Farmers’ Digital Literacy and Its Impact on Agricultural Green Total Factor Productivity: Evidence from China
Abstract
1. Introduction
2. Theoretical Framework and Research Hypotheses
2.1. Analysis of Farmers’ Digital Literacy and Its Impact on AGTFP
2.2. Mediating Role of Social Capital
2.3. The Moderating Role of Agricultural Socialization Services
2.4. The Role of Control Variables in the Impact of Farmers’ Digital Literacy on AGTFP
3. Materials and Methods
3.1. Data Sources
3.2. Model Design
3.2.1. Two-Way Fixed Effects Model
3.2.2. The Mediation Effect Model
3.2.3. The Moderation Effect Model
3.3. Variable Description
3.3.1. Dependent Variable: AGTFP
3.3.2. Core Explanatory Variable: Digital Literacy of Farmers
3.3.3. Mediating Variables
3.3.4. Regulation Variables
3.3.5. Control Variables
4. Empirical Results
4.1. Benchmark Regression
4.2. Endogeneity Test
4.3. Robustness Test
4.4. Heterogeneity Test
4.5. Mechanism Verification
4.5.1. Mediation Effect Test
4.5.2. Moderation Effect Test
4.6. Further Analysis
4.6.1. Feature Importance Analysis
4.6.2. Threshold Effect Analysis
4.6.3. Analysis of the Strength of Directional Contributions
5. Discussion
6. Conclusions
6.1. Key Findings
6.2. Theoretical Significance
6.3. Practical Significance
6.4. Research Limitations and Future Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Appendix B.1. The Neoclassical Theory of Economic Growth
Appendix B.2. Theory of Planned Behavior
References
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Variables | Primary Indicator Dimension | Secondary Indicator Intensity | Meaning | Unit | Symbol |
---|---|---|---|---|---|
AGTFP | Input | Capital | Total liquid and fixed capital investments of farmers in agricultural production | Yuan | a1 |
Labor | Which members of your household participated in agricultural production activities over the past 12 months? | Person | a2 | ||
Rent | Total income from land leasing and total expenditure on land rental | Yuan | a3 | ||
Expectation | Output | The total value of your household’s agricultural and sideline products combined with the total value of your household’s consumption of agricultural and sideline products. | Yuan | b1 | |
Unexpectation | Pollution | Agricultural COD emissions | Ton | c1 | |
Agricultural TN emissions | c2 | ||||
Agricultural TP emissions | c3 | ||||
Subjectivity | How serious do you perceive environmental issues in China, where 0 indicates “not serious” and 10 indicates “very serious”? | Point | c4 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
AGTFP | AGTFP | AGTFP | AGTFP | |
DL | 0.167 *** | 0.138 *** | 0.172 *** | 0.165 *** |
(0.035) | (0.020) | (0.027) | (0.035) | |
Age | 0.033 *** | −0.000 | 0.003 | |
(0.002) | (0.000) | (0.004) | ||
Gender | 0.023 | −0.003 | 0.023 | |
(0.066) | (0.006) | (0.065) | ||
Marriage | −0.005 | −0.002 | −0.004 | |
(0.013) | (0.007) | (0.013) | ||
Old | 0.007 | 0.000 | 0.007 | |
(0.005) | (0.003) | (0.005) | ||
Aveedu | −0.002 | −0.035 *** | −0.022 * | |
(0.011) | (0.006) | (0.011) | ||
Physical | −0.001 | −0.002 | −0.000 | |
(0.002) | (0.002) | (0.002) | ||
Asset | 0.086 ** | 0.048 | 0.018 | |
(0.043) | (0.037) | (0.044) | ||
_cons | 0.743 *** | −1.116 *** | 0.942 *** | 0.692 *** |
(0.008) | (0.131) | (0.081) | (0.257) | |
Controls | NO | YES | YES | YES |
Pid fixed | YES | YES | NO | YES |
Year fixed | YES | NO | YES | YES |
N | 4685 | 4685 | 4685 | 4685 |
R2 | 0.578 | 0.568 | 0.578 | 0.579 |
Variable | First Stage | Second Stage |
---|---|---|
DL | AGTFP | |
(1) | (2) | |
IV | 0.060 ** (0.025) | |
DL | 5.603 *** (2.066) | |
Controls | YES | YES |
Pid fixed | YES | YES |
Year fixed | YES | YES |
Cragg–Donald Wald F | 12.301 | |
Kleibergen–Paap rk Wald F | 17.182 | |
Kleibergen–Paap rk LM | 19.881 | |
N | 4685 | 4685 |
Variables | Model Transformation | Replacement of Explanatory Variables | Bilateral Trimming | Exclusion of Special Samples | ||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
DL | 0.172 *** | 0.058 *** | 0.036 *** | 0.130 *** | 0.179 *** | 0.161 *** |
(0.027) | (0.020) | (0.011) | (0.034) | (0.032) | (0.036) | |
_cons | 0.942 *** | 0.668 ** | 0.686 *** | 0.638 *** | 0.766 *** | 0.753 *** |
(0.081) | (0.286) | (0.257) | (0.229) | (0.183) | (0.260) | |
Controls | YES | YES | YES | YES | YES | YES |
Pid fixed | YES | YES | YES | YES | YES | YES |
Year fixed | YES | YES | YES | YES | YES | YES |
N | 4685 | 3586 | 4685 | 4685 | 4685 | 4643 |
R2 | 0.578 | 0.547 | 0.578 | 0.605 | 0.630 | 0.580 |
Variable | AGTFP | |||||
---|---|---|---|---|---|---|
Female | Male | High Trust | Low Trust | Working Age | Non-Working Age | |
DL | −0.040 (0.056) | 0.279 *** (0.046) | 0.123 ** (0.060) | 0.209 *** (0.052) | 0.110 ** (0.044) | 0.254 *** (0.063) |
_cons | 1.070 ** | 0.455 | 0.446 | 0.365 | 0.625 ** | −0.259 |
(0.416) | (0.323) | (0.717) | (0.320) | (0.304) | (2.836) | |
Controls | YES | YES | YES | YES | YES | YES |
Pid fixed | YES | YES | YES | YES | YES | YES |
