Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Estimation of Carbon Emissions from Agricultural Production
3.2. Data and Variables
3.3. Econometric Model
3.4. Cross−Sectional Dependence Test
3.5. Panel Unit Root Tests
3.6. Panel Cointegration Test
3.7. Causality Test
3.8. Autoregressive Distributed Lag Model (ARDL)
3.9. FMOLS and DOLS
3.10. Variance Decomposition
4. Results
4.1. Cross−Sectional Dependence Tests
4.2. Unit Root Tests
4.3. Panel Cointegration Test
4.4. VAR Stability Test
4.5. Benchmark Results
4.6. Robustness Check
4.7. Granger Causality Test
4.8. Variance Decomposition
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Appendix B.1. LLC Test
Appendix B.2. IPS Tes
Appendix B.3. ADF−Fisher and PP−Fisher Tests
References
- Intergovernmental Panel on Climate Change. Contribution of Working Group I to the Fifth Assessment Report of the Inter−Governmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
- Baloch, M.A.; Khan, S.U.D.; Ulucak, Z.Ş. Poverty and vulnerability of environmental degradation in Sub−Saharan African countries: What causes what? Struct. Chang. Econ. Dyn. 2020, 54, 143–149. [Google Scholar] [CrossRef]
- Dong, F.; Li, X.; Long, R.; Liu, X. Regional carbon emission performance in China according to a stochastic frontier model. Renew. Sust. Energ. Rev. 2013, 28, 525–530. [Google Scholar] [CrossRef]
- Hu, L.; Zhang, X.; Zhou, Y. Farm size and fertilizer sustainable use: An empirical study in Jiangsu, China. J. Integr. Agr. 2019, 18, 2898–2909. [Google Scholar] [CrossRef]
- Dong, H.M.; Li, Y.E.; Tao, X.P.; Peng, X.; Li, N.; Zhu, Z. China greenhouse gas emissions from agricultural activities and its mitigation strategy. Trans. CSAE 2008, 24, 269–273. (In Chinese) [Google Scholar]
- De Janvry, A.; Sadoulet, E. Agricultural growth and poverty reduction: Additional evidence. World Bank Res. Obs. 2010, 25, 1–20. [Google Scholar] [CrossRef]
- Zheng, W.; Luo, B.; Hu, X. The determinants of farmers’ fertilizers and pesticides use behavior in China: An explanation based on label effect. J. Clean. Prod. 2020, 272, 123054. [Google Scholar] [CrossRef]
- Liu, J.; Diamond, J. China’s environment in a globalizing world. Nature 2005, 435, 1179–1186. [Google Scholar] [CrossRef]
- Good, A.G.; Beatty, P.H. Fertilizing nature: A tragedy of excess in the commons. PLoS Biol. 2011, 9, e1001124. [Google Scholar] [CrossRef]
- Ju, X.; Xing, G.; Chen, X.; Zhang, S.; Zhang, L.; Liu, X.; Cui, Z.; Yin, B.; Christie, P.; Zhu, Z. Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proc. Nat. Acad. Sci. USA 2009, 106, 3041–3046. [Google Scholar] [CrossRef]
- Frank, S.; Havlík, P.; Soussana, J.; Levesque, A.; Valin, H.; Wollenberg, E.; Kleinwechter, U.; Fricko, O.; Gusti, M.; Herrero, M.; et al. Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett. 2017, 12, 105004. [Google Scholar] [CrossRef]
- Tian, Y.; Zhang, J.; He, Y. Research on Spatial−Temporal characteristics and driving factor of agricultural carbon emissions in china. J. Integr. Agr. 2014, 13, 1393–1403. [Google Scholar] [CrossRef]
- Liu, Y.; Gao, Y. Measurement and impactor analysis of agricultural carbon emission performance in Changjiang economic corridor. Alex. Eng. J. 2022, 61, 873–881. [Google Scholar] [CrossRef]
- Zhang, L.; Pang, J.; Chen, X.; Lu, Z. Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China’s main grain−producing areas. Sci. Total Environ. 2019, 665, 1017–1025. [Google Scholar] [CrossRef] [PubMed]
