A Study on the Relationship Between Livestock Carbon Emission and Economic Growth in Inner Mongolia

Abstract

The development of animal husbandry directly affects climate change and the ecological environment. This study aims to explore the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia and to provide theoretical and countermeasure support for sustainable development. Based on the environmental Kuznets theory, the present situation of animal husbandry and economic growth in this region is analyzed. By analyzing slaughter and storage data for cow, sheep, hog, and poultry from 2000 to 2022, we calculated carbon emissions using the IPCC coefficient method. The environmental Kuznets curve is used to control variables such as human capital, government intervention, openness, technological innovation, and environmental protection expenditure. The findings show that carbon emissions from cows and sheep have risen significantly over the past 23 years, while the hog industry has remained stable. Both the number of poultry farms and their carbon emissions have declined. Economic growth is one reason for the increase in carbon emissions, while government intervention and openness have had mixed results. To ensure sustainable development, Inner Mongolia should strengthen government supervision, increase investment in environmental protection, expand opening-up, improve rural education, and promote low-carbon growth.

1. Introduction

According to the Second National Pollution Census Bulletin of the State Council, the pollution from livestock production is very prominent, with chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus emissions accounting for a large proportion of agricultural sources, and the development of the livestock industry has a direct negative impact on climate change and the ecological environment. As an important livestock base in China, the livestock industry in the Inner Mongolia Autonomous Region is not only important for local economic growth; it is also crucial for the stable development of the national livestock industry chain. In order to promote sustainable economic development and protect the natural environment, the Inner Mongolia Autonomous Region is implementing the 14th Five-Year Plan for the High-quality Development of Animal Husbandry in the Inner Mongolia Autonomous Region, which clearly points out that animal husbandry development is vital to the maintenance of the national economy and the well-being of the people. In accordance with the relevant requirements of laws and regulations, such as “Regulations on Pollution Prevention and Control of large-scale Livestock and Poultry Farming”, “Technical Policy on Pollution Prevention and Control of Livestock and Poultry Industry”, and “Environmental Protection Law of the People’s Republic of China”, we adhere to the concept of “green water Jinshan is Jinshan Silver Mountain”, implement environmental protection measures, and control the emission of exhaust pollutants.

In this context, with rapid economic growth, will carbon emissions from animal husbandry rise at the same time? Or is there a restraining effect on carbon emissions from animal husbandry driven by economic growth? The study of the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia is helpful to further understand the complex relationship between animal husbandry development and environmental protection and to provide theoretical and policy support for promoting the sustainable development of animal husbandry in Inner Mongolia.

Wang Mingli et al. [1] reviewed the stages and characteristics of China’s livestock industry development over the past 40 years. They conducted an in-depth analysis of the challenges, including tightening resource and environmental constraints, the severe situation of epidemic prevention, a high dependence on core technologies from abroad, and a lack of strong international competitiveness. As environmental issues have intensified, research on controlling carbon emissions from livestock has increased significantly. Shishuai et al. [2] systematically reviewed the development of carbon emissions accounting and carbon footprint methodologies in the livestock sector, highlighting the unique features and limitations of the OECD, IPCC, LCA, and I-O methods. Among them, the life cycle assessment (LCA) method has gained widespread recognition for its ability to comprehensively reflect emissions throughout the livestock lifecycle. However, the existing accounting systems require further refinement and standardization to improve accuracy and consistency.

According to a review of the literature, it is widely acknowledged in academia that manure management and enteric fermentation in ruminants are the primary sources of greenhouse gases (CH₄ and N₂O) in livestock production. Dong Hongmin et al. [3], using the OECD method for methane emissions from ruminants combined with China’s ruminant energy supply characteristics, estimated that methane emissions from ruminants account for 6.9–7.5% of their total energy intake.

Yao Chengsheng et al. [4] applied the LCA method to measure livestock greenhouse gas emissions and found that the total carbon emissions from China’s livestock industry increased from 137.4 million tons in 2000 to 150.6 million tons in 2014, with an average annual growth rate of 0.654%. Livestock production efficiency was identified as a critical factor in mitigating carbon emissions. Population-related variables, such as per capita agricultural productivity and total population growth, significantly contributed to carbon emissions, while urbanization effectively curbed the increase. Wang Zhipeng [5] used this method to accurately estimate carbon emissions from hog farming in Jiangxi Province. Xi Yonglan et al. [6] provided detailed descriptions of three carbon emissions calculation methods—the OECD method, the IPCC coefficients method, and the mass balance method—along with input–output and lifecycle carbon footprint calculation approaches. They concluded that the process-based LCA method is suitable for carbon footprint calculations in livestock farming.

Wu Qiang et al. [7], utilizing the IPCC coefficient method, measured total livestock carbon emissions in China and across 31 provinces from 2001 to 2020. They observed a fluctuating downward trend nationally, with emissions increasingly concentrated in resource-advantaged regions and displaying enhanced spatial correlation and local clustering. These emissions showed significant high–high and low–low aggregation patterns. Carbon transfer exhibited strong spatial–temporal dependencies, with regional backgrounds playing a significant role in emissions impacts. Hu Xiangdong et al. [8], employing the latest IPCC (2006) coefficients, estimated that annual methane emissions from livestock enteric fermentation accounted for 83.2% of total methane emissions. Methane and nitrous oxide emissions from livestock manure were estimated at 1.68 million tons and 577,000 tons, respectively. The reduction in livestock greenhouse gas emissions was primarily attributed to a decline in ruminant numbers. Dabkienė et al. [9] reported that enteric fermentation from family farms accounted for 69.3% of total CH4 emissions, while agricultural soils contributed significantly to N₂O emissions. Benaouda et al. [10] developed an empirical model using IPCC (2019) methods to predict methane emissions from cows in Latin America. They estimated that daily methane emissions ranged between 48.5 and 656 kg per head, with an average of 187 kg per head. Guo Jiao et al. [11], using this methodology, found that China’s livestock carbon emissions experienced two significant fluctuations between 2005 and 2015, with 2008 and 2009 serving as turning points. These changes eventually stabilized, with cow farming being the primary contributor. They suggested that carbon reduction policies should be tailored to provincial greenhouse gas emission structures. Wang Ai’ e et al. [12] estimated greenhouse gas emissions from hog farming in 19 developed provinces of China from 2004 to 2014. Their results showed an initial rise, followed by a decline and another increase, with significant regional differences.