Year fixed | YES | YES | YES | YES | YES | YES |
N | 1821 | 2864 | 2146 | 2539 | 3081 | 1604 |
R2 | 0.631 | 0.552 | 0.569 | 0.591 | 0.550 | 0.626 |
AGTFP (1) | SC (2) | AGTFP (3) | |
---|---|---|---|
DL | 0.165 *** (0.035) | −0.029 *** (0.007) | 0.189 *** (0.035) |
SC | 0.841 *** (0.087) | ||
_cons | 0.692 *** (0.257) | 1.936 *** (0.050) | −0.935 *** (0.305) |
Controls | YES | YES | YES |
Pid fixed | YES | YES | YES |
Year fixed | YES | YES | YES |
N | 4685 | 4685 | 4685 |
R2 | 0.579 | 0.173 | 0.590 |
F | F(11,3365) = 420.63 | F(11,3365) = 63.91 | F(12,3364) = 403.75 |
Variable | Equation (1) | Equation (2) |
---|---|---|
DL | 0.165 *** (0.035) | 0.100 ** (0.040) |
ASS | −0.051 *** (0.011) | |
ASS*DL | 0.081 *** (0.022) | |
_cons | 0.692 *** (0.257) | 0.676 *** (0.256) |
Controls | YES | YES |
Pid fixed | YES | YES |
Year fixed | YES | YES |
N | 4685 | 4685 |
R2 | 0.579 | 0.5815 |
F | F(11,3365) = 420.63 | F(13,3363) = 359.46 |
Variable | Description |
---|---|
Work-related internet usage intensity (x1) | Vayre’s [68] task priority theory defines the importance of the Internet for work as the core motivational driver that enables farmers to access agricultural production information and engage in remote agricultural technology training using digital tools. This study measures farmers’ responses to the question, “When using the Internet, how important is work to you?” using a five-point Likert scale, where 1 denotes “not at all important” and 5 denotes “extremely important”. |
Hedonistic internet behavior motivation (x2) | Based on Van’s [69] hedonistic information system theory, the importance of the Internet for entertainment reflects the frequency with which farmers access agricultural-related leisure information through platforms such as short videos and live streaming. The questionnaire measured this variable using the item, “When using the Internet, how important is entertainment to you?” Responses were recorded on a five-point Likert scale, where 1 indicates “not at all important” and 5 indicates “extremely important”. |
Social network dependency (x3) | Based on Andreassen’s [70] theory of social significance, “network social significance” captures the extent of farmers’ activity in exchanging agricultural information on social platforms such as WeChat and QQ. The survey measured this aspect using the item, “When using the Internet, how important is social interaction to you?” Responses were recorded on a five-point Likert scale, where 1 indicates “not at all important” and 5 indicates “extremely important”. |
Learning-oriented internet usage (x4) | Based on Eynon’s [71] empirical findings on the value of internet learning, “the importance of the internet for learning” is defined as a core cognitive dimension of farmers’ digital technology-enabled learning practices. The questionnaire measured this dimension using the item, “When using the Internet, how important is learning to you?” Responses were rated on a five-point Likert scale, where 1 indicates “not at all important” and 5 indicates “extremely important”. |
Life-empowering internet dependency (x5) | Based on Seifert’s [72] empirical findings on the functional value of the Internet, “the importance of the Internet to daily life” is defined as a core cognitive dimension of digital technology that empowers individuals’ everyday practices. This aspect was measured by the item, “How important are commercial activities to you when using the Internet?” Responses were recorded on a five-point Likert scale, where 1 indicates “not at all important” and 5 indicates “extremely important”. |
Cumulative effect of the digital contact (x6) | Following Sexton’s [73] work on cumulative risk assessment, “amateur Internet usage time” serves as a cumulative indicator of DL, effectively reflecting the extent of an individual’s long-term exposure to and interaction within the digital environment. This aspect was measured by the questionnaire item, “How many hours of your leisure time do you spend online?” |
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Wang, H.; Mao, Y.; Zhou, M.; Li, X. Farmers’ Digital Literacy and Its Impact on Agricultural Green Total Factor Productivity: Evidence from China. Sustainability 2025, 17, 9255. https://doi.org/10.3390/su17209255
Wang H, Mao Y, Zhou M, Li X. Farmers’ Digital Literacy and Its Impact on Agricultural Green Total Factor Productivity: Evidence from China. Sustainability. 2025; 17(20):9255. https://doi.org/10.3390/su17209255
Chicago/Turabian StyleWang, Hubang, Yuyang Mao, Mingzhang Zhou, and Xueyang Li. 2025. "Farmers’ Digital Literacy and Its Impact on Agricultural Green Total Factor Productivity: Evidence from China" Sustainability 17, no. 20: 9255. https://doi.org/10.3390/su17209255
APA StyleWang, H., Mao, Y., Zhou, M., & Li, X. (2025). Farmers’ Digital Literacy and Its Impact on Agricultural Green Total Factor Productivity: Evidence from China. Sustainability, 17(20), 9255. https://doi.org/10.3390/su17209255