Spatiotemporal Variation of Per Capita Carbon Emissions and Carbon Compensation Zoning in Chinese Counties

Abstract

The per capita carbon balance and carbon compensation zoning of Chinese counties from the perspective of major function-oriented zones is important for realizing the carbon peaking and carbon neutral target. In this study, the Kernel-K-means++ algorithm is used and a more comprehensive per capita carbon compensation zoning model is constructed. Based on this, combined with the major function-oriented zones, Chinese counties are divided into per capita carbon compensation-type zones. Further, spatial and temporal characteristics are detected, and suggestions for optimizing low-carbon development are put forward. The main results are as follows: (1) From 2000 to 2017, the per capita carbon emissions (PCO₂) of Chinese counties were large and showed a trend of stable expansion and a southeast–northwest pattern; (2) the per capita carbon emissions of key development zones accounted for the largest proportion of emissions; (3) there were 1410 payment zones, 170 balanced zones, and 242 compensated zones among China’s counties; and (4) 11 types of carbon compensation space optimization zones were finally formed, and low-carbon development directions and strategies were proposed for each type of area. Based on this, this study promotes regional carbon emissions management and reduction in China and provides a reference for other regions to reduce emissions.

1. Introduction

With the problem of global warming becoming more and more serious, the amount of attention being paid to carbon emissions is gradually increasing. As the basic spatial unit of the population and economic activities and the basic local administrative unit in China, counties are also the basic spatial unit of carbon emissions, and carbon emissions pressures coexist with reduction potential. Carbon compensation is an important measure to achieve CO₂ offsetting of emissions from carbon sources through emissions reduction and other measures in order to achieve a regional carbon balance and thus promote overall national emissions reduction and sustainable development. For extensive study areas, zoning is an effective means of controlling carbon emissions. Categorizing regions with similar carbon emissions and background conditions facilitates localized and centralized management and achieves emissions reduction more efficiently. The study of regional differences in per capita carbon emissions (PCO₂), spatial and temporal patterns, and per capita carbon compensation zoning at the county level in China is crucial to the realization of the carbon peaking and carbon neutral target, the implementation of policies related to carbon control and emissions reduction, and the planning of low-carbon development in counties.

At present, carbon emissions research is a popular research topic in geography, ecology, environmental science, and other disciplines, which is mainly divided into carbon emissions simulation and prediction [1,2], spatial and temporal differentiation characteristics [3,4], driving factors [5,6], and so on. Although the results of carbon emissions simulation in the existing research differ, the trend of change is relatively consistent, and the relevant research points out that global carbon emissions have shown an upward trend in most previous years. However, there have been decreases or fluctuations in the recent period [7]. The factors driving carbon emissions vary across sectors—for example, in China, carbon emissions from the power sector are mainly driven by economic growth, whereas the effect of technological progress makes a significant contribution to the carbon intensity of the industrial sector [8], and it is important to note that the driving mechanisms of these various factors are changing dynamically over time [9]. In addition, in terms of spatial and temporal carbon emissions variations, existing studies mostly focused on the analysis of spatial aggregation characteristics, patterns, and evolution [10,11,12,13,14,15]. There are two basic types of such studies, and the first type involves using the boundaries of the administrative region [16], watershed [17], or LUCC [18,19] as the unit to detect the characteristics of the spatial and temporal variations of carbon emissions. There are also studies on the spatialization of carbon emissions statistics by combining nighttime light remote sensing data and other data to detect the spatial and temporal variability of carbon emissions at the raster scale [20,21], and some studies have also combined the two types of data [22,23]. Among them, the study of the spatialization of carbon emissions using nighttime light data is becoming more and more popular. Due to the existence of spatial and temporal variation in carbon emissions, carbon balance has become the focus of carbon emissions reduction research in various regions, and one of the important means of regulating carbon balance is carbon compensation [24].

In terms of carbon compensation, in-depth discussions have been conducted mainly around carbon compensation zoning [24,25,26,27], carbon compensation mechanisms [28,29], carbon compensation influencing factors [30], and carbon compensation in different industries, fields, and sectors. The different fields can be divided into forest carbon compensation [31], agricultural carbon compensation [28,29], and ecosystem compensation [32]. Carbon compensation zoning is one of the main focuses of research in the field of carbon compensation at this stage, and it is also a prerequisite and foundation for the implementation of a regional carbon compensation system [27]. With carbon compensation zoning, it is important to implement carbon compensation techniques and carbon compensation policies in terms of empirical evidence. The current zoning research is mainly based on the carbon compensation rate (carbon absorption/carbon emissions) [33], and the zoning evaluation system is established with the carbon compensation rate, the carbon emissions economic contribution coefficient, and the carbon emissions ecological carrying coefficient, and the existing methods to achieve carbon compensation zoning are mainly SOM [34], the SOM-K-means model [24,27], and the three-dimensional magic method [25,35]. In general, the existing carbon compensation studies have two limits: Firstly, the research on regional carbon compensation and compensation zoning is at the exploratory stage, mainly limited to particular fields; secondly, the existing studies focus on compensation zoning based on carbon balance accounting, the economic contribution rate of carbon emissions, the ecological role of the carbon sink, land use, and major functional zones and fail to consider additional factors such as the regional per capita carbon compensation rate and residents’ economic and energy consumption level; and thirdly, existing studies do not effectively integrate macroscopic patterns and detailed features, i.e., there is a lack of studies with both large-scale study areas and smaller categorical units (counties).

In this study, based on carbon emissions and carbon sequestration and population–economic statistical data, spatial statistical analysis is used to characterize the spatial and temporal variability of PCO₂ in Chinese counties. The Normalized Revealed Comparative Advantage (NRCA) index is used to quantify data on four dimensions, namely, natural, economic, ecological, and energy attributes, and form comprehensive evaluation indexes of per capita carbon compensation zoning in Chinese counties based on the Kernel-K-means++ algorithm to construct a per capita carbon compensation zoning model of Chinese counties, and the zoning model is used for cluster analysis. Finally, optimization recommendations for low-carbon development in each county are provided based on the zoning results.

