Measurement and Influencing Factors of Carbon Emissions of China’s Livestock Husbandry in the Post-COVID-19 Era—Based on the Supply-Side Perspective
Abstract
:1. Introduction
2. Literature Review
3. Measurement of Carbon Emissions of Livestock Husbandry
3.1. Methodology and Indicators of Carbon Emissions of Livestock Husbandry
3.2. Measurement Results of Carbon Emissions of Livestock Husbandry
3.2.1. Dynamic Changes of Carbon Emissions from Livestock Husbandry
3.2.2. Industry Contribution of Carbon Emissions from Livestock Husbandry
3.2.3. Spatial Distribution of Carbon Emissions of Livestock Husbandry
3.2.4. Summary
4. Analysis of the Influencing Factors of Carbon Emissions of Livestock Husbandry
4.1. Panel Regression Models and Indicators
- (1)
- With cultivated land area (culti_land) and grassland area (grassland) as explanatory variables, this paper examines the relationship between land structure and carbon emissions from livestock husbandry. As a space carrier and forage source, cultivated land and grassland have a direct impact on output, as crops and grasses will curb the greenhouse effect. Different forage ratios may cause livestock to generate different amount and types of greenhouse gases.
- (2)
- This paper takes dairy cow inventory (dairy), beef cattle inventory (beef), sheep inventory (sheep), pig inventory (pig), poultry inventory (poultry), and other livestock inventory (other) as explanatory variables to examine the relationship between breeding structure and carbon emissions. Livestock inventory determines the output and the amount and intensity of greenhouse gas, but its impacts vary by breed.
- (3)
- Human capital is measured by the number of employees in state-owned enterprises (SOE) in the industry (stat_owned). Because SOE’s employees are more educated and skilled, and they have both environmental awareness and capability, they are more likely to increase the output and reduce carbon emissions.
- (4)
- Livestock husbandry mechanization is represented by the mechanical power (mech_power). High level means more livestock, which indicates greater emissions. However, high level of mechanization will also increase output and output value and reduce carbon intensity.
- (5)
- Numbers of senior technicians (senior), intermediate technicians (intermediate) and junior technicians (primary) in the industry represent different technology levels. Technology level has a direct impact on the carbon emissions of livestock husbandry. Under different technology levels, the livestock amount, output and output value will have a more complicated relationship between technical level and carbon intensity.
- (6)
- Scale is measured by amounts of large-scale households (large), medium-scale households (medium), small-scale households (small) and below-scale households (below). The impact of scale on carbon emissions in the industry is similar to that of technology.
4.2. Empirical Result Analysis
4.2.1. Analysis of Basic Regression Results
- (1)
- The area of cultivated land is positively correlated with the total carbon emissions of livestock husbandry but has a strong negative correlation with the livestock husbandry’s carbon intensity. This result supports the view that the total carbon emissions or carbon intensity of livestock husbandry are affected by the grain area distribution. Li et al. [23] consider that using grass instead of concentrate can effectively reduce the carbon footprint. However, the test in this paper shows that grassland area has a slight negative correlation with total carbon emissions and carbon intensity. This is likely because of the constant low proportion of grass supplies from the forage.
- (2)
- The inventory of dairy cows, beef cattle or sheep has a strong positive correlation with the total carbon emissions and carbon intensity of livestock husbandry, while the inventory of pigs has a weak negative correlation with the total carbon emissions of livestock husbandry. This is mainly because the emission coefficients of cattle or sheep are high due to the large inventories associated with them, and that the CH4 of enteric emissions is often ignored or difficult to collect and process. In comparison, the emission coefficient of pigs is low as most of the emissions come from manure. China now has mature and advanced manure treatment technology and equipment, which has been widely applied in the pig industry. Meanwhile, the larger husbandry scale of hog farming makes the inventory increase even as total carbon emissions decrease. In addition, the results of this paper are related to the views of Zhan et al. [30] who believe that the number of pigs and sheep is positively correlated with methane emissions, while the number of cattle and camels is negatively correlated with methane emissions.
- (3)
- The number of staff employed by state-owned organizations in livestock husbandry has a weak negative correlation with its total carbon emissions and carbon intensity. This group has a deeper understanding and awareness of emission reduction policies, and they master more diversified technologies and active behaviors in terms of pollution removal or emission reduction. However, the small population of this group makes it difficult to realize their full potential.
