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J. Geogr. Sci. 2023, 33(1): 3-15
DOI: https://doi.org/10.1007/s11442-023-2071-4
© 2023 Science Press Springer-Verla g
Imbalance of inter-provincial forest carbon
sequestration rate from 2010 to 2060 in China
and its regulation strategy
CAI Weixiang1,2, XU Li2, LI Mingxu2, SUN Osbert Jianxin1, *HE Nianpeng2,3,4
1. School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China;
2. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and
Natural Resources Research, CAS, Beijing 100101, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China;
4. Center for Ecological Research, Northeast Forestry University, Harbin 150040, China
Abstract: Forest ecosystem, as a predominant component of terrestrial ecosystems in view of
carbon sinks, has a high potential for carbon sequestration. Accurately estimating the carbon
sequestration rate in forest ecosystems at provincial level, is a prerequisite and basis for
scientifically formulating the technical approaches of carbon neutrality and the associated
regulatory policies in China. However, few researches on future carbon sequestration rates
(CSRs) for Chinese forest ecosystems for provincial-level regions (hereafter province) have
been reported, especially for forest soils. In this study, we quantitatively assessed the carbon
sequestration rates of existing forest ecosystems of all the provinces from 2010 to 2060 using
the Forest Carbon Sequestration model (FCS), in combination with large quantities of
field-measured data in China under three future climate scenarios (RCP2.6, RCP4.5, and
RCP8.5). Results showed that CSRs across provinces varied from 0.01 TgC a–1 to 36.74 TgC
a–1, with a mean of 10.09 ± 0.43 TgC a–1. Inter-provincial differences have been observed in
forest CSRs. Regarding the spatial variations in CSRs on a unit area basis within provinces,
the eastern region provinces have a larger capacity for sequestration than the western region,
while the western region has greater CSR per unit GDP and per capita. Moreover, there are
significant negative correlations between the CSRs per capita in each province and the cor-
responding GDP per capita, under the assumption that GDP per capita is constant in the
future across provinces. In summary, there is a significant regional imbalance in CSR among
provinces. Special technological and policy interventions are required to realize carbon sink
potential sustainably. An overlap in China’s poorer areas and areas with stronger carbon
sinks has indicated that existing policies to support traditional carbon trading are insufficient.
Regulatory measures such as “regional carbon compensation” must be adopted urgently in
line with the Chinese characteristics, so that people in western or underdeveloped regions
can consciously strengthen forest protection and enhance forest carbon sinks through coor-
Received: 2021-11-15 Accepted: 2022-04-21
Foundation: National Natural Science Foundation of China, No.42141004, No.32171544, No.31988102
Author: Cai Weixiang, Master, specialized in ecosystem carbon cycles and regulatory mechanisms. E-mail: caiwx@bjfu.edu.cn
*Corresponding author: He Nianpeng, PhD and Professor, E-mail: henp@igsnrr.ac.cn
This paper is initially published in Acta Geographica Sinica (Chinese edition), 2022, 77(7): 1808‒1820.
www.geogsci.com www.springer.com/journal/11442
4 Journal of Geographical Sciences
dinated regional development while ensuring that China’s forests play a greater role in carbon
neutrality strategies.
Keywords: forest; carbon cycle; carbon sequestration; carbon sink; imbalance; sustainability; carbon neutrality;
carbon trading
1 Introduction
Forests store 45% of the terrestrial carbon, playing an important role in the carbon cycle of
terrestrial ecosystems (Bonan, 2008; Fang et al., 2014; Wen and He, 2016; He et al., 2017).
Therefore, accurate estimates the temporal and spatial distribution and dynamics of forest
carbon sinks under climate change have become increasingly important for the scientific
exploration of Earth systems, along with biological conservation and natural resource man-
agement (Luo et al., 2020).
In 2020, a new national strategy was launched in China to reach its peak total CO2 emis-
sions before 2030 and achieve carbon neutrality before 2060 to combat global climate
change. To achieve carbon neutrality, it is important for industries to reduce emissions. It is
important to improve carbon sinks in forests, grasslands and croplands by strengthening spa-
tial planning and management of land use, as well as by effectively harnessing the carbon
sequestration capacity of these ecosystems. According to the ninth national forest inventory,
China’s forest covering 22.96% of its land area, 33% of which are young forests (National
Forestry and Grassland Administration, 2019). Earlier studies have indicated that China’s
forest vegetation has great potential for sustainable carbon sequestration from 2010 to 2050
(Xu et al., 2010; Ma and Wang, 2011; Hu et al., 2015; He et al., 2017; Tang et al., 2018; Yao
et al., 2018; He et al., 2019; Wang et al., 2020). Besides quantifying the carbon sequestra-
tion capacity of forest ecosystems at the national scale, managers also need to obtain scien-
tific information from each province regarding its forests’ spatio-temporal dynamics. Such
information could aid in carbon reduction and in the implementation of carbon neutrality
action guidelines more effectively (Ma and Wang, 2011). Firstly, assessing the carbon se-
questration potential of forest ecosystems enables provinces to understand their own carbon
sink situation, making it easier for them to draft appropriate afforestation/reforestation and
carbon reduction policies. Secondly, it also provides an opportunity for inter-provincial co-
ordination, and to make carbon sink compensation policies based on the differences and im-
balances in carbon sequestration potential among provinces. However, previous studies fo-
cused only on one or a few provinces (Chen et al., 2018; Chen et al., 2019; Wu et al., 2020),
and the exploration of inter-provincial forest CSRs at the national scale from 2010–2060 has
not been reported.
