Predicting and Optimizing Regional Economic Green Development Pathways: Insights from R-Studio for Sustainable Biotechnology Commercialization

Abstract

Achieving sustainable regional economic growth is crucial for advancing the commercialization of green biotechnology solutions. This study proposes a predictive and optimization framework for assessing regional economic green development levels using multivariate statistical analysis powered by R-Studio. Guided by the "Two Mountains" theory and the "green economy" concept, 43 green development indicators from 31 provincial regions across China were analyzed. R-type cluster analysis identified two comprehensive sets of evaluation indicators representing the current level of economic green development and future sustainable development potential. These indicators were further classified through Q-type sample clustering and discriminant analysis. Principal component scores and Q-type factor scores were employed to quantify and rank regional green development levels, revealing key weaknesses and suggesting targeted optimization pathways. The findings show that the first principal component accounts for 35.36% of variance, the second for 24.86%, and the third for 27.41%, with a cumulative contribution rate of 60.22% for the top two components—sufficient for representing regional green development levels while retaining essential information. The analysis indicates persistent uncoordinated development trends in several regions, characterized by either "economy-led" or "environment-led" growth, with some regions exhibiting low performance in both areas. Notably, Fujian and Hubei provinces demonstrated "coordinated development," highlighting their potential for sustained economic growth supported by green biotechnology. This research provides strategic insights for stakeholders in commercial biotechnology by identifying critical regional development gaps and proposing data-driven optimization pathways to enhance the commercialization and adoption of sustainable biotechnological innovations.