Multi-Criteria LCA Framework for Sustainable Hydropower Refurbishment Design

Abstract

Hydropower refurbishment is increasingly recognized as a key strategy for maintaining renewable electricity generation and minimizing the environmental and social impacts of developing new infrastructure. With much of the global hydropower fleet approaching or exceeding its original design life, refurbishment decisions must strike complex trade-offs between technical performance, environmental impacts, economic viability, and social acceptability. This review provides a comprehensive summary of the scientific and policy literature on sustainable hydropower refurbishment, with a particular focus on the integration of life cycle assessment (LCA) and multi-criteria decision analysis (MCDA) from a circular economy perspective. The study systematically reviews the latest results in the fields of environmental LCA, life cycle costing (LCC), social LCA (S-LCA), and integrated life cycle sustainability assessment (LCSA), highlighting their application in the refurbishment and modernization of hydropower plants. The results show that construction-related impacts, particularly those associated with concrete and steel, dominate the environmental load over the life cycle, making refurbishment and component recycling highly effective strategies for reducing embodied emissions. The integration of LCA and MCDA allows for the transparent prioritization of refurbishment alternatives by explicitly considering stakeholder preferences and trade-offs between environmental, economic, social, and technical criteria. Overall, the results support the use of integrated, multi-criteria life cycle frameworks as reliable decision-making tools for managing sustainable hydropower refurbishment and long-term energy system resilience.

1. Introduction

Hydropower represents one of the cornerstones of global renewable energy infrastructure. It currently contributes approximately 16% of global electricity generation and accounts for over 72% of all renewable electricity generated, strengthening its crucial role in strategies for the deep decarbonization of the energy sector [1]. In addition to contributing to low-carbon energy production, hydropower is also strategically valuable in terms of energy security and grid stability, as it is inherently capable of storage and flexible energy production. However, a significant portion of the global hydropower plant (HPP) fleet is ageing, with turbines and auxiliary systems approaching or exceeding their original design life, thus requiring extensive refurbishment and modernization work [2,3,4].

In addition, modern grid requirements increasingly demand that hydropower plants operate with greater flexibility, for example by providing fast cycles or peak power. Many older facilities were not designed for these operating patterns, leading to faster wear and tear on critical components. These new operating loads expose the limitations of traditional, time-based maintenance models and highlight the need for condition-based and digitally supported maintenance strategies [5,6].

Consequently, while aging infrastructure creates challenges in terms of rising maintenance costs, operational risks, and impacts on sustainability, it also provides an opportunity to rethink refurbishment strategies through the use of digital tools, improved maintenance practices, and circular economy (CE) concepts [7].

Refurbishment, rehabilitation, and modernization have emerged as the primary strategic alternatives to greenfield development. Refurbishment interventions are based on two fundamental objectives: on the one hand, the technical need to improve the efficiency and service life of the equipment and, on the other hand, the increasingly urgent need to comply with the principles of the CE, which requires reducing virgin material consumption and extending the life cycle of equipment [2]. Modernizing existing facilities offers significant opportunities, especially given that many large dams were not originally built for hydropower generation, but rather for flood control or irrigation, for example [8].

The technical scope of a hydropower refurbishment project is determined by a comprehensive diagnostic process that assesses the condition of mechanical, electrical, and architectural components. Unlike new construction, refurbishment is inherently site-specific and constrained by the existing physical footprint and hydraulic parameters of the facility [9]. The primary goal is to restore or exceed the original design performance while extending the facility’s operating life by several decades. Such projects are often motivated by increasing maintenance and repair requirements due to the aging of critical components such as turbine wheels and turbine blades. If this problem is not addressed, deterioration can lead to a significant reduction in efficiency and production capacity, and in severe cases, to the forced shutdown of the facility [10,11]. Therefore, when evaluating refurbishment strategies, the reliability and resilience gains achieved by extending the service life of infrastructure must be quantified, clearly linking improved technical performance to long-term sustainability.

In addition, the incorporation of innovative materials has become one of the most important features of sustainable hydropower renovation. For example, replacing traditional steel components with advanced composite materials can achieve a weight reduction of approximately 50–80%, which reduces the mechanical load on bearings and improves the dynamic response of the system [12]. Furthermore, modern turbine designs can be made “fish-friendly” by using wider blade spacing and lower rotational speeds, which helps to limit pressure fluctuations and shear forces that can otherwise pose a threat to migratory aquatic species [13,14].

The strategic direction of European hydropower refurbishment is determined by a set of interrelated environmental and energy policy measures aimed at aligning infrastructure modernization with the European Union’s long-term sustainability and climate policy goals. A central element of this policy framework is the European Green Deal [15], which sets climate neutrality by 2050 as an overarching goal. Hydropower is regarded as one of the most crucial pillars of the future electric system because its storage capacity and flexibility facilitate the full integration of variable renewables like wind and solar. Under the Green Deal, refurbishment solutions are expected to demonstrate “net-improvements” in socioeconomic and environmental sustainability, particularly concerning the preservation and restoration of biodiversity, in line also with the ‘do no significant harm’ (DNSH) principle [16]. These expectations are reinforced by the Circular Economy Action Plan (CEAP) [17], which complements the Green Deal by promoting the transition from linear “extraction-production-consumption-disposal” production models to strategies that extend the life of assets and optimize resource use [18,19]. Refurbishment is one of the 10R strategies of the circular economy—specifically, a strategy that prioritizes the repair, refurbishment, and modernization of existing assets before replacement—thereby reducing demand for new materials, minimizing waste generation, and reducing environmental impacts throughout the life cycle [20,21]. In the hydropower sector, this means “circularity through design,” where the modernization of turbines and auxiliary equipment is prioritized to close material loops and improve system efficiency. This approach is likely to gain further regulatory significance with the planned introduction of the legally binding Circular Economy Act.

At the same time, the Water Framework Directive (Directive 2000/60/EC) sets environmental objectives for European water bodies [22]. It frequently requires hydropower operators, particularly during license renewals or refurbishment projects, to implement mitigation measures such as fish passage facilities, environmental flow regimes, and hydropeaking reduction strategies. Through these requirements, the framework establishes strict standards for the protection of aquatic ecosystems, ensuring that both existing installations undergoing refurbishment and any new hydropower developments maintain high levels of sustainability, including the preservation of ecological integrity and the morphological health of river systems [23]. In addition, the revised Renewable Energy Directive (RED III) sets a mandatory target of at least 42.5% of gross final energy consumption to be covered by renewable energy sources by 2030. Although RED III establishes a legal presumption of priority public interest for renewable energy development, it maintains requirements for appropriate mitigation measures and compliance with water and biodiversity legislation [24]. Together, these policy instruments form an integrated regulatory environment in which hydropower refurbishment is a key circular economy strategy that simultaneously supports climate change mitigation, resource efficiency, ecological protection, and energy system resilience.

Planning a sustainable refurbishment project is a complex engineering and management task that requires a multi-criteria approach. Sustainability in hydropower development involves three closely linked dimensions: environmental, economic, and social. Despite its importance, there are no universal standards for assessing the sustainability of hydropower, as assessment methods remain fragmented and unharmonized at the global level [25]. To this end, it is no longer sufficient to optimize mechanical efficiency alone; modern projects must deliver outstanding performance in terms of environmental protection, social equity, and economic viability throughout their entire life cycle [3]. The most important methods include Life Cycle Assessment (LCA), Life Cycle Costing (LCC), Social Life Cycle Assessment (S-LCA), and integrated Life Cycle Sustainability Assessment (LCSA) [26,27]. LCA, standardized under ISO 14040/14044 [28,29], provides a standardized framework for assessing the environmental impacts of a product or process throughout its entire life cycle, from raw material extraction and processing through manufacturing, distribution, operation, maintenance, and end-of-life management. LCC focuses on total costs incurred during the life cycle, while S-LCA assesses social and socio-economic impacts, including potential benefits and adverse effects. By combining the results of LCA, LCC, and S-LCA, the LCSA framework provides a holistic sustainability assessment that encompasses environmental, economic, and social aspects [26,27,30,31,32]. In this context, reliable and consistent assessment frameworks are critical for guiding refurbishment and modernization strategies. Promoting standardized evaluation approaches and integrated environmental, social, and economic life cycle assessment (LCA) is essential to realizing the full potential of circular economy strategies in the field of hydropower refurbishment and to supporting long-term sustainability, system resilience and social acceptance [33].

