Spatial–Temporal Patterns of Carbon Sequestration Benefits and Identification of County-Level Compensation Orders in Beijing–Tianjin–Hebei Ecosystems

Abstract

The role of ecosystems in sequestering carbon is becoming increasingly important as China’s “dual-carbon” strategy and the construction of an ecological civilisation continue to be promoted and implemented. The rapid economic development of the Beijing–Tianjin–Hebei region is accompanied by the problem of excessive carbon emissions. Focusing on enhancing the carbon sequestration benefits of ecosystems and coordinating regional development, this paper adopts the model construction method, spatial correlation analysis, and centre of gravity migration analysis to study the spatial and temporal patterns of ecosystems’ carbon sequestration benefits in the counties of Beijing–Tianjin–Hebei, spatial and spatial aggregation, the pattern of centre of gravity migration, and the sequence of county compensation differentials in the period from 2000 to 2020. The results show (1) the carbon account of the Beijing–Tianjin–Hebei region before the revision shows an overall distribution pattern of “low in the north and high in the south”. However, it is important to take into account the differences in the regional area, regional population, and regional GDP and the fact that the analysis of the amount of ecological compensation does not mechanically take into account the absolute magnitude of each value but rather takes into account all the values and then makes a trade-off for the difference in the value of the difference in the background. The spatial distribution of corrected carbon accounts is more even. (2) The high–high aggregation area of ecological compensation from 2000 to 2020 is mainly distributed in a dozen counties, such as Lulong County and Luan County in the eastern part of the Beijing–Tianjin–Hebei region, while the low–low aggregation area is mainly concentrated in counties such as Weichang County and Longhua County in the western and northern parts of the country, and the scope has a tendency to expand. (3) The centre of gravity of the ecological compensation amount moves roughly within the range of 116°17′30″ E–116°30′30″ E, 38°40′ N–38°50′ N, and the overall change rule of northwest to southeast migration is observed, with reciprocal movement in some years. (4) The top 15 lists of eco-compensation pay areas and payment areas have basically remained stable, with only some internal sorting changes, and the overall amount of payment areas is larger than that of payee areas, with sufficient surplus funds to ensure the smooth implementation of eco-compensation work at the county level in Beijing–Tianjin–Hebei. The findings of this paper are important for coordinating the integrated development of Beijing–Tianjin–Hebei and, at the same time, contributing positively to the realisation of China’s carbon peaking and carbon neutrality goals.

1. Introduction

Global climate change is one of the most important environmental issues facing mankind today, and C emissions are particularly important in today’s global state [1], with significant impacts on global warming and the Earth’s environment [2]. Reducing the concentration of CO₂ in the atmosphere is considered to be an essential pathway to combat global warming [3], and carbon sequestration services provided by ecosystems are an important pathway to mitigate the concentration of carbon dioxide. Carbon sequestration is a regulating service provided by ecosystems, which specifically refers to the capture of atmospheric CO₂ by vegetation through photosynthesis and its fixation [4]. At present, the traditional methods of studying carbon sequestration services, such as field sampling [5,6] and forest inventory [7], are highly accurate but have the problem of high cost. With the development of 3S technology, the accuracy of remote sensing data has improved significantly, and the CASA model, GLO-PEM model, InVEST model [8,9,10,11], and so on can account for carbon sequestration services.

Countries around the world have signed a global pact to take measures to reduce greenhouse gas emissions in order to curb the growth of the greenhouse effect and address the challenge of global warming [12]. Australia and New Zealand have taken the lead in implementing carbon-neutral mechanisms, and the UK and Canada have adopted carbon offset policies to reduce carbon emissions [13]. Meanwhile, carbon offset projects have been effective, such as Nicaragua’s grassland and pastoral ecosystem compensation project, which aims to repair damaged ecosystems [14]. The Environmental Quality Incentive Programme (EQIP) in the United States has incentivised companies and individuals to actively reduce carbon emissions and raise the level of carbon emissions through a parallel approach of rewards and penalties [15]. China has also been actively working to reduce carbon dioxide and other greenhouse gas emissions. The country has put forward the strategic goal of “striving to achieve carbon neutrality by 2060”, demonstrating the determination to fulfil its commitment to climate action under the Paris Agreement [12]. By analysing the driving influence of environmental factors on carbon sequestration services, it was found that human activities have a significant impact on carbon sequestration services, and the response curves of different types of carbon sequestration services can verify the impact of human activities on carbon sequestration services [16].

