Dynamic evolution and obstacle factor analysis of green development in China's agriculture and rural areas based on entropy-based TOPSIS model

Abstract

Green development of agriculture and rural areas (GDARA) is an essential part of rural revitalization and high-quality development. Based on 2011 to 2020 provincial panel data from China, the entropy-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model is constructed to measure the level of GDARA, finding that the overall level during the sample period was positive. Then the regional differences mainly from intra-regional were revealed by the methods of Dagum Gini coefficient and Kernel density estimation and were shrinking overall. Besides, the results of Markov chain transfer matrix show the dynamic characteristics clearly, which the low-level regions shift to the middle and high levels with a greater probability. The results of the spatial autocorrelation test display that GDARA has a spatial clustering effect. Finally, the industrial upgrading and the business income of leisure agriculture are respectively proven to hinder GDARA at most by the Obstacle degree model empirically. Based on a series of empirical tests, a few relevant policy recommendations are proposed to promote the road to strong agriculture and rural areas in China.

Keywords

• Green development in agriculture and rural areas
• Indicator system
• Spatial and temporal characteristics
• Obstacle factor
• Spatial autocorrelation