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Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models.

European Energy and Climate Policy has identified strategic energy technologies and designed roadmaps to tackle the individual barriers hindering their effective implementation across Europe. The SET-Plan, the European Union (EU) Energy Roadmap 2050 and various stakeholders' discussions outlined four main decarbonisation routes for the energy sector, namely: energy efficiency, renewable energy sources, nuclear and carbon capture and storage. In the above framework, a set of alternative pathways were designed by identifying outcomes that conform to the objectives of the EU and the SET-plan, in particular competitiveness and a clean, secure and efficient energy system. These pathways are positioned under two key uncertainties; the level of cooperation (i.e. cooperation versus entrenchment) and the level of decentralisation (i.e. decentralisation versus path dependency). The aim of this paper is to assess these pathways, through the active involvement of relevant stakeholders, according to their performance in key areas, such as the regulatory framework, market maturity, cost/ economic factors, towards a sustainable energy system, by using a multicriteria method. The results could assist European policy makers in drawing recommendations by exploring important elements, drivers and factors of the energy transition and the long term impacts of alternative mitigation options on the economy, the energy sector and technology development.

The proposed methodology aims to link potential energy efficiency measures with the progress of official EU & UN indicators, establish a typology structured on a country and sector basis, rank the necessity for energy efficiency between 8 case study countries based on the emerged typology, identify combinations of countries and sectors that are more in need to improve their energy efficiency. point out the multi-benefits of the implementation of energy efficiency measures and their contribution at a country and sector level.

Climate change has been considered one of the most significant risks for sustainability in our century; in order to move towards low-carbon and climate resilient economies, fundamental changes must take place. In this direction, the European Union has set ambitious goals regarding the transition of its Member States to low carbon societies, but the policy strategies to promote this transition must be socially acceptable and supported. So far, climate policies have been evaluated using quantitative methods, including general equilibrium and integrated assessment models but, despite their undoubted contribution to climate modeling, both the quantitative frameworks used for studying climate change and its impacts and those aiming at policy optimization or evaluation feature significant uncertainties and limitations. In order to overcome these issues, a Fuzzy Cognitive Map based approach is proposed, aiming to directly involve stakeholders and assess human knowledge and expertise. The suggested methodological framework can significantly support climate policy making, by supplementing quantitative models and exploring impacts of selected sets of policies, based on qualitative information deriving from a structured stakeholder engagement process. Finally, an innovative approach of incorporating the concept of time into the methodology is proposed and evaluated, in the aim of enhancing the robustness of transition pathways.

This is the 4th chapter of the book "Understanding Risks and Uncertainties in Energy and Climate Policy - Multidisciplinary Methods and Tools for a Low Carbon Society" published in Springer. Abstract Ambitious emission reduction targets are challenging the status quo on designing effective strategies for electricity generation portfolios. In this chapter, we consider the role of low-carbon technologies and determine the cost-benefits of policy strategies to mitigate greenhouse gas emissions in the EU. In particular, we look into how long-term scenarios for transmission expansion and decarbonisation policies influence the evolution of the EU power system infrastructure. We use an EU electricity investment model to determine the optimal portfolio of electricity generation technologies and compute their respective costs and emissions achieved towards 2050. Based on the investment model's results (strategies and suggested portfolios), we investigate how these portfolios perform under divergent policy or geopolitical developments. For this purpose, we apply a robust optimisation tool based on the min-max and the min-max regret criteria, which selects ideal portfolios by stress testing a particular scenario or policy choice under uncertainty of input parameters. Results show that pursuing a strong transmission expansion strategy under the EU PRIMES reference case leads to the maximum regret, while relying on EU scenarios with strong prospects for decarbonisation, either with possibilities or with limitation on transmission expansion, leads to portfolios that exhibit the least variance. However, applying regret analysis on investment costs and total emissions indicates a limited transmission investment case as the more robust one, also noting that a high carbon price will accelerate the energy transition.

This open access book analyzes and seeks to consolidate the use of robust quantitative tools and qualitative methods for the design and assessment of energy and climate policies. In particular, it examines energy and climate policy performance and associated risks, as well as public acceptance and portfolio analysis in climate policy, and presents methods for evaluating the costs and benefits of flexible policy implementation as well as new framings for business and market actors. In turn, it discusses the development of alternative policy pathways and the identification of optimal switching points, drawing on concrete examples to do so. Lastly, it discusses climate change mitigation policies' implications for the agricultural, food, building, transportation, service and manufacturing sectors. ; Open Access Presents a comprehensive tool set of methods for devising energy and climate policies Focuses on methods that are robust and adaptive enough to mitigate risks Investigates implications of climate change mitigation policies to various sectors

