The topic of this article may not meet Wikipedia's
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Konstantinos “Constantine” Spandagos ( Greek: Κωνσταντίνος Σπανδάγος) is a Greek engineer and academic. [1] He is an assistant professor of sustainable energy policy at the University of New Hampshire’s Department of Natural Resources and the Environment. [2] His research combines elements from engineering, artificial intelligence, economics, psychology and data science to address technological, economic, and societal issues of energy and environmental policy in the United States, the European Union and Asia. [2] [3]
Constantine Spandagos | |
---|---|
Born | |
Alma mater |
|
Scientific career | |
Fields | Engineering, Public Policy, Economics |
Institutions | |
Thesis | Integrating behavioral economic principles to energy policy formulation: a fuzzy logic approach for modeling residential cooling energy decisions under bounded rationality and other natural concepts (2017) |
Website |
spandagos |
Constantine Spandagos was born in Athens, Greece, where he graduated from the National Technical University of Athens with a bachelor’s degree in chemical engineering. [3] He subsequently moved to London, United Kingdom, where he earned a master’s degree in environmental technology, and a Ph.D. degree in chemical engineering, both from Imperial College London. [2] He also graduated from the Hong Kong University of Science and Technology with a Ph.D. degree in civil engineering/ energy technology. [4]
Spandagos is currently an assistant professor at the University of New Hampshire in the United States. [2] Previously, he worked as a postdoctoral researcher, first at the Division of Public Policy of the Hong Kong University of Science and Technology, and later in Ireland’s Economic and Social Research Institute [3] and the Department of Economics of Trinity College Dublin. He also worked in the energy technology industry as a data scientist. [3] His research has attracted the attention of major newspapers of record such as The Irish Times [5] and South China Morning Post, [6] scientific media outlets such as Science Trends, [7] and sustainability-focused media such as Green Queen. [8]
During his doctoral studies, Spandagos observed that computational models of energy and economic systems were typically relying on traditional economic theory, which assumed energy consumers to be completely rational (profit-maximizing) and self-interested. [9] That assumption was often transferred into evidence-based energy policy formulation, which is commonly relying on such models. However, modern insights from behavioral economics challenge the full rationality paradigm, demonstrating that human decisions concerning energy consumption can also be not-fully-rational and driven by self-less motivation. [10] Such insights were typically missing in energy systems models, potentially weakening their ability to generate more realistic energy consumption trends and predictions. [11] In view of this, Spandagos developed an interdisciplinary modeling approach that integrates intangible behavioral concepts with quantitative physical factors into a single mathematical framework for improved prediction of human behavior within energy systems. [11] [7] His approach is based on fuzzy logic, a form of many-valued logic which considers "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic. This study, published in Applied Energy, demonstrated the usefulness of integrating behavioral economics concepts and fuzzy logic to enhance the realism of energy systems models, and subsequently the design of sustainable energy policies. [7]
Spandagos’ work has contributed to the understanding of the psychological mechanisms that drive consumers’ energy-saving behaviors and their acceptance of sustainable energy technology. [8] [12] [13] He has also developed machine learning approaches for reliable prediction and targeting of energy poverty in households. [14]
His publications include:
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The topic of this article may not meet Wikipedia's
notability guideline for academics. (July 2024) |
Konstantinos “Constantine” Spandagos ( Greek: Κωνσταντίνος Σπανδάγος) is a Greek engineer and academic. [1] He is an assistant professor of sustainable energy policy at the University of New Hampshire’s Department of Natural Resources and the Environment. [2] His research combines elements from engineering, artificial intelligence, economics, psychology and data science to address technological, economic, and societal issues of energy and environmental policy in the United States, the European Union and Asia. [2] [3]
Constantine Spandagos | |
---|---|
Born | |
Alma mater |
|
Scientific career | |
Fields | Engineering, Public Policy, Economics |
Institutions | |
Thesis | Integrating behavioral economic principles to energy policy formulation: a fuzzy logic approach for modeling residential cooling energy decisions under bounded rationality and other natural concepts (2017) |
Website |
spandagos |
Constantine Spandagos was born in Athens, Greece, where he graduated from the National Technical University of Athens with a bachelor’s degree in chemical engineering. [3] He subsequently moved to London, United Kingdom, where he earned a master’s degree in environmental technology, and a Ph.D. degree in chemical engineering, both from Imperial College London. [2] He also graduated from the Hong Kong University of Science and Technology with a Ph.D. degree in civil engineering/ energy technology. [4]
Spandagos is currently an assistant professor at the University of New Hampshire in the United States. [2] Previously, he worked as a postdoctoral researcher, first at the Division of Public Policy of the Hong Kong University of Science and Technology, and later in Ireland’s Economic and Social Research Institute [3] and the Department of Economics of Trinity College Dublin. He also worked in the energy technology industry as a data scientist. [3] His research has attracted the attention of major newspapers of record such as The Irish Times [5] and South China Morning Post, [6] scientific media outlets such as Science Trends, [7] and sustainability-focused media such as Green Queen. [8]
During his doctoral studies, Spandagos observed that computational models of energy and economic systems were typically relying on traditional economic theory, which assumed energy consumers to be completely rational (profit-maximizing) and self-interested. [9] That assumption was often transferred into evidence-based energy policy formulation, which is commonly relying on such models. However, modern insights from behavioral economics challenge the full rationality paradigm, demonstrating that human decisions concerning energy consumption can also be not-fully-rational and driven by self-less motivation. [10] Such insights were typically missing in energy systems models, potentially weakening their ability to generate more realistic energy consumption trends and predictions. [11] In view of this, Spandagos developed an interdisciplinary modeling approach that integrates intangible behavioral concepts with quantitative physical factors into a single mathematical framework for improved prediction of human behavior within energy systems. [11] [7] His approach is based on fuzzy logic, a form of many-valued logic which considers "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic. This study, published in Applied Energy, demonstrated the usefulness of integrating behavioral economics concepts and fuzzy logic to enhance the realism of energy systems models, and subsequently the design of sustainable energy policies. [7]
Spandagos’ work has contributed to the understanding of the psychological mechanisms that drive consumers’ energy-saving behaviors and their acceptance of sustainable energy technology. [8] [12] [13] He has also developed machine learning approaches for reliable prediction and targeting of energy poverty in households. [14]
His publications include:
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cite web}}
: CS1 maint: url-status (
link)
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cite web}}
: CS1 maint: url-status (
link)
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cite web}}
: CS1 maint: url-status (
link)
{{
cite journal}}
: CS1 maint: multiple names: authors list (
link)
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cite journal}}
: CS1 maint: multiple names: authors list (
link)
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cite journal}}
: CS1 maint: multiple names: authors list (
link)
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cite journal}}
: CS1 maint: multiple names: authors list (
link)