This study explores renewable energy research in Oman, focusing on growth, trends and economic impacts and identifies key contributors, emerging themes and economic ties influencing development.
A bibliometric analysis of 204 studies was conducted to evaluate collaboration networks, citation trends and research performance. Additionally, a vector autoregression (VAR) model was used to examine the associations between economic variables such as economic growth, foreign direct investment and energy consumption.
The findings indicate a consistent increase in research on renewable energy, with publications increasing by 6.3% annually from 2020. Keyword and trend analyses highlight a growing emphasis on regulatory frameworks, with terms such as “law” emerging. A keyword network analysis revealed that renewable energy research in Oman is significantly influenced by regional economic and environmental factors. The VAR model further supports the role of renewable energy in Oman’s economic growth, showing strong correlations between energy consumption, foreign direct investment (FDI) and economic expansion.
The study's data were limited by secondary databases and VAR analysis, suggesting the need for larger datasets and advanced modelling techniques for more accurate results.
This study explores renewable energy research and its economic impact in Oman, providing insights for researchers, policymakers and industry stakeholders on sustainable energy transitions and economic growth. It uniquely combines bibliometric and VAR analyses for a country-specific assessment, quantifying interrelationships between energy consumption, investment and economic output. The findings offer a replicable framework for emerging economies, delivering long-term guidance for policy, investment and sustainable energy planning internationally.
1. Introduction
The global energy landscape is shifting significantly towards renewable sources such as solar, wind, and hydropower as part of the worldwide effort to combat climate change and reduce carbon emissions (Hassan et al., 2024). This transition has been accelerated by international initiatives such as the Paris Agreement. In 2020, renewable energy (RE) accounted for 29% of the global electricity generation (EG), with solar and wind emerging as the fastest-growing sectors (Ram, Aghahosseini, & Breyer, 2020). Many nations are actively exploring alternative energy sources to enhance energy security, reduce dependence on fossil fuels, and drive economic growth. Understanding the social and economic effects of RE, particularly within specific national contexts, is essential for assessing its potential and limitations (Adanma & Ogunbiyi, 2024).
Oman, which is located on the southeast coast of the Arabian Peninsula, has long relied on natural gas and oil to meet its energy needs and support its economy (Amoatey, Al-Hinai, Al-Mamun, & Baawain, 2022). However, Oman's Vision 2040 places strong emphasis on sustainability and seeks to lessen its reliance on fossil fuels, recognizing the need for diversification (Aggarwal, KM, Muslim, & Khan, 2024). Owing to its abundant solar and wind resources, RE is a key component of a country's long-term energy strategy. In 2019, Oman set a goal to generate 30% of its electricity from renewable sources by 2030 (Praveen et al., 2020). Although investments in solar and wind energy are increasing, challenges remain in terms of technological advancements, economic integration, and policy implementation. Interest in Oman's RE sector has grown among researchers and policymakers; however, studies on its effects on local policies, technological advancements, and the economy are still limited (Al-Sarihi & Cherni, 2018). Thus, understanding the wider economic impacts of RE initiatives is essential for future planning, policy formation, and resource allocation.
Although research on the economic effects of RE has been widely conducted globally, studies focusing on specific national contexts, such as Oman, remain scarce. Most existing studies either examine global trends or focus on larger and more developed economies. While some research has explored RE in the Middle East and North Africa (MENA) and other regions, Oman remains inadequately represented in research concerning its economic impacts (Appiah-Otoo & Chen, 2024; Rahman, Rahman, & Akter, 2023). Furthermore, substantial bibliometric studies covering trends and key contributors to Oman's RE research are lacking (He et al., 2024). Additionally, few predictive models assess the economic connections between RE variables such as energy consumption (EC), foreign direct investment (FDI), and economic growth (Iqbal, Tang, & Rasool, 2023). Addressing this gap is essential for advancing scholarly knowledge and providing valuable insights for stakeholders and policymakers involved in Oman's energy transition.
