
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12015-4
10.1016/j.heliyon.2024.e35984
e35984
Research Article
A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach
Jiménez-Gómez L.M. lumjimenezgo@unal.edu.co
⁎
Velásquez-Henao J.D. jdvelasq@unal.edu.co

Facultad de Minas, Universidad Nacional de Colombia, A.A. 1027, Medellín, Colombia
⁎ Corresponding author. lumjimenezgo@unal.edu.co
10 8 2024
30 8 2024
10 8 2024
10 16 e3598431 7 2024
7 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008–2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014–2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

Highlights

• Applied Louvain algorithm to identify five key clusters in real options literature on solar PV generation.

• Real Options Analysis addresses PV investment uncertainties, enhancing flexibility in decision-making.

• Energy policy, technology, costs, and subsidies shape the adoption of renewable energy technologies.

• Monte Carlo methods assess investment timing, market fluctuations, and policy impacts in renewables.

• Integrating PV with smart grids enhances investment value, optimizing subsidies, and reducing inefficiencies.

Keywords

Real options
Photovoltaic power generation
Tech-mining
Uncertainty analysis
Investment decisions
Renewable energy
==== Body
pmcList of abbreviations

PV - Photovoltaic

NPV - Net Present Value

ROA - Real Options Approach

DCF - Discounted Cash Flow

RO - Real Option

RES - Renewable Energy Sources

R&D - Research and Development

FIT - Feed-in Tariff

RQs - Research Questions

PRISMA - Preferred Reporting Items for Systematic Reviews and Meta-Analyses

CSV - Comma-Separated Values

OCC - Occurrences

GCS - Global Citations Score

LCS - Local Citations Score

H-Index - Hirsch Index

G-Index - Egghe's G-Index

M-Index - Individual Hirsch's M-Index

NLP - Natural Language Processing

CCS - Carbon Capture and Storage

SMEs - Small and Medium-sized Enterprises

1 Introduction

Solar photovoltaic (PV) projects, ranging from residential installations to large-scale energy farms, are critical in the global quest for clean and sustainable energy solutions. Yet, they encounter challenges related to investment uncertainties and deficiencies in traditional evaluation methodologies. These challenges are primarily fueled by the urgent need to combat climate change and transition towards a more sustainable, low-carbon future [[1], [2], [3]]. As the energy sector confronts the dual issues of climate change and peak oil, renewable energy stands out as a critical solution, boasting the capability to provide electricity without carbon emissions [1]. However, formidable obstacles such as high initial investment costs, comparatively low returns, and uncertainties about future market trends and technological developments pose significant complications for decision-making processes in renewable energy ventures, including solar PV projects [1,2]. Traditional evaluation methodologies, like the net present value (NPV) criterion, often fail to fully appreciate the strategic aspects of these investments, typically overlooking the inherent risks and uncertainties tied to future benefits [1]. This gap in traditional evaluation is where the Real Options Approach (ROA) comes into play. ROA offers a nuanced perspective, considering firms as investors holding financial options, which affords them the flexibility to make dynamic decisions in uncertain market conditions and evolving technology landscapes [1]. ROA is an invaluable asset, particularly for solar PV projects, where initial investments are non-recoverable, and each decision has a significant impact akin to exercising financial options. It provides strategic flexibility and facilitates informed decision-making, optimizing benefits and minimizing risks [1,2]. The growing incorporation of ROA in the evaluation of PV projects marks a significant shift in perspective, acknowledging the uncertainties inherent in such projects and their capacity for continual adaptation to fluctuating market conditions—elements that traditional evaluation methods often overlook [2].

Real options introduce an innovative perspective on how uncertainty affects the valuation of project investments, effectively addressing the limitations of Discounted Cash Flow (DCF) valuation by contemplating multiple decision-making pathways. This model empowers managers to choose the most beneficial strategies as uncertainties gradually resolve [4]. As new information emerges, managers can exercise real options to adapt and modify their decisions, capitalizing on unexpected developments for the firm's advantage or minimizing losses [4]. This adaptability and strategic foresight render real options particularly apt for assessing utility-scale solar investments, especially considering the scalability of solar projects and the inherent uncertainties related to electricity pricing and policy environments [4]. In financial terms, an option is the right, but not the obligation, to engage in a transaction involving products at a predetermined time and price. Extending this principle to real assets leads to the concept of a Real Option (RO), which can be understood as the right, without the obligation, to make decisions concerning tangible assets [4]. Such choices may include deferring development, constructing prototypes, abandoning projects, adjusting the scale of operations, or switching technologies [5]. This level of managerial flexibility, afforded by real options, significantly elevates the value of projects, offering a strategic advantage in the complex landscape of solar PV project investment [6].

This research meticulously identified nine literature reviews on ROA applications in solar PV projects. The analysis of these reviews reveals that while they collectively contribute valuable insights into the application of the ROA in PV projects and other renewable energy sectors, they do not comprehensively address the following topics: first, the current research landscape lacks a comprehensive thematic analysis across influential literature concerning the ROA. Individual studies concentrate on particular facets or applications of ROA, such as policy evaluation, financial metrics, or specific market conditions. Still, they must offer a broad overview of the predominant thematic domains. Moreover, although these papers address various topics, there needs to be more synthesis that categorizes these topics into well-defined, predominant thematic domains. This deficiency hinders a clearer understanding of the field's most impactful areas of study.

Second, the research on the evolution of significant themes in the ROA applied to PV energy reveals notable gaps. Most studies provide insights confined to specific, static time frames, neglecting to explore how these insights have evolved historically or how past contexts have shaped current research trends. Additionally, there needs to be more longitudinal analysis in the literature. Only some studies track the development or transformation of themes over multiple years, which is essential for understanding how significant themes in this field have evolved. This lack of comprehensive temporal analysis limits the depth of understanding regarding the progression and shifts in thematic focus within the domain.

Third, in the ROA applied to solar PV projects, there is a significant oversight in identifying emerging themes. The majority of documents concentrate on the current methodologies, applications, and outcomes of ROA without focusing on highlighting or predicting emerging trends and future directions in the field. This lack of prospective insights prevents the identification of developing areas of research or emerging themes that are likely to influence the future trajectory and development of ROA in solar PV projects. Therefore, it is crucial to emphasize the importance of identifying and predicting emerging themes in ROA research.

This research aims to close the previous gaps by answering the following research questions:RQ1 ) What are the predominant thematic domains in the most influential literature?

This question aims to identify core themes and consolidate scattered insights across various studies. The literature spans multiple aspects of real options, from policy impacts to technological evolutions. Understanding these predominant themes will provide a structured framework for current and future research, helping stakeholders navigate the complex dynamics of solar PV investments.