2. Materials and Methods

2.1. Study Area

China is located in the eastern part of Asia and on the western coast of the Pacific Ocean, with a total land area of about 9.6 million km², ranking third in the world. China has experienced rapid economic growth and large-scale carbon emissions in recent years. However, it has also been subject to a variety of climate, environmental, ecological, urban heat island, and other issues. Therefore, emissions reduction is urgent in order to achieve the carbon peaking and carbon neutral target and to alleviate these conditions. Meanwhile, China’s vast area, carbon emissions, and its various types of impact on spatial diversity represent significant challenges. The estimation of China as a research area, exploring carbon compensation zoning methodologies and the optimization of policies, provides strong representative data (Figure 1). Further, considering the accessibility of statistical data, the county level was selected to carry out the research.

2.2. Data

In this study, based on the feasibility and completeness of the data acquisition, 1822 counties and districts in China were selected as study areas, excluding data from the Tibet Autonomous Region, Taiwan Province, Hong Kong, and the Macao Special Administrative Region, and the study period was 2000–2017. The data used included 2000–2017 China county-level carbon emissions and carbon sequestration data, county-level population data, county-level GDP data, county-level GDP coal consumption data, etc.; 2000–2017 China county-level carbon emissions and sequestration data from the China Carbon Accounting Database (CEADs) (https://www.ceads.net.cn/data/county/, accessed on 15 May 2023), with strong data time series continuity and a uniform calculation caliber; and 2000–2017 China county-level population, GDP, coal consumption, etc., sourced from various official statistical yearbooks, such as the China County Statistical Yearbook and the China Energy Statistical Yearbook (

Table 1. Overview of county statistics and sources.

Descriptions	Factors	Years	Sources
Carbon emissions scale indicator	Carbon emissions	2000–2017	CEADs
Carbon sequestration scale indicators	Amount of solid carbon	2000–2017
Population indicators	Population	2001–2018 1	China County Statistical Yearbook
Socio-economic indicators	Regional GDP	2001–2018 1
Energy consumption indicators	Regional GDP coal consumption	2001–2018 1
Main functional zone type	Optimized development zone, key development zone, main production zone of agricultural products, key ecological functional zone	\	Government documents on main functional zoning in provinces and cities

¹ The yearbook covers up to the previous year’s data. The year of the data is omitted from Table 1 because the official main functional area planning documents were issued at different times in each province and city.

).

2.3. Methods

In this section, the research process and methodology are presented. The research process of this study is illustrated in Figure 2.

2.3.1. Dagum Gini Coefficient

Dagum [36] decomposed the overall variation into three components of intra-group variation, inter-group variation, and net inter-group variation, and inter-group hypervariable density based on the Gini coefficient was used to effectively address the source of measuring regional variation. In this study, the Dagum Gini coefficient was used as a measure of regional differences in PCO₂ across 1822 county-level units in China, calculated using Equation (1):

$$G_{jh}=\frac{\sum _{i=1}^{nj}{\sum }_{r=1}^{nh}\left|y_{ji}-y_{hr}\right|}{n_j{n}_h\left({\overline{y}}_j+{\overline{y}}_h\right)}$$

(1)

where $j$ and $h$ are the numbers of two regions; $n_j$ and $n_h$ are the number of county units in $j$ and $h$, respectively; $y_{ji}$ and $y_{hr}$ are the PCO₂ of the $i$-th county unit in $j$ and the $r$-th county unit in $h$, respectively; and ${\overline{y}}_j$ and ${\overline{y}}_h$ represent the average value of PCO₂ of all county units in the corresponding regions.

The Dagum Gini coefficient can be specifically decomposed into three components: the contribution of the within-group Gini coefficient to the overall Gini coefficient $G_W$, the contribution of the net difference between groups to the overall Gini coefficient $G_{nb}$, and the contribution of the hypervariable density $G_t$. The relationship between the three components satisfies $G=G_w+G_{nb}+G_t$, and it is calculated via Equations (2)–(5):

$$G={\sum }_{j=1}^k{G}_{jj}{P}_j{S}_j+{\sum }_{j=1}^k{\sum }_{h\ne j}{G}_{jh}{P}_j{S}_h{D}_{jh}+{\sum }_{j=1}^k{\sum }_{h\ne j}{G}_{jh}{P}_j{S}_h{(1-D}_{jh})$$

(2)

$$G_w={\sum }_{j=1}^k{G}_{jj}{P}_j{S}_j$$

(3)

$$G_{nb}={\sum }_{j=1}^k{\sum }_{h\ne j}{G}_{jh}{P}_j{S}_h{D}_{jh}$$

(4)

$$G_t={\sum }_{j=1}^k{\sum }_{h\ne j}{G}_{jh}{P}_j{S}_h{(1-D}_{jh})$$

(5)

where $P_j=\raisebox{1ex}{$n_j$}\!\left/ \!\raisebox{-1ex}{$n$}\right.$ represents the ratio of carbon emissions per capita of county units within $j$ to the overall carbon emissions per capita of county units $n$, and $S_h=\raisebox{1ex}{$n_h{\overline{y}}_h$}\!\left/ \!\raisebox{-1ex}{$n\overline{y}$}\right.$ represents the ratio of carbon emissions per capita of county units within $h$ to the overall carbon emissions per capita of county units $n$.

2.3.2. Moran’s I Index

The global Moran’s I index is usually used to describe the average degree of association of a spatial unit with its surrounding area over the entire region, and it is calculated via Equation (6):

$$I=\frac{n{\sum }_{i=1}^n{\sum }_{j\ne i}^n{W}_{ij}\left(X_i-\overline{X}\right)\left(X_j-\overline{X}\right)}{{\sum }_{i=1}^n{\sum }_{j\ne i}^n{W}_{ij}{\sum }_{i=1}^n{\left(X_i-\overline{X}\right)}^2}$$

(6)

where $n$ is the number of sample counties; $X_i$ and $X_j$ are the per capita carbon emissions of counties $i$ and $j$, respectively; $\overline{X}$ is the mean value of the per capita carbon emissions; and $W_{ij}$ is the spatial weight matrix (1 if adjacent, 0 if not adjacent). At the given significance level, a positive Moran’s I value represents spatial agglomeration (passing the significance test); a negative Moran’s I value represents spatial divergence (passing the significance test).

