• Energy Research

Las políticas energéticas se diseñan con el apoyo de modelos energéticos tecno-económicos que no son capaces de valorar muchos de los impactos ambientales y sociales que la transición energética puede conllevar. La herramienta de código abierto ENBIOS (Environmental and Bioeconomic System Assessment) ha sido diseñada para llenar este vacío y combina dos marcos metodológicos muy reconocidos en el estudio de la sostenibilidad: el análisis de ciclo de vida y el análisis del metabolismo social. Para entender las perspectivas de distintos grupos de interés (política, ciencia, industria y ONGs) sobre las cuestiones que se deben considerar al planificar la transición energética, en LIVEN organizamos un taller en el que participaron 14 personas de distintas entidades. El taller tuvo lugar en Junio de 2022 de forma virtual y siguió una metodología Delphi, con una encuesta inicial que valoraba la importancia de distintos impactos ambientales y sociales de los sistemas energéticos, seguida de una presentación de las respuestas agregadas, una discusión en la que se profundizaba los factores y por último una segunda ronda de la encuesta. En este informe se recogen los resultados del proceso participativo y su implementación en la modelización de impactos ambientales de los escenarios del PNIEC con ENBIOS. This research is funded by the Spanish Research Agency with grant PID2020-119565RJ-I00. Opinions are the authors'.

Micromobility is often thought of as a sustainable solution to many urban mobility challenges. The literature to date, however, has struggled to find consensus on the sustainability of shared and electric scooters, e-bikes, and e-mopeds. This paper uses a Life Cycle Assessment (LCA) approach to calculate the impacts of micromobility modes in three categories: Global Warming Potential (GWP), Particulate Matter Formation, and Ozone Formation. It does so by incorporating the self-reported modal change of each transportation mode: shared e-moped, shared e-bicycle, shared bicycle, and personal e-scooter. The results show that modal change brought by the introduction of shared e-mopeds and shared e-bicycles caused an increase in greenhouse gas (GHG) emissions, while shared bicycles and personal electric scooters decreased GHG emissions. All micromobility modes except personal e-scooters increased particulate matter emissions, but decreased those which were emitted within the city, while they all decreased NOx. The findings of this study suggest new micromobility services are not always the best environmental solution for urban mobility, unless the eco-design of vehicles is improved, and they are strategically used and deployed as part of a holistic vision for transport policy.

Swedish buildings require additional heating inputs for many months each year, much of which comes from district heating systems fed by fuel combustion, electrical devices and recycled heat sources; localised heating is also generated within individual buildings. Optimised projections using the EnergyPLAN model for a so-called "smart energy" scenario predict dramatic reductions in biomass use accompanied by increases in electricity, recycled heat and biogas. Electricity generation routes are also predicted to change, shifting away from nuclear and biomass sources towards wind, solar and biogas technologies. While such transitions are expected to lower greenhouse gas emissions, current assessment methods rarely examine the breadth of other environmental and material supply aspects at play. Here, a novel new approach provides deeper insights into current and future Swedish heating scenarios for 11 key indicators by considering heat production processes over their full life cycles. Results suggest that favourable reductions are likely in five of these indicators, but these benefits are offset by unfavourable outcomes in others, including all three raw material supply indicators. Ultimately, the study provides a novel example of ways in which additional tools can complement existing modelling techniques and expand the scope of available information used to assist heating transition decisions.

