An entropy-based TOPSIS and optimized grey prediction model for spatiotemporal analysis in strategic emerging industry
发布时间：2023年3月
作者：Song Ding a b, Ruojin Li a, Junha Guo a
单位：a School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China
b Center for Regional Economy & Integrated Development, Zhejiang University of Finance & Economics, Hangzhou 310018, China
期刊：Expert Systems with Applications
Volume 213, Part C, 1 March 2023, 119169
摘要：Stepping into the stage of high-quality and innovation-driven development, the 14th Five-Year Plan for China places a high priority on advancing the strategic emerging industry (SEI). An integrated strategic emerging industry-economy-environment (IEE) system is constructed to investigate the sustainability of SEI. The development quality of the three subsystems is assessed by the entropy-based TOPSIS. Furthermore, the evolution trend, spatial distribution, and the prospects of the coupling coordination degree (CCD) for the IEE system are researched. Findings indicate that the respective evolution trend of CCD exhibits an N-shape pattern from 2011 to 2019 in the 11 districts of the Yangtze River Economic Belt. Moreover, mutual promotion and interaction exist between the strategic emerging industrial system and the economic system reflected by analogous evolution patterns. The spatial distribution for CCD is featured with a stepwise decline among these 11 districts from lower to upper reaches of the Yangtze River, mostly attributed to the lagged industrial and economic growth in the districts of upper and middle reaches. As predicted by the improved grey Bernoulli model, the prospect for CCD generally will experience an upward trend. Based on the empirical results, countermeasures are suggested to promote the sustainability and unleash the potential of SEI for advancing high-quality development.
关键词：Strategic emerging industry；Economic growth；Environmental improvement；Grey prediction model；High-quality development
链接：https://doi.org/10.1016/j.eswa.2022.119169