This study makes several important contributions to the existing literature on renewable energy and economic development. First, it provides a bibliometric analysis of renewable energy research in Oman, evaluating research productivity, collaboration networks, and thematic trends. This contributes new insights into the development of systematic research on RE within a resource-dependent economy, a subject that has not been extensively explored in previous research. Second, by focusing on Oman, a nation that is actively diversifying its energy mix while remaining highly dependent on hydrocarbons, this study offers lessons for other hydrocarbon-based economies pursuing sustainability transitions and highlights regional opportunities and challenges in the development of RE. Third, utilising a vector autoregression (VAR) approach to analyse the dynamic relationships between RE adoption and key economic indicators such as Gross Domestic Product (GDP), FDI, and EC, this study contributes to the existing body of literature. Although larger economies have extensively studied the economic effects of RE, this study examines renewable energy policies and their influence on growth trajectories in smaller, oil-dependent contexts, with particular emphasis on Oman's unique position in the RE sector that presents opportunities for growth and development. Finally, by integrating bibliometric and econometric perspectives, the study generates policy-relevant evidence that supports Oman's Vision 2040 and provides broader implications for energy transition strategies in emerging economies.
The main objective of this study was to examine the development, trends, and financial implications of RE research in Oman. Through synthesis and statistical methods, this study conducts a multifaceted analysis to investigate the effects of green innovation and RE on Oman's energy economics over time. The primary objectives of this study were as follows:
To perform the bibliometric analysis by assessing research productivity, trend topics, and key contributors within the RE sector in Oman.
To identify key findings on RE potential, implementation, and technological innovations.
To investigate the relationships between RE variables, such as EC and EG, and economic variables, such as GDP, inflation (IF), and FDI using vector autoregression (VAR) model.
To analyse the contribution of RE to Oman's economic growth, emphasizing the implications for policy and potential growth plans.
The structure of this paper is as follows: Section 2 reviews the literature in the study field. Section 3 outlines the research methodology, covering bibliometric and predictive analysis. Section 4 presents the synthesis analysis results, key findings from the selected studies, and the VAR statistics. Section 5 discusses the results and their implications. Finally, Section 6 concludes the study, highlights its limitations, and provides recommendations for future research.
2. Related works
2.1 Energy economics theory
This study applies energy economics theory to examine Oman's adoption of renewable energy and its economic and industrial development (Bhattacharyya, 2019). It evaluates historical trends in energy consumption and generation, alongside their financial implications. Using a predictive model, the study intends to provide a comprehensive analysis of interactions between renewable energy variables and broader economic indicators such as GDP growth, inflation, and FDI in Oman. This approach offers insights into the feasibility of renewable energy projects, supporting investment and policy decisions while assessing their contribution to sustainable development, in line with Oman's long-term objectives (Al-Sarihi & Cherni, 2018). By adopting this framework, this study enhances understanding of how renewable energy fosters sustainability and economic diversification (Saboori, Zaibet, & Boughanmi, 2022). This aligns with broader data indicating that the utilisation of renewable energy can stimulate economic growth.
2.2 RE research in Oman
With its strong emphasis on renewable resources, Oman's energy sector is diversifying. As one of the regions with the highest levels of solar radiation globally, Oman has a well-established potential for solar energy. Studies indicate that areas such as Salalah and Duqum have the potential to substantially contribute to electricity production (Tabook & Khan, 2021). Despite limited infrastructure development, wind energy is regarded as promising, particularly in Dhofar (Kazem & Khatib, 2013). According to Oman Vision 2040, RE is a crucial component of long-term sustainability and economic growth. Mohammed, Naiyf, Thaer, and Khbalah (2021) emphasized the importance of strategic resource selection and assessed RE resources using an Analytical Hierarchy Process. Elmonshid et al. (2024) investigated the association between carbon emissions and RE use in the Gulf Cooperation Council (GCC). Malik et al. (2019) examined the drivers and barriers that influence RE growth in GCC countries. Al-Maamary, Kazem, and Chaichan (2017) analysed the impact of oil price fluctuations on RE adoption. Additionally, managing human resources and developing a skilled workforce remain key challenges during the transition period (Porkodi et al., 2021). The growing thematic focus on sustainability transitions has been highlighted by recent global bibliometric studies that map research trends and innovations in renewable energy (Mentel, Lewandowska, Berniak-Woźny, & Tarczyński, 2023; Wang, Huang, & Li, 2024). However, similar analyses remain scarce in the context of Oman.