In this study, we used a forest carbon sequestration (FCS) model based on a classical lo-
gistic equation to evaluate the CSRs of forest among provinces (Data for Hong Kong, Macao,
and Taiwan were not included) in China under three future climate scenarios (RCP 2.6, 4.5,
and 8.5). The main objectives of this study were to identify the: (1) CSRs of existing forests
among provinces in China from 2010–2060; (2) regional difference and imbalance in the
carbon sequestration potential of forest ecosystems among provinces. Through the quantita-
tive analysis of inter-provincial forest ecosystem carbon sinks and their regional imbalance,
we discuss forest sink enhancement at the level of technology and policy. We also discuss
regional carbon sink integration and provide guidelines for national and provincial policy-
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 5
makers on forest protection, management, afforestation/reforestation and other strategies of
carbon sink enhancement. Through this, we hope for the gradual integration of regional
carbon sinks and sustainable development.
2 Materials and methods
2.1 Description of the model
Considering the complexity of forest ecosystem carbon cycle mechanism, and the data to
support model building and validation, as well as initial model parameters need to be ob-
tained from a large amount of measured data. Most forest carbon cycle models are parame-
terized or validated by gross primary productivity (GPP), net primary productivity (NPP) or
net ecosystem productivity (NEP) from flux observations. Forest age is rarely taken into
account, resulting in fewer climate-driven models being able to predict forest carbon se-
questration rates (Cao and Woodward, 1998).
Here, we used a forest carbon sequestration (FCS) model on the basis of a classical lo-
gistic equation between forest age and biomass, which is parameterized and validated by
large volumes of vegetation data from field surveys in China (He et al., 2017). The FCS
model was established by measuring carbon sink data and forest age from more than 3300
sample plots of the Chinese Academy of Sciences Carbon Special Project as input parame-
ters. The model was validated by data from 78 forest successions in China and combined
with important factors such as temperature, precipitation and forest age. The construction,
validation and predictions of the FCS model have been published in previous studies (He et
al., 2017; Yan et al., 2020; Cai et al., 2022), making it possible to assess the carbon seques-
tration of forest ecosystems or afforestation in China rapidly and accurately. The model’s
construction and operation process are briefly described below. A detailed description can be
found in previous studies (He et al., 2017).
2.2 Vegetation and soil carbon cycles
With forest development, forest biomass grad-
ually reaches a relative equilibrium state. The
relationship between vegetation biomass and
forest age can be portrayed using a logistic
growth equation (Figure 1) (Xu et al., 2010).
Veg e t ation biomass can be calculated by (1):
00
0
max
()
max
11
t
v tt
t
B
B
Be
B
− ⋅−
=
+ −⋅
(1)
where Bt is the forest vegetation biomass (Mg
ha–1); V0 is the intrinsic growth rate, represent-
ing the maximum growth rate (%) when vege-
tative growth is not limited by the environment,
nutrients, or disturbances; Bmax is the maximum
vegetation biomass under the mature forest
Figure 1 The secondary succession theory (Foun-
dation of FCS model) presented the relationship
between vegetation biomass and forest age (He et al.,
2017). Bt, vegetation biomass (Mg ha–1); Bmax,
maximum vegetation biomass; t, forest age (a); and
Bto, vegetation biomass at t = t0.
6 Journal of Geographical Sciences
scenario (Mg ha–1); and t is the forest age (a).
Annual variation in soil carbon stocks is calculated by the annual input of organic matter
minus the annual decomposition, which is the classical double pool of soil carbon cycle.
When forest factors are relatively stable, the humification and mineralization processes will
tend to balance the SOC level, as shown in (2):
0
t 20
22
exp( ( )
tt
t
II
C C k tt
kk
= − − ⋅ −⋅−
(2)
where Ct is the SOC density (MgC ha–1); It is the annual input of SOC (Mg ha–1 a–1); It = h
(k1 Lt), Lt is the litter content (Mg ha–1); k1 is the litter decomposition coefficient; k2 is the
SOC decomposition rate (a–1); and h is the decay coefficient (0.3) (Foley, 1995).
2.3 Existing forest data
The main parameters of the FCS model are the initial vegetation biomass (B0), stand age (t),
mean annual temperature (MAT, ℃), and mean annual precipitation (MAP, mm). The data
for the existing forest vegetation biomass were obtained from the field survey of the “Stra-
tegic Priority Research Program of the Chinese Academy of Sciences” (XDA05050000),
including the initial vegetation biomass, forest age, litter, and 0–20 cm SOC content in 3365
forest sample plots in China, which are the same as the special features of Proceedings of the
National Academy of Sciences of the USA in 2018 (Tang et al., 2018) They included decid-
uous broadleaf forests (DBF, 806 plots), deciduous needle-leaved forests (DNF, 197 plots),
evergreen broadleaf forests (EBF, 620 plots), evergreen needle-leaved forests (ENF, 1461
plots), and mixed needle-leaved and broadleaf forests (NBF, 281 plots). The plots were each
0.1 hm2, and vegetation biomass was calculated by the relevant allometric equation using
diameter at breast height and tree height (ECSP, 2015).
Climate data, including the mean annual temperature (MAT, ℃) and mean annual precip-
itation (MAP, mm), were obtained from the National Climate Center (http://ncc.cma.
gov.cn/cn/), and simulated using the Regional Climate Model system (RegCM 4.0).The data
was then output (spatial resolution of 1° × 1°) through one-way nesting of the Beijing Cli-
mate Center_Climate System Model Version 1.1 (BCC_CSM1.1) (Gao et al., 2012).