The aim of this paper is to provide a comprehensive analysis of scientific and policy literature on sustainable hydropower refurbishment and redesign. It focuses on the integration of LCA into modernization practices, examines existing multi-criteria assessment frameworks employed in sustainable engineering design, and explores how circular economy principles can guide low-impact refurbishment of hydropower turbines and associated equipment. Additionally, the review considers the role of life cycle–based approaches—including LCA, LCC, Life Cycle Inventory (LCI), and S-LCA—along with relevant environmental indicators, in supporting informed eco-design and decision-making. Through this synthesis, the study aims to identify methodological gaps, highlight best practices, and provide insights to inform more resource-efficient and sustainable strategies for hydropower refurbishment.

The structure of this study is organized as follows: Section 2 describes the research methodology and literature search strategy; Section 3 presents and synthesizes the main findings related to sustainable HPP refurbishment and LCA of HPPs; Section 4 discusses the implications of these findings for decision-making and sustainable modernization of hydropower assets; and Section 5 concludes with key practical insights and policy-oriented recommendations.

2. Materials and Methods

To investigate the multi-criteria LCA framework applied to sustainable hydropower refurbishment design, we conducted a systematic review of scientific literature. The review was based on a comprehensive search of the Web of Science database using a topic-based query that integrated titles, abstracts, and keywords. The search strategy combined several terms relevant to the topic, as follows: “hydropower plant” AND “Life Cycle Assessment”; “hydropower refurbishment”; “Hydropower” AND “circular economy”; “Hydropower” AND “Life Cycle Assessment” AND “Life Cycle Cost”; “Multi-criteria decision analysis” AND “Hydropower”; “Environmental impact” AND “Hydropower turbines”; “Hydropower” AND “Life Cycle Inventory”; “Sustainable hydropower refurbishment”. This initial search yielded 798 records.

The identified documents were then filtered based on predefined inclusion criteria: publication period (between 2010–2026), document type (articles and review articles), language (English), and open access availability. The initial search yielded a significant number of publications, but only those that met all the inclusion criteria were further analyzed. To ensure the relevance of the topic and with the objective of developing a multi-criteria LCA framework for hydropower plant refurbishment, the search string was refined iteratively, with the keyword “Life Cycle Assessment” added as a further AND refinement to “Hydropower” AND “circular economy”, “Multi-criteria decision analysis” AND “Hydropower”, and “Environmental impact” AND “Hydropower turbines”. Iterative refinement of the search strategy increased the accuracy and thematic relevance of the resulting records. After applying these criteria and refinements, 164 studies met all inclusion requirements and were subjected to detailed analysis.

The methodology applied in this study is illustrated in Figure 1. A structured and transparent literature selection process aligned with the PRISMA guidelines, introducing targeted refinements to account for the specific scope of hydropower retrofits and the manageable size of the final analysis sample. The methodological approach aimed to ensure rigor, reproducibility, and relevance in the identification, screening, and inclusion phases.

To further focus on circular and sustainable design approaches, we applied additional refinement. First, we removed duplicate records, then performed a structured, manual full-text assessment to evaluate methodological reliability, thematic relevance, and scope consistency. We excluded studies that did not explicitly integrate LCA into hydropower applications, did not address decision support or multi-criteria evaluation elements, or were not relevant in the context of refurbishment, modernization, or life cycle extension. This systematic and criteria-based screening reduced the data set to 58 publications. The retained studies show direct conceptual and methodological consistency with the proposed multi-criteria LCA framework and provide sufficient quality and thematic coverage to draw reliable conclusions about sustainable hydropower refurbishment.

The final document selection was subjected to bibliometric and keyword co-occurrence analysis using VOSviewer software (version 1.6.20). We set the minimum number of co-occurrences of keywords to 6; thus, 81 of the 2330 keywords met the threshold value (Figure 2). The resulting network visualization is color-coded according to the clusters identified by VOSviewer. Specifically, the red cluster represents the development of hydropower plants, construction stages, operational performance, and socioeconomic aspects; the blue group refers to emissions, reservoirs and dams, energy production, climate change, and small hydropower systems; the green cluster reflects electricity generation and sustainability within broader energy systems; the yellow cluster groups together environmental impact assessment, LCA, environmental performance indicators, and electricity generation indicators (e.g., kWh); the purple cluster covers technological, economic, and methodological aspects, including costs, materials (e.g., steel), water consumption; and the grey cluster represents connecting terms that are closely related to multiple clusters.

3. Results

3.1. Context Analysis

The selected articles provide a complex perspective on the evolution of the research related to the examined concept. The context of these publications describes how the scientific community approaches the topic. The context analysis was conducted on temporal and spatial criteria.

Figure 3 shows the annual distribution of the 58 publications deemed eligible for rating and included in the qualitative analysis. Overall, the research activity shows a clear trend over time, with very limited results in the early years and a significant increase in the number of publications in the most recent period.

Between 2010 and 2014, we identified only two articles suitable for evaluation (one in 2011 and one in 2013), which suggests that there was little or no research directly related to the topic of the study during this period. Research results between 2015 and 2017 remained sporadic and low, with one article in 2015, followed by a slight increase to four articles in 2016 and three articles in 2017.

Between 2018 and 2020, the number of publications stabilized at a relatively low level, with two articles appearing each year. Starting in 2021, there will be a significant change, reflecting growing scientific and practical interest in the topic. In 2021 and 2022, eight articles were published annually, peaking at nine publications in 2023. The high level of publication will continue in the following years, with eight articles appearing in 2024 and nine in 2025. The sample also includes three publications that appeared in journals in 2026.

Overall, this distribution shows that most of the articles analyzed in this study were published in the last five years, highlighting the growing importance and momentum of research on hydropower retrofitting and justifying the focus on recent literature in the qualitative analysis.

In terms of spatial context analysis, Figure 4 shows the geographical distribution of the 58 publications included in the qualitative analysis, illustrating in a color range from light blue (indicating a minimum of 1 publication) to dark blue (indicating the highest concentration of 6 publications) how research on sustainable hydropower refurbishment and LCA is distributed across different regions of the world.

The map shows that research activity is geographically concentrated in a few regions, primarily in countries with long-established hydropower infrastructure and a long history of operation. Brazil stands out as one of the most significant contributors, with the highest publication intensity. This reflects the country’s extensive hydropower capacity and the growing need to modernize and renovate aging facilities.

Several countries in Europe are making significant contributions to research, including Italy, Norway, France, Spain, and the United Kingdom. These countries have mature hydropower fleets and strong research ecosystems, which are likely to drive interest in refurbishment, efficiency improvement, and lifetime extension strategies.

China also plays a prominent role in Asia, in line with its large-scale hydropower development and increasing focus on optimizing, modernizing, and ensuring the sustainability of existing assets. Countries in South and Southeast Asia, Oceania and Africa also contribute to research, but to a relatively limited extent.

3.2. Content Analysis

Global energy consumption continues to rise, necessitating an accelerated transition toward renewable energy sources in order to meet the objectives of the Paris Agreement and the Sustainable Development Goals (SDGs), particularly SDG7, which aims to ensure access to affordable, reliable, and sustainable energy for all [34]. This transition requires a careful balance between environmental, economic, and social dimensions. The electricity sector plays a key role in this process, as its impact extends across the entire value chain, including resource extraction, infrastructure development, operation, and end-of-life management. In this context, hydropower is a cornerstone of the global energy transition, accounting for around 16% of global electricity generation and more than half of total renewable electricity generation [35]. Due to its storage capacity, operational flexibility, and long lifespan, hydropower is uniquely positioned to support system-level decarbonization while contributing to broader sustainability goals [8].