RQ2 ) How have the significant themes within the context of Real Options in photovoltaic (PV) energy evolved?

This inquiry is essential as it traces the trajectory of scholarly discussions and practical applications over time. By examining how themes have evolved, particularly in the wake of rapidly changing market conditions and technological advancements, this research can highlight shifts in strategic priorities and methodological approaches within the sector.

RQ3 ) Which research themes currently emerge in Real Options in PV?

Identifying emerging themes is crucial for anticipating future directions and innovation. As the energy sector adapts to global pressures such as climate change and technological disruption, new themes in accurate options analysis will likely surface, offering fresh perspectives and solutions to ongoing challenges.

Therefore, the main objective of this article is to improve the academic and practical understanding and application of the ROA in solar PV projects. Our study advances this field by adopting a systematic, cluster-based methodology to map out the thematic landscape of this research area. This approach deviates from the narrower focus of existing reviews, offering a comprehensive and unified analysis that categorizes the literature into distinct, well-defined thematic clusters. This refined methodology affords a more focused and in-depth examination of specific aspects of ROA application in solar PV projects. Furthermore, this study illuminates the evolution of the subject matter over a decade through a detailed biennial analysis, identifying thematic clusters within the existing literature. This approach is particularly notable for unveiling emergent trends and challenges in applying ROA to solar PV energy projects, significantly contributing to the field's evolution and comprehension.

Our research method involves meticulously analyzing document descriptors to answer the proposed research questions. We derived these descriptors by combining author and index keywords with noun phrases extracted from the articles' titles and abstracts. This strategy enables an analysis centered on text strings, accurately reflecting the critical concepts in the literature. Importantly, our examination fills a crucial methodological void, as the scrutinized works need a structured approach for determining primary themes. This article employs bibliometric techniques and technology mining methodologies to address these research questions. For RQ1, the analysis involves using the Louvain community detection algorithm on document descriptors, ensuring these descriptors are refined and standardized by removing any that are excessively broad or generic. The documents within each identified cluster are then analyzed. To address RQ2, the same descriptors are used to create thematic clusters for each two years covering the last ten years. This involves examining the biennial variations in these clusters and their constituent descriptors. For RQ3, the concept of emergence, originating from Innovation in Science and Technology, is utilized to pinpoint “hot” descriptors that have recently attracted considerable attention in the research community. These descriptors are grouped using the Louvain algorithm to ascertain today's most pertinent research topics.

The remainder of this work is as follows: Section 2 presents a literature review. Section 3 describes the methodology employed. Section 4 showcases the results obtained. Section 5 discusses these findings. And, Section 5 concludes.

2 Literature review

Integrating ROA into photovoltaic generation systems represents a substantial progression in renewable energy research. This literature review provides an in-depth exploration of this integration, delving into the intricate nuances and complexities that it entails. Across nine comprehensive literature reviews, we investigate various facets of implementing ROA in solar PV projects.

Table 1 presents a series of studies meticulously scrutinizing the landscape of renewable energy project evaluation through the lens of ROA. Among these, the only study conducting a comprehensive literature review on ROA in PV projects is the work by Lazo and Watts [3]. Notably, Lazo and Watts [3] conducted an extensive literature review, categorizing the expansive body of work into 11 key areas. This encompassed an insightful analysis of PV project behavior, operational flexibility, and the intricate evaluation of public policies. The nuanced insights from this review cater to researchers, investors, and policymakers, shedding light on the specificities inherent in PV technology.Table 1 Literature reviews discussing the application of ROA in renewable energy.

Table 1Reference	Year	Document title	Reviewed articles	
[3]	2023	The use of real options approach in solar photovoltaic literature: A comprehensive review	92	
[7]	2022	Methods for Financial Assessment of Renewable Energy Projects: A Review	99	
[1]	2018	Real options analysis of investment in solar vs. wind energy: Diversification strategies under uncertain prices and costs	11	
[8]	2017	Impact of flexibility in public R&D funding: How real options could avoid the crowding-out effect	40	
[9]	2017	Optimal design of subsidy to stimulate renewable energy investments: The case of China	26	
[10]	2016	Application of real options valuation for analysing the impact of public R&D financing on renewable energy projects: A company's perspective	14	
[11]	2014	A real option model for renewable energy policy evaluation with application to solar PV power generation in China	25	
[12]	2013	Valuing Chinese Feed-in Tariffs program for solar power generation: A real options analysis	16	
[13]	2012	Investment in wind power and pumped storage in a real options model	8	

Similarly, the work of Delapedra-Silva et al. [7] is focused on the financial evaluation of Renewable Energy Sources (RES) projects spanning the decade from 2011 to 2020. The study categorized methods into four groups, illuminating the evolving landscape of evaluation techniques within the renewable energy domain. Furthermore, Gazheli and Bergh [1] delved into the application of ROA in renewable energy investment, particularly in diversifying wind and solar PV investments. This study challenged conventional wisdom, providing novel perspectives on optimal investment strategies.

In a different vein, Martín-Barrera et al. [8] centered its exploration on public subsidies for private research and development (R&D) investment, utilizing ROA in the context of an R&D project in solar thermal power. The review proposed an innovative approach to striking a balance between reducing grants and maintaining attractiveness to companies, emphasizing the crucial role of flexibility in project valuation.

Zhang et al. [9] tackled subsidy policies for renewable energy, presenting an RO model to estimate optimal subsidies for solar PV projects in China. Additionally, Martín-Barrera et al. [10] systematically reviewed the financial implications of grants on R&D projects, utilizing ROA to assess the loss of project value attributed to limitations on managerial flexibility imposed by grants.

Further expanding the spectrum, Zhang et al. [11] introduced a comprehensive policy evaluation model, highlighting the effectiveness of ROA over net present value analysis in handling uncertainties in the context of solar PV power generation in China. Meanwhile, Lin and Wesseh [12] extensively examined China's energy landscape, employing an RO pricing approach to quantify the benefits of China's Feed-in Tariff (FIT) policy for solar power generation. This study underscored the intrinsic value of options in anticipating future policy enhancements.

Lastly, Reuter et al. [13] conducted a literature review and economic evaluation of a hybrid technology combining wind power and hydro-pumped storage. The study utilized ROA to assess the profitability of pumped-storage wind-hydro plants, emphasizing the crucial role of public intervention and investment in research and development for the economic viability of this hybrid technology. These reviews contribute to a comprehensive understanding of the multifaceted implications of employing ROA in solar PV projects and beyond.

