The Impact of Environmental Regulation and Technical Cognition on Farmers’ Adoption of Safety Agro-Utilization of Heavy Metal-Contaminated Farmland Soil
Abstract
:1. Introduction
2. Theoretical Analysis and Research Hypotheses
2.1. The Impact of Environmental Regulations on the Adoption Behavior of VIP Technologies by Farmers
2.2. The Mediating Role of Technical Cognition
2.3. The Moderating Role of Self-Efficacy
3. Research Methodology
3.1. Data Sources
3.2. Variable Selection
3.2.1. Dependent Variable
3.2.2. Core Independent Variables
3.2.3. Mediating Variable
3.2.4. Moderator Variables
3.2.5. Control Variables
3.3. Model Settings
3.3.1. Binary Logistic Regression Model
3.3.2. Methods for Mediating and Moderating Effects
4. Results and Analysis
4.1. Total Impact of Environmental Regulations on Farmers’ VIP Technology Adoption Behavior
4.2. Test of the Conditional Mediating Role of Technical Cognition
4.3. Test of the Moderating Effect of Self-Efficacy
5. Conclusions and Recommendations
5.1. Conclusions
5.2. Recommendations
5.3. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Clemens, S.; Aarts, M.G.M.; Thomine, S.; Verbruggen, N. Plant science: The key to preventing slow cadmium poisoning. Trends Plant Sci. 2013, 18, 92–99. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Environmental Protection of the People’s Republic of China; Ministry of Land and Resources of the People’s Republic of China. The Report on the National Soil Contamination Survey. 2014. Available online: https://www.mee.gov.cn/gkml/sthjbgw/qt/201404/t20140417_270670.htm (accessed on 10 April 2023).
- Shang, E.; Xu, E.; Zhang, H.; Huang, C. Spatial-temporal trends and pollution source analysis for heavy metal contamination of cultivated soils in five major grain producing regions of China. Environ. Sci. 2018, 39, 4670–4683. [Google Scholar]
- Song, Y.; Wang, Y.; Mao, W.; Sui, H.; Yong, L.; Yang, D.; Jiang, D.; Zhang, L.; Gong, Y. Dietary cadmium exposure assessment among the Chinese population. PLoS ONE 2017, 12, e0177978. [Google Scholar] [CrossRef] [PubMed]
- Wang, P.; Chen, H.; Kopittke, P.M.; Zhao, F. Cadmium contamination in agricultural soils of China and the impact on food safety. Environ. Pollut. 2019, 249, 1038–1048. [Google Scholar] [CrossRef] [PubMed]
- Ma, L.; Zhou, L.; Song, B.; Wang, F.; Zhang, Y.; Wu, Y. Mercury Pollution in Dry-land Soil and Evaluation of Maize Safety Production in Guizhou Province. Environ. Sci. 2023, 44, 2868–2878. [Google Scholar]
- Xu, C.; Xiong, W.; Liu, J. Research projects promoting green development within China’s agricultural sector: Insights from the National Key R&D Program of China for prevention and control of agricultural non-point source pollution and heavy metal contaminated croplands. J. Agric. Resour. Environ. 2023, 40, 699–704. [Google Scholar]
- Li, X. Safe utilization of heavy metal-contaminated farmland: Goals, technical options, and extendable technology. Chin. J. Eco-Agric. 2020, 28, 860–866. [Google Scholar]
- Huang, D.; Zhu, Q.; Zhu, H.; Xu, C.; Liu, S. Advances and prospects of safety agro-utilization of heavy metal contaminated farmland soil. Res. Agric. Mod. 2018, 39, 1030–1043. [Google Scholar]
- Deng, M.; Li, J. The analysis of the influencing factors of farmers’ VIP technology adoption behaviors. Res. Agric. Mod. 2019, 40, 811–818. [Google Scholar]