The complexity of sustainable hydropower planning requires a decision-making framework that can handle both precise quantitative data and the subjective values of stakeholders. The integration of LCA and multi-criteria decision analysis (MCDA) offers a reliable solution to this problem, providing a structured method for evaluating alternatives and managing trade-offs. While LCA systematically quantifies environmental impacts throughout the life cycle, hydropower refurbishment decisions simultaneously encompass economic feasibility, technical reliability, regulatory compliance, and social acceptance. Since LCA results usually consist of multiple, often conflicting indicators that cannot be directly aggregated or ranked without further decision logic, MCDA allows for the weighting of alternatives, the setting of priorities, and transparent comparison based on criteria defined by stakeholders [36,37,38,39]. In the context of hydropower refurbishment, the integration of LCA and MCDA is essential to generate actionable, policy-relevant decision support from life cycle results. A detailed overview of the review articles analyzed in this study and their contribution to the framework development is provided in Appendix A (

Table A1. Review articles analyzed in detail and their contribution to the present study.

Ref.	Short Citation	Review Type	Main Scope	Main Dimensions Covered	Contribution to This Manuscript (Why Analyzed in Detail)	Main Section(s) Informed
[1]	Gemechu & Kumar (2022)	Narrative review	Use of LCA in hydropower environmental impact assessment	LCA, LCIA, hydropower	Core baseline review for how LCA has been applied to hydropower systems; supports framing of current LCA practices and methodological inconsistencies	Introduction; 3.2.1; 4.1
[3]	Pranoto et al. (2025)	Comprehensive systematic literature review	Hydropower sustainability across environmental, social, economic and technical dimensions	TBL + technical, sustainability assessment	Supports the need for integrated multi-dimensional assessment beyond environmental-only approaches	Introduction; 3.2.2; 4.1
[12]	Quaranta & Davies (2022)	Review	Innovative materials for hydropower engineering (turbines, bearings, seals, dams, waterways)	Materials, eco-design, refurbishment engineering	Supports discussion on material innovation, lightweight composites, durability, and performance implications in refurbishment	Introduction; 3.2.3; 4.2
[25]	Zhang et al. (2021)	Review and modeling paper	Hydropower sustainability assessment methods	Sustainability indicators, assessment models	Used to justify lack of harmonized hydropower sustainability standards and the need for structured frameworks	Introduction; 4.1
[27]	Wulf et al. (2019)	Review	Life-cycle-based sustainability assessment approaches	LCA, LCC, S-LCA, LCSA	Provides methodological grounding for integrated life-cycle sustainability approaches used in the conceptual framework	Introduction; 3.2.1; 3.2.2
[30]	Mwakangale et al. (2025)	Systematic review	Lifecycle-based approaches for hydropower sustainability	LCA/LCC/S-LCA/LCSA in hydropower	Core hydropower-focused lifecycle synthesis; supports state-of-the-art mapping and identification of gaps	Introduction; 3.2.1; 4.1
[31]	Bruno et al. (2025)	Comprehensive overview review	Integrated LCSA approaches (LCA + S-LCA + LCC)	LCSA methodology	Supports the integration logic of environmental-economic-social dimensions in the proposed framework	Introduction; 3.2.1; 3.2.2
[32]	Costa et al. (2019)	Systematic review	LCSA state-of-the-art, challenges, and implementation barriers	LCSA, methodological challenges	Used to substantiate implementation challenges and uncertainty/standardization needs in integrated assessment	Introduction; 3.2.1; 4.1
[33]	Wu (2024)	Review	Circular economy concepts in hydropower generation	CE, sustainable practices, future prospects	Supports CE framing in hydropower and motivates circularity indicators in refurbishment decisions	Introduction; 3.2.3; 4.2
[36]	Campos-Guzman et al. (2019)	Review	LCA + MCDA integration for renewable energy sustainability evaluation	LCA-MCDA, renewable energy	Key methodological reference for combining lifecycle indicators with decision-making/ranking methods	3.2.2; 4.2
[37]	Zanghelini et al. (2017)	Review	MCDA support for LCA results interpretation	LCA interpretation, MCDA methods	Foundational methodological source for explaining why MCDA is needed to interpret multiple LCIA indicators	3.2.1; 3.2.2; 4.1
[43]	Motuzienė et al. (2022)	Review	LCA results across different energy conversion technologies	Comparative energy LCA	Provides comparative context for hydropower within broader energy technologies and supports cross-technology benchmarking discussion	3.2.1; 3.2.2
[46]	Luangphon et al. (2025)	Review	Environmental effects of hydropower plants assessed by LCA	Hydropower LCA, environmental impacts, system boundaries	Core reference for hydropower LCA stages, hotspots, boundary inconsistencies, and limitations	3.2.1; 4.1
[62]	Ubando et al. (2022)	Critical review	Sustainable manufacturability of Archimedes screw turbines	Manufacturing, materials, eco-design, MCDA relevance	Supports component-level sustainable design/refurbishment discussion and material/manufacturing trade-offs	3.2.2; 3.2.3
[64]	Hemmati et al. (2024)	Review	Integrated LCSA methodologies for multiple power generation technologies	LCSA, future energy mix, multi-technology evaluation	Used to position hydropower refurbishment within broader multi-technology sustainability assessment practice	3.2.1; 3.2.2
[68]	Shanbhag et al. (2025)	Review	Predictive maintenance of critical components in hydroelectric turbines	PdM, sensors, digitalization, turbine maintenance	Key support for digitalization, condition-based maintenance, and dynamic inventory updating concepts in DT-DySC-LCA	3.2.1; 4.1; 4.2
[74]	Sebestyén (2021)	Review/synthesis (network-based)	Environmental impact networks of renewable energy power plants	Comparative environmental impacts, renewable systems	Provides comparative context for interpreting hydropower impacts relative to other renewables	3.2.1
[78]	Ferreira & Gonçalves (2021)	Systematic review	Industrial life-extension strategies: remanufacturing and refurbishment	Refurbishment, remanufacturing, life extension, circularity	Important for conceptual transfer of refurbishment/remanufacturing principles to hydropower CE strategies	3.2.2; 3.2.3; 4.1
[92]	Quaranta et al. (2021)	Technology review/perspective review	Environmentally enhanced turbines for hydropower	Fish-friendly turbines, eco-design, ecological mitigation	Supports biodiversity-sensitive refurbishment options and trade-offs between efficiency and ecological performance	3.2.3; 4.2

Note: This table lists review-type scientific articles (including systematic, critical, and methodological reviews) used in the detailed synthesis supporting the proposed DT-DySC-LCA framework. Original case studies, policy documents, standards, and technical reports are excluded.

).

3.2.1. Current LCA Approaches in Hydropower Systems

LCA is a standardized environmental management methodology widely used to identify and quantify the potential environmental impacts of a product, system, or process throughout its entire life cycle, typically from “cradle to grave” [37,38,40,41,42]. Based on life cycle thinking, LCA assesses impact types such as climate change, resource depletion, and toxicity in four mandatory and interrelated phases: goal and scope definition, life cycle inventory analysis, life cycle impact assessment, and interpretation [37,42,43]. The LCI phase forms the analytical backbone of the method, which involves the systematic data collection and modeling of all physical inputs—such as raw materials, water, and energy—and outputs, including emissions to air, soil, and water, as well as waste streams, within the boundaries of the system [37,41,42,44,45].

In hydropower applications, LCA is typically performed using cradle-to-gate or cradle-to-grave system boundaries and covers five main life cycle stages: project preparation, transportation, construction, operation and maintenance (O&M), and decommissioning [42,46]. In the design and planning phase, LCA is increasingly being combined with MCDA to support strategic decisions, such as choosing between reservoir-based and run-of-river systems, identifying sites with the least impact, and balancing environmental performance with technical and economic feasibility [38,47,48]. LCA screening is typically applied to small hydropower to identify environmentally problematic areas at an early stage, particularly those related to raw material extraction and transportation [49,50]. Recent developments, including digital twins (DTs) and inverse design methods, further improve this process by enabling the digital reconstruction and performance optimization of old turbines, even when the original technical documentation is not available [51,52].

