Analysis of the Income Enhancement Potential of the Terrestrial Carbon Sink in China Based on Remotely Sensed Data

Abstract

A key focus of international climate action is achieving a terrestrial carbon sink within the framework of carbon neutrality. For certain regions with vital ecological functions and high poverty rates, the generation of surplus ecological carbon income is crucial for mitigating global inequality. While the evaluation of the economic benefits of carbon income still faces limitations in terms of inadequacy and inaccuracy, enhancing green carbon poverty alleviation schemes is urgently needed. This project introduces an analysis framework for assessing the land-based ecological carbon sink and poverty alleviation potential based on a per capita ideal carbon sink income evaluation, which compares the regional economic benefits of a carbon sink under different carbon price benchmarks and explores tailored green poverty alleviation strategies. It indicates that the per capita ideal carbon sink income in China exhibits a seasonal variation, ranging from approximately USD 16.50 to USD 261.41 per person per month on average. Its spatial distribution shows a pattern of lower values in the central region and higher values in the north and south, following a “high differentiation, low clustering” distribution pattern. The per capita carbon sink income can reach 30% to 70% of the per capita GDP, with a peak value of USD 19,138.10 per year, meeting the minimum livelihood guarantee for the needs in economically underdeveloped areas. Simultaneously, the per capita carbon sequestration income within the Chinese carbon market is expected to demonstrate a yearly ascending trajectory, with an approximate growth rate of USD 23.6 per individual annually. The southwest, northeast, and north China regions can be prioritized as key areas for carbon market development, facilitating more comprehensive inter-regional and sustainable carbon trading. This study plays a significant role in disclosing the regional ecological function and economic benefits, promoting the use of “carbon neutrality” as a driving force to alleviate global inequality and contributing to global climate action and poverty eradication strategies.

1. Introduction

In 2020, China proposed the goal of achieving carbon neutrality by 2060 [1,2]. However, even with the emerging de-carbon technologies, there could still be approximately 0.68 GtC of anthropogenic carbon emissions in 2060 [3,4], which highlights the necessity of utilizing negative emission technologies, such as Carbon Capture, Utilization, and Storage (CCUS), to achieve the goal of carbon neutrality [5]. Currently, China’s terrestrial carbon sink is able to offset around 10% of the anthropogenic emissions annually [6], which amounts to approximately 0.3 GtCb [7]. In fact, the Chinese government initiated the Natural Forest Protection Program (NFPP) in 1998 [8]. Although the primary goal was to strengthen the management and protection of natural forest regions [9], these young forests have the potential to play a crucial role in carbon sequestration in the future [10,11]. In comparison to other negative carbon technologies, terrestrial carbon sinks have a unique advantage in reducing the unfairness caused by economic development because densely forested areas are typically difficult areas in which to develop large-scale industries and commerce [12]. Therefore, the inclusion of terrestrial carbon sinks in carbon trading markets is expected to be an important policy tool to achieve both poverty eradication and the carbon neutrality goals.

The United Nations Sustainable Development Goals (SDGs) have emphasized the importance of climate action and poverty eradication. On one hand, the SDGs advocate for urgent global actions to mitigate the impacts of climate change and reduce greenhouse gas emissions. On the other hand, the SDGs are committed to eliminating poverty and ensuring basic rights and opportunities for all individuals to achieve sustainable development. Additionally, although China has achieved absolute poverty alleviation in 2020 [13], considering the relatively low poverty line standards in China [14] and the risk of reversion to poverty for some residents [15], implementing sustainable support policies for low-income individuals and regions remains a major concern for China’s government [16]. Therefore, discussing and analyzing the leveraging of terrestrial carbon sink resources to help alleviate regional development imbalances holds significant importance. From the perspective of China’s domestic agenda, this represents a convergence point of the carbon neutrality goal and poverty alleviation efforts. From a global perspective, it presents a highly promising policy tool to achieve the SDGs and address global North–South inequalities. However, there is currently a lack of quantitative analysis in the relevant research on the role of terrestrial ecological carbon sinks in reducing poverty and mitigating inequalities in different market trading scenarios.

According to the commonly used definition in ecology, a terrestrial carbon sink is defined as the process by which land ecosystems absorb and store greenhouse gases, thus reducing atmospheric carbon levels [17]. Different indicators provide insights into carbon sinks. Net Ecosystem Productivity (NEP) offers a comprehensive assessment of the carbon balance within an ecosystem. Net Primary Productivity (NPP) quantifies the overall organic matter produced by vegetation through photosynthesis during a specific time period [18], subtracting the portion consumed by the vegetation itself for respiration and growth [19]. Among the indicators used in carbon sink monitoring, the net primary productivity (NPP) of vegetation is a significant measure [20,21], which provides a direct and quantitative characterization of the production capacity of vegetation communities under natural environmental conditions [22]. Meanwhile, comprehensive assessments of carbon sinks should consider factors such as soil carbon fluxes and disturbance components to reveal the dynamic changes in ecosystems and soil carbon.

Currently, the calculation methods for the NPP mainly include statistical models, parameter models, and process models, with the process-based model based on light use efficiency (CASA) being the most widely used. Potter et al. [23] were the first to estimate the NPP of North American vegetation using the CASA model, while Piao et al. [24] estimated the NPP of China’s land vegetation in 1997 using the CASA model. To make the NPP calculations in China more applicable and accurate, extensive regional optimization studies of the CASA model have been conducted domestically. The key focal points of the CASA model optimization include parameters such as light use efficiency and photosynthetically active radiation [25]. Zhu et al. [26] introduced a vegetation cover classification method using measured data to simulate light use efficiency for different vegetation types and estimate the national vegetation NPP at an 8 km resolution from 1989 to 1993. Bao et al. [27] simplified the CASA model’s water stress coefficient estimation for grassland NPP in the Mongolian Plateau using the surface wetness index (LSWI).

Ecological carbon sinks are indispensable for achieving carbon neutrality. They not only play a crucial role in environmental protection but also possess significant economic value. By utilizing the net primary productivity (NPP) for carbon stock accounting, we can unlock the potential of green ecological resources in various regions, enabling the generation of additional carbon sequestration revenue while implementing climate actions in alignment with the Sustainable Development Goals (SDGs). This has profound implications for the development of economically underdeveloped areas.

In recent years, numerous scholars have extensively discussed the value of carbon sinks. Zhang et al. [28] conducted a comprehensive investigation on China’s urban net carbon sink efficiency (NCSE) using the Carbon Emission Efficiency (CEE) and Carbon Sink Value (CSV) approaches. The research also explored the spatial characteristics and influencing factors of the NCSE. Additionally, Lin et al. [29] assessed the forestry productivity across 30 regions in China using the Slacks-based Measure (SBM) method and the Malmquist–Luenberger index, focusing on both the static efficiency and dynamic changes in ecological and economic development through forest carbon sinks. However, there is still a lack of a comprehensive framework for quantitatively assessing the per capita carbon sequestration income, and there is still a gap in the assessment of the spatial distribution of the regional carbon sequestration potential.

To address this issue, this research utilizes the current typical carbon market benchmarks, evaluates the per capita ideal carbon sink income in China integrating social and geographical factors, and proposes an analysis framework to assess the carbon sink potential in different regions of China from 2010 to 2020, which reveal the ecological functionality and economic benefits of carbon sink at the regional level. Through the utilization of this framework, we analyzed the income-generating potential and poverty alleviation capacity in different regions by assigning economic value to terrestrial carbon sequestration resources. This framework can also be employed in the future to analyze the potential effects of implementing similar policies on a global scale. The organization of this paper is as follows: In the second section, we present the overall technical approach of this study, including the algorithmic models and the data sources. In the third section, we provide a detailed analysis of the per capita ideal carbon sink income in China from 2010 to 2020, including the spatial–temporal distribution and regional variations, a comparison of the carbon sequestration economic benefits under different carbon market benchmarks, and an assessment of the relative potential of carbon sequestration income for poverty alleviation. In the fourth section, we discuss the impact of the algorithms and data models on the results, examining the factors contributing to the spatial–temporal distribution, and discuss the patterns of carbon sequestration income under different carbon markets, exploring location-specific carbon sequestration poverty alleviation strategies. The last section concludes the paper.

