Anastasios Karamaneas

Rapid renewable energy deployment is essential for climate change mitigation but often faces opposition due to concerns over potential biodiversity implications. This ‘green versus green’ dilemma is particularly salient in biodiversity-rich nations like Greece, yet regional energy-system implications of spatially constrained renewables remain underexplored. Integrating the regionally disaggregated Greek energy-system model HERMES with geospatial data on existing development restrictions, Natura 2000 protected areas, and protected and endangered flora species distribution, we explore Greece’s decarbonisation pathways under progressively stricter biodiversity constraints for onshore and offshore wind energy. We find that protecting ecologically sensitive areas does not compromise national climate targets and only marginally increases system costs. However, it would force within-country reallocation of installed renewable capacity. We highlight regional energy dynamics related to islands’ energy autonomy and transition justice in coal-producing regions. We argue that strategic spatial planning is critical to reconcile biodiversity conservation with a resilient and just energy transition.

Energy-sector decarbonisation requires large-scale investment in low-carbon technologies, yet only a limited share flows to low- and middle-income countries, partly due to higher financing costs and perceived risks. Most modelling exercises do not fully account for how the cost of capital may vary across regions and technologies, potentially influencing policy insights. We examine how plausible, expert-informed long-term trends in de-risking clean energy and increasing risks for fossil fuels could shape decarbonisation pathways, using an empirical dataset differentiated by country and technology. We also evaluate a “corrective justice” policy that taxes corporate windfall profits and redistributes revenues to support low-carbon investments in higher-risk regions. Results suggest that incorporating differentiated cost-of-capital trajectories may improve mitigation outcomes and help narrow the gap between current commitments and long-term climate targets, while indicating potential underestimation of risks associated with bioenergy-based negative emissions technologies in mitigation scenarios for high-income nations.

CLEWs-EU, an open-source Integrated Assessment Model developed to analyse the coupled climate–land–energy–water (CLEWs) system of the European Union within a single, internally consistent optimisation framework, is presented here. Implemented in OSeMOSYS, the model identifies least-cost system configurations that satisfy exogenously defined energy service demands across electricity and heat supply, buildings, industry, and transport, while simultaneously accounting for land availability, crop production, livestock, forest dynamics, water withdrawals, and climate-sensitive resource constraints. The energy system representation includes primary energy supply, renewable and thermal generation, electricity storage, hydrogen production, and cross-border electricity exchange, with intra-annual time slices capturing seasonal and daily variability in demand and renewable output. The land and water components represent crop types by irrigation class, biomass production, water supply and withdrawals by sector, and land allocation among cropland, pasture, forest, and other uses. Explicit linkages connect irrigation demand, hydropower availability, thermal cooling requirements, and biomass flows into the energy system, enabling assessment of system-wide trade-offs and feedbacks. Baseline projections to mid-century indicate a strong shift toward electrification across end-use sectors, driven primarily by the expansion of renewable electricity generation and the increasing deployment of heat pumps in buildings. Electricity generation is increasingly dominated by wind and solar, supported by storage and intercountry balancing through expanded interconnections. Final energy demand in buildings declines due to efficiency improvements and renovation measures, while transport activity shifts toward electric and, in specific modes, hydrogen-based technologies. Hydrogen supply grows over time, with both domestic production and imports contributing to end-use consumption, particularly in transport and industry.Land-use dynamics reflect increasing competition between food production, biomass supply for energy, and forest-based carbon sequestration. Crop production evolves through shifts in land allocation and irrigation practices, while water withdrawal patterns change substantially across sectors and countries, with agriculture remaining the dominant user in water-stressed regions. Water constraints influence both agricultural output and energy pathways, including hydropower generation and thermal plant cooling. Emissions trajectories vary markedly by sector, with faster declines in power generation and slower reductions in agriculture and certain industrial processes, highlighting persistent mitigation challenges beyond the electricity system.

Pursuing a just, equitable, and desirable sustainable transition requires stakeholders from a diverse set of backgrounds and varying viewpoints to actively engage in deliberations to co-design their future. Facilitating stakeholder engagement activities has so far relied on either qualitative frameworks and processes or simplified quantitative approaches such as surveys, which render eliciting tangible and actionable outcomes challenging. Although group decision making and consensus reaching can leverage the capacity of multi-criteria decision aid to address this gap, tools implementing such processes remain scarce. Here, we present APOLLO-Live, a stakeholder engagement webtool that can be used live in workshops to facilitate deliberations in energy and climate policy. The tool relies on linguistic variables, which are easily comprehensible by the participants, and employs the 2-tuple TOPSIS group decision making method to prioritise needs faced by different communities, as well as solutions that can be implemented to advance the energy transition. It also fleshes out differences in the voting patterns of different groups of voters and calculates a consensus metric to shed light on conflicts arising. Through tips and suggestions provided within the tool and based on multiple rounds of voting, supplemented by live discussions during the workshop, the tool can improve consensus and synthesise multiple perspectives in the produced outcomes, assisting towards bridging the conflicts and producing solutions that are widely accepted. We highlight the functionalities of the webtool, including how it can be used to advance stakeholder dialogues, in two pilot use cases targeting the preferences of the youth in terms of (a) actions to advance climate action and (b) investments to be prioritised. Finally, although the development and use principles followed focused on the energy and climate policy domain, the tool can be used in any application where multi-criteria decision aid and group decision making can potentially be employed.