During the operational phase, LCA is used to evaluate long-term sustainability indicators such as resource efficiency, energy return on investment, and greenhouse gas emissions. In the case of reservoir-type HPPs, the decomposition of flooded biomass is often identified as the most significant problematic area during operation, leading to methane (CH₄) and carbon dioxide (CO₂) emissions from the reservoir surface [46,53,54]. The integration of Internet of Things (IoT) sensors, machine learning (ML), and real-time monitoring supports predictive maintenance (PdM) strategies that reduce unplanned outages and extend the life of critical components [52,55,56]. For long-life infrastructure, the operational phase can account for up to 90% of total life-cycle energy consumption and emissions, including lubricants, periodic replacement of electromechanical equipment, and on-site electricity consumption [57].

Throughout the entire life cycle, LCA studies consistently identify materials, manufacturing, and construction as the main environmental concerns of hydropower projects. Concrete, aggregates, and steel dominate in terms of embodied energy and carbon footprint [46,49,58,59,60,61], with cement production being particularly carbon-intensive; for example, the construction of a concrete dam can exceed 355 tons of CO₂ equivalent per meter of dam width [44]. Consequently, the construction phase is often the most environmentally impactful stage, characterized by large-scale construction works, fuel consumption by heavy machinery, and raw material extraction [44,46,54]. Manufacturing processes, particularly for turbine components, also contribute significantly to this, as they traditionally rely on extractive and energy-intensive techniques [62].

End-of-life considerations, which have been underrepresented in hydropower LCAs to the present, are increasingly recognized as important for overall sustainability assessments. This phase includes the dismantling of dams, canals, and power plants, as well as site restoration. Although the dismantling of large dams remains technically complex and relatively rare, waste management strategies for dismantled concrete and steel play a critical role in overall environmental performance [46,57,59,63]. Recycling these materials can reduce the carbon footprint of decommissioning by up to 95% compared to landfill, underscoring the importance of circular strategies. LCA is also used to compare refurbishment and demolition scenarios and to identify “hidden” resource recovery opportunities, such as capturing methane gases from reservoirs or recovering waste heat from generators [44].

Complementing the environmental dimension, LCC provides an economic perspective by assessing the total economic expenditure associated with a product or infrastructure over its entire lifetime [38,44,64]. Expressed as the net present value (NPV) of capital investment, operation and maintenance, and end-of-life costs, LCC enables the financial evaluation of the environmental impacts identified by LCA and supports more informed economic decision-making [57,63]. Environmental Product Declarations (EPDs) further operationalize life cycle thinking by transforming LCA results into standardized, third-party verified documents that provide information on the environmental performance of products. EPDs governed by product category rules (PCRs) ensure transparency and comparability by prescribing the reporting of certain intermediate indicators, such as global warming potential, acidification, and eutrophication [37,54]. Furthermore, the integration of environmental LCA, LCC, and S-LCA into the life cycle sustainability assessment (LCSA) framework enables a holistic assessment of sustainability performance by addressing environmental, economic, and social dimensions simultaneously at the sectoral or technological level [32,38,45,64,65].

Regional case studies further demonstrate the applicability and variability of LCA in the world’s hydropower systems (see

Table 1. Summary of LCA-based hydropower sustainability assessments from the reviewed literature.

Country	Assessment Focus	Methodological Approach	Key Findings	Refs.
Spain	Renovation of small hydropower nearing concession end	LCA	Renovation can save up to 175 kg CO2-eq/MWh compared to national electricity mix; significant embodied energy and carbon savings	[57]
Iceland	Environmental performance of hydropower	LCA	Carbon intensity ranges from 0.5–21.1 g CO2-eq/kWh; brownfield expansions show the lowest impacts	[54]
Norway	Ecosystem impacts	LCA + ecological analysis	Hydropower is a major driver of aquatic biodiversity loss and habitat fragmentation	[48]
Switzerland	Renewable electricity portfolios for EV charging	LCA + multi-objective optimization	Hydropower and wind consistently ranked as least harmful to human health and ecosystem quality	[42]
China	Large-scale hydropower facilities	LCA	Average emission factor of ~32.6 g CO2-eq/kWh	[66]
	Small hydropower systems	LCA	Small HPPs show lower resource-time footprints per unit of electricity	[67]
India	Site selection and refurbishment	LCA + Economic, Environmental, and Technical Assessment (SEETA)	Integration of social displacement and land use enables strategies that minimize social disruption while maximizing energy output	[47]
Nepal	Technical degradation and environmental performance	Technical & environmental assessment	Sediment erosion and component degradation affect turbine efficiency, cavitation risk, and long-term sustainability	[68]
Brazil	Tropical hydropower life cycle impacts	LCA	When biogenic emissions are excluded, construction dominates life-cycle impacts	[41]
	Carbon intensity (large-scale plants)	LCA	Long operational lifetimes result in low carbon intensity (4.3–5.0 g CO2-eq/kWh)	[69]
Ecuador	Comparison of plant scales	Comparative LCA	Large storage plants distribute environmental burdens more efficiently than small run-of-river plants	[70]
Ghana	Sustainability trade-offs	Integrated sustainability indices	Significant trade-offs between national energy benefits and local socio-economic impacts	[71]
Egypt & Nigeria	Conversion of existing water infrastructure	LCA	High sustainability potential due to avoided dam construction; steel and concrete dominate life-cycle impacts	[72,73]

).

Overall, LCA is a robust, standardized and transparent framework for identifying environmental issues throughout the entire life cycle of complex hydropower systems and comparing their performance with alternative energy technologies, demonstrating that hydropower is generally two orders of magnitude cleaner than coal-based energy [38,69,73,74]. Nevertheless, there are still important limitations, including inconsistent system boundaries, which can lead to emissions being underestimated by up to 27.5%, significant uncertainty in the flow of biogenic greenhouse gases from reservoirs, and a continuing lack of data for older power plants that do not have detailed technical documentation [46,49,52,54,75,76,77]. Furthermore, although LCA provides objective indicators, the interpretation and weighting of results often lead to subjectivity and methodological disagreements among stakeholders [37,49].

From a CE perspective, the application of LCA to hydropower refurbishment must explicitly consider how life extension strategies, material recycling, and end-of-life recovery modify system boundaries and inventory flows. In refurbishment scenarios, the environmental impact of manufacturing new materials can be partially avoided by recycling and reprocessing existing components such as turbine wheels, guide vanes, and structural steel elements [2,46,51,78]. In LCA modeling, this translates into a reduction in virgin material use and changes in end-of-life assumptions in the LCI, particularly regarding recycling rates and material substitution effects. Waste management strategies for demolished concrete and steel have a similar impact on overall environmental performance; when recycling is modeled using a substitution or avoided load approach, the recovery of high-value materials can offset the impact of primary production and reduce the total life cycle carbon footprint [44,46,57]. By incorporating these circular mechanisms into LCI modeling, LCA becomes not only a descriptive environmental accounting tool but also a quantitative tool that assesses the true environmental benefits of hydropower modernization beyond simple efficiency improvements. Despite these challenges, LCA serves as a comprehensive environmental record for hydropower infrastructure, capturing impacts from initial construction through decades of operation to final decommissioning or material recovery, and is thus an essential tool for sustainable hydropower planning, renovation, and policymaking [37,42].

3.2.2. Multi-Criteria Frameworks for Sustainable Refurbishment

The evaluation of sustainable refurbishment and design options for complex energy systems requires decision-making frameworks that can simultaneously process quantitative performance data and qualitative stakeholder preferences [52]. LCA forms the analytical backbone of such assessments, as it systematically quantifies the environmental impacts of a product or infrastructure system throughout its life cycle. However, its results typically consist of multiple, often conflicting indicators—such as trade-offs between global warming potential, ecotoxicity, or resource depletion—that are difficult to interpret on their own [37]. To address this limitation, MCDA is increasingly being integrated into LCA (or its broader extension, LCSA) to provide a structured mathematical approach to aggregating life cycle results and ranking alternatives based on priority weights that reflect stakeholder values. This methodological synergy allows for the simultaneous assessment of environmental, technical, economic, and social dimensions, thereby supporting more transparent and robust sustainability decisions [37,38,39,79].