With advancements in livestock science and technology, economic development in the livestock sector, reductions in carbon emissions, and increased governmental focus on environmental protection, the relationship between livestock carbon emissions and economic benefits has yielded significant findings. Li Bo et al. [13], using cointegration theory and error correction models, explored the long-term stable relationship between livestock economic development and carbon emissions in Hubei Province. Many scholars have applied the environmental Kuznets curve (EKC) model to analyze livestock development trends with remarkable results. Li Yubo et al. [14] studied carbon emissions and economic growth in Jilin Province from 1997 to 2018. They concluded that the relationship follows an “inverted U-shaped” curve that has passed its turning point. Zhang Yuejie et al. [15], applying a vector error correction model, found an “inverted N-shaped” curve in China’s beef industry, indicating that carbon emissions have peaked. Pata Ugur Korkut et al. [16], using Fourier ARDL cointegration and Toda-Yamamoto causality methods, analyzed the ecological footprint and income impact in five highly globalized EU countries (1970–2018). They validated the EKC hypothesis for Switzerland and Denmark but found a U-shaped relationship in Sweden and Austria. Massagony Alfian et al. [17], using an ARDL model, tested the EKC hypothesis for CO₂ emissions in Indonesia, considering income, fossil, and renewable energy consumption, as well as forest conservation policies. They confirmed the EKC feasibility. Mikayilov Jeyhun et al. [18] examined Azerbaijan’s economic growth and carbon emissions, finding that economic growth positively impacted emissions long-term, disproving the EKC hypothesis for Azerbaijan.

Accurately estimating reduction potential is crucial for implementing emission-sharing responsibilities. Wu Xianrong et al. [19] constructed an agricultural economic accounting framework, incorporating expected and undesired outputs, to estimate the marginal carbon abatement cost for 31 provinces in China from 2000 to 2011. They developed an agricultural carbon reduction potential index based on indicators such as per capita agricultural carbon emissions and agricultural carbon shadow prices.

Previous studies on carbon emissions mainly focused on the overall carbon emissions of a region and were not detailed to the field of animal husbandry. However, in the few studies on carbon emissions of animal husbandry, scholars only paid attention to the measurement and emission reduction of carbon emissions in the production process, mainly focusing on the analysis at the ecosystem level, ignoring the role of economic growth. It cannot reflect the mutual feedback mechanism of economic growth and ecosystem, resulting in imperfect explanation of mechanism. Compared with previous studies, this study has the following innovations:

(1)

Inner Mongolia grassland, as one of China’s traditional pastoral areas, is an important animal husbandry production base and main production area of livestock breeding in China, and its animal husbandry industry is relatively developed.

(2)

This study not only estimated the carbon emissions of four major livestock species in Inner Mongolia through the IPCC inventory estimation method, and also empirically analyzed the relationship between the carbon emissions of animal husbandry and economic growth in Inner Mongolia, which is helpful to expand the research scope in the field of environmental economics and explore the interaction between economic growth and environmental quality. It provides theoretical support for the development of ecological economy theory.

2. Theoretical Analysis and Research Framework

This study takes the theory of ethnic habitats as the core theoretical framework, explores the relationship between carbon emissions from animal husbandry and economic growth from the perspective of ethnology, focuses on the geographical characteristics of the Inner Mongolia Autonomous Region, and combines the theory of low-carbon economy to analyze the carbon emission problem in the process of the development of animal husbandry so as to put forward the paths and measures for low-carbon development. In addition, drawing on the theory of the environmental Kuznets curve, the relationship between carbon emissions and economic growth in the animal husbandry industry is explored, and attempts are made to find possible solutions. Accordingly, the theoretical framework for the study of the relationship between livestock carbon emissions and economic growth in Inner Mongolia is constructed, as shown in Figure 1.

As the core theory of this study, the Kuznets curve theory originated from the Kuznets curve proposed by the famous economist Simon Kuznets. This curve explains the “inverted U-shaped” relationship between income equity and economic growth. At first, when the economy starts to grow, income is not distributed fairly, so there is more inequality. However, as the economy grew further, incomes began to become more equal, and equity improved. In 1991, two researchers, Grossman and Krueger, found a similar “inverted U” shape in the relationship between pollution and income. Their research shows that when a country’s per capita income (GDP) reaches about USD 4000, the level of sulfur dioxide pollution begins to change. Below USD 4000, pollution drops; above USD 4000, the pollution starts to increase. In 1996, another researcher, Panayotou, called it the “environmental Kuznets curve”. The idea here is that as the economy grows, pollution may initially rise but will eventually fall, even if the government does not take immediate action. Later, more researchers studied the idea of the environmental Kuznets curve in other fields and found that its conclusions were diversified, with some supporting the inverted U-shape and others showing a U-shape, N-type, monotonic rising, and monotonic falling, and the relationship between pollution and income of different pollutants showed a different pattern.

3. Current Status of Livestock Development in the Inner Mongolia Autonomous Region

The main area of Inner Mongolia’s grassland is 86.667 million hectares, of which 68.18 million hectares are effective natural pastures, accounting for 27% of the country’s grassland area, making it the largest grassland and natural pasture in China. As an important pillar industry in Inner Mongolia, animal husbandry has provided strong support for guaranteeing the national supply of green livestock products, increasing the income of farmers and herdsmen, protecting and improving the grassland ecological environment, and promoting the stable development of the national economy. However, in recent years, under the influence of climate change and diversification of the mode of economic development, the development of animal husbandry in Inner Mongolia has also brought about the problem of environmental pollution in animal husbandry.

