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Constantine Spandagos

Personal Details

First Name:Constantine
Middle Name:
Last Name:Spandagos
Suffix:
RePEc Short-ID:psp197
[This author has chosen not to make the email address public]
https://spandagos.weebly.com/
Twitter: @CSpandagos
Terminal Degree: Centre for Environmental Policy; Imperial College (from RePEc Genealogy)

Affiliation

University of New Hampshire, Department of Natural Resources and the Environment

https://colsa.unh.edu/natural-resources-environment
Durham, NH, USA

Research output

as
Jump to: Articles

Articles

  1. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
  2. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  3. Spandagos, Constantine & Yarime, Masaru & Baark, Erik & Ng, Tze Ling, 2020. "“Triple Target” policy framework to influence household energy behavior: Satisfy, strengthen, include," Applied Energy, Elsevier, vol. 269(C).
  4. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
  5. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).

    Cited by:

    1. Soto, Gonzalo H & Martinez-Cobas, Xavier, 2024. "Green energy policies and energy poverty in Europe: Assessing low carbon dependency and energy productivity," Energy Economics, Elsevier, vol. 136(C).
    2. Song, Malin & Pan, Heting & Shen, Zhiyang & Tamayo-Verleene, Kristine, 2024. "Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value," Energy Economics, Elsevier, vol. 131(C).
    3. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).

  2. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).

    Cited by:

    1. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "How to Encourage Energy Savings Behaviours? The Most Effective Incentives from the Perspective of European Consumers," Energies, MDPI, vol. 14(23), pages 1-25, November.
    2. Khanam, Tahamina & Reiner, David M, 2022. "Evaluating gaps in knowledge, willingness and heating performance in individual preferences on household energy and climate policy: Evidence from the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
    4. Cheng, Xiu & Wu, Fan & Li, Wenbo & Yang, Jiameng & Long, Ruyin, 2024. "What maintains low-carbon consumption behaviors: Evidence from China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

  3. Spandagos, Constantine & Yarime, Masaru & Baark, Erik & Ng, Tze Ling, 2020. "“Triple Target” policy framework to influence household energy behavior: Satisfy, strengthen, include," Applied Energy, Elsevier, vol. 269(C).

    Cited by:

    1. Jarmila Zimmermannova & Richard Smilnak & Michaela Perunova & Omar Ameir, 2022. "Coal or Biomass? Case Study of Consumption Behaviour of Households in the Czech Republic," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Lia Marchi & Jacopo Gaspari, 2023. "Energy Conservation at Home: A Critical Review on the Role of End-User Behavior," Energies, MDPI, vol. 16(22), pages 1-22, November.
    3. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Jin Zhang & Lianrui Ma & Jinkai Li, 2021. "Why Low-Carbon Publicity Effect Limits? The Role of Heterogeneous Intention in Reducing Household Energy Consumption," Energies, MDPI, vol. 14(22), pages 1-17, November.

  4. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.

    Cited by:

    1. Jakučionytė-Skodienė, Miglė & Liobikienė, Genovaitė, 2023. "Changes in energy consumption and CO2 emissions in the Lithuanian household sector caused by environmental awareness and climate change policy," Energy Policy, Elsevier, vol. 180(C).
    2. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    3. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    4. Čičak Josip & Vašiček Davor, 2019. "Determining the Level of Accounting Conservatism through the Fuzzy Logic System," Business Systems Research, Sciendo, vol. 10(1), pages 88-101, April.
    5. Diego Seuret-Jimenez & Tiare Robles-Bonilla & Karla G. Cedano, 2020. "Measurement of Energy Access Using Fuzzy Logic," Energies, MDPI, vol. 13(12), pages 1-13, June.
    6. Prina, Matteo Giacomo & Manzolini, Giampaolo & Moser, David & Nastasi, Benedetto & Sparber, Wolfram, 2020. "Classification and challenges of bottom-up energy system models - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    7. Ma, Yuan & Liu, Changshan, 2023. "Configuration analysis of influencing factors of energy-saving behaviors: From the perspective of consumers’ pro-environmental characteristics and environmentally friendly social atmosphere," Energy, Elsevier, vol. 278(PA).
    8. Spandagos, Constantine & Yarime, Masaru & Baark, Erik & Ng, Tze Ling, 2020. "“Triple Target” policy framework to influence household energy behavior: Satisfy, strengthen, include," Applied Energy, Elsevier, vol. 269(C).
    9. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    10. Jonghoon Ahn, 2021. "Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    11. Jakučionytė-Skodienė, Miglė & Dagiliūtė, Renata & Liobikienė, Genovaitė, 2020. "Do general pro-environmental behaviour, attitude, and knowledge contribute to energy savings and climate change mitigation in the residential sector?," Energy, Elsevier, vol. 193(C).
    12. Qian, Xiaohu & Fang, Shu-Cherng & Huang, Min & Wang, Xingwei, 2019. "Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement," Energy, Elsevier, vol. 177(C), pages 276-292.

