting the entire world, with various natural disasters (e.g., drought, heat waves, heavy rain, floods, and landslides) becoming increasingly frequent or intense [1]. To limit global warming to 1.5 °C above preindustrial levels within this century—a safe threshold indicated by the Intergovernmental Panel for Climate Change (IPCC), it is necessary but not sufficient that carbon neutrality is achieved by mid-21st century. The Paris Agreement on climate change also laid down this target signed by 195 countries, including China. At the general debate of the 75th session of the United Nations General Assembly, President Xi Jinping stated that China is targeting peak CO₂ emissions by the year 2030 and carbon neutrality by 2060. This has made carbon mitigation an essential and urgent issue in China, affecting politics, culture, economy, environment, industry, lifestyle, and ecology.

Carbon neutrality refers to achieving net zero CO₂ emissions by balancing CO₂ emissions with carbon removal (for example, through biogenic CO₂ sequestration). To develop an achievable roadmap to reach carbon neutrality by 2060, it is essential that a clear picture of the magnitude and uncertainty of the current carbon balance emerges, and the challenges to improve it are clearly laid out. That means the Chinese “net carbon budget” must be based on accurate characterization of anthropogenic CO₂ emissions and terrestrial biospheric carbon sinks. In principle only human-managed carbon sinks will be considered under the Paris agreement, but in practice all terrestrial biosphere carbon sinks including natural sinks are included in inventories, given the difficulty to distinguish between managed and unmanaged carbon exchange. A comprehensive assessment of China's carbon balance will help to understand the global carbon cycle, support climate policies, and help projections of climate change for the 21st century. Here, we review the status of China's contribution to carbon neutrality (CCN) at national and regional levels during the last two decades. CCN refers to the difference from the current net carbon budget to net zero CO₂ emission,
where the net carbon budget is the sum of carbon emission and biosphere sequestration. We do not consider carbon transferring and trade (not distinguishing carbon producers and consumers) between China and other countries. We point out remaining uncertainty, weaknesses in our capacity to quantify carbon exchange, and challenges for (inter)national monitoring and verification of China's carbon balance.

(i) National and regional contribution to carbon neutrality. Anthropogenic CO₂ emissions include emission from fossil fuel combustion, oxidation from all energy and industrial processes, as well as cement production. The anthropogenic CO₂ emissions in this work are primarily estimated based on 10 data sources (Text S1 and Table S1 online) [2] and the biosphere CO₂ uptake is inferred from an ensemble of five state-of-the-art so-called “atmospheric inversion systems” (Text S2 and Table S2 online) which estimate natural carbon exchange as constrained by a global network of atmospheric CO₂ mole fraction observations. All five selected inversion models have outputs (surface fluxes of carbon) at a 1° × 1° spatial resolution (Table S2 and Fig. S1 online), and are aggregated to annual or decadal totals from their originally higher temporal resolution. Land use change currently results in a small sink in China [3], which is implicitly included in our inversions [4], while carbon loss by the lateral transport of carbon in air and water and international/provincial trades is excluded in inversions. The national and regional anthropogenic CO₂ emissions, biosphere uptake and CCN during the period 2000–2019 are shown in Table 1, and Fig. 1 and Fig. S1 (online).

All 10 datasets [2] (Text S1 & Table S1 online) show that anthropogenic CO₂ emissions of China increased by 9.02% a⁻¹ on average. Emissions increased from 3.34 ± 0.31 in 2000 to 7.62 ± 1.30 in 2009 and to 10.11 ± 0.90 Gt CO₂ in 2019, with the decadal mean emissions of 5.24 ± 0.34 and 9.34 ± 0.63 Gt CO₂ in the first and second decades, respectively. The decadal mean increase rate went down from 9.02 in the first decade to 2.33% a⁻¹ in the second decade (Fig. 1 and Fig. S2a (online), and Table 1), mainly owing to mitigation policy, improvement of energy efficiency, and cleaner production technology.