2.3 Economic impacts of RE
Numerous studies have examined the economic effects of Oman's adoption of RE. Investments in RE support both environmental sustainability and economic diversification. Saboori et al. (2022) highlight the importance of RE in Oman's economic diversification strategy, which aims to reduce dependency on fossil fuels. Al-Sarihi and Cherni (2018) highlighted the importance of bringing political goals in line with economic realities when discussing the difficulties in funding transitions to RE in Oman. The results of Rahman et al. (2023) underscored the need for policies focusing on RE development, unemployment reduction, and sustainable economic growth in South Asia. Hamed and Özataç (2024) examined the contributions of financial development, institutional quality, and RE use. Li, Samour, Irfan, and Ali (2023) emphasized the role of RE and fiscal policies in reducing carbon emissions in the GCC. These studies illustrate the potential of RE to promote sustainable development, highlighting the necessity for strategic investments and legislative frameworks. Nevertheless, Oman faces challenges in accelerating RE adoption, including financial and regulatory limitations. Although Oman has set a target of 30% RE by 2030, the government is actively working to accelerate progress. Additionally, regulatory hurdles and implementation costs have contributed to delays in Oman's RE expansion (Al-Sarihi & Cherni, 2018). Studies demonstrate that renewable energy subsidies influence both environmental and economic outcomes, while targeted investments generate measurable supply chain and employment impacts (Omoju, Beyene, Ikhide, Dimnwobi, & Ehimare, 2024; Woollacott et al., 2023).
2.4 Research gap
Although studies on RE in Oman exist, significant gaps remain in integrating predictive models and bibliometric analysis to assess its economic effects. While global bibliometric studies exist (Elmonshid et al., 2024), Oman has received limited attention. Furthermore, the energy sector in Oman lacks predictive modelling despite the growing interest in RE (Mohammed et al., 2021). Limited research has been conducted on the impact of RE adoption on Oman's economic structure, particularly using predictive tools (Li et al., 2023). In the context of Oman, the relationship between RE deployment and institutional quality remains unexplored (Hamed and Özataç, 2024). Kwilinski (2024) emphasized the necessity of conducting more detailed bibliometric analyses in research on RE. The adoption of RE has wider economic and policy implications, and these studies highlight the knowledge gaps in these areas (Shafiullah, Miah, Alam, & Atif, 2021). Despite increasing research on RE, there is a lack of comprehensive investigation of its interaction with economic and policy strategies, particularly in Oman.
Moreover, recent studies show that RE subsidies and investments influence both environmental and economic outcomes (Omoju et al., 2024), while also generating local supply chain and employment impacts (Woollacott et al., 2023). However, these analyses are mostly focused on Nigeria, Kenya, or broader global contexts, with limited application to Oman and the GCC region (Abdmouleh, Alammari, & Gastli, 2015; Lahrech, Abu-Hijleh, & Aldabbas, 2024). This emphasises the necessity of investigating the ways in which Oman's policy strategies, economic diversification, and sustainability goals interact with the use of renewable energy.
Based on the reviewed theoretical foundations and empirical findings, this study identifies several gaps in the existing literature. To address these, the following research questions are proposed. These questions help examine related studies, assess economic factors, and inform policy considerations for the long-term growth of Oman's renewable energy sector.
What is the research performance, key trends and contributors in the field of RE research in Oman?
What are the relationships between RE adoption and economic variables, such as EC, FDI, and GDP growth in Oman?