2. Data Processing and Algorithm Models

2.1. Algorithm Models

This research introduces an analysis framework for assessing the land-based ecological carbon sink and poverty alleviation potential based on a per capita ideal carbon sink income evaluation.

The technical workflow for evaluating the national per capita ideal carbon sink income is as follows: (1) Population spatial estimation. We conducted the statistical analysis of the population quantity and its spatiotemporal variations in different regions. (2) Determination of the carbon market benchmarks. We determined the average carbon price under the fluctuations of various typical carbon markets. (3) Terrestrial ecological carbon assessment. We utilized the temperature, precipitation, and ecosystem productivity data to calculate the net primary productivity (NPP) of land ecosystems to estimate the regional carbon sink capacity of the ecosystems. (4) Extraction of effective regions. Based on the land cover type data, the corresponding regions covered by natural vegetation (excluding agricultural land, urban areas, and glacial regions) were extracted as the valid research scope for assessing the terrestrial ecological carbon sinks. (5) Measurement of per capita ideal carbon sink income. We incorporated socioeconomic data such as the NPP, population quantity, and carbon prices, developed a calculation model for the per capita ideal carbon sink income, and assessed the national carbon–economic benefits.

The analysis of the national carbon sink and poverty alleviation potential included the following aspects: (1) Spatial correlation analysis. It involved the local Moran’s I index for spatial clustering analysis and the Getis-Ord Gi* for hotspot and coldspot analysis. (2) Comparative analysis of the spatial–temporal distribution and change trends under multiple market benchmarks. (3) Assessment of the poverty alleviation capacity. We evaluated the relative benefits of carbon sequestration and the potential for poverty alleviation based on socioeconomic indicators. (4) Prediction of the carbon sink economic benefits. We utilized linear regression methods to forecast the potential ideal carbon sink income for the next five years.

The overall methodology framework is shown in Figure 1.

2.1.1. The Carnegie–Ames–Stanford Approach (CASA) Model

The Carnegie–Ames–Stanford Approach (CASA) model [23] is a well-established model for estimating the net primary productivity (NPP). The NPP of land ecosystems is primarily determined by the absorbed photosynthetically active radiation (APAR) by vegetation and the light use efficiency (ε):

NPP(x,t) = APAR(x,t) ∗ ε(x,t).

(1)

In the equation, t represents the time and x represents the spatial location. APAR depends on the vegetation’s proportion of absorbed photosynthetically active radiation, ε refers to the efficiency at which absorbed photosynthetically active radiation is converted into organic carbon, and their formulas are as follows:

APAR(x,t) = SOL(x,t) ∗ FPAR(x,t) ∗ c,

(2)

ε(x,t) = T_ε1(x,t) ∗ T_ε2(x,t) ∗ W_e(x,t) ∗ ε^*.

(3)

In the equation, SOL(x,t) represents the total solar radiation, FPAR(x,t) denotes the fraction of absorbed photosynthetically active radiation (PAR) by the vegetation canopy, and the parameter c represents the proportion of solar radiation in the photosynthetically active wavelength range that can be utilized by the vegetation. The parameters T_ε1 and T_ε2 represent the temperature coefficients that influence the light use efficiency, We is the water stress impact factor, and ε^* denotes the maximum light use efficiency under ideal conditions, which Potter et al. [23] suggested to be 0.389 gC·MJ⁻¹.

2.1.2. Algorithmic Model of Ecosystem Productivity

Net Ecosystem Productivity (NEP) reflects the net carbon exchange in terrestrial ecosystems and provides a direct, qualitative, and quantitative description of the nature and capacity of land-based carbon sinks [30]. In quantitative calculations, the NEP is generally defined as the difference between the net primary productivity (NPP) and the amount of soil heterotrophic respiration (Rh) [31], expressed as:

NEP = NPP − Rh.

(4)

In the equation above, soil heterotrophic respiration (Rh), also known as soil microbial respiration, refers to the process by which microorganisms in the soil decompose organic matter and release CO₂ [32]. It is the primary net output pathway for soil carbon in terrestrial ecosystems [33]. The Rh is closely related to the local hydrothermal conditions. Pei Zhiyong et al. [34] studied the relationship between carbon emissions from soil heterotrophic respiration and environmental factors. Based on this, they established a regression equation between temperature, precipitation, and carbon emissions, which was used to estimate regional soil heterotrophic respiration. This regression equation for estimating soil heterotrophic respiration is as follows:

Rh = 0.22 ∗ (exp(0.0913 ∗ T_a) + ln(0.3145 ∗ R_a + 1) ∗ 30 ∗ 46.5%).

(5)

In the equation, T_a represents the annual average temperature, and R_a represents the annual average precipitation. R_h is measured in g·C·m⁻²·d⁻¹, T_a is measured in °C, and R_a is measured in mm.

2.1.3. Calculation Model for the per Capita Carbon Sink Income

This study presents the innovative concept of a per capita ideal carbon sink income. A per capita ideal carbon sink income in a region is a comprehensive indicator that combines the ecological value and economic value of forest carbon sinks, which is defined as the ratio of the product of net primary productivity (NPP) and carbon price, to the population size. It is of significant importance for quantitatively assessing the current status and potential development of forest carbon sinks in various regions nationwide and studying the ecological poverty alleviation potential in the context of human–environment relationships. The per capita carbon sink income in a specific region m can be derived using the following formula:

I_m = NPP/P_m ∗ M ∗ 10^c.

(6)

In the equation, I_m represents the per capita carbon sink income in region m, NPP represents the net primary productivity of that region, P_m represents the total population in region m for a specific time dimension, and M represents the unit carbon price. 10^c represents a unit scaling factor, and c is a proportionality factor, a constant used for quantitative conversion between the units of the parameters. The unit of I_m is USD, P_m is ten thousand people per square kilometer (10,000 people/km²), and M is USD per ton of CO₂ (USD/tCO₂).

2.1.4. Prediction of the per Capita Income from Carbon Sinks Based on Linear Regression

The linear regression model (LR model) has been widely used in the prediction of carbon sink, population, and carbon price [35,36,37]. Similarly, it can also be applied to the prediction of per capita carbon sink income. The linear regression model is as follows:

$$\widehat{y}=\beta x+\gamma .$$

(7)

In the equation, $\widehat{y}$ is the predicted per capita income from carbon sinks; x denotes the ordinal number of years; $\beta$ reflects the trend of the per capita carbon sink income; when $\beta$ is positive, it indicates that the per capita ideal carbon sink income increases over time, and vice versa, it decreases over time; $\gamma$ is the intercept term of the linear function.

In the solution, we used the classical OLS method [38] to deal with this linear regression equation to obtain the optimal estimation of β and γ, which is calculated as follows.

$$\widehat{\beta }=\frac{\sum (y-\overline{y})(x-\overline{x})}{\sum {(x-\overline{x})}^2},$$

(8)

$$\widehat{\gamma }=\overline{y}-\widehat{\beta }\overline{x}.$$

(9)

In the equation, $\widehat{\beta }$ denotes the optimal estimate of the parameter $\beta$; $\widehat{\gamma }$ denotes the optimal estimate of the parameter $\gamma$. When performing the linear regression, we took the data of the last five years as the model input values to make the model more current. The per capita ideal carbon sink income for 2021 and 2022 was calculated as a validation of the model’s predictions.

2.1.5. Uncertainty of the Model for Calculating the per Capita Income from Carbon Sinks

In order to measure the reliability and validity of the calculation model for the per capita ideal carbon sink income, we developed the equation based on the error transfer formula, to conduct an objective evaluation of the model.