In integrated LCA–MCDA frameworks, MCDA tools are used in several stages of the assessment. When defining the objective and scope, MCDA helps to identify relevant impact types that correspond to the priorities of stakeholders; in the life cycle inventory (LCI) phase, it helps to interpret trade-offs between material, energy, and emission flows; and in life cycle impact assessment (LCIA), MCDA enables the weighting and aggregation of environmental indicators into composite sustainability indices [37]. Commonly used techniques include the analytical hierarchy process (AHP), which determines weights based on pairwise comparisons; TOPSIS, which ranks alternatives based on their proximity to the ideal solution; PROMETHEE and ELECTRE, which use ranking relationships to filter out weaker options [39,47,64,72]; and multi-attribute valuation theory (MAVT), which is often used in energy policy, where trade-offs between criteria are acceptable—for example, higher capital costs in exchange for lower emissions [38,45]. In refurbishment-oriented assessments, particularly in circular economy strategies, the circular economy indicator (CEI) is often incorporated as a key metric for quantifying material circularity, energy efficiency, and extended operational life [2].

Sustainable refurbishment and design increasingly rely on the triple bottom line (TBL), which combines economic growth, social development and environmental protection. In addition to this, we must also include technical criteria as a fourth pillar so that we can have an integrated framework for the energy infrastructure (

Table 2. Four main criteria integrated in multi-criteria frameworks.

Type of Criteria	Focus	Refs.
Environmental criteria	Commonly include global warming potential, acidification, eutrophication, water consumption and abiotic resource depletion	[37,38,45,80,81,82,83,84,85]
Social criteria	Address employment creation, labor safety, human health impacts, and community acceptance, often supported by composite measures such as the Human Development Index (HDI)	[37,39,45,86]
Economic criteria	Is evaluated using LCC indicators such as capital expenditure (CAPEX), net present value (NPV), levelized cost of electricity (LCOE), and payback periods	[39,45,47,57,80,87]
Technical criteria	Focus on technological maturity, system efficiency, reliability, and lifespan extension, which is frequently identified as the most influential factor in refurbishment decision models	[2,39,47,80]

).

The applicability of integrated LCA–MCDA frameworks is well documented in renewable energy systems. In the field of hydropower planning in India, the SEETA model was used to compare large-scale projects, and Teesta Low Dam IV was identified as the most sustainable solution, as it combined strong technical performance with minimal social impacts, including zero agricultural land acquisition [47]. In Mozambique, a TOPSIS-based framework applied to off-grid electrification showed that the hybrid renewable mini-grid scenario reduced greenhouse gas emissions by 77%, reduced electricity costs by approximately 20%, and improved social outcomes through increased local operation and maintenance employment, compared to diesel-only systems [80]. In Latvia, MCDA was used to evaluate the options for pumped storage hydropower plants from a circular economy perspective, concluding that while Daugavpils PSHP was technologically superior due to its high head, the Plavinas PSHP option offered the most favorable economic performance through the reuse of existing emergency spillways [88]. At the component level, fuzzy logic-based MCDA was recommended for decisions related to turbine manufacturability, such as the selection of materials for Archimedes screw turbines, to achieve a balance between durability, corrosion resistance, density, and mechanical performance [62].

In addition to hydropower, LCA-based MCDA approaches are widely used in the energy sector. In Turkey, integrated sustainability assessments ranked hydropower as the most sustainable electricity option when environmental, economic, and social criteria were weighted equally [38,76,89,90], while studies in Bangladesh showed that solar power dominates when economic and social priorities prevail, but hydropower becomes more advantageous when environmental impacts are emphasized [45]. In Greece, the stakeholder-driven MCDA ranked wind energy highest, although assessments prioritizing social acceptance found solar energy to be the most favorable alternative [64]. Comparative analyses in the wind and solar energy sectors show that, although these technologies are often the best in terms of climate change mitigation, their intensive mineral and metal requirements can have a greater impact on the depletion of abiotic resources [38,45,73,74]. MCDA frameworks are also used in waste-to-energy systems, where ranking methods such as PROMETHEE help to reconcile the differing views of stakeholders on pollution control and resource recovery, as well as in the design of electric vehicle infrastructure, where multi-objective optimization is used to balance system costs with impacts on human health, ecosystems, and resource scarcity [37,43,64]. Proven applications in the automotive and aerospace sectors further demonstrate the potential of integrated LCA-MCDA approaches. In these sectors remanufacturing strategies can reduce environmental impacts by 28–75% compared to original manufacturing, underscoring the sustainability potential of life cycle-oriented recovery strategies, while refurbishment can extend the life of components by 20–94% [78].

Conceptually, LCA–MCDA integration can be understood as a decision-making navigation system: LCA provides comprehensive life cycle data on environmental, economic, and social impacts, while MCDA provides decision-making logic that transforms this information into ranked, policy-relevant options based on clear priorities and trade-offs. This integration is therefore essential for managing sustainable refurbishment, the implementation of CE, and the long-term energy system transition.

3.2.3. Circular Economy Principles in Hydropower Refurbishment

CE represents a paradigm shift from the traditional linear “extract-produce-consume-dispose” model towards a regenerative system that aims to use products, materials, and resources for as long as possible while minimizing waste and environmental impacts. Turbine refurbishment plays a central role in this framework as a key life extension strategy, which is particularly relevant for hydropower systems with long-lived and capital-intensive infrastructure [2,78,88].

Refurbishment differs fundamentally from replacement in that it focuses on the in-depth modification and modernization of existing components, such as impellers, guide vanes, and nozzles, to restore or improve performance and extend operating cycles, often at significantly lower environmental and economic costs than complete equipment replacement [2,62,76,91]. In closely related remanufacturing, existing systems are restored to at least their original functional specifications, usually with a warranty, through the strategic reconditioning, redesigning and reuse of parts combined with new components [51,78].

Material innovation plays a central role in circular refurbishment strategies. Advanced polymers, flexible seals, and composite materials are increasingly being used to reduce corrosion, erosion, and leakage, particularly in turbine shafts and auxiliary components [68]. Composite materials manufactured using techniques such as lightweight resin transfer molding offer corrosion resistance, lower friction losses, and weight reductions of 50–80%, which in turn reduce mechanical loads and hydraulic pressure losses by 4–20%, depending on operating conditions [2,62]. Nanocoatings further increase service intervals by improving resistance to cavitation and wear [2]. For smaller-scale applications, additive manufacturing (3D printing) enables the production of non-traditional blade geometries and controllable stepped components with lower material loss and reduced carbon intensity than traditional precision casting [62].

The relevance of these concepts is particularly striking in the hydropower sector, where turbine refurbishment is a critical strategy for increasing electricity generation without building new dams in freshwater ecosystems [77]. With around 60% of European hydropower plants over 40 years old, refurbishment is essential not only to improve efficiency, but also to prevent catastrophic failures and reduce unplanned outages [35,68]. More broadly, the CE approach to energy systems emphasizes resource efficiency, life extension, and system flexibility, including the use of renewable storage technologies such as Pumped-Storage Hydropower Plants (PSHPs) [88].

The principles of eco-design and circular design are essential for translating these strategies into tangible sustainability outcomes in the field of hydropower development and modernization. Eco-design addresses environmental impacts at the earliest stages of design, enabling more effective mitigation throughout the life cycle [54,59]. Additionally, resource efficiency is particularly important, as the environmental footprint of hydropower projects is primarily determined by the construction phase, especially the use of cement and steel. Consequently, designing structures that minimize material use or incorporate low-carbon alternatives can significantly reduce environmental impacts [46,58,66]. Circular design is also essential for addressing inherent trade-offs, including the additional environmental burdens often associated with advanced storage systems and high-performance turbine technologies [59]. For example, environmentally friendly turbines, including fish-friendly or self-lubricating designs, may slightly reduce initial efficiency but significantly reduce ecological damage to aquatic life and improve water quality [2,64,92]. Digitalization further reinforces circular strategies by integrating DTs and IoT sensors into hydropower systems, enabling PdM, real-time operational optimization, and extended component life while reducing unexpected outages [52,93]. Finally, end-of-life planning is an essential element of circular design, ensuring that hydropower infrastructure is designed to be dismantled, retrofitted, or suitable for material recovery, thereby enabling the reuse or remanufacturing of high-value materials, such as reinforced concrete or stone aggregates [2,59].