3.1. Scale of Livestock Farming

The livestock industry in Inner Mongolia is large in scale and covers a wide range of livestock species, mainly including large livestock such as cow, horses, camels, and donkeys, as well as sheep, hog, and poultry. Cow and sheep husbandry is the main pillar of the livestock industry in Inner Mongolia. Inner Mongolia has rich grassland resources, suitable for grazing, so cow and sheep animal husbandry can be developed on a large scale. The vast pastures and high-quality grass on the grassland provide a good growing environment for cows and sheep, making the scale of cow and sheep breeding in Inner Mongolia rank among the top in the country. In addition, the livestock structure of the Inner Mongolia Autonomous Region also includes hog and poultry. They are widely farmed in local rural areas, and the yield and quality of farming has been improved through large-scale farming and modern management.

From 2000 to 2022, the number of cow slaughtered in Inner Mongolia increased from 1,584,300 to 4,288,100, an increase of 2,703,800 heads. As shown in Figure 2, the overall trend is upward despite fluctuations in some years, with the number of heads slaughtered falling to 1,296,300 and 1,490,700 in 2002 and 2003, respectively. The growth rate accelerates after 2016, and the number of stocked cow increases from 3,516,000 to 8,203,600, with a significant increase after 2017 suggesting an improvement in the scale and technology of farming. The increase in the stockpile laid the foundation for the increase in the output, showing the steady expansion of the cow breeding industry and reflecting the steady rise in market demand for beef and its products.

As shown in Figure 3, the sheep output increased from 20,812,100 heads in 2000 to 65,980,800 heads in 2022, during which the output experienced several significant fluctuations, such as 28,677,400 heads and 37,829,900 heads in 2003 and 2004, respectively, which showed a trend of rapid growth. During the period from 2005 to 2018, the output continued to grow, especially after 2016; the number of sheep slaughtered remained at a high level, indicating that the sheep farming industry has been expanding driven by market demand. The stock of sheep increased from 35,515,800 heads in 2000 to 61,240,600 heads in 2022, showing a steady upward trend. Especially after 2003, the stock of sheep increased significantly, showing the rapid expansion of the sheep farming industry. in 2017, the stock was 61,119,300 heads, and in 2022, it slightly decreased to 61,240,600 heads, but the overall trend is still upward, reflecting the continuous growth and stable development of the sheep farming industry.

As shown in Figure 4, Inner Mongolia’s hog slaughter fluctuated greatly between 2000 and 2022. The slaughter fell to 7,759,500 heads in 2007, showing that the hog farming industry was greatly affected by factors such as the market and epidemics. Hog slaughter fell to 7,583,900 heads in 2019 but then rebounded, reaching 8,868,700 heads in 2022, showing the recovery and development of the hog farming industry. The stockpile of hog was 7,382,900 heads in 2000 and increased to 5,970,800 heads in 2022. Overall, the stockpile showed a trend of increasing and then decreasing, especially after 2017, when the stockpile decreased significantly to 4,295,900 heads in 2019, showing the volatility of the hog farming industry. After 2020, the stockpile recovered, but it still did not reach the high level of the early years. Despite the challenges facing the hog farming industry, the recovery and growth in recent years has shown its potential and resilience.

As shown in Figure 5, the poultry slaughtering capacity rose overall between 2000 and 2022, from 33.8 million to 112,611,400 heads, despite short-term fluctuations, such as the sudden increase to 394,241,600 heads in 2002, but then resumed steady growth. Poultry stock increased from 51.93 million heads to 53.9191 million heads, with less fluctuation, showing strong stability and sustained development. Despite the influence of external factors such as the market and the environment, the stockpile remained at a high level, showing a steady increase in the scale and efficiency of farming.

Overall, between 2000 and 2022, Inner Mongolia’s cow, sheep, hog, and poultry farming sectors all achieved significant growth, showing divergent trends. Steady growth in cows and sheep reflects market demand and improved farming techniques; volatility in hog highlights production risks; and recovery in poultry indicates a rebound in demand following market adjustments. Continuous monitoring and analysis of these data not only allows for an understanding of market dynamics but also supports future decision-making, such as optimizing species ratios, improving disease management and adjusting market strategies.

3.2. Value and Share of Livestock Production

With the rapid development of the national economy and the improvement of people’s living standards, the demand for livestock products has been increasing, providing strong support for the development of the livestock industry in Inner Mongolia. The output value of the livestock industry can be used as an important reference to measure the level of economic development of the region, and the total output value of the livestock industry in Inner Mongolia shows a significant upward trend from 2000 to 2022. As shown in Figure 6, the total output value of the pastoral industry was 20.545 billion yuan in 2000, and increased to 187.628 billion yuan in 2022, an increase of nearly eight times. Since 2004, the growth rate of the gross output value of the pastoral industry has accelerated significantly, which is closely related to the modernization and development of the animal husbandry industry in Inner Mongolia and the increase in market demand. The proportion of the total output value of the pastoral industry to the total output value of the primary industry (agriculture, forestry, animal husbandry, and fishery) was 37.83% in 2000, and declined slightly to 43.47% by 2022. Despite fluctuations, this share remains basically stable, reflecting the pillar position of the pastoral industry in Inner Mongolia’s primary industry. Changes in this share are mainly influenced by the development of other agricultural sectors, such as the rate of development of the plantation and fishery industries. The share of total output value of the pastoral sector in Inner Mongolia’s GDP declines from 13.35% in 2000 to 8.10% in 2022. This declining share indicates that despite the increasing gross output value of the pastoral sector, the faster development of Inner Mongolia’s overall economy, especially the rapid growth of industry and services, has led to a relative decline in the share of the pastoral sector in the regional economy, but its growth in absolute terms is still an important contribution to the regional economy. The development of the pastoral industry has not only increased the total regional economy but also provided a stable source of income for farmers and herdsmen. Meat production grew from 1.434 million tons in 2000 to 2.841 million tons in 2022, with an average annual growth rate of about 3.47%. The steady increase in meat production reflects the improved productivity of the livestock sector and growing market demand.

