Abstract

Copper is essential in attaining a sustainable development path due to its prominent role in the electromobility and renewable energy industries. In 2019 refined copper usage was 23.5 million tons, of which primary copper supplied 86.3%, and the remaining 13.7% was provided by secondary metal. In the future, copper recycling would increase significantly concerning primary copper supply to meet the goal of decreasing greenhouse gases emissions. Secondary copper production reduces energy consumption by 85% and greenhouse gas emissions by 65% compared to average primary sources. This study analyzes seven econometric models described in the literature that tried to explain the supply of secondary refined copper worldwide (SRC). Furthermore, this study assesses the forecasting capacity of these models in the short and long-term. Although the explanatory variables selected by previous researchers are theoretically supported, they failed to fully explain SRC. An empirical analysis for the period 1960-2017 confirmed that only two of the eight studied explanatory variables shown to be causal of SRC in the short-term. The re-estimation of the coefficients shows high instability when the number of variables or observations changed, making it difficult to analyze the future market and to assess public policies. Although the explanatory variables share the same long-term trend (cointegrated series), they do not properly forecast the future when evaluated using the Backcasting methodology. Based on these results, it is possible to improve the explanatory and predictive power of the analyzed models and reduce their average error.