The nine reviews previously referenced offered insightful perspectives on the application of the Real Options Approach (ROA) in renewable energy projects. However, these reviews encompassed a broad range of areas, lacking a distinct thematic focus. In stark contrast, Lazo and Watts [3] conducted the only study dedicated exclusively to a literature review of ROA within Photovoltaic (PV) projects. Their work is notably distinct for its targeted domain focus, a sharp departure from the more general thematic spread observed in other reviews.

Our study marks a significant advancement in this field by employing a systematic, cluster-based analysis of the thematic landscape. Whereas previous reviews individually addressed a spectrum of topics, our approach weaves these diverse strands into a coherent narrative, clustering the literature into well-defined thematic categories. This innovative methodology facilitates a deeper, more focused exploration of specific themes pertinent to the application of ROA in solar PV projects.

By segmenting the literature into clear clusters, our research aims to unravel the complex tapestry of concepts and implications associated with the use of ROA in the renewable energy sector, with a particular emphasis on solar PV projects. This comprehensive, clustered approach not only enhances our understanding of the field but also illuminates the multifaceted nature of employing ROA in renewable energy, especially within the solar PV context.

3 Materials and methods

In this research, the standardized research workflow proposed in Refs. [14,15]. This workflow is enriched with elements provided by the PRISMA protocol [16,17] to establish guidelines that enable the reproducibility and replicability of the results. The used workflow is composed of the following steps:1. Study design.

2. Data collection and preparation.

3. Data analysis.

4. Data visualization.

5. Interpretation.

3.1 Study design

Table 2 outlines critical parameters in the study, focusing on data extracted from Scopus until 2023. This choice ensures a contemporary and comprehensive analysis of relevant scholarly literature.Table 2 Parameters of the study.

Table 2Parameter	Value	
Database	Scopus.	
Years of Analysis	To 2023.	
Data Retrieval	November 30, 2023.	
Search String	It is derived using an iterative construction method, which will be elaborated upon in the subsequent section.	
Inclusion Criteria	Articles published in peer-reviewed journals, conference proceedings, books, and book chapters, specifically in English.	
Exclusion Criteria	None.	

In this investigation, the search process in the Scopus database was meticulously designed to identify relevant literature on the intersection of Real Options and Photovoltaic (PV) or Solar technologies. The primary search string was crafted to be comprehensive and effective in capturing pertinent documents. The chosen keywords for this search included “Real Options,” “Photovoltaic,” “PV,” and “Solar.” The objective was to cast a wide net encompassing various facets and perspectives within the specified research domain.

This search string aimed to retrieve publications containing any keywords in the title, abstract, and keywords, ensuring a focused exploration of literature related to Real Options in the context of Photovoltaic and Solar technologies. The process involved iterative refinement to optimize the search strategy, ensuring that the final string effectively encapsulates the targeted themes. This process is illustrated in Fig. 1.Fig. 1 Search string design.

Fig. 1

In the Scopus database search, 159 documents were initially identified. However, during a meticulous validation process, it was discovered that four additional relevant documents should have been included in the initial count. These four documents were uncovered by thoroughly examining the references within review articles identified in the initial search—the comprehensive validation process aimed to ensure the inclusion of all pertinent literature related to the research themes. Therefore, considering the additional four documents found in the references, the final count in the database stands at 163 papers. As shown in Fig. 2.Fig. 2 The PRISMA flow chart.

Fig. 2

As an additional validation step, an exhaustive review was conducted on articles that cited the 163 initially retrieved documents from the database. A comprehensive scan of 1300 citing documents was undertaken to identify any potential additions to the dataset that might have yet to be initially captured. This rigorous process ensured the research landscape was thoroughly explored and no relevant documents were inadvertently overlooked. However, after scrutiny, additional documents were only found in the initially retrieved 163. The absence of new findings in the citing literature reinforces the robustness of the initial search strategy. It affirms the completeness of the dataset, demonstrating that the selected documents effectively encapsulate the relevant literature on RO, Photovoltaic (PV), and Solar technologies within the scope of this investigation.

3.2 Data collection and preparation

Adhering to the methodologies outlined by Refs. [16,18], we systematically retrieved bibliographic information from Scopus in CSV format. This dataset comprised document titles, abstracts, author keywords, author details, source titles, and more. We conducted a thorough process involving computational techniques and manual refinements to ensure data accuracy. This involved preliminary preparation of author keywords, encompassing tasks such as converting texts to uppercase, translating British to American spelling, removing multiple string spaces, and standardizing hyphenated words. These procedures contributed to the dataset's meticulous cleaning, extraction, refinement, and consolidation.

3.3 Data analysis and data visualization

In analyzing the final selected documents, we computed various performance metrics [18,19].

3.3.1 Dominant thematic areas

Term clustering for uncovering dominant themes in a set of documents can be performed using two techniques: the first group involves clustering the author's keywords, index keywords, or a combination of both. It is assumed that these keywords reflect the central themes of each document and capture complex concepts. Keywords, being text strings composed of one or more words, allow for the representation of complex ideas. They are beneficial when technical terms and nomenclature are well-defined and consistently used within a community. However, this approach overlooks other critical concepts embedded in the title or abstract or emerging concepts implicitly defined in the abstracts. This method is commonly used in bibliometric tools such as Bibliometrix.

The second group involves the use of topic modeling techniques. These methodologies help detect latent themes within a group of documents, which might not be explicitly mentioned in the keywords but emerge from the textual content of the papers. A direct issue related to applying this methodology in the context of bibliometrics is that it typically analyzes individual text strings (simple words) making up the texts, thus losing complex concepts usually represented by multi-word keywords. For instance, “real options analysis” is defined as a keyword in clustering analysis, whereas topic modeling algorithms see three individual words: real, options, and analysis.

In this study, a novel technique is introduced that extracts key terms from article texts to preserve compound keywords (comprised of more than one word). This innovative approach not only overcomes the limitations of existing techniques but also combines the advantages of keyword clustering methodologies and topic modeling techniques, offering a unique and promising solution.

Applying the Louvain community detection algorithm to the co-occurrence network constructed from the descriptors facilitated identifying dominant themes about utilizing Real Options and Photovoltaic (PV) or Solar technologies. Each resultant cluster signifies a dominant thematic area, grounded in the assumption that frequently co-occurring descriptors share thematic relationships, elucidated through a co-occurrence matrix. This matrix, a squared representation with rows and columns corresponding to document descriptors, quantifies the instances where a descriptor in one row co-occurs with a keyword in the same column. Transforming this matrix into an equivalent co-occurrence network, nodes represent document descriptors, and links embody the values of the matrix. A zero value denotes the absence of co-occurrence between document descriptors in the same document across the entire dataset, reflecting an absent link in the co-occurrence network between two nodes.

