Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources and Description
2.2. Analytical Framework
2.3. ACEs Measurement Method
2.4. Spatial Autocorrelation Evaluation Model
2.5. STIRPAT Model
- (1)
- The rural population (P). The rural population is an important factor influencing agricultural carbon emissions, and research basically confirms that there is a positive relationship between the two. The more people employed in agriculture, the greater the agricultural carbon emission, and the opposite is true [59].
- (2)
- Agricultural economic factors (A). According to the theory of environmental Kuznets curve, there is an inverted U-shaped relationship between agricultural economic development and carbon emission, i.e., the phenomenon of polluting first and then controlling later. Agricultural economic development is the main factor driving carbon emissions, but whether it will drive the growth of carbon emissions depends on the quality and stage of economic development [60].
- (3)
- Agricultural technology factor (T). Advances in agricultural technology can improve the efficiency of machinery use, which will produce fewer carbon emissions at the same level of output, expressed in terms of total agricultural machinery power [61].
- (4)
- Agricultural industry structure (V). The industrial structure within the agricultural sector has a direct relationship with carbon emissions. Compared with forestry and fishery industries, plantation and livestock contribute the major share of carbon emissions [62].
- (5)
- Urbanization rate (U). Urbanization reduces the rural labor force, which will prompt agricultural production agents to pay attention to scale and intensive operation, which is conducive to saving resources, improving labor productivity, and reducing agricultural carbon emissions. Therefore, we used the urbanization rate as a control variable, which is measured by the proportion of urban population to the total population [63].
- (6)
- Capital factor (C). Public investment in agriculture has a small inhibitory effect on agricultural carbon emissions, but whether its inhibitory effect continues to exist as agriculture continues to develop and as the external environment changes still needs to be explored. Therefore, public investment in agriculture was used as a control variable, specifically expressed as the amount of social fixed asset investment in agriculture, forestry, animal husbandry, and fishery industries [64].
- (7)
- Social concern factors (R). Higher income will enhance farmers’ sustainable development concept and promote their demand for rural environmental quality, and these factors are conducive to reducing agricultural carbon emissions, thus we included rural residents’ income as a control variable as well [65].
3. Results
3.1. Analysis of ACEs
3.1.1. Analysis of the Time Series Characteristics of ACEs
3.1.2. Analysis of the Spatial Characteristics of ACEs
3.2. Global Spatial Autocorrelation Analysis of ACEs
3.2.1. Global Autocorrelation Analysis
3.2.2. Local Autocorrelation Analysis
3.3. Analysis of Driving Factors of ACEs
3.3.1. STIRPAT Model Process
3.3.2. Analysis of STIRPAT Results
4. Discussion
- (1)
- Government departments should make energy saving and emission reduction policies according to local conditions, and the implementation of carbon emission reduction should focus on the areas with high ACEs and prevent the expansion of regional differences in ACEs. For high ACEs areas, to transform into low-carbon agriculture, these areas should scientifically plan the layout of agricultural industries, accelerate the pace of low-carbon science and technology innovation in agriculture, and appropriately reduce the use of agricultural materials such as pesticides, chemical fertilizers, and agricultural films. For low ACEs areas to continue to optimize the structure of agricultural production, they should vigorously develop leisure agriculture, ecological agriculture, and urban agriculture with higher agricultural output values, etc., so that they can move in the direction of less carbon emission development.
- (2)
- Urbanization has a suppressive effect on ACEs, and the construction of new urbanization should be promoted, especially the urbanization of the population, to realize the optimization of industrial structure and the development of clean production through the aggregation and scale of population, land, and other resource factors, thus promoting the smooth transfer of surplus rural labor.
- (3)
- Under the premise of ensuring food security, the industrial structure of plantation and forestry, animal husbandry, and fishery industries need to be further optimized to achieve coordinated development of agriculture, forestry, animal husbandry, and fishery industries. Of course, in the process of agricultural modernization, the promotion of agricultural mechanization should focus on the use of efficiency and strive to reduce the waste of ineffective resources.
