Spatio-temporal differentiation on China’s green agricultural total factor productivity guided by digital technology and fuzzy systems - Changcun Wen; Bao Hu; Yongpeng Sun; Yiping Xiao (Cogent Food & Agriculture)
Chinese agriculture grapples with contemporary challenges involving resource scarcity, environmental concerns, and transformations in agricultural labor structures. This work endeavors to address these challenges and foster sustainable agricultural development by advocating the principles of agricultural green development and sustainability in China. The application principles of digital technology are first expounded alongside a discussion on the foundational concepts of fuzzy systems. Subsequently, an in-depth analysis is undertaken on the composition and trajectory of China’s agricultural total factor productivity (ATFP), culminating in the construction of a Green ATFP model. The findings illuminate the heavy reliance on labor and resources in current agricultural development, with the transformation of labor structure emerging as a pivotal factor influencing progress. Notably, the trends of feminization, aging, and urbanization in rural labor have precipitated a substantial shortage, impeding agricultural development. Furthermore, under the guidance of digital technology and fuzzy systems, this paper establishes the theoretical framework of green ATFP. Evaluation results disclose a noteworthy decrease in the contribution rates of various indicators under the Green ATFP model, with the highest contribution rate approximating 1.23. This implies that the existing measures for China’s green ATFP are still imperfect, signaling a need for greater emphasis on environmental considerations in agricultural development. This work furnishes valuable insights for advancing agricultural development and contributes significantly to the ongoing efforts in promoting the green evolution of Chinese agriculture.