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<p>This study utilizes panel data from 30 provinces in China, spanning from 2003 to 2021, to research the influence of higher education resources on green economy development. First, this study utilized dynamic factor analysis to assess the allocation level of higher education resources in terms of five aspects: human resources, material resources, budget resources, scientific research, and social contribution. Moreover, the Super-efficient Slack-Based Measure (S-SBM) model, incorporating undesired outputs from Data Envelopment Analysis (DEA), and the Global Malmquist-Luenberger (GML) index approach were used to evaluate green economic growth (GTFP). Second, we utilize a Partial Linear Function Coefficient (PLFC) model that differentiates spatio-temporal heterogeneity to evaluate how higher education resources enhance GTFP, and identify the mechanisms that how the digital economy (Digital) and human capital (HC) influence the relationship between higher education resources and GTFP. The findings of this research are as follows. (1) Higher education resources significantly improve GTFP. (2) Higher education resources are influenced by Digital and HC in the promotion of GTFP. (3) The stimulus effects influenced by Digital and HC exhibit significant spatio-temporal variation. The pulling effect is particularly noticeable in provincial capitals and economically developed cities.</p>

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The effect of higher education resources on promoting green economy development

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • Yongchun Sun,
  • Guonian Li

摘要

This study utilizes panel data from 30 provinces in China, spanning from 2003 to 2021, to research the influence of higher education resources on green economy development. First, this study utilized dynamic factor analysis to assess the allocation level of higher education resources in terms of five aspects: human resources, material resources, budget resources, scientific research, and social contribution. Moreover, the Super-efficient Slack-Based Measure (S-SBM) model, incorporating undesired outputs from Data Envelopment Analysis (DEA), and the Global Malmquist-Luenberger (GML) index approach were used to evaluate green economic growth (GTFP). Second, we utilize a Partial Linear Function Coefficient (PLFC) model that differentiates spatio-temporal heterogeneity to evaluate how higher education resources enhance GTFP, and identify the mechanisms that how the digital economy (Digital) and human capital (HC) influence the relationship between higher education resources and GTFP. The findings of this research are as follows. (1) Higher education resources significantly improve GTFP. (2) Higher education resources are influenced by Digital and HC in the promotion of GTFP. (3) The stimulus effects influenced by Digital and HC exhibit significant spatio-temporal variation. The pulling effect is particularly noticeable in provincial capitals and economically developed cities.