2.4 Supplementary data
Population and gross domestic product (GDP) per capita data for each provincial-level re-
gion (Data for Hong Kong, Macao, and Taiwan were not included) were derived from China
Statistical Yearbook 2020 (http:/www.stats.gov.cn/tjsj/ndsj/), and the CSR per capita and
CSR per economic was calculated for each provincial-level region (Data for Hong Kong,
Macao, and Taiwan were not included).
2.5 Statistical analysis
A coefficient of 0.5 had been used to transfer biomass to carbon density (Yu et al., 2014).
First, the tool ‘Extract Multi Values to Points’ in ArcMap was used to extract MAT and MAP
of each site under future climate scenarios, and the future carbon sequestration capacity of
forest ecosystems in each provincial-level region (hereafter province) of China was predict-
ed by FCS model. The changes in carbon density or CSR of vegetation and soil were calcu-
lated for every 10 from 2010–2060. The results were represented as averages of three future
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 7
climate scenarios. Meanwhile, in order to meet the targets of carbon peaking in 2030 and
carbon neutrality in 2060, the period 2010–2030 and 2030–2060 were used in the data pro-
cessing for the analysis.
The spatial distributions of the forests and their carbon densities were mapped using
ArcMap 10.2 (ESRI, Redlands, CA, USA). The data were analyzed and plotted on graphs
using SPSS 25.0 (IBM Corp., Chicago, IL, USA) and Origin 2018 (Origin Lab, Northamp-
ton, MA, USA).
3 Results
3.1 CSR in Chinese forests by province from 2010 to 2060
Forest ecosystems in different Chinese provinces are predicted to have the capacity to se-
quester carbon from 2010–2060 under different climate scenarios, with keep the existing
forest area constant. The vegetation CSR varied between <0.01 TgC a–1 (Shanghai) and
25.67 ± 0.91 TgC a–1 (Heilongjiang Province) (Table 1). Soil CSR varied between <0.01
TgC a–1 (Shanghai) and 15.36 ± 0.94 TgC a–1 (Yunnan Province) (Table 2). Overall, the
CSRs in forest ecosystems over the next 50 years were the largest in Heilongjiang Province
(36.74 ± 1.35 TgC a–1) and the smallest in Shanghai (<0.01 TgC a–1). The carbon sequestra-
tion potential of forest ecosystems in most provinces varied significantly during the period,
which caused inter-provincial differences in CSRs (Table 3).
Table 1 Annual changes of vegetation carbon sequestration rate in Chinese forests by provincial-level region
from 2010 to 2060
Provincial-
level region
Forest area
(104 km2)
CSR of forest vegetation (TgC a–1)
2010–2020
2020–2030
2030–2040
2040–2050
2050–2060
Mean±SD
Anhui
3.12 4.96±0.22a
†
4.79±0.30a 3.39±0.57b 2.53±0.55b 1.24±0.78c 3.38±0.11
Beijing
0.44 0.73±0.02ab 0.83±0.04a 0.66±0.08b 0.51±0.08c 0.21±0.1d 0.59±0.01
Chongqing
3.46 2.58±0.07ab 2.95±0.20a 2.92±0.23a 2.33±0.11b 1.47±0.41c 2.45±0.07
Fujian
8.33
12.32±0.58a
11.36±0.79a 8.07±1.44b 6.34±1.43bc 4.80±2.06c 8.58±0.26
Gansu
2.1 0.64±0.04a 0.73±0.13a 0.77±0.21a 0.83±0.16a 1.01±0.37a 0.79±0.05
Guangdong
10.67
11.82±0.35a
14.23±0.92b 12.19±0.97a 10.08±1.04a 6.26±1.8c 10.91±0.29
Guangxi
12.58
10.76±0.21a
11.31±0.84a 9.8±1.00a 7.58±0.36b 6.94±1.46b 9.28±0.29
Guizhou
6.27 0.81±0.06a 1.63±0.24ab 2.15±0.37b 1.76±0.17b 1.25±0.98ab
1.52±0.17
Hainan
0.92 0.65±0.05a 0.74±0.04b 0.62±0.05a 0.51±0.02c 0.42±0.07c 0.59±0.03
