Spatiotemporal patterns of carbon emissions and carbon balance zoning in Inner Mongolia based on land use change

Under the background of “dual carbon”, Taking the Inner Mongolia Autonomous Region as the research area, based on land use data and local economic data, were used to measure the land use change and carbon emission of the research area from 2000 to 2024, The carbon balance of various cities in Inner Mongolia is zoned and to provide a basis and support for the optimization of territorial space. [Result](1) During the period, the land use dynamics of forest land, construction land and wetland increased, while the changes in the areas of farmland, grassland, water area and unused land decreased. (2) The total carbon emissions of construction land and cultivated land in Inner Mongolia showed an upward trend. The total amount of carbon absorption rose slightly. (3) The total net carbon emissions in 2024 increase by approximately 133% compared to 2000. In terms of carbon budget balance, Hulunbuir City was in a state of carbon balance from 2000 to 2015, but after 2015, all prefecture-level cities have been in an unbalanced state where carbon emissions exceed carbon absorption. (4) The economic contribution rate of carbon emissions in various cities of Inner Mongolia shows significant differences. The cities with an ecological carrying coefficient greater than 1 are Alxa League and Hulun Buir City. (5) Based on the carbon balance analysis, Inner Mongolia are divided into low-carbon development zones, low-carbon economic zones, low-carbon maintenance zones, economic development zones, carbon intensity control zones, and high-carbon optimization zones. The value of this research demonstrates that arid and semi-arid regions can achieve the synergy of ecological protection, economic development, and carbon reduction through land-use optimization. It provides a scientific approach for similar regions to resolve the conflict between development and emission reduction, and it is of great significance for the implementation of the national “dual-carbon” strategy, the construction of ecological security barriers in northern China, and the study of carbon cycles in global arid areas.

1 Introduction

CO₂ emitted from human activities is a key component of greenhouse gases, significantly affecting Earth’s thermal balance. It absorbs and re-emits long-wave radiation from the Earth’s surface. Rising CO₂ levels trap solar radiation, heating the atmosphere and altering global heat distribution. This disrupts ecological balance and climate stability, posing a critical challenge for human production, daily life, and environmental sustainability. For years, countries around the world have recognized the dangers posed by greenhouse gas emissions and have actively worked to control and reduce CO₂ and other greenhouse gases, aiming to mitigate the threat of extreme climate change to global socio-economic development. China’s announcement of its “dual carbon” goals at the 75th UN General Assembly in September 2020 represents a major strategic decision made by this developing nation to balance domestic and international priorities. This commitment not only reflects China’s comprehensive approach to ecological civilization but also demonstrates its systematic planning for overall economic development.

Research on the carbon effects of human production and life has become a hot research area globally for balanced carbon system rescue actions. Foreign scholars developed models for simulating the Earth’s climate physical system, quantifying variations, and predicting global warming caused by carbon dioxide (Hasselmann and Hasselmann, 1991; Manabe et al., 1970). Foreign scholars point out that land use is one of the main human activities causing greenhouse gas emissions (Houghton 2003). Accounts for 30% of the total carbon emissions from human activities worldwide (Houghton and Hackler, 1999). The changes in land use and land management have a significant impact on the loss of terrestrial carbon sinks (Winkler et al., 2023). Chinese scholars reveal that greenhouse gas emissions from land-use change rank as the second largest source of CO₂ emissions, surpassed only by emissions from energy combustion in production and daily activities (Zhang Z. Q. et al., 2022) and represents a significant contributor to global warming (Zhang et al., 2023). Rational land-use planning and management can reduce carbon emissions by 60%–70% (Liu et al., 2022). Domestic and international scholars tend to have consistent results regarding the impact of land use on carbon emissions. Houghton used the “bookkeeping” model to analyze the net carbon flux caused by land use from 1850 to 1990, laying a foundation for subsequent research on carbon emissions from land use (Houghton 1999). Brown and Lugo proposed the plot inventory method, which estimates carbon stocks and carbon emissions in a specific region by setting up sample plots and conducting detailed surveys on them (Brown and Lugo, 1984). Domestic scholars quantification of carbon emissions from land use across different regions (Lv et al., 2023; Guo and Liang, 2022); temporal-spatial distribution patterns and influencing factor analysis (Sun et al., 2025; Wu et al., 2025). The carbon emission coefficient method has become the predominant accounting approach due to its advantages in data accessibility and methodological maturity (Sun et al., 2015). In the early stage, based on remote sensing data, the CLUE-Nuts model was applied to model the changes in the area of crops and natural vegetation in Ecuador, and the impacts of these changes on soil nutrients and carbon status were evaluated (Priess et al., 2001). Nighttime light data has been integrated with provincial-level energy consumption statistics to enhance the accuracy of carbon emission estimations (Lv and Liu 2020). With the deepening of research, analyzing the driving factors of land use-related carbon emissions has gradually become a key focus of studies, The Century model proposes that, from a natural factor perspective, climate change affects the carbon balance of terrestrial ecosystems in multiple aspects (Parton et al., 1993). Regarding socioeconomic factors, scholars have analyzed the influencing factors of carbon emissions from dimensions such as economy, population, energy, and land use structure using a variety of models (Dietz and Rosa, 1994). Scholars have employed Geographically Weighted Regression (GWR) and other methods based on spatiotemporal heterogeneity characteristics of carbon emissions to investigate the underlying driving mechanisms (Zhao et al., 2024; Zhan et al., 2023; He and Yang 2023). Geodetector (Zhang et al., 2025) and LMDI (Huang et al., 2023), For analyzing influencing factors of carbon emissions, the LMDI model has been widely adopted for quantifying emission drivers. Second, the application of Tapio decoupling model (Shan et al., 2024) and OECD decoupling (Zhang C. Y. et al., 2022) framework allows for examining the decoupling relationship between land-use carbon emissions and economic growth, which can provide evidence-based support for formulating regional carbon reduction policies. Third, future carbon emission trends are projected through the application of the LEAP model (Li et al., 2025) and grey prediction methods (Bai et al., 2025) to simulate the evolution of land-use related emissions. The accuracy of land use carbon emission simulation models has been continuously improved, and such models have gradually evolved toward comprehensive models (Zhou et al., 2021). Unfolding along the logical chain of current situation assessment-impact mechanism - policy linkage future prediction, it covers the entire process of land use carbon emission research, reflecting the academic research progression of from description to explanation, from present status to future, and aligns with the research paradigm in this field. It highlights the practical orientation of the research, aligning with the policy demands under the current “dual carbon” targets, and avoids the disconnect associated with purely theoretical studies.

