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Developing the energy profile of mountainous areas

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  • Papada, Lefkothea
  • Kaliampakos, Dimitris

Abstract

The quantification of the increase of energy needs with altitude is undertaken in this paper, in an attempt to highlight the greater vulnerability of mountainous areas to energy poverty. Three different cases have been studied, namely, Austria, Switzerland and north Italy, by applying the method of degree days. The results show that in mountainous areas of little but not insignificant latitudinal variation, such as a country level or a large region within a country which is the usual scale in terms of energy policy, heating and cooling degree days can be predicted based only on altitude, with over 90% accuracy. For this reason, mathematical models – as simple functions of altitude – are suggested, estimating heating and cooling energy demand in a simple and reliable way. As an example, a typical residence at 1200 m in Switzerland has 2 times higher thermal energy needs and a longer heating period by 5 months, compared to the altitude of 200 m. Therefore, mountainous societies are more exposed to energy poverty compared to lowlands and energy policy measures (e.g. subsidies, taxes of fuel prices) should be adapted to their special needs.

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  • Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    • Handle: RePEc:eee:energy:v:107:y:2016:i:c:p:205-214
    DOI: 10.1016/j.energy.2016.04.011
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    References listed on IDEAS

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    1. Xu, Luting & Long, Enshen & Wei, Jincheng & Cheng, Zhu & Zheng, Hanjie, 2021. "A new approach to determine the optimum tilt angle and orientation of solar collectors in mountainous areas with high altitude," Energy, Elsevier, vol. 237(C).
    2. Huaquan Zhang & Yashuang Tang & Martinson Ankrah Twumasi & Abbas Ali Chandio & Lili Guo & Ruixin Wan & Shilei Pan & Yun Shen & Ghulam Raza Sargani, 2022. "The Effects of Ecological Public Welfare Jobs on the Usage of Clean Energy by Farmers: Evidence from Tibet Areas—China," Agriculture, MDPI, vol. 12(7), pages 1-16, June.
    3. Papada, Lefkothea & Kaliampakos, Dimitris, 2018. "A Stochastic Model for energy poverty analysis," Energy Policy, Elsevier, vol. 116(C), pages 153-164.

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