Hebei
3.97 6.41±0.25ab 7.36±0.40b 6.27±0.73ab
5.02±0.92b 2.70±1.09c 5.55±0.12
Heilongjiang
19.77
34.89±0.49a
34.04±0.96a 27.11±2.15b 19.42±1.80c 12.87±3.32d 25.67±0.91
Henan
2.07 2.98±0.06a 3.10±0.19a 2.36±0.20b 1.73±0.15c 0.25±0.36d 2.08±0.06
Hubei
6.21 8.62±0.09a 7.93±0.62a 5.51±0.52b 3.87±0.20c 4.29±0.71c 6.04±0.18
Hunan
8.87 9.73±0.10ab 11.75±0.64c 10.70±0.71bc
9.56±0.83ab 8.98±1.17a 10.15±0.15
Inner Mongolia
16.26
18.75±0.93ab
22.19±1.54b 20.17±2.69ab
18.36±3.99ab 16.00±4.22b 19.09±0.40
Jiangsu
0.31 0.50±0.02a 0.48±0.02a 0.43±0.03b 0.30±0.03c 0.42±0.01b 0.43±0.00
Jiangxi
9.78
10.45±0.37a
12.29±0.68a 10.80±1.24a 9.88±1.46a 6.31±1.98b 9.94±0.25
Jilin
8.33
15.93±0.24a
15.87±0.44a 12.77±0.67b 8.41±0.71c 5.56±1.32d 11.71±0.40
Liaoning
5.57 8.12±0.06a 9.72±0.26a 9.51±0.80a 8.60±1.41a 6.16±1.27b 8.42±0.18
Ningxia
0.07 0.03±0.00a 0.03±0.00a 0.04±0.01a 0.03±0.01a 0.04±0.02a 0.03±0.00
Qinghai
0.29 0.05±0.00a 0.07±0.03ab 0.10±0.04ab
0.14±0.04b 0.14±0.05b 0.10±0.01
(To be continued on the next page)
8 Journal of Geographical Sciences
(Continued)
Provincial-
level region
Forest area
(104 km2)
CSR of forest vegetation (TgC a–1)
2010–2020 2020–2030 2030–2040 2040–2050 2050–2060 Mean±SD
Shaanxi
5.92 6.23±0.12a 6.28±0.37a 5.41±0.43b 4.12±0.18c 2.48±0.65d 4.91±0.13
Shandong
1.83 2.21±0.10a 2.86±0.19a 2.86±0.36a 2.79±0.57a 2.27±0.58a 2.60±0.04
Shanghai
1.31×10-4
<0.01a
<0.01a
<0.01a
<0.01a
<0.01a
<0.01
Shanxi
2.49 2.56±0.06a 3.55±0.25b 3.19±0.28b 2.66±0.25a 1.82±0.38c 2.76±0.05
Sichuan
14.11 2.65±0.04a 3.45±1.21ab 5.63±2.13ab
6.92±1.54b 6.59±3.31b 5.05±0.45
Tianjin
0.03 0.04±0.00ab 0.05±0.00b 0.05±0.01b 0.05±0.01ab 0.03±0.01a 0.05±0.00
Xizang
8.49
2.39±0.27a
3.01±0.76ab
4.10±1.32ab
4.90±1.56b
4.26±1.05ab
3.73±0.07
Xinjiang
2.48 0.22±0.02a 0.28±0.10a 0.40±0.10a 0.34±0.28a 0.60±0.69a 0.37±0.14
Yunnan
18.99 7.14±0.13a 6.84±2.74a 9.58±3.99a 7.23±2.93a 4.91±2.03b 7.14±0.10
Zhejiang
6.06 8.14±0.43a 8.47±0.66a 6.75±1.13ab
5.57±1.26bc 3.62±1.57c 6.51±0.18
Tot a l
189.79
193.32±3.35
208.2±11.48
184.27±21.62
152.39±20.79
113.91±30.45
170.42±4.52
† Change in carbon sequestration rate was presented as mean ± 1 standard deviation on invariable forest area and three
climate scenarios (RCP2.6, RCP4.5, and RCP8.5), and the same small letters indicate no significant difference in carbon
sequestration rates among different periods at p = 0.05 level.
Table 2 Annual changes of soil carbon sequestration rate in Chinese forests by provincial-level region from
2010 to 2060
Provincial-
level region
Forest area
(104 km2)
CSR of forest soil (TgC a
–1
)
2010–2020 2020–2030 2030–2040 2040–2050 2050–2060 Mean±SD
Anhui
3.12
2.45±0.05ab†
3.62±0.12c
3.48±0.22c
2.92±0.43b
2.05±0.30a
2.90±0.03
Beijing
0.44 0.05±0.00a 0.36±0.02c 0.45±0.03d 0.42±0.07cd 0.26±0.04b 0.31±0.01
Chongqing
3.46 2.13±0.03a 3.06±0.09b 3.64±0.11c 3.6±0.19c 3.13±0.16b 3.11±0.01
Fujian
8.33 4.06±0.14a 7.18±0.30b 7.21±0.53b 6.29±1.17b 4.70±0.78a 5.89±0.08
Gansu
2.1
1.21±0.04a
1.46±0.10ab
1.45±0.13ab
1.55±0.21b
1.73±0.19b
1.48±0.01
Guangdong
10.67 4.89±0.08a 9.47±0.29bc 10.74±0.35d 10.21±1.09cd 8.57±0.43b 8.78±0.10
Guangxi
12.58 5.91±0.09a 9.87±0.34b 11.33±0.44d 10.99±0.63cd 10.28±0.48bc
9.67±0.07
Guizhou
6.27 3.91±0.09a 4.36±0.17a 4.44±0.18a 4.34±0.39a 4.33±0.42a 4.28±0.02
Hainan
0.92
0.90±0.00a
0.95±0.02b
0.95±0.02b
0.87±0.03a
0.76±0.03c