5. Conclusions
- (1)
- Jiangsu’s ACEs decreased from 1877.57 × 104 tons in 2005 to 1795.24 × 104 tons in 2020, with an average annual decrease of 0.32%, while the ACED increased from 240.06 t/km2 in 2005 to 245.72 t/km2 in 2020, with an increase of 2.36%. In terms of stages, the trend of “rapid growth—slow decline—accelerated decline” is more obvious; in terms of regions, the high ACEs areas are concentrated in the northern Jiangsu region.
- (2)
- The global Moran’s I index of total ACEs in Jiangsu Province from 2005 to 2020 is positive, ranging from 0.215 to 0.483, with a mean value of 0.394, and the spatial agglomeration is increasing, and the spatial distribution of ACED shows a random characteristic. The local spatial autocorrelation analysis shows that the ACEs in Jiangsu Province form a high–high emission agglomeration centered on Lianyungang and Suqian and a low–low emission agglomeration centered on Zhenjiang, Changzhou, and Wuxi.
- (3)
- Each 1% change in the rural population, economic development level, agricultural technology factor, agricultural industry structure, urbanization level, rural investment, and disposable income per farmer causes changes of 0.112%, −0.127%, −0.116%, 0.192%, −0.110%, −0.114%, and −0.123% in Jiangsu’s ACEs, respectively. Among them, the two factors, rural population and agricultural industry structure, play a role in promoting ACEs; the level of economic development, agricultural technology factors, urbanization level, rural investment, and per capita disposable income of farmers play a suppressive role. The agricultural industry structure has the greatest role in promoting ACEs, and the level of economic development has the most obvious suppressive effect.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Variable | Mean | SD | Min | Max |
---|---|---|---|---|
ACEs/(104t) | 1868.17 | 43.08 | 1766.69 | 1913.94 |
ACED/(t/km2) | 246.93 | 5.35 | 237.38 | 256.34 |
Urbanization rate/(%) | 62.81 | 7.72 | 50.50 | 73.40 |
Rural population/(104) | 2988.41 | 502.49 | 2251.40 | 3756.18 |
Total agricultural output/(108 CNY) | 5470.46 | 1866.34 | 2576.98 | 7952.59 |
Disposable income per rural resident/(CNY) | 13,376.75 | 6243.85 | 5258.00 | 24,198.00 |
Total power of agricultural machinery/(104 kw) | 4290.88 | 701.84 | 3135.33 | 5214.83 |
Total fixed asset investment in agriculture/(108 CNY) | 311.81 | 198.52 | 54.79 | 608.99 |
Share of output value of farming and animal husbandry/(%) | 71.19 | 2.47 | 67.10 | 74.22 |
Carbon Category | Carbon Source | Coefficient | Unit | Refer Source |
---|---|---|---|---|
Agricultural land utilization | Pesticide | 4.934 | Kg C/Kg | ORN |
Agricultural plastic films | 5.180 | Kg C/Kg | IREEA | |
Fertilizer | 0.896 | Kg C/Kg | OPNL | |
Agricultural diesel oil | 0.593 | Kg C/Kg | IPCC (2007) | |
Agricultural irrigation | 266.480 | Kg C/Hm2 | Duan et al. [50] | |