What strategies can support Oman in addressing the challenges of developing a sustainable RE sector while ensuring economic growth and effective policy implementation?
3. Research methodology
This study explores green innovation and RE in Oman through three approaches. First, a bibliometric analysis identifies publication patterns, research trends, and key contributors. Second, a literature-based survey reviews selected studies to summarise developments and policy implications. Finally, the VAR model forecasts the financial impact of RE adoption, offering a data-driven perspective on emerging trends. This combined methodology provides a comprehensive understanding of the sector's evolution, aiding policymakers, researchers, and industry stakeholders in strategic decision-making.
3.1 Bibliometric analysis
A bibliometric analysis was conducted using studies from the past two decades on energy economics in Oman. Bibliometric analysis has limitations due to missing records, inconsistencies, and database biases. Different tools use different mapping principles, algorithms, and visualization techniques, limiting comparability and potentially underrepresenting newer research. Despite its limitations, bibliometric analysis is a powerful method for quantitatively evaluating scientific publications, allowing for topological and temporal representation, measurement of contributors, and performance analysis (Lewandowska, Berniak-Woźny, & Ahmad, 2023; Porkodi & Pundhir, 2025). The literature review involved search strategies and inclusion/exclusion criteria (Porkodi & Raman, 2024). The following subsections describe the stages of the review methodology.
3.1.1 Study selection
Relevant articles were selected from electronic databases, including Scopus, Web of Science, Lens, and Google Scholar, using advanced search queries (Ortega & Delgado-Quirós, 2024). Scopus and Web of Science provide peer-reviewed research, Lens covers patents, and Google Scholar includes preprints and institutional reports and the details of the search is given in Table 1. Around 30 studies on green innovation and RE were examined for keywords to refine the search. Trends, technological advancements, and policy impacts (2005–2025) were analysed. PRISMA guidelines (Page et al., 2021) ensured a systematic selection process, illustrated in Figure 1.
Details of search
| Attributes | Details |
|---|---|
| Keywords | Energy: “green innovation” OR “green initiative” OR “green hydrogen” OR “sustainable hydrogen” OR “eco-innovation” OR “sustainable innovation” OR “green business models” OR “renewable energy” OR “solar energy” OR “wind energy” OR “bioenergy” OR “clean energy” OR “energy transition” Economy: “circular economy” OR “closed-loop economy” OR “circular business models” OR “circular production” OR “Economy” OR “economic impact” OR “business performance” OR “profitability” OR “economic growth” OR “cost savings” OR “competitive advantage” OR “value creation” OR “green finance” OR “finance” OR “investment” OR “ROI” OR “financial sustainability” OR “revenue” OR “cost-benefit analysis” OR “economic policy” OR “sustainable business models” OR “market growth” OR “cost efficiency” OR “fiscal” OR “economic policy” OR “financial performance” OR “economic development” |
| Country | Oman |
| Time frame | 2005–2025 |
| Article searched for | Title, Abstract and Keywords |
| Databases considered | Scopus, Web of Science, Emerald and Google Scholar |
The flowchart is titled “Identification of Studies for Review.” It has three vertical sections on the left labeled “Identification,” “Screening,” and “Inclusion.” Under “Identification,” the first text box has five lines: “Articles identified from the database (n equals 3164),” “Scopus (n equals 804),” “Web of Science (n equals 691),” “Lens (n equals 869),” and “Google Scholar (n equals 800)” A right arrow from this box lead to the second text box in a horizontal arrangement, with the text “Duplicates removed (n equals 1596).” From the first text box, a downward arrow leads to the third text box in the “Screening” section, with the text “Articles screened (n equals 1568).” A right arrow from the third text box leads to the fourth text box in a horizontal arrangement, with the text “Articles excluded (n equals 1036).” A downward arrow from the third text box leads to the fifth text box with the text “Articles sought for retrieval (n equals 532).” A right arrow from the fifth text box points to the sixth text box in a horizontal arrangement, with the text “Articles not retrieved (n equals 186).” A downward arrow from the fifth text box leads to the seventh text box with the text “Articles assessment for eligibility (n equals 346).” A right arrow from the seventh text box points to the eighth text box in a horizontal arrangement, with the text “Articles excluded through exclusion criteria (n equals 142).” A downward arrow from the seventh text box leads to the ninth text box in the “Inclusion” section, with the text “Articles included (n equals 204).”Steps in the study selection phase. Source: The authors
