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. ORCID: https://orcid.org/0000-0002-2890-7523 (2016). Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China. Journal of Cleaner Production 137 752-761. 10.1016/j.jclepro.2016.07.161.

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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.

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