Agricultural cultivation | 312.600 | Kg C/Km2 | Wu et al. [51] | |
Rice cultivation | Rice | 5110.92 | Kg C/Hm2 | Liu et al. [52] |
Livestock breeding emissions | Cattle | 415.91 | Kg C/Year | IPCC (2007) |
Sheep | 35.182 | Kg C/Year | IPCC (2007) | |
Pigs | 34.091 | Kg C/Year | IPCC (2007) |
Year | Agricultural Land Utilization | Rice Cultivation | Livestock and Poultry Breeding | ACE /104 t | Growth Rate /% | ACED (t/km2) | |||
---|---|---|---|---|---|---|---|---|---|
CE /104 t | PERC /% | CE /104 t | PERC /% | CE /104 t | PERC /% | ||||
2005 | 548.45 | 29.21 | 1210.76 | 64.49 | 118.36 | 6.30 | 1877.57 | - | 240.06 |
2006 | 557.97 | 29.47 | 1224.50 | 64.68 | 110.68 | 5.85 | 1893.15 | 0.83 | 237.38 |
2007 | 557.45 | 29.61 | 1224.47 | 65.03 | 100.88 | 5.36 | 1882.80 | −0.55 | 241.38 |
2008 | 559.82 | 29.83 | 1220.78 | 65.05 | 96.19 | 5.13 | 1876.80 | −0.32 | 242.17 |
2009 | 566.29 | 29.96 | 1219.13 | 64.50 | 104.80 | 5.54 | 1890.23 | 0.72 | 244.07 |
2010 | 569.60 | 29.93 | 1221.50 | 64.17 | 112.34 | 5.90 | 1903.44 | 0.70 | 243.44 |
2011 | 566.50 | 29.60 | 1223.32 | 63.92 | 124.12 | 6.49 | 1913.94 | 0.55 | 247.25 |
2012 | 563.23 | 29.62 | 1225.63 | 64.46 | 112.45 | 5.91 | 1901.31 | −0.66 | 247.69 |
2013 | 560.10 | 29.52 | 1224.89 | 64.55 | 112.54 | 5.93 | 1897.52 | −0.20 | 248.49 |
2014 | 559.31 | 29.46 | 1227.02 | 64.63 | 112.23 | 5.91 | 1898.57 | 0.06 | 250.31 |
2015 | 549.84 | 29.19 | 1223.61 | 64.96 | 110.20 | 5.85 | 1883.64 | −0.79 | 249.89 |
2016 | 543.69 | 29.16 | 1220.42 | 65.45 | 100.56 | 5.39 | 1864.67 | −1.01 | 250.61 |
2017 | 534.01 | 29.18 | 1203.75 | 65.78 | 92.20 | 5.04 | 1829.95 | −1.86 | 250.41 |
2018 | 522.04 | 28.76 | 1211.59 | 66.75 | 81.62 | 4.50 | 1815.25 | −0.80 | 255.72 |
2019 | 515.19 | 29.16 | 1203.42 | 68.12 | 48.08 | 2.72 | 1766.69 | −2.68 | 256.34 |
2020 | 508.73 | 28.34 | 1210.19 | 67.41 | 76.32 | 4.25 | 1795.24 | 1.62 | 245.72 |
AAGR/% | −0.50 | − | 0.00 | − | −2.88 | − | −0.30 | 0.16 |
City | 2005 | 2010 | 2015 | 2020 | ||||
---|---|---|---|---|---|---|---|---|
ACEs (104 t) | ACED (t/km2) | ACEs (104 t) | ACED (t/km2) | ACEs (104 t) | ACED (t/km2) | ACEs (104 t) | ACED (t/km2) | |
Nanjing | 96.87 | 241.42 | 79.53 | 237.21 | 76.91 | 242.70 | 66.41 | 264.09 |
Wuxi | 63.46 | 339.34 | 51.54 | 284.90 | 39.87 | 230.28 | 31.52 | 242.86 |
Xuzhou | 204.74 | 199.80 | 215.39 | 195.97 | 211.89 | 182.56 | 189.85 | 160.83 |
Changzhou | 68.77 | 285.49 | 62.36 | 269.92 | 57.48 | 267.79 | 42.45 | 257.19 |
Suzhou | 74.53 | 239.79 | 71.64 | 265.43 | 63.87 | 255.27 | 54.93 | 262.01 |
Nantong | 164.71 | 188.72 | 158.63 | 185.54 | 152.15 | 182.05 | 146.41 | 186.20 |
Lianyungang | 150.06 | 270.43 | 163.77 | 276.69 | 166.89 | 263.34 | 166.69 | 263.33 |
Huai’an | 266.37 | 358.62 | 282.81 | 362.79 | 290.69 | 365.34 | 299.56 | 369.92 |
Yancheng | 270.85 | 198.78 | 297.66 | 203.86 | 303.53 | 212.73 | 307.06 | 221.02 |
Zhenjiang | 68.03 | 291.41 | 62.99 | 264.34 | 60.73 | 257.34 | 49.55 | 273.81 |
