Introduction

With rapid economic development, the unsustainable increase in energy consumption and carbon emissions has exacerbated ecological and environmental problems, and the conflicts between human development and environmental protection have become increasingly prominent1,2,3. In 2018, the IPCC (Intergovernmental Panel on Climate Change) reached a consensus on the carbon peak and carbon neutralization (hereinafter referred to as “dual carbon”)4. Carbon neutrality has become one of the key factors in mitigating climate change5. At the general debate of the 75th United Nations General Assembly in 2020, China proposed to achieve the goal of “dual carbon” in two stages6. Controlling greenhouse gas emissions and achieving the ‘dual carbon’ goals have become a global consensus to address climate warming. Therefore, the Chinese government has set a “dual carbon” goal that aims to achieve peak carbon in 2030 and carbon neutrality in 20607. Clarifying the current status of the carbon budget and formulating appropriate carbon management policies for different regions have significant guiding significance for achieving the dual carbon goal.

Carbon budget accounting is the premise of formulating different carbon strategies and is also the primary task to achieve the dual carbon goal8. The accounting methods for carbon emissions mainly include the life cycle method, the input-output method, remote-sensing estimation methods, emission coefficient methods, and factor decomposition methods. The life cycle method is mainly used to analyze the carbon emissions generated by products in various stages such as manufacturing, circulation, use, and recycling and then to analyze the impact of product life cycle on the environment9. It is often used for accounting and evaluation of carbon emissions from enterprise products or construction activities10. The input-output method is derived from the input-output model and calculates direct and indirect carbon emissions based on the input-output Table11. The remote-sensing estimation method is used to estimate the amount of carbon emissions by constructing a correlation model between carbon emissions and remote-sensing data12. The factor decomposition method is mostly used for simulation assessment of the impact of carbon emissions reduction policies on social development, rather than for the total amount of the carbon budget13. Comparatively, the emissions coefficient method is more suitable for accounting for the carbon budget under complex socio-economic relationships and is widely used at global14,15,16, national17, and provincial18 scales. However, this method is highly dependent on energy consumption data, which are difficult to obtain at county or smaller scale. Related studies have shown a strong correlation between energy consumption and economic development19,20,21. Therefore, this study’s aim is to use economic data to invert energy consumption and then to substitute it into the emissions coefficient method to achieve better carbon budget accounting at county scale.

Carbon comprehensive zoning research is an effective way to achieve low-carbon development and is also an important measure to alleviate the contradiction between economic development and environmental protection22. It has received widespread attention in various fields such as carbon compensation mechanisms23, factors influencing carbon balance24, and carbon management zoning25. One of the important research methods of carbon comprehensive zoning is carbon compensation zoning. The focus of carbon compensation research is to divide the study area into a payment zone, a compensation zone, and a balance zone based on differences in carbon balance and other conditions in different regions26. Unlike carbon compensation zones, carbon management zones focus on spatial planning and strategic design of regional carbon management methods based on comprehensive consideration of low-carbon development level and ecological land-type carbon balance capacity27. Currently, most studies have focused on carbon compensation zoning28,29,30, with relatively little research on carbon management zoning. Establishing an indicator system and conducting low-carbon development evaluations is an important method for carbon management zoning. However, based on existing studies, there are still problems such as complex indicator systems, different selection tendencies of the indicators, difficulty in implementation, and low comparability31. Therefore, this study aims to develop a simple and clear carbon management zoning evaluation standard that can effectively reduce emissions and increase sinks at the application level.

The Fenhe River Basin plays a crucial role in the ecological and economic system of the Yellow River Basin, China. In addition, the Fenhe River Basin is located in the central part of Shanxi Province and is an important energy base in China. The long-term economic development model dominated by energy consumption has resulted in a significant amount of carbon emissions. However, there is currently relatively little research on carbon balance in the Fenhe River Basin. Therefore, conducting carbon management zoning research here has high representativeness for achieving regional carbon balance and exploring carbon management strategies for resource-based regions.