The Inner Mongolia region, with its vast territory and extensive grasslands and forests, possesses significant carbon absorb potential, serving as a crucial ecological security barrier. In recent years, the ongoing implementation of the “Three-North Shelterbelt” Development Program has enhanced regional greening and carbon absorb. Particularly in combating Inner Mongolia’s four major deserts and sandy lands, integrated measures like straw checkerboard barriers, shrub planting, large-scale ecological afforestation, and the Grain-for-Green Program have significantly improved vegetation coverage. The regions unique natural ecology and energy structure have collectively boosted the annual carbon absorb capacity of grassland, forest, and wetland ecosystems, providing solid support for ecological value transformation and green low-carbon development. Although geographically classified within North China, Inner Mongolia’s elongated terrain spans China’s northeast, north, and northwest, bordering multiple provinces. Its substantial carbon sink capacity can offset overestimated emission responsibilities for neighboring regions. Consequently, carbon emissions from land-use changes have become a critical factor in restructuring low-carbon-oriented territorial spatial planning. In recent years, Inner Mongolia’s economic development has been accompanied by growing demand for construction land, particularly evident in the urban integration processes of the Hubao-Eyu urban agglomeration and the Hubao-Ewu energy-resource-dependent urban agglomerations. The expansion of built-up areas coincides with intensified intercity economic linkages and industrial structure homogenization, predominantly reliant on energy-intensive sectors. This has resulted in a regional economic profile characterized by heavy industrialization and a high-carbon energy mix. Existing studies have analyzed the spatiotemporal patterns and driving factors of land-use carbon emissions at the county level in Inner Mongolia (Wu et al., 2025). There is a notable lack of carbon balance zoning schemes proposed based on land-use carbon emissions. As a drought and semi-arid region, the Inner Mongolia area needs to focus on the impact of grassland degradation on carbon sinks, the expansion of construction land, and the carbon sink potential of unused land, among other specific issues. Therefore, this study utilizes land-use data at the provincial and prefectural levels in Inner Mongolia Autonomous Region to quantify spatiotemporal emission patterns through carbon emission coefficient methods. Building on this foundation, we assess the economic contribution rate of emissions and the ecological carbon absorption index by integrating. Since the beginning of the new century, the area of urban built-up districts in Inner Mongolia has expanded rapidly, and the area of construction land has grown at a fast pace. In 2013, the area of urban built-up districts was double that of 2000. The acceleration of urban scale expansion ceased in 2014, which marks the beginning of a period when Inner Mongolia’s urbanization shifted from scale expansion to functional improvement. The land use pattern has generally experienced the characteristics of “early stability - mid-term rapid change - late stability”. The selection of the study period (2000–2024) can well reflect the land use changes and carbon emission effects in the study area.

2 Study area overview

The Inner Mongolia Autonomous Region is located in northern China (Figure 1), with a terrain that slopes from northeast to southwest, presenting an elongated shape. It spans a total area of 1.183 million square kilometers, accounting for approximately 12.32% of China’s total land area. Spanning China’s northeast, northwest, and north China regions, it borders Mongolia and Russia to the north, Ningxia, Shaanxi, Shanxi, and Hebei to the south, Gansu Province to the west, and Heilongjiang and Jilin Provinces to the east. The region is predominantly characterized by the Mongolian Plateau, featuring complex and diverse land forms. Except for the southeastern portion, most of the territory consists of high plains, with deserts distributed in the western extremity. The central and eastern areas, comprising relatively small plains, serve as the primary zones for grain and cash crop production in Inner Mongolia. Inner Mongolia is endowed with abundant natural resources, epitomized by the regional descriptor “forests in the east, minerals in the west, agriculture in the south, and pastoralism in the north”. It belongs to the typical agro-pastoral transitional zone in northern China. Land use shows the characteristic of alternating conversion between farmland and grassland. The climate is mainly temperate continental, with a large annual temperature range and decreasing precipitation from east to west. It ranks first nationally in grassland coverage, forest area, and per capita arable land. According to 2024 remote sensing data on land use, the regions land cover distribution is as follows (Figure 2):cropland (12.07%), forest (15.7%), grassland (47.34%), water bodies (0.47%), construction land (1.02%), unused land (23.39%), and wetlands (0.01%). Grasslands occupy the largest proportion and are an important foundation for developing animal husbandry; an economy based on the development of animal husbandry has become the main economy of Inner Mongolia.

Figure 1

Figure 1. Location map of inner Mongolia autonomous region. (a) Location map of Inner Mongolia. (b) Distribution map of cities in Inner Mongolia. (c) Elevation map of Inner Mongolia.

