Factors affecting industrial localization of timber mills in Peninsular Malaysia by econometric and spatial analysis

Nur Amira Zainul Armir (), Sarani Zakaria (), Rawshan Ara Begum (), Noratiqah Mohd Ariff (), Norshamliza Chamhuri (), Jalaluddin Harun (), Noorlaila Mohd Talib () and Mohd Amin Kadir ()
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Nur Amira Zainul Armir: National University of Malaysia
Sarani Zakaria: National University of Malaysia
Rawshan Ara Begum: National University of Malaysia
Noratiqah Mohd Ariff: National University of Malaysia
Norshamliza Chamhuri: National University of Malaysia
Jalaluddin Harun: Malaysia Timber Industry Board
Noorlaila Mohd Talib: Malaysia Timber Industry Board
Mohd Amin Kadir: Malaysia Timber Industry Board

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 6, No 17, 7833-7850

Abstract: Abstract This article aims to shed the light about the important geographical factors that influence the localization of Peninsular Malaysia timber mills to further understand the employment of sustainable timber industry practice as well as proposing the general raw materials’ supply chain in Peninsular Malaysia timber industry. This study is performed by using secondary data from Moderate Resolution Imaging Spectroradiometer land cover V6 MCD12Q1 data of NASA LPDAAC Collections from US Geological Survey and Malaysia Timber Industry Board for geographical factors as well as primary, secondary and tertiary timber mills’ information, respectively. The analysis from negative binomial regression analysis reveals that the geographical location for logs’ storage and transportation is affecting the primary timber mills’ localization, while a number of primary mills and secondary mills are affecting by the locations of raw materials’ supplies. This study gives an impactful insight for the Peninsular Malaysia timber industry in creating the timber industry’s policy regarding the roles of timber mills as the linkages in coordinating a sustainable timber industry.

Keywords: Spatial distribution; Negative binomial regression analysis; Raw materials’ supply (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01760-y

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