Driving patterns of industrial green transformation: A multiple regions case learning from China

Abstract

A core issue for achieving successful industrial green transformation is to identify what driving factors under which kinds of synergistic combination methods with how strong driving strengths will lead to the superior driving patterns of green transformation. This paper focuses on the novel perspective of learning and mining historical cases of industrial green transformation in China's multiple regions, and a three-phase case learning theoretical framework of learning-validation-generalization based on rough set theory is constructed to identify the driving patterns from regional level. The extracted decision rules set reveals the potential synergistic relationships of different heterogeneous driving factors on industrial green transformation from the multiple paths of structure transformation and efficiency transformation. The results show that the key driving factors of structure transformation and efficiency transformation presents certain differences and obvious spatial effect. The higher resource allocation efficiency, stronger investment scale and lower resource endowment dominate the industrial green transformation's core features, which distinguish the superior driving patterns from the inferior, in China's eastern, central and western regions respectively. The driving patterns provide policy-makers extra insights to guide different regions to adjust and optimize their green transformation through developing the regional advantages and bypassing the insufficiencies in historical cases.