Table1_Comparing Cars With Apples? Identifying the Appropriate Benchmark Countries for Relative Ecological Pollution Rankings and International Learning.pdf

One of the most difficult tasks that economies face is how to generate economic growth without causing environmental damage. Research in economic complexity has provided new methods to reveal structural constraints and opportunities for green economic diversification and sophistication, as well as the effects of economic complexity on environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, and used this information to identify the best learning partners. This matters, because there are substantial differences in the environmental damage caused by the same product in different countries, and green diversification needs to be complemented by substantial efficiency improvements of existing products. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create first a relative ecological pollution ranking (REPR). Then, 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 sustainable 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 cleaner global production system.

各国经济体面临的最棘手挑战之一，便是如何在不造成环境破坏的前提下实现经济增长。经济复杂性（economic complexity）领域的研究已催生全新分析方法，可用于揭示绿色经济多样化与高级化进程中的结构性约束与发展机遇，同时还能阐释经济复杂性对各类环境污染指标的影响。然而截至目前，尚无研究针对生产结构与经济复杂性水平相近的国家开展生态效率对比，并以此为依据筛选最佳学习伙伴。这一研究空白意义重大，因为同类产品在不同国家造成的环境破坏存在显著差异，且绿色经济多样化离不开现有产品的实质性效率提升作为支撑。本文依托覆盖99个国家的774类出口商品、二氧化碳（CO₂）排放量及生态足迹数据集，首先构建了相对生态污染排名（relative ecological pollution ranking, REPR）。随后，本文借助网络科学方法，基于出口相似度较高但污染水平差异显著的国家配对，构建了最佳学习伙伴的基准网络。这一基准网络具备重要价值：考虑到各国可能专注于截然不同的经济活动领域，它能够为效率提升与可持续生产筛选出合适的参考国家。最后，本文完成了三项核心工作：①阐明了在当前全球产出水平下可实现的大规模效率提升空间；②明确了彼此间最具学习借鉴价值的国家组合；③为构建更清洁的全球生产体系，夯实了国际谈判的信息基础。