- Ridzuan, N.H.A.M.; Marwan, N.F.; Khalid, N.; Ali, M.H.; Tseng, M. Effects of agriculture, renewable energy, and economic growth on carbon dioxide emissions: Evidence of the environmental Kuznets curve. Resour. Conserv. Recycl. 2020, 160, 104879. [Google Scholar] [CrossRef]
- Freibauer, A.; Rounsevell, M.D.A.; Smith, P.; Verhagen, J. Carbon sequestration in the agricultural soils of Europe. Geoderma 2004, 122, 1–23. [Google Scholar] [CrossRef]
- Carauta, M.; Troost, C.; Guzman−Bustamante, I.; Hampf, A.; Libera, A.; Meurer, K.; Bönecke, E.; Franko, U.; Ribeiro Rodrigues, R.D.A.; Berger, T. Climate−related land use policies in Brazil: How much has been achieved with economic incentives in agriculture? Land Use Policy 2021, 109, 105618. [Google Scholar] [CrossRef]
- Xie, Z.; Wu, R.; Wang, S. How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression. J. Clean. Prod. 2021, 307, 127133. [Google Scholar] [CrossRef]
- Subhan, D.S.; Hafeez, D.A.; Waheed, H. Impact of Energy Consumption on Economic Growth of Pakistan. Int. J. Mech. Eng. Technol. 2015, 688, 424–436. [Google Scholar] [CrossRef]
- Lei, X.U.; Dong, J.; Zhang, J.F.; Lu, L.I. System simulation and policy optimization of agricultural carbon emissions in Hubei province based on SD model. Resour. Devel. Mark. 2017, 33, 1031–1035. (In Chinese) [Google Scholar]
- Xu, X.; Yang, H.; Yang, H. The threshold effect of agricultural energy consumption on agricultural carbon emissions: A comparison between relative poverty regions and other regions. Environ. Sci. Pollut. R. 2021, 28, 55592–55602. [Google Scholar] [CrossRef]
- Yangyang, Z.; Jianli, L. Effect of agricultural production efficiency on carbon emissions: Spatial spillovers and threshold characteristics. J. Beijing Univ. Aeronaut. Astronaut. Soc. Sci. Ed. 2021, 34, 96–105. (In Chinese) [Google Scholar]
- Jiang, L.; Zhang, J.; Wang, H.H.; Zhang, L.; He, K. The impact of psychological factors on farmers’ intentions to reuse agricultural biomass waste for carbon emission abatement. J. Clean. Prod. 2018, 189, 797–804. [Google Scholar] [CrossRef]
- Kipling, R.P.; Taft, H.E.; Chadwick, D.R.; Styles, D.; Moorby, J. Challenges to implementing greenhouse gas mitigation measures in livestock agriculture: A conceptual framework for policymakers. Environ. Sci. Policy 2019, 92, 107–115. [Google Scholar] [CrossRef]
- Guan, X.; Ma, W.; Zhang, J.; Feng, X. Understanding the extent to which farmers are capable of mitigating climate change: A carbon capability perspective. J. Clean. Prod. 2021, 325, 129351. [Google Scholar] [CrossRef]
- Koondhar, M.A.; Udemba, E.N.; Cheng, Y.; Khan, Z.A.; Koondhar, M.A.; Batool, M.; Kong, R. Asymmetric causality among carbon emission from agriculture, energy consumption, fertilizer, and cereal food production—a nonlinear analysis for Pakistan. Sustain. Energy. Technol. Assess. 2021, 45, 101099. [Google Scholar] [CrossRef]
- Liu, H.; Li, J.; Li, X.; Zheng, Y.; Feng, S.; Jiang, G. Mitigating greenhouse gas emissions through replacement of chemical fertilizer with organic manure in a temperate farmland. Sci. Bull. 2015, 60, 598–606. [Google Scholar] [CrossRef]
- Fan, S.; Hazell, P.; Thorat, S. Government spending, growth and poverty in rural India. Am. J. Agric. Econ. 2000, 82, 1038–1051. [Google Scholar] [CrossRef]
- Tang, L.; Sun, S. Fiscal incentives, financial support for agriculture, and urban−rural inequality. Int. Rev. Financ. Anal. 2022, 80, 102057. [Google Scholar] [CrossRef]
- Rada, N.; Valdes, C. Policy, technology, and efficiency of Brazilian agriculture. SSRN Electron. J. 2012, 137, 1–43. [Google Scholar] [CrossRef]