Figure 2

Figure 2. Land use in inner Mongolia from 2000 to 2024.

3 Materials and methods

3.1 Materials

The land-use data in this study were sourced from the Annual China Land Cover Dataset (CLCD) developed by Prof. Huang’s team at Wuhan University. This dataset, derived from Landsat imagery, provides continuous land cover classifications from 2000 to 2024 at 30-m spatial resolution (Yang and Huang 2021). The land-use data for the years 2000, 2005, 2010, 2015, 2020, and 2024 were processed using Arc GIS 10.3, involving extraction, clipping, and reclassification operations. Seven land cover types were identified: cropland, forest, grassland, water bodies, wetlands, construction land, and unused land. The data type is remote raster data.

The basic geographic data mainly include: the provincial boundary of the Inner Mongolia Autonomous Region and the administrative division boundaries of leagues and cities within its jurisdiction are sourced from the 1:4million National Fundamental Geographic Information Database of the National Geographic Information Resource Directory Service System. The data type is vector data. The Digital Elevation Model (DEM) data is obtained from the Geospatial Data Cloud. The data type is raster data.

The socio-economic data are sourced from the China City Statistical Yearbook, Inner Mongolia Statistical Yearbook, as well as league/city statistical yearbooks and statistical bulletins. The data type is Panel data.

Data serves as the core factual basis for supporting research conclusions. By obtaining land use spatial data through remote sensing image interpretation and systematically collecting land use change survey data, all derived from objective data rather than subjective inference. It involves demonstrating the rationality of the study period selection, so as to avoid lopsided in the study period setting caused by data gaps. It lays the groundwork for subsequent research by ensuring the spatiotemporal consistency of data, avoiding errors caused by data mismatches in subsequent analyses, and enhancing the credibility of the research on the correlation between land use changes and carbon emissions.

3.2 Methods

3.2.1 Land use dynamic degree

The Land Use Dynamic Degree (LUDD) model provides a quantitative assessment of both the magnitude and rate of land use changes across various categories within a given region. In essence, it converts the dynamic process of land use into comparable and quantifiable rate values through the area change data over the time dimension. Key interpretation of LUDD values:A higher absolute LUDD value signifies more pronounced transformation of the specific land use type into other categories. Conversely, a lower absolute value indicates greater stability of the land use type, suggesting minimal changes occurred during the study period. The computational formula for the Single Land Use Dynamic Degree is expressed as:

$K_i=\frac{U_b-U_a}{U_a}\times \frac{1}{T}\times 100\%$

The formula is defined as: K_i represents Land Use Dynamic Degree Methods, U_a and U_b represents the dynamic degree index of a specific land-use type during the study period (km²). T represents the study period.

3.2.2 Land-use carbon emission estimation

The estimation was conducted using the carbon emission coefficient method. Its core logic can be summarized as follows: different land use types have different carbon metabolism functions due to differences in coverage characteristics and the intensity of human activities. The key to measurement is classification quantification and systematic summation, avoiding the one-sidedness of focusing on a single type or a single process. Where the area of each land use type was multiplied by its corresponding carbon emission coefficient (Table 1), and the results were summed to derive the total carbon emissions for each land category. The calculation formula is expressed as (Chen et al., 2024):

$C_e=\sum e_i=\sum a_i\times {\alpha }_i$

Table 1

Table 1. Carbon emission coefficients of different land use types.

The formula is defined as: Ce represents Total carbon budget, e_i Carbon emissions from land type“i”, The parameter a_i represents the carbon emission coefficient for the i-th land use category. The parameter α_i represents the areal extent of the i-th land use category. Cropland exhibits dual carbon source-sink characteristics through agricultural activities: while crop photosynthesis absorbs atmospheric CO₂, subsequent plant respiration releases it back into the atmosphere over short cycles, resulting in limited net carbon absorb. Therefore, this study adopts a net carbon emission coefficient for cropland (calculated as gross emission coefficient minus sink coefficient), ultimately treating cropland as a carbon source in the accounting framework.

3.2.3 Land-use carbon emission estimation

This study identifies forest land, grassland, water bodies, wetlands, and unused land as carbon sinks. The carbon absorption coefficients are shown in Table 2, and the calculation formula is as follows (Chen et al., 2024):

$C_e=\sum e_i=\sum s_i\times {\alpha }_i$

Table 2

Table 2. Carbon sequestration coefficients of different land use types.

The formula is defined as: C_e represents Total carbon budget, e_i represents Carbon absorption from land type“i”. The parameter a_i represents the carbon emission coefficient for the i-th land use category. The parameter α_i represents the areal extent of the i-th land use category.

3.2.4 Measurement of net carbon emissions by land use type

The net carbon emissions of the region are obtained by calculating the difference between carbon emissions and carbon absorption, Carbon emission accounting is not an independent process; it is a comprehensive calculation based on the sub-item calculation of carbon source emissions and the sub-item calculation of carbon sink absorption. The core logic can be summarized as calculate separately first, then aggregate, and finally subtract. Using the following formula (Deng et al., 2024):

$E_e=\left(E_{cons}+E_{crop}\right)-\sum E_i$

The formula is defined as follows: E_e represents the net carbon emissions; E_cons denotes carbon emissions from construction land; E_crop indicates carbon emissions from cropland; E_i is the total carbon absorb (or absorption) from forest land, grassland, water bodies, unused land, and wetlands.