Overall, integrating circular economy principles and eco-design strategies provides a solid and comprehensive framework for promoting the sustainability of hydropower refurbishment. By combining environmental, economic, and social considerations with practical refurbishment and design solutions, this integrated approach supports informed decision-making and enables the transition to more flexible, resource-efficient, and environmentally friendly hydropower systems.

4. Discussion

4.1. Gaps and Limitations

Hydropower refurbishment has several distinctive characteristics compared to other infrastructure systems, such as wind energy facilities or transport infrastructure, which have important implications for LCA. Hydropower plants are long-lived, capital-intensive assets, with architectural structures often designed for a lifetime of 80–100 years. Refurbishment therefore generally focuses on electromechanical components, such as turbines, generators, and control systems, while the primary concrete and hydraulic infrastructure remains in place. In contrast, wind energy systems often undergo complete power upgrades or significant component replacements over shorter lifetimes (20–40 years). In terms of materials, reinforced concrete and structural steel dominate in hydropower systems, which largely determine the life-cycle impacts. This contrasts with wind turbines, where composite materials and rare earth metals pose significant end-of-life challenges [38,45].

Since hydropower refurbishment primarily extends the lifetime without replacing the entire system, LCA modeling must address the distribution of existing embedded impacts, dynamic efficiency degradation, and lifetime extension within functional units [2,51,78]. These characteristics methodologically distinguish the LCA of hydropower refurbishment from other infrastructure assessments and justify the development of sector-specific, dynamic frameworks that are tailored to their long service life, material requirements, and ecological interactions.

Despite the growing body of literature on hydropower sustainability, this study identifies several critical research gaps (

Table 3. Gaps and limitations of creating a multi-criteria LCA for hydropower refurbishment.

Gaps	Limitations	Refs.
Technological underrepresentation in hydropower lifecycle data	A strong bias towards large-scale projects limits the availability of data on the environmental performance of small-scale facilities.	[46,53,54]
Lack of detailed technical documentation for old hydroelectric power plants.	Many of the power plants built in the mid-20th century do not have complete documentation, which poses a significant obstacle to performance optimization.	[51,52,55,56]
Empirical validation of integrated LCA-MCDA frameworks is limited.	Many proposed frameworks remain theoretical and have not been validated based on real-world refurbishment case studies.	[37,39]
Refurbishment-specific LCA methodologies remain underdeveloped	Most existing LCAs use static system boundaries that do not adequately reflect the dynamic nature of refurbishment, deterioration, and life extension.	[42,46,76]
Constrained system boundaries in hydropower LCA	Existing hydropower LCAs often use inconsistent or narrowly defined system boundaries, which can lead to incomplete representation of upstream material flows and circular processes related to refurbishment.	[46]
Social dimension of hydropower refurbishment is weakly represented	They primarily focus on economic and technical criteria.	[2,42,47]
Digitalization remains insufficiently embedded in LCI and LCA	Although DT, IoT technologies, and PdM systems are increasingly being applied in the hydropower industry, their potential for improving LCI modeling and LCA is rarely exploited.	[37,52,62,68]

) that limit the effective application of multi-criteria life cycle frameworks in refurbishment decision-making.

Finally, the review underscores the growing alignment between scientific developments and policy objectives. At the political level, the European Green Deal, the EU Taxonomy and the CEAP provide a coherent and supportive regulatory framework for sustainable hydropower refurbishment in Europe. In addition, targeted funding mechanisms under Horizon Europe, such as the REVHYDRO project [94], actively support innovative refurbishment technologies, digitalization, and circularity indicators. Aligning future methodological developments in multi-criteria LCA frameworks with these policy instruments will be essential to ensure their practical application and effectiveness as decision-support tools for hydropower modernization.

These identified gaps directly motivate the development of a dynamic, digitally enabled, and circular life-cycle framework for hydropower refurbishment, which is introduced in Section 4.2.

4.2. Proposed Novel LCA Framework for Sustainable Hydropower Refurbishment Design

a.

Rationale and Novelty

The results of the literature review show that sustainable hydropower refurbishment cannot be adequately addressed by single-indicator assessments or purely technical optimization approaches. Rather, renovation decision-making requires an integrated framework that can simultaneously consider the environmental, economic, technical, and social dimensions of the entire life cycle of hydropower facilities. To this end, the study proposes a conceptual, multi-criteria, life-cycle-based framework that integrates the principles of LCA, MCDA, and circular economy into a unified decision-support structure specifically tailored to the refurbishment and redesign of hydropower plants.

The framework begins with problem definition and boundary setting, where the objectives of refurbishment are clearly defined, including extending the operating life of HPPs, reducing the use of new materials, increasing recycling of materials, mitigating environmental impacts, and complying with regulatory requirements [57,62,76,77,88,95]. The boundaries of the system are defined in relation to the baseline for refurbishment, specifically comparing refurbishment alternatives with replacement or decommissioning.

The second stage involves the development of a refurbishment-specific LCI. As detailed historical documentation is often lacking for aging HPPs, this phase increasingly relies on digital reconstruction methods, including DTs, condition monitoring systems, ML, IoT and PdM data [51,52,55,64,68,93]. The inventory explicitly considers material reuse, remanufacturing, extended maintenance intervals, and alternative end-of-life solutions, reflecting the dynamic nature of refurbishment strategies. This is followed by an assessment of life cycle impact and costs. Environmental impacts are quantified using LCA indicators such as climate change potential, resource depletion, water consumption, and ecosystem impacts [37,38,45,80,81,82,83,84,85]. Economic performance is assessed using LCC, which includes investment costs, operating and maintenance costs, lifetime extension benefits, and avoided replacement costs [39,45,47,57,80,87]. Where data allow, S-LCA is used to assess impacts related to worker safety, local employment, and community acceptance.

In addition, CEIs and technical performance indicators are being introduced in the framework. Circularity is quantified using indicators such as material recycling rates, extended component life, recyclability, and resource efficiency [2,51,62,68,78]. Technical indicators include system reliability, maintained efficiency even with flexible operating systems, and increased resistance to cycles and load changes [2,39,47,80]. Together, these indicators ensure that refurbishment solutions are evaluated not only on their environmental performance but also on their long-term operational viability.

Despite the rapid growth of life-cycle–based studies in the hydropower sector, refurbishment decision support remains constrained by (i) static system boundaries that do not represent component deterioration, flexible operation, and maintenance/refurbishment events; (ii) incomplete technical documentation for legacy assets, which limits the quality of LCI; and (iii) weak integration of site-specific ecological sensitivities and circular economy mechanisms into the modeling logic. To address these limitations, we propose a Digital-Twin enabled Dynamic, Spatiotemporal and Circular LCA framework (DT-DySC-LCA) specifically tailored for hydropower refurbishment design (Figure 5).

The proposed framework extends conventional LCA-MCDA practice by introducing three original contributions:

(i)

Dynamic LCI updating through digital-twin and IoT/SCADA data streams, allowing the inventory to reflect measured operational states, degradation patterns, and refurbishment events;

(ii)

A spatiotemporal impact layer that links hydropower operation (e.g., hydropeaking, ramping, seasonal constraints) to location-dependent ecological stressors, supporting compliance-oriented assessments (e.g., WFD-aligned decision contexts); and

(iii)

Circularity by design embedded in the LCI through parametric representation of reuse, remanufacturing, recyclability, and design for disassembly (DfD), enabling transparent quantification of circular strategies and their influence on life-cycle impacts.

In addition, DT-DySC-LCA includes a structured uncertainty and value of information (VoI) module to prioritize data acquisition in legacy hydropower plants where documentation gaps and measurement limitations are common.

b.

Framework Overview and Modules

DT-DySC-LCA is structured into six interoperable modules (

Table 4. Refurbishment scenarios evaluated in DT-DySC-LCA (example set).