- Liu, Q.; Xiao, H. The impact of farmland management scale and fiscal policy for supporting agriculture on agricultural carbon emission. Resour. Sci. 2020, 42, 1063–1073. (In Chinese) [Google Scholar] [CrossRef]
- Han, J.Y.; Qu, J.S.; Xu, L.; Li, H.J.; Liu, L.N. The spatial effect of agricultural finance on agricultural greenhouse gas emission intensity: An empirical analysis based on the spatial Durbin model. J. Ecol. Rural Environ. 2021, 37, 1404–1412. (In Chinese) [Google Scholar]
- Huang, X.H.; Yang, F.; Lu, X. Urbanization, Spatial Spillover Effect, and Agricultural Carbon Emission: Empirical Analysis Based on the Data of Provincial Panel from 2007 to 2019. East China Econ. Manag. 2022, 36, 107–113. (In Chinese) [Google Scholar]
- Chen, M.M.; Chen, Y.Y. Does financial support for agriculture and financial support for agriculture promote the low−carbon development of agriculture−Research Based on STIRPAT model. Financ. Dev. Rev. 2022, 2, 29–41. (In Chinese) [Google Scholar]
- Fan, S.; Gulati, A.; Thorat, S. Investment, subsidies, and pro−poor growth in rural India. Agric. Econ. 2008, 39, 163–170. [Google Scholar] [CrossRef]
- Zheng, H.; Chuan, L.; Zhao, J.; Sun, S.; Zhang, J. Overview of Water and Fertilizer Integration Development. In Proceedings of the 2016 International Conference on Advances in Energy, Environment and Chemical Science, Paris, France, 22–24 July 2015; Atlantis Press: Amsterdam, The Netherlands, 2016; pp. 273–277. [Google Scholar]
- Wang, Y.; Zhu, Y.; Zhang, S.; Wang, Y. What could promote farmers to replace chemical fertilizers with organic fertilizers? J. Clean. Prod. 2018, 199, 882–890. [Google Scholar] [CrossRef]
- Guo, L.; Li, H.; Cao, X.; Cao, A.; Huang, M. Effect of agricultural subsidies on the use of chemical fertilizer. J. Environ. Manag. 2021, 299, 113621. [Google Scholar] [CrossRef] [PubMed]
- Scholz, R.W.; Geissler, B. Feebates for dealing with trade−offs on fertilizer subsidies: A conceptual framework for environmental management. J. Clean. Prod. 2018, 189, 898–909. [Google Scholar] [CrossRef]
- Vercammen, J. Farm bankruptcy risk as a link between direct payments and agricultural investment. Eur. Rev. Agric. Econ. 2007, 34, 479–500. [Google Scholar] [CrossRef]
- Li, W.; Wei, X.; Zhu, R.; Guo, K. Study on Factors Affecting the Agricultural Mechanization Level in China Based on Structural Equation Modeling. Sustainability 2019, 11, 51. [Google Scholar] [CrossRef]
- Jiang, M.; Hu, X.; Chunga, J.; Lin, Z.; Fei, R. Does the popularization of agricultural mechanization improve energy−environment performance in China’s agricultural sector? J. Clean. Prod. 2020, 276, 124210. [Google Scholar] [CrossRef]
- Yi, F.; Sun, D.; Zhou, Y. Grain subsidy, liquidity constraints and food security—Impact of the grain subsidy program on the grain−sown areas in China. Food Policy 2015, 50, 114–124. [Google Scholar] [CrossRef]
- Johnson, J.M.; Franzluebbers, A.J.; Weyers, S.L.; Reicosky, D.C. Agricultural opportunities to mitigate greenhouse gas emissions. Environ. Pollut. 2007, 150, 107–124. [Google Scholar] [CrossRef] [PubMed]
- West, T.O.; Marland, G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: Comparing tillage practices in the United States. Agric. Ecosyst. Environ. 2002, 91, 217–232. [Google Scholar] [CrossRef]
- Dubey, A.; Lal, R. Carbon footprint and sustainability of agricultural production systems in Punjab, India, and Ohio, USA. J. Crop Impr. 2009, 23, 332–350. [Google Scholar] [CrossRef]
