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To achieve sustainable development, it is necessary to consider the business model adjustment of the industry in advance, from the development stage to the mature stage. In China, strategic emerging industries are industries that achieve technological breakthroughs, but such industries often have the characteristics of high investment, high technology, high risk, high returns, and long research and development times. This type of industry relies heavily on national resource support from the exploration period to the development period, but its high-profit characteristics also attract policy bias from the governments of other countries internationally. Therefore, understanding the resource requirements of such industries in different periods in advance will help the government to adjust resource allocation and strategic layout through policy means. This will facilitate the smooth transition of the entire industry from the development period to the mature period, and achieve its overall sustainable development. To assist the government in achieving reasonable predictions for policy adjustments, this study focuses on the biopharmaceutical industry, which is one of the representatives of the strategic emerging industries in China’s Yangtze River Delta. Considering that policies are not used in a single manner, and that the observation period needs to span the development and platform periods of the industry, the traditional Qualitative Comparative Analysis method (QCA) does not consider the analysis of data from multiple periods. Therefore, this study innovatively uses the Multi-Time Qualitative Comparative Analysis method (mtQCA), adding the dimension of time change and exploring the policy configuration logic behind the differences in local industrial innovation performance. Extracting general rules from specific policy configuration patterns is meaningful for a better analysis and resolution of complex, dynamic management issues, which will promote the sustainable development of strategic emerging industries.
Xiao et al. (Tue,) studied this question.