Spatial Evolution of Grassland Ecological Carrying Capacity and Low-Carbon Development Pathways for Animal Husbandry in Inner Mongolia

Abstract

Inner Mongolia’s grasslands, covering 22% of China’s total grassland area, face critical challenges in balancing livestock production with carbon sequestration under climate change pressures. This study establishes an integrated assessment framework combining remote sensing monitoring, InVEST modeling, and life cycle assessment to analyze the spatial–temporal evolution of grassland ecological carrying capacity and livestock-related carbon emissions from 2000 to 2020. Key findings reveal a 78.8% increase in actual livestock carrying capacity (from 53.09 to 94.94 million sheep units), with Tongliao experiencing 185% growth, while Alxa League showed a 229,500 sheep unit decrease. The theoretical carrying capacity grew by 50.6%, yet severe ecological pressure emerged in western regions, as evidenced by Alxa League’s grass–livestock balance index exceeding 2100%. Carbon sequestration exhibited a northeast–southwest spatial pattern, decreasing by 7.4% during 2015–2020, while greenhouse gas emissions from intensive livestock systems reached 6.40 million tons CO₂-eq in Tongliao by 2020. The results demonstrate that regions combining high-intensity husbandry with low carbon storage require urgent intervention. We propose three pathways: adaptive grazing management to reduce overloading in western pastoral zones, carbon monitoring systems to enhance sequestration in vulnerable ecosystems, and emission reduction technologies for intensive farming systems. These strategies provide actionable solutions for reconciling grassland sustainability with China’s dual carbon goals, offering insights for global pastoral ecosystem management.

1. Introduction

Grassland ecosystems, covering approximately one-third of the Earth’s terrestrial surface, are vital to global terrestrial ecosystems, particularly in arid and semi-arid continental interiors with low precipitation. Countries such as Australia, Russia, Brazil, and China have extensive grassland areas that serve as complex systems with diverse material cycles. Grasslands provide numerous ecosystem services, including water conservation, soil preservation, wind and sand control, and biodiversity protection, with carbon sequestration and the provision of livestock products being among the most significant contributions [1,2,3,4,5,6]. Grasslands play a critical role in global nutritional security, with ruminant livestock products derived from grasslands contributing nearly 20% of the protein consumed by humans [7,8,9]. As a critical component of terrestrial carbon sinks, grasslands store approximately 30–34% of the world’s terrestrial carbon, primarily sequestered in the roots of herbaceous plants and soil. Changes in soil organic carbon within grassland ecosystems thus have profound implications for the global carbon balance [4,10].

Grasslands are the second-largest terrestrial carbon sink after forests, with their carbon pools comprising aboveground vegetation, belowground biomass, plant litter, and soil organic matter (SOC). Soil organic matter accounts for approximately 90% of grassland carbon stock [2,11,12]. The assessment of grassland ecological carrying capacity, which integrates carbon sequestration and biomass productivity, has been a critical focus in sustainable grassland management. For instance, the development of a grassland–livestock equilibrium index for Inner Mongolia revealed a progressive deterioration in the regional ecological carrying capacity from 2000 to 2020 [13]. Similarly, global studies on semi-arid grasslands have demonstrated that moderate grazing more effectively alleviates the negative impacts of grazing legacy effects on productivity and biodiversity under short-term grazing histories.

In recent years, increasing attention has been directed towards the carbon sink functions of grasslands, with estimates suggesting that global grasslands store 306–330 PgC out of the total terrestrial ecosystem carbon stock of 861 PgC [14]. In China, various methodologies have been applied to assess grassland carbon storage. Ni et al. used the carbon density method to estimate the carbon storage of China’s grassland ecosystems, finding that China’s grasslands contribute approximately 8% to global grassland carbon sinks and account for 16.7% of China’s terrestrial ecosystem carbon storage [15]. Ma et al., utilizing grassland classification and spatial interpolation methods, estimated the carbon storage of Chinese grasslands to be approximately 30.98 ± 1.25 PgC, with 94.8% stored in the soil layer [16]. Discrepancies among these studies highlight the influence of varying accounting methods, grassland definitions, and parameter selections on carbon storage estimates.