- Tong, X.; Xia, W.; Lin, Z.; Huang, D.; Wu, J.; Ding, H. Study on the implementation effects of different implementation entities of heavy metal contaminated cultivated land. Chin. J. Environ. Manag. 2020, 12, 121–129. [Google Scholar]
- Hao, L.; Li, Y.; Zhang, C.; Zhao, Z. An Empirical Study on the Control Policy of Heavy Metal Pollution and Stakeholders’ Feedback Mechanism in Cultivated Land: The Investigation from a Pilot. Chin. J. Environ. Manag. 2018, 10, 21–24. [Google Scholar]
- Newman, T.P.; Fernandes, R. A re-assessment of Factors Associated with Environmental Concern and Behavior Using the 2010 General Social Survey. Environ. Educ. Res. 2015, 22, 153–175. [Google Scholar] [CrossRef]
- Ma, W.K.; Chan, A. Knowledge Sharing and Social Media: Altruism, Perceived Online Attachment Motivation, and Perceived Online Relationship Commitment. Comput. Hum. Behav. 2014, 39, 51–58. [Google Scholar] [CrossRef]
- Liu, X.; Ren, T.; Ge, J.; Liao, S.; Pang, L. Heterogeneous and Synergistic Effects of Environmental Regulations: Theoretical and Empirical Research on the Collaborative Governance of China’s Haze Pollution. J. Clean. Prod. 2022, 350, 131473. [Google Scholar] [CrossRef]
- Huang, X.L.; Cheng, L.L.; Chien, H.; Jiang, H.; Yang, X.; Yin, C. Sustainability of returning wheat straw to field in Hebei, Shandong and Jiangsu provinces: A contingent valuation method. J. Clean. Prod. 2019, 213, 1290–1298. [Google Scholar] [CrossRef]
- Harper, J.K.; Roth, G.W.; Garalejić, B.; Škrbić, N. Programs to promote adoption of conservation tillage: A Serbian case study. Land Use Policy 2018, 78, 295–302. [Google Scholar] [CrossRef]
- Zhang, Z.; Liu, Y.; Zhou, Q. Study on farmers’ active choice, government multidirectional intervention and the adoption of pro-environmental agricultural technology in forestry-fruit industry. Res. Agric. Mod. 2022, 43, 638–647. [Google Scholar]
- Tang, L.; Luo, X.; Zhang, J. Environmental policies and farmers’ environmental behaviors: Administrative restriction or economic incentive. China Popul. Resour. Environ. 2021, 316, 147–157. [Google Scholar]
- Zhang, H.; Li, J.Y.; Shi, D. Research on the influence of environmental regulation and ecological cognition on farmers’ organic fertilizer adoption behavior. Chin. J. Agric. Resour. Reg. Plan. 2021, 42, 42–50. [Google Scholar]
- Shi, Z.; Fu, Y. Contradiction Between Green Production Intention and Farmers’ Behavior from the Perspective of Technology Diffusion Conditions:Taking Adoption of Pollution-Free Pesticide Technology as An Example. J. Agro-For. Econ. Manag. 2022, 21, 29–39. [Google Scholar]
- Yu, W.; Luo, X.; Li, R.; Xue, L.; Huang, L. The paradox between farmer willingness and their adoption of green technology from the perspective of green cognition. Resour. Sci. 2017, 39, 1573–1583. [Google Scholar]
- Ajzen, I. From Intentions to Actions: A Theory of Planned Behavior; Springer: Berlin/Heidelberg, Germany, 1985. [Google Scholar]
- Wang, X.; Yan, G. Technology cognition, environmental regulation and farmers’ straw return technology adoption behavior. World Agric. 2022, 4, 57–68. [Google Scholar]
