Comparing cars with apples? Identifying the appropriate benchmark countries for relative ecological pollution rankings and international learning

TL;DR

This paper develops a relative ecological pollution ranking for countries by combining data envelopment analysis and economic complexity, using network science to identify suitable benchmark countries for efficiency improvements and cleaner production.

Contribution

It introduces a novel approach that integrates ecological efficiency and economic complexity to identify benchmark countries for environmental improvements.

Findings

Large efficiency improvements are possible within current global output levels.

Identifies countries that can serve as effective learning partners.

Enhances the information base for international negotiations on clean production.

Abstract

Research in Data Envelopment Analysis has created rankings of the ecological efficiency of countries' economies. At the same time, research in economic complexity has provided new methods to depict productive structures and has analyzed how economic diversification and sophistication affect environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, combining the strengths of both approaches. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create a relative ecological pollution ranking (REPR). Moreover, we use methods from network science to reveal a benchmark network of the best learning partners based on country pairs with a large extent of export similarity, yet significant differences in pollution values. This is important because it helps to reveal adequate benchmark countries for efficiency improvements and cleaner production, considering that countries may specialize in substantially different types of economic activities. Finally, the article (i) illustrates large efficiency improvements within current global output levels, (ii) helps to identify countries that can best learn from each other, and (iii) improves the information base in international negotiations for the sake of a clean global production system.