In summary, due to the lack of energy consumption data, carbon budget and revenue accounting at the county level remain challenging. Although nighttime light remote sensing data can simulate county-level carbon emissions, there is still room for improvement in terms of data stability and model accuracy. Furthermore, existing carbon management zoning methods face issues such as complex indicator systems and difficulties in implementation. Therefore, the main objectives are as follows: (1) establish a carbon budgeting and accounting method based on energy consumption per unit of GDP and analyze the spatial and temporal variation characteristics of the carbon budget at the county level; (2) construct a clear carbon management zoning indicator system from three aspects: socio-economic, resource utilization, and the ecological environment and conduct comprehensive carbon management zoning; (3) propose corresponding low-carbon development strategies for different types of carbon management. The research results can provide reference for achieving “dual carbon” strategic goals, guiding low-carbon development, and improving the carbon management mechanism in the Fenhe River Basin. In addition, it can also provide reference experience for other similar studies.

Methodology

Study area

The Fenhe River has the largest natural runoff in Shanxi Province (Fig. 1) and is also the second largest tributary of the Yellow River. The principal stream is 716 km long, with a total drainage area of 3.9 × 104 km2, accounting for about one-quarter of the province’s area, involving more than 40 counties (cities, districts) with a residential population of 13.15 million and accounting for 38.76% of the province’s population and a regional GDP of about 50% of the province. The Fenhe River Basin accounts for one-quarter of the province’s area, supporting more than one-third of the province’s population and contributing nearly half the province’s GDP. As an important ecological function area, a densely populated area, a main grain and cotton production area, and an economically developed area in Shanxi Province, the Fenhe River Basin occupies an important position in the economic and social development of Shanxi Province and has also become an important location for Shanxi Province to achieve the “double carbon” goal. It is of great significance to evaluate and analyze the evolution of the spatial and temporal pattern of the carbon budget in the Fenhe River Basin and to reconcile the carbon balance relationships among different counties and cities within the basin to achieve the “dual carbon” goals in the basin.

Fig. 1
figure 1

Location and counties of the Fenhe River basin.

Full size image

Data source

The main data used for this research included socio-economic, energy consumption, land-use, and night-time light remote-sensing data. The socio-economic and energy consumption data were obtained from provincial municipal statistical yearbooks, urban yearbooks, county statistical yearbooks, and the “China Energy Statistical Yearbook” (http://www.stats.gov.cn/). Land-use data were sourced from the resource and environmental science data center of the Chinese Academy of Sciences (https://www.resdc.cn/), and six land-use types were derived, including farmland, forest land, water area, grassland, built-up land, and unused land.

Methods

According to the land-use status classification, the regional land-use carbon budget is divided into two aspects: carbon emissions of economic and social activities, and carbon sinks in the natural ecosystem. The carbon emission process mainly considers energy consumption and human and livestock respiration. The natural ecosystem mainly uses cultivated land, forest land, grassland, wetland, and unused land as carbon sinks. The total carbon budget comes from the difference between carbon source emissions and carbon sink absorption.

Carbon emissions

The carbon emissions from energy consumption were calculated as follows:

$${E_i}={K_i}\alpha ,$$
(1)
$${K_i}={\varepsilon _j} \times GD{P_i},$$
(2)

where Ei is the carbon emissions from energy consumption in county i, Ki is the energy consumption of county i, \(\alpha\)is the carbon emission coefficient, and \({\varepsilon _j}\) is the standard energy consumption per unit of GDP, calculated based on provincial-level data. All energy consumption was converted into standard coal consumption, and \(\alpha\) represents the carbon emission coefficient of standard coal, which was set to 0.7559 t/t8.

The calculation formula for carbon emissions from human and livestock respiration is:

$${M_i}=\sum {P_i} \times {\theta _i},$$
(3)

where Mi is the carbon emissions from human and livestock respiration in county i, Pi is the number of people and livestock living in county i, and \({\theta _i}\) is the annual carbon emissions of each person or animal. Two main types of livestock were considered, cattle and pigs, and \({\theta _i}\) for each person or animal was set to 0.079t/a, 0.796t/a, and 0.082t /a32.

