Predicting the Material Footprint in Germany between 2015 and 2020 via Seasonally Decomposed Autoregressive and Exponential Smoothing Algorithms

Johannes Buhl, Christa Liedtke, Sebastian Schuster and Katrin Bienge
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Johannes Buhl: Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany
Christa Liedtke: Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany
Sebastian Schuster: Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany
Katrin Bienge: Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany

Resources, 2020, vol. 9, issue 11, 1-17

Abstract: Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whether the Material Footprint decreases accordingly. We studied the Material Footprint of 113,559 users of an online footprint calculator and predicted the Material Footprint by seasonally decomposed autoregressive (STL-ARIMA) and exponential smoothing (STL-ETS) algorithms. We find a relatively stable Material Footprint for private consumption. The overall Material Footprint decreased by 0.4% per year between 2015 and 2020 on average. The predictions do not suggest that the Material Footprint of private consumption follows the reduction path of 3.3% per year that will lead to the sustainable consumption of natural resources.

Keywords: material footprint; sustainable consumption; time series analyses; forecasting (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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