2.3.3. Normalized Revealed Comparative Advantage Index

The NRCA index is primarily an indicator of product competitiveness, obtained by Yu et al. [37] by improving the Revealed Comparative Advantage (RCA) index constructed by Balassa [38], which was used in this study to discriminate the dominant attributes of the per capita carbon compensation zoning in Chinese counties. It is calculated using Equation (7):

$${NRCA}_j^i=\raisebox{1ex}{$X_j^i$}\!\left/ \!\raisebox{-1ex}{$X$}\right.-\raisebox{1ex}{$X_j{X}^i$}\!\left/ \!\raisebox{-1ex}{$XX$}\right.$$

(7)

where $X_j^i$ denotes the index value of attribute $j$ of county unit $i$, $X_j$ denotes the total index value of attribute $j$ of all county units, $X^i$ denotes the total index value of all attributes of county unit $i$, and $X$ denotes the total index value of all county units and attributes. If ${NRCA}_j^i>0$, it means that the county-level unit has a comparative advantage in this attribute; otherwise, it means that the county-level unit does not have a comparative advantage in this attribute.

2.3.4. Comprehensive Evaluation Index of Per Capita Carbon Compensation Zoning

Existing studies point out that the carbon cycle process at the county scale has obvious natural–social binary cycle characteristics [39] and its carbon balance has both natural and social attributes [40]. Based on the regional binary carbon balance theory [40] and existing studies [24,27], comprehensive evaluation indicators for per capita carbon compensation zones were constructed based on four perspectives: the natural background, socio-economic factors, the ecological environment, and energy consumption construction, and the construction process diagram is shown in Figure 3.

The per capita carbon compensation rate was used as an indicator of the natural background attributes; the per capita economic contribution coefficient of carbon emissions, calculated based on the GDP, population, and carbon emissions data of the selected regions, was used as an indicator of socio-economic attributes; the per capita ecological support coefficient of carbon emissions calculated based on the selected carbon emissions data and carbon sequestration data was used as an indicator of socio-ecological attributes; and the per capita regional GDP energy consumption level (per capita regional GDP coal consumption), calculated based on selected regional GDP coal consumption and population data, was used as an energy consumption attribute. The mechanism of per capita carbon compensation zoning in Chinese counties was investigated from four perspectives: natural background attributes, socio-economic attributes, ecological and environmental attributes, and energy consumption attributes. The above indicators are shown in

Table 2. County data on per capita carbon compensation zoning variables.

Descriptions	Factors	Years	Sources
Natural background attribute indicators	Per capita carbon compensation rate (PCR)	2000–2017	Per capita carbon sequestration/per capita carbon emission
Socio-economic attribute indicators	Per capita economic contribution coefficient of carbon emissions (PECC)	2000–2017	(Per capita GDP/per capita total GDP)/(per capita carbon emissions/per capita total carbon emissions)
Socio-ecological attribute indicators	Per capita ecological support coefficient of carbon emissions (PESC)	2000–2017	(Per capita carbon sequestration/per capita total carbon sequestration)/(per capita carbon emissions/per capita total carbon emissions)
Energy consumption attribute indicators	Per capita regional GDP coal consumption (PRGDPCC)	2000–2017	Regional GDP coal consumption/population

and Equations (8)–(11).

The county PCR for China was selected as an indicator reflecting the scale attributes of per capita carbon compensation, PECC was selected as an indicator of the socio-economic attributes of per capita carbon compensation, PESC was selected as an indicator of the ecological and environmental attributes of per capita carbon compensation, and PRGDPCC was selected as an indicator of the energy consumption attributes of per capita carbon compensation partition. The PCR is calculated using Equation (8):

$$PCR=\frac{{PCA}_i}{{PC}_i},i=\mathrm{1,2},\dots ,n$$

(8)

where ${PCA}_i$ is the per capita carbon sequestration of the $i$-th county unit, and ${PC}_i$ is the per capita carbon emissions of the $i$-th county unit. As shown in Equation (8), the PCR index only considers the relationship between per capita carbon sequestration and per capita carbon emissions in a county itself.

Based on the carbon emissions ecological carrying coefficient and carbon emissions ecological carrying factor proposed by Lu et al. [41], in this study, the per capita carbon emissions economic contribution coefficient (PECC) and per capita carbon emissions ecological carrying coefficient (PESC) are proposed. PECC indicates the socio-economic attributes of per capita carbon compensation and is used to reflect the socio-economic benefits of per capita carbon compensation in the county, and it is calculated via Equation (9):

$$PECC=\frac{{PG}_i}{PG}/\frac{{PC}_i}{PC}$$

(9)

where ${PG}_i$ and $PG$ are the GDP per capita of the $i$-th county unit and the total per capita GDP of 1822 county units, respectively; ${PC}_i$ and $PC$ are the carbon emissions per capita of the $i$-th county unit and the total per capita carbon emissions of 1822 county units, respectively.

PESC denotes the eco-environmental attributes of per capita carbon compensation, used to reflect the eco-environmental benefits of per capita carbon compensation in the county, and it is calculated via Equation (10):

$$PESC=\frac{{PCA}_i}{PCA}/\frac{{PC}_i}{PC}$$

(10)

where ${PCA}_i$ and $PCA$ are the per capita carbon sequestration of each county unit and the total per capita carbon sequestration of 1822 county units, respectively, and ${PC}_i$ and $PC$ are the per capita carbon emissions of each county unit and the total per capita carbon emissions of 1822 county units, respectively. As shown in Equation (10), the PESC index takes the correlation between counties into account.

PRGDPCC is used to indicate the level of energy consumption of the residential economy to represent the energy consumption attribute of per capita carbon compensation, and it is calculated via Equation (11):

$$\mathrm{P}\mathrm{R}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}\mathrm{C}=\frac{{\mathrm{R}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}\mathrm{C}}_i}{{\mathrm{P}\mathrm{o}\mathrm{p}}_i},i=\mathrm{1,2},\dots ,n$$

(11)

where ${\mathrm{P}\mathrm{R}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}\mathrm{C}}_i$ is the level of economic energy consumption of residents in the $i$-th county unit, ${\mathrm{P}\mathrm{o}\mathrm{p}}_i$ is the population in the $i$-th county unit, and there are $n$ county units in total. This index represents the per capita economic energy consumption level of the region.