Carbon sinks

The calculation formula for carbon absorption of various types of carbon sinks is:

$${N_i}=\sum {L_{ij}} \times {\delta _j},$$
(4)

where \({N_i}\) is the carbon absorption of county i, \({L_{ij}}\) is the area of land-use type j in county i, and \({\delta _j}\) is the carbon emissions coefficient of land-use type j, including farmland, grassland, forest land, and water area and was set to -0.13t/(ha·a)33, -0.298t/(ha·a)34, -5.77t/(ha·a)35, and − 0.022t/(ha·a)36 respectively.

The calculation formula for the total carbon budget is:

$${C_i}={E_i}+{M_i}+{N_i},$$
(5)

where \({C_i}\) is the total amount of the carbon budget in county i.

Using the equidistant method, taking 40%, 80%, 120%, and 160% of the average carbon emissions and absorption in each year as a standard, carbon emissions and absorption were divided into five levels: very low, low, medium, high, and very high.

Index system

To form a spatial pattern of carbon management in which the regional population, economy, resources, and environment are coordinated, the carbon management types of the river basin districts and counties were categorized. Based on existing studies8,27,37, the index system was established by comprehensively considering the strength of social and economic development, the consumption of natural resources, and the protection of the ecological environment, as shown in Table 1. Data in 2023 were collected and employed to conduct the carbon management zoning.

Table 1 Index system of carbon management type zoning.
Full size table

Entropy weight method

The raw data were first normalized as follows:

$${\text{Positive}}:x_{{ij}}^{\prime }=0.99 \times \frac{{{x_{ij}} - {\text{min}}\left( {{x_{ij}}} \right)}}{{\hbox{max} \left( {{x_{ij}}} \right) - {\text{min}}\left( {{x_{ij}}} \right)}}+0.01,$$
(6)
$${\text{Negative}}:~x_{{ij}}^{\prime }=0.99 \times \frac{{{\text{max}}\left( {{x_{ij}}} \right) - {x_{ij}}}}{{\hbox{max} \left( {{x_{ij}}} \right) - {\text{min}}\left( {{x_{ij}}} \right)}}+0.01,$$
(7)

where \(x_{{ij}}^{\prime }\) is the normalized value of each indicator, \({x_{ij}}\) is the value of indicator i in city j, and \(\hbox{max} \left( {{x_{ij}}} \right)\) and \({\text{min}}\left( {{x_{ij}}} \right)\) are the maximum and minimum values of indicator i.

Entropy theory was used to obtain the weight of each indicator38 as follows:

$${H_j}= - \frac{1}{{lnm}}\mathop \sum \limits_{{i=1}}^{m} {P_{ij}}\ln \left( {{P_{ij}}} \right),~\;\;\;~\left( {{e_j} \in \left[ {0,1} \right]} \right)$$
(8)
$${W_j}=\frac{{1 - {H_j}}}{{n - \mathop \sum \nolimits_{{j=1}}^{m} {H_j}}}~,$$
(9)

where m is the number of counties, n is the number of indicators, Pij represents the portion of indicator j in county i, and Wj is the weight of indicator j.

The composite index of each subsystem (including the socio-economic index, SEI, the resource utilization index, RUI, and the ecological environment index, EEI) is calculated as follows:

$$F=\mathop \sum \limits_{{j=1}}^{n} {W_j}x_{{ij}}^{\prime },$$
(10)

where F represents SEI, RUI, or EEI respectively when employing different indicators in each subsystem.

Three-dimensional space model

Based on the values of each index, the natural breakpoint method was used to divide the SEI, RUI, and EEI into four grades respectively that represent the development status of different counties (Table 2).

Table 2 Value settings of different grades for SEI, RUI, and EEI.
Full size table

This study constructed a three-dimensional space for carbon management zoning. RUI, EEI, and SEI represent (x, y ,z) in three-dimensional space, respectively. Because each index has four grades, it will form a 4 × 4 × 4 three-dimensional space, including 64 units (Fig. 2). Each unit represents the performance of a different county in the three subsystems. According to the rules in Table 3, different carbon management types were categorized.

Fig. 2
figure 2

Three-dimensional space for carbon management zoning.