0.89±0.00
Hebei
3.97 1.13±0.06a 3.79±0.16bc 4.68±0.29d 4.46±0.68cd 3.22±0.44b 3.46±0.05
Heilongjiang
19.77 1.18±0.53a
13.32±0.70b
16.56±1.25c 14.14±1.44b 10.18±0.83d 11.08±0.47
Henan
2.07 1.61±0.01a 2.46±0.06b 2.49±0.08b 2.16±0.18c 1.72±0.12a 2.09±0.01
Hubei
6.21
5.60±0.03a
7.54±0.21b
7.11±0.18c
5.93±0.31a
4.35±0.08d
6.11±0.04
Hunan
8.87 2.92±0.03a 7.22±0.23b 9.05±0.28cd 9.45±0.82c 8.57±0.31d 7.44±0.10
Inner Mongolia
16.26 1.33±0.24a 9.6±0.49b 13.58±1.07c 14.78±2.90c 14.07±1.84c 10.67±0.22
Jiangsu
0.31 0.11±0.01a 0.27±0.00b 0.33±0.01c 0.29±0.02b 0.38±0.01d 0.28±0.00
Jiangxi
9.78
6.61±0.09a
9.69±0.26b
10.48±0.50b
10.33±1.21b
7.88±0.75c
9.00±0.11
Jilin
8.33 2.74±0.06a 7.75±0.13b 8.78±0.30c 6.82±0.63d 4.77±0.28e 6.17±0.12
Liaoning
5.57 2.55±0.02a 3.70±0.07b 5.76±0.37cd 6.28±0.95c 5.15±0.47d 4.69±0.08
Ningxia
0.07 0.01±0.00a 0.03±0.00b 0.04±0.00c 0.04±0.01cd 0.05±0.01d 0.03±0.00
Qinghai
0.29
<0.01a
0.02±0.01a
0.04±0.02a
0.09±0.04b
0.10±0.02b
0.05±0.00
Shaanxi
5.92 4.14±0.03a 6.16±0.14b 6.85±0.19c 6.45±0.27b 5.46±0.25d 5.81±0.01
Shandong
1.83 0.17±0.03a 0.35±0.06a 1.48±0.15b 1.90±0.41b 1.68±0.27b 1.12±0.03
Shanghai
1.31×10
-4
<0.01a <0.01a <0.01a <0.01a <0.01a <0.01
Shanxi
2.49
1.06±0.01a
2.32±0.09b
2.78±0.12c
2.72±0.25c
2.40±0.12b
2.26±0.02
(To be continued on the next page)
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 9
(Continued)
Provincial-
level region
Forest area
(104 km2)
CSR of forest soil (TgC a–1)
2010–2020 2020–2030 2030–2040 2040–2050 2050–2060 Mean±SD
Sichuan
14.11 6.51±0.20a 7.15±0.76a 8.09±1.18ab 9.84±1.69b 9.90±1.78b 8.30±0.08
Tianjin
0.03 0.01±0.00a 0.03±0.00b 0.04±0.00c 0.04±0.01c 0.03±0.01bc
0.03±0.00
Xizang
8.49 2.53±0.06a 2.66±0.36a 3.43±0.64a 4.84±1.08b 4.93±0.56b 3.68±0.08
Xinjiang
2.48 0.74±0.04a 1.65±0.02b 1.58±0.08b 1.36±0.34ab 1.52±0.84b 1.37±0.20
Yunnan
18.99 18.72±4.64a
16.63±2.08ab
15.08±1.95ab
14.56±2.66ab 11.8±1.10b 15.36±0.94
Zhejiang
6.06 5.46±0.11a 7.37±0.24b 7.39±0.47b 6.60±0.92b 4.93±0.69a 6.35±0.05
Tot a l
189.79 90.64±4.20
150.03±5.32
169.32±9.25
164.30±19.38
138.90±9.99
142.64±0.
54
† Change in carbon sequestration rate was presented as mean ± 1 standard deviation on invariable forest area and
three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), and the same small letters indicate no significant difference in
carbon sequestration rates among different periods at p = 0.05 level.
Table 3 Annual changes of ecosystem carbon sequestration rate in Chinese forests by provincial-level region
from 2010 to 2060
Provincial-
level region
Forest area
(104 km2)
CSR of forest ecosystem (TgC a–1 )
2010–2020 2020–2030 2030–2040 2040–2050 2050–2060 Mean±SD
Anhui
3.12 7.41±0.28ab
†
8.41±0.41b 6.87±0.79a 5.44±0.98c 3.28±1.08d 6.28±0.11
Beijing
0.44 0.78±0.02a 1.19±0.05b
1.11±0.11bc
0.93±0.15ac
0.47±0.14d 0.90±0.01
Chongqing
3.46 4.71±0.10a 6.02±0.29b 6.56±0.34b 5.93±0.29b 4.60±0.57a 5.56±0.07
Fujian
8.33 16.38±0.71ab
18.55±1.07b
15.28±1.97ab
12.63±2.61bc
9.49±2.83c 14.47±0.23
Gansu
2.1 1.85±0.08a 2.19±0.23ab
2.21±0.34ab
2.38±0.36ab
2.74±0.56b 2.27±0.05
Guangdong
10.67 16.71±0.43a 23.69±1.21b 2
2.93±1.32bc
20.3±2.13c 14.83±2.23a 19.69±0.22
Guangxi
12.58 16.66±0.29a 21.17±1.17b 21.13±1.43b 18.57±0.95a 17.22±1.93a 18.95±0.27
Guizhou
6.27 4.72±0.15a 5.99±0.40ab
6.59±0.55b 6.11±0.55ab
5.58±1.39ab
5.80±0.17
Hainan
0.92 1.54±0.05a 1.69±0.05b 1.58±0.