Energy-sector decarbonisation, which is a prerequisite to tackling climate change, entails major low-carbon technology investments across the globe. However, currently only a fraction of those investments is directed towards emerging and developing economies, partly due to large disparities in financing conditions and perceived investment risks. Modelling exercises underpinning decarbonisation strategies typically do not consider how costs of capital may evolve across regions and technologies, which can have a strong impact on resulting policy insights. We explore the impacts of various plausible, expert-informed, long-term trends of (de-)risking investments in clean energy versus fossil-fuel technologies on decarbonisation pathways on top of an empirical dataset of costs of capital, differentiated by technology and country. We also assess the effects of a ‘corrective justice’ policy of risk underwriting for low-carbon investments, whereby corporate windfall profits are taxed, and the revenues redistributed as subsidies towards higher-risk regions. Results indicate considerable mitigation and investment potential in better informed planning with plausible long-term cost-of-capital projections, which could contribute towards bridging the emissions gap between efforts reflecting the ambition embedded in Nationally Determined Contributions and longer-term climate targets, as well as possible underestimation of the risk entailed in bioenergy-based negative emissions technologies in mitigation scenarios for developed nations.

Pursuing a just, equitable, and desirable sustainable transition requires stakeholders from a diverse set of backgrounds and varying viewpoints to actively engage in deliberations to co-design their future. Facilitating stakeholder engagement activities has so far relied on either qualitative frameworks and processes or simplified quantitative approaches such as surveys, which render eliciting tangible and actionable outcomes challenging. Although group decision making and consensus reaching can leverage the capacity of multi-criteria decision aid to address this gap, tools implementing such processes remain scarce. Here, we present APOLLO-Live, a stakeholder engagement webtool that can be used live in workshops to facilitate deliberations in energy and climate policy. The tool relies on linguistic variables, which are easily comprehensible by the participants, and employs the 2-tuple TOPSIS group decision making method to prioritise needs faced by different communities, as well as solutions that can be implemented to advance the energy transition. It also fleshes out differences in the voting patterns of different groups of voters and calculates a consensus metric to shed light on conflicts arising. Through tips and suggestions provided within the tool and based on multiple rounds of voting, supplemented by live discussions during the workshop, the tool can improve consensus and synthesise multiple perspectives in the produced outcomes, assisting towards bridging the conflicts and producing solutions that are widely accepted. We highlight the functionalities of the webtool, including how it can be used to advance stakeholder dialogues, in two use cases targeting the preferences of the youth in terms of (a) actions to advance climate action and (b) investments to be prioritised. Finally, although the development and use principles followed focused on the energy and climate policy domain, the tool can be used in any application where multi-criteria decision aid and group decision making can potentially be employed.

Pursuing a just, equitable, and desirable sustainable transition requires stakeholders from a diverse set of backgrounds and varying viewpoints to actively engage in deliberations to co-design their future. Facilitating stakeholder engagement activities has so far relied on either qualitative frameworks and processes or simplified quantitative approaches such as surveys, which render eliciting tangible and actionable outcomes challenging. Although group decision making and consensus reaching can leverage the capacity of multi-criteria decision aid to address this gap, tools implementing such processes remain scarce. Here, we present APOLLO-Live, a stakeholder engagement webtool that can be used live in workshops to facilitate deliberations in energy and climate policy. The tool relies on linguistic variables, which are easily comprehensible by the participants, and employs the 2-tuple TOPSIS group decision making method to prioritise needs faced by different communities, as well as solutions that can be implemented to advance the energy transition. It also fleshes out differences in the voting patterns of different groups of voters and calculates a consensus metric to shed light on conflicts arising. Through tips and suggestions provided within the tool and based on multiple rounds of voting, supplemented by live discussions during the workshop, the tool can improve consensus and synthesise multiple perspectives in the produced outcomes, assisting towards bridging the conflicts and producing solutions that are widely accepted. We highlight the functionalities of the webtool, including how it can be used to advance stakeholder dialogues, in two use cases targeting the preferences of the youth in terms of (a) actions to advance climate action and (b) investments to be prioritised. Finally, although the development and use principles followed focused on the energy and climate policy domain, the tool can be used in any application where multi-criteria decision aid and group decision making can potentially be employed.

Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.