An issue frequently encountered in research reports is the need for more preprocessing of descriptors. The failure to unify text strings serving as conceptual synonyms poses challenges for clustering algorithms, resulting in the formation of clusters that are challenging to interpret and subject to scrutiny. This challenge arises due to the necessity of clustering techniques for each text string to embody a distinct concept. Such criteria are compromised when, for instance, terms like “real option” and “real options” remain unharmonized. Consequently, different text strings denoting the same concept may be allocated to disparate clusters, leading to the dispersion of associated terms across multiple groups.

Homogenizing conceptual synonyms poses a non-trivial challenge, particularly as the database size increases exponentially. This issue, extensively explored in tech-mining, has prompted the proposal of various approaches. Fig. 2 illustrates the methodology employed in this study using guidelines and methods put forth by Refs. [20,21], and [22].

The employed methodology is inherently iterative, recognizing the challenges posed by a direct review of the descriptors to standardize text strings, especially given the extensive volume of keywords within the database. Examining terms organized in clusters proves more manageable than scrutinizing a list individually. Furthermore, the primary goal is not flawless homogenization but ensuring consistency within these clusters. It is pertinent to mention that non-homogenized terms with very low frequencies are anticipated to have minimal impact on the results. Therefore, the focus is on standardizing terms with high-frequency occurrences. The initial step involves constructing a thesaurus to homogenize text strings within the original descriptors, with each unique text string in the descriptors having an entry in the thesaurus.

A significant challenge arises from including abbreviations in descriptors, complicating the identification of conceptual synonyms as terms with and without corresponding abbreviations are spatially distant in the thesaurus. The initial step in transforming the thesaurus involves replacing abbreviations to address this. The iterative process entails applying the revised thesaurus, generating clusters, reviewing each cluster, and adjusting the thesaurus until convergence is attained. Convergence is determined by the consistency of the most frequently occurring words within a cluster. Notably, this verification is omitted for words with low frequency.

The procedure depicted in Fig. 3 starts by constructing a thesaurus comprising all descriptors found in the database. Descriptors are defined as the combination of author and index keywords and noun phrases extracted from the titles and abstracts. Initially, this process gathers 432 author keywords, 1095 index keywords, and 3609 noun phrases.Fig. 3 Used methodology to obtain the dominant themes from database descriptors.

Fig. 3

The next phase involves identifying all abbreviations within these descriptors and their complete forms, such as expanding “RO” to “Real Options.” During this stage, abbreviations in the descriptors are substituted with their whole forms. This step results in a collection of 357 author keywords, the same number of index keywords, and 3327 noun phrases.

Beginning with the third step, a computer-assisted manual refinement process, which is iterative, is initiated. The thesaurus is utilized for the descriptors of each document, followed by the implementation of a clustering technique using the Louvain community detection algorithm. The terms within each cluster are meticulously reviewed, leading to adjustments that consolidate terms representing the same concepts. It's important to note that clustering at this stage is primarily to aid in reviewing and refining descriptors.

3.3.2 Evolution of cluster themes over biennial periods

Identifying predominant clusters for each biennial period will be conducted to examine the progression of themes in two-year intervals. The creation of a Sankey diagram will follow this to illustrate the transition of terms from one two-year period to the next. Lastly, the research papers associated with each cluster within every two years will be scrutinized.

3.3.3 Emergent themes

Identifying emerging themes is closely associated with the emergence concept discussed in science and technology innovation (Porter et al., 2019). This study utilizes this concept to objectively identify the topics within ROA in PV that garner significant research interest. Emergence is grounded in four key elements: novelty, persistence, community, and growth. Typically, ten years is examined for detecting emerging themes. This timeframe includes a three-year base period, followed by a seven-year active period, with the final three years being the recent period.

The criteria and their explanations are as follows:a) Novelty: The topic is either newly introduced during the base period or needs more attention. The descriptor is found in 15 % of the records from the base period, at most.

b) Persistence: The topic has been continuously explored over various periods, indicating its significance. The descriptor appears in at least seven documents and is present across three or more periods (which need not be consecutive).

c) Community: The topic is being researched by two or more independent groups, signaling its relevance to the academic community. This is measured by the involvement of at least two separate organizations in the topic.

d) Growth: There is an increasing academic interest in the topic. A growth rate quantifies this in the recent period that is at least double that of the base period.

After identifying the emerging descriptors, the Louvain clustering algorithm extracts the relevant themes. The benchmarks for these parameters draw from Garner et al. [23], and Porter et al. [24].

4 Results

4.1 Basic global indicators

This bibliographic dataset, covering the timespan from 2001 to 2023, comprises 163 scientific publications with an annual growth rate of 24.79 %. The average age of these documents is 6.18 years, with an average citation rate of 12.78 per document, translating to 0.56 citations per year. The dataset spans 100 sources, averaging 1.63 documents per source. It includes a variety of document types, with articles (102) being the most common, followed by conference papers (42). The authorship data reveals 386 authors, indicating a collaborative trend with 3.08 authors and 3.38 co-authors per document. The authors are affiliated with 217 organizations in 45 countries. However, single-authored documents are minimal (9), reflecting a 19.35 % rate of international co-authorship. The dataset contains 432 raw author keywords and 1095 raw index keywords.

4.2 Annual production

Fig. 4 displays the annual publication count of documents from 2001 to 2023. Citations per year are denoted above each data point. The trend illustrated by this curve suggests an increasing focus within the scientific community on employing real options to evaluate photovoltaic generation projects.Fig. 4 Annual scientific production.

Fig. 4

Table 3 presents the annual performance metrics of the bibliographic dataset. The inspection reveals several trends and facts. From 2001 to 2023, the number of publications per year generally increased, peaking in 2022 with 20 documents. However, the global citations show a less consistent trend. Early years like 2003 and 2006 stand out for high average citations per document despite a low publication count, indicating a few highly impactful works. A significant increase in global and average citations per document began around 2012, peaking in 2015 and 2016. These years show exceptionally high average citations per document, with 2015 having the highest at 30.2857 and 2016 leading in global citations at 368. This spike suggests an increased interest and impact in the field during this period. From 2017 onwards, there's a noticeable fluctuation in the data. While the number of publications remains relatively high, there's a decrease in the mean global citations per document, especially in 2023, which drops to 1.28571, indicating a possible decline in the impact of recent publications. Additionally, the mean international citations per year, which adjusts for the age of the documents, show a peak in 2021 at 3.74 but then declines sharply in 2023.Table 3 Annual performance metrics.

