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Predicting the factors influencing construction enterprises' adoption of green development behaviors using artificial neural network.
- Source :
- Humanities & Social Sciences Communications; 7/15/2022, Vol. 9 Issue 1, p1-12, 12p
- Publication Year :
- 2022
-
Abstract
- The construction industry occupies a high proportion of the global economy. However, with the energy consumption of construction enterprises, it still brings a series of serious environmental problems. Construction enterprises should take active green development behavior to respond. Based on enterprises' green development behavior, this paper explores the influencing factors of green development behavior adopted by construction enterprises in China. Through literature analysis, this paper identifies that construction enterprises' adoption of green development behaviors is influenced by technological, organizational and environmental factors. Then this paper constructs an index system of the influencing factors of green development behaviors adopted by enterprises. According to the data of construction enterprises from 2000 to 2020 of National Bureau of Statistics, an artificial neural network is used to construct the prediction model of influencing factors of green development behavior adopted by construction enterprises. The conclusions are as follows. (1) Construction enterprises' adoption of green development behavior shows an upward trend over time. (2) Market share of construction enterprises is the most important factor to promote construction enterprises' adoption of green development behavior. (3) The prediction model of influencing factors constructed in this paper is verified to be effective by the technology-organization-environment framework. This paper provides a reference for construction enterprises and the government to promote enterprises to adopt green development behavior, which is beneficial for construction enterprises to achieve green development faster. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- Volume :
- 9
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Humanities & Social Sciences Communications
- Publication Type :
- Academic Journal
- Accession number :
- 158021560
- Full Text :
- https://doi.org/10.1057/s41599-022-01263-9