Overall, the development of the livestock industry in Inner Mongolia during the period 2000–2022 has gone through stages of rapid growth, steady improvement, and modernization. The significant growth in the gross value of livestock production and meat production reflects the progress of the regional livestock industry and the increase in market demand. Although the share of the pastoral industry in regional GDP has declined, its importance in the primary sector and its contribution to the regional economy remain significant.

3.3. Measurement of Carbon Emissions from Livestock

In order to accurately assess the environmental impacts of the livestock sector, this paper uses the 2006 IPCC Inventory Guidelines and Emission Factors to estimate the carbon emissions of the livestock sector in Inner Mongolia.

According to the relevant literature, GHG emissions from livestock come from CH₄ in the digestive tract, CH₄ in feces, and N₂O from animals, so the GHG emissions from livestock farming will be estimated from this perspective. In this paper, the research objects are cows, sheep, and hogs, and the emission factor method proposed by Zhang Jinxin et al. [20] is borrowed to estimate the CO₂ emissions in Inner Mongolia. The following steps are used to perform the detailed calculations:

E = GWP_CH4 × AAP_i × δ_1i + GWP_CH4 × AAP_i × δ_2i + GWP_N2O × AAP_i × δ_3i,

(1)

where E represents the total carbon emission from livestock; AAP_i denotes the average annual feeding quantity of the ith livestock; δ_1i, δ_2i, and δ_3i denote the emission coefficients of gastrointestinal fermentation CH₄, fecal fermentation CH₄, and N₂O of the ith livestock, respectively; and GWP_CH4 and GWP_N2O denote the greenhouse efficiency index of CH₄ and N₂O, respectively.

According to the different feeding cycles of animals, the average annual feeding quantity was adjusted by referring to the theory proposed by Hu Xiangdong et al. [8]. See Equations (2) and (3) for details:

$$AA{P}_i=\left\{\begin{array}{c}{C}_i\times {\displaystyle \frac{Days\_alive}{365}}, r\ge 1\\ {\displaystyle \frac{C_{i-1}+C_{i+1}}{2}}, r<1,\end{array}\right.$$

(2)

$$\mathrm{r}={\displaystyle \frac{\mathrm{number} \mathrm{of} \mathrm{slaughtered} \mathrm{for} \mathrm{meat}}{\mathrm{number} \mathrm{of} \mathrm{stocking}}},$$

(3)

where AAP_i denotes the annual average stocking of a certain livestock in year I; r is the stocking rate of livestock; Days_alive is the life cycle of livestock (days); C_i denotes the end-of-year stocking in year i for livestock with a stocking rate ≥ 1; C_i−1 denotes the end-of-year stocking in year i − 1 for livestock with a stocking rate < 1; and C_i+1 denotes the end-of-year stocking in year i + 1 for livestock with a stocking rate < 1.

The emissions of CH₄ mainly originated from enteric fermentation and manure management of ruminants, and the emissions of N₂O mainly originated from manure treatment of animals. The CH₄ coefficients and N₂O coefficients were adopted from the data obtained by Hu et al. [8] with reference to the guidelines of the IPCC on greenhouse gas emissions in 2006. The detailed values are shown in

Table 1. Greenhouse gas emission factors for livestock.

Livestock Breeds	CH4 Emission Factor (kg/Unit/Year)		N2O Emission Factor (kg/Unit/Year)
	Gastrointestinal Fermentation	Fecal Fermentation
Cow	57.4	8.5	1.195
Hog	1	3.5	0.53
Sheep	5	0.16	0.33
Poultry	----	0.02	0.02

Source: The emission coefficients of CH₄ and N₂O are derived from the data obtained by Xiangdong Hu et al. [6] according to IPCC (2006).

.

The greenhouse effect indices in this paper are from IPCC (2006), and the CH₄ and N₂O greenhouse effect indices are converted into carbon dioxide equivalent units of 21 and 310, respectively.

The resulting stocking rate r, average annual feeding capacity AAP, and annual carbon emissions E for each year were calculated as follows.

Table 2. Rate of livestock production by year.

Years	Cow	Sheep	Hog	Poultry
2000	0.4506	0.5860	1.1531	0.6509
2001	0.5374	0.6105	1.1471	0.6810
2002	0.3960	0.5456	1.2297	7.4996
2003	0.3638	0.6444	1.2226	1.2996
2004	0.3620	0.7113	1.2773	2.1699
2005	0.3752	0.8325	1.3738	1.6643
2006	0.4024	0.8957	1.4050	1.7675
2007	0.4110	0.9626	1.2173	2.1046
2008	0.4103	1.0121	1.2682	1.5329
2009	0.4436	1.0273	1.2915	1.9829
2010	0.4505	1.0231	1.3268	2.0635
2011	0.4835	1.0046	1.3229	1.8712
2012	0.5063	1.0479	1.3555	2.1228
2013	0.5228	1.0309	1.3615	2.5888
2014	0.5341	1.0173	1.3893	2.1820
2015	0.4864	0.9686	1.3922	2.3466
2016	0.5188	1.0845	1.4207	2.2777
2017	0.5535	1.0239	1.8176	2.1896
2018	0.6087	1.0648	1.8017	1.9423
2019	0.6122	1.0807	1.7654	1.9655
2020	0.5915	1.0988	1.3894	1.9015
2021	0.5602	1.0924	1.4383	1.9394
2022	0.5227	1.0774	1.4853	2.0885

provides the livestock rearing rate in Inner Mongolia from 2000 to 2022. Using the average annual stocking rate in

Table 3. Average annual number of animals raised (10 thousand).