07ab
1.38±0.05c 1.18±0.09d 1.48±0.03
Hebei
3.97 7.54±0.31ab
11.15±0.56c 10.95±1.02c 9.48±1.61bc
5.91±1.53a 9.01±0.12
Heilongjiang
19.77 36.07±0.63a 47.36±1.66b 43.67±3.39b 33.57±3.22a 23.05±4.11c 36.74±1.35
Henan
2.07 4.59±0.07a 5.56±0.25b 4.85±0.28a 3.89±0.32c 1.97±0.47d 4.17±0.06
Hubei
6.21 14.23±0.11a 15.47±0.84b 12.61±0.69c 9.80±0.50d 8.64±0.77e 12.15±0.16
Hunan
8.87 12.65±0.12a 18.97±0.87b 19.75±0.98b 19.02±1.65b 17.55±1.42b 17.59±0.10
Inner
Mongolia
16.26 20.08±1.16a 31.79±2.02b 33.74±3.76b 33.15±6.89b 30.07±6.03b 29.77±0.42
Jiangsu
0.31 0.61±0.03a 0.76±0.02b 0.76±0.04b 0.59±0.05a 0.80±0.00b 0.70±0.00
Jiangxi
9.78 17.06±0.46ab
21.97±0.94c 21.28±1.74c 20.21±2.68bc
14.19±2.72a 18.94±0.24
Jilin
8.33 18.67±0.30a 23.62±0.57b 21.55±0.96c 15.23±1.34d 10.32±1.58e 17.88±0.48
Liaoning
5.57 10.68±0.07a 13.42±0.32bc
15.27±1.17c 14.87±2.36c 11.32±1.73a 13.11±0.21
Ningxia
0.07 0.04±0.00a 0.06±0.01ab
0.08±0.01bc
0.08±0.01bc
0.09±0.03c 0.07±0.00
Qinghai
0.29 0.05±0.01a 0.09±0.04a
0.13±0.06ab
0.23±0.08b 0.24±0.07b 0.15±0.01
Shaanxi
5.92 10.37±0.15a 12.44±0.51b 12.26±0.62b 10.57±0.43a 7.94±0.89c 10.72±0.13
Shandong
1.83 2.38±0.12a 3.21±0.25ab
4.33±0.51bc
4.7±0.97bc 3.95±0.84c 3.71±0.05
Shanghai
1.31×10-
4
<0.01a <0.01a <0.01a <0.01a <0.01a <0.01
Shanxi
2.49 3.61±0.07a 5.87±0.33b 5.98±0.39b 5.38±0.50b 4.21±0.48a 5.01±0.03
(To be continued on the next page)
10 Journal of Geographical Sciences
(Continued)
Provincial-
level region
Forest area
(104 km2)
CSR of forest ecosystem (TgC a–1 )
2010–2020 2020–2030 2030–2040 2040–2050 2050–2060 Mean±SD
Sichuan
14.11 9.17±0.16a 10.60±1.97ab
13.72±3.30ab
16.76±3.17b 16.50±5.09b 13.35±0.48
Tianjin
0.03 0.05±0.00a 0.08±0.01b 0.09±0.01b
0.09±0.02b
0.07±0.02ab
0.08±0.00
Xizang
8.49 4.92±0.34a 5.67±1.12a
7.52±1.96ab
9.74±2.58b 9.19±1.57b 7.41±0.03
Xinjiang
2.48 0.96±0.04a 1.93±0.12a 1.98±0.18a
1.71±0.62a 2.12±1.53a 1.74±0.34
Yunnan
18.99 25.86±4.71a 23.47±3.78a 24.66±5.67a
21.80±5.59a 16.72±3.09b 22.50±0.99
Zhejiang
6.06 13.60±0.54ab
15.84±0.89b
14.14±1.60ab
12.17±2.18a 8.55±2.26c 12.86±0.19
Tot a l
189.79
283.96±3.39
358.23±16.67
353.59±30.73
316.69±40.12 252.81±40.39 313.06±4.72
†Change in carbon sequestration rate was presented as mean ± 1 standard deviation on invariable forest area and three
climate scenarios (RCP2.6, RCP4.5, and RCP8.5), and the same small letters indicate no significant difference in carbon
sequestration rates among different periods at p = 0.05 level.
3.2 Changes in CSR per unit area of forests among provinces
Compared with the 2030–2060 period, CSR per unit area of the forest ecosystem in Chinese
provinces larger varied from 2010–2030 (Figure 2). The provinces in the northwest (Xin-
jiang, Tibet, Qinghai) and southwest (Yunnan and Guizhou) had smaller changes in forest
CSR per unit area (0.32–2.15 MgC ha–1 a–1), while the provinces in the eastern region are
larger (Jiangsu and Anhui) (3.05–3.83 MgC ha–1 a–1). The result revealed that the per capita
CSR of provinces varied from 0.01–1.50 MgC a–1 per capita in the 2010–2030 period, and
from 0.01–2.52 MgC a–1 per capita in the 2030–2060 period (Figure 3), with significant dif-
ferences among provinces.
Figure 2 Changes in carbon sequestration rates (CSR) per unit area of forest by province at different periods.
Panel a is the period of 2010–2030 and panel b is 2030–2060. Note: This figure has been prepared based on the
standard map provided by the Ministry of Natural Resources of the People’s Republic of China, which can be
found on the service website (GS (2019)1698). The base map was not modified.
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 11
Figure 3 Annual changes in per capita carbon sequestration rate (CSR) among provinces from 2010–2030 (a)
and 2030–2060 (b). Note: This figure has been prepared based on the standard map provided by the Ministry of
Natural Resources of the People’s Republic of China, which can be found on the service website (GS (2019)1698).
The base map was not modified.
Overall, provinces such as Tibet, Inner Mongolia, Heilongjiang and Jilin showed higher
CSRs per unit of economic whereas the more economically developed cities on the eastern
coast have lower CSRs (Figure 4).
There are significant negative correlations between the CSR per capita of each province and
its GDP per capita. Provinces such as Heilongjiang and Inner Mongolia have a higher CSR
per capita, while more developed ones (such as Beijing, Shanghai, Jiangsu and Tianjin) are
lower (Figure 5).