The flowchart is titled “Identification of Studies for Review.” It has three vertical sections on the left labeled “Identification,” “Screening,” and “Inclusion.” Under “Identification,” the first text box has five lines: “Articles identified from the database (n equals 3164),” “Scopus (n equals 804),” “Web of Science (n equals 691),” “Lens (n equals 869),” and “Google Scholar (n equals 800)” A right arrow from this box lead to the second text box in a horizontal arrangement, with the text “Duplicates removed (n equals 1596).” From the first text box, a downward arrow leads to the third text box in the “Screening” section, with the text “Articles screened (n equals 1568).” A right arrow from the third text box leads to the fourth text box in a horizontal arrangement, with the text “Articles excluded (n equals 1036).” A downward arrow from the third text box leads to the fifth text box with the text “Articles sought for retrieval (n equals 532).” A right arrow from the fifth text box points to the sixth text box in a horizontal arrangement, with the text “Articles not retrieved (n equals 186).” A downward arrow from the fifth text box leads to the seventh text box with the text “Articles assessment for eligibility (n equals 346).” A right arrow from the seventh text box points to the eighth text box in a horizontal arrangement, with the text “Articles excluded through exclusion criteria (n equals 142).” A downward arrow from the seventh text box leads to the ninth text box in the “Inclusion” section, with the text “Articles included (n equals 204).”Steps in the study selection phase. Source: The authors
In the preliminary search of the study selection process, 3,164 articles were obtained from different academic databases and 1,596 duplicate documents were found and removed. The screening phase filtered 1,568 articles and eliminated 1,036 articles. The inclusion criteria, which included articles published in peer-reviewed journals, articles written in English, and articles on Oman, were used to screen the articles by assessing the abstract. A total of 532 articles were retrieved, 346 of which were obtained for further evaluation. The reviewers eventually determined the eligibility of these articles by examining complete articles based on several inclusion/exclusion criteria. Finally, 204 articles were selected for review analysis, after 142 articles were eliminated. Moreover, the references of the selected articles were evaluated and no additional studies were conducted in this manner.
The inclusion criteria used in the study include:
Articles written in English
Peer-reviewed articles
Articles that focus on at least one form of RE
Articles that examine at least one economic or financial factor, such as investment, economic growth, production costs, and trade in the energy sector
The exclusion criteria employed in the study include:
Studies that focus solely on literature reviews
Studies that do not focus on Oman
Studies that focus only on green innovation without addressing economic aspects
3.1.2 Study analysis
The selected articles were downloaded as CSV files with all associated data including titles, keywords, abstracts, authors, affiliations, citations, publisher details, and funding information. The CSV file was imported into bibliometric tools for analysis. This study utilized Biblioshiny, an open-source program that provides a variety of bibliometric analyses and imports data from multiple sources, including the Web of Science and Scopus (Aria & Cuccurullo, 2017). It offers a user-friendly interface for conducting bibliometric analyses, including science mapping and performance analysis, making it suitable for examining research trends and scholarly impacts. However, its limitation lies in restricted customization and computational scalability when handling very large datasets.