Years	Cow	Sheep	Hog	Poultry
2000	340.41	3609.27	419.84	5049.16
2001	339.46	3751.66	433.00	5224.91
2002	355.42	3983.03	413.49	4644.49
2003	421.02	4635.11	390.53	830.13
2004	493.05	4935.07	432.24	2250.43
2005	572.80	5456.46	474.47	1609.96
2006	596.91	5242.08	519.92	1616.91
2007	653.47	5277.69	382.66	991.01
2008	640.67	4126.67	422.33	938.57
2009	678.55	4388.38	435.46	1012.53
2010	649.20	4436.63	450.69	1046.44
2011	653.03	4356.33	446.35	1043.89
2012	623.47	4430.42	463.80	1269.28
2013	627.80	4439.22	459.59	1506.75
2014	641.70	4656.75	458.67	1293.98
2015	642.73	5537.76	443.07	1266.32
2016	663.57	4907.88	448.39	1301.66
2017	635.53	5143.45	453.19	1139.21
2018	641.13	5252.63	441.86	1106.45
2019	643.66	5308.22	374.00	1248.53
2020	679.28	5485.58	365.96	1207.82
2021	745.74	5511.25	400.88	1231.91
2022	807.51	5423.08	437.36	1326.65

and the carbon emission data in

Table 4. Annual carbon emissions (10,000 tons).

Years	Cow	Sheep	Hog	Poultry	Total
2000	597,189.51	760,327.76	108,654.31	33,425.41	1,499,596.99
2001	595,531.65	790,324.70	112,060.68	34,588.87	1,532,505.90
2002	623,531.08	839,064.05	107,010.47	30,746.52	1,600,352.12
2003	738,616.44	976,431.22	101,069.41	5495.49	1,821,612.56
2004	864,982.27	1,039,620.79	111,862.86	14,897.83	2,031,363.75
2005	1,004,891.68	1,149,456.81	122,791.60	10,657.93	2,287,798.01
2006	1,047,180.29	1,104,296.57	134,555.01	10,703.92	2,296,735.79
2007	1,146,406.32	1,111,797.12	99,032.48	6560.50	2,363,796.42
2008	1,123,959.41	869,323.98	109,298.83	6213.30	2,108,795.53
2009	1,190,414.19	924,456.88	112,696.27	6702.92	2,234,270.27
2010	1,138,924.02	934,620.50	116,638.68	6927.43	2,197,110.63
2011	1,145,634.41	917,704.22	115,515.56	6910.58	2,185,764.76
2012	1,093,775.82	933,313.26	120,031.01	8402.61	2,155,522.71
2013	1,101,380.93	935,165.91	118,941.08	9974.69	2,165,462.61
2014	1,125,757.62	980,990.52	118,703.69	8566.16	2,234,018.00
2015	1,127,573.38	1,166,584.52	114,666.84	8383.04	2,417,207.77
2016	1,164,125.26	1,033,893.31	116,043.93	8617.01	2,322,679.51
2017	1,114,942.06	1,083,520.19	117,284.47	7541.58	2,323,288.29
2018	1,124,757.64	1,106,519.06	114,352.87	7324.73	2,352,954.31
2019	1,129,196.15	1,118,228.87	96,791.34	8265.26	2,352,481.63
2020	1,191,686.10	1,155,591.88	94,709.74	7995.80	2,449,983.51
2021	1,308,280.20	1,161,000.94	103,748.31	8155.28	2,581,184.72
2022	1,416,646.40	1,142,425.92	113,188.91	8782.46	2,681,043.68

, it can be seen that both cows and sheep show a steady growth trend: the average annual stocking rate of cows was 3,404,100 heads in 2000, and experienced several significant rises during the period, increasing to 8,075,100 heads in 2022. Correspondingly, carbon emissions from cows increase from 5,971,895,100 tons in 2000 to 1,416,646,400 tons in 2022. Despite the significant increase in the number of cows, the percentage increase in carbon emissions is relatively small. The annual average number of sheep was 3609.27 million heads in 2000, reaching 5423.08 million heads in 2022. This increase is also particularly significant after 2003, with carbon emissions from sheep increasing from 76,032,277,600 tons in 2000 to 11,424,259,200 tons in 2022, with the growth trend being largely consistent with the growth in the number of sheep.

The average annual number of hogs was 4,198,400 heads in 2000, and it increased slightly to 4,373,600 heads in 2022. This figure was relatively flat over the 22-year period, but significant fluctuations occurred in some years, such as 2006 and 2021 (as shown in Figure 3). Despite little change in hog rearing, there was a slight increase in carbon emissions, which increased from 108,654,531,000 tons in 2000 to 113,189,100 tons in 2022 (as shown in Figure 4).

In contrast, the average annual stocking of poultry has declined over the past few years: the average annual stocking of poultry was 50,491,600 heads in 2000, decreasing to 13,266,500 heads in 2022 (as shown in Figure 3). The simultaneous decline in poultry rearing and carbon emissions has resulted in a decrease in carbon emissions from poultry from 334,254,100 tons in 2000 to 87,824,600 tons in 2022 (as shown in Figure 4).

According to the annual carbon emissions of various types of livestock in Table 4, the total carbon emissions of animal husbandry in Inner Mongolia are summarized, as shown in Figure 7.

