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A platform for research: civil engineering, architecture and urbanism
The influencing factors of China's green building development: An analysis using RBF-WINGS method
https://orcid.org/0000-0001-8889-9839
https://orcid.org/0000-0001-8529-7189
Abstract Energy efficiency and emission reduction in construction industry has been concerned in the world. Green building become the focus of academic research in recent years. To fundamentally realize the green development of construction industry, it is necessary to systematically analyze the influencing factors of green building development. On the basis of science and technology investment, industrial size, industry potentiality, and policy incentives, this paper introduced the green financial supportive factor and established a multi-layer green building influencing factors index system. Radial basis function neural network is adopted to improve the Weighted Influence Non-linear Gauge System (WINGS) model, namely RBF-WINGS model, and direct strength-influence matrix is determined. This method objectively analyzed the weights of the green building development influencing factors. The result indicates that science and technology input is the fundamental influencing factor, while industrial size and green financial support are the main influencing factors for the development of green building. This study provides theoretical evidence for the development of green buildings in China and offers decision-making reference for government departments.
Highlights Green finance factors are introduced in the green building influencing factors index system. The analysis is conducted using WINGS algorithm by adopting the RBF neural network. Green building development is mainly influenced by investment in science and technology. Green financial support can boost the development of green building.
The influencing factors of China's green building development: An analysis using RBF-WINGS method
https://orcid.org/0000-0001-8889-9839
https://orcid.org/0000-0001-8529-7189
Abstract Energy efficiency and emission reduction in construction industry has been concerned in the world. Green building become the focus of academic research in recent years. To fundamentally realize the green development of construction industry, it is necessary to systematically analyze the influencing factors of green building development. On the basis of science and technology investment, industrial size, industry potentiality, and policy incentives, this paper introduced the green financial supportive factor and established a multi-layer green building influencing factors index system. Radial basis function neural network is adopted to improve the Weighted Influence Non-linear Gauge System (WINGS) model, namely RBF-WINGS model, and direct strength-influence matrix is determined. This method objectively analyzed the weights of the green building development influencing factors. The result indicates that science and technology input is the fundamental influencing factor, while industrial size and green financial support are the main influencing factors for the development of green building. This study provides theoretical evidence for the development of green buildings in China and offers decision-making reference for government departments.
Highlights Green finance factors are introduced in the green building influencing factors index system. The analysis is conducted using WINGS algorithm by adopting the RBF neural network. Green building development is mainly influenced by investment in science and technology. Green financial support can boost the development of green building.
The influencing factors of China's green building development: An analysis using RBF-WINGS method
https://orcid.org/0000-0001-8889-9839
https://orcid.org/0000-0001-8529-7189
Abstract Energy efficiency and emission reduction in construction industry has been concerned in the world. Green building become the focus of academic research in recent years. To fundamentally realize the green development of construction industry, it is necessary to systematically analyze the influencing factors of green building development. On the basis of science and technology investment, industrial size, industry potentiality, and policy incentives, this paper introduced the green financial supportive factor and established a multi-layer green building influencing factors index system. Radial basis function neural network is adopted to improve the Weighted Influence Non-linear Gauge System (WINGS) model, namely RBF-WINGS model, and direct strength-influence matrix is determined. This method objectively analyzed the weights of the green building development influencing factors. The result indicates that science and technology input is the fundamental influencing factor, while industrial size and green financial support are the main influencing factors for the development of green building. This study provides theoretical evidence for the development of green buildings in China and offers decision-making reference for government departments.
Highlights Green finance factors are introduced in the green building influencing factors index system. The analysis is conducted using WINGS algorithm by adopting the RBF neural network. Green building development is mainly influenced by investment in science and technology. Green financial support can boost the development of green building.
The influencing factors of China's green building development: An analysis using RBF-WINGS method
Wang, Wei (author) / Tian, Ze (author) / Xi, Wenjia (author) / Tan, Yi Rong (author) / Deng, Yao (author)
Building and Environment ; 188
2020-10-31
Article (Journal)
Electronic Resource
English
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