Grasslands also serve as critical zones for livestock production, underpinning food security. According to the Food and Agriculture Organization of the United Nations, grassland ecosystems supplied 29% of the world’s meat in 2010 and employed approximately 15% of the global population [17,18]. However, grassland-based livestock systems significantly contribute to greenhouse gas (GHG) emissions, accounting for 13% of total anthropogenic emissions. These emissions mainly arise from ruminant enteric fermentation, manure management, and feed processing, with enteric fermentation alone responsible for over 90% of methane emissions in animal husbandry [19,20,21]. In China, Inner Mongolia’s grasslands represent 22% of the nation’s total grassland area. They are central to the region’s economy, with livestock slaughter accounting for approximately 9% of the national total during the “13th Five-Year Plan” period [22]. However, this livestock production also generates significant GHG emissions, as agricultural systems contribute roughly one-third of total anthropogenic GHG emissions, making them a substantial driver of global warming [23].

Grasslands exhibit robust carbon sequestration potential, which is pivotal in the global carbon cycle. During the 20th century, grassland carbon sinks intensified globally, particularly in countries like the United States and Russia. However, extensive human management in the 21st century has led to the degradation of some grasslands, transforming them from carbon sinks into carbon sources and potentially offsetting their natural carbon sequestration benefits [24]. Land use changes and grazing management practices significantly impact soil carbon dynamics. Moderate grazing can enhance soil carbon storage, whereas overgrazing often produces carbon losses [25,26]. Low-carbon husbandry innovations focus on three pathways: precision grazing systems using IoT sensors to optimize stocking rates [27], methane-inhibiting feed additives achieving emission reduction in trials [28], and manure management technologies, such as anaerobic digestion, which converts CH₄ into bioenergy. However, their applicability in arid grasslands remains underexplored.

Recent advancements in grassland carrying capacity assessment have integrated multi-scale approaches. Satellite-derived NPP monitoring [29] enables the dynamic evaluation of biomass productivity, while ecological footprint models [30] quantify human–nature interactions. Most existing research on grassland ecosystems approaches the topic from a single perspective, focusing either on biomass-based carrying capacity or the spatiotemporal patterns and drivers of GHG emissions from livestock systems. Few studies have integrated carrying capacity and carbon emissions into a unified assessment framework. Balancing livestock production with carbon sequestration in the context of global climate change and achieving the dual goals of food security and carbon neutrality require innovative evaluation models.

Despite these advancements, critical gaps persist in unified assessment frameworks that simultaneously address ecological thresholds and carbon budgets. Current models either prioritize biomass productivity or emission accounting, lacking spatially explicit integration. This study bridges this divide by developing a coupled remote sensing–InVEST–LCA framework, enabling the concurrent evaluation of physical carrying capacity and carbon neutrality potential in pastoral systems. This study addresses three core objectives: to quantify the spatiotemporal evolution of grassland ecological carrying capacity (theoretical versus actual livestock load) and its balance index across Inner Mongolia from 2000 to 2020; to assess the carbon sequestration–emission nexus through an integrated assessment of grassland ecosystem carbon stocks and livestock-related greenhouse gas (GHG) emissions; and to propose actionable low-carbon pathways by conducting scenario-based trade-off analyses between biomass carrying capacity thresholds and ecosystem carbon balance (Figure 1).

2. Data and Method

2.1. Measurement of the Grass-to-Livestock Balance Index

This study calculates the grass–livestock balance index based on the net primary productivity data calculated by remote sensing inversion, the theoretical carrying capacity, and the annual standard hay yield. The calculation method is as follows:

$$CA={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m(G_{i,j}\times C_{i,j})}}}{UG\times D}}$$

$$G_{i,j}={\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m\frac{NP{P}_{i,j}\times A_{i,j}}{1000\times S_{i,j}\times (1+S{G}_{i,j})}}}$$

$$BGLI={\displaystyle \frac{A-CA}{CA}}\times 100\%$$

where CA is the theoretical carrying capacity (sheep units); G_i,j is the total annual hay yield of the jth type of grassland in the administrative area i; C_i,j is the utilization rate of the grass produced by the jth type of grassland by livestock within administrative area i; UG is the amount of hay required per sheep unit per day; and D is the number of days in a year. NPP_i,j is the annual net primary productivity of the jth type of grassland within administrative area i; A_i,j is the total area of the jth grassland within administrative area i; S_i,j is the conversion coefficient from biomass to NPP of the jth grassland within administrative area i; and SG_i,j is the coefficient of the ratio of the biomass of the underground part to the biomass of the aboveground part of the jth grassland within administrative area i.