- (4)
- The mechanical power of livestock husbandry has a positive relationship with its total carbon emissions and carbon intensity. The allocation of dung collection, storage, separation and other machinery for anaerobic digestion and solid–liquid separation can also effectively reduce carbon emissions [38]. At the same time, with the increase of temperature and ventilation, methane emissions will also increase, while temperature control and ventilation equipment in livestock houses play an active role in reducing methane emissions from livestock [39]. However, such special machinery for livestock husbandry is mainly used to increase productivity, such as grass and silage harvesters, forage processing and feeding machines, product collection and primary processing machines, loading and unloading machines and transporters. While these machines reduce the carbon amount per unit of livestock, they increase the total amount of livestock, ultimately leading to an increase in the total carbon emissions of livestock husbandry. However, machinery mainly used for pollution removal or emission reduction has received little attention, especially machinery related to manure removal, manure treatment, and environmental control in livestock houses.
- (5)
- The number of senior technicians in livestock husbandry has a significantly weak positive correlation with its total carbon emissions, but a weak negative correlation with its carbon intensity. This might be caused by the fact that senior technicians have a deeper understanding of policies as leaders in the production process, and so have the awareness and ability to enhance the technology of this sector in line with policy goals to increase output and reduce emissions. However, senior technicians account for a small proportion, and thus have a limited impact on carbon emission reduction. The number of intermediate technicians in livestock husbandry has a significant positive correlation with its total carbon emissions or its carbon intensity, while the number of junior technicians is positively correlated with its total carbon emissions and carbon intensity. It is possible that the abovementioned junior and intermediate technicians focus their techniques on output growth.
- (6)
- The amount of households engaging in large-scale livestock husbandry has a negative relationship with its total carbon emissions and carbon intensity. The amount of households engaging in medium-scale livestock husbandry is significantly positively correlated with its total carbon emissions but negatively correlated with its carbon intensity. As for households engaging in small-scale livestock husbandry, the household amount is positively related to its total carbon emissions and negatively related to its carbon intensity. In comparison, the amount of households engaging in below-scale livestock husbandry is negatively correlated with its total carbon emissions, and significantly and strongly negatively correlated with its carbon intensity. For large-scale livestock husbandry production, one of the ways to curb carbon emissions is to seek higher output efficiency and lower emissions per livestock. Moreover, the test shows that medium-scale or small-scale livestock husbandry is positively correlated with its total carbon emissions, while below-scale livestock husbandry is negatively correlated with its total carbon emissions and carbon intensity but has the highest emission per livestock, which might be caused by differences in decontamination technology or equipment at different scales. Since large-scale livestock farming is subject to the strongest policy constraints, it generally has chain-type, high-end machines equipping the livestock house for manure removal, manure treatment, environmental control and other technologies and equipment. Husbandry infrastructure nowadays is even equipped with grassland construction, epidemic prevention and other machinery, leading to a greater ability of large-scale decontamination than the growth and accumulation effects of its manure. On the other hand, due to the loose supervision that medium- or small-scale livestock husbandry operations receive, they often have fragmented and low-end decontamination technology or equipment. As a result, their decontamination ability cannot keep up with the growth and accumulation effects of manure. Despite the fact that below-scale farmers have the worst equipment but the highest discharge per livestock, their small amount of livestock inventory cause almost no growth and little accumulation effect of manure.
4.2.2. Examination of the Influence of Technical and Scale Structure
- (1)
- Regarding the number of technicians at all levels of grassland stations, the presence of intermediate or primary technicians at veterinary stations is negatively correlated with total carbon emissions. The number of technicians at all levels of improvement stations, senior technicians of veterinary stations and primary technicians of grassland stations is negatively correlated with its carbon intensity. In general, the number of technicians of grassland stations, veterinary stations, and improvement stations of livestock husbandry has a negative relationship with its carbon emissions. The different types of technology mastered by technicians at each workstation are reasons for the differential impact. In terms of specific technologies, methanogenic immunization can reduce the number or activity of methanogenic bacteria in the rumen, thus directly reducing methane emissions [40]. Livestock and poultry breed improvement technology can control carbon intensity from the breeding source, and by improving genetic traits, excellent varieties with higher feed conversion rate and less gas emissions can be obtained [41].
