Environmental conscience, external incentives and social norms in rice farmers’ adoption of pro-environmental agricultural practices in rural Hubei province, China

ABSTRACT

This paper attempts to address a recurring theme in agricultural resources and environmental economics – the low adoption rates of pro-environmental agricultural practices in many developing countries. By improving the Norm-activation model, this paper incorporates external incentives and social norms into the framework and employs multivariate probit and ordered probit models to explore how environmental conscience, external incentives and social norms influence rice farmers’ adoption behaviours and intensive use of pro-environmental agricultural practices. The case study, involving 954 household-level data of rice farmers from rural Hubei province, China, reveals that the adoption rates of certain practices are very low, and that only 6.5% rice farmers adopt three or more pro-environmental agricultural practices. Results show that straw returning and soil testing and fertilizer recommendation are complementaries, and that environmental conscience, external incentives and social norms all positively affect the adoption behaviours, while the adoption intensity is significantly influenced by awareness of consequences, perceived efficacy, external incentives and descriptive norm. These findings underscore that policy interventions to improve rice farmers’ environmental conscience, to provide well-designed external incentives and to activate social norms are needed to enhance the adoption of pro-environmental agricultural practices in developing countries.

GRAPHICAL ABSTRACT

KEYWORDS:

• Environmental conscience
• external incentives
• social norms
• rice farmers
• multiple adoption
• pro-environmental agricultural practices

Acknowledgements

The authors would like to show our sincere gratitude to the editor and the anonymous reviewers for their time and valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The reasons for these variables selected are detailed in the third section of this paper.

2 Before the regression is estimated, the independent variables are checked for the existence of multicollinearity. No serious problem was found. The work exampled by Gaur and Gaur [53] shows that the multicollinearity exists when a value of variance inflation factor (VIF) is greater than 5. While according to the multicollinearity test with SPSS software, the maximum VIF value for this study is 1.358, far less than 5, which means that the multicollinearity test of independent variables is acceptable for this study.

Additional information

Funding

This research is financially supported by the National Natural Science Foundation of China (71703051/71333006/71503074), the Key Program of Philosophy and Social Sciences Research, Ministry of Education of China (15JZD014), the Soft Science Research Project of Technology Innovation in Hubei Province (2018ADC036), and the Humanity and Social Science Youth foundation of Ministry of Education (17YJC790169).