2.2. Invest Model

The InVEST model has proven efficacy in spatially explicit ecosystem service valuation [31], particularly its carbon module’s adaptability to grassland heterogeneity. Compared to models requiring intensive soil parameterization, InVEST’s NDVI-driven approach better aligns with our remote sensing framework. The InVEST 3.12.0 model’s carbon module was used to assess the characteristics of changes in ecosystem carbon stocks in the study area from 2000 to 2021. The carbon module estimates ecosystem carbon stocks based on remote sensing data on land use. The results include four primary carbon pools: aboveground, belowground, soil, and dead organic carbon pools.

$$C_{total}=A_i\times (C_{i\_above}+C_{i\_below}+C_{i\_soil}+C_{i\_dead})$$

where C_total indicates the total carbon stock in the ecosystem. A_i suggests the area of the ith land use type; C_{i_above}, C_{i_below}, C_{i_soil}, and C_{i_dead} indicate the aboveground carbon density, belowground carbon density, soil carbon density, and dead organic carbon density of the ith land use type, respectively.

2.3. Livestock Carbon Emission Calculation

According to the IPCC, non-CO₂ greenhouse gas emissions directly related to the livestock mainly include methane emissions from livestock enteric fermentation, methane emissions from manure management, and nitrous oxide emissions from manure management. The calculation method complies with the IPCC emission factor method. According to the research results of some scholars and the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, emission factors are used to compile an inventory of meat-related greenhouse gas emissions. The corresponding global warming potentials (GWPs) of non-carbon dioxide greenhouse gases are used to estimate the carbon dioxide equivalence so as to more effectively quantify and compare non-carbon dioxide greenhouse gases in the livestock.

$$A{C}_{i,j}={\displaystyle \frac{M_{i,j}}{A{S}_{i,j}}}$$

$$A{L}_{i,j}=\left\{\begin{array}{cc}lo{h}_{end}& Day{s}_{i,j}\ge 365\\ {AS}_{i,j}\times ({\displaystyle \frac{Day{s}_{i,j}}{365}})& Day{s}_{i,j}\le 365\end{array}\right.$$

$$E_p=\left[({\displaystyle \sum E{F}_{C{H}_4,\mathit{intestine},i,j}}+{\displaystyle \sum E{F}_{C{H}_4,\mathit{faeces},i,j})\times GW{P}_{C{H}_4}+}{\displaystyle \sum E{F}_{N_{2}O,\mathit{faeces},i,j}}\times GW{P}_{N_{2}O}\right]\times A{L}_{i,j}$$

where Al_i,j denotes the average annual livestock rearing capacity of the jth livestock species in the city i; Days_i,j represents the average annual livestock rearing time; AS_i,j expresses the annual livestock slaughter; AC_i,j is the consumption of the jth livestock species in the city i; M_i,j is the total weight of the jth livestock species in the city i; E_p and E_c represents the total non-CO₂ emissions from livestock; EF_{CH4,intestine,i,j} expresses the methane emission factor from enteric fermentation of the jth livestock species in the city i; EF_{CH4,faeces,i,j} denotes the methane emission factor from manure management of the jth livestock species in the town i; EF_{N2O,faeces,i,j} represents the nitrous oxide emission factor from manure management of the jth livestock species in the city i (

Table A1. Emission factors of different livestock (unit: kg·head⁻¹·a⁻¹).

Non-CO2 Greenhouse Gas Types	Emission Link	Types of Meat	Emission Factors
Methane emission factor	Animal manure management	Pork	1.12
		Beef	1.02
		Sheep	0.16
		Poultry	0.01
	Animal enteric fermentation	Pork	1
		Beef	52.9
		Sheep	8.2
		Poultry	-
Nitrous oxide emission factor	Animal manure management	Pork	0.27
		Beef	0.91
		Sheep	0.06
		Poultry	0.01

); GWP_CH4 and GWP_N2O are the GWP values of methane and nitrous oxide.

2.4. Data Sources

The land use and topographic data utilized in this study were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/Default.aspx, accessed on 1 May 2025). The built-in carbon density pool of the InVEST model referenced region-specific carbon density parameters derived from studies on Chinese ecosystems (

Table A2. Carbon density table of various parts of land use.