Full size image
Table 3 Classification criteria for carbon comprehensive management types.
Full size table

Results

Spatial and temporal characteristics of the carbon budget

Overall characteristics of the carbon budget

As shown in Table 4, carbon emissions in the Fenhe River Basin showed a significant increasing trend from 2000 to 2023, and total carbon emissions increased from 2489.74t to 9967.61t, giving a net increase of 7477.87t. From the perspective of carbon emissions proportion, carbon emissions from energy consumption were the main source of carbon emissions in the research area, accounting for over 93% in each year. The proportion of carbon emissions from human and livestock respiration decreased from 6.77% to 1.91% during this period. Although the population has increased by 18.03%, the significant increase in energy consumption brought about by rapid economic development is the main reason for the increase in carbon emissions in the Fenhe River Basin during this period.

Table 4 Carbon emissions, carbon absorption, and proportions in each study area in 2000, 2005, 2010, 2018, and 2023.
Full size table

Compared to 2000, carbon absorption in 2023 increased to 11.08 t (Table 4). Carbon absorption in the remaining years fluctuated slightly but overall showed an increasing trend. In terms of carbon absorption ratio, forest carbon absorption accounted for over 92% in each year and was the main type of carbon absorption. The rapid development of the economy and the rapid expansion of cities were the main characteristics of Fenhe River Basin development during this period. On the one hand, the expansion of built-up land has led to a reduction in areas such as farmland, forest land, and grassland. On the other hand, the policy of returning farmland to forests and grasslands implemented by the Shanxi government has led to an increase in the areas of forest and grassland. As a result, the areas of forest and grassland changed very little, keeping carbon absorption basically stable.

Characteristics of carbon budget at County scale

The carbon emissions in each county (or district) showed a substantial difference. For example, the county with the lowest carbon emissions in 2000 was Lan County, with total carbon emissions of 7.64 × 104 t, and the highest county was Xiaodian District, with total carbon emissions of 246.41 × 104 t, which is 32 times the former amount. In 2023, the county with the lowest carbon emissions was Fenxi County, with total carbon emissions of 29.08 × 104 t, and the county with the highest carbon emissions was again Xiaodian District, with total carbon emissions of 1363.27 × 104 t, or 48 times the former amount. Other years showed similar characteristics, indicating that there were significant differences in economic development and population size among counties (or districts) in the Fenhe River Basin.

As for different carbon emission levels in each year (Fig. 3), nine counties (or districts) were at a high or very high level in 2000, all located around Taiyuan and Linfen City. Their population accounted for 21.95% of the counties in the Fenhe River Basin, but the carbon emissions of the nine counties (or districts) accounted for 58.11% of the total amount in the basin. In 2005, the number of counties with high and very high emissions increased to 13, accounting for 31.71% of the total. Their proportion of carbon emissions had also increased, reaching 66.62%. Compared to 2005, the number of counties with high and very high emissions decreased to 12 in 2010, and their proportion of total carbon emissions also decreased to 65.82%. In 2018 and 2023, the number of counties with high and very high emissions was basically stable, at 11 and 12 respectively, and accounted for 64.68% and 67.02% of total carbon emissions in the basin.

Fig. 3
figure 3

Spatial and temporal distribution of carbon emissions in the Fenhe River Basin from 2000 to 2023.

Full size image

Carbon absorption in each county (or district) also showed huge differences. Houma County had the lowest amount of carbon absorption, and the changes in each year were not significant, with the average carbon absorption remaining at 0.29 × 104 t. The county with the highest carbon absorption was Jiaocheng County, where average carbon absorption reached 73.59 × 104 t, which was nearly 256 times that of Houma County.

As shown in Fig. 4, the spatial pattern of carbon absorption in the Fenhe River Basin was basically consistent in the study period. There were 14 counties at the high and very high carbon absorption level, including Shouyang, Wenshui, Jiangxian, Yicheng, Jingle, Guxian, Lanxian, Loufan, Gujiao, Jiaokou, Ningwu, Yangqu, Xiangning, and Jiaocheng counties, with an average proportion of 70.42% of total carbon absorption. These 14 counties are mainly located in key ecological function protection areas in the Fenhe River Basin, including water conservation areas and national and provincial nature reserves, all of which have high forest coverage rates.

Fig. 4
figure 4

Spatial and temporal distribution of carbon absorption in the Fenhe River Basin in 2000 and 2023.