Considering that NPP, P_m, and M are independent of each other, the equation takes the following form:

$$∆I_m^2=({\left(∆NPP\ast M\ast P_m\right)}^2+{\left(∆M\ast NPP\ast P_m\right)}^2+{\left(∆P_m\ast NPP\ast M\right)}^2)\ast {(\frac{{10}^c}{P_m})}^2.$$

(10)

In the equation, $∆I_m$ denotes the uncertainty of the per capita carbon sink income in region m, $∆NPP$ denotes the uncertainty of the net primary productivity, $∆M$ denotes the uncertainty of the carbon price, and $∆P_m$ denotes the uncertainty of the population density. The relative uncertainty is given by the following equation:

$${\mu }_I=∆I_m/I_m\ast 100\mathrm{\%}.$$

(11)

In the equation, ${\mu }_I$ denotes the relative uncertainty of the per capita ideal carbon sink income. It better reflects the credibility of the per capita ideal carbon sink income calculations. For the population data, we used the relative uncertainty to replace the calculation, and the relative uncertainty value was 0.21%, which referred to [39]. For NPP and M, the absolute uncertainty is given by the standard deviation formula:

$$∆=\sqrt{\frac{\sum {\left(x-\overline{x}\right)}^2}{n}}.$$

(12)

In the equation, x denotes NPP or M; $\overline{x}$ denotes the mean value of the variable; n denotes the number of observations of the variable; ∆ denotes the uncertainty of the variable.

2.2. Data Preparation

2.2.1. Population Data

The LandScan global population dynamics dataset for the years 2010–2020 was downloaded from Google Earth Engine (GEE) “https://landscan.ornl.gov (accessed on 30 July 2023)” [40], which has a spatial resolution of 1 km (30″ × 30″) and consists of annual data. This dataset is an annual statistical data with one period per year, which includes information on the global population density spatial distribution, the population counts per unit area, and so on.

2.2.2. Climate Data

The required climate data for the model included the temperature and cumulative precipitation data, which were obtained from the China National Science and Technology Resources Sharing Service Platform.

For the temperature data, the 1991–2020 China Surface Climate Normal Dataset (monthly temperature values) was downloaded from the National Meteorological Science Data Center “http://data.cma.cn (accessed on 30 July 2023)” [41]. This dataset comprised observed data from China’s surface meteorological stations, archived by the National Meteorological Information Center, which underwent quality control, control, and correction, resulting in a high-quality set of ground-based basic data. These temperature data were spatialized, resampled, and interpolated to generate a grid of the monthly average temperature data for China from 2010 to 2020, with a resolution of 0.0083333°. For the precipitation data, the 1901–2021 1 km resolution monthly precipitation dataset for China was downloaded from the China National Tibetan Plateau Data Center “https://data.tpdc.ac.cn (accessed on 30 July 2023)” [42]. This dataset had a spatial resolution of 0.0083333° (approximately 1 km).

The climate data used in this study exhibited monthly statistical characteristics. Two temporal scales, namely the annual average and monthly average, were employed to analyze the temperature and precipitation patterns. The annual average temperature (cumulative precipitation) data were obtained by calculating the average temperature (cumulative precipitation) values across the twelve months within a year. In order to investigate the seasonal variations in more detail, the monthly average temperature (cumulative precipitation) data were derived by averaging the temperature (cumulative precipitation) values for each individual month over an eleven-year period, which resulted in the determination of the average monthly temperature (cumulative precipitation) values throughout the entire study duration.

2.2.3. Land Cover Type Data

The land cover-related dataset used in this study was the MODIS/Terra + Aqua Global Land Cover Type yearly SIN Grid 500 m dataset, named the MCD12Q1 v006 product [43], covering the period from 2010 to 2020. It was derived from the MODIS Terra and Aqua reflectance data through supervised classification and postprocessing. The land cover types in this dataset were based on the Land Cover Classification System (LCCS) developed by the Food and Agriculture Organization (FAO), utilizing a new classification scheme. The specific classification scheme is outlined in

Table 1. IGBP global land cover classification system [44].

Code	Type	Description
1	Evergreen Needleleaf Forests	Forests dominated by evergreen coniferous trees with a canopy cover greater than 60%, a height exceeding 2 m, and green foliage throughout the year.
2	Evergreen Broadleaf Forests	Forests dominated by evergreen broadleaf trees with a canopy cover greater than 60%, a height exceeding 2 m, and green foliage throughout the year.
3	Deciduous Needleleaf Forests	Forests dominated by deciduous coniferous trees with a canopy cover greater than 60%, a height exceeding 2 m, and a certain leaf shedding cycle.
4	Deciduous Broadleaf Forests	Forests dominated by deciduous broadleaf trees with a canopy cover greater than 60%, a height exceeding 2 m, and a certain leaf shedding cycle.
5	Mixed Forests	A mosaic of the aforementioned forest types, with each type having a canopy cover not exceeding 60%.
6	Closed Shrublands	Woody vegetation with a canopy cover greater than 60%, a height less than 2 m, and evergreen or deciduous characteristics.
7	Open Shrublands	Woody vegetation with a canopy cover ranging from 10% to 60%, a height less than 2 m, and evergreen or deciduous characteristics.
8	Wooded Grasslands	Areas with forest cover ranging from 30% to 60% and a height exceeding 2 m, mixed with herbaceous or other understory vegetation systems.
9	Sparse Trees and Shrubs	Areas with forest cover ranging from 10% to 30%, and a height exceeding 2 m, mixed with herbaceous or other understory vegetation systems.
10	Grasslands	Areas covered predominantly by herbaceous vegetation, with forest and shrub cover less than 10%.
11	Permanent Wetlands	Extensive areas covered by water (freshwater, brackish, or saline) and characterized by herbaceous or woody vegetation, serving as transition zones between land and water.
12	Farmland	Areas covered by cultivated crops, including bare soil after harvest. Perennial woody crops can be classified under appropriate forest or shrubland categories.
13	Urban and Built-up Areas	Land areas covered by buildings and urban infrastructure.
14	Mosaic of Cropland and Natural Vegetation	A mix of cropland, trees, shrubs, and grassland, with none of the cover types exceeding 60%.
15	Snow and Ice	Areas permanently covered by snow or ice.
16	Bare Ground	Areas with exposed soil, sand, or rocks, with vegetation cover less than 10%.
17	Water Bodies	Oceans, lakes, reservoirs, and rivers, which can be freshwater or saline.

.

2.2.4. Carbon Market Price Data

The international carbon market is composed of compliance markets and voluntary markets. Compliance markets, also known as mandatory markets, are governed by national, regional, or provincial laws that require emission sources to meet greenhouse gas reduction targets [45]. The carbon credits generated and traded in compliance markets are used to offset emissions in order to comply with regulations. Voluntary carbon markets allow emitters to offset their unavoidable emissions by acquiring carbon credits, which are market-based indicators generated by actions that eliminate or reduce greenhouse gas emissions.

Considering factors such as market supply and demand, carbon stock levels, and economic development trends, based on annual carbon market reports from international organizations, the representative reference carbon prices selected for this study are presented in

Table 2. Annual average carbon prices (2010–2020) in different carbon market benchmarks [46,47] *.

Year	Mandatory Market			Voluntary Market Offset	Average Carbon Price
	Chinese Carbon Market	U.S. Carbon Market	EU Carbon Market
2020	5.49	5.13	18.54	2.49	7.91
2019	4.07	4.94	24.51	4.30	9.45
2018	4.83	4.30	16.37	3.01	7.13
2017	3.91	3.94	6.24	3.16	4.31
2016	4.08	5.24	4.88	3.10	4.33
2015	5.74	5.90	7.69	3.26	5.65
2014	7.74	3.93	6.76	3.80	5.56
2013	4.64	3.85	6.06	4.93	4.87
2012	4.64	3.85	6.06	5.87	5.11
2011	4.64	3.85	6.06	6.20	5.19
2010	4.64	3.85	6.06	6.00	5.14

* In this table, the unit for carbon price is standardized as USD · t⁻¹ · CO₂⁻¹.