- Li, W.; Wang, G. Effects of social capital and technology cognition on farmers’ adoption of conservation tillage in black soil areas. Chin. J. Eco-Agric. 2022, 30, 1675–1686. [Google Scholar]
- Yu, Y.; Xu, X. Study on the influence of risk aversion and value perception on farmers’ willingness to adopt VIP cadmium reduction technology—The investigation of farmers in Jiangsu, Hunan and Jiangxi provinces id taken ad an example. World Agric. 2022, 9, 101–112. [Google Scholar]
- Lu, S.; Zhang, X. Research on the Kiwifruit Growers’ Adoption of Green Production Technology. North Hortic. 2023, 7, 141–148. [Google Scholar]
- Zhou, W.; Guo, G. Self-efficacy: The Conception, Theory and Applications. J. Renmin Univ. China 2006, 1, 91–96. [Google Scholar]
- Bandura, A.; Freeman, W.; Lightsey, R. Self-efficacy: The exercise of control. J. Cogn. Psychother. 1999, 13, 158–166. [Google Scholar] [CrossRef]
- You, L.; Shen, Z.; Huo, X. Does confidence affect farmers’ lending behavior—An empirical study on two villages. J. Shanxi Univ. Financ. Econ. 2022, 44, 42–56. [Google Scholar]
- Xu, X.; Zhao, Y.; Shi, R.; Qian, P.; Song, S. The Adoption of SOR Theory in the Field of Library and Information Science in China: Traceability, Application and Future Prospects. Inf. Doc. Serv. 2022, 43, 98–105. [Google Scholar]
- Zhang, S.; Chen, R.; Luo, Y. A study on the path of high-quality development of agriculture embedded in Notarization based on the micro perspective of the new agricultural producers. Issues Agric. Econ. 2020, 6, 66–74. [Google Scholar]
- Liu, X.; Zhou, L.; Ying, R. Research on Ecological Compensation Policy Choice and Combination for Heavy Metal Pollution Control of Cultivated Land. China Land Sci. 2021, 35, 88–97. [Google Scholar]
- Travis, C.M.; Nijkamp, P. Valuing Environmental and health risk in agriculture: A choice experiment approach to pesticides in Italy. Ecol. Econ. 2008, 67, 598–607. [Google Scholar] [CrossRef]
- Yang, X.; Qi, Z.; Chen, X.; Yang, C. Government training, technology cognition and farmer Eco-agricultural technology adoption behavior—Taking rice and shrimp co-culture technology as an example. Chin. J. Agric. Resour. Reg. Plan. 2021, 42, 198–208. [Google Scholar]
- Ren, Z.; Guo, Y. The effect of environmental regulation and social capital on farmers’ adoption behavior of low-carbon agricultural technology. J. Nat. Resour. 2023, 38, 2872–2888. [Google Scholar] [CrossRef]
- Li, M.; Lu, Q.; Qiao, D. Technological cognition, government support and farmers’ adoption of water-saving irrigation technology. J. Arid Land Resour. Environ. 2017, 31, 27–32. [Google Scholar]
- Su, L.; Swanson, S.R. The effect of destination social responsibility on tourist environmentally responsible behavior: Compared analysis of first-time and repeat tourists. Tour. Manag. 2017, 60, 308–321. [Google Scholar] [CrossRef]
- Luo, L.; Fu, H.; Liu, Y.; Li, D. Risk perception digital literacy and farmers’ willingness to participate in E-commerce in COVID-19—An analysis based on the survey data of citrus farmers. J. Agrotech. Econ. 2022, 9, 83–99. [Google Scholar]
- Long, C.; Shan, J.; Chai, X. Antecedents of Residents’ Brand Ambassadorial Behavior for Tourism Destination: An Empirical Study Based on MOA Model. Econ. Probl. 2023, 6, 96–105. [Google Scholar]
