Driving factors of material consumption in European countries – spatial panel data analysis

ABSTRACT

The aim of this study is to identify the main drivers of material consumption measured by DMC per capita. Due to data availability, the study is limited to European countries in 2000–2016. We analyse panel data compiled from the Eurostat database. At first, we estimated the fixed-effects model with robust standard errors [Arellano, Manuel. 2003. Panel Data Econometrics. Oxford: Oxford University Press]. Then we applied the method proposed by Baltagi and Wu [1999. “Unequally Spaced Panel Data Regressions with AR(1) Disturbances.” Econometric Theory 15: 814–823] for unequally spaced panel data regression models with AR(1) remainder disturbances (implemented in Stata – xtregar). Finally, we estimated the spatial autocorrelation model (SAR) to account for spatial dependencies in the data (Stata – xsmle). Results show the strong coupling of material consumption and GDP per capita. Another strongly significant factors are final energy consumption per capita and the share of the construction sector in GDP. We received mixed results on the impact of investments and R&D expenditures depending on model specification.

KEYWORDS:

• Sustainability
• material consumption
• material flow analysis (MFA)
• dematerialization
• panel data
• spatial econometrics

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 It is estimated that by 2050 the resource efficiency should be increased 4–10 times. See also: European Commission. Roadmap to a Resource Efficient Europe, COM(2011) 571 Final, European Commission: Brussels, Belgium, 2011.

2 MF indicator is also used under the term Raw Material Consumption (RMC). By definition RMC = domestic extraction + raw material equivalents (RME) of imports – RME of exports. See also: European Commission (2014).

3 May 2019.

4 The term ‘effect of scale’ used in the paper could be a bit confusing. In economics, the effect of scale is usually understood as the increasing efficiency with the increasing scale of production. Here, we use this term to describe the increasing material demand with the increasing production volume.

5 See also: Gretl User's Guide, Chapter 19. Robust covariance matrix estimation, http://gretl.sourceforge.net/gretl-help/gretl-guide.pdf

6 See also: xtregar – Fixed- and random-effects linear models with an AR(1) disturbance, https://www.stata.com/manuals13/xtxtregar.pdf