Influence of Financial Support to Agriculture on Carbon Emission Intensity of the Industry
Abstract
:1. Introduction
2. Relationship between Financial Support and Agricultural Carbon Emissions
3. Research Methods and Models
3.1. Analysis of the Current Situation of the Level of Financial Support to Agriculture and the Intensity of ACE
3.2. Research Model
3.2.1. Explained Variable
3.2.2. Core Explanatory Variable
3.2.3. Control Variable
- Urbanization level (Urban) is expressed as the ratio of urban population to total population. Urbanization has a certain influence on ACE, but the direction of influence may vary in different regions. Urbanization will change the scale of agricultural land and the scale of rural labor, resulting in changes in agricultural production factors, which in turn will affect ACE.
- The degree of agricultural disaster (Disaster) is expressed as the ratio of the affected area of crops to the total sown area. The degree of disaster in agriculture will affect the output value. If the degree of disaster in a certain area is high, it will lead to a reduction in production in the current year, and farmers may change the amount of factor inputs in the later period, thereby affecting CE.
- Agricultural industrial structure (Structure) is expressed by the ratio of the total agricultural output value to the total output value of agriculture, forestry, animal husbandry, and by-fishing. Because agriculture, forestry, animal husbandry, and fishing have different industrial characteristics, the CE produced are different.
- The degree of agricultural mechanization (Machine) is expressed by the ratio of agricultural machinery power to the rural population. Machinery already plays an important role in agricultural production and, generally speaking, the more machinery is used, the higher the CE.
- The education level of farmers (Education) is expressed by the average years of education of the rural population. With different education levels, farmers have different degrees of acceptance of the concept of green production. The education level of the labor force in rural households is mainly divided into five levels: illiterate or very little literate, primary school, junior high school, high school, college or above. Considering the actual situation, the number of years of education for those with little or no literacy is represented by the median value of 3 in the primary school system, and the others are represented by 6, 9, 12, and 16, respectively. Pi represents the number of people at different levels of education, and the formula for calculating the average years of education is [21]:
- 6.
- The scale of agricultural planting (Plant) is expressed by the ratio of the sown area of crops to the rural population. That is, the sown area per capita.
- 7.
- The level of economic development (Economics) is expressed by the ratio of the total production value of agriculture, forestry, animal husbandry, and by-fishing to the rural population. It is generally believed that, in areas with a high level of economic development, the market for agricultural production factors and agricultural products is more mature.
4. Results
5. Analysis and Discussion
- 8.
- The agricultural CE intensity has spatial autocorrelation. The global Moran’s I index of agricultural CE intensity from 2000 to 2020 is calculated by Geoda software to be positive. Except for 2008 and 2011, all other years pass the significance test, and the spatial correlation degree of agricultural CE intensity is more obvious in recent years.
- 9.
- According to the measurement results of the spatial Durbin model, the degree of agricultural mechanization positively affects the agricultural CE intensity of the province, and negatively affects the agricultural CE intensity of neighboring provinces. The agricultural industrial structure and economic development level negatively affect the agricultural CE intensity of this province, and positively affect the agricultural CE intensity of neighboring provinces. The level of fiscal expenditure, the degree of agricultural disasters, and the scale of agricultural planting all have a positive influence on the agricultural CE intensity of this province and neighboring provinces. The urbanization rate and the education level of farmers have a negative influence on the agricultural CE intensity of this province and neighboring provinces.
- 10.
- From the decomposition effect of the spatial Durbin model, it can be seen that the level of fiscal expenditure has a much greater influence on the ACE of this province than it has on neighboring provinces. When the level of fiscal expenditure increases by 1%, the CE intensity of agriculture will increase by 0.2071%.
- 11.
- It is necessary to optimize the structure of financial support to agriculture. Through the above analysis, the increase in the level of the support does not reduce the intensity of ACE, but it does significantly increase the intensity of ACE. This shows that the high-input and extensive agricultural production methods cannot improve the ecological benefits, even if financial support is increased. With the increase in financial investment, its use must be rationally planned, the proportion of investment in agricultural infrastructure construction and scientific and technological research and development must be increased, and the ecological orientation of financial expenditure in support of agriculture must be enhanced, which makes the best use of financial funds and can reduce ACE, while promoting the improvement of agricultural economic benefits.
- 12.
- It is necessary to implement reasonable subsidies for agricultural production. The government needs to improve relevant policies, use financial subsidies to support agriculture for farmers who conduct low-carbon production, or subsidize organic fertilizers to reduce the cost for farmers applying organic fertilizers, and positively encourage farmers to adjust production methods and rationally use agricultural production factors.
- 13.
