Role of Social Learning in the Diffusion of Environmentally-Friendly Agricultural Technology in China
| Autor: | Wanjiang Yang, Kai Li, Qi Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Learning from demonstration
Agricultural machinery Renewable Energy Sustainability and the Environment business.industry Geography Planning and Development Spatial error 0211 other engineering and technologies 021107 urban & regional planning 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law Environmental economics Social learning 01 natural sciences Environmentally friendly environmentally-friendly rice fertilizer-reducing and pesticide-reducing technologies social learning spatial error model (SEM) Work (electrical) Agriculture China business 0105 earth and related environmental sciences |
| Zdroj: | Sustainability; Volume 10; Issue 5; Pages: 1527 |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su10051527 |
| Popis: | Reducing the use of chemical inputs is an urgent and challenging task in the transformation toward environmentally-friendly agriculture in China, especially when the efficacy of alternative control measures is not yet fully understood. Based on the data from 601 rice farmer households regarding their adoption of fertilizer- and pesticide-reducing technologies in Zhejiang and Jiangsu Provinces, this study investigated whether social learning can promote the diffusion of fertilizer- and pesticide-reducing technologies, and whether the role of social learning varies when the technologies differ. Empirical analysis using the spatial error model (SEM) showed that social learning positively affects the diffusion of ecological technologies, but the role of social learning varies when the technology characteristics differ. Learning from neighbors promotes the adoption of labor-intensive and high-skilled technologies, but this strategy does not work well in capital-intensive technologies. However, learning from demonstration significantly affected the diffusion of capital-intensive and high-skilled technologies, but did not work well for labor-intensive technologies. |
| Databáze: | OpenAIRE |
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