Based on the natural background attributes, socio-economic attributes, ecological environment attributes, and energy consumption attributes of the per capita carbon compensation in counties, the NRCA index of each attribute was calculated to set the evaluation index of per capita carbon compensation zoning. Considering the four factors of nature, economy, ecology and energy, four comprehensive evaluation indicators for per capita carbon offset zoning were established (

Table 3. Comprehensive evaluation index of counties for per capita carbon compensation zoning.

Descriptions	Factors	Years	Interpretation
Natural background property indicators	Natural NRCA index (NRCAPCR)	2000–2017	NRCA index of carbon compensation rate per capita for each county unit
Socio-economic attribute indicators	Economic NRCA index (NRCAPECC)	2000–2017	NRCA index of economic contribution factor of carbon emissions per capita
Ecological attribute indicators	Ecological NRCA index (NRCAPESC)	2000–2017	NRCA index of ecological carrying factor of carbon emissions per capita
Energy consumption attribute indicators	Energy NRCA index (NRCAPRGDPCC)	2000–2017	NRCA index of energy consumption levels per capita regional GDP

). Regarding the per capita carbon compensation division scheme, the study area was divided into three types: paying, balancing, and receiving [27]. Among them, the payment zone was defined as the area that needs to be compensated by economic or non-economic means in the per capita carbon compensation behavior, the equilibrium area was defined as the area that does not need to pay and receive compensation in the per capita carbon compensation behavior, and the compensated zone was defined as the area that receives economic or non-economic compensation in the per capita carbon compensation behavior.

2.3.5. Per Capita Carbon Compensation Zoning Model Based on Kernel-K-means++ Algorithm

The architecture and implementation strategy of the per capita carbon compensation zoning model designed in this study is shown in Figure 4. A four-dimensional framework of per capita carbon compensation zoning was constructed based on four perspectives, namely, the natural background, socio-economic factors, the ecological environment, and energy consumption (Figure 4), and four indicators, namely, the per capita carbon compensation rate, the per capita carbon emissions economic contribution coefficient, the per capita carbon emissions ecological carrying capacity coefficient, and the per capita economic energy consumption level, to calculate the NRCA index according to the theoretical framework; the NRCA index of four aspects was calculated using Equation (7); and comprehensive evaluation indicators for per capita carbon compensation zoning in Chinese counties were formulated. The dataset was normalized and feature extraction and dimensionality reduction were performed via Kernel PCA; then, the data were clustered using the K-means++ algorithm, and thus, the clustering results were acquired. The specific steps are as follows:

• Step 1: normalizing the raw data so that different features take the same range of values;
• Step 2: using the Kernel PCA method to map the data into the high-dimensional space, selecting the appropriate kernel function, and performing parameter adjustment and experimental verification to obtain better mapping results;
• Step 3: using the K-means++ algorithm to cluster the mapped data, selecting the appropriate number of clusters, and performing experimental verification and adjustment to obtain better clustering results;
• Step 4: analyzing and interpreting the clustering results to gain a deeper understanding of the data.

3. Results

3.1. Analysis of Regional Differences in Carbon Emissions Per Capita in Counties in China

3.1.1. General Differences in PCO₂ in China’s Counties

During the study period, the overall Dagum Gini coefficient showed an overall upward trend from 0.48 in 2000 to 0.51 in 2017, although it decreased in 2002 and 2003, indicating a gradual widening of the overall gap in carbon emissions per capita in China’s counties (Figure 5).

3.1.2. Spatial Differences in Per Capita Carbon Emissions in China’s Counties

According to the geographic zoning map of China (Figure 1b), county PCO₂ was statistically analyzed in the eastern, central, western, and northeastern parts of China.

(1) Intra-economic regional differences. The evolutionary trends of the differences in PCO₂ among the four major economic regions in China’s counties from 2000 to 2017 were plotted (Figure 6). First, in terms of the size of the differences, the mean values of the Dagum Gini coefficients of the eastern, central, western, and northeastern regions of the four major economic regions from 2000 to 2017 were 0.423, 0.371, 0.413, and 0.47, respectively, indicating that the difference in PCO₂ was the largest in the northeastern region and the smallest in the central region. In addition, the Dagum Gini coefficients of the central, western, and northeastern regions as a whole showed different degrees of decline, unlike the eastern region, which showed an overall increasing trend. The main changes in Gini coefficients of carbon emissions differences per capita in the region were characterized as follows: (1) That of the eastern region increased abruptly to 0.614 in 2010, (2) that of the central region increased abruptly to 0.6 in 2007 and shrank sharply to 0.265 in 2010, (3) that of the western region fluctuated from 2000 to 2003 and shrank sharply to 0.341 in 2008, (4) and that of the northeastern region fluctuated from 2000 to 2003 and then declined overall.

(2) Differences between economic regions. Trends in PCO₂ in China’s counties during the period of 2000–2017 were plotted for differences between economic regions (Figure 7). In terms of the size of the differences, the order of the differences in carbon emissions per capita between economic regions from 2000 to 2017, from largest to smallest, was east–northeast (0.491), east–west (0.459), west–northeast (0.455), central–northeast (0.451), east–central (0.428), and central–west (0.412). Among them, the differences in PCO₂ between east and central, east and west, and east and northeast showed an overall increasing trend. In 2007, the difference in PCO₂ between east and central expanded sharply and then showed a slowly increasing trend.

3.1.3. Sources and Contributions of Differences in PCO₂ by Economic Region in China

Trends in the sources and contributions to differences in PCO₂ across economic regions in China’s counties over the period of 2000–2017 were plotted (Figure 8). The mean values of the contribution within each economic region, the contribution between each economic region, and the contribution of hypervariable density were 32.256%, 16.180%, and 51.564%, respectively, indicating that the sources leading to the variation of PCO₂ in Chinese counties included hypervariable density (which accounted for about half of the overall variation), variation within each economic region, and variation between each economic region, in that order.