Figure 4 Changes in forest carbon sequestration rate (CSR) on per unit of GDP among provinces. Note: This
figure has been prepared based on the standard map provided by the Ministry of Natural Resources of the People’s
Republic of China, which can be found on the service website (GS (2019)1698). The base map was not modified.
12 Journal of Geographical Sciences
Figure 5 Relationship between per capita forest carbon sequestration rate and GDP per capita at provincial level
in China
4 Discussion
4.1 Significant differences in forest carbon sequestration potential among provinces
During the 2010–2060 period, results show a large regional imbalance in the CSR between
provinces in China, with the CSR of forest vegetation varying between 0.01 and 25.67 TgC
a–1 and that of soil varying 0.01–15.36 TgC a–1 among provinces. The CSR in forest ecosys-
tems was the largest in Heilongjiang Province (36.74 ± 1.35 TgC a–1) and the smallest in
Shanghai (<0.01 TgC a–1). This is closely related to economic development and climatic
conditions in each province. Economic development inevitably comes at the risk of damag-
ing current forests, while climatic conditions determine whether forests are suitable for
growth.
The results estimating the CSR of forest vegetation at the national scale differed signifi-
cantly from earlier studies (Xu et al., 2010; Pan et al., 2011; Fang et al., 2014; Hu et al.,
2015), but were similar to the results of recent studies (Tang et al., 2018; Yao et al., 2018).
Varying data sources and methodologies are important reasons for the differences. None of
the earlier studies at the national scale have systematically estimated CSRs of forest ecosys-
tems in Chinese provinces under future climate scenarios, especially for soil. More im-
portantly, most earlier studies estimated forest CSR at the national scale without providing
inter-provincial data clearly (Xu et al., 2017; 2018a; 2018b; 2019), which made it impossi-
ble for us to compare between provinces. Regarding methodology, the FCS model was based
on a classical logistic equation to establish the relationship between forest age and biomass,
and was parameterized and validated using numerous field measurements in China, which
could better simulate the changes in CSR during the natural succession of forest ecosystems
(He et al., 2017; Yan et al., 2020; Cai et al., 2022). Meanwhile, the FCS model also consid-
ered the influence of future climate change on forest ecosystems by incorporating data under
different future climate scenarios. However, the model only uses mean annual climate pa-
rameters, and is not sensitive to the overall response of climate data. As for the data source,
the forest sample plots were obtained from a large number of field surveys conducted by the
“Climate Change: Carbon Budget and Relevant Issues” of the Chinese Academy of Sciences,
which were carried out in strict accordance with field survey specifications and were highly
representative (ECSP, 2015). However, there are some uncertainties in this study; the defini-
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 13
tion of mature forest age is a widely debated scientific issue and we set the age of mature
forest stands at 100 a (Liu et al., 2014), which may have allowed the carbon sequestration
potential of forests to have been over or underestimated in some provinces. Although previ-
ous studies have shown that forests mature at 100–400 a (Guariguata and Ostertag, 2001),
some studies showed that defining the mature forest across regions is difficult due to differ-
ences in species, forest type and climate (Martin et al., 2016). Theoretically, FCS model can
be used to carry out more systematic simulations on basis of different forest types and spe-
cies, but there are still uncertainties in the spatial data products available at the national scale
for these parameters, which need to be improved and refined in future studies. As the econ-
omy and public awareness develop, forest area may increase from largescale afforesta-
tion/reforestation and ecological restoration or decrease in response to social development
demands, but we assume that the existing forest area in each region will remain unchanged
for the next 50 a, which may lead to underestimation of the results.
4.2 Strengthening regional regulation to improve the carbon sequestration potential
of forests among provinces
Forests in different Chinese provinces, especially Heilongjiang, Sichuan and Yunnan, have
great potential for carbon sequestration over the next 50 years. However, to maintain and
increase the carbon sequestration potential of the provinces’ forests, long-term effective for-
est management techniques and new socio-economic policies are needed.
In general, forest management practices to conserve and sequester C can be grouped into
three major categories: (i) maintaining existing C pools (e.g., slowing deforestation and for-
est degradation), (ii) expanding existing C sinks and pools (e.g., increasing C density by
modifying forest structure and growth processes), (iii) creating new C sinks and pools by
expanding tree and forest cover (afforestation and reforestation) (Peng et al., 2008). As the
CSR of forests may vary among provinces (Tables 1–3), strengthening the research and de-
velopment of technologies with the function of enhancing forest carbon sinks according to
its characteristics is urgent. When the CSR of forests is low, appropriate technical measures
such as forest soil fertilization, soil improvement, forest thinning, and rational forest har-
vesting (Figure 6) can be used to not only increase CSR but also lengthen such periods. In
addition to clarifying the principles and approaches for improving the capacity of forest
carbon sinks, it is important to quantitatively assess the sustainability and consistency of
forest carbon sinks, which is the new requirement for sink enhancement technologies for
carbon neutrality. Carbon neutrality is a long-term goal; temporary and effective sink en-
hancement measures should be promoted with caution, because inappropriate forest man-
agement practices and short-term sink enhancement measures may also reduce the carbon
sink of forests or even convert them to carbon sources (Hyvonen et al., 2007). Meanwhile,
carbon neutrality should be integrated with the improvement of ecosystem quality. The de-
velopment and promotion of carbon sink enhancement technologies that may cause damage
to ecosystem quality should be treated with caution.