3.2.5 Economic contribution rate of carbon

Economic Contribution Rate of Carbon reflects the impact of regional carbon emissions on economic efficiency. Essentially, it evaluates the dependence and contribution level of regional economic development to carbon emissions by quantifying the proportion of carbon emissions corresponding to unit economic growth. It is a core tool for assessing the synergy between the economy and ecology. The index calculation is based on regional comparison and total association, with the core logic summarized as first calculating the proportion, then calculating the ratio, and it needs to be combined with the target region and reference regions. calculated as (Yang et al., 2022):

$ECC=\frac{G_n/G}{E_n/E}$

The formula is defined as follows: G_n and G represent the GDP of the n th prefecture-level city and Inner Mongolia region; E_n and E denote the carbon emissions of the n th prefecture-level city and Inner Mongolia. ECC > 1 indicates a higher carbon emission economic contribution rate, suggesting greater contribution to economic efficiency. 0 < ECC < 1 implies a lower carbon emission economic contribution rate, reflecting a smaller contribution to economic efficiency.

3.2.6 Carbon absorption ecological carrying coefficient

Measures the proportion of carbon absorption within a study area relative to the total carbon absorb capacity, reflecting the scale of carbon sink potential. By quantifying the carbon absorb capacity in relation to carbon emissions, it is possible to determine whether regional ecosystems can support current and future carbon emission demands. The calculation of this indicator is based on the balance between carbon absorb and carbon emissions. The core logic can be summarized as first calculating supply and demand, and then calculating the ratio, without relying on external reference regions, requiring only the regions own data on carbon absorb and carbon emissions. The calculation formula is as follows (Xia and Yang, 2022):

$ESC=\frac{A_n/A}{E_n/E}$

The formula is defined as follows: A_n and A Carbon absorb of the n-th city and Inner Mongolia, E_n and E Carbon emissions of the n-th city and Inner Mongolia, ESC > 1 indicates a higher carbon absorb carrying coefficient, demonstrating stronger carbon sink capacity in the region, 0<ESC < 1, indicates a lower carbon absorb carrying coefficient, suggesting weaker carbon sink capacity in the region.

4 Results

4.1 Land use dynamic degree

Based on the proportion of different land use types in Inner Mongolia (Table 3), the percentage of each land type relative to the total study area remained relatively stable. However, the area of all land use types changed during the study period (Table 4). During 2000–2024, the land use dynamic degrees of forest land, construction land, and wetlands increased by 0.149%, 5.091%, and 1.097%, respectively. Construction land exhibited the highest land use dynamic degree, the main reason is that Inner Mongolia’s economy is small in the early stage, regional development lags behind, and in the later stage, relying on energy and local characteristic industries to continuously promote economic development, population growth has expanded the requirements for housing, public facilities and infrastructure, and the key areas of industrial expansion need to invest in the construction of factories, warehouses, parks and other construction land demand is increasing, the construction land area base is small, the growth is fast, and the land use dynamics are high. Wetlands, though accounting for a relatively small proportion of the total land area, exhibited significant fluctuations in land use dynamic degree across different periods. This indicates that Inner Mongolia’s wetland conservation efforts have achieved remarkable results, particularly during 2020–2024, when the change rate reached 65.478%. According to the Third National Land Survey, the region now boasts 131 protected wetland sites, including: Internationally Important Wetlands, Nationally Important Wetlands, Autonomous Region-Level Important Wetland, National Wetland Parks, Autonomous Region Wetland Parks. Meanwhile, the area changes in farmland, grassland, water bodies, and unused land were relatively minor, with rates of −0.018%, −0.025%, −0.407%, and −0.123%. This trend reflects: Urban expansion pressures increased demand for construction land due to regional economic growth. Despite urban sprawl, local governments have maintained strict regulatory control over construction land, ensuring the preservation of ecological land reserves.

Table 3

Table 3. Proportion of different land use types in inner Mongolia (2000–2024) (unit: %).

Table 4

Table 4. Land use dynamic degree in inner Mongolia at different stages (2000–2024).

4.2 Spatiotemporal evolution of land-use carbon budget patternes

4.2.1 Temporal variation of land-use carbon budget in inner Mongolia

From 2000 to 2024, the total carbon emissions of construction land and cropland in Inner Mongolia showed an upward trend (Figure 3a), with total carbon emissions increasing from 4.82 × 10⁷t in 2000 to 10.0 × 10⁷t in 2024, an increase of about 108%. Among them, the carbon emissions of construction land increased from 4.24 × 10⁷t in 2000 to 9.42 × 10⁷t in 2024, and the proportion of carbon emissions from construction land in total carbon emissions increased from 89.7% to 94.19%. Cropland carbon emissions showed stable fluctuations during the study period, decreasing from 5.83 × 10⁶t to 5.81 × 10⁶t, with a slight decrease of about 0.34%. The main reason is that urban land expansion is a typical feature in the process of urbanization, and the impact on carbon emissions has a significant two-way effect. Although carbon sink compensation is achieved through the planning of green infrastructure such as vegetation in urban construction, it is difficult to achieve a balance in carbon budget in the face of the reality of large carbon emissions.

Figure 3

Figure 3. Temporal variation of carbon budget in inner Mongolia (2000–2024).

The total carbon absorption in Inner Mongolia increased from 1.21 × 10⁷t in 2000 to 1.24 × 10⁷t in 2024, an increase of about 2.47% (Figure 3b), and the proportion of total carbon absorption in forest land, grassland, water area and unused land in the total carbon absorption during the study period remained basically stable, and the carbon absorption of forest land accounted for the highest proportion of carbon absorption from forest land from 2000 to 2024, followed by grassland, with an average of about 9.76%. The carbon absorption of wetlands is the smallest due to the limited area.

