Coupling Coordination Analysis of Regional IEE System: A Data-Driven Multimodel Decision Approach

Abstract

Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system.

Sustainable Supply Chains in Industrial Engineering and Management

“…Yang et al [5] built a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional innovation-economy-ecology (IEE) system. This method was used to test the coupling coordination degree of the Anhui IEE system and optimization measures were proposed.…”

Data-Driven Evaluation and Recommendations for Regional Synergy Innovation Capability

“…Economic spillover capability and knowledge creation capability are two kinds of results stemming from the operation of regional innovation activities [46]. Economic spillover capability directly produces economic value [61]. This study also uses GDP, new product sales revenue of industrial enterprises above the designated size, and turnover of the technology market to reflect economic spillover capability.…”