By Lucia Rigamonti, Jouni Havukainen and Minna Havukainen
Abstract
The transition to renewable energy is essential for tackling climate change, but reducing carbon emissions alone does not necessarily make an energy technology sustainable. A comprehensive assessment also needs to consider environmental impacts beyond climate change, economic consequences, and social effects across the entire life cycle. The article will introduce Life Cycle Sustainability Assessment (LCSA) as a promising approach for bringing these dimensions together and explain why doing so remains challenging — from data availability and methodological inconsistencies to the difficulty of integrating environmental, economic, and social indicators. Finally, it will look at emerging approaches that could help overcome these limitations, including digital technologies, prospective assessment methods, and new decision-support tools.
Beyond Decarbonisation: Assessing the Energy Transition Across Its Life Cycle
The global energy system must shift to renewable energy to address climate change. The International Energy Agency (IEA) set out a pathway in its 2021 “Net Zero Roadmap for the Global Energy Sector”, subsequently updated [1]. Under the IEA Stated Policies Scenario (STEPS), total energy supply increases by 30% between 2022 and 2050. By contrast, the updated Net Zero Emissions (NZE) scenario projects a reduction in global energy demand of around 15% from approximately 600 EJ in 2022 while solar and wind expand rapidly, replacing coal, oil and natural gas [2]. Electricity production is nevertheless expected to grow by more than 2.5-fold, driven by electrification.
The post-transition energy system is expected to rely heavily on solar and wind electricity to provide energy services directly, rather than through conversion of fuels by heat engines. This would improve final-energy conversion efficiency [3]. Research on 100% renewable energy systems has grown considerably [4], and many scholars agree that a renewable world could be achieved before 2050 [5], although feasibility may differ across regions [6].
Yet the transition cannot be assessed through carbon indicators alone. Its broader environmental, economic, and social implications – including equity and distributional effects – must also be considered. Life cycle thinking (LCT) offers a framework for doing so.
LCT expands the focus beyond a product’s production site to include its whole life cycle: resource extraction, manufacturing, distribution, use and end-of-life treatment through reuse, recycling, recovery or disposal. Its aim is to avoid burden shifting—for example, reducing energy use during operation without increasing the material impacts of production. In this sense, LCT extends cleaner-production approaches to the sustainability of the entire product life cycle [7].
Three main methodologies that use LCT for sustainability assessment are: Life Cycle Assessment (LCA), Life Cycle Costing (LCC) and Social Life Cycle Assessment (S-LCA). LCA evaluates environmental impacts, considering inputs such as energy and materials, outputs such as emissions and waste, and categories including climate change, eutrophication, acidification and resource depletion. LCC assesses costs across the life cycle, including acquisition, operation, maintenance and disposal. S-LCA evaluates potential positive and negative social and socio-economic impacts on workers, consumers, communities and society. When these three methodologies are applied jointly to the same system, the result is a sustainability assessment based on the LCT approach, known as Life Cycle Sustainability Assessment (LCSA).
Challenges in applying Life Cycle Thinking methodologies to the energy transition
LCA, LCC and S-LCA face both methodological and practical challenges when used to assess the energy transition. Moreover, the three approaches differ in methodological maturity and practical application, making LCSA difficult to operationalise. LCSA inherits the limitations of each method and adds the challenge of combining them coherently.
These challenges matter because energy technologies can look favourable when assessed against a single indicator or at a single stage of their life cycle, while creating impacts elsewhere. A solar panel or a wind turbine may reduce operational emissions but still require energy- and material-intensive supply chains, land, infrastructure, maintenance and end-of-life management. Likewise, a technology with a low life-cycle climate footprint may involve high upfront costs, unevenly distributed local impacts or uncertain benefits for workers and communities. The purpose of life cycle sustainability assessment is not to identify a single perfect technology, but to make such trade-offs visible and comparable.
Life Cycle Assessment (LCA)
For LCA, a first challenge is defining the functional unit: the reference against which a system is assessed. In energy studies, this can range from one megajoule of an energy carrier to the energy required to meet the needs of an entire region. The choice determines whether studies can be consistently compared. Defining system boundaries is similarly complex and may be subjective; excluding life-cycle stages can introduce bias. Other difficult choices concern the treatment of multi-functional processes and the inclusion of spatial and temporal differences [8].
Life cycle inventory (LCI) data collection is time-consuming and prone to error. Primary data are often unavailable, particularly for new renewable technologies, so researchers rely on literature, databases and assumptions. Databases may rely on different system models and may not reflect rapidly evolving solar and wind technologies. Limited transparency about assumptions further constrains comparison between studies. Prospective LCA (pLCA) seeks to account for changes in foreground and background systems [9] but requires additional information and introduces uncertainty.
Climate-change assessment is relatively established, whereas human toxicity, biodiversity and ecosystem services remain harder to assess [10]. The interpretation phase must also test how modelling and data choices affect results. This requires sensitivity and uncertainty analysis, but data gaps can limit both.