- Wu, Y.; Dong, J. Relationship within capital endowment, technology cognition and farmers’ low carbon use intention of cultivated land. Acta Agric. Zhejiangensis 2021, 33, 2423–2434. [Google Scholar]
- Yue, M.; Zhang, L.; Wan, J. Can the expansion of land scale promote farmers to adopt organic fertilizer technology?: An expatiation from the perspective of the subject capacity. J. China Agric. Univ. 2022, 27, 236–248. [Google Scholar]
- Xue, C.; Li, H. Environmental Knowledge and Pro-environmental Behaviors of Households: An Analysis Based on Mediation Effect of Environmental Capability and Moderation Effect of Social Norms. Sci. Technol. Manag. Res. 2021, 41, 231–240. [Google Scholar]
- Yue, M.; Zhang, L.; Zhang, J. Land fragmentation and farmers’ environmental–friendly technology adoption decision: Taking soil measurement and fertilization technology as an example. Resour. Environ. Yangtze Basin 2021, 30, 1957–1968. [Google Scholar]
- Sang, X.; Luo, X.; Huang, Y.; Tang, L. Relationship between policy incentives, ecological cognition, and organic fertilizer application by farmers: Based on a moderated mediation model. Chin. J. Eco-Agric. 2021, 29, 1274–1284. [Google Scholar]
- Li, Y.; Wang, X.; Hao, L.; Liu, Y.; Jiang, L. An Analysis on Treatment of Heavy-Metal Soil Contamination: Characteristics and Determinants of Farmers’ Treatment Methods. Chin. Rural Econ. 2017, 1, 58–67, 95. [Google Scholar]
- Preacher, K.J.; Rucker, D.D.; Hayes, A.F. Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivar. Behav. Res. 2007, 42, 185–227. [Google Scholar] [CrossRef] [PubMed]
- Hayes, A.F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. J. Educ. Meas. 2014, 51, 335–337. [Google Scholar]
- Hayes, A.F. An index and Test of Linear Moderated Mediation. Multivar. Behav. Res. 2015, 50, 1–22. [Google Scholar] [CrossRef] [PubMed]
- Davison, A.C.; Hinkley, D.V. Bootstrap Methods and Their Application; Cambridge University Press: Cambridge, UK, 1997; Volume 1. [Google Scholar]
- Chen, R.; Zheng, Y.; Liu, W. Mediation Analysis: Principles, Procedures, Bootstrap Methods and Applications. J. Mark. Sci. 2013, 14, 120–135. [Google Scholar]
- Li, X.; Liang, H. Agrotechnical Training, Technology Access and Farmers’ Water-saving Irrigation Technology Adoption Behavior—An Example from Inner Mongolia Autonomous Region. Water Sav. Irrig. 2021, 2, 95–104. [Google Scholar]
- Ma, R.; Xiao, H.; Gao, B.; Qiao, G. Research on the farmers’ resource conservation technology adoption behavior: Dual perspectives of endogenous drive and external situation. J. Arid Land Resour. Environ. 2023, 37, 28–36. [Google Scholar]
- Xu, Y.; Mu, Y. Study on the Impact of Income Uncertainty on the Dynamic Decision of Water-saving Technology Adoption—Adjustment from the Government Promotion Forms. Chin. J. Agric. Resour. Reg. Plan. 2023, 44, 15–23. [Google Scholar]
- Shi, Z.; Zhang, H. Research on Social Norms, Environmental Regulations and Farmers’ Fertilization Behavior Selection. Chin. J. Agric. Resour. Reg. Plan. 2021, 42, 51–61. [Google Scholar]
- Wang, T.; Yang, H. Social norms, ecological cognition and film recycling willingness: Based on the moderating effect of environmental regulation. J. Arid Land Resour. Environ. 2021, 35, 14–20. [Google Scholar]