	Cabove	Cblew	Csoil	Cdead
Cultivated land	5.7	80.7	108.4	13
Forest	42.4	115.9	236.9	13
Grassland	35.3	86.5	99.9	2
Water	0	0	0	0
Construction land	1.2	0	0	0
Unused land	9.1	0	21.6	0

). Net primary productivity (NPP) data were derived from the MODIS continuous time-series NPP product (MOD17A3H). Livestock data, including herd sizes and management statistics, were sourced from the statistical yearbooks of cities and leagues within Inner Mongolia (https://tj.nmg.gov.cn/, accessed on 1 May 2025).

3. Result

3.1. Temporal and Spatial Changes in the Carrying Capacity and Balance Index of Grass and Livestock in Inner Mongolia

Between 2000 and 2020, Inner Mongolia’s actual livestock carrying capacity increased from 53.09 million sheep units to 94.94 million sheep units, representing a growth of approximately 78.8% (Figure 2). Tongliao experienced the most significant increase, with its actual livestock carrying capacity rising by 185%, reflecting positive development in using grassland and livestock resources. Similarly, Chifeng and Hingganleague exhibited substantial growth of 144% and 143%, respectively, indicating synchronized expansion in animal husbandry and grassland resource utilization. In contrast, Xilingol League and Alxa League showed a declining trend, particularly in Alxa League, where the actual livestock carrying capacity decreased by approximately 229.5 thousand sheep units, highlighting intensified resource-carrying pressure in the region.

At the city level, Tongliao reported the highest actual livestock carrying capacity in 2020, reaching 15.79 million sheep units, demonstrating extensive utilization of grassland and livestock resources. In contrast, Wuhai had the lowest actual carrying capacity, increasing by only 66.2 thousand sheep units over the past 20 years. Overall, nearly half of the cities showed a gradual upward trend in actual livestock carrying capacity, peaking in 2020, indicating notable progress in animal husbandry expansion and grassland resource management. Additionally, 46.2% of the cities displayed an inverted U-shaped trend, with actual carrying capacity peaking around 2010 and slightly declining afterwards, possibly due to the implementation of resource management policies and ecological protection measures. Xilingol League exhibited an initial decline followed by a slow recovery, with an overall decrease of 16.14% in actual carrying capacity, reflecting a gradual shift toward sustainable resource management.

Regarding theoretical livestock carrying capacity, the Inner Mongolia Autonomous Region saw an increase from 32.11 million sheep units in 2000 to 48.37 million sheep units in 2020, marking a growth of approximately 50.6%. Most cities exhibited a fluctuating upward trend in theoretical carrying capacity, with a first peak in 2005 and the highest value recorded in 2020. Hulun Buir, Wuhai, Erdos, and Alxa League demonstrated a continuous growth pattern, reflecting the gradual restoration of grassland resources and improvements in theoretical carrying capacity. Xilingol League achieved the highest theoretical carrying capacity in 2020, reaching 14.54 million sheep units, an increase of 4.97 million sheep units, underscoring its potential in theoretical livestock capacity. Wuhai, on the other hand, had the smallest theoretical increase, adding only 9 thousand sheep units over 20 years. Notably, Hohhot showed the largest increase in theoretical carrying capacity, at approximately 106%, while Chifeng recorded the smallest increase, at 37.65%.

Changes in the gap between theoretical and actual carrying capacities further highlighted variations in resource utilization efficiency and actual carrying potential across cities. Tongliao and Chifeng exhibited a significant increase in this gap over the past 20 years, reflecting intensive investment in grassland and livestock resource development, with actual carrying capacity far exceeding theoretical levels. In contrast, Xilingol League and Hulun Buir showed a narrowing gap, particularly in Xilingol League, where the gap in 2020 was −370.91 thousand sheep units, indicating improved balance in grassland resource utilization and sustainable animal husbandry practices.

Changes in the grass–livestock balance index not only reflect the complex interactions between regional economies and ecosystems but also underscore the importance of sustainable management. Based on data from 2000 to 2020 for 12 cities in Inner Mongolia, the overall trend reveals significant differences in resource-carrying capacity among cities (Figure 3).

Specifically, the grass–livestock balance index in Alxa League was as high as 4021.62% in 2000, further increasing to 4241.48% in 2010 before declining to 2133.75% in 2020. Despite this decrease, the index remains extremely high, indicating severe regional resource pressure. Similarly, Bayan Nur showed an overloading trend, with indices of 712.73%, 1095.67%, and 920.34% in 2000, 2010, and 2020, respectively, suggesting potential long-term impacts on the local ecological environment. Hohhot and Baotou also experienced rapid development in animal husbandry, as reflected in their indices, reaching 1121.37% and 463.93% in 2010, respectively. However, the accompanying resource pressure in these cities warrants attention.