.

2.2.5. Net Primary Productivity Data

The net primary productivity (NPP) data used in this study were derived from the MODIS/Terra Global Land Net Primary Production annual SIN grid 500 m dataset, named the MOD17A3HGF v006 product [48], covering the period from 2010 to 2020. This dataset calculated the net primary production based on the Carnegie–Ames–Stanford Approach (CASA) model.

3. Experimental Results and Analysis

3.1. Spatial Distribution Characteristics of the per Capita Ideal Carbon Sink Income

The assessment of the per capita ideal carbon sink income starts from the ideal carbon sequestration status of land-based natural vegetation, taking into account the regional socioeconomic variations. Utilizing data on temperature, precipitation, carbon price, population, NPP (net primary productivity), and so on, based on the per capita ideal carbon income analysis framework and the relevant econometric models described in Section 2.1, we calculated the average per capita ideal carbon income for different carbon market benchmarks in China from 2010 to 2020 and extracted the valid data regions with land surface covered by natural vegetation (meeting the conditions for land-based ecological carbon sequestration). Under mandatory carbon markets, the peak per capita ideal carbon sink income nationwide reached USD 19,138.10 per person per year in the European Union carbon market, approximately USD 8546.70 per person per year in the United States carbon market, and approximately USD 9534.88 per person per year in the Chinese carbon market. For voluntary carbon offset markets, the peak per capita ideal carbon sink income nationwide was approximately USD 8080.64 per person per year.

As an example, taking the carbon price benchmark of the European Union carbon market, the spatial distribution of the per capita ideal carbon sink income at a 500 m grid scale for the average national values from 2010 to 2020 based on natural vegetation ecosystems is shown in Figure 2. Overall, the economic benefits derived from the ecological value of the terrestrial carbon sinks in China were considerable in the ideal state. There were significant spatial differences in the per capita ideal carbon sink income, with large variations between the peak values and the low values. The spatial distribution generally exhibited a central low and a surrounding high pattern, with significant high-income regions in the southwest and northeast, displaying a strong gradient of income levels between regions. On a national scale, the geographical distribution of the annual per capita ideal carbon sink income aligned well with the differentiation of vegetation zones. The high-value distribution was mainly located within the three major forest regions in China, namely the Northeast Forest Region, Southwest Forest Region, and Southeast Forest Region.

In order to explore the similarity and correlation between the carbon sink income potential of spatial units and their neighboring units, the high and low-value aggregation areas of the annual average per capita ideal carbon sink income at the municipal level were identified, as shown in Figure 3. Using a specified critical distance or distance threshold for assigning a weight of 1 to neighboring elements as a conceptualization of spatial proximity, the local Moran’s I was employed for spatial clustering and outlier analysis, as shown in Figure 3a. Hotspot and coldspot analysis were conducted using the Getis-Ord Gi* method, as shown in Figure 3b.

Overall, there were significant spatial variations in the clustering and hotspot/coldspot patterns of the per capita ideal carbon sink income in China. The distribution exhibited a general trend of lower or colder values in central regions and higher or hotter values in the southwest and northeast. There was a significant aggregation of high values and low values within the respective regions, indicating strong spatial correlation and radiative clustering of the per capita ideal carbon sink income distribution. Using the EU carbon market as an example, the southwestern and northeastern regions of China showed highly clustered high-income characteristics in terms of the per capita ideal carbon sink income. These hotspots exhibited a point-like radiation diffusion pattern towards the inland regions, at a 99% significant confidence level. Simultaneously, the subtropical evergreen broad-leaved forest regions in the southeastern part of the country also demonstrated a small-scale clustering of a high per capita income, with Hainan Island’s southern forest area exhibiting a significant regional hotspot. The central and eastern parts of China showed a large-scale distribution of low per capita ideal carbon sink income, among which the western Inner Mongolia Plateau, characterized by lower natural vegetation coverage, and the Yellow River estuary alluvial fan region were two prominent coldspot centers.

Using the local Moran’s I as a measure of correlation, the presence of outliers in the annual average per capita ideal carbon sink income across the country was relatively weak. However, there was a significant outlier coldspot in the relatively low-lying Northeast Plain, which formed a low-value area within the high-value clustering of income. Additionally, mountainous regions in the inland areas such as the Qinling Mountains and Jiangnan Hills exhibited significant outlier hotspots, indicating large carbon storage and high-income potential in these areas within a large-scale low-value region.

Based on the administrative divisions at different levels in China, the evaluation results were analyzed and aggregated at the provincial and municipal levels, as shown in Figure 4. Specifically, Figure 4a presents the statistical results of the average annual per capita ideal carbon sink income at the provincial level from 2010 to 2020, while Figure 4b shows the corresponding results at the municipal level. Concentrating on the natural vegetation ecosystem in China and using the EU carbon market as a benchmark, the average annual per capita ideal carbon sequestration income ranged from USD 35.75 to 2079.66 per person per year at the provincial level and from USD 5.89 to 3613.81 per person per year at the municipal level. The disparity in the per capita ideal carbon sequestration income between different cities was more pronounced than at the provincial level, with differences in carbon sequestration benefits exceeding USD 3000 per person per year in some regions.

Through a comprehensive analysis of Figure 4a,b, it is evident that the economic benefits of carbon sinks varied greatly among regions. The cities in the southwest and northeast regions of China demonstrated considerable potential for increasing the ideal carbon sink income, with provinces such as Yunnan, Heilongjiang, and Jilin leading the way. On the other hand, cities in the central and eastern regions of China showed relatively weaker driving forces for green carbon sinks, with limited potential for increasing the ideal carbon sink income in Shandong and Jiangsu provinces, located downstream of the Yellow River.

The provincial-level statistical analysis of the average annual per capita ideal carbon sink income in China from 2010 to 2020, based on the carbon prices in various carbon markets, is presented in

Table 3. The provincial-level statistical analysis of the average annual per capita ideal carbon sequestration income from 2010 to 2020 under different carbon markets *.