Taizhou | 148.38 | 264.54 | 142.99 | 249.99 | 140.34 | 241.54 | 129.18 | 249.10 |
Suqian | 159.37 | 238.62 | 170.08 | 241.60 | 173.58 | 243.80 | 174.94 | 234.40 |
Yangzhou | 141.44 | 301.80 | 144.05 | 288.02 | 145.72 | 286.21 | 136.69 | 286.06 |
Year | ACEs | Z-Value | p-Value | ACED | Z-Value | p-Value |
---|---|---|---|---|---|---|
2005 | 0.215 | 1.871 | 0.049 | 0.023 | 0.579 | 0.256 |
2006 | 0.256 | 2.105 | 0.033 | −0.010 | 0.411 | 0.305 |
2007 | 0.276 | 2.216 | 0.025 | −0.015 | 0.408 | 0.323 |
2008 | 0.322 | 2.448 | 0.022 | −0.006 | 0.446 | 0.302 |
2009 | 0.361 | 2.673 | 0.012 | −0.022 | 0.362 | 0.335 |
2010 | 0.371 | 2.720 | 0.010 | −0.026 | 0.344 | 0.354 |
2011 | 0.390 | 2.847 | 0.012 | −0.008 | 0.462 | 0.318 |
2012 | 0.408 | 2.912 | 0.009 | −0.014 | 0.433 | 0.336 |
2013 | 0.424 | 3.044 | 0.007 | −0.002 | 0.496 | 0.307 |
2014 | 0.436 | 3.066 | 0.005 | 0.008 | 0.571 | 0.283 |
2015 | 0.458 | 3.104 | 0.004 | 0.009 | 0.589 | 0.276 |
2016 | 0.472 | 3.254 | 0.004 | 0.027 | 0.677 | 0.267 |
2017 | 0.471 | 3.245 | 0.003 | 0.059 | 0.888 | 0.195 |
2018 | 0.482 | 3.270 | 0.005 | 0.045 | 0.793 | 0.228 |
2019 | 0.483 | 3.248 | 0.003 | 0.015 | 0.593 | 0.281 |
2020 | 0.481 | 3.149 | 0.004 | 0.308 | 0.683 | 0.249 |
Factors | Initial Eigenvalues | Extraction Sums of Squared Loadings | ||||
---|---|---|---|---|---|---|
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 6.146 | 87.805 | 87.805 | 6.146 | 87.805 | 87.805 |
2 | 0.765 | 10.930 | 98.735 | 0.765 | 10.930 | 98.735 |
3 | 0.056 | 0.802 | 99.537 | |||
4 | 0.021 | 0.299 | 99.837 | |||
5 | 0.009 | 0.132 | 99.969 | |||
6 | 0.001 | 0.020 | 99.989 | |||
7 | 0.001 | 0.011 | 100.000 |
Variable | Factors | |
---|---|---|
FAC1 | FAC2 | |
InP | −0.176 | 0.023 |
InA | 0.197 | −0.089 |
InT | 0.182 | −0.038 |
InV | −0.263 | 1.088 |
InU | 0.174 | −0.011 |
InC | 0.179 | −0.031 |
InR | 0.192 | −0.069 |
Parameter | Unnormalized Coefficient | Standardization Coefficient | t-Test | p-Value | |
---|---|---|---|---|---|
Nonstandard Coefficient | SE | Beta | |||
Constant | 0.000 | 0.208 | 0.000 | 0.000 | 1.000 |
FAC1 | −0.632 | 0.215 | −0.632 | −2.944 | 0.011 |
FAC2 | 0.024 | 0.215 | 0.024 | 0.113 | 0.412 |
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Hu, C.; Fan, J.; Chen, J. Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China. Int. J. Environ. Res. Public Health 2022, 19, 12463. https://doi.org/10.3390/ijerph191912463
Hu C, Fan J, Chen J. Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China. International Journal of Environmental Research and Public Health. 2022; 19(19):12463. https://doi.org/10.3390/ijerph191912463
Chicago/Turabian StyleHu, Chao, Jin Fan, and Jian Chen. 2022. "Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China" International Journal of Environmental Research and Public Health 19, no. 19: 12463. https://doi.org/10.3390/ijerph191912463