Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology
2.2. Specific Model Used
3. Data Selection and Model Construction
3.1. Data Sources
- Limited coverage of influencing factors due to constraints in sample selection and data collection methods;
- Inadequate consideration of economic factors, costs of technological transformation, and impacts on agricultural ecological systems;
- Potential biases or limitations in the statistical methods used for analysis;
- Insufficient depth in exploring certain variables crucial to understanding the research question;
- Challenges in generalizing findings beyond the specific context of the study area.
3.2. Model Setting
3.3. Variable Selection
3.3.1. Dependent Variable
3.3.2. Explanatory Variables
4. Empirical Analysis
4.1. Descriptive Statistics of Sample Variables
4.2. Multicollinearity Test
4.3. Analysis of Factors Influencing Farmers’ Willingness to Adopt Green Production Technologies
4.4. Robustness Checks
5. Research Results
6. Policy Recommendations for Promoting Green Agricultural Production
6.1. Improving Green Certification Standards and Systems
6.2. Enhancing the Traceability System for Agricultural Products and Increasing Transparency of Production Information
6.3. Synchronously Enhancing Regulation of Banned Pesticides and Promoting Green Production
6.4. Innovating Subsidy Approaches through Enhanced Transparency and Traceability
6.5. Innovating Subsidy Methods through Enhanced Transparency and Traceability
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Huang, Y.; Luo, X.; Li, R.; Zhang, J. Farmers’ cognition, external environment, and willingness to produce green agriculture—Based on survey data from 632 farmers in Hubei Province. Resour. Environ. Yangtze Basin 2018, 27, 680–687. [Google Scholar]
- Yi, F.; Wang, F.; Ke, Y.; Bai, Z.; Chen, S. Research on Farmers’ Cognition, Economic Incentives, and Adoption Behavior of Green Pest Control Technologies: Based on Survey Data from 347 Betel Nut Plantation Households in Wanning City, Hainan Province, China. For. Econ. 2023, 7, 52–53. [Google Scholar]
- Hou, X.; Liu, T.; Huang, T. Farmers’ Adoption of Green Agricultural Technologies and Income Effects. J. Northwest AF Univ. 2019, 3, 121–122. [Google Scholar]
- Chen, N. Factors influencing farmers’ green production behavior from the perspective of rural revitalization. Agric. Technol. 2021, 41, 144–149. [Google Scholar]
- Li, Y.; Bai, J.; Wang, J.; Liu, F. Analysis of factors influencing the advancement of high-quality wheat green industry technologies—A case study of high-quality wheat production areas in Hebei Province. Sci. Technol. Manag. Res. 2020, 40, 240–248. [Google Scholar]
- Zhang, H. Analysis of the impact of government intervention and market incentives on farmers’ adoption of green pest control technologies. Fujian Tea 2020, 42, 55–56. [Google Scholar]
- Xiong, Y.; Guo, Y. Game analysis of relevant stakeholders in the adoption of green control technologies. Agric. Prod. Qual. Saf. 2019, 3, 87–92. [Google Scholar]
- Yao, X.; Nie, Z. Study on the behavior of empty-nest farmers adopting green agricultural technologies from the perspective of green agricultural subsidies. J. Xinyang Agric. For. Univ. 2017, 3, 20–24. [Google Scholar]
- Zhou, L.; Feng, J.; Cao, G. Study on the Adoption Behavior of Green Agricultural Technology by Farmers—A Survey of Farmers in Hunan, Jiangxi, and Jiangsu. Rural. Econ. 2020, 3, 93–101. [Google Scholar]
- Fan, T.; Yu, Y. The Impact of Land Trusteeship on Farmers’ Adoption of Green Agricultural Production Technologies—A Case Study of M Village in Shandong Province. Anhui Agric. Sci. Bull. 2021, 3, 13–16. [Google Scholar]
- Wang, R.; Han, X.; Cui, M.; Zheng, F. The Income-Enhancing Effect of Farmers’ Adoption of Green Production Technologies: A Perspective from Quality Economics. Res. Agric. Mod. 2021, 3, 462–473. [Google Scholar]
