Authors: | Asonitou, Sofia Ntanos, Stamatios Anagnostopoulos, Theodoros Gkika, Eleni Kyriakopoulos, Grigorios |
Issue Date: | 20-Apr-2020 |
Journal: | Sustainability |
Volume: | 12 |
Issue: | 7 |
Keywords: | Business investment, Data mining, Intelligent predictive analytics, Renewable energy sources, Sustainable management |
Abstract: | Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents' perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest. |
ISSN: | 2071-1050 |
DOI: | 10.3390/su12072817 |
URI: | https://uniwacris.uniwa.gr/handle/3000/1880 |
Type: | Article |
Department: | Department of Business Administration |
School: | School of Administrative, Economics and Social Sciences |
Affiliation: | University of West Attica (UNIWA) |
Appears in Collections: | Articles / Άρθρα |
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