Full size image

From the perspective of net carbon emissions, as the economy and population gradually increased, the net carbon emissions of each county also showed a significantly increasing trend. Due to the lack of significant changes in carbon absorption in each county during the research period, during which carbon emissions increased significantly, the spatial pattern of net carbon emissions in each county is basically consistent with the spatial pattern of carbon emissions (Fig. 3). In 2000, 12 counties were designated as net carbon-absorbing counties where carbon absorption exceeded carbon emissions, accounting for 29.27% of all counties. The remaining counties were net carbon-emitting counties, accounting for 70.73% of total emissions. In 2005 and 2010, the number of net carbon-absorbing counties decreased to six, accounting for 14.63% of all counties. By 2018, the number of net carbon-absorbing counties had decreased further to only two, and in 2023, all counties had become net carbon-emitting counties (Fig. 5).

Fig. 5
figure 5

Number of net carbon-absorbing (NCB) and net carbon-emitting (NCE) counties from 2000 to 2023.

Full size image

Carbon management zoning

Measurement of carbon management indicators

Based on the index system and calculation method described above, SEI, RUI, and EEI were calculated to represent the development status of each county in the socio-economic subsystem, resource utilization subsystem, and ecological environmental subsystem respectively (Fig. 6). From the perspective of socio-economic subsystems, there were significant differences in SEI values between counties (districts). Yingze, Xinghualing, and Xiaodian had relatively higher SEI values and were all located around Taiyuan City, which is the largest city in the basin. The per capita GDP, population density, economic density, and urbanization rate of these counties (districts) were all higher than the others, indicating higher performance in socio-economic coupling. However, higher population density and economic density entail a more obvious contradiction between humans and the environment and more carbon emissions, which should be attended to and optimized.

Fig. 6
figure 6

Result of the subsystem index for each county.

Full size image

From the perspective of resource utilization subsystems, Jingle, Guxian, Fushan, Loufan, and Lanxian had relatively higher RUI values. The RUI values in counties such as Jinyuan, Jiancaoping, and Xiaoyi were relatively low, indicating a relatively low degree of land resource utilization and the absence of either reasonable or excessive development of land resources. Unreasonable land expansion and occupation of ecological land caused by rapid economic development were the main problems preventing sustainable use of land resources. As for the ecological environmental subsystem, Loufan and Jingle had the highest EEI values, followed by Ningwu and Yangqu. The RUI values of other counties (districts) were all less than 0.5, indicating a smaller forest and grassland area with more carbon emissions caused by socio-economic development. This phenomenon indicates that the carbon absorption of ecological land in most counties in the Fenhe River Basin does not have a significant neutralizing effect on carbon emissions.

Result of carbon management zoning and development strategy

According to the different grades of SEI, RUI, and EEI and the classification criteria of carbon management zoning (Table 3), a three-dimensional space was generated for each county in the Fenhe River Basin (Fig. 7). When the three-dimensional coordinates (x, y,z) of the elements were all less than 2, the county was assigned to the first level of zoning, which was called the carbon emissions control area. Twenty counties were identified as being in the control area, including Hejin, Xinjiang, Jinyuan, Qingxu, and others (Fig. 8). The socio-economic development, resource utilization, and ecological environment of these counties were all lagging behind, and regional economic development and the ecological environment were in a low-level coupled state. Therefore, these counties were further designated as a comprehensive restricted area for the subtype. The future development strategy is to revise the energy structure, advance substitution by clean energy sources, and enhance the development and utilization of renewable energies such as solar and wind power. The strategy also includes encouraging clean and efficient utilization of coal, enhancing coal utilization efficiency, decreasing coal consumption, and mitigating pollutant emissions. Leveraging local resource advantages and industrial foundations, actively fostering and expanding emerging industries to establish new economic growth hubs, and diminishing the prevalence of high-energy consumption and high-pollution industries are also part of the plan. Additional initiatives aim to bolster carbon sequestration capacity, reinforce ecosystem protection and restoration efforts, safeguard forests, wetlands, rivers, and other ecosystems, regulate forest harvesting rigorously, intensify afforestation and wetland rehabilitation, and augment the carbon sequestration potential of ecosystems.

Fig. 7
figure 7

Three-dimensional unit distribution of carbon management zones of counties in the Fenhe River Basin.