As the pace of China’s promotion of ecological civilisation gradually increases, the value of the ecological compensation mechanism becomes more and more significant [17]. In September 2021, the General Office of the State Council issued the Opinions on Deepening the Reform of the Ecological Protection Compensation System, stressing the need to continue to improve the ecological compensation mechanism and stimulate high-quality development of the ecological environment [18,19]. Carrying out research on ecological compensation mechanisms is of practical significance for coordinating fair and sustainable development among regions and is crucial for constructing reasonable ecological compensation standards, as well as determining priority zoning [20].

At present, scholars at home and abroad have made great achievements in the study of ecological compensation standards; in the quantitative method of ecological compensation standards, most of the existing studies have adopted the model construction method [21,22,23], the conditional value method [24,25], the cost method [26,27], and other methods. The model construction method is based on the principle of “who benefits, who pays” and builds a model to determine the ecological compensation standard, which is more direct, accurate, and widely used; the conditional value method determines the ecological compensation standard by considering the individual’s willingness to pay or acceptance, which improves the environmental quality and operability. However, it is largely affected by the cognitive and educational level of stakeholders [28]; opportunity cost refers to the cost of giving up or losing the opportunity for economic development in order to protect the ecological function of the area supplying ecosystem services [29], and the compensation standard is usually calculated on the basis of the direct cost of inputs, which makes it more suitable for the situation where the social and economic benefits can not be directly estimated [30]. In terms of the research object of ecological compensation, existing studies at home and abroad have focused on single aspects, such as watershed water resource management [31], forests [32,33], grasslands [34,35], and environmental protection and restoration [36], as well as regional and holistic aspects, such as provinces [37,38], counties [39], and urban agglomerations [40,41], and the overall research involves a more comprehensive and rich object.

The Beijing–Tianjin–Hebei region is densely populated, industrially concentrated, and has great development potential, but rapid industrialisation and urbanisation have led to a certain degree of environmental pollution and carbon emissions [42]. Although there have been studies on the quantification of ecological compensation, there are still problems, such as the short span of the study and the lack of generality of the results; at the same time, there are fewer articles that take the county as the scale of the study and correct the model to take into account the ontological differences in regional population, area, and GDP. Therefore, based on the perspective of Beijing–Tianjin–Hebei counties, this paper constructs a carbon account correction model and carries out a study on the quantification of eco-compensation and its spatiotemporal differentiation characteristics through software such as ArcGIS, taking into account the differences in population, area and GDP background within the region.

We also analysed the centre of gravity of eco-compensation from the perspective of Beijing–Tianjin–Hebei counties. Finally, we identified the eco-compensation differentials under the county scale of Beijing–Tianjin–Hebei, with a view to providing relevant references for the construction of a perfect eco-compensation mechanism in Beijing–Tianjin–Hebei.

2. Materials and Methods

2.1. Research Area

The Beijing–Tianjin–Hebei region is located in the North China Plain (113°27′–119°50′ E and 36°05′–42°40′ N) (Figure 1), with the Loess Plateau to the west and the Bohai Sea to the east. The area is bounded by the Taihang Mountains in the west and the Yanshan Mountains in the north. North of the Yanshan Mountains is the Zhangbei Plateau, and the rest is the Haihe Plain. Of the three areas, Beijing City is predominantly mountainous, with mountains accounting for about 62% of the area. Tianjin City is predominantly plain, with more than 90% of the area being plain. Hebei Province has a varied topography, consisting mainly of mountains, plains, and plateaus. The overall topography of the Beijing–Tianjin–Hebei region is characterised by a high northwest and a low southeast, with a total area of 216,000 sq km, of which about 65,000 sq km are cultivated, accounting for 5.0% of China. Forest land accounts for about 75,000 sq km, accounting for 2.6% of China; garden land accounts for 12,000 sq km; grassland accounts for 20,000 sq km; wetlands occupy an area of 2000 sq km. Beijing has an area of 718,200 sq km of forested land, accounting for 0.33% of the country’s total forested area. The area of forest land in Tianjin is 136,400 sq km, accounting for 0.06% of the national area. Hebei Province has a forested area of 5,026,900 sq km, accounting for 2.28% of the country’s forested area. The fragile ecosystems and low ecological carrying capacity of the Beijing–Tianjin–Hebei region are due to the low regional forest cover, the low quality of forested land, and the overly homogenous nature of forested land. The healthy and sustainable development of the Beijing–Tianjin–Hebei region, as the most important urbanised region in China, is of great significance to China’s economic and ecologically sustainable development.

