Reprint

End-Users’ Perspectives on Energy Policy and Technology

Edited by
January 2021
166 pages
  • ISBN978-3-0365-0015-7 (Hardback)
  • ISBN978-3-0365-0016-4 (PDF)

This is a Reprint of the Special Issue End-Users’ Perspectives on Energy Policy and Technology that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

This Special Issue (SI) deals with different end-users’ perspectives on newly developed energy policy and technology. Although the importance of end-users’ preferences is not totally new to the energy sector, this issue needs to be urgently and consistently addressed if new policies, projects, and technologies are to be introduced successfully. The eight papers included in this SI are focused on various issues such as modeling the future energy demand, household energy consumption behavior, public perceptions of new energy technologies and projects, and ICT–energy efficiency interrelationship. Some papers also analyze end-users’ experiences with recently introduced energy technologies. Based on these eight articles with various topics, this SI will provide fruitful insights in assessing and forecasting the evolution of the future energy sector. I hope this SI can contribute to the increase in communication and cooperation among academic researchers as well as practitioners in energy fields.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
energy demand; CO2 emissions; Indonesia; households; energy consumption; pro-environmental behavior; conceptual framework; energy technology; energy security; public opinion; cost-benefit analysis; energy strategy; improved cook stoves; Honduras; occupant behaviour; socio-economic profile; survey; energy efficiency; persuasion; intervention; pro-environmental behaviour change; workplace; choice experiment; renewable energy; willingness to accept; multinomial logit models; LCOE; stochastic; solar PV; South Korea; renewable energy; data center; thermal characteristics analysis; machine learning; energy efficiency; clustering; unsupervised learning