4.2.2 The spatial variation of carbon budget in land use in inner Mongolia

The carbon emission data from land use in Inner Mongolia were imported into Arc GIS 10.3 and classified using the natural breaks method (Figure 4). The results show that carbon emissions across different leagues and cities in Inner Mongolia exhibit a spatial distribution pattern of “high in the east, moderate in the center, and low in the west.” In terms of total carbon emissions: In the early period, the highest emissions were concentrated in Tongliao City in eastern Inner Mongolia, while the lowest emissions were found in Alxa League in the western region; In the later period, the highest-emission zones expanded southward and northward from Tongliao to Hulunbuir City and Chifeng City, which became secondary high-emission areas. Other leagues and cities maintained relatively low emission levels. The total annual average carbon emissions of Tongliao, Chifeng and Hulunbuir cities are 16.4 × 10⁶t, 10.8 × 10⁶t, 8.49 × 10⁶t, and the average carbon emissions during the study period are located in Inner Mongolia Third, the above three areas are located in the eastern part of Inner Mongolia, the terrain is dominated by plains, which lays a good foundation for agricultural development. Typical development areas dominated by agriculture and animal husbandry, and some areas have agricultural and pastoral interlaced zones as semi-agricultural and semi-pastoral areas, with significant agricultural carbon emissions. During the study period, the growth rates of agricultural land area in Tongliao, Chifeng and Hulunbuir were 2.91%, −3.51% and 7.6%, showing the characteristics of large and stable carbon emissions. The growth rate of construction land area was 71.6%, 106.4% and 132.2%, and the carbon emissions showed the characteristics of large volume and rapid growth, and the overall carbon emissions of construction land were higher than those of agricultural land, which was the main carbon source in the region.

Figure 4

Figure 4. Spatial variation of carbon emissions in inner Mongolia (2000–2024).

During the study period, the cities with the fastest growth rates in carbon emissions were Ordos City and Alxa League, with increases of 296% and 207%. However, the cities with rapid emission growth did not coincide with those having the highest absolute emission increments. Alxa League, located in western Inner Mongolia, is characterized by a desert-gobi mosaic landscape. Despite having the largest territorial area in the region, its urban development has been constrained by harsh environmental conditions. The urban construction scale remains limited, and urbanization progresses slowly due to sparse population and insufficient developmental momentum. A significant proportion of its land consists of unused areas. At the beginning of the study period, the baseline area of construction land in Alxa League was relatively small. Although urban expansion during the research phase led to a multiplicative increase in built-up land, the absolute magnitude of this growth remained modest in scale.

The carbon absorb data from land use in Inner Mongolia were imported into ArcGIS 10.3 and classified using the natural breaks method (Figure 5). The results reveal that carbon absorb across different leagues and cities in Inner Mongolia primarily exhibits a spatial pattern of “high in the east and low in the west,” with pronounced proximity characteristics. A clear boundary exists between high and low carbon absorb zones, and no significant changes were observed in this spatial pattern during the study period. During the study period, Hulunbuir City accounted for approximately 75.65% of the regional average in total carbon absorb. Forest carbon sinks constituted 97.48% of the city’s total carbon absorb, primarily due to forest coverage exceeding 59% of its territorial area - significantly higher than China’s national average of 23%. These forests are predominantly distributed across the Greater Khingan Mountains and their extensions, forming an ecological barrier zone with minimal human disturbance, which significantly enhances their carbon absorb capacity. Additionally, grassland areas cover about 30% of the territory but contribute only 1.8% to the regional carbon absorb total. The regions of Xing’an League, Tongliao City, Chifeng City, and Xilingol League demonstrate high carbon absorb capacity. These four areas primarily rely on forest and grassland carbon sinks. Although grassland areas are more extensive than forested areas, forests exhibit significantly greater carbon absorb potential than grasslands. During the study period, all regions experienced varying degrees of forest area expansion, attributable to local government policies addressing over-cultivation in agro-pastoral transition zones. Through subsidy programs, these policies promoted the conversion of farmland to forests (Grain for Green) and implemented grassland ecological restoration projects by returning grazing land to grassland. Furthermore, the advancement of the “Three-North Shelterbelt” Program has led to simultaneous improvements in both forest coverage and comprehensive grassland vegetation coverage, while achieving concurrent reductions in desertification and sandy land area.

Figure 5

Figure 5. Spatial Variation of Carbon absorb in Inner Mongolia (2000–2024).

4.3 Analysis of net carbon emission changes from land use in inner Mongolia

By calculating the difference between carbon emissions and carbon absorption in Inner Mongolia as a whole, the net carbon emissions in Inner Mongolia are obtained (Figure 6). The total net carbon emissions in 2024 will reach 8.76 × 10⁷t, an increase of about 133% compared with 3.62 × 10⁷t in 2000, and the increase in cultivated land area and construction land area will be −591 km² and 6,380.7 km² respectively, with growth rates of −0.42% and 122.19% respectively, that is, the total net carbon emissions of construction land area will be about 3.96 × 10⁵t for every 1% increase. It can be seen that the advancement of urbanization and the development of industry are the reasons for the increase in total net carbon emissions.

Figure 6

Figure 6. Net carbon emissions in inner Mongolia (2000–2024).

By calculating the difference between carbon emissions and carbon absorption in various cities, the carbon balance status of each city in Inner Mongolia was measured (Figure 7). Among the 12 prefecture-level cities in the region, only Hulunbuir City reached the balance from 2000 to 2015, mainly because the area of forest land and grassland in the area is large, the carbon absorb effect is good, with the increase in the area of construction land and the proportion of cultivated land, carbon emissions also increase, during the period the gap between carbon emissions and carbon absorption gradually narrowed, in a state of carbon balance, after 2015, all prefectures and cities are in an imbalance state of carbon emissions exceeding carbon absorption, finding a balance between carbon income and balance is an urgent problem for all regions.