Fig. 1. National and regional CO₂ emissions, uptakes and contribution to carbon neutrality for the baseline and mitigation scenarios. (a) National annual CO₂ emissions, biosphere uptake and contribution to carbon neutrality (CCN, the difference from the current net cumulative CO₂ emissions to net zero emission = biosphere uptake (positive value) plus carbon capture and storage (CCS) minus gross positive emissions). (b) Comparison of China's scenarios of CO₂ emission peak and neutrality with other key nations and community. The CO₂ emission peak and neutrality are respectively set to be achieved by 2030 and by 2060
for two mitigation scenarios: the scenario (brown solid line) assumes that the emissions would increase at the same rate as the mean rate (2.33% a⁻¹) of the past decade (2010 – 2019) and the scenario of the “14th five” year plan of China (dashed red line): assuming that the emissions would increase at the mean rate (2.33% a⁻¹) during the past decade (2010 – 2019) and additionally depressed by 18% per 5-year period according to the “14th five” year plan of China [5]; and net cumulative emissions were calculated as the scenario of the “14th five” year plan of China offset by biosphere uptake in the past and during the period 2020 – 2030 offset by the past two decades’ mean value of 1.52 Gt CO₂ a⁻¹ (Table 1). EU refers European Union. Emissions were estimated from the ten emission datasets (Table S1 online) and the uptake was inferred from the atmospheric CO₂ mole fraction measurements by an ensemble of five world-wide state-of-the-art inversion systems at a 1° × 1° resolution (Text S2 and Table S2 online). (c) Carbon balance in the nine regions of China. The nine regions are Northeast China, Inner Mongolia, Northwest China, North China, Central China, Tibetan Plateau, Southeast China, South China, and Southwest China. Decadal mean annual CCN of these nine regions (color map, CCN = biosphere uptake (dark blue columns, inverted using CT-China with GB + 7 Chinese background sites) minus emissions (grey columns) during 2010 – 2019. An estimate of biosphere uptake inverted by an ensemble of five models with GB data (green column) during 2010-2019 and an averaged estimate (light blue column) reported by Piao et al. [6] using three different methods: biomass and soil carbon inventories extrapolated by satellite greenness measurements, ecosystem models and atmospheric inversions, and an inversion result with GB data (purple column) by Piao et al. [6] during 1980s-1990s are also shown for comparison. (d) National emissions for the baseline and mitigation scenarios. The historical emissions (grey solid line), the emission scenario with the same increasing rate as the mean rate (2.33% a⁻¹) of the past decade (2010-2019) (dashed grey line), the scenario of the “14th five” year plan (red slid line), the scenario of IPCC-SSP1 [7] (solid purple) and emissions following the scenario of CEDSP2035 (China’s Energy Development Strategic Plan 2035, orange solid line and area) [8] are compared here. Gross negative emissions show as negative values: historical biosphere uptake (dark green), future biosphere uptake (= the past two decades’ mean value of 1.52 Gt CO₂ a⁻¹, light green) and CCUS (carbon capture, utilization and storage, blue solid line). Net cumulative emissions (CEDSP2035) (thick black solid line) was calculated as emissions following the scenario of CEDSP2035 (China’s Energy Development Strategic Plan 2035) minus gross negative emissions (biosphere uptake +CCUS [5]). (e) Annual regional aggregate gross emissions during the periods 2000-2009 and 2010-2019 and in 2019. (f) Annual regional aggregate biosphere uptake during the periods 2000-2009 and 2010-2019 and in 2019. (g) Annual CCN during the period 2010-2019 calculated as the difference in the emission and the biosphere uptake by an ensemble of five inversion models and by CTC, respectively. All the inversions do not consider the lateral transport of carbon in air and water and international trade. a gigaton (Gt CO₂) = 1×10¹⁵ g CO₂.