- Ji, J.; Xie, Y. The Influence of Multi-Agent Governance on Farmers’ Green Production Behavior—Empirical Study Based on the Survey Data of 872 Tea Farmers in Fujian Province. For. Econ. 2023, 45, 5–28. [Google Scholar]
Content of the Survey | Options | Frequency | Percentage |
---|---|---|---|
Gender | Woman | 127 | 28.41 |
Male | 320 | 71.59 | |
Age | 30 years old and under | 10 | 2.24 |
31–40 years old | 37 | 8.28 | |
41–50 years old | 81 | 18.12 | |
51–60 years old | 175 | 39.15 | |
61 years old and over | 144 | 32.21 | |
Level of education | Illiteracy | 20 | 4.47 |
Primary school | 119 | 26.62 | |
Middle school | 172 | 38.48 | |
High school | 103 | 23.04 | |
College and above | 33 | 7.38 | |
Type of business | Individual | 372 | 83.22 |
Large-scale grain farmers | 41 | 9.17 | |
Cooperative | 34 | 7.61 | |
The scale of rice cultivation | Less than 0.2 hectare | 151 | 33.78 |
0.2–0.34 hectare | 148 | 33.11 | |
0.34–0.47 hectare | 67 | 14.99 | |
0.47–0.67 hectare | 26 | 5.82 | |
0.67 hectares and above | 55 | 12.30 | |
Family income from agriculture | Less than USD 2765 | 305 | 68.23 |
USD 2765–5530 | 60 | 13.42 | |
USD 5530–8295 | 7 | 1.57 | |
USD 8295–11,060 | 14 | 3.13 | |
USD 11,060 and above | 61 | 13.65 |
Variable Type | Variable Name | Meaning and Valuation | Mean | Standard Deviation |
---|---|---|---|---|
Dependent variable | Variety | Do you cultivate cadmium-low-accumulation varieties? No = 0, yes = 1 | 0.76 | 0.43 |
Irrigation | Do you maintain a certain flooding depth during rice growth, draining and drying the field 7–10 days before harvest? No = 0, yes = 1 | 0.54 | 0.50 | |
pH | Do you evenly apply sufficient lime and promptly till the soil? No = 0, yes = 1 | 0.54 | 0.50 | |
Core explanation Variable | Guidance regulation | How often did you attend training sessions on the safe utilization of technology for heavy metal-polluted farmland this year? 0 = 1, 1 = 2, 2 = 3, 3 = 4, 4 or more = 5 | 2.87 | 1.11 |
Incentive regulation | Mean value of farmers’ evaluation of the quantity and time of distribution of materials | 4.46 | 0.80 | |
Constraint regulations | Do you feel that regulatory authorities adequately supervise the implementation of safe utilization technology for heavy metal polluted farmland? Very poorly = 1, less well = 2, average = 3, fairly well = 4, very well = 5 | 4.06 | 1.29 | |
Intermediary variable | Technical Cognition | Calculated based on the entropy weight method | 0.54 | 0.22 |
moderator variable | Self-efficacy | The mean value of farmers’ capacity to access information, capital investment, labor inputs, and material acquisition capacity | 3.04 | 1.11 |
Control variable | Age | <30 = 1, 31~40 = 2, 41~50 = 3, 51~60 = 4, 61 and above = 5 | 3.91 | 1.01 |
Educational attainment | Illiterate = 1, Primary school = 2, Middle school = 3, High school = 4, College and above = 5 | 3.02 | 0.99 | |
Participating in the management entity | Participation in new agricultural enterprises: no = 0, yes = 1 | 0.17 | 0.37 | |
Family income from agriculture | Actual annual household income from agriculture (USD): below 2765 = 1, 2765 to 5530 = 2, 5530 to 8295 = 3, 8295 to 11,060 = 4, 11,060 and above = 5 | 1.81 | 1.42 | |
Number of family farmers | Number of laborers in the household engaged in agricultural production | 1.88 | 0.83 | |