Code	Administrative Region	Mandatory (Compliance) Market						Voluntary Market Carbon Offset
		European Union (EU)		United States		China
		Mean	Maximum	Mean	Maximum	Mean	Maximum	Mean	Maximum
1	Beijing	475.88	4605.90	251.40	2208.09	287.47	2503.07	252.87	2216.28
2	Tianjin	122.96	2607.77	57.72	1278.82	65.62	1445.56	57.18	1232.97
3	Hebei	521.29	7407.06	232.74	3307.15	259.95	3689.32	216.76	3125.31
4	Shanxi	358.15	5207.52	155.44	2329.96	173.20	2606.60	143.06	2289.23
5	Inner Mongolia	693.36	7099.10	305.17	3146.76	338.91	3503.48	280.95	2924.48
6	Liaoning	932.12	9133.61	430.72	4078.89	482.25	4550.50	407.89	3856.47
7	Jilin	1471.73	9057.15	680.85	4044.75	761.22	4512.41	637.09	3824.19
8	Heilongjiang	2079.66	8598.39	921.70	3839.87	1025.33	4283.84	851.55	3630.48
9	Shanghai	35.75	4307.89	18.60	2265.53	21.25	2594.48	19.40	2271.91
10	Jiangsu	53.36	4465.08	24.97	1961.08	28.05	2192.31	24.11	1940.40
11	Zhejiang	792.42	8945.23	363.54	4191.17	407.29	4691.81	346.89	4125.15
12	Anhui	583.64	7615.02	271.63	3414.59	304.23	3810.73	261.33	3361.39
13	Fujian	901.81	15,384.38	397.34	6878.24	443.06	7675.04	370.40	6571.96
14	Jiangxi	704.53	13,748.96	316.61	6131.03	353.90	6840.24	301.51	5796.08
15	Shandong	217.31	7278.24	100.14	3365.29	112.53	3772.74	94.58	3236.10
16	Henan	407.48	6524.64	181.93	2993.52	203.02	3345.53	171.25	2890.04
17	Hubei	471.91	10,328.25	212.95	4608.48	237.72	5140.55	201.21	4351.11
18	Hunan	464.20	11,360.94	208.15	5047.35	232.54	5628.35	197.30	4748.25
19	Guangdong	1034.93	14,936.70	463.81	6670.44	518.65	7441.69	442.03	6306.70
20	Guangxi	816.20	14,763.62	362.40	6601.49	404.49	7373.70	339.66	6331.99
21	Hainan	931.62	13,208.89	420.76	5925.81	470.39	6613.92	400.81	5632.70
22	Chongqing	290.07	6414.95	129.87	2984.46	144.93	3336.66	121.86	2900.78
23	Sichuan	972.69	12,318.17	433.29	5501.05	482.63	6137.09	407.71	5201.08
24	Guizhou	350.83	9723.46	156.78	4326.59	174.96	4820.48	146.93	4028.90
25	Yunnan	1524.92	19,138.09	683.76	8546.70	763.83	9534.88	649.19	8080.64
26	Tibet	920.76	18,475.23	391.94	8239.88	434.11	9191.13	365.89	7787.19
27	Shaanxi	522.37	10,353.02	231.86	4623.46	258.72	5158.03	217.51	4371.34
28	Gansu	591.03	9732.03	262.74	4346.46	292.62	4849.23	246.04	4110.84
29	Qinghai	886.53	6380.08	394.58	2849.22	439.58	3178.65	370.04	2693.85
30	Ningxia	123.35	4404.31	55.74	2048.72	62.32	2249.16	51.87	1973.27
31	Xinjiang	709.70	7041.22	313.91	3128.10	348.46	3554.83	289.04	3046.71
32	Taiwan	1134.11	15,447.05	548.86	6817.71	625.65	7581.66	609.60	6333.87
33	Hong Kong	477.21	8437.75	195.66	3744.68	213.15	4178.93	169.52	3542.70
34	Macao	36.90	102.46	19.61	55.94	23.31	64.42	21.43	57.96

* In this table, the unit for the annual average per capita ideal carbon sink income is USD per capita per year.

. It is shown that provinces such as Yunnan, the Tibet Autonomous Region, Fujian, Guangdong, and the Guangxi Zhuang Autonomous Region had the highest values of average annual per capita ideal carbon sink income, ranking at the top of the mainland provinces. Under different carbon price scenarios, their income could reach up to USD 19,138.09 per person per year, indicating prominent ecological carbon sink economic benefits at a local scale. Heilongjiang, Yunnan, and Jilin were the top three regions nationwide with the highest average annual per capita ideal carbon sink income. Under the EU carbon market benchmark, their incomes could reach USD 2079.66 per person per year, USD 1524.92 per person per year, and USD 1471.73 per person per year, respectively, highlighting their strong regional carbon sink revenue generation capacity.

Overall, cities in the southern and northeastern regions of China exhibited significant prospects for carbon sequestration economic development. In smaller-scale regions with high carbon sequestration potential and low human resources, the maximization of carbon sink benefits can be better achieved.

3.2. Trend in the per Capita Ideal Carbon Sink Income

The monthly average per capita ideal carbon sink income for the period of 2010–2020 was analyzed and statistically summarized. The comprehensive visualization of the monthly per capita ideal carbon sink income is shown in Figure 5. Using a yearly time scale, the seasonal variation in the national average per capita ideal carbon sink income ranged from USD 16.50 to 261.41 per person per month, with the maximum value reaching as high as USD 3736.80 per person per month. This maximum value exceeded ten times the average monthly disposable income per capita in China (which was approximately USD 275.92 between 2010 and 2020).

Overall, the per capita ideal carbon sink income in China exhibited significant fluctuations throughout the year, demonstrating strong seasonality. The annual variation showed a distinct pattern of peaks and troughs, with lower values during the winter months and higher values during the summer months. This income reached its peak in August, while the troughs occurred in January and February. Additionally, there were noticeable monthly fluctuations in the per capita ideal carbon sink income, and the largest variations in carbon sink benefits throughout the year were observed between August and September.

The annual values of the per capita ideal carbon sink income based on the different benchmarks of mandatory (compliant) markets represented by the EU carbon market, the U.S. carbon market, and China’s carbon market, as well as voluntary market carbon offsetting, are shown in Figure 6. Figure 6a–c represent the Chinese, U.S., and EU mandatory carbon markets, while Figure 6d represents the voluntary market carbon offsetting. The horizontal axis represents the study years. The left vertical axis corresponds to the average range of the per capita ideal carbon income nationwide, represented by line data, while the right vertical axis represents the maximum range of the per capita ideal carbon income nationwide, represented by the bar data.

Significant differences in the per capita ideal carbon sink income were observed across the different market carbon price levels from 2010 to 2020 nationwide.

The average annual per capita ideal carbon sink income range under the Chinese carbon market was approximately USD 271.67 to 543.92 per person per year, with the highest per capita income reaching a peak of USD 15,206.78 in 2014. Under the U.S. carbon market, the average annual per capita ideal carbon sink income range was approximately USD 273.76 to 412.71 per person per year, with the highest income reaching USD 11,591.73 in 2015. Overall, in both the Chinese and U.S. mandatory carbon markets, the economic benefits of domestic ecological carbon sinks exhibited relatively consistent patterns during the study period, with weak interannual fluctuations and strong stability. Comparatively, the Chinese carbon market had a higher annual per capita ideal carbon sink income and relatively significant volatility compared to the U.S. carbon market.

From 2010 to 2020, the annual per capita ideal carbon sink income under the EU carbon market showed two distinct phases: a relatively stable phase and an explosive growth phase. During China’s twelfth five-year plan period, the fluctuation in the annual per capita ideal carbon sink income was similar to other mandatory markets, showing a slow upward trend with an average range of approximately USD 432.67 to 537.92 per person per year. However, during the thirteenth five-year plan period (2016–2020), the EU carbon market experienced significant volatility in the domestic ecological carbon sink economic benefits due to the market carbon price fluctuations. It entered an explosive growth phase in 2018 and reached the highest average value of USD 1677.99 per person per year in 2019, with the highest per capita income reaching as high as USD 48,039.60 per person per year. The income level during this period was more than four times higher than the stable phase, demonstrating significant interannual differences.

Under the benchmark of voluntary market carbon offsetting, the average annual per capita ideal carbon sink income in China fluctuated in the range of approximately USD 200 to 400 per person per year, with the highest income ranging from USD 5000 to 11,000 per person per year. The overall economic benefits were significantly lower than those of the mandatory carbon markets. The interannual variation in the ideal carbon sink income amount was weak, gradually declining from 2010 to 2020, reaching the lowest average value of USD 167.14 per person per year in the study period by 2020.

The spatial distribution of the annual per capita ideal carbon income exhibited substantial variation across different carbon market benchmarks, rendering it a crucial and fluctuating dependent variable. Considering standard deviation as an evaluation indicator, the deviation and spatial dispersion of per capita ideal carbon sink income under four typical carbon markets are shown in Figure 7.

For the mandatory carbon markets, the distribution differences in the carbon sink economic benefits between the Chinese carbon market and the national carbon market were relatively stable. However, under the EU carbon market, the economic benefits were significantly influenced by price fluctuations, resulting in an evident income imbalance trend. Under the benchmark of voluntary market carbon offsetting, the distribution differences in carbon sink economic benefits gradually decreased and tended to achieve regional balance across the country.

