Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China

Wang T, Yu Y, Zhou W, Liu B, Chen D, & Zhu B (2016). Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China. Journal of Cleaner Production 137: 752-761. DOI:10.1016/j.jclepro.2016.07.161.

[img] Text
Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China.pdf - Accepted Version
Restricted to Repository staff only until 25 July 2018.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB)
[img] Text
Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China supplementary data.docx - Supplemental Material
Restricted to Repository staff only until July 2018.
Available under License Creative Commons Attribution.

Download (15kB)

Abstract

Material productivity (MP), measured as economic output (such as Gross Domestic Product, GDP) per corresponding material input, is gained significant interest of becoming a widespread environmental sustainability indicator. The study of MP's dynamics is very important for policy-making on how to improve MP. This paper applies the auto-regressive distributed lag (ARDL) model to investigate the dynamic impacts of energy intensity for secondary industry (SEI), tertiary industry value added per GDP (TVA), trade openness (TO) and domestic extraction per capita (DEC) on MP in the case of China during the period from 1980 to 2010. The validated and robust results of the model confirm the existence of cointegration among the variables both in the long and short run. The impacts of selected socioeconomic factors can be summarized as follows: 1) In the long run, an SEI decrease driven by technology improvement is found to be the main driver of MP, and a 1% decrease in SEI results in a 0.432% increase in MP; 2) The magnitude of the impact of TVA on MP is higher over the short run than over the long run; 3) TO can reluctantly promote MP both in the long and short run; 4) DEC exhibits fundamentally different behaviors in the long and short run. DEC is not a strongly significant factor for MP, and the magnitude of the impact is very weak in the long run. However, it has the greatest negative impact on MP in the short run, as a 1% increase in DEC results in a 0.519% decrease in MP, which demonstrates that the marginal revenue of resource input has already dramatically declined. These insights from the study could be considerably helpful for sustainable resource management and material productivity enhancement.

Item Type: Article
Uncontrolled Keywords: Material productivity; Socioeconomic factors; ARDL (auto-regressive distributed lag); China
Research Programs: Energy (ENE)
Depositing User: Luke Kirwan
Date Deposited: 28 Jul 2016 06:39
Last Modified: 23 Aug 2016 13:06
URI: http://pure.iiasa.ac.at/13433

Actions (login required)

View Item View Item

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313