Here, we extensively review and analyze China’s historical terrestrial CO₂ uptake during 1980s-2020s using different approaches, such as biomass and soil carbon inventories, ecosystem models, eddy covariance (EC) flux products from machine learning and atmospheric inversions constrained by various atmospheric data sets[4,6,9]. These estimates of net terrestrial carbon sinks over China range between 1.21 ± 1.32 [4] and 5.39 ± 1.39.[9] Gt CO₂ a⁻¹. As reviewed by Chen et al. [4], Wang et al.’s [9] estimate is about three times higher than that of other estimates, and would account by itself for about 30%-50% of the global land biosphere carbon sink [3,4]. The high range of atmospheric-CO₂ based estimates of the Chinese land biosphere carbon sink is often difficult to reconcile with those from bottom-up approaches, which use ecological surveys and other accounting data to construct a nationwide terrestrial net sink [4]. For example, carbon uptake in East Asia (mainly including China, Mongolia, Japan, and Korea) was estimated to be 1.17 ± 0.40 Gt CO₂ a⁻¹
during 2000–2009 using satellite greenness measurements, terrestrial ecosystem carbon cycle models and atmospheric inversion models [4]; much higher than an ecosystem-model-based estimate of 0.43 ± 0.29 Gt CO₂ a⁻¹ during the period 1982–2010 [10].

In this study the agreement between methods is much higher. We estimate the Chinese terrestrial biosphere uptake from an ensemble of five inversions constrained by the global atmospheric CO₂ data [4] (Text S2 online) for the periods 2000–2009, 2010–2019 and the year 2019 to be -1.63 ± 1.15, -1.41 ± 1.08, and -1.67 ± 0.89 Gt CO₂ a⁻¹, respectively (Table 1 and Fig. 1c, f). Using our collected 82 EC flux tower measurements over Chinese terrestrial ecosystems and 9 sites located in its neighboring regions (Text S2 online), the Chinese CO₂ uptake is estimated to be 1.64 ± 0.12 Gt CO₂ a⁻¹ ([4], also see Table 1). This agreement is independent of the choice of sites included (Wang et al. [9] used additional Chinese tower observations): we also estimated the Chinese land sink using CTC (CT-China) [4] constrained by the global CO₂ data and the additional data from the six CMA (China Meteorological Administration) CO₂ sites ((Lin'an (30.30°, 119.72°), Longfengshan (44.70°, 127.60°), Shangdianzi (40.65°, 117.12°), Shangri-La (28.00°, 99.40°), Jinsha (29.63°, 114.22°), and Akedala (47.10°, 87.93°)). For the period 2010–2019 and the year 2019 we find -1.46 ± 1.08 and -1.68 ± 0.89 Gt CO₂ a⁻¹, respectively, which is close to the estimates by our ensemble of five inversions with the global atmospheric CO₂ datasets only (Fig. 1f). This ensemble thus represents the lower range of net carbon uptake estimates for China, agrees with bottom-up, and robustly captures variability in China's carbon sinks driven by El Niño events (2002/2003, 2005/2006, and 2019) and weather extremes (2009/2010 drought) (Fig. S2b online).

Provincial emissions in 2019 are close to their average values during 2010-2019. In 2019 (Fig. S3e online), the five highest emitting provinces were Jiangsu, Shandong, Hebei, Inner Mongolia, and Shanxi, with emissions ranging from 0.83 to 0.58 Gt CO₂ a⁻¹. The five lowest emitting provinces are Tibet, Hainan, Qinghai, Beijing, and Tianjin, with emissions ranging from 7.65 to 76.40 Mt CO₂ a⁻¹. The average emission of the top five provinces in 2019 (356.46 Mt CO₂ a⁻¹) is about 10 times higher than that of the five lowest emitting provinces (40.49 Mt CO₂ a⁻¹). Among the top five emitters, Shandong Province has the largest energy consumption, while Shanxi and Inner Mongolia have the lowest carbon emission efficiency (CEE), and the worst performance in carbon emission reduction across recent years [11,12].
Among the five lowest emitters, Beijing and Tianjin have the highest gross domestic product (GDP) per capita with highest energy efficiency, whereas Tibet, Hainan, and Qinghai have the lowest GDP per capita, the largest energy consumption and lower CEE [11]. Both provincial aggregate CO₂ emissions and CEE, and their growth rates in China, have significant spatial heterogeneity and differences between various periods [12]. These differences across provinces are driven, among others, by variation in population, economic growth, regional structure, energy efficiency, industrial structure, energy mix, and emission intensity [11,12]. It has been found that scale economy, industrial structure, degree of opening up, foreign/provincial direct investment, energy intensity, government interference, ownership structure, and capital-labor ratio have different impacts on the CEE across provinces [13].

