A multi-layer network perspective on green finance and clean energy industry synergistic development and mutual reinforcement: Mechanism analysis, correlation effect and enhancement path

• Published in: Renewable energy Vol. 240; p. 122209
• Main Authors: Wang, Kexin, Ding, Rui, Xiao, Wenqian, Liang, Juan, Hong, Yuxuan, Peng, Lina, Zou, Jian, Jiang, Shuyue
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2025
• Subjects: China; Clean energy; Driving factors; energy industry; finance; Geographic detector; Green finance; Multi-layer network; Network structure characterization; renewable energy sources; society; sustainable development; China; Multi-layer network; Driving factors; Green finance; Network structure characterization; Clean energy; Geographic detector
• DOI: 10.1016/j.renene.2024.122209
• ISSN: 0960-1481

The synergistic development of green finance and clean energy (SDGC) is the cornerstone for driving green and low-carbon transformation and achieving sustainable development goals. This article examines the interaction mechanisms between green finance (GF) and clean energy (CE), constructs both single-layer correlation networks and multi-layer networks (MN) based on panel data from 30 provinces in China spanning from 2007 to 2022, analyzes the characteristics of multi-layer network nodes and structures, and identifies main driving factors by using geographical detector. The findings reveal that the correlation degree of China's GF, CE, and their synergistic development (SD) network has shown a continuous improvement trend. Compared to single-layer networks, the network structure and related attributes of MN exhibit more optimized performance. With the integrated development of each layer network, four primary communities emerge within the MN: the Beijing-Tianjin-Hebei (BTH) community, the Northeast community, the North China community, and the Central-Western community. Factors such as economy, population, and society influence SDGC correlation network patterns to varying degrees, and the influence of interactions among these factors is even more prominent. Therefore, each province should clarify its role within the network and promote SDGC from the dual perspectives of fostering its own and overall development. •The relationship between GF and CE is studied from the perspective of multi-layer networks.•The influencing factors on SDGC are explored with the geographic detector model.•The dynamic development trajectory of SDGC is explored under policy guidance.