Table 3Year	Documents	Global Citations	Mean Global Citations	Mean Global Citations per Year	
2001	1	0	0.0	0.0	
2003	1	7	7.0	0.33	
2005	6	19	3.17	0.17	
2006	1	9	9.0	0.5	
2007	2	9	4.5	0.26	
2008	3	27	9.0	0.56	
2009	4	1	0.25	0.02	
2010	3	15	5.0	0.36	
2011	5	37	7.4	0.57	
2012	8	119	14.88	1.24	
2013	7	146	20.86	1.9	
2014	5	90	18.0	1.8	
2015	7	212	30.29	3.37	
2016	13	368	28.31	3.54	
2017	12	201	16.75	2.39	
2018	11	193	17.55	2.92	
2019	10	186	18.6	3.72	
2020	12	113	9.42	2.35	
2021	18	202	11.22	3.74	
2022	20	111	5.55	2.78	
2023	14	18	1.29	1.29	

4.3 Basic performance metrics for constituents

These 163 published articles were authored by 386 researchers affiliated with 217 organizations in 45 countries. Table 4 lists the organizations belonging to the top 20 most frequent organizations and the top 20 most cited organizations. The table is dominated by China (with 12), followed by Italy (with 4) and South Korea (with 3). The table, which sorts the organizations by the number of publications, reveals several patterns and trends. The top-ranked organization, Nanjing University of Aeronautics & Astronautics (China), leads in all metrics: number of publications and global and local citations. Its dominance is marked by the highest H-index and G-index, indicating a high volume and impact of publications. Chinese universities dominate the top ranks, with China University of Petroleum, University of Padova (Italy), and Massachusetts Institute of Technology (USA) following. The high rank of the Massachusetts Institute of Technology is notable due to its significantly lower global citations compared to its OCC rank, suggesting a focus on quality over quantity. Lower-ranked organizations, despite having fewer publications, sometimes show disproportionately high global citations. For example, Xi'an Jiaotong-Liverpool University, ranking 47th in occurrences, is 6th in global citations, indicating a significant impact relative to its publication count. This trend is consistent among other lower-ranked organizations, suggesting that fewer publications do not necessarily equate to lower impact.Table 4 Organizations performance metrics.

Table 4Organization	Rank	Rank	Rank	OCC	Global	Local	H	G	M	
	OCC	GCS	LCS		Citations	Citations	Index	Index	Index	
Nanjing Univ. of Aeronaut. & Astronaut. (CHN) *	1	1	1	7	481	94		5	7	
China Univ. of Petroleum (CHN) *	2	2	2	5	141	26	4	4	0.57	
Univ. of Padova (ITA) *	3	3	3	5	114	24	4	3	0.44	
Massachusetts Inst. of Technol. (USA)	4	26	40	5	51	5	4	2	0.19	
RWTH Aachen Univ. (DEU)	5	28	22	5	45	9	4	2	0.4	
China Univ. of Geosciences (CHN) *	6	5	20	4	86	9	3	3	0.6	
Nac. Univ. of Singapore (SGP)	7	49	94	4	26	1	3	2	0.43	
Tianjin Univ. (CHN)	8	59	44	4	21	5	3	2	0.23	
China Univ. of Mining & Technol. (CHN) *	9	4	26	3	91	8	3	3	0.6	
Jiangsu Univ. (CHN) *	10	20	8	3	58	19	2	2	0.29	
Yonsei Univ. (KOR)	11	24	18	3	54	10	3	2	0.25	
Georgia Inst. of Technol. (USA)	12	31	7	3	39	21	3	2	0.23	
Fondazione Eni Enrico Mattei (ITA)	13	43	42	3	27	5	3	2	1.0	
Univ. of Bari (ITA)	14	53	28	3	24	8	3	2	0.75	
Univ. of Sydney (AUS)	15	60	25	3	20	9	3	2	0.6	
City Univ. of Hong Kong (HKG)	16	66	97	3	19	1	2	2	0.33	
Pontifícia Univ. Católica Do Rio de Janeiro (BRA)	17	169	169	3	1	0	1	1	0.14	
China Univ. of Mining and Technol. (CHN) *	18	15	33	2	73	6	2	2	0.4	
Minist. of Sci. and Technol. (CHN) *	19	16	34	2	73	6	2	2	0.4	
VU Univ. Amsterdam (NLD) *	20	19	27	2	61	8	2	2	0.33	
Xi'an Jiaotong-Liverpool Univ. (CHN)	47	6	9	1	85	18	1	1	0.12	
Soongsil Univ. (KOR)	48	7	10	1	83	18	1	1	0.11	
Sungkyunkwan Univ. (KOR)	49	8	11	1	83	18	1	1	0.11	
Minjiang Univ. (CHN)	50	9	4	1	80	24	1	1	0.09	
Univ. of Liberia (LBR)	51	10	5	1	80	24	1	1	0.09	
Xiamen Univ. (CHN)	52	11	6	1	80	24	1	1	0.09	
Comenius Univ. (SVK)	53	12	57	1	74	3	1	1	0.08	
Int. Inst. for Appl. Syst. Analysis (AUT)	54	13	58	1	74	3	1	1	0.08	
Vienna Univ. of Econ. and Bus. (AUT)	55	14	59	1	74	3	1	1	0.08	
Sistema Iniziative Locali S.P.A. (ITA)	56	17	13	1	62	16	1	1	0.11	
Southeast Univ. (CHN)	57	18	39	1	62	5	1	1	0.14	

The 163 analyzed articles are associated with 45 countries. The leading countries are China (with 40 documents), the United States (with 26 papers), Italy (with 14 papers), South Korea, and Brazil (with ten documents each). There is low international collaboration:• China shows international co-authorship with the United States and Singapore (2 documents with each one), among others.

• The United States has an international co-authorship with South Korea, Italy, and China (2 documents with each one), among others.

• Italy shows international co-authorship with the United States (2 documents).

• South Korea shows only international co-authorship with the United States (2 documents).

• Brazil shows only international co-authorship with Canada and Portugal (1 document with each).

4.4 Obtained clusters

Table 5 presents clusters obtained from the combination of author keywords, index keywords, and NLP phrases extracted from the abstracts.Table 5 Obtained clusters of terms.