- Breitung, J.; Pesaran, M.H. Unit roots and cointegration in panels. In The Econometrics of Panel Data; Springer: Berlin/Heidelberg, Germany, 2008; pp. 279–322. [Google Scholar]
- Sarafidis, V.; Wansbeek, T. Cross−Sectional dependence in panel data analysis. Econ. Rev. 2011, 31, 483–531. [Google Scholar] [CrossRef]
- Breusch, T.S.; Pagan, A.R. The LaGrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 1980, 47, 239–253. [Google Scholar] [CrossRef]
- Pesaran, M.H. General diagnostic tests for cross section dependence in panels. SSRN Electron. J. 2004, 69, 1240. [Google Scholar] [CrossRef]
- Mahadeva, L.; Robinson, P. Unit Root Testing to Help Model Building; Centre for Central Banking Studies, Bank of England: London, UK, 2004. [Google Scholar]
- Kasman, A.; Duman, Y.S. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Econ. Modell. 2015, 44, 97e103. [Google Scholar] [CrossRef]
- Hossain, M.S. Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy 2011, 39, 6991e6999. [Google Scholar]
- Levin, A.; Lin, C.; James Chu, C. Unit root tests in panel data: Asymptotic and finite−sample properties. J. Econom. 2002, 108, 1–24. [Google Scholar] [CrossRef]
- Im, K.S.; Pesaran, M.H.; Shin, Y. Testing for unit roots in heterogeneous panels. J. Econom. 2003, 115, 53–74. [Google Scholar] [CrossRef]
- Maddala, G.S.; Wu, S. A comparative study of unit root tests with panel data and a new simple test. Oxf. B. Econ. Stat. 1999, 61, 631–652. [Google Scholar] [CrossRef]
- Choi, I. Unit root tests for panel data. J. Int. Money Financ. 2001, 20, 249–272. [Google Scholar] [CrossRef]
- Hadri, K. Testing for stationarity in heterogeneous panel data. Econom. J. 2000, 3, 148–161. [Google Scholar] [CrossRef]
- Kao, C. Spurious regression and residual−based tests for cointegration in panel data. J. Econom. 1999, 90, 1–44. [Google Scholar] [CrossRef]
- Granger, C.W.J. Investigating causal relations by econometric models and cross−spectral methods. Econometrica 1969, 37, 424–438. [Google Scholar] [CrossRef]
- Pesaran, M.H.; Shin, Y. An autoregressive distributed lag modelling approach to cointegration analysis. Econom. Soci. Monogr. 1998, 31, 371–413. [Google Scholar]
- Pesaran, M.H.; Shin, Y.; Smith, R.J. Bounds testing approaches to the analysis of level relationships. J. Appl. Econ. 2001, 16, 289–326. [Google Scholar] [CrossRef]
- Pesaran, M.H.; Pesaran, B. Working with Microfit 4.0; Camfit Data Ltd.: Cambridge, UK, 1997. [Google Scholar]
- Ghatak, S.; Siddiki, J. The use of ARDL approach in estimating virtual exchange rates in India. J. Appl. Stat. 2001, 28, 573–583. [Google Scholar] [CrossRef]
- Pedroni, P. Fully modified OLS for heterogeneous cointegrated panels. Dep. Econ. Work. Pap. 2000, 15, 93–100. [Google Scholar]
- Stock, J.H.; Watson, M.W. A simple MLE of cointegrating vectors in higher order integrated systems. NBER Work. Pap. 1989, 61, 783–820. [Google Scholar]
- Hamit−Haggar, M. Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective. Energ. Econ. 2012, 34, 358–364. [Google Scholar] [CrossRef]
- Kao, C.; Chiang, M.H. On the estimation and inference of a cointegrated regression in panel data. In Nonstationary Panels, Panel Cointegration, and Dynamic Panels; Emerald Group Publishing Limited: Bingley, UK, 2001. [Google Scholar]
- Ismael, M.; Srouji, F.; Boutabba, M.A. Agricultural technologies and carbon emissions: Evidence from Jordanian economy. Environ. Sci. Pollut. Res. 2018, 25, 10867–10877. [Google Scholar] [CrossRef] [PubMed]
- National Bureau of Statistics of China (NBSC). China Statistical Yearbook; National Bureau of Statistics of China: Beijing, China, 2021.