Through natural restoration and growth, forest ecosystems can not only improve the eco-
logical environment and forest health, but also achieve the long-term forest carbon sink,
which is highly desirable (Jin et al., 2020). However, we must pay great attention to their
long-term sustainability, disturbance factors and potential risks. Forest management strate-
14 Journal of Geographical Sciences
gies must be developed for each province according to local conditions. Clay et al. (2019)
observed that reasonable harvesting and appropriate fires would increase the carbon seques-
tration capacity of forests, while excessive logging and extreme fires would do the opposite.
A study on Pinus sylvestris found that heavy intercutting reduces photosynthesis as well as
biomass and soil carbon fluxes, thereby reducing the positive effect of fertilizer application
on carbon sink potential, so the interaction between intercutting measures and fertilizer ap-
plication can be used rationally and is important in guiding management to increase forest
carbon sink (Jörgensen et al., 2021). Moreover, forest fire policies should be revisited to
optimize the fire-disturbing properties of long-term carbon sinks. Moreover, low-intensity
fires must be actively employed to improve forest structure, enhance forest productivity, and
boost carbon sequestration (Wright et al., 2020). Extensive outbreaks of pests and diseases
also have a significant impact on the structure and quality of forest ecosystems, which can
greatly threaten the long-term carbon sink of forest ecosystems (Hyvonen et al., 2007). Bio-
logical protection should be fully utilized and strengthened to avoid the loss of carbon sinks
caused by pests and diseases as far as possible. In conclusion, to realize the carbon seques-
tration potential of forests in each province from 2020–2060 (or even longer), we must at-
tach importance to the harmonious development of human beings and nature and actively
promote forest conservation. We must follow the two-pronged approach of “increasing” and
“preserving” sinks while also adopting scientific and reasonable long-term forest manage-
ment measures that are appropriate to each province.
Most of the regions with high contributions to carbon neutrality are economically under-
developed or less economically developed provinces. Therefore, we must not expect to rely
on traditional carbon trade (e.g., Clean Development Mechanism, CDM) to make up for the
huge imbalance in the inter-provincial carbon sinks. Firstly, compared with the European
Union (EU) and other countries, China’s carbon trade market is not perfect and its policies
are less flexible. Secondly, there are strong regional differences in China’s naturals, society
and economy, which are completely different from those of developed countries in Europe
and the US. Thirdly, the carbon trade only captures a small portion of carbon compared to
ecosystem carbon sinks; it is therefore difficult to reflect its contribution to carbon neutrality
targets. Besides strengthening the carbon trade system, we should also take special measures
such as carbon sink compensation or an eco-environmental compensation tax to address the
regional imbalance of carbon sinks (Figure 6). It must be ensured that people in western re-
gions or underdeveloped provinces are willing to establish long-term forest carbon sinks,
consciously protect and enhance them, and prevent the development of these provinces to
achieve the national ecosystem carbon sink target. This will result in poverty in these areas
due to the long-term protection of forest carbon sinks and the restriction of economic de-
velopment. Once the phenomenon of “carbon sink poverty” emerges, it will reduce the sub-
jective willingness of people in the region to protect forests and carbon sinks, making it dif-
ficult to realize the long-term carbon sequestration potential of forest ecosystems and affect
the national strategic goal of carbon neutrality (Tong et al., 2020). In conclusion, the nation-
al and local governments should focus on the regional imbalance based on the CSR per cap-
ita, economic development level, climate and soil characteristics. Novel measures such as
national carbon trading markets and inter-regional carbon sink compensation in policy mak-
ing should be optimized. We recommend choosing a pair or pairs of provinces and cities
CAI Weixiang et al.: Imbalance of inter-provincial forest carbon sequestration rate from 2010 to 2060 in China 15
with low GDP but high forest CSRs and high GDP but low forest CSRs, and compensating
in both directions for common development. We also recommend providing effective de-
velopment support to economically underdeveloped provinces and increasing the carbon
sequestration potential of the forest ecosystems so that they can make greater contributions
to achieving carbon neutrality. This will promote synergy among several strategic objectives
such as national rural revitalization, coordinated regional development and common pros-
perity to help achieve the country’s double hundred goals.
Figure 6 Requirement for developing a combination of technologies and novel policies to enhance forest carbon
sequestration in a new era. CSR, carbon sequestration rate
5 Conclusion
Chinese forest ecosystems have a huge carbon sequestration potential over the next 50 a, and
the range of forest CSRs among provinces is 0.01–36.74 TgC a–1, with a large imbalance in
the forest ecosystem CSR among provinces. The forest CSR per unit area in each province is
greater in the east than in the west, while the CSR per unit GDP and the CSR per capita are
larger in the west. There is a significant negative correlation between the CSR per capita in
each province and its GDP per capita. To enhance the long-term carbon sequestration capac-
ity of forest ecosystems, it is necessary to adopt appropriate forest management measures
such as selective logging, thinning, rationalization of stand structure, and prevention of pests
and fires, thus realizing the sustainable development of forest carbon sinks in each province.
Besides traditional carbon trade, it is also necessary for the country to combine regional for-
est carbon sequestration potential with regional economic development and policy formula-
tion. Strong compensation and supporting regulatory policies will ensure that people in
western regions or underdeveloped provinces will consciously generate, protect and enhance
forest carbon sinks. In short, there is an urgent need in the new era to study and build a sys-
tem that combines forest carbon sequestration technologies and policies to ensure that they
meet the national strategic goal of carbon neutrality by 2060, while also synergizing with the
16 Journal of Geographical Sciences
national strategic goals of rural revitalization, coordinated regional development and com-
mon prosperity.
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