13433 17 68573 4 189199 document 68573 Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China.pdf application/pdf ef442ddb8432cf7abb21d50cdde6b8d2 MD5 1355872 2016-07-28 06:33:05 https://pure.iiasa.ac.at/id/eprint/13433/1/Dynamics%20of%20material%20productivity%20and%20socioeconomic%20factors%20based%20on%20auto-regressive%20distributed%20lag%20model%20in%20China.pdf 13433 1 1 application/pdf text en public cc_by_nc_nd

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

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

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/68574 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/68574 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/68574 archive 353 disk0/00/01/34/33 2016-07-28 06:39:48 2021-08-27 17:27:29 2016-07-28 06:39:48 article show Wang T. Yu Y. Zhou W. Liu B. Chen D. Zhu B. 7782 0000-0002-2890-7523 pub prog_ene Material productivity; Socioeconomic factors; ARDL (auto-regressive distributed lag); China 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. 2016-11-20 published Elsevier 10.1016/j.jclepro.2016.07.161 345 public Journal of Cleaner Production 137 752-761 TRUE 1879-1786 FALSE no info:eu-repo/semantics/article