Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China?
Abstract
:1. Introduction
2. Theoretical Basis and Research Assumptions
2.1. The Effects of the Digital Economy on Carbon Emission Reduction in Livestock Husbandry
2.2. The Spatial Effects of Carbon Emission Reduction in Livestock Farming Empowered by the Digital Economy
3. Materials and Methods
3.1. Econometric Model
3.1.1. Benchmark Regression Model
3.1.2. Mediating Effect Regression Model
3.1.3. Spatial Measurement Model
3.2. Variable Selection
3.2.1. Explained Variable: Carbon Emission Intensity of Livestock Sector
3.2.2. Core Explanatory Variables: Digital Economy
3.2.3. Control Variables
3.2.4. Mediating Variables
3.3. Data Sources
4. Results and Discussion
4.1. Carbon Intensity of Livestock Husbandry
4.2. Benchmark Regression Results
4.3. Robustness Testing and Endogeneity Processing
4.3.1. Robustness Testing
4.3.2. Endogeneity Processing
4.4. Heterogeneity Analysis
4.5. Mechanism Analysis
4.6. Further Study: Spatial Spillover Effect
5. Conclusions
5.1. Research Conclusions
5.2. Recommendations
- (1)
- Establish digital strategic thinking and encourage the digital transformation of livestock farming. It is necessary to accelerate the promotion of the deep integration of the digital economy and the traditional livestock farming industry. The government should accelerate the coverage and application of big data, artificial intelligence, the Internet of Things, blockchain, and other digital technologies in the whole industry chain of animal husbandry, enhance the coverage of intelligent farm bases, and ultimately promote the development of precision animal husbandry. The digital economy should be applied to improve the level of scale in livestock farming, reduce the proportion of small farmers in the livestock industry, and build up a “scale, specialization, standardization, intensification, and greening” model of livestock development;
- (2)
- Tailor policies based on local conditions to achieve synergistic development of a low-carbon transition in the livestock sector. The focus of the carbon emission reduction task for livestock farming is in the western and northeastern regions, which are the main livestock farming provinces in China and also the areas with the most serious carbon emissions. The government should give the western and northeastern regions of livestock farming carbon emission reduction more fiscal support and policy inclination, but also accelerate the improvement of western and northeastern regions’ digital economy development level. The construction of smart farms should be laid out according to the resource advantages of the western and northeastern regions and modern manure resource treatment equipment should be constructed to improve the efficiency of resource utilization and production efficiency. The government should make a concerted effort to stimulate the growth of the western region and revitalize the northeast. Effective talent introduction policies should be developed to attract the inflow of talent in light of the significant brain leakage in the northeast and western regions. In terms of livestock species heterogeneity, the government should focus on laying out digital carbon reduction programs for non-dairy cattle and pigs to accelerate the greening development of non-dairy cattle and pig farming;
- (3)
- Improve the level of digital skills and the digital literacy of farmers and provide human capital support for the livestock sector. The local governments should coordinate the formulation of action programs to enhance farmers’ digital literacy and skills, attach importance to online training methods, introduce various types of smart farming machinery to farmers, and make farmers pay attention to the production methods of smart farming. It is also important to provide live training on e-commerce, teach farmers to use new media tools to capture market information in time, and use mobile phone applications to carry out digital marketing, so as to cultivate new types of farmers with digital awareness and skills. It is also necessary to vigorously publicize and explain to farmers the knowledge of digital security and raise their awareness of digital security;
- (4)
- Strengthening regional linkages and leveraging spillover and learning effects between regions. The government should use the interconnectivity, interactivity, and high permeability of digital technology to build an effective knowledge-sharing platform and technology transfer mechanism to promote the flow and sharing of knowledge and information between regions and to promote the synergistic regional development of the low-carbon transformation of the livestock sector. Academic seminars, commercial forums, and cooperative forums on the digital revolution of animal husbandry should be actively organized by local governments. The government should accelerate the establishment of a model in which a digital animal husbandry low-carbon development demonstration base leads other regions to realize a low-carbon transformation of the animal husbandry industry.