Table 5Cluster Name	Keywords	Percentage	Main Terms	
Optimizing Investments	27	26.7	Uncertainty Analysis; Optimal Investment Timing; Monte Carlo Methods; Energy Markets; Investment Evaluation; Sensitivity Analysis; Least Square Monte Carlo; Electric Power Transmission Networks; Smart Grids; Intelligent Systems	
Strategic Investment Analysis	24	23.8	Investment Decisions; Present Values; Economics; Decision Making; Net Present Value; Managerial Flexibility; Defer Option; Cash Flow Analysis; Binomial Decision Tree; Project Investment	
Energy Policy	22	21.8	Carbon Emissions; Uncertainty; Energy Policy; Investment Costs; Feed-In-Tariffs; Carbon; Carbon Prices; Climate Change; Project Values; Power Plants	
Optimizing Power Generation	19	18.8	Costs; Electricity Prices; Stochastic Process; Power Generation; Electricity Generation; Risk Assessment; Optimization; Dynamic Programming; Strategic Planning; Strategy Investment	
Wind Energy Investments	9	8.9	Wind Energy; Renewable Energy Technology; Cost Benefit Analysis; Economic Viability; Energy Prices; Future Electricity Price; Cost Effectiveness; Energy Productions; Capital Investments	

5 Discussion

This section presents the answers to the research questions defined in the introduction. The research questions are:RQ1: What are the predominant thematic domains in the most influential literature?

RQ2: In what ways have the significant themes within the context of Real Options in photovoltaic (PV) energy evolved?

RQ3: Which research themes are currently emerging in the field of Real Options in PV?

5.1 Dominant themes (RQ1)

5.1.1 Optimizing Investments

In the field of renewable energy, particularly solar PV systems and energy storage, understanding the dynamics of investment timing and managing uncertainties is crucial. Jeon et al. [25] emphasizes the need for dynamic and uncertainty considerations in optimizing financial subsidies for PV technology, highlighting the role of policymakers in subsidy allocation. Biondi and Moretto [26] on the Italian PV market illustrate how uncertainties in energy prices and PV costs affect investment timing, emphasizing the need for strategic planning to maximize investor returns.

Tian et al. [27] extends this perspective, showing that uncertainties in investment costs and electricity prices can devalue PV investments, advocating for delaying investments until conditions are more favorable. Sim [28] employs a System Dynamics approach and Monte Carlo simulations for sensitivity analysis, evaluating the economic feasibility of renewable energy investments and highlighting the impact of uncertainty factors on investment values.

Furthermore, Bertolini et al. [29] introduces smart grids in the Italian electricity market, revealing their potential to enhance investment value in domestic PV plants. This enhancement is attributed to increased managerial flexibility and active market participation, demonstrating intelligent systems' role in optimizing investment strategies. Collectively, these insights emphasize the intricate relationships among uncertainty analysis, optimal investment timing, Monte Carlo methods, and intelligent systems in shaping investment strategies within energy markets.

5.1.2 Strategic investment analysis

In the renewable energy sector, specifically for solar photovoltaic (PV) systems, investment decisions hinge on precise evaluations of present values and economics, often utilizing advanced decision-making frameworks. Zhang et al. [11] underscores the superiority of ROA over traditional Net Present Value (NPV) in assessing solar PV investments in China. ROA provides a dynamic evaluation, adapting to uncertainties such as energy costs and price subsidies, offering a more flexible and responsive analysis compared to the static nature of NPV.

Martinez-Cesena et al. [30] shows that investment decisions on domestic PV systems, traditionally based on discounted cash flow criteria like NPV, can benefit from considering options to postpone investments. This study underscores the value of managerial flexibility in deferring investments, using ROA to account for rapid technological advancements in PV, and enhancing the economic attractiveness of these projects.

Penizzotto et al. [6] introduces a sophisticated method based on stochastic simulation, linear regression, and dynamic programming for PV power investments in existing buildings. This approach evaluates the option value of deferring investment decisions, considering uncertainties like declining costs and fluctuating tariffs, to identify the optimal investment time for irreversible PV assets.

Fioriti et al. [31] on off-grid microgrids in developing countries proposes a novel stochastic dynamic method for sizing microgrids. This method captures uncertainties in load growth and component aging, illustrating different capacity expansion strategies under various scenarios, which traditional NPV methods might not adequately address.

Finally, Ashuri and Kashani [32] utilize RO theory to evaluate investments in solar-ready buildings. This model incorporates the experience curve for technological changes and an energy price modeling component, comparing financial risk profiles for investments in solar-ready buildings versus fixed installations. This approach recognizes the importance of flexibility and future market conditions in investment decisions, often overlooked in NPV calculations.

5.1.3 Energy policy

In renewable energy investments, especially in solar photovoltaic (PV) power generation, the dynamics are shaped by an intricate web of factors. These include carbon emissions, market and policy uncertainties, fluctuating investment costs, the mechanisms of Feed-in Tariffs, varying carbon pricing, and the overarching impact of climate change. Together, these elements substantially sway both the valuation of projects and the strategic development of power plants, intertwining environmental considerations with economic and policy-driven forces.

Uncertainty in CO2 prices and investment costs critically impacts the investment values and optimal timing for renewable energy projects. This uncertainty affects FIT, an essential policy tool to encourage renewable energy development, demanding optimal levels to effectively attract investments [33]. The role of FITs is crucial in offsetting high investment costs and uncertainties associated with renewable energy, positioning solar power as a competitive alternative in China's energy future [12,34].

Promoting renewable energy, integral to energy policy, entails considering investment decisions' environmental uncertainties and market effects. This includes the influence of public incentives on investments in technologies like wind power and hydro-pumped storage installations [13]. Decisions to transition from coal-fired to solar photovoltaic power plants are influenced by various factors, including the market price of electricity, fossil fuel cost, carbon dioxide price, and long-term changes in FIT policy [35]. These aspects collectively illustrate how carbon emissions, uncertainty, energy policy, investment costs, FIT, and carbon prices interrelate and impact project values and power plants in the context of climate change.

5.1.4 Optimizing Power Generation

The interaction between costs, electricity prices, stochastic processes, and strategic investment planning in the renewable energy sector presents a complex and dynamic landscape. This multifaceted relationship is influenced by several key factors, such as risk assessment, applying optimization techniques, and dynamic programming. These elements play a critical role in guiding informed and strategic investment decisions in the rapidly evolving field of renewable power generation.

Zhang et al. [36] emphasizes the importance of optimal investment portfolio strategies for electric power enterprises. Utilizing real option and portfolio optimization methods, the study evaluates and mitigates risks in project investments, especially under fluctuating policy scenarios like subsidy phase-outs or changes in carbon reduction standards. This approach is critical to achieving high expected value while minimizing investment risks.

Guno et al. [37] explores optimal investment strategies for integrating solar photovoltaic systems in residential buildings, particularly in developing countries with limited grid infrastructure. Employing an ROA under fluctuating electricity prices, the study compares different investment payment schemes to identify the most economically viable options, highlighting the importance of strategic planning and risk assessment in dynamic market environments.