Figure 7

Figure 7. Carbon balance patterns of prefecture-level cities in inner Mongolia (2000–2024).

4.4 Carbon balance zoning and optimization recommendations for inner Mongolia

By calculating the Economic Contribution Rate of Carbon and Carbon Absorption Ecological Carrying Coefficient the ecological carrying index of carbon absorption, referring to the relevant literature (Xia and Yang, 2022), the cities of Inner Mongolia are divided into six carbon balance zones (Table 5), and the results of the economic contribution rate of carbon emissions (Figure 8) and the ecological carrying index of carbon absorption (Figure 9) are visualized. In 2024, the economic contribution coefficient of the five prefectures and cities will be >1, of which Ordos is as high as 3.39, mainly because Ordos has a huge economy, rapid development, energy economy dominant, and income is related to the role of multiple factors such as local resource endowment and location advantages, which has become an important engine for economic development in Inner Mongolia. The economic contribution coefficient of Hohhot, Baotou and Alxa League is >2, and the economic contribution coefficient of Wuhai City is >1. Hohhot, Baotou and Ordos are closely related to each other, geographically adjacent, and the developed transportation network provides convenience for exchanges between the two places, and can effectively promote the allocation of resources in the market. The cities with a carbon absorption ecological carrying coefficient of >1 are Alxa League and Hulunbuir City, and the average ecological carrying coefficients of carbon absorption in the two places during the study period are 2.30 and 6.52, which can play a positive role in carbon emission reduction in their jurisdictions. The other prefectures and cities are <1, Wuhai City, Tongliao City, Baotou City, The ecological carrying coefficient of carbon absorption in Bayannur City is even less than 0.1, and the carbon absorb capacity is weak, which is not conducive to the absorption of carbon emissions.

Table 5

Table 5. Characteristics of carbon balance zones in inner Mongolia.

Figure 8

Figure 8. Economic contribution rate of carbon emissions by prefecture in inner Mongolia (2000–2024).

Figure 9

Figure 9. Carbon absorption ecological carrying coefficient by prefecture in inner Mongolia (2000–2024).

From the perspective of carbon emission balance zoning, the 12 prefecture-level divisions in Inner Mongolia are categorized into six distinct zones: low-carbon development zone, low-carbon economy zone, low-carbon maintenance zone, economic development zone, carbon intensity control zone, and high-carbon optimization zone (Figure 10). The analysis reveals that, with the exception of Hulunbuir City, all other divisions maintained stable carbon balance zoning characteristics throughout the study period without significant changes.

Figure 10

Figure 10. Carbon balance zoning of prefecture-level cities in inner Mongolia (2000–2024).

Hulunbuir City transitioned from low-carbon development zone-low-carbon economic zone-economic development zone. The region has one of the four major grasslands in the world, with wide forest coverage, rich carbon sink resources, low intensity of human activities, and relatively slight impact on the original ecological environment. During the period from 2000 to 2015, the total carbon absorption in the region was higher than the total carbon emissions, and the gap narrowed year by year, and after 2020, carbon emissions exceeded the carbon absorption, and the growth rate of carbon emissions reached 12.44% in 2020 and then fell back to 9.2% in 2024, which also shows that the city’s carbon emissions are effectively controlled. The total GDP accounts for 6.6% of Inner Mongolia’s total, and the carbon emission intensity per unit of GDP is large, so there is a lot of room for improvement in the economic development of such areas with large carbon sink functions. On the one hand, it is to promote economic ecology, develop the tourism industry based on local nature reserves, ecological function areas, and key areas of scenic spots, cultivate and expand the “eco-tourism” model, and stimulate economic growth. On the other hand, it is to promote the low-carbon industry, grasp the lifeblood of industrial economic development, promote industrial ecology, and carry out the transformation and upgrading of traditional industries according to the direction of “stock greening”, and cultivate emerging industries with high scientific and technological content, strong development ability and large industrial relevance, so as to achieve the purpose of energy conservation and emission reduction.

Alxa League is a low-carbon maintenance area, with a high economic contribution rate and ecological carrying coefficient of carbon emissions, carbon emissions greater than carbon absorption, but a low base, of which carbon emissions show a significant growth trend, the absolute amount is limited, and the contribution of regional carbon neutrality requires a certain long-term accumulation. The economic contribution rate of Alxa League carbon emissions shows an inverted N-shaped downward trend, the carbon emission intensity per unit of GDP increases, and there is a big gap between the growth rate of economic volume and the growth rate of carbon emissions, but it does not show a serious imbalance between economic development and carbon emissions. In addition to the basic urban construction, there are also a large number of factory construction, so it is necessary to promote the circular transformation of the park area and realize the green and low-carbon circular development of the park.