Full size image

When a county had at least one category with a level of 3 in the spatial unit coordinates (x, y, z), but the levels of the other category elements were less than 3, the county was classified as being in an optimization area, which included 9 counties (Fig. 8). This indicated that at least one of the three subsystems was tending towards the level of comparative advantage. According to the index value of different subsystems, these counties were further divided into three subtypes: high socio-economic development areas, high resource utilization areas, and high ecological environment areas. The high socio-economic development area included Xiaodian and Jiancaoping, the high resource utilization area included Xiangfen and Wanrong, and the high ecological environment area included Jiangxian, Xiangning, Yicheng, Jiaocheng, and Shouyang. Although these counties had advantages in one aspect, they were not significant. The development strategy of the optimization area is to further develop the advantages and reconcile the contradictions in the other two aspects while developing. Taking the high socio-economic development area as an example, the strategy is to facilitate the modernization of conventional sectors by integrating cutting-edge and advanced technologies to elevate them to high-end, intelligent, and environmentally sustainable levels. This includes fostering and fortifying nascent industries to generate industrial clustering effects and propel the industrial landscape towards high-end and low-carbonization industries. Further plans aim to enhance the modern service sector by leveraging our strategic geographical and resource advantages to elevate its contribution to the national economy.

Fig. 8
figure 8

Carbon management type and subtype zoning in the Fenhe River Basin.

Full size image

The remaining counties were assigned to the emissions reduction area, where there was at least one category with a level of 4 in the spatial unit coordinates. This indicated that at least one of the three subsystems in the unit was at the absolute advantage level. Similarly, according to different advantages, the emissions reduction area was further divided into three subtypes: the high socio-economic development area, the high resource utilization area, and the high ecological environment area. For the high socio-economic development area, including Yinze and Xinghualing, economic and social development was in an advantageous position over the whole region. However, the ecological and resource utilization levels were low. The future development strategy mainly focuses on implementing a circular economy system, which is essential to promote efficient resource utilization and minimize waste generation. Enhancing waste management through improved garbage classification, recovery, and comprehensive utilization is crucial. This includes establishing a robust waste collection, transportation, and treatment system to enhance resource recovery rates. Moreover, prioritizing ecosystem protection by safeguarding forests, rivers, lakes, and other ecosystems is vital to enhance carbon sequestration capacity. Urban greening initiatives should be intensified to expand green spaces, enhance urban green coverage, and improve the overall urban ecological environment. The high resource utilization area included Jingle, Loufan, Fushan, Guxian, Fenxi, and Lanxian. The cultivated land resources in these counties were of extremely high quality, and the built-up land had not been overexploited. Moreover, the ecological environment level was everywhere greater than 3, indicating that carbon sinks such as forest land, grassland, and water areas were in a relatively advantageous position. In the future, these counties should avoid imbalance between built-up and agricultural land caused by excessive development and focus on improving the supply of agricultural products, enhancing comprehensive agricultural production capacity, vigorously developing modern agriculture, strictly controlling development intensity, and avoid gradually reducing land occupation by rural residential areas. The high ecological environment area included Ningwu, Yangqu, Gujiao, and Jiaokou. These counties were characterized by a good natural ecological background and relatively reasonable resource utilization, but social and economic development was lagging behind other counties. The development strategy for these counties is to strictly abide by the ecological bottom line, change the ecological advantages into industrial advantages according to local conditions, and promote the green development of the social economy. The entry of highly energy-consuming and highly polluting industries should be severely restricted, and resource advantages should be utilized to develop ecotourism.

Discussion

In the study area, carbon emissions surged from 2484.74 × 104t to 9967.61 × 104t from 2022 to 2023, marking a fourfold increase. Population growth minimally impacted carbon emissions, with the primary driver of this surge being the substantial rise in energy consumption. The prevalence of heavy industries reliant on coal and chemicals in the region resulted in significant energy usage, fueling the spike in carbon emissions alongside economic expansion39. To foster low-carbon development in the future, it is imperative to optimize industrial structures and enhance energy efficiency. Throughout the study period, the carbon sequestration capacity of the Fenhe River Basin remained constant. This stability can be attributed to preservation of forest and grassland areas, which serve as primary carbon sinks and have remained largely unchanged due to the implementation of various ecological conservation policies.

