ALBERT

Description: This report presents 100 research questions that have been identified by scientific experts as key priorities for Social Science and Humanities (SSH) research on renewables, in order to inform and support EU-funded research and innovation leading to achieve climate-neutrality by 2050. The questions together aim: To promote SSH research that contributes to better understanding the meaning and conditions of just transitions to renewables-based energy systems, by recognising the social conditions and consequences of using and further implementing renewable energy technologies.

Description: In line with its commitments to lower carbon emissions under the Paris Agreement and its own 2030 Climate & Energy Framework, the European Union (EU) has committed to increase the share of renewable energy use-around 15% in 2018-to be at least 32% by 2030. Achieving this will require a major reconfiguration of current energy systems in what could be seen as an example of a socio-technical transition or, more specifically, of an 'energy transition'. The key driver of this transition will be the electrification of heating and mobility functions. However, owing to the intermittent nature of most renewable energy sources (RES), this will need to be accompanied by the increased decentralisation and digitalisation of electricity networks. Existing energy system modelling softwares can simulate the dynamics of many of these processes. Nevertheless, they generally do not adequately capture the social and ecological aspects of the technologies that will drive this transition. Accordingly, the report aims to identify ways that future modelling applications-such as the ENVIRO and QTDIAN modules to be developed within the current project-can be used to address this gap and what information, theories, frameworks and methodologies exist that can guide such processes. Section 2 reveals that hydropower looks set to be replaced by wind energy as the dominant RES for electricity generation in the EU. Several other technologies, particularly solar photovoltaic and bioenergy, are also predicted to contribute. Changes in the mix of energy supply technologies is expected to be accompanied by changes at the energy demand end, most notably via the increased integration of digital technology to form 'smart grid' networks. The functionality of such networks relies heavily on devices that can attenuate electrical energy in order to address the intermittency issues of RES and many technologies, old and new, are available at all scales. Understanding these trends will allow us to identify the energy supply and energy demand technologies that should best be considered within the forthcoming modelling studies. Similarly, it is recognised that achieving a just and sustainable energy transition will also require changes within society itself. Accordingly, a selection of six key social trends relating to the energy transition are identified. Collectively, these trends suggest that addressing issues of social acceptance, democracy and justice are likely to greatly improve the success of transition processes. An understanding of these trends will allow us to identify the drivers and constraints that apply to modelling processes and data relating to past trends will be used to guide the formulation of specific modelling scenarios. A number of frameworks and theories that can be used to conceptualise the social processes and processes of technological emergence within broader energy transition processes are discussed in section 3. Firstly, the four main theoretical foundations for visualising transitions are identified as the Multi-Level Perspective (MLP), the Technological Innovation System (TIS), Strategic Niche Management (SNM) and Transition Management (TM). All four-and the MLP in particular-can be used to understand how structural changes occur in energy systems and how to guide sustainable energy transition processes. In any case, as these frameworks do not fully represent exchanges between societies and the ecosystem, so-called socio-ecological system (SES) frameworks are also discussed. Lastly, two approaches for quantifying the rates of technological progress and market impact for burgeoning technologies are discussed. Together, it is hoped that this information can be used to conceptualise and predict the myriad potential transition pathways that are to be developed using the ENVIRO and QTDIAN modules. This is perhaps particularly true of the QTDIAN module which specifically aims to use theoretical insights from these sources to guide the formulation of a series of new model toolboxes. While qualitative methods have tended to dominate the approaches taken to transition theory in the past, section 4 presents a summary of six existing frameworks and approaches that have found use in the quantitative modelling of energy transitions. The first of these-the use of integrated assessment models (IAMs)-involves the integration of multiple existing quantitative models, is already widely employed to simulate transition scenarios at larger scales and is perhaps the most relevant to the current project. The remaining five model categories are a group of more abstract frameworks and approaches that attempt to model complex systems, behaviours and dynamics, often at finer levels of detail. This includes agent-based models (ABMs)-the most commonly used to date-as well as the broadly classified group of complex systems models, evolutionary economics models, socio-ecological systems models and system dynamics models. Most of these are not able to model the social-cultural, organisational, institutional and political aspects of a system, their interplay, or their feedbacks with the surrounding environment, underlining the need for further development. Nevertheless, the overview of the current status quo in real-world transition modelling provides an understanding of the available options for the development of the ENVIRO and QTDIAN modules. It also provides an element of contextual background to other modelling activities within the SENTINEL project as a whole, particularly those involving ABM and IAM approaches. The findings of the report will act as the foundation for the development of the ENVIRO and QTDIAN modules that will allow social and ecological factors and impacts to be integrated into the energy system modelling platform of the SENTINEL project. It will also serve to open doors to the continued integration of social and environmental factors into future energy system models by demonstrating the ways in which societal and technological trends can be integrated into energy system modelling projects.

Description: Although energy models advance rapidly in terms of technical and techno-economic details, social and political aspects and environmental burdens beyond greenhouse gas emissions are currently underrepresented. However, in light of the European Green Deal and the EU Energy Union Strategy, models must advance in terms of social and environmental considerations to support decision- and policymakers in adequately addressing that environmental burden and to put “citizens at its core” of the energy transition. In this deliverable, we present key user-needs for environmental and social aspects that need to be better represented in energy system models (Section 2), and how we have developed and adapted the modelling tools ENVIRO, QTDIAN, and ATOM in response to the identified user needs. We show three main user needs regarding social aspects, specifically (i) social impacts on energy politics and policies, (ii) the social acceptance of energy technologies and infrastructure, and (iii) consumers’ behavior in energy models. We furthermore show that users consider relevant the following factors within the environmental aspects of energy scenarios: (iv) demand of raw materials/ circularity, (v) the implications on nature and biodiversity, as well as (vi) full life-cycle impacts and externalization. ENVIRO and QTDIAN are being developed within SENTINEL in a participatory process by engaging with stakeholders in the information and development stages of the model implementation. In contrast, ATOM is adapted by considering user-needs especially in the implementation stage. We conclude that we have benefited from the insights of model users and other stakeholders, and that this will allow us to make our modelling tools fit-for-purpose. All three modelling tools will support decision-makers by answering the most important of the questions users have risen within the SENTINEL stakeholder engagement process. Model-linking within the WP2 and other WPs will ensure that the understanding of environmental and social aspects is strengthened in energy system models and will be embedded in the overall SENTINEL platform.