Wuhai City, Ordos City, Baotou City, and Hohhot City are carbon intensity control areas, with a high economic contribution rate of carbon emissions, but the ecological carrying coefficient is low, and the overall carbon emission is large and the carbon absorption is large. As far as the economic development of such areas is concerned, the agglomeration effect of the Hohhot capital area and the spillover effect of the leading economic development of Ordos have driven the economic development of the surrounding areas, and the economic contribution rate has remained at a high level. However, these areas are rich in energy, Ordos is dominated by the coal mining industry, energy resources and other pillar industries have formed an energy-relying industrial structure, Baotou relies on its own superior resource conditions and policy support, and continues to develop and gradually grow into a comprehensive industrial city centered on steel and a variety of light industry and chemical industry. Therefore, in promoting economic development, it is necessary to pay attention to the quality of development, and in the selection of industrial introduction, it is more inclined to meet the regional standards of “low pollution and carbon emissions”. On the other hand, increase investment in regional environmental protection, change terminal treatment to source reduction, whole process control, improve the quality of the ecological environment and reduce regional environmental risks. At the same time, with the help of Hohhot, Ordos and Baotou City, which have the R&D foundation of universities and scientific research institutes, we will focus on solving the problem of disconnection between scientific and technological innovation and enterprises, promote the development of industries to high-end and intelligent, and overcome the difficulties of sustainable development and green development of local industries. Cities maintain internal greening stocks, expand increments, and enhance their carbon absorb capacity through the construction of wetland parks, urban parks, urban forests, etc.

Bayannur City, Ulanqab City, Xilin Gol League, Chifeng City, Tongliao City, and Xing’an League belong to the high-carbon optimization zone. The carbon emissions of such regions are much greater than the carbon absorption, and the carbon emissions per unit of GDP are large, and the economic efficiency of carbon emissions and the ecological carrying index of carbon absorption are low. In order to meet the needs of development, it has undertaken the industrial gradient transfer of some surrounding cities, and the corresponding carbon emission intensity has gradually increased, so that the economic contribution rate of carbon emissions and the ecological carrying coefficient of carbon absorption in this area have shown a downward trend year by year. The carbon absorption ecological carrying index of Bayannur City and Tongliao City is less than 0.1, and agricultural land and construction land account for a large proportion, becoming the main carbon source. For the above regions, the first to undertake the transfer will select the introduced industries, eliminate industries with high energy consumption, high pollution and high emissions, and make good use of local renewable energy to promote green economic development. Secondly, actively promote ecosystem protection and restoration, improve inefficient forests, and maintain forest and grass stocks; Carry out integrated management of undeveloped land such as deserts, wastelands, and Gobi, scientifically promote large-scale land greening, expand green ecological space, build green ecological barriers in desert areas, and maintain a scientific and orderly increase in the scale of forests and grasslands to increase the capacity of carbon sinks.

5 Discussion

This paper mainly analyzes the spatio-temporal pattern and carbon emission balance zone in Inner Mongolia, The accurate quantification of dynamic changes in land use clearly reveals the differential evolution law of land use structure during the urbanization process. The characteristics of carbon sources, carbon sinks, and their spatial patterns fill the gap in the research on long-term spatial differentiation of carbon budgets in Inner Mongolia. The identification of carbon balance status provide a key temporal reference for regional carbon management. Starting from 2015, all prefectures and cities were in an imbalance in which carbon emissions exceeded carbon absorption. A single measure of carbon emissions lacks discussion on the carbon economic contribution rate and the ecological carrying capacity coefficient of carbon emissions (Wu et al., 2025). This leads to a focus solely on emissions, neglects the balance between carbon reduction and economic development, fails to distinguish between the differences between “high emissions with high value” and “high emissions with low value,” and severs the symbiotic relationship between carbon and ecology—ultimately resulting in ecological risks caused by carbon reduction measures. The innovative coupled analysis of economic contribution and ecological carrying capacity offers a new quantitative tool for evaluating the coordination between regional “economy and ecology”. The proposal of differentiated territorial spatial optimization strategies and the formulation of targeted zonal optimization measures break through the traditional “homogeneity” planning model. Compared with the economically highly developed Yangtze River Delta region—where cities are small in area, human and logistics activities are frequent, and energy consumption is high—territorial optimization plans there mostly focus on urban population carrying capacity and the expansion of urban land (Du et al., 2024). In contrast, the Inner Mongolia region features a vast territory and low population density. As the “Three-North Shelterbelt” serves as a crucial ecological protection barrier and the region contains numerous ecological reserves, territorial optimization in Inner Mongolia tends to prioritize the transformation and upgrading of the industrial structure, as well as the maintenance of existing ecological green space and the increase in its area. It’s providing an operable spatial governance plan for the green, low-carbon, and circular development of Inner Mongolia. Supported by long-term data, supplemented by multi-dimensional indicator innovation and spatial differentiation analysis, this study provides a new paradigm for research on land use and carbon cycling in arid and semi-arid regions of northern China, and also offers a scientific basis for the realization of regional “dual carbon” goals. Mainly has the following shortcomings: 1. The carbon emission coefficient and carbon absorption coefficient in this paper mainly refer to previous studies, especially the carbon emission coefficient of construction land is not detailed enough, the regional characteristics of land use and natural resources are obviously different, and the accounting results are reference, but there are also uncertainties, so it is also necessary to calculate the correlation coefficient in line with Inner Mongolia according to the actual situation of regional carbon sources and carbon sinks. Considering the regional characteristics of Inner Mongolia, such as its rich coal resources and large proportion of grassland ecosystems, localized parameters including “resource utilization intensity” and “ecological restoration measures” should be incorporated into the traditional carbon coefficient calculation model. An association model of “land use type - regional characteristics - carbon coefficient” should be established to calculate land-use-specific and region-specific carbon coefficients that are more in line with the actual conditions of Inner Mongolia. This will replace the current “general coefficients” and reduce the uncertainty of accounting results. 2. Existing research has penetrated into the spatiotemporal pattern of carbon emissions at the county scale and explored the influencing factors, and the decoupling relationship with economic growth needs to be further discussed. In the future, systematic research can be conducted around the framework of “decoupling state identification - driving factor analysis - collaborative path construction”. The Tapio decoupling model or Logarithmic Mean Divisia Index (LMDI) decomposition method can be adopted to quantify the decoupling types of “economic growth - carbon emissions” in each district over a long time horizon, and clarify the spatiotemporal variation characteristics of decoupling states across different regions. By integrating methods such as Geographically Weighted Regression (GWR) and Spatial Dubin Model (SDM), this study can analyze the dominant driving factors of decoupling states in different regions, identify the key constraint factors for the “synergy between economic growth and carbon emission reduction”, and propose synergy plans for carbon emission reduction and economic development in Inner Mongolia’s agricultural development zones, animal husbandry development zones, semi-agricultural and semi-pastoral development zones, and industrial development zones.

