Can digital technology promote sustainable agriculture? Empirical evidence from urban China

Figures & data

Figure 1. Logic diagram of the impact of digital technology on SA.

Table 1. SA evaluation indicator system

Primary Indicators	secondary indicator	tertiary indicators	Specific indicators	+/-
Sustainable gricultural	Poverty alleviation and income increase	Condition	Incidence of rural poverty (%)	-
			Engel’s coefficient for rural inhabitants (%)	-
			Percentage of rural residents’ expenditure on education, culture and recreation (%)	+
			Rural sanitary latrine penetration rate (%)	+
			Rural health technicians per 1,000 population (persons)	+
		Production efficiency	Land productivity (million yuan/ha)	+
			Agricultural labor productivity (yuan/person)	+
		Economic performance	Urbanization rate (%)	+
			Per capita net income of farmers (yuan)	+
			Growth rate of per capita income of farmers (%)	+
			Ratio of income of urban and rural residents (%)	+
			Main business income of agricultural product processing enterprises above designated size (billion yuan)	+
	Food security	Resource base	Water resources per capita (cubic meters per person)	+
			Area of arable land in common rural use (thousands of hectares)	+
			Effective irrigated area (thousands of hectares)	+
		Production toughness	Total power of agricultural machinery per capita (kW)	+
			Comprehensive grain production capacity (tons)	+
			Poultry egg production (tons)	+
		Structurally sound	Grain sowing ratio (-)	+
			Oilseed seed production ratio (-)	+
	Green agriculture	Environmental protection	Pesticide use (tons)	-
			Fertilizer use in tons	-
			Rural greening rate (%)	+
		Contamination treatment	Comprehensive utilization rate of livestock and poultry waste (%)	+
			Percentage of administrative villages with domestic sewage treatment (%)	+
			Percentage of administrative villages that dispose of domestic waste (%)	+
		Agricultural carbon emissions	Carbon emissions from fertilizers (tons)	-
			Pesticide carbon emissions (tons)	-

Table 2. Indicators for measuring the level of digital technology development

Primary Indicators	Secondary indicator	Specific indicators	+/-
Digital technology	Digital interconnection	Number of Internet broadband access subscribers (1000 households)	+
		Proportion of administrative villages with Internet broadband service (%)	+
		Cable television coverage (%)	+
		Number of international Internet users (households)	+
	Digital informatization	Cell phone subscribers (10,000 households)	+
		Total postal operations (ten thousand yuan)	+
		Total telecommunication business (ten thousand yuan)	+
		Number of employees in the information transmission, computer services and software industry (10,000 persons)	+
	Digital technology innovation	Number of patent applications for the digital economy (number)	+
		Number of invention patents authorized (pieces)	+

Table 3. Results of descriptive statistics of variables

	variable	mean	sd	min	max	N
Explanatory variable	digital	0.1976	0.0804	0.0056	0.6564	4416
Explained variable	SA PI	0.7195 0.3759	0.0250 0.0496	0.6291 0.2975	0.8439	4416
					0.6608	4416
	FS	0.5474	0.0155	0.2510	0.6379	4416
	GA	0.5992	0.0623	0.3809	0.9287	4416
Intermediary variable	Labor_rate Technique	0.2599 1.2234	0.0905 0.4591	0.0441 0.1801	0.6899	4416
					3.5830	4416
Control variables	Tech	13.7423	4.8336	2.2046	38.2790	4416
	Edu	1.9898	0.6968	0.3256	5.4039	4416
	House	10.6779	3.8221	1.7425	30.4642	4416
	Water	13.7496	4.8559	2.2568	37.0296	4416
	Road	13.7605	4.8394	2.3429	36.4349	4416
	Lnargri	7.3669	0.7141	3.6227	8.7397	4416
	Gini	0.4285	0.0461	0.3502	0.5097	4416
	RD	1.4625	0.6807	0.1802	6.4444	4416
	Tecniq	0.0082	0.0116	0.0000	0.1750	4416

