Agricultural mechanization, large-scale operation and agricultural carbon emissions

Figures & data

Table 1. Carbon sources, coefficient and reference sources of agricultural carbon emissions

Carbon Source	Carbon Emission Coefficient	Reference Source
Reduction of fertilizer application	0.8956 kg/kg	Oak Ridge National Laboratory (ORNL)
Pesticide use	4.9341kg/kg	Oak Ridge National Laboratory(ORNL)
Agricultural film usage	5.1800kg/kg	Institute of Agricultural Resources and Ecological Environment, Nanjing Agricultural University
Diesel usage	0.5927kg/kg	United Nations Intergovernmental Panel of Experts on Climate Change (IPCC)
Seeded area	312.60kg/hm^2	College of Biology and Technology, China Agricultural University
Irrigated area	25kg/hm^2	Dubey and Lal (2009)

Figure 1. Agricultural carbon emissions of various provinces in China in different years: (a) 2005, (b) 2010, (c) 2015 and (d) 2019.

Table 2. Variable description and descriptive statistics

Variable Name	Meaning of Variables	Obs	Mean	SD	Min	Max
CARBON	Total carbon emissions of various carbon sources (10000 tons), take the logarithm	450	5.739	1.019	2.692	7.197
MACH	Total power of agricultural machinery (10000 kilowatts), take the logarithm	450	7.585	1.086	4.543	9.499
SCALE	Large-scale operation (hectare/person), take the logarithm	450	−0.575	0.438	−1.566	0.720
AGR	Industrial upgrading	450	0.892	0.058	0.672	0.997
URB	Urbanization level	450	1.644	1.664	0.367	8.600
ROAD	Grade highway mileage (km), take the logarithm	450	11.375	0.849	9.000	12.670

Table 3. Estimation results of two-way fixed effect model

Variables	(1)	(2)	(3)	(4)	(5)
MACH	0.3484***	0.3491***	0.3028***	0.2792***	0.0215**
	(15.2368)	(15.1033)	(11.6646)	(10.4377)	(2.1321)
SCALE	0.1671***	0.1671***	0.1941***	0.1904***	0.0548***
	(5.6994)	(5.6935)	(6.5135)	(6.4552)	(5.1873)
AGR		−0.0532	−0.1687	−0.3502	0.1060
		(−0.2210)	(−0.7064)	(−1.4400)	(1.1892)
URB			−0.1009***	−0.0941***	−0.0242**
			(−3.7110)	(−3.4855)	(−2.4725)
ROAD				0.0916***	−0.0005
				(3.1387)	(−0.0412)
L.CARBON					0.9200***
					(51.7391)
Constant	3.2073***	3.2479***	3.8274***	3.1656***	0.2841**
	(18.3083)	(12.7971)	(12.9892)	(8.7992)	(1.9986)
Province FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observations	420	420	420	420	420
R^2	0.6034	0.6035	0.6166	0.6258	0.9563

Notes: Data in () are z-test values; ***, ** and * represent the significance level of 1%, 5% and 10% respectively; All R² here are R² in the group.

Table 4. Unit root check of panel data

Variables	LLC	IPS	ADF-Fisher	Results
CARBON	−0.205	.1585	34.903	unstable
DCARBON	−7.867***	−8.800***	181.627***	stable
MACH	−5.952***	−4.191***	89.288***	stable
DMACH	−7.784***	−9.386***	154.285***	stable
SCALE	−5.872***	−3.774***	96.957***	stable
DSCALE	−2.884***	−9.203***	103.476***	stable
AGR	−3.150***	−3.146***	45.909	unstable
DAGR	−9.153***	−9.483***	212.744***	stable
URB	−3.660***	−1.168	56.185	unstable
DURB	−7.198***	−8.007***	100.764***	stable
ROAD	8.750	−6.435***	280.776***	unstable
DROAD	−12.565***	−1.090***	161.978***	stable

Notes: DCARBON, DMACH, DSCALE, DAGR, DURB and DROAD respectively represent the first-order difference of each variable; *** represents the significance level of 1%.

Table 5. Results of co-integration test

Co-integration Test Methods	Name of statistics	Value of statistics	P value
Weserlund	Variance ratio	9.2501	.0000***
Pedroni	Modified Phillips-Perron t	8.3037	.0000***
Kao	Modified Dickey-Fuller t	−7.3724	.0000***

Note: *** represents the rejection of the null hypothesis and the acceptance of the alternative hypothesis at the 1% significance level.

Table 6. Lag order of the model

Lag Orders	AIC	BIC	HQIC
1	−21.004	−18.672*	−20.077
2	−21.450*	−18.549	−20.293*
3	−21.218	−17.662	−19.795
4	−21.255	−16.937	−19.521

Note: * represents the optimal lag order under the information criterion.

Figure 2. Square root test diagram of adjoint matrix.