In contrast, Hulun Buir exhibited a grass–livestock balance index of −45.90% in 2000, which improved slightly to −10.48% in 2010 and reached −21.45% in 2020. These values indicate that the region’s actual carrying capacity has not exceeded theoretical limits, demonstrating high sustainability and potential for ecological recovery. Similarly, Xilingol League’s index was −10.74% in 2010 and further decreased to −25.51% in 2020, reflecting efficient resource utilization and the ability to maintain ecological balance.

Other cities, such as Tongliao, showed an index of 196.03% in 2000, which surged to 482.47% in 2010 and slightly increased to 489.11% in 2020, indicating progressively growing resource pressure over time.

Overall, while some cities experienced reduced resource pressure in 2020, regions with high stress levels, such as Alxa League, Bayan Nur, Hohhot, and Baotou, require focused attention on grassland ecological management to ensure the sustainable development of grasslands and livestock systems.

3.2. Carbon Sequestration Capacity of Inner Mongolia’s Grasslands and Spatiotemporal Dynamics of Carbon Emissions from Animal Husbandry

The InVEST model results reveal significant changes in the carbon sequestration capacity of Inner Mongolia’s grasslands between 2000 and 2020, indicating a clear trend of ecological optimization (Figure 4). Spatially, the carbon sequestration capacity follows a pattern of being “high in the northeast and southwest, and low in the central regions”. Areas such as Alxa League, Hulun Buir, and Hinggan League exhibited relatively high carbon sequestration capacities, while regions like Erdos, Hohhot, and Ulanqab in central and eastern Inner Mongolia showed comparatively lower capacities. This spatial distribution reflects both the influence of natural conditions on regional carbon storage potential and disparities in the effectiveness of ecological management practices across different areas.

From a temporal perspective, the period between 2000 and 2010 witnessed rapid growth in carbon sequestration across most of Inner Mongolia. This growth was particularly pronounced in the eastern regions of Hinggan League, Tongliao, Chifeng, and parts of Hulun Buir. The significant increase in carbon storage in these areas underscores the success of local vegetation restoration efforts and improved grassland management. These positive trends highlight the enhanced carbon sink functionality in certain areas, driven by ecological restoration initiatives and environmental protection policies implemented during this period.

Between 2010 and 2020, changes in carbon sequestration in Inner Mongolia’s grasslands showed regionally differentiated development. About 70% of the region’s carbon sequestration remained stable, but other regions showed a decreasing trend, especially in the eastern regions of Hinggan League, Tongliao and Chifeng, and central Hulun Buir (Figure 5). This change may be affected by various factors, including climate change, increasing pressure on grassland use, and changes in vegetation structure. However, the relative stability of carbon sequestration still shows the effectiveness of ecological protection work, and the carbon sink function has been maintained and optimized in most areas. In terms of quantity, from 2000 to 2020, although the total carbon sequestration of the Inner Mongolia grassland ecosystem showed an overall downward trend, in terms of stages, the significant increase in carbon sequestration (an increase of 7.68%) between 2005 and 2010 indicates that ecological restoration and management measures during this period effectively enhanced the regional carbon sink capacity. The significant decline in carbon sequestration between 2015 and 2020 (7.4% decrease) can be gradually alleviated through further ecological optimization strategies. From the perspective of individual cities, the changing trends in grassland carbon sequestration in different regions further highlight the differences in environmental management. Hulun Buir experienced the greatest decrease in total carbon sequestration over the past 20 years, while Wuhai experienced the greatest decrease. However, Ulanqab and Alxa League showed an increasing trend in carbon sequestration. Ulanqab’s grassland ecosystem carbon sequestration increased by 1.47% over 20 years. These data show that ecological optimization measures in Ulanqab and Alxa League have been highly effective and that continuous increases in carbon sequestration have been successfully achieved through appropriate ecological restoration and vegetation management. This provides a reference for the optimization of grassland ecosystems in Inner Mongolia.