4. Description of the Research Methodology and Variables

4.1. Selection of Variables

In verifying the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia, the selection of appropriate indicators is of great significance to the accuracy of the results of this study. By reading the research results of the study’s predecessors, it can be seen that the selection of previous studies on economic development and environmental indicators is mainly the regional gross domestic product, human capital, government intervention, the level of openness, and technological innovation, as well as environmental protection inputs.

(1)

The explanatory variable is carbon emissions from animal husbandry, referring to the emission factor method of Zhang Jinxin et al. [20] to measure carbon dioxide emissions in Inner Mongolia.

(2)

The explanatory variable in this paper is the gross regional product. This, first, is because the gross regional product can reflect the regional economic development situation more realistically; second, the availability of the gross regional product is higher, and the statistical caliber is the most consistent, which is convenient for the mutual comparison between different regions.

(3)

The control variables in this paper are the five indicators of technological innovation, environmental protection expenditure, government intervention, openness level, and human capital. Among them, technological innovation, environmental protection expenditure, and government intervention are borrowed from Zhao Feifei et al. [21], who used the general public budget expenditure in science and technology expenditures (10,000 yuan), energy saving and environmental protection expenditures (10,000 yuan), and the city’s general public budget financial expenditures as a proportion of the regional GDP; the level of openness is a reference to the ratio of the total import and export trade to the regional GDP by Xu Yingying [22]; the human capital is borrowed from Chen Yao [23], who used the number of rural population to measure rural human capital. The details are shown in

Table 5. Variable selection.

Variables Types	Variables Names	Variables Definition
Explained variable	E	Using the emission factor method proposed by Zhang Jinxin et al. [20] to estimate the carbon dioxide emissions of Inner Mongolia (10,000 tons)
Explaining variable	GDP	Gross regional product of the year (100 million yuan)
Control variables	R&D	Expenditure on science and technology in the general public budget (10 thousand yuan)
	EPE	Expenditure on energy conservation and environmental protection in general public budgets (10 thousand yuan)
	GOV	The proportion of financial expenditure in the general public budget of the city to the regional GDP
	OPEN	Total import and export trade as a share of regional GDP
	P	Rural population (10,000)

.

4.1.1. Data Sources

This article selects Inner Mongolia time series data from 2000–2022 to analyze the shape of the environmental Kuznets curve (EKC) of Inner Mongolia’s animal husbandry industry. The data on carbon emissions from the livestock industry in Inner Mongolia are adopted from Section 3.3, Estimation of Carbon Emissions from the Livestock Industry, and the data are taken from the Inner Mongolia Statistical Yearbook and the China Animal Husbandry and Veterinary Medicine Yearbook. The data for the explanatory variables include the gross regional product, and the data involved in the control variables include the general public budget expenditure of the local finance, science, and technology expenditure. Energy conservation and environmental protection expenditure, total import and export trade, and the number of rural population are all from the Inner Mongolia Statistical Yearbook.

4.1.2. Descriptive Statistics

Fiscal data for Inner Mongolia from 2000 to 2022 are shown in

Table 6. Financial data of Inner Mongolia over the years.

Year	General Public Budget Expenditure (0.1 Billion Yuan)	Total Import and Export Trade (0.1 Billion Yuan)	Regional GDP (0.1 Billion Yuan)
2000	261.06	168.78	1539.12
2001	335.98	210.90	1713.81
2002	413.33	248.73	1940.94
2003	471.09	257.70	2388.38
2004	602.75	335.09	2942.35
2005	734.61	416.58	3523.70
2006	914.97	464.40	4161.75
2007	1082.31	565.71	5166.93
2008	1454.57	610.55	6242.11
2009	1926.84	461.85	7104.22
2010	2273.50	577.43	8199.86
2011	2989.21	752.27	9458.12
2012	3425.99	707.48	10,470.14
2013	3686.52	731.17	11,392.42
2014	3879.98	894.04	12,158.22
2015	4252.96	792.54	12,948.99
2016	4512.71	772.78	13,789.26
2017	4529.93	940.86	14,898.05
2018	4831.46	1034.35	16,140.76
2019	5100.91	1097.80	17,212.53
2020	5270.16	1051.63	17,258.04
2021	5239.57	1236.49	21,166.00
2022	5887.70	1524.37	23,158.65

.

Table 7. Descriptive statistics for each variable.

Variables	Sample Size	Mean	Standard Deviation	Minimum	Maximum
E	23	2,182,414.325	307,585.3135	1,499,596.99	2,681,043.68
EPE	23	993,233.3913	559,472.895	146,960	1,752,452
GOV	23	0.262978261	0.052515253	0.1696	0.3284
OPEN	23	0.08406087	0.025017753	0.056	0.1281
P	23	1063.426087	210.2793428	754	1371.3
R&D	23	208,296.4783	134,453.37	22,214	429,858
GDP	23	97,814,934.78	65,065,510.15	15,391,200	231,586,500

provides descriptive statistical information on the livestock industry in Inner Mongolia from 2000 to 2022, involving indicators on livestock carbon emissions, environmental protection expenditures, government intervention, openness level, human capital, technological innovation, and gross regional product. From the data of animal husbandry carbon emission, the average value of animal husbandry carbon emission is about 2,182,414.325, and the standard deviation is 307,585.3135, which indicates that the animal husbandry carbon emission fluctuation of the Inner Mongolia animal husbandry industry in the past twenty-three years is small; the average technological innovation and environmental protection expenditures are more than 208 million yuan and 993 million yuan, respectively, and the energy-saving and environmental protection expenditures have a large standard deviation, which indicates that Inner Mongolia has large energy-saving environmental protection. Gross regional product (GDP) is an important indicator for measuring the overall economic situation of Inner Mongolia, with an average GDP of 978,149.34 billion yuan. The range of GDP is relatively large, with the maximum value higher than the minimum value of about 216,195.3 million yuan, showing that the overall economy of the Inner Mongolia region is showing a certain trend of growth, and the development of the animal husbandry industry has an important impact on the overall regional economy. The standard deviations of the government intervention and openness level, both of which are the proportion of general public budget expenditures and total imports and exports to GDP, are 0.052515253 and 0.025017753, respectively, indicating that the economic situation of Inner Mongolia is influenced by domestic and international trade policies and market demand. The standard deviation of the rural population is 210.2793428, with a minimum value of 754 and a maximum value of 1371.3, indicating that the rural population of Inner Mongolia has been relatively stable over the past twenty-three years, and that human capital is relatively abundant.