We calculate regional aggregate carbon uptakes from gridded values at a 0.1°×0.1° resolution, interpolated from outputs of the ensemble of the five inversions at a 1°×1° resolution using Kriging (Fig. S1 online). Fig. 1c provides mean annual aggregate CO₂ emissions (10 datasets on average), biosphere uptake (ensemble of five inversions with global atmospheric CO₂ data and CTC with the additional 6 CMA sites) and CCN (ensemble-emissions) during 2010-2019 in the nine regions of China following the regional classification by Piao et al. [6]. The carbon uptake estimates in these nine regions based on different approaches (inversions and inventory) by Piao et al. [6] during 1980s-1990s are also compared in Fig. 1c. At this aggregate scale, the three most emitting regions during 2010-2019 are Inner Mongolia (1.91 Gt CO₂ a⁻¹, 20.5% of China), north China (1.71 Gt CO₂ a⁻¹, 15.9% of China), and central China (1.42 Gt CO₂ a⁻¹, 13.5%), accounting for half of China's emissions (Fig. 1c, e); while Tibetan Plateau only accounts for about 1% of the total (Fig. 1c, e).

The regions with the largest carbon sinks using CTC constrained by all available CO₂ sites are northeast China (0.068 Gt C a⁻¹) and north China (0.061 Gt C a⁻¹) which respectively account for 17% and 15% of the total country area. The two lowest biosphere carbon uptake regions are northwest China (0.033 Gt C a⁻¹) and South China (0.010 Gt C a⁻¹) (Fig. 1c, f). These differences among the nine regions over China can mainly explained by the differences in the provincial pattern of rapid afforestation [4,6,14], climate change [6] and enhancement of soil carbon sinks over grassland due to accelerated vegetation growth by warming and wetting and by protection measures from the beginning of this century [6,14].

Fig. 1g shows CCN values during 2010-2019, calculated as the difference of the
218 averaged emissions based on ten datasets and uptake. The regions with the largest
219 negative values (least contribution to reaching carbon neutrality) are Inner Mongolia
220 (-0.461 Gt C a⁻¹) and North China (-0.362 Gt C a⁻¹), only the Tibetan Plateau has a
221 positive contribution to carbon neutrality (0.022 Gt C a⁻¹) as its estimated terrestrial
222 sinks exceed its fossil fuel emissions.

223 (ii) Sources of uncertainties in carbon budgeting. Carbon emissions are calculated
224 from activity data and emission factors (EFs). Uncertainties of emission estimates are
225 considerable (Fig. S2a online) and are likely to be mainly systematic errors, related to
226 underlying biases of activity data and the EFs used. There is a large discrepancy
227 among the current available datasets (Table S1 online), ranging from 8.93 Gt CO₂ for
228 the year 2014 (China's official greenhouse gas inventory report: NCCC) to 10.54 Gt
229 CO₂ (NJU) and with a standard deviation (σ) of 0.52 Gt CO₂. The uncertainties
230 mainly stem from: (1) different EFs choices (obtained from either the IPCC default
231 values or local optimized values from different sources) and different treatments
232 (being constant over time or changing with time); (2) differences in activity data; and
233 (3) differences in emission definitions (emission sectors). Among them, the difference
234 in EF values of coal is the major source because the EF values of raw coal have a
235 large range of 0.491 to 0.746 t C tcoal⁻¹. The Emissions Database for Global
236 Atmospheric Research (EDGAR) used 0.713 t C per t of coal from the IPCC
237 recommended default values, while Carbon Emission Accounts and Datasets
238 (CEADs) used 0.499 based on large-sample measurements [2,15]. The estimates using
239 the locally measured EFs were typically lower and considered to be more realistic,
240 such as CEADs and Liu et al.[15] (Table S1 online). The uncertainties in estimates of
241 emissions from China were reported to be around ±10% (for ±1σ) [3].

242 The United Nations Framework Convention on Climate Change (UNFCCC)
243 regulations that national emission statistics "include greenhouse gas emissions and
244 removals taking place within national territory and offshore areas over which the
245 country has jurisdiction" and are called territorial emission inventories [3]. Carbon
246 Dioxide Information Analysis Center (CDIAC) follows this regulation while other
247 datasets do not. This definition difference is another source of uncertainties.