Table 4. Benchmark regression results of digital technology affecting sustainable agricultural development and subsystems

VARIABLES	SA		PI		FS		GA
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Digital	0.2253***	0.0457***	0.4335***	0.0737***	0.0682***	0.0163*	0.5141***	0.0926**
	(15.14)	(5.86)	(16.72)	(3.17)	(14.85)	(1.88)	(10.71)	(2.28)
Tech		0.0008***		0.0015***		0.0003***		0.0018***
		(9.84)		(8.11)		(2.97)		(7.99)
Edu		0.0117***		0.0239***		0.0030***		0.0274***
		(17.79)		(14.77)		(4.25)		(11.90)
House		0.0007***		0.0013***		0.0003***		0.0015***
		(9.30)		(7.59)		(3.11)		(6.63)
Water		0.0007***		0.0013***		0.0003***		0.0017***
		(9.85)		(7.53)		(3.49)		(7.24)
Road		0.0007***		0.0013***		0.0001		0.0017***
		(9.34)		(7.31)		(0.91)		(7.74)
Gini		−0.0026*		−0.0072*		−0.0004		−0.0048
		(−1.83)		(−1.95)		(−0.20)		(−1.23)
RD		0.0026***		0.0052***		−0.0004		0.0068***
		(4.39)		(2.79)		(−0.76)		(4.21)
Tecniq		0.0999***		0.3005***		0.0422		0.1751**
		(4.43)		(4.41)		(1.32)		(2.53)
Constant	0.6750***	0.6322***	0.2902***	0.2132***	0.5340***	0.5387***	0.4976***	0.3728***
	(229.47)	(52.04)	(56.63)	(7.29)	(587.90)	(69.47)	(52.43)	(10.26)
year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
N	4416	4416	4416	4416	4416	4416	4416	4416
R^2	0.954	0.973	0.931	0.951	0.859	0.863	0.952	0.968

Note: t-values are reported in parentheses in the table, ***, ** and * indicate that the regression results passed the significance test at 1%, 5% and 10% confidence levels, respectively.

Table 5. Robustness test of digital technology affecting sustainable agricultural development

VARIABLES	Excluding systemic changes in macro factors				Substitution of explanatory variables		instrumental-variable method
	(1) SA	(2) PI	(3) FS	(4) GA	(5) SA1	(6) SA1	(7) SA	(8) SA
Digital	0.0457*** (5.84)	0.0737*** (3.16)	0.0163* (1.87)	0.0926** (2.27)	0.0591*** (6.66)	0.0591*** (6.64)	0.1168*** (8.09)	0.0233** (2.02)
Control	YES	YES	YES	YES	YES	YES	NO	YES
year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
Prov FE	YES	YES	YES	YES	NO	YES	NO	NO
Kleibergen-Paap rk LM statistic							74.474 [0.000]	51.724 [0.000]
Kleibergen-Paap rk Wald F-statistic							404.429 {16.38}	370.177 {16.38}
R^2	0.973	0.951	0.863	0.968	0.977	0.977	0.296	0.636
N	4416	4416	4416	4416	4416	4416	4416	4416

Note: ***, **, and * denote significance levels at 1%, 5%, and 10%, respectively; () values are robust standard errors, [] values are p-values, and {} values are critical values at the 10% level of the Stock-Yogo weak identification test.