- Heggie, K.; Savage, C. Nitrogen yields from New Zealand coastal catchments to receiving estuaries. N. Z. J. Mar. Freshw. Res. 2009, 43, 1039–1052. [Google Scholar] [CrossRef]
- Ongley, E.D. Control of Water Pollution from Agriculture; FAO Irrigation and Drainage Paper 55; Food and Agriculture Organization of the United Nations: Rome, Italy; GEMS/Water Collaboration Centre Canada Centre for Inland Waters: Burlington, ON, Canada, 1996. [Google Scholar]
- Ongley, E.D.; Xiaolan, Z.; Tao, Y. Current status of agricultural and rural non-point source pollution assessment in China. Environ. Pollut. 2010, 158, 1159–1168. [Google Scholar] [CrossRef]
- Wauters, E.; Mathijs, E. The adoption of farm level soil conservation practices in developed countries: A meta-analytic review. Int. J. Agric. Resour. Gov. Ecol. 2014, 10, 78–102. [Google Scholar] [CrossRef]
- Dessart, F.G.; Barreiro-Hurlé, J.; van Bavel, R. Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. Eur. Rev. Agric. Econ. 2019, 46, 417–471. [Google Scholar] [CrossRef]
- Baregheh, A.; Rowley, J.; Sambrook, S. Towards a multidisciplinary definition of innovation. Manag. Decis. 2009, 47, 1323–1339. [Google Scholar] [CrossRef]
- Esser, K. Factors influencing the adoption of green technologies by farmers: A review. Sustainability 2018, 10, 2774. [Google Scholar]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Serebrennikov, D.; Thorne, F.; Kallas, Z.; McCarthy, S.N. Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature. Sustainability 2020, 12, 9719. [Google Scholar] [CrossRef]
- Läpple, D.; Van Rensburg, T. Adoption of organic farming: Are there differences between early and late adoption. Ecol. Econ. 2011, 70, 1406–1414. [Google Scholar] [CrossRef]
- Läpple, D.; Kelley, H. Spatial dependence in the adoption of organic drystock farming in Ireland. Eur. Rev. Agric. Econ. 2015, 42, 315–337. [Google Scholar] [CrossRef]
- Dabiah, A.T.; Alotibi, Y.S.; Herab, A.H. Attitudes of Agricultural Extension Workers toward the use of Electronic Extension Methods in Agricultural Extension in the Kingdom of Saudi Arabia. Int. J. Agric. Biosci. 2023, 12, 104–109. [Google Scholar]
- Mzoughi, N. Farmers adoption of integrated crop protection and organic farming: Do moral and social concerns matter. Ecol. Econ. 2011, 70, 1536–1545. [Google Scholar] [CrossRef]
- USDA NRCS. USDA NIFA Conservation Effects Assessment Project (CEAP) Fact Sheets. USDA NRCS NIFA. 2011. Available online: http://www.soil.ncsu.edu/publications/NIFACEAP (accessed on 2 February 2018).
- Pannell, D.J.; Marshall, G.R.; Barr, N.; Curtis, A.; Vanclay, F.; Wilkinson, R. Understanding and promoting adoption of conservation practices by rural landholders. Aust. J. Exp. Agric. 2006, 46, 1407–1424. [Google Scholar] [CrossRef]
- Greiner, R.; Gregg, D. Farmers’ intrinsic motivations, barriers to the adoption of conservation practices and effectiveness of policy instruments: Empirical evidence from northern Australia. Land Use Policy 2011, 28, 257–265. [Google Scholar] [CrossRef]
- Chouinard, H.H.; Wandschneider, P.R.; Paterson, T. Inferences from sparse data: An integrated, meta-utility approach to conservation research. Ecol. Econ. 2016, 122, 71–78. [Google Scholar] [CrossRef]
- Savage, J.; Ribaudo, M. Improving the Efficiency of Voluntary Water Quality Conservation Programs. Land Econ. 2016, 92, 148–166. [Google Scholar] [CrossRef]
- Liu, T.; Bruins, R.J.F.; Heberling, M.T. Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis. Sustainability 2018, 10, 432. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Li, M. The Impact of Farmers’ Professional Cooperatives on the Accessibility of Financing for Farmers. J. Jishou Univ. (Nat. Sci. Ed.) 2019, 40, 83–90. [Google Scholar]