2.2. Data and Methods

2.2.1. Carbon Emission and Absorption Modelling

Carbon emission: Considering the limitations and uncertainties of obtaining fossil energy consumption data from the bottom-up through county statistical yearbooks, county carbon emission data from the China Carbon Accounting Database (https://www.ceads.net.cn (accessed on 15 September 2023)) was selected for this study as the basic data for the subsequent research. This data uses the particle swarm optimisation–backpropagation (PSO-BP) algorithm to unify the scales of DMSP/OLS and NPP/VIIRS satellite imagery to estimate the CO₂ emissions of 2735 counties in China from 1997 to 2017. In this thesis, the study period is defined as 2000–2020, and the missing carbon emission data for 2018, 2019, and 2020 are completed by linear interpolation using the carbon emission intensities of the previous years.

Carbon sequestration: The present study was conducted by calculating the net primary productivity of the ecosystem (NEP). Unit conversions and regional statistics were then performed to obtain carbon sequestration for each county. The calculation of NEP requires the use of three values: net primary productivity (NPP) of vegetation, soil anaerobic respiration ($R_h$), and soil respiration ($R_s$). Subtract NPP from soil heterotrophic respiration ($R_h$) to obtain NEP. A positive value of NEP indicates that the vegetation plays the role of a carbon sink and vice versa. The unit of NEP is ${\mathrm{kg}/(\mathrm{m}}^2\times \mathrm{a})$, and the formula is as follows:

$$NEP=NPP-R_h$$

(1)

Soil anaerobic respiration was calculated as $R_h$ using the model developed by Zhang Mei [43] et al.

$$R_h=0.6163 R_s^{0.7918}$$

(2)

Soil respiration was calculated as $R_s$ using the soil respiration model developed by Chen [44] et al.

$$R_s=1.55e^{0.031T}•\frac{P}{P+0.68}•\frac{SOC}{SOC+2.23}$$

(3)

In this equation, T is the mean annual air temperature (°C); P is the annual precipitation (m); and SOC is the soil carbon density ($\mathrm{kg}/{\mathrm{m}}^2$).

2.2.2. Ecological Compensation Modelling

With the above carbon emission and carbon sequestration modelling, we can obtain the regional carbon account with the following equation:

$$C_s=C_e-C_a$$

(4)

where C_s is the pre-correction carbon account, C_e is the total carbon emissions, and C_a is the total carbon sequestration in t/CO₂. However, at this point, the carbon accounts are absolute data ignoring regional population, area, and GDP, and to eliminate the background differences, we have made statistically significant corrections, which are calculated as follows:

$$C_s^A=\overline{A}(\frac{C_s}{A}-\frac{C_T}{A_T})$$

(5)

$$C_s^P=\overline{P}(\frac{C_s}{P}-\frac{C_T}{P_T})$$

(6)

$$C_s^G=\overline{G}(\frac{C_s}{G}-\frac{C_T}{G_T})$$

(7)

where $C_s^A$ is the carbon account in blocks corrected for the area factor, and $C_T$ is the sum of all carbon accounts in the study area (t/a), $\overline{A}$ is the mean value of the area of each block in the study area. $A_T$ is the total area of the study area (km²); $C_s^P$ is the carbon account for blocks corrected to take into account the population factor. $\overline{P}$ is the average value of the population in each block of the study area. $P_T$ is the total population of the study area, individually; $C_s^G$ is the carbon account for blocks corrected to take into account the GDP factor. $\overline{G}$ is the average value of GDP for each block in the study area. $G_T$ is the total GDP of the study area in millions of dollars.

$$C_s^{\prime }=(C_s^A+C_s^P+C_s^G)/3$$

(8)

$${\mathrm{BCE}}_k=C_s^{\prime }\cdot V_{c{o}_2}$$

(9)

where BCE_k is the compensation for region k and $V_{c{o}_2}$ is the carbon trading price, which is taken as the average value of the trading price in China’s carbon trading market from 2013 to 2020 (CNY 27.31).

2.2.3. Ecological Compensation Differential Order Identification

The ecological compensation differential refers to the principle that regions with lower levels of economic development have a more urgent need for ecological compensation than regions with higher levels of economic development, and this principle is used to determine the priority of ecological compensation in each region [45]. The formula for calculating the ecological compensation differential order is as follows:

$$\left|{\mathrm{CCPS}}_i\right|=\frac{C_s^{\prime }\cdot V_{c{o}_2}}{{\mathrm{GDP}}_i}$$

(10)

where GDP_i is the GDP of place I, $V_{c{o}_2}$ is the unit price of carbon, and |CCPS_i| is the corrected ecological compensation differential order for region i. The magnitude of the |CCPS_i| value represents the importance of ecological compensation funds to the region. When a place has a carbon surplus area, it should be compensated, and at the same time, CCPS_i < 0 indicates that the place is in urgent need of eco-compensation funds, and at this time, the smaller the value of CCPS_i, the higher the order of being compensated. When a place is a carbon deficit area, the funds should be paid, and at the same time, CCPS_i > 0. The larger value of CCPS_i indicates that the payment of eco-compensation funds has a larger touch on the local area, and at this time, the larger the CCPS_i is, the more the order of payment is.