4.2. Model Construction

In this section, on the basis of constructing the theoretical model of the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia, we will set up the following logarithmic model to alleviate the influence of heteroskedasticity and carry out the empirical test of the EKC curve model on the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia in order to further clarify the relationship between the two changes.

LnE = C + αLnGDP + Control + ε,

(4)

LnE = C + αLnGDP + βLnGDP² + Control + ε,

(5)

LnE = C + αLnGDP + βLnGDP² + γLnGDP³ + Control + ε,

(6)

where LnE is the Ln value of carbon emissions from animal husbandry; LnGDP is the Ln value of gross regional product; α, β, and γ are variable parameters; Control is a control variable; and ε is a random error term.

5. Empirical Analysis of the Relationship Between Carbon Emissions from Animal Husbandry and Economic Growth in Inner Mongolia

Non-Linear Regression

STATA 15 was used to analyze the regression of the time series data of Inner Mongolia for the past 23 years, in which the first two columns did not include the quadratic term, the middle two columns included only the quadratic term, and the last two columns included the cubic term on top of the quadratic term.

As shown in

Table 8. Results of regression analysis.

Variables	Estimators	p-Values	Estimators	p-Values	Estimators	p-Values
C	5.481287	0.161	78.32722	0.002	−1120.074	0.186
Ln GDP	0.4257866	0.067	−8.118024	0.005	193.2589	0.175
Ln GDP2	——	——	0.2505302	0.004	−11.0012	0.167
Ln GDP3	——	——	——	——	0.209027	0.158
EPE	−0.000000709	0.147	−0.00000101	0.019	−0.000000768	0.083
GOV	6.329177	0.007	7.434547	0.000	7.92967	0.000
OPEN	0.4954135	0.923	−5.357042	0.246	−7.5471	0.118
P	0.0004378	0.488	0.0008997	0.103	0.0008714	0.105
R&D	−0.000000444	0.732	−0.00000258	0.049	−0.00000249	0.052
R-squared	0.7686	——	0.8532	——	0.8690	——
F-statistic	11.07	——	15.78	——	14.93	——
Curve shape	linear type	——	U type	——	N type	——
Inflection point	None	——	16.201687	——	17.5435	——

Note: Data are used to reject the original hypothesis at the 5% significance level.

, in the model containing only primary terms, the estimated value of LnGDP is 0.4257866, with a p-value of 0.067, and among the control variables, only GOV has a significant effect on carbon emissions, with an estimated value of 6.329177 and a p-value of 0.007, which suggests that government interventions have a significant positive effect on carbon emissions, while R&D and EPE have a very insignificant negative effect on carbon emissions. The R² of the model is 0.7686, and the F-statistic is 11.07, indicating that the model has a good overall fit. When the quadratic term is added, the estimated value of LnGDP is −8.118024, with a p-value of 0.005, and the estimated value of the quadratic term, LnGDP², is 0.2505302, with a p-value of 0.004. Among the control variables, GOV is still significant, and the p-values of EPE and R&D have reached the significance level (p-values of 0.019 and 0.049, respectively). The R² is improved to 0.8532 and the F-statistic is 15.78. The model fitting effect is further improved, indicating that the model’s ability to explain the data is enhanced after adding the quadratic term. According to the curve shape analysis, it shows a typical U-shaped curve with the inflection point at 16.201687. Further introducing the cubic term variable, the curve shape is N-shaped, with the inflection point at 17.5435, which indicates that the growth rate of LnE relative to LnGDP changes after this point. The R² increases to 0.8690, and the F-statistic is 14.93, which indicates that the model fitting effect is further improved.

Therefore, according to the regression results after adding control variables, the expression of the environmental Kuznets curve function in Inner Mongolia can be expressed as follows:

LnE = 193.2589 LnGDP − 11.0012LnGDP² + 0.209027LnGDP³ − 0.000000768EPE + 7.92967 GOV − 7.5471OPEN + 0.0008714P − 0.00000249R&D − 1120.074.

(7)

According to the model results, GDP growth has a significant positive effect on carbon emissions, and the percentage of carbon emissions increases significantly when GDP grows by one unit. However, carbon emissions show a nonlinear trend with economic growth, different from the traditional inverted U-shaped curve presented by the EKC curve, showing a clear N shaped. The inflection point is LnGDP = 17.5435, and the actual converted GDP is 415,953,990,000 yuan, indicating that the carbon emissions turn from a downward trend to an upward trend at this level of GDP (as shown in Figure 8). This inflection point has important policy implications, indicating that special attention needs to be paid to carbon emission control measures at this level of the economy. The coefficient of environmental protection expenditure is −0.000000768, indicating that increasing environmental protection expenditure reduces carbon emissions, but the effect is very weak. The coefficient of government intervention is 7.92967, indicating that the stronger the government intervention, the more carbon emissions are likely to increase. The coefficient of openness level is −7.5471, implying that increasing the level of openness helps to reduce carbon emissions. In addition, the coefficient of population is 0.0008714, and the coefficient of technological innovation input is −0.00000249, indicating that these two factors have a small and opposite effect on carbon emissions.

6. Conclusions and Recommendations for Response

6.1. Main Findings

This paper chooses to use regional gross domestic product as an economic indicator and carbon emissions from animal husbandry as an environmental indicator and measures the relationship between animal husbandry and economic growth in Inner Mongolia based on time-series data over a 23-year period from 2000 to 2022 and revalidates the environmental Kuznets curve.

