Need-responsive accessibility supports equitable sustainable development and reshapes migration as evolving human needs
Abstract
Realizing the Sustainable Development Goals’ commitment to “leave no one behind” requires accessibility that responds to evolving human needs, yet prevailing accessibility assessments remain static and sector-specific, overlooking hierarchical need evolution and behavioral feedback. We develop a Needs-responsive Accessibility Framework that integrates Maslow’s hierarchy with the Sustainable Development Goals to assess accessibility across five need tiers and their coupling coordination degree in China (2000–2020). By incorporating migration data, the framework further reveals accessibility-migration interactions. Results show that national accessibility increased by 21.26 points, corresponding to a compound annual growth rate of 3.39%, with development priorities shifting from physiological to higher-order needs—echoing Maslow’s theory. Meanwhile, east-west disparities narrowed and coupling coordination degree improved from 0.41 (uncoordinated) to 0.71 (moderately coordinated), indicating a more balanced human-need system. Migration motives likewise evolved from material to self-actualization needs, whose contribution rose from 20% to 45% as higher-order opportunities improved. These findings establish a demand-oriented framework for designing more inclusive sustainable strategies, particularly in resource-constrained regions. Accessibility in China rose from 2000 to 2020, and east-west disparities narrowed, system coordination improved while migration motives shifted toward self-actualization needs, according to an analysis that uses a needs-responsive accessibility framework integrating SDGs, Maslow’s hierarchy, and migration data.
// Source
Authors: Ting Zhou, Chunlin Huang, Fanglei Zhong, Xiaoyu Song, Jinliang Hou, Ying Zhang, Xin Li, Huadong Guo
Institutions: Chinese Academy of Sciences, Institute of Tibetan Plateau Research, University of Chinese Academy of Sciences, Minzu University of China, Northwest Institute of Eco-Environment and Resources, Lanzhou Jiaotong University, Beijing Institute of Big Data Research, International Research Center of Big Data for Sustainable Development Goals