Table 7. GMM estimation results of PVAR model

Explanatory Variables	Explained Variables
	h_DCARBON	h_DMACH	h_DSCALE	h_DAGR	h_DURB	h_DROAD
L.h_DCARBON	0.2882** (0.1125)	0.7343*** (0.2659)	−0.1703 (0.1916)	−0.0204 (0.0174)	−0.2456 (0.3017)	0.2515** (0.1173)
L.h_DMACH	0.0476** (0.0209)	0.0895 (0.0665)	0.0321 (0.0296)	−0.0025 (0.0034)	−0.0159 (0.0367)	0.0332** (0.0167)
L.h_DSCALE	0.0018 (0.0231)	−0.1059 (0.0794)	0.0593 (0.0774)	−0.0046 (0.0046)	0.0239 (0.0729)	0.0082 (0.0226)
L.h_DAGR	0.2491 (0.2921)	0.4629 (0.9248)	−0.0065 (0.4997)	0.2569** (0.1035)	−0.3494 (0.4903)	0.5946 (0.4695)
L.h_DURB	0.0976* (0.0572)	−0.3501 (0.2225)	0.0675 (0.1123)	0.0223* (0.0131)	−0.0434 (0.1564)	−0.1107 (0.0762)
L.h_DROAD	0.1237*** (0.0454)	−0.1126 (0.1496)	0.0646 (0.0829)	0.0297** (0.0116)	−0.0873 (0.1291)	0.3906*** (0.0849)

Data in () are standard deviation; ***, ** and * represent the significance level of 1%, 5% and 10% respectively; “h_” indicates the “Helmert” conversion of the original variable.

Table 8. Granger causality test results

Causal Relationships		Lag the Variable by Two Orders
Null hypothesis: The latter sequence is not the Granger reason of the former sequence		Chi-square	Degree of Freedom	P Value	Conclusion: Whether to Reject the Null Hypothesis
DCARBON	DMACH	7.612	2	0.022	YES
	DSCALE	1.540	2	0.463	NO
	DAGR	3.325	2	0.190	NO
	DURB	4.015	2	0.134	NO
	DROAD	8.682	2	0.013	YES
	All variables are not the reason	26.146	10	0.004	YES
DMACH	DCARBON	9.317	2	0.009	YES
	DSCALE	1.841	2	0.398	NO
	DAGR	0.784	2	0.676	NO
	DURB	3.248	2	0.197	NO
	DROAD	0.718	2	0.698	NO
	All variables are not the reason	63.472	10	0.000	YES
DSCALE	DCARBON	2.245	2	0.325	NO
	DMACH	5.751	2	0.056	YES
	DAGR	1.169	2	0.558	NO
	DURB	0.719	2	0.698	NO
	DROAD	1.375	2	0.503	NO
	All variables are not the reason	9.506	10	0.485	NO
DAGR	DCARBON	2.098	2	0.350	NO
	DMACH	0.996	2	0.608	NO
	DSCALE	9.755	2	0.008	YES
	DURB	3.239	2	0.198	NO
	DROAD	8.663	2	0.013	YES
	All variables are not the reason	19.568	10	0.034	YES
DURB	DCARBON	0.797	2	0.671	NO
	DMACH	0.797	2	0.671	NO
	DSCALE	1.597	2	0.450	NO
	DAGR	3.487	2	0.175	NO
	DROAD	2.439	2	0.295	NO
	All variables are not the reason	31.011	10	0.001	YES
DROAD	DCARBON	4.738	2	0.094	YES
	DMACH	8.385	2	0.015	YES
	DSCALE	0.172	2	0.918	NO
	DAGR	3.019	2	0.221	NO
	DURB	2.114	2	0.347	NO
	All variables are not the reason	22.783	10	0.012	YES

Figure 3. Impulse response diagram.

Table 9. Results of PVAR variance decomposition

Variables	Number of Periods	DCARBON	DMACH	DSCALE	DAGR	DURB	DROAD
DCARBON	1	1.000	0.000	0.000	0.000	0.000	0.000
	5	0.824	0.071	0.002	0.019	0.041	0.043
	10	0.802	0.084	0.002	0.024	0.037	0.051
DMACH	1	0.003	0.997	0.000	0.000	0.000	0.000
	5	0.079	0.822	0.010	0.006	0.076	0.007
	10	0.094	0.804	0.010	0.008	0.074	0.010
DSCALE	1	0.038	0.000	0.962	0.000	0.000	0.000
	5	0.040	0.009	0.934	0.007	0.007	0.002
	10	0.040	0.009	0.934	0.007	0.007	0.002
DAGR	1	0.019	0.047	0.001	0.932	0.000	0.000
	5	0.028	0.041	0.023	0.791	0.070	0.048
	10	0.030	0.041	0.023	0.789	0.070	0.048
DURB	1	0.000	0.001	0.019	0.001	0.980	0.000
	5	0.011	0.005	0.019	0.007	0.949	0.008
	10	0.013	0.006	0.019	0.007	0.945	0.009
DROAD	1	0.000	0.032	0.000	0.000	0.001	0.966
	5	0.071	0.092	0.002	0.006	0.044	0.784
	10	0.102	0.099	0.002	0.010	0.041	0.746

Dubey, A., & Lal, R. (2009). Carbon footprint and sustainability of agricultural production systems in Punjab, India, and Ohio, USA. Journal of Crop Improvement, 23(4), 332–350. https://doi.org/10.1080/15427520902969906

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