In addition, different sources of variation in PCO₂ in China’s counties showed different trends that can be derived from Figure 8. Among them, the variance within each economic region and the variance between each economic region showed a stable and increasing trend, whereas the super-variance density showed a more significant decreasing trend, from 58.542% in 2000 to 39.845% in 2017, which is a significant decrease but was located in the main contribution position.

3.2. Spatial and Temporal Patterns of PCO₂ in Chinese Counties

3.2.1. Spatial and Temporal Pattern of Carbon Emissions Per Capita in China’s Counties

According to Figure 9, the spatial distribution of per capita carbon emissions in Chinese counties was generally characterized by a high level in the northwest and a low level in the southeast, with high-value counties and regions being more common in the northwest and low-value regions being more common in the southeast. The distribution of per capita carbon emissions in counties was concentrated and clearly differentiated, roughly opposite to the spatial distribution of the Hu Huanyong line (the line of population density comparison) [42]. The counties in northwest China were the areas with high per capita carbon emissions, and this phenomenon became bigger and bigger with time, which may have been caused by many factors. These regions belong to the northwestern half of the Hu Huanyong line and are sparsely populated, with traditional production methods, more carbon sources, and smaller populations, leading to relatively high per capita carbon emissions. The southeast region had low per capita carbon emissions, and its high level of economic development, large population, and well-developed tertiary industry may have led to lower per capita carbon emissions. Over the course of time, per capita carbon emissions in most counties in China showed an increasing trend, especially in some counties in the northwestern region, where per capita carbon emissions increased over time, and the number of high-value counties rose significantly in 2017. This may have been due to the increasing level of China’s economic development and the intensity of spatial development of the country’s territory over time, leading to increasing natural and anthropogenic carbon sources and increasing per capita carbon emissions.

Looking at the per capita carbon emissions of each main functional area, key development zones ranked third in number but first in per capita carbon emissions, whereas optimized development zones reached a per capita carbon emissions share of about 5% in the period of 2000–2017, with 48 county-level units. Key development zones bore heavy responsibility for population absorption, economic development, and industrial agglomeration, and their per capita carbon emissions were significantly higher than those of restricted development zones (the main agricultural product-producing zones and key ecological functional zones), which were the main pressure areas for per capita carbon emissions in Chinese counties (Figure 10). The per capita carbon emissions share of key development zones and key ecological functional zones increased year after year, whereas the per capita carbon emissions share of the main agricultural product-producing zones decreased year after year. The increase in per capita carbon emissions in the key ecological functional zones may have been partly due to the gradual increase in the development of the region and the increase in eco-tourism and other industries that generate more carbon emissions. The decrease in per capita carbon emissions in the main agricultural production zones may have been due to the reduction in carbon emissions sources caused by the return of farmland to forests and grasslands and the increase in the abandonment of farmland.

3.2.2. Spatial Clustering Characteristics of PCO₂ in Chinese Counties

The global Moran’s I indices for 2000–2017 were all greater than 0 and passed the significance test at the 99.99% level, and China’s PCO₂ was moderately autocorrelated in space during the study period (

Table 4. Moran’s I index of per capita county-level carbon emissions in China, 2000–2017.

Years	Moran’s I	Z	P
2000	0.302	19.5241	0.000100
2005	0.370	23.7615	0.000100
2010	0.421	27.1650	0.000100
2017	0.511	32.690	0.000100

).

The global Moran’s I index values showed an increasing trend from 2000 to 2017, indicating that the spatial autocorrelation of carbon emissions per capita in China was increasing, and the Moran’s I indexes of carbon emissions per capita in Chinese counties were all greater than zero and passed the significance test at the 99.99% level, whereas the districts and counties with similar levels of carbon emissions per capita tended to have a concentrated distribution. Meanwhile, the Moran’s I index gradually increased from 2000 to 2017, and the rate of increase for the index was higher in the early period than in the later period (the Moran’s I index increased from 0.302 in 2000 to 0.511 in 2017).

3.3. Per Capita Carbon Compensation Zoning and Optimization Analysis in Chinese Counties

3.3.1. Analysis of the NRCA Index

Based on the index values of the natural background attribute, socio-economic attribute, ecological environment attribute, and energy consumption attribute, the NRCA index was used to measure the comparative advantage of each type of attribute in China’s counties’ per capita carbon compensation zoning, which provided a basis for the zoning and optimization of per capita carbon compensation.

The results show (Figure 11) that most of the dominant areas of natural background attributes in per capita carbon compensation in Chinese counties were located in the southwest, the upper northern part of northeast China, Qinghai, and parts of Xinjiang, indicating that these two major functional zones belonged to the dominant areas of per capita carbon sinks. Most of the disadvantaged areas of the natural background attributes of per capita carbon compensation in Chinese counties were located in the southeast coastal region and parts of the Central Plains and Inner Mongolia, indicating that the per capita carbon compensation rate in these areas was low and that the per capita carbon emissions far exceeded the per capita carbon sequestration, constituting a disadvantaged area of per capita carbon sinks.

The advantageous areas of per capita carbon compensation economic attributes were mainly located in northeastern and central China, western Xinjiang, and southeastern China, where the economic contribution of carbon emissions was high, whereas some areas, such as northwestern Inner Mongolia, Gansu, Qinghai province, and Ningxia province, were the disadvantageous areas of carbon compensation economic attributes, where the economic contribution of carbon emissions was weak, the economic output efficiency was low, and the economic development may have been relatively more sloppy and lagging.

In terms of per capita carbon compensation for ecological attributes, the most advantageous regions were mainly located in the mountainous regions of southwest China, the Daxinganling Mountains in northeast China, the Tianshan Mountains in Xinjiang, and the mountainous regions in the south. These areas play an important ecological role in water resource protection, wind and sand control, and climate regulation, and have a high ecological carrying capacity of PCO₂.

