A novel approach for developing composite eco-efficiency indicators: The case for US food consumption

Abstract

Eco-efficiency analysis can provide useful information for sustainability benchmarking of products and sectors while assessing and monitoring their economic and environmental performances. The eco-efficiency is defined as a ratio between economic performance and environmental impact. With multiple environmental and economic metrics, the eco-efficiency assessment is computationally complex. One common aspect of this complexity is associated with the importance (a.k.a. Relative weights) of sustainability indicators in the presence of high multicollinearity. A novel weighting method integrating two well-established methods for reducing the multicollinearity consequence during the aggregation process is presented in the study. The proposed method's mathematical and operational procedures, called Weighted Penalized Maximum Likelihood Estimation (W-PMLE), are demonstrated for the eco-efficiency analysis of U.S food consumption. The eco-efficiency analysis results revealed that the CO2 emissions, the level of consumption of the metallic mineral, and water were the most critical to the eco-efficiency performance of U.S. consumption.