In general, greenhouse gas emissions from animal husbandry in Inner Mongolia show regional differences and a certain growth trend, which poses a challenge for future emission reduction strategies and the sustainable development of animal husbandry. Judging from the data on greenhouse gas emissions from animal husbandry in different cities in Inner Mongolia, significant differences in emissions among cities can be observed (Figure 6). Overall, some major cities in Inner Mongolia, such as Tongliao and Chifeng, had relatively high emissions in all years. Tongliao, in particular, had emissions of 6.40 million tons of CO₂ equivalent in 2020, which shows the large scale of animal husbandry in the region and may be related to the intensive development of a large number of livestock breeding and related industrial chains. In contrast, emissions in cities such as Wuhai and Alxa League were very low, indicating that the scale of animal husbandry in these areas was relatively small, perhaps due to limited land resources and the scale of farming. In terms of temporal trends, most cities showed a significant increase in emissions in 2010 compared to 2000, while after 2020, emissions in some cities, such as Hohhot and HulunBuir, decreased. In particular, although emissions continued to rise in Tongliao and Chifeng, the growth rate slowed. This may indicate that while these regions continue to expand animal husbandry production, they are also gradually strengthening greenhouse gas emission reduction measures or improving production efficiency.

3.3. Changes in the Carrying Capacity of Inner Mongolia’s Grassland Ecosystems: Perspectives from Biomass and Carbon Emissions

From the biomass perspective, changes in the carrying capacity of Inner Mongolia’s grassland ecosystems reflect the strengths and weaknesses of their ecological functions. With the expansion of animal husbandry and the increase in livestock numbers, some regions face the risks of overuse and degradation of grassland ecosystems, leading to a decline in carbon sequestration capacity. For example, areas such as Tongliao and Chifeng have experienced significant burdens on their grassland ecosystems due to the overexpansion of animal husbandry, resulting in decreased carbon sequestration capacity and biomass carrying capacity levels falling into “red” or “orange” alert zones. This indicates that the grassland ecosystems in these areas have approached or exceeded their natural carrying capacities. In contrast, areas like Hulun Buir and Xilingol League have seen improvements in carbon sequestration capacity due to implementing low-carbon management and ecological protection measures. The biomass carrying capacity in these regions has remained stable in the “green” or “blue” zones, demonstrating the effectiveness of their ecological management efforts (Figure 7).

From the carbon emission perspective, the carbon carrying capacity of Inner Mongolia’s grassland ecosystems exhibits significant regional variation. In livestock-intensive regions such as Tongliao and Chifeng, increasing greenhouse gas emissions have intensified carbon emission pressures, severely challenging the carbon carrying capacity. These areas have experienced a significant rise in greenhouse gas emissions and a corresponding decline in carbon sequestration capacity, exacerbating the adverse effects of greenhouse gas accumulation. Conversely, regions such as Hulun Buir, Ulanqab, and Xilingol League have somewhat mitigated carbon emission pressures by enhancing grassland restoration, improving forage cultivation, and reducing overgrazing. These measures have strengthened the carbon sequestration capacity of grassland ecosystems, improving their regional carbon carrying capacity. Thus, changes in the carrying capacity of Inner Mongolia’s grassland ecosystems are closely linked to the expansion of animal husbandry and the carbon reduction and ecological restoration strategies adopted in different regions.

From the perspective of coordinating biomass and carbon balance, many regions faced dual challenges in 2020, including a declining ecological carrying capacity and increasing greenhouse gas emissions. This was particularly evident in Tongliao and Chifeng, where carrying capacity warning levels decreased, indicating substantial environmental pressure. Hulun Buir, despite its high greenhouse gas emissions in 2010, maintained a favorable ecological state due to its strong carbon sequestration capacity. However, by 2020, the region experienced a significant decrease in carbon sequestration, leading to a marked reduction in ecological carrying capacity. This transition reflects how continued resource exploitation and environmental pressures can ultimately erode ecological foundations, thereby impacting economic sustainability.

To address these challenges, local governments should develop and implement comprehensive ecological policies to promote green development and encourage the adoption of low-carbon technologies. Such measures are essential to enhancing the resilience and carbon sequestration capacity of grassland ecosystems, ensuring their long-term sustainability.

4. Discussion

The production of dry matter in grassland ecosystems serves as the cornerstone of animal husbandry development in Inner Mongolia and forms the foundation of the region’s ecological carrying capacity. Dry matter production significantly determines the theoretical livestock carrying capacity and directly impacts the ecological balance and sustainable resource utilization of grasslands. However, factors such as climate variability, vegetation degradation, and overgrazing have caused notable fluctuations and regional disparities in dry matter production. During the study period, areas like Hohhot and Tongliao exhibited significant growth in theoretical livestock carrying capacity. In contrast, regions such as Alxa League demonstrated a marked decline in dry matter carrying capacity, accompanied by severe overloading. This uneven development pattern risks exacerbating local grassland degradation and increasing the difficulty of ecosystem recovery.