The areas of per capita carbon compensation where energy consumption attributes were dominant were mainly distributed in the Beijing–Tianjin–Hebei region, the eastern part of the northeast region, the southeastern coastal region, the middle Yellow River Basin region, Qinghai, eastern Gansu, and a few counties in the south of the western region. The areas of per capita carbon compensation where energy consumption attributes were dominant played a major feedback role on the PCO₂ of Chinese counties, and most of the carbon emissions originated from human energy consumption.

3.3.2. Per Capita Carbon Compensation Zoning Results

The per capita carbon compensation zoning model constructed by integrating the Kernel-K-means++ algorithm was used to cluster and analyze the NRCA index of four attributes, and the 1822 county units were classified into 1410 payment zones, 170 balanced zones, and 242 compensated zones. The per capita carbon compensation zoning was then overlaid with the main functional zoning and finally reconstructed into 11 types (Figure 12 and

Table 5. Key indicators of per capita carbon compensation types in county-level regions of China.

Spatial Partitioning of Per Capita Carbon Compensation (Number of Units)	Per Capita Total GDP (%)	Share of Total Per Capita Carbon Emissions (%)	Mean Economic Contribution Factor of Per Capita Carbon Emissions	Mean Ecological Carrying Capacity Coefficient of Per Capita Carbon Emissions
Payment zone—optimized development zone (46)	10.645	4.310	4.106	0.031
Payment zone—key development zone (397)	34.500	34.618	3.588	0.405
Payment zone—main production zone of agricultural products (461)	29.161	17.272	4.47	0.516
Payment zone—key ecological function zone (506)	13.675	27.491	3.128	1.956
Balanced zone—optimized development zone (2)	0.083	0.114	1.39	0.355
Balanced zone—key development zone (29)	0.927	1.652	1.119	0.483
Balanced zone—main production zone of agricultural products (90)	2.131	4.371	0.907	1.697
Balanced zone—key ecological function zone (49)	0.916	2.525	0.697	2.225
Compensated zone—key development zone (33)	1.336	1.218	1.76	1.847
Compensated zone—main production zone of agricultural products (129)	4.681	4.165	1.833	2.435
Compensated zone—key ecological function zone (80)	1.945	2.264	1.526	3.833

).

3.4. Spatial Optimization Analysis of Per Capita Carbon Compensation Zoning in Chinese Counties

3.4.1. Payment Zone

Based on the zoning results, the payment zone is densely distributed and extensive, mainly located in southeast China, northeast China, and northwest Xinjiang, with a high level of economic development (per capita GDP share of up to 87.981%), high per capita carbon emissions contribution (per capita carbon emissions share of up to 83.691%), high per capita carbon emissions economic contribution capacity, and low per capita carbon emissions ecological carrying capacity. There was a serious mismatch between the two levels (the economic contribution coefficient of carbon emissions per capita was 6.338, and the ecological carrying capacity of carbon emissions per capita was 0.469). This zone includes:

(1) The payment zone—optimized development zone. The payment zone—optimized development zone consists of 46 county-level units, which are mainly located in the Bohai Sea Economic Zone, Yangtze River Delta region, and Pearl River Delta region and belong to highly urbanized and industrialized areas with relatively rapid economic development (the largest per capita total GDP share was 10.645%), large PCO₂ (the per capita total carbon emissions share was 34.618%), and high PCO₂ economic benefits (the average value of the economic contribution coefficient of carbon emissions per capita was 4.106), but the ecological environment is under great pressure (the average value of the ecological bearing coefficient of carbon emissions per capita was 0.031) and facing serious pressure to achieve emissions reduction. The main optimization direction is to promote the construction of industrial agglomeration to improve the economic contribution capacity of carbon emissions per capita and at the same time protect water resources and ecological environment resources and improve ecological environment carrying capacity. A new window and strategic space for opening up to the outside world should be formed while at the same time building an urban green isolation zone and coastal protection forests in order to protect the quality of the water environment in the near-shore sea.

(2) Payment zone—key development zone. The payment zone—key development zone consists of 398 county-level units, which are mainly distributed in the southeast coastal region, the Tianshan region of Xinjiang, the river-loop region, and the Sichuan Basin. This region has a high level of economic development and is the region that had the highest percentage of total GDP per capita among all sub-regions (34.500% of total GDP per capita) and is also the region that had the highest total carbon emissions per capita among all sub-regions (34.618% of total carbon emissions per capita). Although it has a good economic contribution benefit of carbon emissions per capita, it has high ecological and environmental pressure (economic contribution coefficient of carbon emissions per capita. The average value of per capita carbon emissions economic contribution coefficient was 3.588, the average value of the per capita carbon emissions ecological bearing coefficient was 0.405, and the pressure to achieve emissions reduction is greater. The main optimization direction is to form a new window and strategic space for opening up to the outside world. At the same time, it is necessary to build urban green isolation zones and coastal protection forests, strengthen the control of land-based pollutant emissions, and protect the quality of the water environment in the near-shore sea.

(3) Payment zone—agricultural products main production zone. The payment zone—agricultural products main production zone consists of 461 county-level units, mainly located in the northeast region, the North China Plain, and the Middle and Lower Yangtze River Plain. This region is mostly located in the plain area, with excellent agricultural conditions and an agricultural foundation, and is an important agricultural product base, but is more affected by human production activities. Compared with the main agricultural product-producing areas in the balanced zone and the compensated zone, this region has better economic contribution efficiency of carbon emissions per capita (the average value of economic contribution coefficient of carbon emissions per capita was 4.47) and average ecological carrying capacity (the average value of ecological carrying coefficient of carbon emissions per capita was 0.516) and creates the second largest total GDP per capita with lower carbon emissions (the percentage of total GDP per capita was 29.161%, and the percentage of total carbon emissions per capita was 17.1%). The total carbon emissions per capita was 17.272%. The main optimization direction is the implementation of the geographical indication brand project and origin protection project. At the same time, we should consolidate the achievements of returning farmland to forests, continue to implement the natural forest resources protection project and comprehensive management of small watersheds, and strengthen the construction of wildlife biodiversity reserves.