- Peng, C. The Basic Framework of China’s Agricultural Subsidies, Policy Performance, and Directions for Momentum Transformation. Theor. Explor. 2017, 3, 18–25. [Google Scholar]
- Chen, W. Institutional Constraints and Policy Recommendations for the Green Transformation of Farmers’ Production Under the Strategy of Rural Revitalization—Based on In-depth Interviews with 47 Conventional Production Farmers. Exploration 2018, 3, 136–145. [Google Scholar]
- Yang, C.; Qi, Z.; Huang, W.; Chen, X. The Impact of Benefit Perception on Farmers’ Adoption Behavior of Green Production Technologies—A Heterogeneity Analysis Based on Different Production Stages. Resour. Environ. Yangtze Basin 2021, 30, 448–458. [Google Scholar]
- Nie, W.; Zuo, T.; Chen, J. Analysis of Factors Influencing Farmers’ Perception of Agricultural Green Development and Adoption of Green Production Behaviors. J. Northeast. Agric. Univ. (Soc. Sci. Ed.) 2020, 18, 9. [Google Scholar]
- Min, J.; Kong, X. Research Progress on Agricultural Non-Point Source Pollution in China. J. Huazhong Agric. Univ. (Soc. Sci. Ed.) 2016, 2, 59–66. [Google Scholar]
- Yu, W.; Luo, X.; Li, R.; Xue, L.; Huang, L. Study on the Discrepancy between Willingness and Behavior of Farmers to Adopt Green Technologies from the Perspective of Green Cognition. Resour. Sci. 2017, 8, 1573–1583. [Google Scholar]
- Zhang, M.; Zhang, C.; Li, F.; Liu, Z. Green Finance as an Institutional Mechanism to Direct the Belt and Road Initiative towards Sustainability: The Case of China. Sustainability 2024, 16, 6164. [Google Scholar] [CrossRef]
Type | Variable Name | Variable Definition | Mean | Standard Deviation |
---|---|---|---|---|
Dependent variable | Willingness to adopt green production technologies (Y) | “Very Unwilling” = 1; “Unwilling” = 2; “Neutral” = 3; “Willing” = 4; “Very Willing” = 5 | 4.251 | 0.035 |
Explanatory variables | Sex (X1) | “Male” = 1; “Female” = 0 | 0.755 | 0.019 |
Age (X2) | “40 years and under” = 1; “40 to 50 years” = 2; “50 to 60 years” = 3; “Above 60 years” = 4 | 2.188 | 0.035 | |
Level of education (X3) | “Junior High School and Below” = 1; “High School or Technical/Vocational School” = 2; “Community College or Technical Institute ” = 3; “Bachelor’s Degree and Above” = 4 | 2.056 | 0.038 | |
Government subsidy amount (X4) | “No Subsidy” = 1; “0 to 50,000 yuan” = 2; “50,000 to 100,000 yuan” = 3; “Above 100,000 yuan” = 4 | 1.851 | 0.048 | |
Land management area (X5) | “0 to 32.94 acres” = 1; “32.94 to 65.88 acres” = 2; “65.88 to 98.82 acres” = 3; “98.82 to 131.76 acres” = 4; “over 131.76 acres” = 5 | 2.307 | 0.061 | |
Sales performance of green products (X6) | “Very Difficult” = 1; “Quite Difficult” = 2; “Moderate” = 3; “Relatively Easy” = 4; “Very Easy” = 5 | 2.713 | 0.042 | |
Presence of sales quality inspection (X7) | “Yes” = 1; “No” = 0 | 0.854 | 0.015 | |
Traceability of agricultural products (X8) | “Yes” = 1; “No” = 0 | 0.715 | 0.020 | |
Assessment of enforcement intensity against the use of banned pesticides (X9) | “Very Small” = 1; “Somewhat Small” = 2; “Moderate” = 3; “Somewhat Large” = 4; “Very Large” = 5 | 3.891 | 0.046 | |
Membership in a cooperative (X10) | “Yes” = 1; “No” = 0 | 0.291 | 0.020 |
Sample Characteristics | Categorical Indicators | Frequency | Percentage | Sample Characteristics | Categorical Indicators | Frequency | Percentage |
---|---|---|---|---|---|---|---|
Sex (X1) | Male | 394 | 75.5 | Sales Performance of Green Products (X6) | Very difficult | 55 | 10.5 |
Female | 128 | 24.5 | Quite difficult | 153 | 29.3 | ||
Age (X2) | 40 years and under | 100 | 19.2 | Moderate | 218 | 41.8 | |
40 to 50 years | 247 | 47.3 | Relatively easy | 79 | 15.1 | ||
50 to 60 years | 152 | 29.1 | Very easy | 17 | 3.3 | ||
Above 60 years | 23 | 4.4 | Presence of Sales Quality Inspection (X7) | Yes | 446 | 85.4 | |