Carbon budget accounting is crucial to assess regional carbon emissions and devising regional low-carbon development strategies. Among the various methods available, carbon budget accounting based on land use is the most widely used25,40. However, this approach is constrained by challenges related to the availability of energy consumption data and estimation methodologies. Several studies have used night light remote-sensing data to estimate carbon emissions from energy consumption41,42,43,44. However, this method encounters issues such as intricate data processing and the need for enhanced simulation accuracy45. Given the strong association between energy consumption and economic progress, this study chose to use energy consumption per unit of GDP as a proxy for estimating carbon emissions. Limited by technological level, energy consumption per unit GDP remained stable over the study period. Consequently, this approach offers a more precise estimation of energy consumption at the current economic development stage, thereby facilitating a more accurate assessment of carbon emissions stemming from energy use. Nonetheless, one drawback of this method is its failure to account for variations among different regions during the estimation process. Therefore, this method is only applicable to regions with small spatial differences in industrial structure. In the future, comparative research can be carried out on methods of estimating carbon emissions from energy consumption, and more accurate and effective estimation methods must be developed that can provide basic support for regional carbon budget accounting and carbon management zoning.

Carbon management zoning is essential for developing regional low-carbon strategies. This study establishes an index system based on social economic development, resource utilization, and the ecological environment. Taking the Fenhe River Basin as a case study, a three-dimensional spatial model was introduced to assess the level of low-carbon development across different counties for more accurate results. However, due to limitations in county-level data availability, there is still potential for enhancing the constructed index system. Future research should consider regional resource capacity, carbon emission intensity, economic development status, and carbon pricing to create a more comprehensive and practical evaluation system for carbon management zoning in regional development. This can establish the groundwork for enhancing the carbon compensation framework, exploring diverse pathways to achieve dual carbon goals, and realizing coordinated regional carbon emissions reduction objectives.

Conclusion

Using a land-use carbon budget accounting framework, this study examined the spatial and temporal dynamics of carbon budgets and the interplay of carbon budget balances in the Fenhe River Basin at county level. Furthermore, an evaluation index system was used to delineate carbon management zones within the basin. The results show that:

  1. (1)

    From 2000 to 2023, carbon emissions in the Fenhe River Basin increased significantly, with a net increase of 7477.87 tons, among which carbon emissions from energy consumption predominated. The spatial distribution of carbon emissions varied considerably, with the maximum and minimum emissions differing by a factor of nearly 32. The spatial distribution of carbon emissions was basically consistent with the economic pattern.

  2. (2)

    Under the influence of vegetation spatial pattern, carbon absorption in the Fenhe River Basin remained stable from 2000 to 2023, and forest absorption was dominant. The spatial distribution of carbon absorption varied greatly, and the proportion of carbon absorption in 14 counties with very high carbon absorption levels reached 70.42%. Such counties were mainly ecological functional areas or nature reserves.

  3. (3)

    The coupling development effect among the three subsystems of social economy, resource utilization, and the ecological environment was found to be weak in every county. Counties with higher levels of social and economic development tended to exhibit lower scores in the ecological environmental subsystem, whereas counties with higher ecological environmental scores tended to show slower progress in social and economic development.

  4. (4)

    The counties within the Fenhe River Basin were categorized into three types: emissions reduction areas, optimization areas, and control areas. Each type was further subdivided into four subtypes: a comprehensive restricted area, a high socio-economic development area, a high resource utilization area, and a high ecological environment zone, based on the distinct characteristics of the three subsystems. Corresponding development strategies were then proposed for each type to provide a theoretical underpinning for achieving low-carbon county development.

As an exploratory study, this study only divides carbon management types into zones, without considering the carbon compensation relationships between different regions or the value standards for calculating carbon compensation in each region. In the future, regional resource capacity, carbon emission intensity, economic development conditions, and carbon prices should be considered from multiple perspectives. These will be explored and improved in subsequent research to improve the carbon management system, further explore diversified paths to achieve the “dual carbon” goals, and achieve regional coordinated carbon reduction development goals.