The scale of rice cultivation | Area under rice cultivation (hectare): <0.2 = 1, 0.2~0.34 = 2, 0.34~0.47 = 3, 0.47~0.67 = 4, 0.67 and above = 5 | 2.30 | 1.32 | |
Degree of cropland fragmentation | Cultivated land area/cultivated plots | 1.03 | 0.79 | |
Water retention of arable land | Poor = 1, Moderate = 2, Excellent = 3 | 2.09 | 0.59 | |
Average distance to irrigation water sources | Average distance from irrigation water source to arable land (Km): below 0.05 = 1, 0.05 to 0.1 = 2, 0.1 to 0.5 = 3, 0.5 to 1 = 4, 1 and above = 5 | 2.37 | 1.34 |
Variant | Variety | Irrigation | pH | ||||||
---|---|---|---|---|---|---|---|---|---|
B | Standard Error | Exp (B) | B | Standard Error | Exp (B) | B | Standard Error | Exp (B) | |
Guidance regulation | 0.820 *** | 0.176 | 2.270 | 1.850 *** | 0.254 | 6.361 | 1.680 *** | 0.229 | 5.365 |
Incentive regulation | 0.448 *** | 0.164 | 1.565 | −0.222 | 0.198 | 0.801 | −0.190 | 0.186 | 0.827 |
Constraint regulation | 0.462 *** | 0.111 | 1.588 | 0.934 *** | 0.175 | 2.545 | 0.693 *** | 0.150 | 1.999 |
Age | −0.165 | 0.153 | 0.847 | 0.328 ** | 0.158 | 1.388 | 0.323 ** | 0.150 | 1.382 |
Educational attainment | −0.094 | 0.158 | 0.910 | 0.377 ** | 0.166 | 1.457 | 0.288 * | 0.157 | 1.334 |
Participating in the management entity | 0.300 | 0.691 | 1.349 | 2.127 *** | 0.707 | 8.392 | 1.795 *** | 0.648 | 6.017 |
Family income from agriculture | −0.043 | 0.181 | 0.958 | −0.135 | 0.165 | 0.873 | −0.065 | 0.158 | 0.937 |
Number of family farmers | 0.242 | 0.185 | 1.274 | 0.765 *** | 0.204 | 2.150 | 0.656 *** | 0.191 | 1.926 |
The scale of rice cultivation | 0.041 | 0.138 | 1.042 | 0.223 | 0.148 | 1.250 | 0.026 | 0.138 | 1.027 |
Degree of cropland fragmentation | 0.082 | 0.243 | 1.085 | −0.516 ** | 0.212 | 0.597 | −0.328 * | 0.198 | 0.720 |
Water retention of arable land | 0.513 ** | 0.254 | 1.670 | 0.934 *** | 0.273 | 2.545 | 0.914 *** | 0.259 | 2.494 |
Average distance to irrigation water sources | −0.086 | 0.111 | 0.918 | 0.154 | 0.118 | 1.166 | 0.103 | 0.111 | 1.109 |
Constant | −5.066 | 1.228 | 0.006 | −14.104 | 1.827 | 0.000 | −11.882 | 1.606 | 0.000 |
−2 log-likelihood | 339.955 | 310.672 | 341.396 | ||||||
Cox Snell R2 | 0.285 | 0.496 | 0.460 | ||||||
Negolko R2 | 0.428 | 0.663 | 0.614 | ||||||
chi-square test | 149.739 | 306.563 | 275.212 | ||||||
Predictive accuracy | 85.20% | 85.00% | 82.30% |
Variant | Variety | Irrigation | pH | |||
---|---|---|---|---|---|---|
B | Standard Error | B | Standard Error | B | Standard Error | |
Guidance regulation | 0.820 *** | 0.178 | 1.850 *** | 0.383 | 1.680 *** | 0.300 |
Incentive regulation | 0.448 ** | 0.183 | −0.222 | 0.182 | −0.190 | 0.186 |
Constraint regulation | 0.462 *** | 0.129 | 0.934 *** | 0.203 | 0.693 *** | 0.180 |
Age | −0.165 | 0.172 | 0.328 ** | 0.178 | 0.323 ** | 0.161 |
Educational attainment | −0.094 | 0.156 | 0.377 ** | 0.177 | 0.288 * | 0.164 |
Participating in the management entity | 0.300 | 1.437 | 2.127 *** | 0.871 | 1.795 *** | 0.780 |
Family income from agriculture | −0.043 | 0.241 | −0.135 | 0.191 | −0.065 | 0.180 |
Number of family farmers | 0.242 | 0.207 | 0.765 *** | 0.253 | 0.656 *** | 0.215 |
The scale of rice cultivation | 0.041 | 0.165 | 0.223 | 0.167 | 0.026 | 0.158 |
Degree of cropland fragmentation | 0.082 | 0.204 | −0.516 *** | 0.210 | −0.328 ** | 0.193 |