5.3. Research Limitations and Areas for Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Category | CH4 Emission Factor | N2O Emission Factor | |
---|---|---|---|
Enteric Fermentation | Manure Treatment | Manure Treatment | |
dairy cows | 68 | 16 | 1 |
non-dairy cows | 51.4 | 1.5 | 1.37 |
horses | 18 | 1.64 | 1.39 |
mules/donkeys | 10 | 0.9 | 1.39 |
pigs | 1 | 3.5 | 0.53 |
camels | 46 | 1.92 | 1.39 |
goats | 5 | 0.17 | 0.33 |
sheep | 5 | 0.15 | 0.33 |
rabbits | 0.254 | 0.08 | 0.02 |
poultry | 0 | 0.02 | 0.02 |
Primary Indicators | Weights | Secondary Indicators | Unit | Indicator Attribute |
---|---|---|---|---|
digital infrastructure | 0.013 | Density of Internet access ports | Ten thousand units/ten thousand person | + |
0.053 | Number of domain names | Ten thousand units | + | |
0.092 | Number of web pages | Ten thousand units | + | |
0.012 | Mobile phone penetration rate | % | + | |
0.019 | Fiber optic cable density | Ten thousand units/ten thousand person | + | |
0.023 | Number of mobile phone base stations | Ten thousand units | + | |
digital industrialization | 0.079 | Total postal business | Billions RMB | + |
0.050 | Total telecommunication business | Billions RMB | + | |
0.012 | Internet penetration rate | % | + | |
0.076 | Software business revenue | Billions RMB | + | |
industrial digitization | 0.088 | Express mail volume | Ten thousand units | + |
0.055 | E-commerce sales | Billions RMB | + | |
0.012 | Share of enterprises in e-commerce Trading activities | % | + | |
0.012 | Digital Financial Inclusion Index | - | + | |
0.035 | Rural broadband access users | Ten thousand units | + | |
0.017 | Number of computers per 100 rural inhabitants at the end of the year | - | + | |
0.001 | Proportion of administrative villages with Internet broadband service | % | + | |
digital economic development environment | 0.053 | Number of patent applications | Ten thousand units | + |
0.045 | Percentage of information technology employees | % | + | |
0.254 | R&D Expenditure | billions | + |
Variable | (1) | (2) | (3) |
---|---|---|---|
OLS | FE | SYS-GMM | |
−0.599 ** (0.01) | |||
digital | −1.058 ** (0.04) | −0.988 ** (0.08) | −4.045 *** (0.03) |
pgdp | −0.227 (0.16) | −0.105 (0.82) | 0.602 ** (0.03) |
indust | 0.053 (0.35) | −0.044 (0.59) | −0.310 (0.23) |
agglom | −0.322 *** (0.00) | 0.028 (0.78) | 0.140 (0.63) |
regula | −0.000 (0.47) | −0.000 (0.75) | −0.000 (0.79) |
invest | −10.892 *** (0.00) | −4.444 *** (0.04) | −7.941 (0.26) |
market | −0.210 *** (0.00) | 0.028 (0.39) | 0.161 (0.24) |
constant | 11.937 *** (0.00) | 8.275 (0.12) | −3.181 (0.34) |
Sample size | 270 | 270 | 270 |
AR(1) p-value | 0.037 | ||
AR(2) p-value | 0.108 | ||
Hansen J test p-value | 0.146 | ||
R-square | 0.635 | 0.739 |
Variable | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
Digital | −0.991 * | −1.061 * | −0.094 * | −1.644 ** | −2.023 *** |
(0.082) | (0.06) | (0.09) | (0.02) | (0.593) | |
Control variables | YES | YES | YES | YES | YES |
Year fixed effects | YES | YES | YES | YES | YES |
Provincial fixed effects | YES | YES | YES | YES | YES |
R-squared | 0.744 | 0.739 | 0.738 | 0.351 | 0.790 |
Stage 1: Development of the Digital Economy | Stage 2: Carbon Emission Intensity of Livestock Husbandry | |||
---|---|---|---|---|
Variable | Coefficient | Standard Error | Coefficient | Standard Error |
Carbon emission intensity of livestock husbandry | −6.978 *** | 1.166 | ||
Instrumental variable | 0.159 *** | 0.030 | ||
Kleibergen–Paap rk LM | 11.74 *** | |||
Kleibergen–Paak rK Wald F | 29.09 *** | |||
Cragg–Donald Wald F | 50.93 *** | |||
Sample size | 180 |
Variable | 2013–2017 | 2018–2021 | Northeast | West | East | Central |
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Digital | 1.804 | −1.886 ** | −4.652 * | −2.326 * | −0.766 | 3.387 |
(0.21) | (0.05) | (0.08) | (0.07) | (0.30) | (0.42) | |
Constant | 0.827 | 10.173 ** | 40.221 ** | −1.892 | 4.624 | 47.007 * |
(0.92) | (0.05) | (0.05) | (0.82) | (0.65) | (0.09) | |
Control variables | YES | YES | YES | YES | YES | YES |