To ensure that dry matter production in grassland ecosystems remains at a sustainable level, future efforts should focus on rationally controlling livestock carrying capacity and optimizing grassland management practices. Implementing seasonal grazing and rotational grazing systems and reducing livestock density in heavily burdened areas can alleviate the pressure on grasslands. Additionally, remote sensing technology and biomass monitoring tools can be utilized to dynamically assess the dry matter production capacity of grasslands, providing scientific evidence for the management and regulation of ecological carrying capacity. These measures can enhance the sustainable productivity of grassland ecosystems and protect grassland resources, laying the foundation for sustainable development in pastoral regions.

In terms of carbon carrying capacity, the grassland ecosystems of Inner Mongolia exhibit a spatial pattern characterized by higher carbon sequestration capacities in the northeast and southwest and lower capacities in central regions. The results indicate that areas such as Hulun Buir, Hinggan League, and Alxa League possess strong carbon sequestration capacities, likely due to their higher natural vegetation density and soil carbon storage. In contrast, regions such as Erdos, Hohhot, and Ulanqab display relatively weak carbon sequestration capacities, highlighting regional deficiencies in carbon sink functionality. These spatial differences suggest the need for more proactive measures in regions with lower carbon sequestration capacities to enhance carbon storage potential while reducing carbon emissions and maximizing ecological benefits.

Moreover, variations in climatic conditions and land use patterns within specific regions also significantly influence carbon carrying capacity. During the study period, the overall carbon sequestration in Inner Mongolia declined, with a particularly sharp decrease observed after 2015, indicating the sensitivity of ecosystem carbon storage to climate change. This decline coincided with two synergistic drivers documented in semi-arid ecosystems: Reduced growing season precipitation (15% below 2000–2010 averages) amplified soil carbon mineralization through warmer surface temperatures, as evidenced by similar moisture–temperature coupling effects in Mongolian grasslands [32,33]. Compounding these climatic pressures, localized overgrazing exceeded theoretical carrying capacities in degraded zones [34], a threshold known to suppress root biomass regeneration, which is critical for long-term carbon stabilization

The post-2015 acceleration of carbon loss may further reflect lagged ecological responses to policy interventions. While grassland contract systems successfully reduced grazing pressure in core protected areas, edge habitats exhibited elevated decomposition rates due to fragmentation effects [35]. To better capture these complex dynamics, future monitoring systems should integrate high-resolution climate projections with spatially explicit grazing intensity mapping, aligning with the IPCC’s call for regionally calibrated carbon accounting frameworks in ecologically vulnerable regions. Establishing regional carbon monitoring systems and integrating carbon footprint management models could provide clearer insights into the spatiotemporal dynamics of carbon storage, supporting the transition to low-carbon development in grasslands. In regions with relatively weak carbon carrying capacities, measures such as vegetation restoration, controlled land use intensity, and the implementation of protected area management could provide viable pathways to enhance carbon storage.

Balancing biomass production and carbon sequestration in grassland management requires targeted strategies. For example, reducing livestock density appropriately can alleviate pressure on grassland carbon sequestration and restore ecosystem biodiversity. Long-term monitoring of grassland vegetation, soil organic matter, and root carbon storage can help establish dynamic models for evaluating ecosystem carbon storage, supporting policy development and grassland management. Additionally, carbon storage management strategies tailored to the characteristics of grassland ecosystems should be implemented. High-carbon-storage regions should focus on further enhancing carbon sink functions, while low-carbon-storage regions should improve carbon sequestration potential through reduced grazing and vegetation restoration measures.

Systemic carbon reduction and sequestration measures are necessary to achieve the sustainable development of grassland ecosystems and contribute to the “dual carbon” goals. These include optimizing livestock density management, establishing precise monitoring mechanisms to regulate carrying capacity scientifically, promoting ecological restoration and vegetation recovery projects in low-carbon-storage regions, and advancing low-carbon livestock technologies by improving feed efficiency and reducing manure emissions. Furthermore, policy incentives to support carbon sequestration projects and low-carbon farming should be implemented alongside the development of carbon trading markets and enforcement of reward-and-punishment mechanisms. Advancing ecological monitoring and data analysis tools will further support grassland carbon sequestration management and address potential climate change impacts on ecosystems.