- (2)
- For medium- or small-scale beef cattle, layers of all scales, and small- or below-scale broiler chicken breeding, the number in a household is negatively correlated with its total carbon emissions. In medium-scale dairy cows, large-, medium- or small-scale beef cattle, small-scale sheep, swine farming of all scales, small- or below-scale layers, and large-, medium- or small-scale broiler breeding, the number in households is negatively related to its carbon intensity. In general, large-scale non-grass-fed livestock such as swine, layer, and broiler, and beef cattle under medium-scale, dairy cows, sheep and other grass-fed livestock are more likely to reduce carbon emissions generated by livestock husbandry. As mentioned above, if the decontamination capacity of a certain scale is greater than the effect of the growth and accumulation of manure, there may be an extensive but low emission effect. However, such a situation relies on the premise that manure is the main pollutant, indicating that large-scale non-grass-fed livestock husbandry is more likely to reduce carbon emissions. For grass-fed livestock—the main CH4 emitter—large-scale breeding with an increasing amount of livestock will increase and concentrate CH4, because the current technical equipment for gas emission reduction is neither mature nor widely used. To make things worse, the environment-controlled systems commonly deployed in large-scale operations reinforce this effect. As the CH4 increase and concentration effects of households below the medium-scale are weaker than those of large-scale breeding, the popularity of environment-controlled systems with them is also lower, so the CH4 emissions of the grass-fed livestock that are below the medium-scale are covered by the environmental carrying capacity.
4.2.3. Summary
5. Conclusions and Recommendations
6. Deficiency and Prospect
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
LCA | Life Cycle Assessment |
LMDI | Logarithmic Mean Index Method |
OLS | Ordinary Least Squares |
FE | Fixed Effects Model |
IPCC | Intergovernmental Panel on Climate Change |
F-test | Joint Hypotheses Test |
LSDV | Least Square Dummy Variable |
LM | Lagrange Multiplier Method |
MLE | Maximum Likelihood Estimation |
RE | Random Effects Model |
FE-TW | Two-way Fixed Effects Model |
SOE | State-owned Enterprises |
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Province | Total Carbon Emissions | Carbon Intensity | Major Grain Producing Areas | Economic Region | ||||
---|---|---|---|---|---|---|---|---|
Proportion in China | Ranking | Emission Area | Proportion in China | Ranking | Intensity Area | |||
Henan | 8.760% | 1 | High | 1.618% | 23 | Low | Yes | Central |
Neimenggu | 8.508% | 2 | High | 9.745% | 3 | High | Yes | West |
Sichuan | 7.628% | 3 | High | 2.865% | 12 | Low | Yes | West |
Shandong | 6.676% | 4 | High | 1.972% | 17 | Low | Yes | East |
Yunnan | 6.479% | 5 | High | 3.790% | 7 | Low | No | West |
Xinjiang | 5.298% | 6 | Medium | 11.084% | 1 | High | No | West |
Hunan | 5.047% | 7 | Medium | 4.014% | 6 | Medium | Yes | Central |
Hebei | 4.825% | 8 | Medium | 1.282% | 28 | Low | Yes | East |
Heilongjiang | 4.542% | 9 | Medium | 1.391% | 25 | Low | Yes | Northeast |
Gansu | 4.076% | 10 | Medium | 9.212% | 4 | High | No | West |
Qinghai | 3.821% | 11 | Medium | 10.973% | 2 | High | No | West |
Liaoning | 3.479% | 12 | Medium | 1.796% | 20 | Low | Yes | Northeast |
Hubei | 3.316% | 13 | Medium | 1.843% | 19 | Low | Yes | Central |
Jilin | 3.255% | 14 | Medium | 2.022% | 16 | Low | Yes | Northeast |
Guizhou | 3.085% | 15 | Medium | 5.992% | 5 | Medium | No | West |
Guangxi | 2.950% | 16 | Low | 2.490% | 13 | Low | No | West |
Jiangxi | 2.502% | 17 | Low | 2.901% | 10 | Low | Yes | Central |