3.3. Prediction of the per Capita Carbon Sink Income

Based on the linear regression model, we used the data between 2017 and 2020 to establish a LR model to predict the change trend of the per capita carbon sink income in the next few years. The coefficient of determination (R²) of this linear equation is 0.4377, and the equation is as follows:

$$y=23.617x+88.601.$$

(13)

In the formula, x represents the ordinal number of the year, for example, 2021 is the 12th year from 2010, and y represents the predicted per capita carbon sink income.

It can be concluded from the coefficients of the equation that the change trend of the per capita carbon sink income in the Chinese market is increasing year by year, and the growth rate is USD 23.617 per year per capita.

Concurrently, we calculated the per capita carbon sink income for the years 2021 and 2022, and the results are presented in Figure 8. The actual computation results for the year 2021 amounted to USD 370.39 per capita, while for the year 2022, they reached USD 412.49 per capita. As for the projected outcomes, the estimated values for 2021 and 2022 were USD 372.01 per capita and USD 395.62 per capita, respectively. Evidently, the striking proximity observed between the actualized calculations and the forecasted values bolstered the veracity and soundness of the predictive model.

3.4. The Significance of the per Capita Ideal Carbon Sink Income for Poverty Alleviation

In the current context of “carbon neutrality”, exploring the potential carbon sink benefits and their relative contribution to national productivity and poverty alleviation capabilities is of crucial importance. Harnessing the ecological carbon sink for increased economic gains has a significant potential to achieve simultaneous poverty eradication and ecological preservation.

The Gross Domestic Product (GDP) is a core indicator used to measure the economic situation and development level of a country or region, which plays an important role in objectively assessing the poverty alleviation efforts in different regions and the corresponding market size for carbon sinks.

In this regard, the concept of relative carbon sink benefits is introduced, which refers to the proportion of the per capita ideal carbon sink income to the per capita GDP during the same period. The assessment of the annual average ecological carbon revenue relative efficiency under different carbon market benchmarks from 2010 to 2020 is shown in Figure 9. Based on this, the highest value of the relative benefits across the country was selected as the maximum relative benefit. Here, the maximum relative benefit under voluntary market carbon offsetting was 400%, while it was 475% for the Chinese carbon market, 426% for the U.S. carbon market, and as high as 955% for the European Union carbon market. Overall, the relative benefit level of the ecological carbon sink income in China is relatively high, with an average ranging from 30% to 70%, with a significant imbalance across the country.

To further assess the poverty alleviation needs in different regions of the country, additional indicators such as the average standard and expenditure level of rural subsistence allowances were introduced. The average standard of minimum living allowances in urban and rural areas is an important system for providing subsistence allowances to impoverished individuals, while the expenditure level of rural minimum living allowances is a key parameter for measuring the basic living guarantee for impoverished individuals. During the study period from 2010 to 2020, the average expenditure level of rural minimum living allowances in China was approximately USD 151.20, the average standard of rural minimum living allowances was around USD 579.8, the average standard of urban minimum living allowances was about USD 839.6, and the average per capita GDP was approximately USD 7909.3. These serve as important reference points for understanding the poverty alleviation needs and the level of economic development in different regions of the country.

We used the four indicators mentioned above as threshold values to analyze the carbon sink poverty alleviation capacity, as shown in Figure 9. Overall, the per capita ideal carbon sink income from terrestrial ecosystems in China could generally meet the average expenditure level of the rural minimum living allowances during the same period, and most regions had incomes above the average standard of the rural minimum living allowances. In regions such as Southwest China and Northeast China, the per capita ideal carbon sink income exceeded the average standard of the urban minimum living allowances. Under the EU carbon market, the economic benefits derived from carbon sinks in some forested areas of the southern part of the Yungui Plateau could even exceed the per capita Gross Domestic Product during the same period.

From the analysis of Figure 9 and Figure 10, it can be concluded that the per capita ideal carbon sink income obtained through voluntary carbon offset transactions could basically meet the average standard of the rural minimum living allowances during the same period, which has largely met the poverty alleviation needs in various regions. The carbon sink poverty alleviation indicators in the compulsory markets had a higher achievement rate compared to the voluntary market, with most regions tending to reach the low-income threshold for both urban and rural areas, indicating a strong potential for poverty alleviation.

Comprehensive analysis of the per capita ideal carbon sink income and national per capita GDP distribution allows us to assess the carbon poverty alleviation potential in representative regions. We selected the three provinces in Northeast China (Heilongjiang, Jilin, and Liaoning) and four provinces in Southwest China (Sichuan, Guizhou, Yunnan, and Tibet Autonomous Region) to focus on the carbon sink poverty alleviation capacity in these key areas. The carbon sink income potential in the three provinces of Northeast China from 2010 to 2020 is shown in Figure 11, while the carbon offset income potential in the four provinces of Southwest China is depicted in Figure 12. In these figures, Figure 11a and Figure 12a represent the highest carbon sink income potential, which is the ratio of the maximum annual per capita ideal carbon sink income to the corresponding per capita GDP, and Figure 11b and Figure 12b represent the average carbon sink income potential.

The average regional carbon sink income potential in the three provinces of Northeast China ranged from 1% to 8%, with a maximum potential of up to 70%. Heilongjiang Province had a significantly higher average potential compared to the other two provinces, indicating a relatively strong prospect for carbon sink poverty alleviation, while Liaoning Province demonstrated relatively weaker benefits from carbon sink poverty alleviation. In the four provinces of Southwest China, the average regional carbon sink income potential ranged from 2% to 14%, with a maximum potential of up to 220%. The benefits of a carbon sink income were particularly prominent in certain local areas. Yunnan Province and the Tibet Autonomous Region had relatively high carbon sink poverty alleviation benefits, with the ability to maintain carbon sink income levels significantly higher than the regional per capita GDP income.

Overall, the three provinces in Northeast China and the four provinces in Southwest China demonstrate considerable potential for carbon sequestration and income generation. However, the Southwest provinces exhibit more pronounced regional imbalances and development potentials.

4. Discussion

4.1. Causes of the Spatiotemporal Distribution of the per Capita Ideal Carbon Sink Income

4.1.1. Spatial Differentiation Characterized by Low Values in the Central Region and High Values in the North and South

Overall, China’s terrestrial carbon sink ecological value can generate substantial economic benefits under ideal conditions. The national distribution of the per capita ideal carbon sink income exhibited a general trend of lower values in central regions and higher values in northern and southern regions. The spatial variations in the per capita ideal carbon sink income were influenced by the interplay of the regional vegetation ecosystems and the socioeconomic environments. The analysis of the key regions was as follows:

The Yun-Gui Plateau and the Hengduan Mountains region are markedly influenced by the Indian Ocean monsoon throughout the year [49]. Benefiting from favorable geographical location and altitude, this region enjoys overall superior water and thermal conditions. It boasts extensive forest coverage, diverse plant species, and excellent ecological conditions. Furthermore, the regional industrial development has progressed relatively slowly, with a lower level of exploitation. This unique dual development characteristic enables the region to exhibit exceptional overall capacity for increasing the carbon sinks and contributing to poverty alleviation. The northeastern region, encompassing the Da Hinggan Mountains, Xiao Hinggan Mountains, and Changbai Mountains, serves as a significant ecological carbon sink area with substantial potential for carbon sink income development with highly stable and productive ecosystems. Simultaneously, the distribution of natural vegetation in these regions is more scattered resulting in larger industrial and urban land areas. Consequently, the per capita ideal carbon sink income exhibited significant dispersion, with high-value figures prominent. The Qinling Mountains and southern hilly areas are located in China’s typical subtropical monsoon region, characterized by diverse plant communities and stable ecosystems with a higher carbon sequestration capacity. The southeastern region, including the middle and lower reaches of the Yangtze River, is experiencing rapid urban expansion and economic growth. There is a growing demand for environmental protection and ecological restoration. With a relatively substantial per capita ideal carbon income, this region has promising prospects for green economic development.

