Modelling Hazard for Tailings Dam Failures at Copper Mines in Global Supply Chains

Sören Lars Nungesser and Stefan Pauliuk ()
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Sören Lars Nungesser: Industrial Ecology Group, Faculty of Environment and Natural Resources, University of Freiburg, D-79106 Freiburg, Germany
Stefan Pauliuk: Industrial Ecology Group, Faculty of Environment and Natural Resources, University of Freiburg, D-79106 Freiburg, Germany

Resources, 2022, vol. 11, issue 10, 1-27

Abstract: The global mining industry generates several billion tons of waste every year. Much of it is stored in liquid form, known as tailings, in large impoundments. Recent dam failures at tailing ponds with catastrophic outcomes have raised public concern, such that industry initiatives and investors are beginning to address the problem. So far, a lack of publicly available data makes an independent and comprehensive risk assessment challenging. We introduce a simple and transparent hazard indicator built from environmental proxy variables and screen a global sample of 112 copper mines for natural hazards regarding tailings dams. In a second step, material footprints of copper for the European Union and five major economies are estimated and compared using a Multi-Regional Input–Output model, shedding light on the regions of origin. Finally, hazard scores are linked to regional copper footprints to identify hotspots in supply chains of final consumption. The most hazardous mines are located in Chile and Peru including some of the world’s largest copper producers. China and the US have the largest copper ore footprints and per capita values in the US were 25 times larger than in India. The United States’ and European footprints are satisfied by domestic extraction to about 66 and 40 percent respectively. Copper from Poland contributes around 19 and 28 percent to supply chains of German and European final demand respectively and, as a consequence, Poland constitutes the main hazard hotspot for Europe’s copper supply chain.

Keywords: MRIO modelling; tailings dam failures; hazard score; copper supply chain modelling; material footprint; shared responsibility models (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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