Guangdong | 2.427% | 18 | Low | 3.187% | 8 | Low | No | East |
Anhui | 2.287% | 19 | Low | 1.626% | 22 | Low | Yes | Central |
Jiangsu | 1.793% | 20 | Low | 1.313% | 26 | Low | Yes | East |
Shaanxi | 1.777% | 21 | Low | 1.402% | 24 | Low | No | West |
Chongqing | 1.491% | 22 | Low | 2.442% | 14 | Low | No | West |
Shanxi | 1.489% | 23 | Low | 2.335% | 15 | Low | No | Central |
Ningxia | 1.096% | 24 | Low | 2.922% | 9 | Low | No | West |
Fujian | 1.065% | 25 | Low | 1.302% | 27 | Low | No | East |
Zhejiang | 0.843% | 26 | Low | 1.906% | 18 | Low | No | East |
Hainan | 0.686% | 27 | Low | 2.885% | 11 | Low | No | East |
Tianjin | 0.354% | 28 | Low | 1.685% | 21 | Low | No | East |
Beijing | 0.275% | 29 | Low | 0.980% | 30 | Low | No | East |
Shanghai | 0.170% | 30 | Low | 1.024% | 29 | Low | No | East |
I | II | |
---|---|---|
C | C_intens | |
culti_land | 0.0234 | −1.150 *** |
(0.0972) | (0.361) | |
grassland | −0.000818 | −0.0170 |
(0.00500) | (0.0241) | |
dairy | 0.0311 | 0.178 ** |
(0.0234) | (0.0803) | |
beef | 0.245 *** | 0.221 ** |
(0.0548) | (0.0853) | |
sheep | 0.248 *** | 0.269 |
(0.0752) | (0.299) | |
pig | −0.0195 | 0.0472 |
(0.0682) | (0.491) | |
poultry | 0.0412 | 0.0215 |
(0.0779) | (0.400) | |
other | 0.0189 | 0.233 |
(0.0133) | (0.139) | |
stat_owned | −0.00222 | −0.00643 |
(0.00765) | (0.0269) | |
mech_power | 0.0394 | 0.0361 |
(0.0277) | (0.105) | |
senior | 0.0539 * | −0.0677 |
(0.0288) | (0.112) | |
intermediate | −0.104 * | 0.398 ** |
(0.0583) | (0.187) | |
primary | 0.0539 | 0.150 |
(0.0602) | (0.182) | |
large | −0.00366 | −0.0456 |
(0.0241) | (0.0753) | |
medium | 0.0730 ** | −0.100 |
(0.0261) | (0.0990) | |
small | 0.00480 | −0.195 |
(0.0539) | (0.129) | |
below | −0.0293 | −0.542 ** |
(0.0495) | (0.247) | |
year | Controlled | Controlled |
_cons | 3.468 *** | 10.95 *** |
(1.098) | (3.117) | |
N | 233 | 233 |
R2 | 0.849 | 0.614 |
I | II | |
---|---|---|
C | C_intens | |
hus_sen | 0.0301 | 0.271 |
−0.0349 | −0.288 | |
imp_sen | 0.0153 | −0.0968 |
−0.0247 | −0.101 | |
gra_sen | −0.00752 | 0.00156 |
−0.00441 | −0.0312 | |
fed_sen | −0.00401 | −0.0947 |
−0.0102 | −0.0743 | |
vet_sen | 0.0107 | −0.101 ** |
−0.0121 | −0.0434 | |
dairy_lar | 0.00728 | 0.0169 |
−0.0185 | −0.135 | |
beef_lar | 0.012 | −0.157 * |
−0.0125 | −0.0894 | |
sheep_lar | 0.00677 | 0.0629 |
−0.00816 | −0.0749 | |
pig_lar | −0.0334 | −0.27 |
−0.036 | −0.184 | |
layer_lar | −0.00486 | 0.115 |
−0.0113 | −0.0793 | |
broiler_lar | −0.00416 | −0.235 |
−0.0103 | −0.142 | |
culti_land | Controlled | Controlled |
grassland | Controlled | Controlled |
dairy | Controlled | Controlled |
beef | Controlled | Controlled |
sheep | Controlled | Controlled |
pig | Controlled | Controlled |
poultry | Controlled | Controlled |
other | Controlled | Controlled |
stat_owned | Controlled | Controlled |
mech_power | Controlled | Controlled |
year | Controlled | Controlled |
_cons | 2.089 *** | 13.47 ** |
−0.566 | −5.467 | |
N | 134 | 134 |
R2 | 0.949 | 0.566 |
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Hao, G.; Zhu, H.; Cui, Y. Measurement and Influencing Factors of Carbon Emissions of China’s Livestock Husbandry in the Post-COVID-19 Era—Based on the Supply-Side Perspective. Sustainability 2023, 15, 913. https://doi.org/10.3390/su15020913
Hao G, Zhu H, Cui Y. Measurement and Influencing Factors of Carbon Emissions of China’s Livestock Husbandry in the Post-COVID-19 Era—Based on the Supply-Side Perspective. Sustainability. 2023; 15(2):913. https://doi.org/10.3390/su15020913
Chicago/Turabian StyleHao, Guoming, Honghui Zhu, and Yechen Cui. 2023. "Measurement and Influencing Factors of Carbon Emissions of China’s Livestock Husbandry in the Post-COVID-19 Era—Based on the Supply-Side Perspective" Sustainability 15, no. 2: 913. https://doi.org/10.3390/su15020913