Abstract
In the interests of the environment, many countries set limits on the use of non-renewable energy sources and promote renewable energy sources through policy and legislation. Consequently, the demand for components for renewable energy systems exhibits an upward trend. For this reason, managers, investors, and banks are interested in knowing whether investing in a business associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is worthy of a penny. Using a sample for the period of 2015–2018, we apply a new approach to panel data, extending existing research using Classification Trees with the k-Nearest Neighbor and Altman model. Our aim is to analyze the financial conditions of enterprises to identify key indicators that distinguish companies producing PV system components (labeled “green, G”) from companies that do not manufacture PV components (“red, R”). Our results show that green companies can be distinguished from red companies at classification accuracies of 86% and 90% for CRT and CHAID algorithms in Classification Trees method and 93% for k-Nearest Neighbor method, respectively. Based on the Altman model and the analysis of crucial ratios, we also find that green businesses are characterized by lower financial performance although future ratio values may equal or exceed the values for the red companies if current upward trends are sustained. Therefore, investing in green companies presents a viable alternative.
Highlights
Debates about renewable energy sources have become more prominent in recent decades with increased public acceptance and a positive perception of renewable energy source [1]
The aim was to determine whether it is possible to distinguish between companies that are connected with production of renewable energy and those that are not involved in the production of renewable energy on the basis of our variables (X1–X92)
Our results indicate that that green and red companies can be classified with 86%, 90%, and 93% accuracy according to the Classification and Regression Trees (CRT), Chi-squared Automatic Interaction Detector (CHAID) algorithms, and k-Nearest Neighbors method, respectively
Summary
Debates about renewable energy sources have become more prominent in recent decades with increased public acceptance and a positive perception of renewable energy source [1]. Given the negative effects of the increased consumption of fossil fuels which include climate change and global warming, policymakers and researchers have shown an increased focus on and preference for renewable energy sources. This increasing preference has resulted in the share of electricity generated from renewable sources exceeding 25% in 2018 and is consistent with an upward trend in the production of renewable energy over the three preceding decades [2]. This, in turn, implies increased demand for components used in the production of electricity from renewable sources; solar modules, solar cells, silicon rods, photovoltaic equipment, and equipment Given these targets and preference for renewable energy sources, there has been much interest in renewable energy and related aspects
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