Table 6. Mechanism test of digital technology affecting SA development

VARIABLES	Panel A: Mediating effects of Labor_rate
	Labor_rate (1)	SA (2)	PI (3)		FS (4)		GA (5)
Digital	0.1816	0.0228***	0.0193		0.0113		0.0419***
	(14.40)	(4.90)	(1.52)		(1.64)		(3.27)
labor_rate		0.1258***	0.2996***		0.0273***		0.2790***
		(22.40)	(19.59)		(3.29)		(18.01)
control variable	YES	YES	YES		YES		YES
year FE	YES	YES	YES		YES		YES
City FE	YES	YES	YES		YES		YES
N	4416	4416	4416		4416		4416
R^2	0.9859	0.9760	0.9548		0.8636		0.9706
Sobal test		P = 0.000 (Z = 12.116)	P = 0.000 (Z = 11.250)		P = 0.000 (Z = 11.605)		P = 0.001 (Z = 3.210)
VARIABLES	Panel B: Mediating Effects of Technique
	Technique (1)	SA (2)	PI (3)		FS (4)		GA (5)
Digital	0.5154**	0.0426***	0.0724***		0.0071		0.0902***
	(2.43)	(9.17)	(5.60)		(1.27)		(6.95)
tecnique		0.0060***	0.0025***		0.0179***		0.0046***
		(17.47)	(2.68)		(43.79)		(4.82)
control variable	YES	YES	YES		YES		YES
year FE	YES	YES	YES		YES		YES
City FE	YES	YES	YES		YES		YES
N	4416	4416	4416		4416		4416
R^2	0.8440	0.9749	0.9506		0.9067		0.9684
Sobal test		P = 0.016 (Z = 2.403)	P = 0.030 (Z = 2.167)		P = 0.072 (Z = 1.798)		P = 0.015 (Z = 2.422)
VARIABLES	Panel C: Moderating effect of finance
	SA		PI			FS	GA
Finance	0.0029**		0.0134***			0.0026	0.0021
	(2.03)		(3.70)			(0.95)	(0.57)
Digital	0.0524***		0.0760***			0.0165*	0.1273***
	(5.89)		(3.69)			(1.85)	(4.85)
Finance*Digital	0.0241***		0.0907***			0.0025	0.0371**
	(4.29)		(6.82)			(0.37)	(2.47)
Control	YES		YES			YES	YES
year FE	YES		YES			YES	YES
City FE	YES		YES			YES	YES
N	2,760		2,760			2,760	2,760
R^2	0.9780		0.9670			0.8370	0.9740

Note: Robust t-statistics in parentheses. *** p < 0.01. ** p < 0.05. *p < 0.1.

Table 7. Test of regional heterogeneity in the impact of digital technology on SA development and subsystems

VARIABLES	Panel A: Geographic heterogeneity
	Midwest	Eastern	Midwest	Eastern	Midwest	Eastern	Midwest	Eastern
	(1) SA	(2) SA	(3) PI	(4) PI	(5) FS	(6) FS	(7) GA	(8) GA
Digital	0.0381***	0.0433***	0.0675*	0.0443	0.0211*	0.0015	0.0570	0.1253***
	(4.83)	(3.48)	(1.89)	(1.61)	(1.77)	(0.19)	(1.09)	(3.25)
Constant	0.6204***	0.6434***	0.1607***	0.3739***	0.5333***	0.5637***	0.3634***	0.2965**
	(50.08)	(18.54)	(5.03)	(6.23)	(56.07)	(44.40)	(11.31)	(2.43)
Control	YES	YES	YES	YES	YES	YES	YES	YES
year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
R^2	0.971	0.969	0.934	0.966	0.838	0.916	0.970	0.952
N	3,072	1,344	3,072	1,344	3,072	1,344	3,072	1,344
VARIABLES	Panel B: Urban hierarchy
	Peripheral city	Center city	Peripheral city	Center city	Peripheral city	Center city	Peripheral city	Center city
	(1) SA	(2) SA	(3) PI	(4) PI	(5) FS	(6) FS	(7) GA	(8) GA
Digital	0.0650***	0.0176	0.0960***	0.0606	0.0128	0.0253	0.1712***	−0.0392
	(9.79)	(1.25)	(5.38)	(0.97)	(1.59)	(1.32)	(9.64)	(−0.46)
Constant	0.6386***	0.6246***	0.2213***	0.2676***	0.5370***	0.5698***	0.3966***	0.2655*
	(50.82)	(17.11)	(6.19)	(4.98)	(54.44)	(29.09)	(13.73)	(1.97)
Control	YES	YES	YES	YES	YES	YES	YES	YES
year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
R^2	0.967	0.984	0.929	0.978	0.812	0.968	0.966	0.976
N	3904	512	3904	512	3904	512	3904	512

Note: Robust t-statistics in parentheses. *** p < 0.01. ** p < 0.05. *p < 0.1.