(1)

The research objects of this paper are cows, sheep, hog, and poultry. Drawing on the emission factor method of Zhang Jinxin et al. [20], the carbon dioxide emissions of Inner Mongolia are measured, and the results show that the livestock industry in Inner Mongolia achieved significant growth between 2000 and 2022 and that the average annual feeding capacity and carbon emissions of all types of livestock increased. However, the carbon emission problem has been gradually highlighted along with the increase in feeding capacity, with the average annual feeding capacity and carbon emission of cow, sheep and hog showing an increasing trend, while on the contrary, the average annual feeding capacity and carbon emission of poultry have decreased.

(2)

Through regression analysis, it is further pointed out that environmental protection expenditure, openness level, and technological innovation all show a negative correlation with the carbon emissions of the livestock industry, and government intervention, human capital, and the carbon emissions of the livestock industry show a positive correlation.

(3)

From the results of our data analysis, Inner Mongolia animal husbandry carbon emissions and the introduction of the three terms of regional gross domestic product after the introduction of economic growth show an N-shaped relationship, and there is an inflection point, which is 415,953,900,000 yuan. Initial economic growth is accompanied by a rapid rise in carbon emissions. Medium-term economic growth helps to control carbon emissions. And further economic growth in the later period brings about a rise in carbon emissions. In view of this complex relationship, it is necessary to take corresponding measures at different stages of economic development in order to realize the coordinated development of economic growth and carbon emission control.

6.2. Countermeasures Suggestions for the Development of Livestock Husbandry in Inner Mongolia

Although the model shows that environmental protection expenditures have a small impact on carbon emissions, increasing environmental protection investment is still an effective means of reducing carbon emissions [24]. Especially in the stage of rapid economic growth, the importance of environmental protection cannot be ignored. By increasing the investment in environmental protection funds, the construction and operation of pollution control facilities can be strengthened, the environmental monitoring system can be improved, and the capacity of environmental enforcement can be enhanced so as to better control and reduce carbon emissions.

Secondly, upgrading technological innovation is the key to coping with the carbon emission problem [25]. Investment in technological innovation should continue to be increased, especially to support the development and application of low-carbon and green technologies. Promoting the research, development, and popularization of clean energy technologies, new energy-saving technologies and carbon capture and storage technologies can not only directly reduce carbon emissions but also inject new momentum into economic development. Through technological innovation, energy utilization efficiency can be improved and dependence on fossil energy can be reduced, thereby lowering carbon emissions. At the same time, enterprises and scientific research institutions are encouraged to strengthen technical cooperation and exchanges, promote the transformation and application of scientific and technological achievements, and form a good ecosystem for technological innovation.

Optimizing industrial structure is also an important way to reduce carbon emissions. The modernization and scaling up of animal husbandry should be promoted to enhance production efficiency and reduce carbon emission intensity. Modernized animal husbandry can not only improve resource utilization efficiency but can also reduce carbon emissions through scientific management and advanced technological means. For example, it promotes advanced feeding technology and equipment, improves feed formulas, and reduces carbon emissions from livestock and poultry. At the same time, it actively promotes the green transformation of the industrial chain and encourages the development of a circular economy to reduce resource waste and environmental pollution.

The government of Inner Mongolia plays a significant role in carbon emission control, and government intervention should be further strengthened. The government should strengthen the supervision and control of carbon emissions from the animal husbandry industry through policy guidance, laws, and regulations. It should formulate and improve carbon emission standards and implement a strict environmental protection review system to ensure that enterprises comply with environmental protection regulations in the production process. At the same time, the Inner Mongolia government should increase its support for low-carbon industries by providing incentives such as financial subsidies and tax incentives to encourage enterprises to invest in low-carbon technologies and equipment. By strengthening policy guidance and supervision, the whole society should be promoted to participate in the management of carbon emissions.

Finally, expanding the level of openness is also of great significance to reducing carbon emissions. Increasing the level of openness can help introduce advanced technology and management experience, thereby reducing carbon emissions. By learning from the successful experiences and practices of foreign countries in low-carbon development, and in light of the unique environment and animal husbandry situation in Inner Mongolia, we can avoid introducing high-pollution and high-energy-consumption projects, promote the green development of the regional economy, and enhance the region’s ability to control carbon emissions.

At different stages of economic growth, the government can take steps to help the economy grow while also reducing carbon emissions. Here is what the government can do at each stage, i.e., before, during, and after the turning point (when the economy grows more, and emissions may start to increase).

Before the turning point (when the GDP is low or medium and emissions are just starting to go up), economic growth has a smaller impact on carbon emissions. The government can spend more money on clean energy (like solar, wind, and natural gas) and on green technology (like energy-saving equipment). This can help improve the energy system. The government can also set limits on how much energy factories use and how much carbon they release to encourage cleaner ways of making products. At the same time, by teaching people about protecting the environment through community and media, they can help people become more eco-friendly and make greener choices.

When the GDP reaches the turning point, carbon emissions may go from going down to going up. At this stage, the government needs to take stronger steps to control emissions while also keeping the economy steady. For example, they could start charging a carbon tax or make companies pay for carbon emissions. This can encourage companies to produce in a cleaner way and raise money for green projects. The government can also support green supply chains and give people more eco-friendly product choices. This helps industries move towards a low-carbon future.

After the GDP passes the turning point, carbon emissions could increase quickly. At this stage, the government needs even stricter steps to stop emissions from growing too much while still supporting the economy. They should set stricter limits on emissions and clear goals for each industry. For companies that go over the limit, the government can apply penalties like higher carbon taxes. The government should also work with other countries and regions to learn and use new low-carbon technology, which will keep the economy strong.