The workflow diagram of the system in this paper is shown in Figure 2.

2.2.4. Data Sources

This study involves data on carbon emissions, land use types, regional socio-economics, and China’s administrative boundaries, as shown in

Table 1. Data sources.

Data Category	Data Sources	Time	Comments
Carbon emission data	China Carbon Accounting Database https://www.ceads.net/user/index.php?id=1057&lang=en (18 September 2023)	2020	county
NPP	MODIS data set	2000–2020	500 m
Regional socio-economic data	Statistical information such as provincial statistical yearbooks	2000–2020	county
Administrative boundary vector data	National Geographic Information Resources Catalogue Service System: 1:1 million national basic geographic database	2020	1:1 million

. Considering the administrative division adjustment and data availability, Dongcheng District, Xicheng District, Haidian District, Chaoyang District, Fengtai District, and Shijingshan District were merged into the Beijing Municipal District. The Heping District, Hexi District, Hedong District, Nankai District, Hebei District, and Hongqiao District were merged into the Tianjin Municipal District. All prefectural-level cities and counties in the Hebei Province were also processed according to the same criteria. In contrast, some county names were still used due to data problems, and other missing data were interpolated from data of similar years or filled in by reference to the average value of data from neighbouring counties.

3. Results

3.1. Comparative Analysis of the Characteristics of Spatial and Temporal Differentiation before and after the Revision of the Carbon Accounts

As shown in Figure 3, from 2000 to 2020, Beijing–Tianjin–Hebei’s carbon emissions were increasing as a whole, of which the trend of increase was particularly obvious along the southeast coast. The positive areas were obviously greater in number than the negative areas. The urban agglomerations of Beijing, Tianjin, and Shijiazhuang exhibited a spatial pattern where high values were concentrated at the center and gradually dispersed towards the periphery. The low values were mainly located in the northern districts and counties, with the representatives of the Weichang County, Longhua County, and Fengning Manchu Autonomous County. On the time scale, there is a gradual increase in the number of positive areas over time, with the addition of Beijing, Tianjin, and the counties around the Handan municipal district in 2010 compared to 2000. In 2015, net carbon emissions from Fengning Manchu Autonomous County, as well as Weichang County, increased significantly compared to 2010. By 2020, the high values are mainly located in Beijing, Tangshan, Xingtai, and around the Handan municipal district.

The corrected spatial and temporal characteristics of carbon accounts are shown in Figure 4. Overall, the distribution of carbon accounts in Beijing–Tianjin–Hebei from 2000 to 2020 still shows the distribution trend of “low in the north and high in the south”, but the gap between the counties is significantly narrowed, and the spatial distribution of carbon accounts is more even. The northern part of the Beijing–Tianjin–Hebei region is the main carbon sequestration area, the southern part is the carbon emission area, and the Binhai New Area in the centre is the main carbon account peak area. In the northern part of the region, there is a trend of “decreasing first and then increasing”; the counties around Zhangjiakou City and Luanping County show a trend of gradual increase; in the central part of the region, the highest value of the carbon account is always located in the Binhai New Area; in the southern part of the region, there is a trend of “increasing first and then decreasing” in the carbon account as a whole, but Shijiazhuang City and its municipal districts show a steady increase.

Comparing the spatial and temporal variations of carbon accounts before and after the amendment, it is found that the carbon accounts of Beijing–Tianjin–Hebei before and after the amendment show the distribution pattern of “low in the north and high in the south”, but the spatial distribution of carbon accounts after the amendment is more even, which is more conducive to easing the economic conflicts among counties and promoting the smooth implementation of ecological compensation compared with the distribution pattern of “two extremes of high and low, more high and less low” before the amendment.

Zhang Zhengfeng et al. used a gravity model to empirically analyse the spatial correlation of carbon emissions/sinks at the county scale in Beijing–Tianjin–Hebei, divided the carbon balance into zones, and concluded that carbon sinks showed a high distribution in the north and a low distribution in the central, southern, and eastern parts of the country [46]. Xia Siyou et al. used the index of dominant attributes of carbon offset zones and the SOM-K-means clustering model to classify the carbon offset type zones of the Beijing–Tianjin–Hebei urban agglomeration and came up with the distribution trend of “high in the north and low in the south” [47]. Xiao Weiwen et al. measured the carbon emissions and carbon footprint of land use in the Beijing–Tianjin–Hebei region based on the Carbon Footprint Ecological Compensation Model (CFEM). They concluded that the carbon emissions in the Beijing–Tianjin–Hebei region have a decreasing trend from north to south, which is basically the same as that of this paper [48]. Meanwhile, compared with the mathematical models used in existing studies, the eco-compensation model used in this paper further makes the distribution of carbon accounts at the county level more even and can identify the key counties in the region and promote the smooth implementation of eco-compensation to a greater extent.

