52 research outputs found
A Nexus-Based Impact Assessment of Rapid Transitions of the Power Sector: The Case of Greece
No full textPower system transformation can unleash wide-ranging effects across multiple, frequently interlinked dimensions such as the environment, economy, resource systems, and biodiversity. Consequently, assessing the multidimensional impacts of power system transformation, especially under rapid transitions, has become increasingly important. Nonetheless, there is a gap in the literature when it comes to applying such an analysis to a Mediterranean country facing structural socioeconomic challenges. This paper explores the potential multifaceted implications of rapidly decarbonizing the Greek power sector by 2035, focusing on the local-level consequences. The evaluation criteria encompass the cost-optimal power mix, power costs, land use, biomass utilization, GDP, and employment. In this effort, a technology-rich cost optimization model representing Greece’s power sector is linked to a global Computable General Equilibrium (CGE) macroeconomic model focusing on the Greek economy. The results indicate that a fast decarbonization of the Greek power sector could trigger positive socioeconomic consequences in the short- and medium-term (GDP: +1.70, employees: +59,000 in 2030), although it may induce negative long-term socioeconomic effects due to increased capital investment requirements. Additionally, the impact on land use may only be trivial, with the potential to decrease over time due to the de-escalation of biomass power generation, thereby reducing the risk of harming biodiversity.Peer reviewe
Koutsandreas_et_al_2023_ESR_dataset
No full text<p>This dataset contains the underlying raw modelling data for the journal article by Koutsandreas et al., 2023 published in Energy Strategy Reviews in November 2023.</p>
Harnessing machine learning algorithms to unveil energy efficiency investment archetypes
No full textPublisher Copyright: © 2024 The Author(s)Increasing transparency about the performance of different projects is crucial to reducing the heterogeneity in the energy efficiency services market, thereby upscaling investments. In this context, machine learning algorithms could assist in identifying and analyzing energy efficiency project archetypes, although this field has so far been explored with a limited view in the literature. This paper aims to address this gap by identifying energy efficiency investment families and the determinant factors of the classification scheme, using machine learning. In this effort, it hinges on a wide range of indicators from implemented projects around Europe and the USA, including investment profitability, initial investment, risk of failure, intervention type, life measure, region of implementation and building type. The analysis employs two clustering approaches, namely Partitioning Around Medoids (PAM) and K-means, determining the number of clusters based on the Silhouette index and total within–cluster sum of squares. The results indicate that energy efficiency investments can be classified into three categories: (i) “junk investments”, characterized by low–profitability (IRR∼10%), moderate risk, and extended horizons; (ii) “safe profitability”, distinguished by high profitability (IRR∼30%) and minimal risk; and (iii) “high stakes”, described by exceptionally high profitability (IRR∼40%), coupled with a substantial risk. Next to profitability and risk of failure, also energy efficiency intervention and building type (sector) emerge among the most influential factors in the classification scheme. Feature importance shows a significant sensitivity to the chosen classification model.Peer reviewe
On the macroeconomic and societal ramifications of green hydrogen policies — The case of Greek transport sector
No full textPublisher Copyright: © 2025 The AuthorsGreen hydrogen has emerged as a key tool for decarbonizing hard-to-abate sectors and stabilizing variable power generation. However, its diffusion can raise energy costs, thereby leading to adverse economy-wide impacts. A gap exists in the literature concerning the evaluation of green hydrogen's macroeconomic impact in countries with both high potential for hydrogen production and structural economic challenges. This paper addresses this gap by examining the macroeconomic footprint of green hydrogen diffusion in the Greek transport sector over 2030–2050, considering various scenarios concerning which economic agents undertake implementation costs. We employ an integrated modelling framework composed of a technology-rich optimization model for power generation and transportation planning in Greece, OSeMOSYS-PORTAGE, and a general equilibrium model illustrating the interactions between domestic and global economic agents, GTAP-Greece, coupled to the GTAP database version 11. The results indicate that when domestic economic agents fully cover the costs, a moderate negative impact (∼1.5 % in 2050) is induced on the Greek economy, unveiled less intensely when households alone finance these costs. Conversely, if the costs transferred to households are alleviated through an external to the economy grant, a profound expansionary effect (∼2.5 % in 2050) emerges, albeit at the expense of the economy's competitiveness and extroversion.Peer reviewe
The role of nuclear power on the road to net zero: a review of its benefits, drawbacks, risks and risk mitigation strategies
No full textOn the interplay of circular economy and decarbonization policies in Europe: A review on their synergies and trade-offs
No full textA stochastic fuzzy multicriteria methodology for energy planning decision support : Case study of the electrification of the Greek road transport sector
No full textPublisher Copyright: © 2025 The AuthorsProviding robust planning insights requires transitioning from single-criterion, definitive frameworks to approaches that handle trade-offs and communicate result conditionality. This paper introduces an integrated methodology for energy planning, combining life-cycle impact assessment with decision support. Our framework incorporates often-overlooked aspects in energy planning (e.g., resource depletion and biodiversity) and allows us to reflect stakeholder risk profiles, preferences, and uncertainties about future energy and economic states. Scenarios are handled with the fuzzy group utility and maximum regret measures, while strategies are evaluated with the fuzzy VIKOR and TOPSIS methods. The framework advances existing multicriteria approaches by innovatively combining and contextually adapting existing methods, embedding them within a robust modelling framework. It provides comprehensive decision support by identifying optimal strategies across decision-maker profiles and explicitly communicating result contingency and diversity linked to uncertainties and policy preferences. The methodology is demonstrated for Greece's road transport sector, evaluating three fleet electrification strategies for 2040. Results revealed that aggressive electrification may induce significant negative repercussions on resources, human health, and ecosystems. Conversely, moderate electrification emerged as the most effective strategy in 79 % of cases across risk profiles and policy objective portfolios, suggesting the combination of technological shifts with resource-neutral lifestyle changes for road transport decarbonization.Peer reviewe
ASO Author Reflections: Minimally Invasive Surgery for Hepatocellular Carcinoma in the Setting of Portal Vein Hypertension
No full textMinimally Invasive Surgery for Hepatocellular Carcinoma in the Setting of Portal Vein Hypertensio
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