In recent years, there have been substantial efforts to improve the efficiency of production resources, including energy use, to reduce the human footprint from economic activities. Increasing production capacity and incorporating new technologies that improve energy efficiency in the production process are two primary challenges faced by developing countries, where capital goods imports could play a key role in addressing both challenges. This paper contributes to the empirical literature by examining the relationship between energy intensity, economic structure, and capital goods imports in a set of 36 upper-middle income economies in the period 2000–2019. The empirical strategy recognizes the existing heterogeneity among the broad group of countries in the sample by implementing the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm, a state-of-the-art unsupervised machine learning technique which allows identification of clusters of countries and years. The results show the existence of ten clusters, where energy intensity has the most relevant positive associations with industry share, trade openness, and merchandise imports. Improvements in regulatory quality are associated with lower energy intensity. The direction and strength of the relationship between energy intensity and capital goods imports depend on the cluster; nonetheless, it is usually a weak relationship. Policy implications are discussed.
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All Science Journal Classification (ASJC) codes
- Ingeniería ambiental
- Gestión y eliminación de residuos
- Gestión, supervisión, políticas y leyes