Life Cycle Costing (LCC)
Data quality is also central to LCC. Comparing mature and emerging technologies is difficult because costs can vary greatly. For example, LCC studies of bio-based and electrolysis-based hydrogen may yield inaccurate results when technology variability is not adequately represented [11]. Limited primary data for emerging technologies, such as Proton Exchange Membrane (PEM) electrolysers, as well as fluctuating costs for renewable-energy infrastructure, maintenance and decommissioning reduce reliability [12]. Dynamic LCC approaches that include learning curves could help [13], but costs and resource availability remain strongly region-specific, limiting comparability.
LCC must also decide whether and how to include external costs: the costs associated with environmental and social impacts. There is currently no harmonised international standard for their inclusion. Some studies omit them; others include only a CO2 tax; still others monetise environmental impacts using different approaches. Monetisation can also create double counting when an environmental externality is reported in both environmental and economic results.
Social LCA (S-LCA)
Renewable energy is often expected to produce social benefits, as recognised in EU policy, but evaluating them is difficult. Social impacts depend on temporal and geographical context, and even the boundaries of a system may be understood differently across cultures. There is no universally agreed definition of what is socially “good”: wellbeing, health, economic security, social connections and fulfilment may all matter, but their significance varies across places and over time [14].
Qualitative evidence is therefore important. In a Namibian energy-sector case study, Van der Veen et al. (2025) [15] showed that issues such as land ownership, enforcement capacity, monitoring, education, and norms relating to ethnicity, religion and gender can be essential, yet difficult to capture through technical, indicator-based assessments. The recently published ISO standard, ISO 14075:2024, identifies stakeholder categories but does not provide a corresponding, harmonised list of social impact categories and indicators.
Data availability and consistency are further obstacles [16]. Relevant indicators are limited, comparable indicator sets are not available for every product system, and hypothetical future scenarios are difficult to score. Two main S-LCA approaches exist: reference-scale (Type I) and impact-pathway (Type II). However, a review of electricity-generation studies identified only Type I applications [17].
Life Cycle Sustainability Assessment (LCSA)
LCSA first faces the data, boundary and indicator problems of its component methods. Reviews of solar energy systems [18], energy-sector applications [19] and solid oxide fuel cells [20] identify gaps in materials data, background process choices and the selection of environmental, economic and social indicators. The lack of standardisation makes results difficult to compare and may hinder LCSA’s intended role in decision-making and policy development.
There is also no consensus on whether, or how, to integrate LCA, LCC and S-LCA. Some studies use visual methods, such as colour-gradient scales. Others assign weights to, and prioritise, different sustainability dimensions. In an assessment of bio-based and fossil transport fuels, Ekener et al. (2018) [21] found no single universally sustainable option: the result depended on stakeholder priorities. Integration therefore cannot remove value judgements; it must make them explicit.
From assessment to action
LCSA can help us look beyond carbon emissions and ask a broader question: what are the environmental, economic and social consequences of an energy technology throughout its life cycle? But it is not yet a simple or fully standardised methodology. Its wider use in policy and regulation will depend on better data, clearer assumptions and more consistent methods.
New technologies may make this easier. Digital tools, including product passports, sensors and data-sharing systems, could improve the traceability of materials and make information more accessible. Artificial intelligence and other data-driven approaches may also help analyse complex evidence. At the same time, combining life-cycle methods with energy-system models, economic models and grid analysis could produce assessments that are better suited to real-world decisions.
However, technology alone will not decide what a sustainable energy system looks like. Researchers, policymakers, industry and communities will need to agree on which impacts matter most, how they should be measured, and how to address trade-offs between them. A solution that performs well on climate change may be more expensive, require more resources, or create local social pressures.
The goal is not to identify a universally “best” technology, but to make energy choices more transparent, informed and fair.
References
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Bio
Lucia Rigamonti is Associate Professor at the Department of Civil and Environmental Engineering at Politecnico di Milano, where her research and teaching focus on Life Cycle Thinking methodologies and their application to sustainability assessment. Her work covers methodological developments as well as applications in waste management, circular economy strategies, and environmental engineering. She is co-coordinator of the AWARE (Assessment on Waste and Resources) research group and founder of the Polimi LCA Network. https://rigamonti.faculty.polimi.it/#about
Jouni Havukainen is Professor of Environmental Sustainability of Energy Systems at the Department of Sustainability Science at LUT University. His research focuses on integrating sustainability considerations into the systemic changes taking place in the energy sector, with particular emphasis on the decarbonization of energy production, Power-to-X solutions, renewable fuels, and offshore wind energy. https://www.lut.fi/en/profiles/jouni-havukainen
Minna Havukainen is a postdoctoral researcher at LUT School of Energy Systems. Her research focuses on the societal impacts of renewable energy and the energy transition, particularly in vulnerable communities and within planetary boundaries.