Scenario ID	Description	Digitalization	Eco-Design/Circularity	Expected Effect on Lifecycle
S0	Minimal intervention/run to failure baseline	None	Low	Higher unplanned repairs, higher downtime impacts
S1	Conventional refurbishment (major at fixed interval)	Low	Medium	Efficiency recovery, reduced failures vs. S0
S2	Eco-design refurbishment (DfD + material optimization)	Low	High (DfD, higher reuse/reman)	Lower virgin materials, improved EoL recovery
S3	PdM-enabled refurbishment (IoT/ML, condition-based)	High	Medium	Fewer premature replacements, fewer outages
S4	Integrated circular-digital refurbishment (Eco + PdM)	High	High	Lowest material intensity, optimized intervention timing
S5	Biodiversity enhanced option (fish-friendly + operation constraints)	Medium	Medium–High	Lower ecological risk, potential trade-off in CAPEX/η

), designed to be implemented as a decision-support workflow compatible with ISO 14040/14044 principles while enabling refurbishment-specific dynamics. The framework integrates environmental LCA, LCC, optional S-LCA, and MCDA, with circularity indicators acting as model parameters rather than standalone metrics.

• Module A. Goal, scope, and refurbishment baselines

The assessment begins by defining refurbishment objectives (e.g., lifetime extension, efficiency recovery, reduction in virgin materials, biodiversity risk mitigation, DNSH compliance) and selecting system boundaries. Unlike conventional “cradle to grave” static models, DT-DySC-LCA formalizes a refurbishment baseline by comparing alternatives against a reference case of continued operation with minimal intervention (or run to failure), and against replacement or decommissioning when relevant.

A functional unit (FU) is defined to preserve comparability while reflecting real refurbishment outcomes. A recommended FU for refurbishment decisions is:

“1 MWh net electricity delivered to the grid over the residual lifetime under a specified flexibility requirement (e.g., number of starts, ramping rate, hydropeaking constraints).”

This FU allows explicit representation of operational flexibility, which is increasingly relevant in modern power systems and can significantly alter maintenance burdens and inventory flows.

• Module B. Dynamic system representation and state variables

The core innovation is the representation of hydropower refurbishment as a time-dependent system. The system is discretized into time steps (e.g., monthly or annual) over a residual lifetime horizon (e.g., 20–40 years). Each time step is characterized by state variables and event triggers that influence the LCI:

• Component health index, H(t) (e.g., runner, guide vanes, bearings, seals);
• Efficiency trajectory, η(t) and performance losses due to wear;
• Operational profile, F(t) (load factor, starts, ramping, hydropeaking frequency);
• Auxiliary energy use, E_aux(t) (cooling, lubrication, pumps);
• Consumables and fluids, L(t) (lubricants, seals, coatings);
• Maintenance/refurbishment event indicator, M(t) (none/minor/medium/major).

Hydropower refurbishment is modeled over a discrete time horizon:

$$t\in \{\mathrm{0,1},2,\dots ,T\}$$

(1)

where T represents the residual lifetime of the hydropower plant (years).

The gross electricity required to deliver one functional unit is adjusted by time-dependent efficiency:

$$E_{\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{s}\mathrm{s}}(t)={\displaystyle \frac{1}{\eta (t)}}$$

(2)

where η(t) is the net turbine–generator efficiency at time t.

Each critical component is characterized by a normalized health index:

$$0\le H\left(t\right)\le 1$$

(3)

where H(t) = 1 denotes pristine condition and H(t) = 0 denotes functional failure.

A generic deterioration function links operational stressors to component condition:

$${\displaystyle \frac{dH(t)}{dt}}=f(S(t),R(t),Se(t),C(t),V(t))$$

(4)

where:

• S(t) = start–stop cycles,
• R(t) = ramping intensity,
• Se(t) = sediment load,
• C(t) = cavitation risk,
• V(t) = vibration intensity.

An intervention is triggered when the health index reaches a threshold:

$$M(t)=\{\begin{array}{cc}1,& H(t)\le H^{\ast }\\ 0,& H(t)>H^{\ast }\end{array}$$

(5)

where H* is the intervention threshold.

Efficiency loss accumulates over time as degradation progresses:

$$\eta (t)={\eta }_0-{\int }_0^{t}g({\displaystyle \frac{dH\left(\tau \right)}{d\tau }})$$

(6)

where:

• ${\eta }_0$ is the nominal efficiency at commissioning or immediately after major refurbishment,
• η(t) is the net turbine–generator efficiency at time t;
• H(τ) is the normalized component health index (H = 1 pristine condition, H = 0 failure);
• ${\displaystyle \frac{dH\left(\tau \right)}{d\tau }}$ is the rate of component degradation over time;
• g is a degradation to efficiency coupling function, translating health deterioration into efficiency losses;
• τ is the integration time variable;
• t is the elapsed operational time since the reference state.

Following a refurbishment event at time:

$$\eta \left(t_r^{+}\right)=\eta \left(t_r^{-}\right)+\Delta {\eta }_{\mathrm{r}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{r}\mathrm{b}}$$

(7)

where:

• Δη_refurb represents efficiency recovery due to refurbishment,
• η₀ is the nominal efficiency;
• t⁻_r and t⁺_r denote the instants immediately before and after refurbishment.

Auxiliary electricity consumption depends on operation and component condition:

$$E_{\mathrm{a}\mathrm{u}\mathrm{x}}\left(t\right)=h\left(F\right(t),H(t\left)\right)$$

(8)

where F(t) represents the operational profile.

Lubricants and consumables increase with degradation and interventions:

$$L\left(t\right)=k\left(M\right(t),H(t\left)\right)$$

(9)

This formulation is intentionally modular; project teams can use empirical rules, physics-informed models, or data-driven (ML) predictors depending on data availability. The key requirement is that deterioration affects both energy performance and intervention frequency, thereby altering inventory flows.

• Module C. Digital-twin and measured-inventory LCI engine

To overcome the lack of legacy documentation, DT-DySC-LCA relies on a measured-inventory approach, digital-twin reconstruction and IoT/SCADA measurements (e.g., vibration, temperature, oil analysis, pressure, flow, start/stop logs) are used to populate or validate LCI parameters. Data sources may include:

• SCADA and operational logs (starts, load, ramping, flow regimes);
• Condition monitoring systems (vibration spectra, bearing temperatures, partial discharge);
• Maintenance management systems (MMS) (work orders, component replacements, downtime);
• Digital reconstruction for geometry inference and performance mapping;
• Procurement records for materials and spare parts (mass, alloys, coatings).

The LCI is updated at each time step:

$$LCI\left(t\right)=LC{I}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}+\Delta LC{I}_{\mathrm{o}\mathrm{p}}\left(t\right)+\Delta LC{I}_{\mathrm{m}\mathrm{a}\mathrm{i}\mathrm{n}}\left(t\right)+\Delta LC{I}_{\mathrm{r}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{r}\mathrm{b}}\left(t\right)$$

(10)

where:

• LCI_base is the baseline inventory associated with the initial system configuration,
• ΔLCI_op(t) represents operation-related flows (e.g., auxiliary electricity, consumables, losses),
• ΔLCI_maint(t) accounts for maintenance-related flows (e.g., spare parts, transport, workshop processes),
• ΔLCI_refurb(t) captures refurbishment or major overhaul interventions occurring at time t.

This structure enables explicit modeling of how predictive maintenance (PdM) strategies reduce unplanned interventions, improve availability, and shift environmental burdens from reactive replacement to planned refurbishment.

• Module D. Embedded circularity by design parameterization

Circular economy strategies are embedded directly in the inventory via parameters that modify material flows and end-of-life (EoL) treatments:

• Reuse rate (r_u): fraction of components reused without reprocessing;
• Remanufacturing rate (r_rm): fraction restored to specification through reconditioning;
• Recycling rate (r_rc): fraction recycled into secondary materials;
• Design for disassembly score (DfD): influences dismantling energy, labor, and achievable r_u/r_m/r_rc;
• Material substitution factor (s_m): accounts for alternative materials/coatings influencing lifespan and replacement frequency.

Material demand at time t is defined as:

$$m_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{d}}\left(t\right)=m_{\mathrm{v}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}}\left(t\right)+m_{\mathrm{s}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{y}}\left(t\right)+m_{\mathrm{r}\mathrm{e}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}}\left(t\right)+m_{\mathrm{r}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}}\left(t\right)$$

(11)

Virgin material requirements are reduced by circular strategies:

$$m_{\mathrm{v}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}}\left(t\right)=m_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{d}}\left(t\right)·(1-r_u-r_{rm})·(1-{\alpha }_{\mathrm{s}\mathrm{e}\mathrm{c}})$$

(12)

where:

• m_demand(t) is the total material demand at time t,
• r_u = reuse rate,
• r_rm = remanufacturing rate.