(4) Payment zone—key ecological function zone. The payment zone—key ecological function zone consists of 505 county-level units, mainly located in the Sichuan Basin, southern Xinjiang, Inner Mongolia, the northern northeastern region, the two lakes region, and the southeastern mountainous region. As the sub-region with the largest share, the economic situation of this zone does not match the carbon emissions situation (the share of total GDP per capita was 13.675%, and the share of total carbon emissions per capita was 27.491%), the economic contribution of carbon emissions per capita is high (the average value of economic contribution coefficient of carbon emissions per capita was 3.128), and the ecological carrying capacity of carbon emissions per capita is high (the average value of ecological carrying coefficient of carbon emissions per capita was 1.956). The main optimization direction is to establish an ecological compensation mechanism, strengthen the natural restoration function of the ecosystem, develop environmentally bearable industries according to local conditions, and export ecological products.

3.4.2. Balanced Zone

The balanced zone is mainly located in parts of northern and northeastern China and some mountainous and hilly areas in southwest China, with an overall sporadic and short linear distribution. The economic development of the equilibrium zone is relatively slow (the percentage of GDP per capita was relatively low, at 4.057% of the total), the percentage of carbon emissions per capita is relatively small (carbon emissions per capita accounted for 8.662% of the total carbon emissions per capita), the ecological function and ecological carrying capacity of the carbon sink per capita are strong (the ecological carrying coefficient of carbon emissions per capita was 1.575), and the economic contribution capacity of carbon emissions per capita and the ecological carrying capacity of carbon emissions per capita are in a relatively matching state (the economic contribution coefficient of per capita carbon emissions was 1.82, and the ecological carrying capacity of per capita carbon emissions was 1.575), indicating that the economic development of the balanced zone is relatively balanced and will not cause an excessive impact on the ecological environment.

(1) Balanced zone—optimized development zone. The number of counties in the balanced zone—optimized development zone is very small. The per capita economic contribution and per capita carbon emissions of this zone are low (0.083% of total GDP per capita and 0.114% of total carbon emissions per capita), and the ecological carrying capacity is weak (the average value of economic contribution coefficient of carbon emissions per capita was 1.39, and the average value of ecological carrying coefficient of carbon emissions per capita was 0.355). The main optimization direction is to optimize the layout of urban functions, focus on the greening of towns and traffic arteries, improve the ecological system of urban forest parks and green channels, and enhance the carrying capacity of the ecosystem.

(2) Balanced zone—key development zone. The balanced zone—key development zone consists of 29 county-level units, showing a sporadic distribution, mainly located in the western region of Xinjiang, the eastern region of Qinghai, the southern region of Liaoning, the North China Plain, and the Yunnan-Guizhou region. This zone constitutes fewer counties and regions, with lower economic development (0.927% of total GDP per capita), lower carbon emissions per capita (1.652% of total carbon emissions per capita), slightly higher economic benefits of carbon emissions per capita (average value of economic contribution coefficient of carbon emissions per capita was 1.119), and a relatively stronger ecological carrying capacity of carbon emissions per capita (the average value of the ecological carrying coefficient of carbon emissions per capita was 0.483). The main optimization directions include forming intensive and efficient urban clusters or dense urban areas, promoting comprehensive water environment management in important watersheds (Wujiang River, Lancang River, etc.), protecting water-bearing areas and biodiversity, and improving the ecological carrying capacity of the environment.

(3) Balanced zone—main production zone of agricultural products. The balanced zone—main production zone of agricultural products consists of 90 county-level units, scattered in the northeast region, the North China Plain, and the southwest mountainous region, whose per capita carbon emissions are more than twice the per capita GDP, with weak economic development and low economic contribution efficiency of per capita carbon emissions (2.131% of total per capita GDP, 4.371% of total per capita carbon emissions, and an average value of economic contribution coefficient of per capita carbon emissions of 0.907), but the ecological carrying capacity is strong (the average value of the ecological carrying coefficient of carbon emissions per capita was 1.697). The main optimization direction is to strengthen ecological protection; maintain a stable landscape system structure of hills, forests, grasses, and farmlands; and correctly handle the relationship between agricultural production, ecological protection, and resource development.

(4) Balanced zone—key ecological functional area. The balanced zone—key ecological functional area consists of 49 county-level units, mainly located near Kashgar in Xinjiang, part of the North China Plain, and part of the Northeast Plain, with a small per capita carbon emissions share (2.525% of total per capita carbon emissions share), weak economic development, and little economic contribution to per capita carbon emissions (0.916% of total per capita GDP and an average value of economic contribution coefficient of per capita carbon emissions of 0.697), but the ecological carrying capacity of carbon emissions per capita is strong (the average value of the ecological carrying coefficient of carbon emissions per capita was 2.225). The main optimization direction is to control the development intensity and carry out integrated management of small watersheds and the construction of silt dams to promote ecosystem restoration.

3.4.3. Compensated Zone

The compensated zone is mainly located in the northeastern plain and mountainous areas of the country’s counties, including the mountainous and hilly areas in the southern region, Xinjiang, and some counties in Hainan, and shows a scattered and small cluster-like distribution. The economic development of the compensated zones is relatively limited (the per capita GDP share was relatively low, at 7.962% of the total), the per capita carbon emissions share is the smallest (the per capita carbon emissions accounted for 7.647% of the total per capita carbon emissions), the per capita carbon sink ecological function and ecological carrying capacity is strong (the per capita carbon emissions ecological carrying coefficient was 1.886), and the per capita carbon emissions economic contribution is low (the per capita carbon emissions economic contribution coefficient was 0.286).

(1) Compensated zone—key development zones. The compensated zone—key development zones are composed of 33 county-level units, mainly located in the southwest mountainous region, the middle reaches of the Yellow River, the vicinity of Kashgar in Xinjiang, and the Yili region, whose economic development and PCO₂ are relatively well matched (1.336% of total GDP per capita and 1.218% of total carbon emissions per capita, respectively), with a larger economic contribution of carbon emissions per capita (the average value of the economic contribution coefficient of carbon emissions per capita was 1.76) and stronger ecological and environmental functions (the average value of the ecological bearing coefficient of carbon emissions per capita was 1.847). The main optimization direction is to expand green ecological space; build an ecological pattern of the organic integration of forest, grassland, rivers, and lakes; and improve the carbon sink capacity of the ecosystem.