Education Level (X3) | Junior high school and below | 149 | 28.5 | No | 76 | 14.6 | |
High school or vocational/technical secondary school | 226 | 43.3 | Traceability of Agricultural Products (X8) | Yes | 373 | 71.5 | |
Junior college or higher vocational education | 116 | 22.2 | No | 149 | 28.5 | ||
Bachelor’s degree and above | 31 | 5.9 | Assessment of Enforcement Intensity against the Use of Banned Pesticides (X9) | Very small | 20 | 3.8 | |
Government Subsidy Amount (X4) | No subsidy | 279 | 53.4 | Quite small | 33 | 6.3 | |
0 to 50,000 yuan | 118 | 22.6 | Moderate | 105 | 20.1 | ||
50,000 to 100,000 yuan | 49 | 9.4 | Relatively large | 190 | 36.4 | ||
Above 100,000 yuan | 76 | 14.6 | Very large | 174 | 33.3 | ||
Land Management Area (X5) | 0 to 32.94 acres | 196 | 37.5 | Membership in a Cooperative (X10) | Yes | 152 | 29.1 |
32.94 to 65.88 acres | 148 | 28.4 | No | 370 | 70.9 | ||
65.88 to 98.82 acres | 71 | 13.6 | |||||
98.82 to 131.76 acres | 36 | 6.9 | |||||
Over 131.76 acres | 71 | 13.6 |
Model | Unstandardized Coefficient | Standardized Coefficients | t | Significance | Collinearity Statistics | ||
---|---|---|---|---|---|---|---|
B | Standard Error | Beta | Tolerance | VIF | |||
X1 | −0.031 | 0.080 | −0.017 | −0.389 | 0.698 | 0.965 | 1.036 |
X2 | 0.010 | 0.045 | 0.010 | 0.231 | 0.817 | 0.928 | 1.078 |
X3 | 0.029 | 0.041 | 0.031 | 0.714 | 0.475 | 0.926 | 1.080 |
X4 | 0.043 | 0.032 | 0.059 | 1.365 | 0.173 | 0.957 | 1.045 |
X5 | 0.017 | 0.025 | 0.030 | 0.692 | 0.490 | 0.949 | 1.054 |
X6 | 0.101 | 0.037 | 0.120 | 2.770 | 0.006 | 0.945 | 1.058 |
X7 | 0.235 | 0.099 | 0.103 | 2.370 | 0.018 | 0.945 | 1.058 |
X8 | 0.172 | 0.077 | 0.096 | 2.245 | 0.025 | 0.957 | 1.045 |
X9 | 0.138 | 0.033 | 0.181 | 4.205 | 0.000 | 0.959 | 1.043 |
X10 | 0.073 | 0.075 | 0.041 | 0.964 | 0.336 | 0.985 | 1.015 |
Item | Model 1 | Model 2 | ||||||
---|---|---|---|---|---|---|---|---|
Estimate | Standard Error | Wald | Significance | Estimate | Standard Error | Wald | Significance | |
X1 | 0.062 | 0.202 | 0.094 | 0.759 | ||||
X2 | 0.041 | 0.112 | 0.136 | 0.712 | ||||
X3 | 0.029 | 0.103 | 0.081 | 0.776 | ||||
X4 | 0.137 | 0.081 | 2.882 | 0.090 * | 0.138 | 0.079 | 3.047 | 0.081 * |
X5 | 0.008 | 0.063 | 0.014 | 0.905 | ||||
X6 | 0.242 | 0.092 | 6.819 | 0.009 *** | 0.235 | 0.091 | 6.587 | 0.010 *** |
X7 | 0.542 | 0.245 | 4.889 | 0.027 ** | 0.559 | 0.244 | 5.238 | 0.022 ** |
X8 | 0.529 | 0.192 | 7.611 | 0.006 *** | 0.538 | 0.191 | 7.926 | 0.005 *** |
X9 | 0.420 | 0.083 | 25.524 | 0.000 *** | 0.415 | 0.083 | 25.236 | 0.000 *** |
X10 | 0.177 | 0.190 | 0.876 | 0.349 |
Ordered Logistic | Ordered Probit | Applying a 1% Winsorization to the Government Subsidy Amount | Applying a 1% Winsorization to the Land Management Area | |
---|---|---|---|---|
X1 | 0.062 | 0.003 | 0.039 | 0.048 |
X2 | 0.041 | 0.022 | 0.042 | 0.064 |
X3 | 0.029 | 0.031 | 0.044 | 0.038 |
X4 | 0.137 * | 0.075 * | 0.140 * | 0.134 * |
X5 | 0.008 | 0.012 | 0.016 | 0.014 |
X6 | 0.242 *** | 0.154 *** | 0.245 *** | 0.237 ** |
X7 | 0.542 ** | 0.317 ** | 0.531 ** | 0.495 ** |
X8 | 0.529 *** | 0.273 ** | 0.511 *** | 0.415 *** |
X9 | 0.420 *** | 0.225 *** | 0.419 *** | 0.526 *** |
X10 | 0.177 | 0.105 | 0.164 | 0.167 |
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Fan, X.; Meng, G.; Zhang, Q. Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies. Sustainability 2024, 16, 5657. https://doi.org/10.3390/su16135657
Fan X, Meng G, Zhang Q. Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies. Sustainability. 2024; 16(13):5657. https://doi.org/10.3390/su16135657
Chicago/Turabian StyleFan, Xiaojuan, Guanghui Meng, and Qingming Zhang. 2024. "Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies" Sustainability 16, no. 13: 5657. https://doi.org/10.3390/su16135657