Agaton and Karl [38] use ROA to assess the attractiveness of renewable electricity generation investments in the Philippines. This research addresses the challenges of competitive oil prices and high renewable energy investment costs, showing how dynamic optimization and consideration of price uncertainties can significantly influence investment decisions.

Das Gupta [39] focuses on the policy value of capacity additions and investment in renewable energies in India, using global coal prices as a stochastic variable in an RO model. The study suggests immediate deployment of additional wind and solar technologies as optimal, given the current trigger price of coal.

Asano et al. [40] studies investment strategies in microgrid systems under uncertainty in fuel prices, particularly natural gas. The findings indicate that optimal investment strategies shift towards including renewable power generation as fuel price volatility increases.

5.1.5 Wind energy investments

The provided text focuses on the economic aspects of investing in renewable energy technologies, specifically photovoltaic (PV) and wind energy. Gazheli and van den Bergh [1] examine the impact of uncertainty in electricity prices and technological costs on solar PV and wind energy investments. It suggests that the choice between investing in solar or wind depends on their initial costs and learning rates, with the importance of future electricity prices in determining the best investment strategy.

In contrast, Li et al. [41] explores diversified investment strategies in solar PV and hydropower, applicable to the dynamics between solar PV and wind energy. This work highlights the influence of learning rates and initial costs on investment decisions, emphasizing that increased capital investments can lead to earlier investment opportunities due to the learning curve effect.

5.1.6 Conclusions

The presented clusters analysis aimed at identifying the predominant themes in the literature on the ROA to both solar PV projects and residential applications reveals several vital domains: investment optimization, strategic investment analysis, energy policy, power generation optimization, and considerations for wind energy investments. These themes emphasize the complexity and dynamic nature of financing renewable energy, advocating for a flexible approach that adapts to uncertainties and rapid technological and market changes. The transition from traditional financial metrics, such as NPV, to more sophisticated models like ROA signifies a more profound comprehension of renewable energy projects' unique risks and opportunities. Additionally, the literature points to the significant impact of policy instruments, such as FITs and carbon pricing, on investment decisions. This intricate interaction between financial modeling, policy frameworks, and strategic planning necessitates continuous research to enhance the application of ROA, ensuring its efficacy in guiding investments amid the changing dynamics of the renewable energy sector.

5.2 Thematic evolution (RQ2)

5.2.1 Primary themes for each biennial period

Fig. 5 depicts the biennial progression of thematic clusters, highlighting the top five terms in frequency for each cluster. The clusters are color-coded according to the initial term of each cluster. The diagram also shows the movement of terms between clusters. Additionally, Table 6 summarizes the primary theme associated with each cluster.Fig. 5 Primary themes for each biennial period. Four main themes are identified, each represented by a unique color.

Fig. 5

Table 6 Themes of clusters biennially.

Table 6Year	Cluster	Theme	
2014–2015	1	The economic viability of hybrid power plants depends on balancing energy production variability, investment costs, and future electricity prices to determine optimal storage size and investment timing.	
	2	Investment decisions in energy markets leverage intelligent systems and numerical models like Monte Carlo methods to navigate uncertainties in costs and market dynamics, optimizing timing and strategies under stochastic processes.	
2016–2017	1	Energy investment decisions are shaped by a complex mix of costs, electricity prices, climate change, and policy, requiring flexible decision-making approaches like Real Options Analysis to address uncertainties.	
	2	Monte Carlo methods and Real Options Models, addressing uncertainties in investment costs, electricity, and carbon prices, are essential in evaluating renewable energy investments, highlighting the impact of technological progress and market conditions on investment decisions.	
	3	market systems and support schemes shape solar photovoltaic investment timing, with reforms and FIT impacting decision-making and project launch timing.	
	4	Flexible public R&D funding mechanisms are designed to boost private investment and minimize the crowding-out effect in research and development projects.	
2018–2019	1	Investment decisions in the energy sector are shaped by managerial flexibility, optimal timing, and market evolution, with studies emphasizing the importance of smart grids, FIT, and ROA.	
	2	Uncertainties in electricity prices and learning rates crucially impact investment thresholds and strategies in renewable energy, particularly wind and solar.	
	3	Transition strategies from coal-fired to renewable energy in China are shaped by investment costs, carbon pricing, and subsidy policies, impacting the feasibility of CCS and PV technologies.	
	4	Studies in power generation economics highlight how investment costs, electricity tariffs, and policy incentives critically affect renewable energy projects' strategic planning and timing.	
	5	Uncertainty analysis in renewable energy investments significantly impacts R&D value, optimal timing, and carbon reductions, as studies on South Korea's energy policy and residential PV systems highlight.	
2020–2021	1	Uncertainty analysis and Monte Carlo methods are crucial for optimizing energy investments, as shown in various studies in renewable technology and microgrids.	
	2	The interplay between coal-fired power generation, renewable energy projects, changing volatility, and policy alignment impacts investment cost-effectiveness.	
	3	Various studies demonstrate the interplay among investment decisions, present values, economics, and energy policy.	
	4	The integration of solar PV in developing nations underscores the significance of timely investments and cost-effective payment schemes as catalysts for fostering the adoption of renewable energy and mitigating greenhouse gas emissions.	
2022–2023	1	Several studies exemplify the relationships among keywords in renewable energy economics, highlighting the importance of sensitivity and uncertainty analysis, cost-benefit assessment, and incentives in shaping investment decisions in this sector.	
	2	Cash flow analysis, net present value, managerial flexibility, optimization, and strategic planning are interrelated concepts in renewable energy project investment and climate change mitigation.	
	3	The relationship between keywords related to renewable energy investment decisions, optimal timing, and decision-making is explored through studies on factors like phased investments, deferment options, and market conditions.	
	4	Considering market uncertainty and conditions, exploring ways to enhance investment strategies for renewable energy and storage systems.	
	5	Uncertainty influences solar project valuation, while price volatility impacts low-carbon cogeneration investment decisions, highlighting the role of flexibility.	

5.2.2 Conclusions

The biennial thematic analysis presented in Table 6 reflects a significant evolution in the ROA applications within the solar PV sector, demonstrating a progression from basic economic viability analyses to more sophisticated, integrated financial and strategic planning influenced by a confluence of market, policy, and environmental factors. From 2014 to 2023, themes have shifted from focusing on the balancing of energy production variability and investment costs in hybrid power systems to more complex considerations of market dynamics and policy impacts on investment decisions. Notably, the increased incorporation of intelligent systems, Monte Carlo methods, and sensitivity analyses indicates a growing emphasis on managing uncertainties more effectively. The progression also shows an enhanced focus on the interplay between traditional energy sources and renewable energy, reflecting broader energy transition strategies. This dynamic evolution highlights the sector's adaptive response to external pressures such as climate change, policy reforms, and technological advancements, reinforcing the need for flexible, robust decision-making frameworks that can accommodate the evolving landscape of renewable energy investments.