- Ma, L.; Long, H.; Tang, L.; Tu, S.; Zhang, Y.; Qu, Y. Analysis of the spatial variations of determinants of agricultural production efficiency in China. Comput. Electron. Agric. 2021, 180, 105890. [Google Scholar] [CrossRef]
Carbon Source | Carbon Emission Coefficient | Reference |
---|---|---|
Fertilizer | 0.8956 kg/kg | Oak Ridge National Laboratory [45] |
Pesticides | 4.9341 kg/kg | Oak Ridge National Laboratory |
Mulches | 5.18 kg/kg | Institute of Resource, Ecosystem, and Environment of Agriculture, Nanjing Agricultural University [13] |
Diesel | 0.5927 kg/kg | Intergovernmental Panel on Climate Change IPCC [13] |
Plowing | 312.6 kg/hm2 | College of Biological Sciences, China Agricultural University |
Irrigation | 25 kg/hm2 | [46] |
Pigs | 34.0910 kg/(each·year) | Intergovernmental Panel on Climate Change IPCC [13] |
Cattle | 415.91 kg/(each·year) | Intergovernmental Panel on Climate Change IPCC [13] |
Sheep | 35.1819 kg/(each·year) | Intergovernmental Panel on Climate Change IPCC [13] |
Agricultural electricity | CO2: 0.7921 t·MWh−1 | China’s Ministry of Ecology and Environment |
Variables | Definition | Measurement |
---|---|---|
Agricultural carbon emissions (percarbon) | Average carbon emissions from agricultural production | Total agricultural carbon emissions /Arable land area |
chemical fertilizer use (perfertilizer) | Average chemical fertilizer consumption in agricultural production | Total chemical fertilizer consumption /Arable land area |
financial support for agriculture (agriratio) | The ratio of agriculture, forestry, and water in financial expenditure | Total financial expenditure of agriculture, forestry, and water/Total financial expenditure |
Test | Statistic | Prob. |
---|---|---|
Breusch−Pagan LM | 2281.3470 | 0.0000 |
Pesaran scaled LM | 62.5970 | 0.0000 |
Pesaran CD | 7.2394 | 0.0000 |
Variables | Level | First−Difference | ||||
---|---|---|---|---|---|---|
Intercept | Intercept and Trend | Conclusion | None | Intercept and Trend | Conclusion | |
LLC test | ||||||
Lnpercarbon | 0.6898 | 0.9999 | N | 0.0000 | 0.0000 | S |
Lnperfertilizer | 0.0000 | 1.0000 | U | 0.0000 | 0.0000 | S |
Lnagriratio | 0.0000 | 0.7712 | U | 0.0000 | 0.0000 | S |
IPS test | ||||||
Lnpercarbon | 0.9691 | 1.0000 | N | 0.0000 | 0.0000 | S |
Lnperfertilizer | 0.1394 | 1.0000 | N | 0.0000 | 0.0000 | S |
Lnagriratio | 0.0000 | 1.0000 | U | 0.0000 | 0.0000 | S |
ADF−Fisher Chi−square test | ||||||
Lnpercarbon | 0.9037 | 0.9564 | N | 0.0000 | 0.0000 | S |
Lnperfertilizer | 0.2152 | 1.0000 | N | 0.0000 | 0.0000 | S |
Lnagriratio | 0.0000 | 1.0000 | U | 0.0000 | 0.0000 | S |
PP−Fisher Chi−square test | ||||||
Lnpercarbon | 0.9539 | 1.0000 | N | 0.0000 | 0.0000 | S |
Lnperfertilizer | 0.0194 | 1.0000 | N | 0.0000 | 0.0000 | S |
Lnagriratio | 0.3108 | 1.0000 | N | 0.0000 | 0.0000 | S |
Null Hypothesis | t−Statistics | Prob. | |
---|---|---|---|
ADF | No co−integration | −6.523558 | 0.0000 |
Variable | Coefficient | Std.Error | t−Statistic | Prob. |
---|---|---|---|---|
Long Run Equation | ||||
Lnperfertilizer | 1.1713 | 0.0392 | 29.9010 | 0.0000 |
Lnagriratio | −0.2015 | 0.0550 | 3.6665 | 0.0003 |
Short Run Equation | ||||
CointeQ01 | −0.0447 | 0.0173 | −2.5799 | 0.0103 |
D(Lnperfertilizer) | 0.6500 | 0.0649 | 10.0108 | 0.0000 |
D(Lnagriratio) | −0.0551 | 0.0148 | −3.7361 | 0.0002 |