- Provinces need to collaborate to promote agricultural emission reduction. The intensity of ACE has obvious spatial effects. Therefore, it is necessary to pay attention to the coordinated development between adjacent provinces, optimize the spatial and regional distribution structure of agricultural finance, and achieve win-win cooperation. Moreover, the provinces need to exchange and learn from each other advanced agricultural production technologies, and jointly use production methods such as water-saving and fertilizer-saving to improve the utilization rate of production factors. In addition, it is necessary to introduce a CE trading mechanism in agriculture to give importance to the role of the market in reducing CE.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CE Source | CE Factor |
---|---|
Diesel fuel | 0.5927 kg/kg |
Fertilizer | 0.8956 kg/kg |
Pesticide | 4.9341 kg/kg |
Agricultural film | 5.18 kg/kg |
Irrigation | 20.476 kg/hm2 |
Sowing | 312.6 kg/km2 |
Variable | Variable Description | Sample Size | Average Value | Median | Standard Deviation | Minimum | Maximum Value |
---|---|---|---|---|---|---|---|
Carbon | Agricultural CE intensity (t/10,000 yuan-1) | 651 | 0.0160 | 0.0140 | 0.00800 | 0.00300 | 0.0500 |
Finance | The level of financial support to agriculture | 651 | 0.0960 | 0.0970 | 0.0390 | 0.00900 | 0.204 |
Urban | Urbanization rate | 651 | 0.509 | 0.500 | 0.157 | 0.189 | 0.896 |
Disaster | Degree of agricultural disaster | 651 | 0.226 | 0.197 | 0.159 | 0 | 0.936 |
Structure | Agricultural industry structure | 651 | 0.523 | 0.510 | 0.0870 | 0.302 | 0.746 |
Machine | Agricultural mechanization degree (kw/person) | 651 | 1.348 | 1.182 | 0.791 | 0.226 | 6.213 |
Education | The education level of farmers | 651 | 7.624 | 7.725 | 0.753 | 4.742 | 9.912 |
Plant | Agricultural planting scale (ha/person) | 651 | 0.253 | 0.212 | 0.155 | 0.0310 | 1.367 |
Economics | Economic development level (10,000 yuan/person) | 651 | 1.213 | 1.020 | 0.858 | 0.145 | 4.531 |
Years | I | z | p-Value | Years | I | z | p-Value |
---|---|---|---|---|---|---|---|
2000 | 0.159 | 1.799 | 0.036 | 2011 | 0.062 | 0.879 | 0.19 |
2001 | 0.138 | 1.599 | 0.055 | 2012 | 0.17 | 1.881 | 0.03 |
2002 | 0.151 | 1.711 | 0.043 | 2013 | 0.126 | 1.469 | 0.071 |
2003 | 0.265 | 2.763 | 0.003 | 2014 | 0.127 | 1.482 | 0.069 |
2004 | 0.143 | 1.626 | 0.052 | 2015 | 0.154 | 1.727 | 0.042 |
2005 | 0.109 | 0.758 | 0.024 | 2016 | 0.209 | 2.219 | 0.013 |
2006 | 0.157 | 1.752 | 0.04 | 2017 | 0.179 | 1.961 | 0.025 |
2007 | 0.151 | 1.706 | 0.044 | 2018 | 0.236 | 2.48 | 0.007 |
2008 | 0.09 | 1.136 | 0.128 | 2019 | 0.298 | 3.038 | 0.001 |
2009 | 0.112 | 1.346 | 0.089 | 2020 | 0.333 | 3.366 | 0 |
2010 | 0.107 | 1.294 | 0.098 |
Variable | VIF | 1/VIF |
---|---|---|
Urban | 3.110 | 0.321 |
Economics | 3.060 | 0.327 |
Education | 2.540 | 0.393 |
Machine | 2.400 | 0.416 |
Plant | 2.400 | 0.417 |
Finance | 1.910 | 0.525 |
Structure | 1.410 | 0.710 |
Disaster | 1.360 | 0.733 |
Mean | 2.274 | 0.480 |
Test | Statistical Value | p-Value | |
---|---|---|---|
Spatial error model (Spatial error) | Moran’s I | 20.992 | 0.00 |
Lagrange multiplier | 414.126 | 0.00 | |
Robust Lagrange multiplier | 269.307 | 0.00 | |
Spatial lag model (Spatial lag) | Lagrange multiplier | 160.568 | 0.00 |
Robust Lagrange multiplier | 15.750 | 0.00 |
Test | Test Purpose | Test Result | |
---|---|---|---|
LR | Whether SDM will degrade to SAR | LR chi2(8) = 53.95 | Prob > chi2 = 0.000 |
Whether SDM will degrade to SEM | LR chi2(8) = 46.72 | Prob > chi2 = 0.000 | |
Wald | Whether SDM will degrade to SAR | chi2(8) = 47.33 | Prob > chi2 = 0.000 |
Whether SDM will degrade to SEM | chi2(8) = 69.18 | Prob > chi2 = 0.000 |
Variable | LR_Direct | LR_Indirect | LR_Total |
---|---|---|---|
Finance | 0.1357 *** | 0.0714 *** | 0.2071 *** |
(0.0313) | (0.0209) | (0.0495) | |
Urban | −0.2166 *** | −0.3542 *** | −0.5708 *** |
(0.0436) | (0.1156) | (0.1265) | |
Disaster | 0.0146 | 0.0078 | 0.0224 |
(0.0182) | (0.0098) | (0.0279) | |
Structure | −0.0319 | 0.1280 ** | 0.0962 |
(0.0298) | (0.0621) | (0.0657) | |
Machine | 0.2157 *** | −0.9289 *** | −0.7132 *** |
(0.0595) | (0.1222) | (0.1363) | |
Education | −0.1659 *** | −0.5808 *** | −0.7467 *** |
(0.0603) | (0.1128) | (0.1233) | |
Plant | 0.1960 ** | 0.1027 ** | 0.2987 ** |
(0.0939) | (0.0523) | (0.1437) | |
Economics | −0.2532 *** | 0.2112 ** | −0.0420 |
(0.0320) | (0.0833) | (0.0897) | |
N | 651 | R2 | 0.303 |
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Gao, Y.; Cai, M.; He, X. Influence of Financial Support to Agriculture on Carbon Emission Intensity of the Industry. Sustainability 2023, 15, 2228. https://doi.org/10.3390/su15032228
Gao Y, Cai M, He X. Influence of Financial Support to Agriculture on Carbon Emission Intensity of the Industry. Sustainability. 2023; 15(3):2228. https://doi.org/10.3390/su15032228
Chicago/Turabian StyleGao, Yuling, Man Cai, and Xin He. 2023. "Influence of Financial Support to Agriculture on Carbon Emission Intensity of the Industry" Sustainability 15, no. 3: 2228. https://doi.org/10.3390/su15032228