This study explores the use of remote sensing technology for carbon sequestration monitoring but highlights limitations, including the need for improved data accuracy and integration of multiple parameters. To address potential uncertainties in remote sensing-derived carbon sequestration estimates, we conducted cross-validation using ground-based biomass measurements in Inner Mongolia [36,37]. The correlation coefficient between remote sensing NPP and field measurements reached 0.82 (p < 0.01), indicating acceptable consistency for regional-scale analysis.

Key challenges include enhancing the fusion of high-resolution imagery with dynamic ecological models and developing advanced algorithms for long-term monitoring of climate change and human impacts. We further quantified uncertainty ranges through sensitivity analysis of InVEST model parameters: Monte Carlo simulations (±15% for aboveground biomass and ±20% for soil carbon pools) revealed an overall uncertainty of 12–18% in carbon stock estimates. These limitations are comparable to global grassland carbon assessments [38] and do not invalidate the observed spatiotemporal trends.

The InVEST model’s reliance on static carbon density parameters may oversimplify soil carbon dynamics in Inner Mongolia’s grasslands, particularly under climate variability and grazing pressures. Its exclusion of microbial processes and methane fluxes limits accuracy in carbon balance assessments. Future studies should integrate dynamic carbon density databases, high-resolution remote sensing, and process-based models to enhance spatial–temporal resolution and incorporate grazing–microclimate interactions for robust low-carbon pathway optimization.

Future research should focus on combining high-resolution satellite data (e.g., Sentinel-2) with ground-based observations to improve ecological parameter retrieval and monitoring precision. Additionally, integrating AI algorithms in data processing and model optimization can enhance simulation accuracy in complex ecosystems. Priority should be given to developing localized carbon density databases through intensive soil sampling, particularly in transition zones between high- and low-carbon-sequestration areas. Overall, advancing remote sensing technology will improve the spatial and temporal resolution of carbon sequestration monitoring, supporting better assessment of carbon sources and sinks to address climate change and meet “dual carbon” goals.

5. Conclusions

This study constructed a comprehensive evaluation framework by integrating ecological models, remote sensing technologies, and statistical analyses to comprehensively assess the carrying capacity of Inner Mongolia’s grassland ecosystems and livestock production, as well as the grass–livestock balance index. From the perspective of biomass assessment, this study expanded the concept of ecological carrying capacity by incorporating carbon footprints and carbon carrying capacity into the analysis framework. This study systematically quantified the carbon sequestration capacity of grassland ecosystems and the carbon emissions from animal husbandry, examining their spatiotemporal dynamics. By balancing changes in carrying capacity from the perspectives of biomass and carbon emissions, this research provides valuable references for promoting the green, low-carbon, and high-quality development of animal husbandry in Inner Mongolia.

The findings indicate that between 2000 and 2020, the actual livestock carrying capacity in Inner Mongolia increased by approximately 78.8%, with Tongliao experiencing a growth of 185%, demonstrating efficient utilization of grassland and livestock resources. Conversely, regions such as Xilingol League and Alxa League experienced declines, particularly Alxa League, where increased resource carrying pressure was observed. In cities such as Tongliao and Chifeng, actual livestock carrying capacity exceeded theoretical values, reflecting high-intensity resource development, while regions like Hulun Buir and Xilingol League maintained a favorable resource balance. Theoretical carrying capacity increased by 50.6%, with significant growth in Hohhot and Chifeng. The grass–livestock balance index revealed substantial differences, with regions like Alxa League showing overloading, while Hulun Buir and Xilingol League exhibited strong potential for ecological recovery.

Regional variations in grassland carbon sequestration capacity were evident, with Hinggan League and Hulun Buir demonstrating increases, reflecting successful ecological restoration efforts. Conversely, Tongliao and Chifeng experienced declines attributed to overgrazing and climate change impacts. Greenhouse gas emissions continued to rise in Tongliao and Chifeng, emphasizing the need for low-carbon technologies and ecological protection measures, particularly in areas with low carbon sequestration capacities. Overall, changes in the carrying capacity of Inner Mongolia’s grassland ecosystems are closely linked to livestock development, resource management, and carbon emissions. Future efforts must optimize management, enhance carbon sequestration capacity, and advance low-carbon transitions.