A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan

While Germany and Japan are going through major energy reforms, natural gas consumption is taking a growing share in their energy supply. This paper adopts a Machine Learning approach to assess the causal link between natural gas consumption and economic growth for both economies. A Causal Direction from Dependency (D2C) algorithm with the interconnection of the sub-class is employed using yearly data from 1970 to 2018. The interconnections of the sub-classes are found for both economies, indicating evidence of causalities operating in both directions. In addition, the propagation over the seven eras is linear and homogeneously continue for Japan, while this effect meets a stabilization phase in the fifth era for Germany. The empirical findings claim strong support for the existence of a bidirectional causality between these variables in Germany and Japan, which is in line with the feedback hypothesis. Although the strength of this bidirectional relationship is clear for both economies, its time propagation is expected to be longer for Japan. Accordingly, this study claims that the gas supply should be further strengthened to progressively replace the most polluting fuels (oil and coal) and ensure a feasible transition towards a renewable path. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Germany and Japan are using a Machine Learning approach to study the relationship between natural gas consumption and economic growth, finding evidence of bidirectional causality. The research suggests that strengthening gas supply to replace the most polluting fuels is crucial for a feasible transition towards renewable energy.