5.3 Emergent themes (RQ3)

The study identifies 31 novel descriptors in renewable energy research, organized into four clusters using the Louvain community detection algorithm.

5.3.1 Cluster 1. real options and renewable energy investments

This cluster examines the role of Real Options in managing uncertainties in solar energy investments. It explores decision-making processes, emphasizing deferment options and sensitivity analysis. The use of sophisticated models like the Least Square Monte Carlo method is highlighted for assessing investment viability under fluctuating electricity prices and market conditions. This cluster emphasizes economic analysis using Real Options, exploring the impact of housing markets and climate change policies on renewable energy projects.

5.3.2 Cluster 2. real options in energy investments

Focusing on applying Real Options across various energy investments, this cluster underlines the importance of managerial flexibility and risk assessment in project valuation. Covering diverse contexts, from solar energy projects in Italy to hydroponic farm development in the UAE, it demonstrates the need for adaptive strategies to cope with market uncertainties. The studies utilize advanced financial tools like binomial decision trees to comprehensively evaluate net present values and cash flows, incorporating flexibility and adaptability.

5.3.3 Cluster 3. real options, energy policy, and power plant analysis

This cluster centers on the relationship between Real Options and energy investment under uncertainties, focusing on transitioning from coal-fired to renewable energy sources. It scrutinizes the comparative benefits of CCS retrofitting versus PV investments in China, the valuation of rooftop PV systems, and the flexibility in decision-making processes. Themes also include the viability of renewable energy investments post-policy support and the cost-effectiveness of renewable adoption among SMEs.

5.3.4 Cluster 4. real options in power generation and market dynamics

Addressing Real Options' role in energy investment and policy decision-making, this cluster examines optimal investment strategies for power enterprises and the value of capacity additions in renewable energies in India. It also explores the valuation of defer and relocation options in PV investments, the energy exchange among prosumers in a smart grid, and India's shift towards renewable energy sources.

5.3.5 Conclusions

The emergent themes identified in the analysis of recent ROA applications in solar PV energy investments delineate a sophisticated evolution in the field, as articulated in four distinct clusters. These clusters reveal a refined focus on managing uncertainties through advanced modeling techniques, such as the Least Square Monte Carlo method and binomial decision trees, which underscore the integration of economic analysis with practical decision-making. This integration is particularly notable in contexts ranging from housing markets influenced by climate change policies to the flexibility required in transitioning energy systems from coal to renewable sources. Furthermore, the clusters highlight the increasing relevance of ROA in diverse energy investment scenarios—ranging from traditional power generation to innovative hydroponic farms—emphasizing the critical role of managerial flexibility and strategic adaptability in navigating the complexities of market and policy dynamics. Such advancements reflect a deeper comprehension of the multifaceted impacts of policy changes, market volatility, and environmental sustainability on renewable energy projects. This understanding points towards a future where ROA, with its innovative nature, could serve as a cornerstone for strategic planning and policy formulation in the renewable energy sector, offering a hopeful prospect for the industry.

5.4 Limitations of the study

The study examining the application of the Real Options Approach (ROA) to solar photovoltaic (PV) projects from 2008 to 2023 recognizes several limitations that could affect the breadth and depth of its findings. However, it is important to note that these limitations have been comprehensively understood and addressed. Firstly, the scope of the literature reviewed may not cover all relevant sources due to restrictions to specific journals, databases, or geographic areas, potentially excluding significant studies published in less accessible outlets or languages other than English. Methodologically, relying on document descriptors for literature analysis could miss nuanced insights and bias the findings towards frequently cited topics, overlooking emerging research areas. Additionally, the rapid evolution of technology and policy post-2023 might render some findings less relevant to current and future contexts. The focus on solar PV projects explicitly limits the generalizability of the results to other renewable energy technologies or projects. Issues with the data sources' completeness, accuracy, and reliability also pose concerns, alongside potential misrepresentations due to the technological specificity and assumptions used in modeling ROA for solar PV. Furthermore, economic factors and market dynamics that significantly influence investment decisions might need to be fully accounted for, and the study may underrepresent interdisciplinary influences such as political, social, and environmental factors, all of which can profoundly affect the viability and success of solar PV projects. Acknowledging and addressing these limitations is crucial for contextualizing the research findings and maintaining the study's integrity and utility.

6 Conclusions

This study examines scholarly work on Real Options in Solar Photovoltaic (PV) Projects available in Scopus, utilizing a database of 163 documents acquired through a thorough search process. The research differentiates itself from other publications by employing descriptors (author keywords, index keywords, and noun phrases from titles and abstracts) instead of the usual keyword-based approach, resulting in the identification of five primary clusters: 1) Optimizing Investments; 2) Strategic Investment Analysis; 3) Energy Policy; 4) Optimizing Power Generation; 5) Wind Energy Investments. These clusters were derived using an iterative method that involved synthesizing conceptually synonymous terms and removing ambiguous or overly general ones, a technique more akin to Tech-mining topic identification than standard bibliometric analyses.

The study also tracks the biennial evolution of these thematic clusters to understand shifts in academic interest. Unique to this paper is the objective use of the concept of emergence, sourced from scientific and technological innovation literature, to pinpoint current “hot” topics in research, as opposed to the subjective methods often seen in review papers.

The investigation reveals four major themes in the application of Real Options Analysis: 1) Managing uncertainties in solar investments, 2) Applying Real Options across various energy projects, 3) Investigating energy policy and investment amidst uncertainty, and 4) Examining power generation and market dynamics in renewable energy sectors.

Beyond these emerging themes, the article suggests conducting analyses like this study for each identified dominant theme.

Data availability

The document database downloaded from Scopus will be made available on request.

CRediT authorship contribution statement

L.M. Jiménez: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. J.D. Velásquez: Conceptualization, Formal analysis, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Luis Miguel Jiménez-Gómez reports financial support was provided by 10.13039/501100002753 Universidad Nacional de Colombia . Juan David Velasquez-Henao reports financial support was provided by 10.13039/501100002753 Universidad Nacional de Colombia . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank the peer reviewers for their valuable feedback, and I would like to acknowledge the support from the 10.13039/501100002753 Universidad Nacional de Colombia .
==== Refs
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