Given the international efforts in tackling climate change as well as the potential dependence on conventional energy imports and the adverse economic environment, countries in the European Union face significant challenges in the critical task of enhancing energy efficiency. Approaches exclusively oriented on detailed quantitative modelling tools like energy system and climate-economy models, however, tend to exclude certain policy instruments and risks, and be too formalised or complex for policymakers to participate, understand and trust. Several decision support frameworks have been proposed for bridging the policy-model gap and helping policymakers confidently select among a number of alternative strategies. This paper employs the expert-driven method of fuzzy cognitive mapping, a semi-quantitative modelling technique in which system dynamics are captured and simulated against different scenarios. To this end, an innovative decision support tool for building and simulating complex fuzzy cognitive maps for assessing policy strategies with the help of experts, ESQAPE, is introduced and presented. An application in Greece shows that long-term energy efficiency measures focusing mainly on behavioural change in the residential sector – as opposed to services in the private and public sectors – are perceived to be more sustainable in a socio-economically optimistic future; this is not the case when challenges across the mitigation and adaptation axes are expected to be higher.

In alignment with the European Union's legislation, Greece submitted its final 10-year National Energy and Climate Plan (NECP) in December 2019, setting more ambitious energy and climate targets than those originally proposed in the draft version of the document. Apart from higher penetration of renewable energy sources (RES), the final NECP projects also zero carbon use in power generation till 2030. Although decarbonization has long been regarded beneficial for economies that base their energy production on coal, as it is the case with Greece, the macroeconomic and societal ramifications of faster transitions to carbon-free economies remain highly unexplored. Under this context, in this paper, we soft-link energy models, namely Times-Greece and Primes, with a macroeconomic model, namely Global Trade Analysis Project (GTAP), to measure the effects of the final and draft NECPs on the Greek economy and evaluate the impact of higher decarbonization speeds. We find that the faster transition scenario displays both economic and societal merits, increasing Gross Domestic Product (GDP) and household income by about 1% and 7%, respectively.

Significant renewable energy (RE) investments have to be implemented in order to achieve the ambitious RE targets set in the EU for 2020 and beyond. Moreover, a great amount of capital has to be leveraged, as these projects are followed by high investment and financing costs. Main aim of this paper is the provision of a comprehensive assessment of the existing risk elements of RE investments in relation to the respective policies and the evaluation of their impact on the weighted average cost of capital (WACC) in Greece. A consultation procedure with key national energy stakeholders took also place, including policy makers, project developers, investors, equity providers, bankers and energy analysts in the Greek RE market, in order to provide a validation of the respective results. It has been concluded that the policy design risk represents the risk element with the greatest impact on the cost of capital and, thus, the level of RE investments' deployment. Βased on the cost of capital valuation process followed, the WACC was estimated to reach approximately 12% for onshore wind and little lower values for solar PV projects in Greece. ; This work was supported by the Intelligent Energy Europe (IEE) Programme within the framework of the project "DIA-CORE - Policy dialogue on the assessment and convergence of RES policies in EU Member States" (Project Number: IEE/12/833/SI2.645735), the Horizon 2020 Programme of the European Commission in the context of the project "TRANSrisk - Transitions pathways and risk analysis for climate change mitigation and adaptation strategies" (Grant Agreement No. 642260) and the European Climate Foundation within the framework of the Pricetag project (Project Number: EPONL17190).

Ambitious emission reduction targets are challenging the status quo on designing effective strategies for electricity generation portfolios. In this chapter, we consider the role of low-carbon technologies and determine the cost-benefits of policy strategies to mitigate greenhouse gas emissions in the EU. In particular, we look into how long-term scenarios for transmission expansion and decarbonisation policies influence the evolution of the EU power system infrastructure. We use an EU electricity investment model to determine the optimal portfolio of electricity generation technologies and compute their respective costs and emissions achieved towards 2050. Based on the investment model's results (strategies and suggested portfolios), we investigate how these portfolios perform under divergent policy or geopolitical developments. For this purpose, we apply a robust optimisation tool based on the min-max and the min-max regret criteria, which selects ideal portfolios by stress testing a particular scenario or policy choice under uncertainty of input parameters. Results show that pursuing a strong transmission expansion strategy under the EU PRIMES reference case leads to the maximum regret, while relying on EU scenarios with strong prospects for decarbonisation, either with possibilities or with limitation on transmission expansion, leads to portfolios that exhibit the least variance. However, applying regret analysis on investment costs and total emissions indicates a limited transmission investment case as the more robust one, also noting that a high carbon price will accelerate the energy transition. ; publishedVersion ; © The Author(s) 2019 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.