3.2. Analysis of Spatial Correlation and Centre of Gravity Migration of Ecological Compensation

3.2.1. Local Moran Index

As shown in Figure 5, the spatial distribution of the ecological compensation amount from 2000 to 2020 has obvious spatial and temporal heterogeneity. Specifically, the northwestern region displays a low–low aggregation concentration, while the eastern region demonstrates a high–high aggregation concentration. In the southern region, there is a sporadic distribution of individual low–low aggregation areas and low–high aggregation areas, and the aggregation areas have been expanding to varying degrees over time. In 2000, the low–low aggregation area was 13 counties, including Weichang County, Longhua County, and Fengning Manchu Autonomous Region; the high–high aggregation area was eight counties, including Huanghua City, Binhai New Area, and Jinghai County; and the low–high aggregation area was six counties, including Baodi County, Ninghe County, and Fengnan City, which had low ecological compensation, while their neighbouring counties had high ecological compensation. In 2005, the low–low aggregation zone reduced the number of counties in Luanping and Chengde and increased the number of counties in Longyao and Julu, bringing the total to 15 counties. The high–high aggregation area added Ninghe and Fengnan counties, bringing the total to 15 counties. The low–high aggregation area reduced the counties of Ninghe and Fengnan and added Yongqing County to bring the total to three counties. In 2010, compared to 2005, the low–low agglomeration area decreased in the Kuan Cheng Manzu Autonomous Region and increased by eight counties, including Luanping and Xinhe, bringing the total to 22 counties. The low–high aggregation area increased by five counties, including Wuqiang and Ninghe, to eight counties. The high–high aggregation area reduced the counties of Jinghai, Wuqing, and Ninghe to nine counties. In 2015, compared to 2010, the low–low aggregation area was reduced by eight counties, including Wuqiang County and Wuyi County. The low–high aggregation area reduced Yongqing, Luquan, and Luannan counties and added Changli, Luannan, and Linzhang counties to bring the total to eight counties. The high–high aggregation area reduced Xianghe County, Luannan County, etc., and added eight counties, including Funing County. In 2020, compared with 2015, the low–low aggregation area reduced counties such as Miyun and Qinghe and increased Xinji and Zhangbei counties to bring the total to 17 counties. The low–high aggregation area reduced Changli County and increased Magnet County to eight counties. The high–high aggregation area reduced Jingxing County and Ci County and added six counties, including Lulong County and Luan County, to bring the total to 18 counties.

3.2.2. Centre of Gravity Migration

From Figure 6, it can be seen that the centre of gravity of the ecological compensation amount during the period of 2000–2020 roughly shifted within the range of 116°17′ E–116°30′ E, 38°40′ N–38°50′ N; i.e., it transformed into Wen’an County, Renqiu County, and Dacheng County. The study time can be roughly divided into four phases: From 2000 to 2004, the study site shifted approximately 9 km from its initial coordinates at (116°23′ E, 38°42′ N) to a new location at (116°17′ E, 38°43′ N). From 2004 to 2017, the study area underwent a northeasterly and then a southeasterly movement, covering a distance of approximately 13.3 km from (116°17′ E, 38°43′ N) to (116°32′ E, 38°41′ N). From 2017 to 2019, the study site experienced a further displacement of about 20.7 km, relocating from (116°32′ E, 38°41′ N) to (116°26′ E, 38°40′ N). Finally, from 2019 to 2020, the study area moved approximately 8.9 km from (116°26′ E, 38°40′ N) to (116°28′ E, 38°41′ N). Overall, these changes over 20 years indicate a consistent northwest-to-southeast migration pattern.

3.3. County Compensation Orders Identification

In 2000 and 2005, there were 96 payment zones and 73 compensation zones, respectively, and in 2010, 2015, and 2020, there were 100, 133, and 138 payment zones and 69, 36, and 31 compensation zones, respectively. The ratio of the number of payment zones to the number of compensated areas varied between 1.3 and 4.5; i.e., the number of payment zones was consistently greater than the number of compensated areas. The total compensation payments in the five nodal years were CNY 7.7 billion, CNY 11.944 billion, CNY 16.981 billion, CNY 18.857 billion, and CNY 22.251 billion. The total amount of compensation received was CNY 1.63 billion, CNY 2.966, CNY 3.298 billion, CNY 2.519 billion, and CNY 2.540 billion. The ratio of the total amount of payment in the payment area to the compensation in the compensation area ranged from 3.9 to 8.9. It can be found that ecological compensation under the Beijing–Tianjin–Hebei county-level threshold of vision can be realised with payments greater than reimbursements and with sufficient surpluses as reserves. Due to space limitations, the top 15 counties and districts ranked in the payment zones and compensation zones in five years were selected to be displayed. It can be seen from

Table 2. Ecological compensation orders in 2000, 2005, 2010, 2015, and 2020.