  5. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.

    Cited by:

    1. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    2. Mehmood, Sajid & Lizana, Jesus & Núñez-Peiró, Miguel & Maximov, Serguey A. & Friedrich, Daniel, 2022. "Resilient cooling pathway for extremely hot climates in southern Asia," Applied Energy, Elsevier, vol. 325(C).
    3. Bass, Brett & New, Joshua, 2023. "How will United States commercial building energy use be impacted by IPCC climate scenarios?," Energy, Elsevier, vol. 263(PE).
    4. Piotr Narowski & Dariusz Heim & Maciej Mijakowski, 2024. "New External Design Temperatures and Geospatial Models for Poland and Central Europe for Building Heat Load Calculations," Energies, MDPI, vol. 17(16), pages 1-21, August.
    5. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
    6. Shamim Akhtar & Muhamad Zahim Bin Sujod & Syed Sajjad Hussain Rizvi, 2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
    7. Lizana, Jesus & de-Borja-Torrejon, Manuel & Barrios-Padura, Angela & Auer, Thomas & Chacartegui, Ricardo, 2019. "Passive cooling through phase change materials in buildings. A critical study of implementation alternatives," Applied Energy, Elsevier, vol. 254(C).
    8. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    9. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).
    10. Yang, Yuchen & Javanroodi, Kavan & Nik, Vahid M., 2021. "Climate change and energy performance of European residential building stocks – A comprehensive impact assessment using climate big data from the coordinated regional climate downscaling experiment," Applied Energy, Elsevier, vol. 298(C).
    11. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Ge, Lurong & Ge, Tianshu & Wang, Ruzhu, 2022. "Facile synthesis of Al-based MOF and its applications in desiccant coated heat exchangers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    13. Dino, Ipek Gürsel & Meral Akgül, Cagla, 2019. "Impact of climate change on the existing residential building stock in Turkey: An analysis on energy use, greenhouse gas emissions and occupant comfort," Renewable Energy, Elsevier, vol. 141(C), pages 828-846.
    14. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    15. Tarroja, Brian & Chiang, Felicia & AghaKouchak, Amir & Samuelsen, Scott & Raghavan, Shuba V. & Wei, Max & Sun, Kaiyu & Hong, Tianzhen, 2018. "Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California," Applied Energy, Elsevier, vol. 225(C), pages 522-534.
    16. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    17. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    18. Scarpa, Federico & Tagliafico, Luca A. & Bianco, Vincenzo, 2021. "Financial and energy performance analysis of efficiency measures in residential buildings. A probabilistic approach," Energy, Elsevier, vol. 236(C).
    19. Marek Kraft & Przemysław Aszkowski & Dominik Pieczyński & Michał Fularz, 2021. "Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings," Energies, MDPI, vol. 14(15), pages 1-12, July.
    20. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    21. Agathokleous, R. & Barone, G. & Buonomano, A. & Forzano, C. & Kalogirou, S.A. & Palombo, A., 2019. "Building façade integrated solar thermal collectors for air heating: experimentation, modelling and applications," Applied Energy, Elsevier, vol. 239(C), pages 658-679.
    22. Qian Chen & Muhammad Burhan & M Kum Ja & Muhammad Wakil Shahzad & Doskhan Ybyraiymkul & Hongfei Zheng & Xin Cui & Kim Choon Ng, 2022. "Hybrid Indirect Evaporative Cooling-Mechanical Vapor Compression System: A Mini-Review," Energies, MDPI, vol. 15(20), pages 1-17, October.
    23. Jiang, Dachuan & Xiao, Weihua & Wang, Jianhua & Wang, Hao & Zhao, Yong & Li, Baoqi & Zhou, Pu, 2018. "Evaluation of the effects of one cold wave on heating energy consumption in different regions of northern China," Energy, Elsevier, vol. 142(C), pages 331-338.
    24. Nnaemeka Vincent Emodi & Taha Chaiechi & ABM Rabiul Alam Beg, 2018. "The impact of climate change on electricity demand in Australia," Energy & Environment, , vol. 29(7), pages 1263-1297, November.
    25. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).
    26. Burleyson, Casey D. & Voisin, Nathalie & Taylor, Z. Todd & Xie, Yulong & Kraucunas, Ian, 2018. "Simulated building energy demand biases resulting from the use of representative weather stations," Applied Energy, Elsevier, vol. 209(C), pages 516-528.
    27. Tarroja, Brian & Chiang, Felicia & AghaKouchak, Amir & Samuelsen, Scott, 2018. "Assessing future water resource constraints on thermally based renewable energy resources in California," Applied Energy, Elsevier, vol. 226(C), pages 49-60.

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