Year fixed effects | YES | YES | YES | YES | YES | YES |
Provincial fixed effects | YES | YES | YES | YES | YES | YES |
Observations | 270 | 270 | 27 | 99 | 90 | 54 |
R-squared | 0.119 | 0.631 | 0.996 | 0.723 | 0.842 | 0.827 |
Variable | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
Non-Dairy Cows | Dairy Cows | Pigs | Poultry | Sheep | |
Digital | −5.042 ** | 1.553 | −1.228 *** | 1.257 *** | 0.467 |
(0.04) | (0.40) | (0.00) | (0.00) | (0.64) | |
Constant | 5.087 | −1.644 | −3.442 | −1.112 | −11.370 * |
(0.76) | (0.99) | (0.13) | (0.63) | (0.10) | |
Control variables | YES | YES | YES | YES | YES |
Year fixed effects | YES | YES | YES | YES | YES |
Provincial fixed effects | YES | YES | YES | YES | YES |
Observations | 270 | 270 | 270 | 270 | 270 |
R-squared | 0.116 | 0.175 | 0.324 | 0.423 | 0.129 |
Variable | Technological Progress Effect | Human Capital Effect | Mode of Production Transformation Effects |
---|---|---|---|
Sobel | −0.814 *** | −1.99 * | −0.447 *** |
a coefficient | 1.901 *** | 0.462 ** | −1.464 *** |
b coefficient | −0.428 *** | −4.304 *** | 0.306 *** |
Indirect effect | −0.814 *** | −0.199 * | −0.447 *** |
Direct effect | −2.136 *** | −2.751 *** | −2.503 *** |
Total effect | −2.950 *** | −2.950 *** | −2.950 *** |
Percentage of mediation effects | 0.276 | 0.068 | 0.152 |
Year | Carbon Emission Intensity | Digital Economy | ||
---|---|---|---|---|
Moran’s I | p-Value | Moran’s I | p-Value | |
2013 | 0.338 | 0.000 | 0.199 | 0.022 |
2014 | 0.339 | 0.000 | 0.261 | 0.005 |
2015 | 0.341 | 0.000 | 0.331 | 0.001 |
2016 | 0.204 | 0.014 | 0.218 | 0.010 |
2017 | 0.211 | 0.01 | 0.22 | 0.010 |
2018 | 0.295 | 0.024 | 0.207 | 0.013 |
2019 | 0.25 | 0.01 | 0.2 | 0.015 |
2020 | 0.259 | 0.01 | 0.173 | 0.027 |
2021 | 0.193 | 0.064 | 0.22 | 0.037 |
Model | Index | Adjacency Weight Matrix | Economic Weight Matrix | Economic–Geography Nested Weight Matrix | |||
---|---|---|---|---|---|---|---|
Value | p-Value | Value | p-Value | Value | p-Value | ||
spatial lag model(SAR) | LM | 3.754 | 0 | 8.767 | 0 | 26.418 | 0 |
Robust-LM | 0.958 | 0.321 | 22.112 | 0 | 3.661 | 0.056 | |
LR | 36.69 | 0 | 65.58 | 0 | 35.96 | 0 | |
Wald | 46.75 | 0 | 23.34 | 0 | 14.77 | 0 | |
spatial error model(SEM) | LM | 15.877 | 0 | 38.948 | 0 | 7.19 | 0 |
Robust-LM | 4.763 | 0.029 | 7.927 | 0.005 | 15.285 | 0 | |
LR | 53.64 | 0 | 84 | 0 | 35.13 | 0 | |
Wald | 24.1 | 0 | 17.65 | 0 | 19.93 | 0 | |
Hausman | 38.8 | 0 | 49.73 | 0 | 25.41 | 0.05 |
Variable | Adjacency Weight Matrix | Economic Weighting Matrix | Economic–Geography Nested Weights Matrix | |||
---|---|---|---|---|---|---|
Main | Wx | Main | Wx | Main | Wx | |
Digital | −2.068 *** | −2.173 *** | −1.130 ** | −4.515 *** | −1.438 *** | −4.340 *** |
0 | 0 | −0.02 | 0 | 0 | −0.01 | |
Control variables | YES | YES | YES | |||
Year fixed effects | YES | YES | YES | |||
Provincial fixed effects | YES | YES | YES | |||
ρ | 0.370 *** | 0.393 *** | 0.414 *** | |||
0 | 0 | 0 | ||||
0.010 *** | 0.010 *** | 0.010 *** | ||||
0 | 0 | 0 | ||||
Observation | 270 | 270 | 270 | 270 | 270 | 270 |
R-squared | 0.696 | 0.696 | 0.694 | 0.694 | 0.723 | 0.723 |
Adjacency Weight Matrix | Economic–Geography Nested Weights Matrix | |||||
---|---|---|---|---|---|---|
Variable | Direct Effects | Indirect Effects | Total Effect | Direct Effects | Indirect Effects | Total Effect |
digital | −2.355 *** | −4.422 *** | −6.777 *** | −1.513 *** | −7.994 *** | −9.507 *** |
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) |
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Su, T.; Li, C. Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China? Agriculture 2024, 14, 1494. https://doi.org/10.3390/agriculture14091494
Su T, Li C. Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China? Agriculture. 2024; 14(9):1494. https://doi.org/10.3390/agriculture14091494
Chicago/Turabian StyleSu, Tiantian, and Cuixia Li. 2024. "Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China?" Agriculture 14, no. 9: 1494. https://doi.org/10.3390/agriculture14091494