Table 8. Regression results of the model of digital technology affecting SA and subsystem thresholds

variant		Moderator variable
		(1) SA	(2) PI	(3) GA
		Digital	Digital	Digital
Threshold value	q1	0.2282	0.2226	0.2282
	q2		0.2533
Digital-I (Th≤q1)		0.0149*** (3.0976)	−0.0870*** (−6.92)	0.0348*** (2.6101)
Digital-I (q1 <Th<q2)		0.0359*** (7.7898)	−0.0296** (−2.02)	0.0741*** (5.8018)
Digital-I (Th≥q2)			0.01947* (1.67)
Control		YES	YES	YES
City-year FE		YES	YES	YES
N		4416	4416	4416
R^2		0.9070	0.8848	0.8552

Note: Robust t-statistics in parentheses. *** p < 0.01. ** p < 0.05. *p < 0.1.

Table 9. Characterizing the “spread” of digital technology and SA, 2005–2020

year	Digital		SA
	Moran’s I	Z-value	Moran’s I	Z-value
2005	0.076***	15.264	0.082***	16.643
2006	0.080***	16.007	0.094***	18.717
2007	0.082***	16.350	0.091***	18.132
2008	0.081***	16.168	0.088***	17.583
2009	0.082***	16.452	0.089***	17.803
2010	0.075***	15.169	0.095***	18.934
2011	0.073***	14.708	0.083***	16.613
2012	0.082***	16.465	0.093***	18.552
2013	0.075***	15.121	0.085***	16.937
2014	0.072***	14.593	0.086***	17.127
2015	0.072***	14.502	0.089***	17.649
2016	0.075***	15.139	0.086***	17.202
2017	0.071***	14.283	0.084***	16.845
2018	0.075***	14.997	0.092***	18.228
2019	0.069***	13.988	0.087***	17.341
2020	0.070***	14.151	0.084***	16.752

Note: *** p < 0.01. ** p < 0.05. *p < 0.1.

Table 10. Regression results of the spatial model of the impact of digital technology on sustainable agricultural development

Model setup	SLM			SDM
	(1)	(2)	(3)	(4)	(5)	(6)
Space matrix typology	geographic distance	adjacency matrix	Economic distance nesting	geographic distance	adjacency matrix	Economic distance nesting
ρ	0.7868*** (0.0457)	0.2479*** (0.0138)	0.1082*** (0.0192)	0.5034*** (0.0966)	0.2136*** (0.0207)	0.0848*** (0.0272)
Digital	0.0462*** (0.0046)	0.0448*** (0.0045)	0.0446*** (0.0046)	0.0458*** (0.0045)	0.0440*** (0.0044)	0.0440*** (0.0047)
W*Digital				0.0871*** (0.0684)	−0.0048 (0.0087)	−0.0207 (0.0112)
Control	YES	YES	YES	YES	YES	YES
Direct effect	0.0470*** (0.0048)	0.0456*** (0.0046)	0.0449*** (0.0048)	0.0466*** (0.0047)	0.0444*** (0.0046)	0.0439*** (0.0048)
Spillover effect	0.1787*** (0.0029)	0.0138*** (0.0017)	0.0053*** (0.0012)	0.2261*** (0.1559)	0.0052 (0.0102)	−0.0187 (0.0117)
Aggregate effect	0.2257*** (0.0603)	0.0595*** (0.0062)	0.0502*** (0.0054)	0.2727* (0.1569)	0.0495*** (0.0120)	0.0251* (0.0129)
LogL	18074.3833	18157.2697	18017.9214	18122.3412	18189.2075	18028.4268
R^2	0.5362	0.8409	0.8813	0.6970	0.8630	0.8833

Note: Robust standard errors are reported in parentheses in the table. *** p < 0.01. ** p < 0.05. * p < 0.1.