Sort	Orders	2000	2005	2010	2015	2020
		County
Compensated area	1	Chongli County	Chongli County	Fuping County	Chicheng County	Chicheng County
	2	Chicheng County	Chicheng County	Guyuan County	Fengning County	Fengning County
	3	Guyuan County	Guyuan County	Fengning County	Fuping County	Fuping County
	4	Fuping County	Weichang County	Chongli County	Weichang County	Chongli County
	5	Longhua County	Shangyi County	Weichang County	Longhua County	Weichang County
	6	Fengning County	Fuping County	Shangyi County	Chongli County	Kangbao County
	7	Weichang County	Fengning County	Chi County	Guyuan County	Longhua County
	8	Zhuolu County	Longhua County	Kangbao County	Zhuolu County	Zhuolu County
	9	Qinglong County	Kangbao County	Longhua County	Xinglong County	Guyuan County
	10	Laiyuan County	Zhangbei County	Zhuolu County	Yangyuan County	Xinglong County
	11	Kangbao County	Zhuolu County	Zhangbei County	Chengde County	Yangyuan County
	12	Chengde County	Xinglong County	Xinglong County	Kangbao County	Chengde County
	13	Huai’an county	Chengde County	Yangyuan County	Pingquan County	Yixian County
	14	Luanping County	Pingquan County	Boye County	Yixian County	Pingquan County
	15	Xinglong County	Qinglong County	Chengde County	Mentougou District	Yanqing County
Sort	Priority order	2000	2005	2010	2015	2020
		County
Payment zone	1	Gaoyi County	Beichen District	Beichen District	Shangyi County	Tang Hai County
	2	Binhai New Area	Qinhuangdao Municipal District	Gaocheng City	Qinhuangdao Municipal District	Qinhuangdao Municipal District
	3	Nangong City	Wu’an City	Wu’an City	Tang Hai County	Shangyi County
	4	Qinhuangdao Municipal District	Gaocheng City	Dongli District	Ningjin County	Ningjin County
	5	Wu’an City	Tangshan City Area	Qinhuangdao Municipal District	Haixing County	Dachang County
	6	Ningjin County	Zhangjiakou Municipal District	Zhangjiakou Municipal District	Handan City District	Haixing County
	7	Dongli District	Binhai New Area	Tangshan City Area	Jingxing County	Wuqiao County
	8	Beichen District	Dongli District	Tongzhou District	Xuanhua County	Handan City District
	9	Gaocheng City	Ningjin County	Binhai New Area	Wuqiao County	Xuanhua County
	10	Zhangjiakou Municipal District	Tianjin Municipal District	Luancheng County	Lincheng County	Wangdu County
	11	Tangshan City Area	Tongzhou District	Hengshui City Area	Dachang County	Lulong County
	12	Yongnian County	Hengshui City Area	Xianghe County	Nanpi County	Rongcheng County
	13	Tongzhou District	Luancheng County	Potou City	Wangdu County	Jingxing County
	14	Fangshan District	Anxin County	Ningjin County	Huanghua City	Nanpi County
	15	Handan City Jurisdiction	Baoding City Area	Tianjin Municipal District	Luannan County	Zhangbei County

that the top 15 counties and districts ranked in the different order of the ecological compensation of the compensation districts, and the payment zones were basically stable and unchanged in the years 2000, 2005, 2010, 2015, and 2020. Only a certain order of change occurred internally: the municipal area of Qinhuangdao City ranked second among the payment zones in the years 2005 and 2015. Qinhuangdao Municipal District in 2005, 2015, and 2020 ranked second in the payment zones, and Beichen District was the first in the payment zones in both 2010 and 2015. Chicheng County, Fengning Manchu Autonomous County, and Fuping County were ranked in the top three of the compensated areas on several occasions.