248 Consumption-based emission inventories equals territorial (provincial) emission
249 minus emission due to exports and plus emissions from imports, providing additional
250 information to provincial-based emissions that can be used to quantify emission
251 transfers by the trade of products between provinces. This information is useful for
252 making provincial policy compensation by the central government of China.

253 Accurate assessment of the strength of China's terrestrial ecosystem carbon sink
254 is key to evaluate effectiveness of national and provincial carbon neutrality actions.

255 However, large uncertainties in current carbon sink estimations still exist [4,6].

256 During the last four decades, several approaches have been developed to quantify
257 carbon sequestration in terrestrial ecosystems at the regional scales including
258 atmospheric inversions [4,6], process-based ecosystem models [3,10], inventory
259 approaches [6] and EC flux data-oriented machine learning models [4].

260 The state-of-the-art inversion systems still produce a large divergence in estimate
261 of biosphere carbon sink, and such divergence likely stems from the choices of
262 atmospheric CO2 observations, atmospheric transport models, assimilation methods
263 and inversion configurations, such as assimilation framework, resolution, time step,
264 lag window (if applicable), a priori information, assumed fossil emissions, and
265 number of unknowns in the state vector. Among all of these reasons, a sparse network
266 of in situ atmospheric CO2 observations over China is a bottleneck [4]. Though
267 satellite CO2 observations have an extensive spatial coverage, their poor precision
268 (often far exceeding 1 ppm) and spatial biases (e.g., the data coverage would be
269 affected by aerosol pollution in northern China and clouds in southern China) restrict
270 their application. A trustworthy biosphere surface fluxes of China strongly depends on
271 expansion of a high precision CO2 monitoring network and improvement of
272 atmospheric inversion methodology. It is noticed that the inverted CO2 uptake by
273 terrestrial ecosystems at provincial levels exhibit large uncertainties mainly owing to
274 the very limited number of in situ atmospheric CO2 observation sites over China [4].

275 In addition, year-to-year variations in the terrestrial CO2 uptake are generally large
276 and their values depend on climate variations and human management of ecosystems
277 (Fig. S2b online). Atmospheric inversion excludes also international/provincial trade
278 and crop biomass burning, it hence might overestimate terrestrial carbon sinks, mainly
279 in croplands. This uncertainty should be considered, especially for CNN assessment at
280 a provincial level. Scrutiny by a large inversion modeling community, therefore, is
281 needed to obtain a robust estimate of China's uptake at regional and finer scales to
282 meet carbon neutrality assessment requirement.

283 There are different advantages to the use of top-down inversions and the three
284 commonly used bottom-up approaches including process-based ecosystem models
285 [3,4,10], biomass and soil carbon inventories extrapolated by satellite greenness
measurements and machine learning models based on all land surface available data [4,6]. Hence, to optimally estimate Chinese and provincial biosphere carbon sinks, an integration method involving both top-down and bottom-up approaches is needed.

Improvement of ecosystem process model is also necessary as it is critical to project future ecosystem uptake [10] and to assess future national CNN under climate change and mitigation scenarios.

(iii) Challenges and regional-oriented smarter strategies. Some countries have already reached their “carbon emission peak”, at least in terms of direct territorial emissions. For instance, the European Union, USA, and Japan reached their peak emission in 1979, 2006, and 2006, respectively (Fig. 1b). To achieve “carbon neutrality” in 2050, they still need to strongly accelerate emission reductions (Fig. 1b). However, China’s aggregate carbon emissions are still increasing. They should peak by 2030, and then be reduced to reach the point of “carbon neutrality” before 2060. We can clearly see from the two roadmap scenarios (Fig. 1b) that from either the amount (pressure) of emission reduction or from the time between the years of carbon peak and carbon neutrality, the downward slopes of carbon emissions are steeper and more challenging for China than for developed countries. China’s national CO₂ emissions for the baseline and mitigation scenarios are shown in Fig. 1d. The scenario of IPCC’s SSP1 aims for green growth (sustainable development) and its target is to reduce the projected increase of global mean temperature from 3°C to 2 or 1.5°C (in line with current policy targets) [3,7]. Following the SSP1 storyline, China would nearly achieve carbon neutrality by 2060 (Fig. 1d). Following China’s energy development strategic plan 2035 (CEDSP2035) [8] and including implementation of CCUS (carbon capture, utilization and storage, 3.5-3.8 Gt CO₂ a⁻¹ at 2060), China may achieve the target of carbon neutrality before 2060. However, considering GDP would be double once in 2035 and again in 2060 on the current baseline according to CEDSP2035, the mitigation roadmaps are ambitious and very challenging.