Secondary (recycled) material demand

$$m_{\mathrm{s}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{y}}\left(t\right)=m_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{d}}\left(t\right)·r_{rc}·{\alpha }_{\mathrm{s}\mathrm{e}\mathrm{c}}$$

(13)

where:

• r_rc = recycling rate,
• α_sec = effective substitution ratio of secondary materials and can be aligned with the selected allocation approach (e.g., substitution or cut-off), which must be transparently reported.

By treating CE parameters as inventory modifiers, DT-DySC-LCA quantifies the environmental benefit of circularity through reduced virgin material demand, changed refurbishment frequency, and improved EoL recovery.

$$C{E}_{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}}={\displaystyle \frac{{\sum }_t\left(m_{\mathrm{s}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{y}}\right(t)+m_{\mathrm{r}\mathrm{e}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}}(t)+m_{\mathrm{r}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}}(t\left)\right)}{{\sum }_t{m}_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{d}}\left(t\right)}}$$

(14)

• Module E. Spatiotemporal ecological layer for water and biodiversity

Hydropower sustainability is strongly site-dependent. DT-DySC-LCA therefore includes a spatiotemporal layer that characterizes local ecological sensitivity and links it to operational profiles. Spatial attributes may include:

• river typology/ecoregion, habitat connectivity, protected areas;
• WFD status, environmental flow requirements;
• fish migration sensitivity (seasonality), sediment regime, temperature vulnerability.

Ecological pressure proxy (spatiotemporal layer):

$$P_{\mathrm{e}\mathrm{c}\mathrm{o}}\left(t\right)=\left[{\beta }_{1}R\right(t)+{\beta }_{2}S(t\left)\right]·S_{\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{l}}$$

(15)

where

• P_eco(t) is the ecological pressure indicator;
• R(t) is ramping intensity;
• S(t) is start–stop frequency;
• S_ecol is the site-specific ecological sensitivity factor;
• β₁,β₂ are weighting coefficients.

Dynamic life cycle cost formulation

$$LCC=\sum _{t=0}^T{\displaystyle \frac{C_{\mathrm{o}\mathrm{p}}\left(t\right)+C_{\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}}\left(t\right)+C_{\mathrm{r}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{r}\mathrm{b}}\left(t\right)+C_{\mathrm{d}\mathrm{t}}\left(t\right)}{(1+i{)}^t}}$$

(16)

where i is the discount rate.

• where
• C_op(t) are operating costs,
• C_maint(t) maintenance costs,
• C_refurb(t) refurbishment costs,
• C_dt(t) downtime costs,
• i is the discount rate.

Operational attributes such as hydropeaking intensity, ramping rate, and seasonal operating constraints are mapped to ecological pressure proxies. The framework does not replace detailed ecological modeling; instead, it introduces a structured, transparent approach to incorporate location-dependent ecological risk into multi-criteria evaluation and to enable policy-aligned comparison of refurbishment alternatives (e.g., “fish-friendly runner” vs. conventional design, changes in hydropeaking control strategies).

• Module F. Uncertainty quantification and Value-of-Information (VoI)

Given that refurbishment LCAs often face incomplete data, DT-DySC-LCA includes uncertainty characterization (parameter ranges, probability distributions) for key variables such as component masses, deterioration rates, biogenic emissions (for reservoirs where relevant), recycling yields, and downtime impacts. In addition, a VoI component supports data prioritization by identifying which measurements (e.g., vibration sensors, oil particle monitoring, flow instrumentation) most reduce decision uncertainty, particularly when MCDA rankings are sensitive to a small set of uncertain parameters. This module is particularly suited for legacy assets where monitoring investments must be justified through measurable sustainability and economic benefits.

c.

Scenario Definition and Decision Outputs

DT-DySC-LCA evaluates a set of refurbishment alternatives (Table 4) spanning conventional refurbishment, eco-design variants, and digitally enabled PdM strategies. At each time step, the framework produces:

• Environmental impacts (LCIA indicators) aggregated over the residual lifetime and optionally disaggregated over time;
• Economic results via LCC (NPV, CAPEX/OPEX, avoided replacement costs, downtime costs);
• Optional social metrics (e.g., worker safety proxies, local employment effects, acceptance indicators);
• Technical performance indicators (availability, efficiency recovery, expected life extension);
• Circularity outcomes (mass loop closure, reuse/reman/recycle fractions, DfD-enabled recovery).

Results are subsequently integrated through MCDA (Table 4) to rank alternatives under stakeholder-defined weighting schemes. Importantly, DT-DySC-LCA supports robust decision insights by combining (i) dynamic impact profiles (showing when impacts occur and which events cause them), (ii) hotspot identification (materials, maintenance events, auxiliary energy), and (iii) sensitivity and uncertainty analysis (highlighting parameters that change rankings). This combination enables refurbishment decisions that are not only “environmentally preferable” on average but also resilient to uncertainty, aligned with regulatory constraints, and operationally feasible.

d.

Practical Implementation Pathway

For practical adoption in real refurbishment projects, DT-DySC-LCA can be implemented in three incremental levels:

Level 1 (MVP): annual time steps; a limited set of critical components (e.g., runner, bearings, seals); three intervention classes (minor/medium/major); circularity parameters for metals and consumables; simplified ecological sensitivity scoring.

Level 2 (Enhanced): monthly steps; inclusion of hydropeaking and seasonal constraints; DT-assisted geometry reconstruction; expanded EoL scenarios with DfD impacts.

Level 3 (Advanced): near-real-time inventory updating; probabilistic deterioration models; VoI-driven monitoring design; integration with plant-level DT dashboards and automated reporting of refurbishment sustainability KPIs.

This staged approach ensures that the framework is both scientifically rigorous and implementable in refurbishment settings with heterogeneous data availability.

5. Conclusions

This review critically examined the current state of sustainable hydropower refurbishment from the perspective of LCA and MCDA. The analysis confirms that the refurbishment and modernization of existing hydropower plants is one of the most effective strategies for maintaining renewable electricity generation while avoiding the significant environmental and social burdens associated with the development of new hydropower plants. Given the long lifetime, capital intensity, and site-specific constraints of hydropower infrastructure, refurbishment decisions inherently involve complex trade-offs that cannot be adequately captured by a single indicator or purely technical optimization approaches.

The reviewed scientific literature shows that LCA provides a rigorous and standardized approach to quantifying environmental impacts across all stages of the life cycle, but it is not sufficient on its own to support refurbishment planning decisions. Integrating LCA with LCC, S-LCA, and MCDA allows for the structured aggregation of diverse indicators and the explicit incorporation of stakeholder preferences. This integrated LCA-MCDA approach provides a robust decision support framework for evaluating refurbishment alternatives in terms of environmental performance, economic feasibility, social acceptability, and technical reliability.

Across diverse geographical locations, construction and in particular the use of concrete and steel, consistently dominate the environmental impacts of the life cycle. Consequently, refurbishment strategies that emphasize the reuse, remanufacturing, geometric adaptation, and life extension of components result in significant reductions in embodied emissions and material requirements compared to replacement or new construction solutions. The integration of circular economy principles further improves the sustainability performance by promoting resource efficiency, durability design, and end-of-life material recovery, thereby aligning hydropower refurbishment with broader circular and climate policy goals.

Future research should prioritize the development of harmonized LCI tailored to refurbishment, the standardization of CEIs applicable to hydropower systems, and the integration of governance and social dimensions into multi-criteria sustainability frameworks. Strengthening the link between methodological developments and regulatory instruments, such as the EU Green Deal and CEAP, will be essential for transforming integrated life-cycle frameworks into operational decision-making tools. Overall, multi-criteria life-cycle assessment frameworks provide a scientifically robust and policy-relevant basis for steering sustainable hydropower refurbishment design and ensuring the long-term resilience of renewable energy systems.