4. Discussions

4.1. Spatial Correlation and Centre of Gravity Shift in Ecological Compensation

By analysing the Moran Index of eco-compensation in the Beijing–Tianjin–Hebei region, it can be seen that the high–high aggregation area of eco-compensation is mainly located near Tianjin, around Cangzhou and Handan. In contrast, the low–low aggregation area of eco-compensation is mainly concentrated in the northern counties of the Beijing–Tianjin–Hebei region, Weixian County, Laiyuan County, Fuping County of Baoding City, as well as the vicinity of Xingtai and Hengshui, and the eco-compensation in these and the neighbouring counties are all relatively low and low. The main reason for this is that the northern counties of the Beijing–Tianjin–Hebei region, Weixian County of Baoding City, Laiyuan County, and Fuping County have high vegetation cover, especially in the northern counties of the Beijing–Tianjin–Hebei region, which are sparsely populated, where human economic activities cause less damage to the ecological environment, and where the vegetation grows luxuriantly so that the carbon sequestration capacity is stronger. The neighbourhood of Tianjin, the vicinity of Cangzhou, and the vicinity of Handan, on the other hand, have high population densities and have a stronger impact on the environment, and therefore emit more carbon, a conclusion similarly reached in the study by Zhang Zhengfeng et al. [49].

Compared with other articles related to ecological compensation amount, this paper explores for the first time the centre of gravity shift law of Beijing–Tianjin–Hebei ecological compensation amount and finds that the centre of gravity of ecological compensation amount moved in a zigzag-like trajectory between 2000 and 2020. However, the overall direction shifted from the northwest to the southeast by about 9 km from 2000 to 2004. Combining the data on carbon emissions and carbon sequestration from 2000 to 2004, it can be found that the centre of gravity migration during this period was mainly due to the increase in carbon emissions in Zhangjiakou, Baoding, and some parts of Beijing. Among them, Zhangjiakou City Municipal District and Dafang Hui Autonomous County had higher increases. From 2004 to 2017, it moved first to the northeast and then to the southeast, with a migration path of Renqiu County–Wen’an County–Dacheng County. The main reason for this is the substantial increase in carbon emissions in coastal areas, such as Tangshan, Qinhuangdao, and Tianjin, which led to the migration of the centre of gravity to the east during this period. The centre of gravity of eco-compensation credits from 2017 to 2020 showed a reciprocal movement in the east–west direction. During the 20 years, the overall change trend pattern of northwest-to-southeast migration was observed. The shifting path of the centre of gravity of the Beijing–Tianjin–Hebei eco-compensation credits shows that the high-carbon emission zone expands to the coastal areas, such as Qinhuangdao, Tangshan, and Cangzhou [50].

The low ecological compensation aggregation area is concentrated in the north and west of Beijing and Hebei Province. In contrast, the high ecological compensation aggregation area is concentrated in the central and south of Beijing Hebei and most of Tianjin, and the shift in the centre of gravity of ecological compensation is closely related to the urban economy, urban population, and industrial structure. Rational planning of industrial layout, implementation of afforestation, and delineation of the “three lines”, including permanent basic farmland, ecological red line, and urban growth boundary, can generally improve the situation.

4.2. Ecological Compensation Differential Order Identification

Most of the existing results use GDP as a reference indicator, Wang et al. [51] compared compensation with regional GDP, and Yu et al. [2] calculated the compensation capacity, which was expressed as the ratio of regional GDP per capita.

This paper chooses GDP as the reference index for priority zoning, and in terms of ecological compensation differential zoning, the ratio of the total ecological compensation amount in the payment area to the total ecological compensation amount in the recipient area is approximated to be 3:1. The ratio of the number of the payment area to the number of the recipient area is approximated to be 4:7. The combined ratio of the amount of compensation to the number of the ratio, it can be basically deduced that the Beijing–Tianjin–Hebei county level is able to realise an overall balance between revenue and expenditure in terms of the amount of ecological compensation. In 2000, 2005, 2010, 2015, and 2020, the top 15 counties in terms of priority of ecological compensation between compensated areas and paying areas have basically remained stable, with only some internal changes in the order, with Fengning Manchu Autonomous County, Fuping County, Chongli County, and Weichang County consistently ranking among the top seven paying areas, and Chicheng County, with the exception of 2010, consistently ranking as the top two paying areas. Qinhuangdao Municipal District has been among the top five payee districts, and at the same time, it can be seen that most of the payer districts are compensated more than the payee districts. In terms of time, carbon emissions in the Beijing–Tianjin–Hebei region continued to increase from 2000 to 2013 and decrease from 2014 to 2020. From a spatial perspective, high-carbon emission areas are mainly concentrated in coastal areas, such as Qinhuangdao, Tangshan, and Cangzhou. In contrast, low-carbon emissions are mainly distributed in areas with high vegetation cover, such as Chengde, which is closely related to the local economy, urban population, and industrial structure. In summary, it is inferred that the order of ecological compensation difference between 2000 and 2020 in the Beijing–Tianjin–Hebei region during the 21 years did not change much, and the overall ecological compensation amount of the paying area was greater than the compensation amount of the receiving area, which indicates the balance of income and expenditure of ecological compensation in the Beijing–Tianjin–Hebei region.