The northwestern region, encompassing the Qilian Mountains, Hexi Corridor, and northern mountains, extends deep into the inland areas of China. Characterized by a dry climate and significant temperature variations, the natural vegetation mainly consists of deserts, desert grasslands, and sandy areas. The ecological environment is relatively vulnerable; consequently, this region exhibited significant low carbon sink economic benefits across a wide geographical range. The central and northern regions situated within the Yellow River basin possess relatively weak water conservation capacity and limited water resources, influenced by soil and water conservation capabilities. The natural vegetation is predominantly composed of broadleaved forests, shrublands, and mixed grasslands, where the carbon sink capacity is unstable and their productivity is relatively low, resulting in a relatively low per capita ideal carbon sink income.

4.1.2. Large Disparities and Small Clusters in Spatial Distribution Patterns

Based on the comprehensive analysis, there is a large difference between the peak and trough values of China’s per capita ideal carbon sink income, with obvious spatial differences and a strong gradient of high and low income amounts between regions. These variations demonstrate a “large difference” characteristic. Additionally, the distribution regions showed a notable “high value–high value” and “low value–low value” aggregation pattern, indicating a “small clustering” rule. This suggests a strong spatial correlation and radiation aggregation. China’s vast territory and diverse climate types contribute to pronounced zonal and regional differentiations in natural zones, resulting in a rich variety of vegetation types and complex ecosystems. The imbalances and strong fluctuations in ecosystem productivity directly influence the complexity of carbon sequestration and the distribution of carbon sinks and sources within the country, leading to significant spatial variations in the per capita ideal carbon sink income. There are localized clusters of small-scale spatial anomalies in the per capita ideal carbon income, particularly in the Northeast Plain and Sichuan Basin, which are primarily attributed to the low vegetation coverage caused by local industrialization and urbanization.

4.2. Analysis of the Carbon Sink Income Potential under Different Carbon Market Benchmarks

This study evaluated and analyzed the per capita ideal carbon sink income nationwide, utilizing four different types of carbon markets as benchmarks: the European Union Emissions Trading Scheme (EU ETS), the United States carbon market, and the Chinese carbon market, representing mandatory (compliance) markets, and the voluntary market carbon offsets. The specific details are as follows:

The trends in the economic benefits of carbon sinks under the Chinese carbon market and the U.S. carbon market were generally consistent, exhibiting a year-by-year upward trend with periodic fluctuations. The spatial distribution differences were relatively stable, and the average per capita ideal carbon sink income was close to the average standard of the minimum living standard in urban areas of China. Compared to the U.S. carbon market, the Chinese carbon market showed a higher overall potential for increasing the carbon sink economic benefits, with relatively significant volatility. Under the EU carbon market, the per capita ideal carbon sink income experienced a relatively flat phase (2010–2015) followed by a surge phase (2016–2020). The economic benefits increased significantly in the latter phase with strong volatility. Notably, there were pronounced inter-annual and spatial imbalances, but the income levels were generally able to meet the minimum security target for urban and rural areas. In the voluntary market carbon offsets, the economic benefits of carbon sinks showed overall weak volatility, exhibiting a stable and gradual decline. The distribution differences across the country gradually diminished, approaching regional balance. This market could broadly meet the needs of poverty alleviation in less developed rural areas.

In conclusion, the year-to-year fluctuation in the carbon price amounts plays a decisive role in the changes in the carbon sink economic benefits. Simultaneously, China’s uncertain stability and productivity of vegetation ecosystems lead to variations in carbon sink storage and carbon sequestration; the rapid growth of the population and gross domestic product (GDP) not only affects the distribution of the natural vegetation but also impacts the regional development balance and the urgency of poverty alleviation efforts.

4.3. Data Accuracy and Algorithm Model Optimization

Taking the Chinese market in 2020 as an example, based on the results of the error transfer formula, the uncertainty of the per capita income from carbon sinks was USD 6.25/person, and the relative uncertainty was 1.69%, thus indicating that our results had sufficient reliability. The absolute and relative uncertainties of the per capita carbon sink income in the Chinese market from 2010 to 2020 are shown in

Table 4. Uncertainty of the per capita carbon sink income in the Chinese market.

Year	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Carbon Sink Income	331.28	326.45	339.28	327.78	543.92	401.51	284.97	271.67	333.00	278.64	368.52
Absolute Uncertainty	5.29	5.39	5.57	5.75	8.17	6.45	5.03	4.83	5.73	4.99	6.25
Relative Uncertainty	1.60%	1.65%	1.64%	1.76%	1.50%	1.61%	1.76%	1.78%	1.72%	1.79%	1.69%

.

In summary, the credibility and accuracy of the open-source data and the feasibility and applicability of the algorithm model in this research were demonstrated through the data processing and result validation. The conclusions and expectations of the national carbon sequestration potential analysis were generally consistent.

In calculating the per capita ideal carbon sink income from 2010 to 2020, the study adopted the average carbon price as a constant factor, which was calculated by taking the average of the highest and lowest carbon prices at the end of each month. However, it should be noted that carbon prices exhibit strong volatility and instability as indicators of the dynamic international carbon market. When extending the research period beyond ten years, the universality of the average carbon price diminishes significantly. Therefore, it is necessary to refine the time resolution of carbon sink prices and adopt a better value selection method to accurately reflect the value of carbon sinks associated with spatiotemporal changes.

The algorithm model proposed in this study heavily relied on existing satellite remote sensing data and observed data from meteorological and hydrological stations. The accuracy and timeliness of the model were influenced to some extent by the quality and precision of the available data. Moreover, the per capita ideal carbon sink income calculation model was based on the light use efficiency (CASA) and C-FIX models, which are recognized as internationally accepted and applicable LUE models that widely applied in large-scale studies of terrestrial ecosystem productivity and carbon balance. However, considering strong zonal variations and spatiotemporal fluctuations of vegetation, a single model may have limited adaptability to the diversity and differences of vegetation types. In order to achieve better applicability in diverse scenarios and enhance the robustness across different spatial and temporal scales, future research will integrate multiscale and multidomain ecosystem productivity accounting models to refine the algorithms and the related parameters.

4.4. Incorporating the CAPM Model into the Carbon Sink Asset Evaluation

The Capital Asset Pricing Model (CAPM)—an economic model used to evaluate the risk and expected return of assets—can be utilized to assess the potential carbon sink income and analyze the correlation between carbon trading and international market fluctuations [50]. Previously, Ristino and Michel [51] applied the CAPM model to research the mechanisms of the U.S. carbon trading market. In this section, we incorporate the CAPM model into the carbon sink asset evaluation, discuss the sensitivity of carbon trading to the market, and evaluate the potential carbon income. The value of carbon sink assets can be expressed by the following formula:

R_i = R_f + β × (R_m − R_f).

(14)

R_f represents the time value of assets, which is the return generated based on the risk-free interest rates; in this research, we used the three-month U.S. Treasury bond yield (from the first day of each month) from 2014 to 2022. R_i is the project return rate; here, we used the carbon emission futures’ yield from the Intercontinental Exchange (ICE). R_m is the market return; here, we used the return of the S&P 500 index. The β coefficient represents the sensitivity of the asset to systematic risk, which can measure the sensitivity of carbon trading to systematic risk in the overall market. It is calculated as the covariance between R_i and R_m divided by the variance of R_m, which is expressed as follows:

β = Cov (R_i, R_m)/Var (R_m).

