Impact of climate changes on manufacturing: Hodrick-Prescott filtering and a partial least squares regression model. (8th May 2020)

• Record Type: Journal Article
• Authors: Chen, Keyao; Wang, Guizhi; Chen, Jibo; Yuan, Shuai; Wei, Guo
• Is Part Of: International journal of computational science and engineering. Volume 22:Number 2/3(2020)
• Page Start: 211
• Page End: 220
• Publication Date: 2020-05-08
• Subjects: climatic output -- HP filter -- multicollinearity -- partial least squares regression; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

In order to explore the impact of climate change on manufacturing outputs in Nanjing, China, this paper first adopts a polynomial function to retrieve trend values of manufacturing output, and then elaborates to manipulate the Hodrick-Prescott (HP) filtering to isolate the parts of manufacturing outputs that are caused by the climate factors. Subsequently, the paper attempts to construct a partial least squares regression (PLSR) model covering meteorological factors (e.g., average annual temperature, precipitation, sunshine hours and four quarters' average temperatures) and manufacturing meteorological outputs. The results show that an increased average temperature and average precipitation yield negative impacts on manufacturing and production; while in winter, higher temperature offers benefits to manufacturing on the contrary. Finally, this paper studied the changes of manufacturing outputs in Nanjing for different climate scenarios.