Resource efficiency awareness of companies
DOI:
https://doi.org/10.29015/cerem.981Keywords:
Energy, Resource Efficiency, Machine LearningAbstract
Aim: This paper aims to identify the characteristic variables that influence firms’ resource efficiency awareness and subsequently group countries based on the similarities of these influencing factors.
Design / Research methods: We utilize data from the GESIS Data Archive and Flash Eurobarometer, which conducted a survey in 2017 across 36 European countries and the United States of America. Machine learning tools are applicable to the analysis. Specifically, the Chi-squared independence test is applied to determine the impact of characteristic variables on resource efficiency awareness. Following this, unsupervised learning (clustering) algorithms are used to identify countries that exhibit similar patterns.
Conclusions/findings: The findings reveal that turnover performance over the past two years and last year's turnover significantly influence firms’ resource efficiency awareness, while factors such as employee number of the company, one-person company or not (sole proprietorship), and the establishment year of the company do not seem to have a notable effect. The impact of the customer profile of the firm on resource efficiency awareness remains uncertain. Based on these dependency results, the study identifies ten potential clusters of countries with similar characteristics in terms of resource efficiency awareness and related factors.
Originality/value of the article: Machine learning methods are relatively novel approaches that have gained prominence with the rise of extensive datasets. As a result, the paper exhibits originality in terms of research methodology. Furthermore, when considering resource efficiency as a significant topic, the article holds considerable importance.
Implications of the research (if applicable): Utilizing the findings of the paper, it becomes possible to develop an application suitable for an Erasmus project. Given that resource efficiency is one of Erasmus’ critical focal points in 2023, the likelihood of approval is considerably elevated.
Keywords: Energy, Resource Efficiency, Machine Learning.
JEL: F64, C55
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