(2) Compensated zone—main production zone of agricultural products. The compensated zone—main production zone of agricultural products consists of 129 counties. In these areas, the economic development and per capita carbon emissions are well matched (4.681% of total GDP per capita and 4.165% of total carbon emissions per capita, respectively), the economic contribution of per capita carbon emissions is high (the average value of the economic contribution of per capita carbon emissions was 1.833), and the ecological carrying capacity is strong (the average value of the ecological carrying coefficient of carbon emissions per capita was 2.435). The main direction of optimization includes the rational planning of agricultural land, the comprehensive control of desertification and rock desertification, and the formation of eco-friendly agricultural belts.

(3) Compensated zone—key ecological function zone. The compensated zone—key ecological function zone consists of 80 county-level units, mainly located in the northeast; the center; some parts of Guangdong, Gansu, and Xinjiang; and other northwest areas. The economic development of this zone is at an average level and the per capita carbon emissions are low (1.945% of total GDP per capita and 2.264% of total carbon emissions per capita, respectively), and the economic contribution capacity of per capita carbon emissions is average, whereas the ecological carrying capacity is very strong (the average value of the economic contribution coefficient of per capita carbon emissions was 1.526, and the average value of the ecological carrying coefficient of per capita carbon emissions was 3.833). The main optimization direction is to develop succession and alternative industries, mainly ecological tourism, special breeding, green food processing, etc., to form an ecologically dominant industrial pattern.

4. Discussion

In this study, a new per capita carbon compensation zoning model was proposed and the per capita carbon compensation zoning of Chinese counties was realized, which was verified as being reliable and have the potential to be applied to other similar studies. In addition, the research features and innovations of this study were mainly reflected in the following two aspects:

(1) The per capita carbon compensation rate was selected as a natural background attribute indicator; the level of residential economic energy consumption was selected as an energy consumption indicator to be added to the model; methods such as the K-means and SOM-K-means classification models, which were mostly used in the existing research on carbon compensation zoning, were further developed; a per capita carbon compensation county zoning model incorporating the Kernel-K-means++ algorithm was constructed; and per capita carbon compensation zoning of the counties in China was realized. Previous classification models in this research area did not consider the benefits of integrating data from all input aspects. In contrast, our updated model considers the complexities of multidimensional data and integrates natural context, socio-economic, ecological, and energy consumption aspects. This approach provides an advantage and makes our new model superior to previous models. This new model integrates the main functional zoning of the country with the real background conditions of each county, and the results of regional division have spatial autocorrelation, which is of practical significance in promoting emissions reduction. Additionally, since the new model incorporates nature, ecology, and energy consumption indicators, it is more effective in analyzing and planning sustainable development.

(2) Based on the county scale, results of China’s per capita carbon compensation zoning were obtained and an optimization analysis was carried out. Most of the existing studies on carbon compensation zoning in China were conducted at the spatial scale of provinces and urban clusters but not at the spatial scale of counties. This study took the major counties in China as the unit, which provided a more detailed spatial scale and more accurately reflects the characteristics of China’s carbon emissions; in addition, 1822 counties were used, which covers most of China’s regions, and the study also had great macro-analysis conditions.

Due to the limited research conditions, the time series of the study was only selected as 2000 to 2017, and full time series coverage was not achieved from 2018 to 2023. Since the obtained county-level carbon balance data do not include Tibet, Hong Kong, Macao, and Taiwan and need to be matched with county-level economic data and since the division of China’s county-level units varies with the time series, the carbon balance data and economic data of only 1822 county-level units in China were obtained, which cannot cover all county-level units in China. Considering the availability of data and the applicability of the zoning model, only four indicators, namely, the per capita carbon compensation rate, the per capita carbon emissions ecological carrying coefficient, the per capita carbon emissions economic contribution coefficient, and the level of economic energy consumption of the population, were selected for clustering and partitioning, and population, industrial structure, and technological level were not included in the comprehensive evaluation index. The existing deficiencies of the above study will be further explored in future research. Moreover, there is also potential for future improvement to the new model, which is improved based on the kernel-PCA and K-means++ algorithms due to the kernel-PCA property, so there is also a possibility of overfitting in the new model, which can be further improved in the future.

5. Conclusions

In this study, based on county carbon emissions, carbon sequestration, and economic data, the variable dataset of per capita carbon compensation zoning in Chinese counties was constructed, regional differences in PCO₂ at the county scale were explored, and a four-dimensional framework of carbon compensation zoning was constructed. Based on this, a comprehensive evaluation index and a per capita carbon compensation zoning model were constructed. We also tried to propose a spatial low-carbon optimization strategy to promote carbon neutrality from the perspectives of payment, balanced, and compensated zones.

The main results are as follows: During the period of 2000–2017, the overall differences in PCO₂ were large and showed a steady widening trend in China’s counties, generally showing distribution characteristics of low in the southeast and high in the northwest, with obvious spatial autocorrelation. In addition, the model constructed in this study is based on the Kernel-K-means++ algorithm, which integrates natural, economic, ecological, and energy factors and more comprehensively realizes the per capita carbon compensation zoning in Chinese counties. Based on the model, the 1822 counties and districts in China were divided into 1410 payment zones, 170 balanced zones, and 242 compensated zones. By combining the results of carbon compensation zoning with the major function-oriented zones, 11 types of zones were finally formed, and the direction and strategy of low-carbon development for each type of zone were proposed. The improved model and optimization suggestions proposed in this study can contribute to a reduction in China’s carbon emissions in order to achieve the target of carbon peaking and carbon neutrality and, at the same time, have certain reference significance for the management of and reduction in carbon emissions in similar regions.

The implementation of sub-regional recommendations should be considered in order to achieve the goal of carbon peaking and carbon neutrality. We suggest that, based on the background characteristics of the study’s delineated regions and the recommendations therein, this plan will develop emissions reduction pathways and low-carbon industries. This strategy will keep carbon emissions under control while adapting to local conditions, and with the development of the carbon emissions situation, longer time series studies can be conducted and more appropriate strategies for cutting carbon emissions can be developed.