Title: Impact of climate changes on manufacturing: Hodrick-Prescott filtering and a partial least squares regression model

Authors: Keyao Chen; Guizhi Wang; Jibo Chen; Shuai Yuan; Guo Wei

Addresses: National Climate Center, China Meteorological Administration, Beijing 100081, China ' School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China ' Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA

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.

Keywords: climatic output; HP filter; multicollinearity; partial least squares regression.

DOI: 10.1504/IJCSE.2020.107343

International Journal of Computational Science and Engineering, 2020 Vol.22 No.2/3, pp.211 - 220

Received: 06 Mar 2019
Accepted: 18 Jul 2019
Published online: 18 May 2020 *

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