Water retention of arable land | 0.513 ** | 0.270 | 0.934 *** | 0.321 | 0.914 *** | 0.308 |
Average distance to irrigation water sources | −0.086 | 0.120 | 0.154 | 0.140 | 0.103 | 0.125 |
Constant | −5.066 | 1.305 | −14.104 | 2.221 | −11.882 | 1.732 |
VIP Technology | Conditional Indirect Effect | Moderated Mediation Effect | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Environmental Regulation | Self- Efficacy | Coefficient | Boot Standard Error | 95% Confidence Interval | Index | Boot Standard Error | 95% Confidence Interval | |||
Lower Limit | Upper Limit | Lower Limit | Upper Limit | |||||||
Variety | Guidance regulation | Low level | 0.615 | 0.174 | 0.353 | 1.036 | 0.183 | 0.135 | −0.063 | 0.478 |
average | 0.819 | 0.191 | 0.555 | 1.307 | ||||||
High level | 1.023 | 0.296 | 0.593 | 1.741 | ||||||
Incentive regulation | Low level | 0.327 | 0.105 | 0.171 | 0.585 | 0.093 | 0.069 | −0.024 | 0.250 | |
average | 0.431 | 0.118 | 0.256 | 0.723 | ||||||
High level | 0.534 | 0.169 | 0.292 | 0.956 | ||||||
Constraint regulation | Low level | 0.339 | 0.106 | 0.184 | 0.597 | 0.103 | 0.076 | −0.027 | 0.272 | |
average | 0.453 | 0.107 | 0.310 | 0.724 | ||||||
High level | 0.567 | 0.161 | 0.349 | 0.976 | ||||||
Irrigation | Guidance regulation | Low level | 0.156 | 0.421 | −0.637 | 1.009 | 0.873 | 0.471 | 0.301 | 2.047 |
average | 1.126 | 0.361 | 0.755 | 2.107 | ||||||
High level | 2.096 | 0.794 | 1.276 | 4.232 | ||||||
Incentive regulation | Low level | 0.150 | 0.223 | −0.256 | 0.618 | 0.430 | 0.246 | 0.122 | 1.056 | |
average | 0.629 | 0.202 | 0.386 | 1.177 | ||||||
High level | 1.107 | 0.426 | 0.614 | 2.261 | ||||||
Constraint regulation | Low level | 0.113 | 0.241 | −0.337 | 0.624 | 0.511 | 0.264 | 0.173 | 1.177 | |
average | 0.681 | 0.206 | 0.465 | 1.230 | ||||||
High level | 1.249 | 0.447 | 0.746 | 2.427 | ||||||
pH | Guidance regulation | Low level | 0.285 | 0.330 | −0.352 | 0.946 | 0.662 | 0.343 | 0.167 | 1.510 |
average | 1.021 | 0.260 | 0.687 | 1.715 | ||||||
High level | 1.757 | 0.564 | 1.036 | 3.248 | ||||||
Incentive regulation | Low level | 0.193 | 0.169 | −0.087 | 0.558 | 0.324 | 0.183 | 0.070 | 0.775 | |
average | 0.553 | 0.165 | 0.343 | 0.990 | ||||||
High level | 0.913 | 0.329 | 0.490 | 1.775 | ||||||
Constraint regulation | Low level | 0.163 | 0.179 | −0.164 | 0.549 | 0.378 | 0.184 | 0.103 | 0.841 | |
average | 0.583 | 0.146 | 0.401 | 0.970 | ||||||
High level | 1.003 | 0.307 | 0.596 | 1.831 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, X.; Li, J.; Lin, Z.; Ma, L. The Impact of Environmental Regulation and Technical Cognition on Farmers’ Adoption of Safety Agro-Utilization of Heavy Metal-Contaminated Farmland Soil. Sustainability 2024, 16, 3343. https://doi.org/10.3390/su16083343
Guo X, Li J, Lin Z, Ma L. The Impact of Environmental Regulation and Technical Cognition on Farmers’ Adoption of Safety Agro-Utilization of Heavy Metal-Contaminated Farmland Soil. Sustainability. 2024; 16(8):3343. https://doi.org/10.3390/su16083343
Chicago/Turabian StyleGuo, Xinyuan, Jizhi Li, Zejian Lin, and Li Ma. 2024. "The Impact of Environmental Regulation and Technical Cognition on Farmers’ Adoption of Safety Agro-Utilization of Heavy Metal-Contaminated Farmland Soil" Sustainability 16, no. 8: 3343. https://doi.org/10.3390/su16083343