6 Conclusion

1. From 2000 to 2024, the land use dynamics of forest land, construction land and wetland increased by 0.149%, 5.091% and 1.097% respectively, and the land use dynamics of construction land were the highest, and the changes in the area of farmland, grassland, water area and unused land decreased by 0.018%, 0.025%, 0.407% and 0.123% respectively. Economic development and population growth have increased the demand for housing, public facilities, and infrastructure. The demand for construction land in key areas of industrial expansion continues to grow. With a relatively small initial base of construction land and rapid short-term growth in construction land, the dynamics of land use are at their highest.

2. From 2000 to 2024, the total carbon emissions of construction land and cultivated land in Inner Mongolia showed an upward trend, with total carbon emissions increasing from 4.82 × 10⁷t in 2000 to 10.0 × 10⁷t in 2024, of which the carbon emissions of construction land increased from 4.24 × 10⁷t in 2000 to 9.42 × 10⁷t in 2024, and the carbon emissions of cropland showed stable fluctuations during the study period, decreasing from 5.83 × 10⁶t to 5.81 × 10⁶t. Construction land and cropland are the main carbon sources. Spatial characteristics: Carbon emissions in various cities in Inner Mongolia show the distribution characteristics of “high in the east, followed by the central region, and low in the west”. The expansion of urban land has a significant impact on carbon emissions. Urban expansion alters the original land use types, occupying areas such as forests and grasslands. The construction of urban infrastructure consumes a large amount of resources, and the increased commuting distances caused by urban sprawl lead to a higher reliance on private cars, resulting in an increase in transportation-related carbon emissions.

The total carbon absorption in Inner Mongolia increased from 1.21 × 10⁷t in 2000 to 1.24 × 10⁷t in 2024, an increase of about 2.47%. In terms of spatial characteristics, the carbon absorption of various cities in Inner Mongolia mainly showed “high in the east and low in the west”, and the boundary between the high carbon absorption area and the low carbon absorption area was obvious, and there was no significant change during the study period.

3. The total net carbon emissions in 2024 will reach 8.46 × 10⁷ tons, an increase of about 133% compared with 3.62 × 10⁷ tons in 2000, and the increase in cropland area and construction land area will be −591 km² and 6,380.7 km² respectively, with a growth rate of −0.42% and 122.19% respectively, the total net carbon emissions of construction land area will be about 3.96 × 10⁵ tons for every 1% increase in construction land area. From 2000 to 2015, Hulunbuir City was in a state of carbon balance, and after 2015, all prefectures and cities were in an imbalance in which carbon emissions exceeded carbon absorption.

4. The economic contribution rate of carbon emissions in various cities in Inner Mongolia shows significant differences, indicating that the economic contribution rate and carbon emission rate of the region are relatively imbalanced, and the economic contribution rate of >1 is less than half, and the number of cities with an economic contribution rate of >1 in 2024 has decreased by 2 compared with 2000, and the spatial distribution characteristics are high in the west and low in the east. From 2000 to 2024, the prefectures and cities with a carbon ecological carrying coefficient of >1 are Alxa League and Hulunbuir City, and the carbon ecological carrying capacity of other prefectures and cities has developed slowly and has a lot of room for improvement. The spatial distribution is characterized by “high-low-high” from west to east. This is mainly due to the fact that Hailunbuir City initially had large areas of forests and grasslands, and the abundant carbon sinks reduced atmospheric carbon concentration and maintained the balance of the carbon cycle. During the study period, along with the expansion of construction land, the areas of forests and grasslands remained stable, but the gap between total carbon sinks and carbon emissions reached a critical point. After 2015, carbon emissions exceeded the total carbon sinks and the imbalance gradually widened.

5. According to the economic efficiency of carbon emissions and the carrying coefficient of carbon absorption, the 12 prefectures and cities under the jurisdiction of Inner Mongolia are divided into low-carbon development zones, low-carbon economic zones, low-carbon maintenance zones, economic development zones, carbon intensity control zones, and high-carbon optimization zones. Hulunbuir City transitioned from “low-carbon development zone-low-carbon economic zone-economic development zone”. Alxa League is a low-carbon maintenance area, Wuhai City, Ordos City, Baotou City, and Hohhot City are carbon intensity control areas, with a high economic contribution rate of carbon emissions, Bayannur City, Ulanqab City, Xilin Gol League, Chifeng City, Tongliao City, and Xing’an League belong to the high-carbon optimization zone.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JM: Writing – review and editing, Writing – original draft. HuZ: Investigation, Writing – review and editing. BT: Writing – review and editing, Data curation. HoZ: Writing – review and editing, Formal Analysis. YL: Supervision, Writing – original draft, Writing – review and editing, Funding acquisition.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by the Qingdao University Outstanding Achievement Award, grant number RZ2100004657.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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