4.3. Strengths and Weaknesses of the Modelling Approach

This paper further explains the local evolutionary characteristics of ecological compensation credits at the county level, as well as the heterogeneity in spatial distribution by combining the relevant data on population, area, GDP, carbon emissions, carbon sequestration, pre-corrected net carbon emissions, and post-corrected net carbon emissions in the study area with local Moran’s indices and centre of gravity migration analyses. However, there are still limitations in this paper: (1) The ecological compensation model covers multiple aspects and domains, and the ecological compensation standard determined by a single indicator may be unfair, which needs to be verified through field surveys [52]. (2) Due to the limitation of data availability, the model validation in this paper only analyses the ecological compensation from 2000 to 2020, and the results of the study have temporal limitations, which can be further deepened by strengthening the tracking and research of future change trends [53]. (3) The Beijing–Tianjin–Hebei region is China’s three major urban agglomerations. The development gap is the largest, the most serious environmental pollution of the urban agglomerations; it is necessary to improve the Beijing–Tianjin–Hebei ecological compensation mechanism as soon as possible to alleviate the Beijing–Tianjin–Hebei region faced with serious environmental pollution and ecosystem degradation problems. Existing ecological compensation has a variety of compensation modes, such as policy support and social security, so it is possible to carry out research on ecological compensation from multiple perspectives and at multiple levels to further improve the effectiveness and realistic operability of compensation, to effectively coordinate the relationship between the various subjects of interest in the inter-regional area, and to promote coordinated regional development.

5. Conclusions

Based on the carbon emission and carbon sequestration data of ecosystems in Beijing–Tianjin–Hebei counties from 2000 to 2020, the spatiotemporal heterogeneity of ecosystem carbon sequestration benefits and ecological compensation in the Beijing–Tianjin–Hebei region was analysed by using the research methods of model construction, local Moran’s Index, the centre of gravity relocation, and identification of compensatory differential sequences with the specific conclusions as follows:

(1)

Comparing the spatial and temporal distribution of carbon accounts before and after the amendment, it is found that before and after the amendment, the Beijing–Tianjin–Hebei region as a whole shows a distribution pattern of “low in the north and high in the south”, and after taking into account the background differences between regions, the carbon accounts are more evenly distributed in space. Before the amendment, the overall carbon emissions in Beijing–Tianjin–Hebei showed an increasing trend during the 21a period, with more positive areas than negative ones and the positive areas gradually increasing. After the correction, the gap between counties is significantly reduced, with the northern region showing a trend of “decreasing first and then increasing” and the southern region showing a trend of “increasing first and then decreasing”, which is more conducive to easing the economic conflicts between counties and promoting the smooth implementation of eco-compensation work;

(2)

Analyses through the Moran Index of the amount of ecological compensation between 21a revealed strong local spatial correlations. Of these, in the five representative years selected, the high–high aggregation area increased in all four years except 2010, when the high–high aggregation area decreased by four counties. The region is mainly located in Lulong County and Luan County in the eastern part of Beijing, Tianjin, and Hebei. The low–low aggregation area is mainly concentrated in the western and northern counties of the study area, such as Weichang County and Longhua County, and has a tendency to expand in scope. The centre of gravity of the eco-compensation amount shifts roughly between Wen’an County, Renqiu County, and Dacheng County. By analysing the four study periods of 2000–2004, 2004–2017, 2017–2019, and 2019–2020, this paper finds that the overall pattern of change in migration from the northwest to the southeast is shown, with the characteristic of reciprocal movement in some years;

(3)

By summarising and comparing the data, it is found that the Beijing–Tianjin–Hebei region can basically achieve ecological compensation revenue and expenditure balance. In the five representative years, Qinhuangdao Municipal District and Beichen District ranked among the top two paying districts for long periods of time, while Chicheng County, Fengning Manchu Autonomous County, and Fuping County ranked among the top three receiving districts many times. Overall, the top 15 counties and districts in the different order of ecological compensation between the compensated and paying districts are basically stable and unchanged. However, the internal rankings have been adjusted to a certain extent. The overall compensation amount of the paying districts is greater than that of the compensated districts so that the ecological compensation revenue and expenditure under the Beijing–Tianjin–Hebei county-level threshold of vision can achieve a surplus, which can guarantee the smooth implementation of compensation work;

(4)

The findings of this thesis will play an important role in coordinating the development of Beijing–Tianjin–Hebei, advancing the task of ecological civilisation and environmental protection and contributing to the achievement of the dual-carbon goal in terms of policy significance. The findings will also provide a reference value for all levels of government when implementing ecological goals and specific initiatives.