To achieve carbon neutrality by 2060 for China, the nine large regions face large differences in their challenges (Fig. 1g). There are large differences among regions in terms of the gross emissions (Fig. 1e), the possible time to achieve the carbon peak, the level of social and economic development, the industrial structure and energy structure, the clean production technology and efficiency, the level of emission reduction technology, the amount of energy consumption and CO₂ emission per unit GDP, the financial base, and the status and potential of ecosystem carbon uptake.
Therefore, each province and/or city should develop local roadmaps.

Given the large carbon transfers between provinces through trade, as well as the imbalance in regional development, achievable and reasonable carbon neutral compensatory policies will probably be necessary to meet the wishes of equity, balance, and sustainability of regional development, and to ensure the national objective of achieving carbon neutrality by 2060.

## Conflict of interest

The authors declare that they have no conflict of interest.

## Acknowledgments

This work was supported by the National Key Research & Development Program of China (2018YFA0606001 and 2017YFA0604302) and the National Natural Science Foundation of China (41977404 and 42141018). We acknowledge Drs. Prabir Patra (Japan Agency for Marine-Earth Science and Technology), Yosuke Niwa (National Institute for Environmental Studies), Ingrid T. Luijkx (Wageningen University), and Andy Jacobson (NOAA Earth System Research Laboratory) for providing their inversion results. We thank Lifeng Guo and Junting Zhong (Chinese Academy of Meteorological Sciences) for their help in the figure preparation.
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<table><caption>**Table 1** China's CO₂ budget during the period 2000 - 2019<sup>a)</sup></caption><thead><tr><th></th><th></th><th>2000-2009</th><th>2010-2019</th><th>2019</th></tr></thead><tbody><tr><td rowspan="2">Emission</td><td>Mean (Gt CO₂ a⁻¹)</td><td>5.24 ± 0.34</td><td>9.34 ± 0.63</td><td>10.11 ± 0.90</td></tr><tr><td>Growing Rate (% a⁻¹)</td><td>9.02</td><td>2.33</td><td>0.26<sup>b)</sup></td></tr><tr><td rowspan="2">Sink</td><td>Mean (Gt CO₂ a⁻¹)</td><td>-1.63 ± 1.15</td><td>-1.41 ± 1.08</td><td>-1.67 ± 0.89</td></tr><tr><td>Changing Rate (% a⁻¹)</td><td>3.68</td><td>4.96</td><td>-10.55<sup>b)</sup></td></tr><tr><td rowspan="2">CCN</td><td>Mean (Gt CO₂ a⁻¹)</td><td>-3.61 ± 0.36</td><td>-7.93 ± 0.59</td><td>-8.01 ± 0.90</td></tr><tr><td>Growing Rate (% a⁻¹)</td><td>12.96</td><td>0.86</td><td>6.14</td></tr></tbody></table>

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a) Mean emissions were calculated from the 10 emission datasets (Table S1 online) and mean sinks were the ensemble mean of the five inversions (Table S2 online). The relative growing (changing) rate (in Gt CO₂ a⁻¹) for a decade (e.g., 2000-2009) was calculated by fitting a linear trend during the certain period. Contribution to carbon neutrality (CCN) refers to the difference from the current net carbon budget to net zero CO₂ emission. b) The changing rate of 2019 relative to 2018 was calculated as the difference between the two years and then normalizing to the absolute annual value in 2018.

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Baozhang Chen is a professor of the Nanjing University of Information Science and Technology and Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences. He received his Ph.D. degree from the University of British Columbia and has long been dedicating to research on carbon cycle and interaction between land the atmosphere, and development of state-of-the-art land models and atmosphere inversion systems.

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审图号:GS京(2022)0484号

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