C | 0.0484 | 0.0212 | 2.2830 | 0.0230 |
Variable | Coefficient | Std.Error | t−Statistic | Prob. |
---|---|---|---|---|
FMOLS | ||||
Lnperfertilizer | 0.9267 | 0.0230 | 40.2135 | 0.0000 |
Lnagriratio | −0.1563 | 0.0125 | −12.5067 | 0.0000 |
DOLS | ||||
Lnperfertilizer | 0.9710 | 0.0214 | 45.3548 | 0.0000 |
Lnagriratio | −0.1713 | 0.0134 | −12.7897 | 0.0000 |
Null Hypothesis: | F−Statistic | Prob. |
---|---|---|
LNPERFERTILIZER does not Granger Cause LNPERCARBON | 6.4082 | 0.0000 |
LNPERCARBON does not Granger Cause LNPERFERTILIZER | 8.2025 | 0.0000 |
LNAGRIRATIO does not Granger Cause LNPERCARBON | 6.7647 | 0.0000 |
LNPERCARBON does not Granger Cause LNAGRIRATIO | 0.8131 | 0.6164 |
LNAGRIRATIO does not Granger Cause LNPERFERTILIZER | 2.6765 | 0.0044 |
LNPERFERTILIZER does not Granger Cause LNAGRIRATIO | 2.6333 | 0.0050 |
Period | S.E. | Lnpercarbon | Lnperfertilizer | Lnagriratio |
---|---|---|---|---|
Variance Decomposition of Lnpercarbon: | ||||
1 | 0.15 | 100.00 | 0.00 | 0.00 |
2 | 0.20 | 99.63 | 0.09 | 0.27 |
3 | 0.23 | 99.52 | 0.18 | 0.29 |
4 | 0.26 | 99.40 | 0.32 | 0.28 |
5 | 0.27 | 99.24 | 0.50 | 0.26 |
6 | 0.29 | 99.05 | 0.71 | 0.24 |
7 | 0.30 | 98.82 | 0.95 | 0.23 |
8 | 0.30 | 98.57 | 1.21 | 0.22 |
9 | 0.31 | 98.30 | 1.49 | 0.21 |
10 | 0.31 | 98.02 | 1.77 | 0.21 |
11 | 0.32 | 97.72 | 2.07 | 0.21 |
12 | 0.32 | 97.43 | 2.36 | 0.21 |
13 | 0.32 | 97.13 | 2.65 | 0.21 |
14 | 0.32 | 96.84 | 2.94 | 0.22 |
15 | 0.33 | 96.55 | 3.22 | 0.22 |
Variance Decomposition of Lnperfertilizer: | ||||
1 | 0.15 | 88.81 | 11.19 | 0.00 |
2 | 0.20 | 88.18 | 11.70 | 0.13 |
3 | 0.24 | 87.60 | 12.29 | 0.11 |
4 | 0.26 | 87.15 | 12.76 | 0.09 |
5 | 0.27 | 86.72 | 13.20 | 0.09 |
6 | 0.29 | 86.28 | 13.61 | 0.10 |
7 | 0.30 | 85.84 | 14.02 | 0.14 |
8 | 0.30 | 85.39 | 14.42 | 0.19 |
9 | 0.31 | 84.92 | 14.82 | 0.26 |
10 | 0.31 | 84.44 | 15.22 | 0.35 |
11 | 0.31 | 83.95 | 15.61 | 0.44 |
12 | 0.32 | 83.46 | 15.99 | 0.55 |
13 | 0.32 | 82.97 | 16.37 | 0.67 |
14 | 0.32 | 82.46 | 16.75 | 0.79 |
15 | 0.32 | 81.95 | 17.12 | 0.92 |
Variance Decomposition of Lnagriratio: | ||||
1 | 0.23 | 0.67 | 20.86 | 78.47 |
2 | 0.31 | 0.79 | 27.80 | 71.41 |
3 | 0.36 | 0.88 | 29.15 | 69.97 |
4 | 0.40 | 0.93 | 29.19 | 69.88 |
5 | 0.43 | 0.96 | 28.71 | 70.32 |
6 | 0.46 | 0.98 | 28.01 | 71.00 |
7 | 0.48 | 1.00 | 27.21 | 71.79 |
8 | 0.49 | 1.00 | 26.37 | 72.62 |
9 | 0.51 | 1.00 | 25.54 | 73.45 |
10 | 0.52 | 1.00 | 24.74 | 74.26 |
11 | 0.53 | 0.99 | 23.99 | 75.02 |
12 | 0.54 | 0.98 | 23.30 | 75.72 |
13 | 0.55 | 0.97 | 22.67 | 76.36 |
14 | 0.55 | 0.95 | 22.12 | 76.93 |
15 | 0.56 | 0.94 | 21.65 | 77.42 |
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Guo, L.; Guo, S.; Tang, M.; Su, M.; Li, H. Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China. Int. J. Environ. Res. Public Health 2022, 19, 7155. https://doi.org/10.3390/ijerph19127155
Guo L, Guo S, Tang M, Su M, Li H. Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China. International Journal of Environmental Research and Public Health. 2022; 19(12):7155. https://doi.org/10.3390/ijerph19127155
Chicago/Turabian StyleGuo, Lili, Sihang Guo, Mengqian Tang, Mengying Su, and Houjian Li. 2022. "Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China" International Journal of Environmental Research and Public Health 19, no. 12: 7155. https://doi.org/10.3390/ijerph19127155