Raw materials and their related environmental impacts will play a key role in the implementation of renewable energy infrastructures for decarbonization. Despite the growing amount of data quantifying raw materials for energy production technologies, few examples of these data sources are being included in current energy system models. Accordingly, this paper introduces possible pathways for integrating material-specific life cycle assessment outputs and material metabolism indicators into energy system models so that raw material requirements, and their associated impacts, can be accounted for. The paper discusses the availability of life cycle inventories, impact assessment methods and important output indicators. The material metabolism indicators most relevant to the current policy debate surrounding the European Green Deal-namely, material supply risk and contribution of recycled materials to total supply-are also discussed alongside the value of adding this information to energy system models. A methodology for using data from both approaches is offered and operationalised using four sub-technologies of both wind turbines and solar photovoltaic panels as case studies. The results show that considerable variation exists between and within the two groups for all indicators. The technologies with the lowest global warming potential, cumulative energy demand and supply risk are turbines with gearbox double-fed induction generators and cadmium telluride photovoltaics. Furthermore, wind turbines exhibit significantly higher recycling rates than photovoltaics. Ultimately, the integration of such methodologies into energy system models could greatly increase the awareness of raw material issues and guide policies that maximise compatibilities between resource availability and cleaner energy systems.

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. Following a brief introduction to the key concepts involved, Section 2 provides a summary of the current energy system at the global and EU scale, followed by a detailed investigation into the technologies most relevant to the transition towards the greater use of renewable energy. This includes all important energy supply, demand and storage technologies. Recognising that achieving a just and sustainable energy transition will also require changes within society itself, a selection of six key social trends relating to the energy transition are also discussed. Collectively, these trends suggest that addressing issues of social acceptance, democracy and justice are likely to greatly improve the success of transition processes. Section 3 outlines a number of frameworks and theories that can be used to conceptualise the social processes and processes of technological emergence likely to occur within broader energy transition processes. 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. Two further approaches for quantifying the rates of technological progress and market impact for burgeoning technologies are also 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. Lastly, 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. Collectively, the findings of the report 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 also serves 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.

Altres ajuts: acords transformatius de la UAB Unidad de excelencia María de Maeztu CEX2019-000940-M Energy models are used to inform and support decisions within the transition to climate neutrality. In recent years, such models have been criticised for being overly techno-centred and ignoring environmental and social factors of the energy transition. Here, we explore and illustrate the impact of ignoring such factors by comparing model results to model user needs and real-world observations. We firstly identify concrete user needs for better representation of environmental and social factors in energy modelling via interviews, a survey and a workshop. Secondly, we explore and illustrate the effects of omitting non-techno-economic factors in modelling by contrasting policy-targeted scenarios with reality in four EU case study examples. We show that by neglecting environmental and social factors, models risk generating overly optimistic and potentially misleading results, for example by suggesting transition speeds far exceeding any speeds observed, or pathways facing hard-to-overcome resource constraints. As such, modelled energy transition pathways that ignore such factors may be neither desirable nor feasible from an environmental and social perspective, and scenarios may be irrelevant in practice. Finally, we discuss a sample of recent energy modelling innovations and call for continued and increased efforts for improved approaches that better represent environmental and social factors in energy modelling and increase the relevance of energy models for informing policymaking.

Altres ajuts: acords transformatius de la UAB Unidad de excelencia María de Maeztu CEX2019-000940-M Many of the long-term policy decisions surrounding the sustainable energy transition rely on models that fail to consider environmental impacts and constraints beyond direct greenhouse gas emissions and land occupation. Such assessments offer incomplete and potentially misleading information about the true sustainability issues of transition pathways. Meanwhile, although decision-makers desire greater access to a broader range of environmental, material and socio-economic indicators, few tools currently address this gap. Here, we introduce ENBIOS, a framework that integrates a broader range of such indicators into energy modelling and policymaking practices. By calculating sustainability-related indicators across hierarchical levels, we reach deeper understandings of the potential energy systems to be derived. With ENBIOS, we analyse a series of energy pathways designed by the Calliope energy system optimization model for the European energy system in 2030 and 2050. Although overall emissions will drop significantly, considerable rises in land, labour and critical raw material requirements are likely. These outcomes are further reflected in unfavourable shifts in key metabolic indicators during this period; energy metabolic rate of the system will drop by 25.6%, land requirement-to-energy will quadruple, while the critical raw material supply risk-to-energy ratio will rise by 74.2%. Heat from biomass and electricity from wind and solar are shown to be the dominant future processes across most indicator categories.