(15)

In the above formulas, R_m, R_f, and R_i are measured in percentages. To evaluate the potential of the carbon sink income and the correlation between the carbon asset risk and market risk, based on the carbon market benchmark in the USA during the study period, the CAPM model was established. The carbon sink revenue trend fitting graph is shown in Figure 13, and the statistical results of the CAPM regression analysis are presented in

Table 5. (a) Regression Results of Carbon Emission Futures of the CAPM analysis in the U.S. Carbon Market (2014–2022); (b) Variance Analysis of Carbon Emission Futures of the CAPM analysis in the U.S. Carbon Market (2014–2022).

(a)
Statistical Parameter				Value
Multiple R				0.994
R-Square				0.988
Adjusted R-Square				0.988
Standard Error				0.125
Observations (number of data points)				108
(b)
	Coefficients	Standard Error	t Stat	p-Value	Lower *	Upper *
α	0.02707	0.01236	2.19075	0.03066	0.00257	0.05158
Rm − Rf	1.00161 (β)	0.01057	94.72396	0.00029	0.98065	1.02257

* The upper and lower bounds are defined at a confidence level of 95%.

. Its regression analysis was conducted to calculate β, α, and R-square; among these, the value of the β coefficient indicates the relationship between the asset and the overall market volatility, α represents the asset’s excess return rate, and R-square measures the regression model’s ability to explain the variability of the dependent variable.

Based on the scatter plot in Figure 13 and regression results in Table 5, we can draw the following preliminary conclusions: the β value close to 1 indicates that the CAPM model calculated a strong correlation between the carbon futures prices and the overall market risk. In future carbon trading, market-wide factors such as macroeconomic conditions, policy adjustments, and changes in the energy market may significantly influence the carbon prices, necessitating close monitoring of market dynamics. The R-square value close to 1 indicates that the CAPM model effectively explained the variations in the carbon futures prices. A positive α value suggests that carbon futures may have positive excess returns, indicating favorable performance in the market and further highlighting the high potential of carbon income, which may attract more investors to participate in carbon trading, stimulate market activity, and contribute to climate action while indirectly helping poverty alleviation. It further validates the reliability of the previous findings and demonstrates the substantial potential for carbon sequestration revenue development.

Despite its usefulness, the CAPM model has limitations due to the complex nature of the carbon market and environmental actions. To comprehensively assess the carbon market’s performance and environmental impact, it is crucial to consider additional factors and incorporate alternative analytical methods in future carbon trading and environmental initiatives.

4.5. Exploration of Poverty Alleviation Programs Based on the Analysis of the Carbon Sink Income Generation Potential

Overall, the analysis of the spatial distribution of per capita ideal carbon sink income in China reveals significant regional disparities in the ecological carbon sink economic benefits. This provides a solid material foundation and objective conditions for cross-regional carbon compensation and the realization of a well-functioning carbon market-driven system. The southwestern region emerges as a significant hotspot for carbon sink economic benefits, which exhibits strong correspondence with regions of low per capita GDP values during the same period in China. The northeastern region also shows a concentration of high carbon sink income values, driven by its status as a key region for industrial and agricultural development, where development demands align with ecological resource demands. The central-northern and eastern regions, with a widespread aggregation of low per capita ideal carbon sink income values, correspond significantly to areas of high per capita GDP and total GDP values in China. With economic and population development, the demand for environmental protection and ecological restoration has surged, creating relatively broad prospects for green economic development in these regions.

Therefore, the southwestern forested area, characterized by a high carbon sink intensity, increasing economic demands, and a broad market for green development, possesses a significant potential for carbon sink income generation and poverty alleviation. It can become a key implementation area for poverty alleviation through the utilization of ecological carbon sink resources. Efforts should be made to fully leverage the economic benefits of the surplus carbon sink in the region, explore the possibilities for interregional carbon compensation, establish and improve the carbon trading systems, ensure comprehensive and accurate carbon information disclosure, and expand the industrial and regional shares in the carbon market. Forest carbon sink poverty alleviation projects should be implemented, integrating ecological utilization with poverty alleviation efforts, to develop optimal regional carbon sink income schemes that complement ecological construction. Leveraging land carbon sink can contribute to poverty alleviation and rural revitalization. In terms of the horizontal spatial comparison, the central and western regions of China are influenced by a certain degree of temperate continental climate. They have a lower per capita ideal carbon sink income and a relatively weaker potential for carbon sink income enhancement. The ecological environment in these regions is fragile, and protective surface development should be carried out adaptively. Clean energy sources such as solar and wind energy can be transformed into economic advantages to avoid the resource destruction and environmental pollution [52] caused by traditional energy acquisition [53]. With diverse topography and a rich historical and cultural heritage, the development of a green ecological tourism industry complemented by red cultural elements can promote the growth of the tertiary sector. At the same time, the central and eastern regions of China, characterized by urbanization and high population concentration but with a relatively low potential for carbon sink development, can become key trading partners in the domestic carbon market against the backdrop of “carbon neutrality.” It is essential to convert the ecological benefits into economic benefits and actively participate in the domestic and international carbon trading market, which promotes the dual-cycle development strategy. By leveraging carbon sink transactions, it is possible to drive compensation from developed regions to underdeveloped regions, transforming poverty alleviation from being driven by morality and policies to being driven by interests and the market. This will align with national “carbon neutrality” strategies and achieve the process of “The Rich First Pushing Those Being Rich Later”.

5. Conclusions

This research proposed an analytical framework that integrates social and geographical factors such as carbon market prices, population distribution, and terrestrial carbon sequestration to assess the per capita ideal carbon income in different regions of China from 2010 to 2020 under various carbon price scenarios and to predict the development trend of per capita carbon sequestration income. Based on multiple indicators including correlation, hotspots, spatial deviation, and relative profitability, it addresses the disclosure of regional ecological functionality and economic benefits under different carbon price benchmarks and assesses the potential of terrestrial carbon sequestration resources for income generation and poverty alleviation, exploring location-specific carbon trading mechanisms and green carbon sequestration poverty alleviation strategies.

This analytical framework highlights the important role of terrestrial carbon sequestration resources in addressing poverty eradication and regional environmental inequalities in China. The research reveals that the per capita ideal carbon income in China exhibits a seasonal pattern with higher values in the summer and lower values in the winter. Spatially, it shows a general trend of lower values in central regions and higher values in northern and southern regions, indicating a “large disparity, small aggregation” distribution pattern. After validation through the error propagation method, the uncertainty of per capita carbon sink income calculated by this approach was 1.51%, objectively reflecting the reliability of the results. In the coming years, the per capita carbon sink income in the Chinese carbon market is projected to exhibit an annual upward trend, with an estimated growth rate of USD 23.6 per person per year. China’s terrestrial carbon sequestration potential is substantial, and there are significant differences in per capita ideal carbon income under different carbon markets. The economic value of the carbon sequestration resources in China can generally meet the minimum subsistence needs in economically underdeveloped regions, with the southwest, northeast, and north China being prominent areas. Therefore, considering the regional ecological functionality and economic development, these regions can be prioritized in the carbon markets for more comprehensive and sustainable cross-regional carbon trading, promoting compensation from developed regions to underdeveloped ones, and establishing a positive mechanism for poverty alleviation driven by carbon markets.

Despite its usefulness, this study had certain limitations in terms of real-time applicability and adaptability in evaluating the income enhancement potential from the terrestrial carbon sink. The research algorithm relied on remote sensing data and carbon market statistics as crucial inputs, demanding relatively high precision and coverage for the data used. Additionally, the study’s findings solely provide an overall assessment of the economic benefits of terrestrial natural system ecological carbon sequestration. For regions with distinct regional variations and significant spatiotemporal fluctuations in land cover types, further evaluations may require tailored assessment approaches to account for the specific geographical characteristics and dynamic changes. To a certain extent, this analytical framework can contribute to global climate action and poverty eradication strategies by analyzing the potential of using terrestrial carbon sequestration resources to promote inequality reduction and uneven improvement on a global scale, thus assisting countries in formulating more rational and effective policies to collectively support the achievement of the United Nations SDGs.