Additionally, VOSviewer's advanced bibliometric network visualization capabilities were employed, particularly for the term co-occurrence analysis. Generating software-based maps that depict keywords and their relationships through nodes and edges facilitates the identification of thematic structures and research topic relationships (Van Eck and Waltman, 2010). Its limitation is that while it provides strong visualization, it has limited statistical depth and may oversimplify complex relationships. Furthermore, additional software, including Microsoft Excel and online visualization tool such as Flourish, were used for clear and enhanced visual representation. The drawback is that, in contrast to automated bibliometric platforms, these tools are primarily descriptive and manual, which may limit advanced analytical accuracy and reproducibility. However, this study effectively utilised a combination of tools to utilizes the strengths of each method, ensuring comprehensive bibliometric coverage, clear visualisation, and precise interpretation of research trends and patterns.
3.2 VAR model
A time-series analysis using a VAR model was conducted with 39 years of secondary data (1985–2023) for five variables in Oman: GDP growth (%), FDI (net inflows, % of GDP), IF (inflation, GDP deflator, %), EG (electricity generation, TWh), and EC (annual change in primary EC, %). As of January 2025, data for 2024 remain unavailable. GDP, FDI, and IF data were sourced from the World Data Bank, while EG and EC were obtained from Our World in Data. Preprocessing included differencing for stationarity, followed by Augmented Dickey-Fuller (ADF) tests to determine optimal lags. Granger Causality (GC) and Johansen's Cointegration (JC) tests assessed variable relationships. The VAR model was then fitted for forecasting and dynamic interaction analysis using the Impulse Response Function (IRF), which evaluates variable responses to shocks over time. Model stability was examined to ensure accuracy, aiding in the assessment of time-dependent financial and economic relationships.
The VAR model's main drawback is its inability to capture nonlinearities or structural breaks in economic relationships, as it assumes linear interdependencies. Its strength, however, lies in its capacity to model multiple dynamic interactions, making it particularly effective for analysing interconnected energy and macroeconomic variables in a time-dependent setting. Thus, despite these limitations, the VAR model provides robust insights into dynamic interrelationships and remains highly appropriate for this study's objectives.
4. Results
4.1 Synthetic analysis
According to the summary of selected articles, 204 studies from 137 sources (2007–2025) comprised 82.84% journals, 14.22% conferences, and 3% books. The studies had 6.29% annual growth, 18.52 citations per study, 5,000 references, 395 keywords, 909 authors, and 23.53% involved international coauthors. The bibliometric analysis results are presented in tabular and visual form. Figure 2 illustrates annual production and performance. Publications increased from 2007 to 2025, surging after 2017 due to Oman's RE initiatives and the 2020 target for 2025. Early publications had high citation impact, notably in 2012 (121 citations) and 2015 (131 citations). Growth peaked between 2023–2024 with 43 publications, but citations declined sharply by 2024 (three citations). The decline in 2025 indicates recent research has not yet accumulated citations.
The horizontal axis of the grouped vertical bar graph is titled “Publication Year” and shows the years 2007, 2009, 2010 to 2025. The horizontal axis ranges from 0 to 1000, in increments of 200. For each year, two bars are shown for “Number of publications” and “Number of citations.” The legend at the top indicates that the green bar represents the “Number of publications” and the blue bar represents the “number of citations.” The data for the graph is as follows: 2007: Number of publications: 1, Number of citations: 3. 2009: Number of publications: 2, Number of citations: 50. 2010: Number of publications: 2, Number of citations: 4. 2011: Number of publications: 2, Number of citations: 54. 2012: Number of publications: 3, Number of citations: 121. 2013: Number of publications: 4, Number of citations: 14. 2014: Number of publications: 1, Number of citations: 61. 2015: Number of publications: 3, Number of citations: 131. 2016: Number of publications: 2, Number of citations: 10. 2017: Number of publications: 10, Number of citations: 278. 2018: Number of publications: 4, Number of citations: 21. 2019: Number of publications: 14, Number of citations: 406. 2020: Number of publications: 12, Number of citations: 230. 2021: Number of publications: 20, Number of citations: 422. 2022: Number of publications: 35, Number of citations: 792. 2023: Number of publications: 43, Number of citations: 548. 2024: Number of publications: 43, Number of citations: 121. 2025: Number of publications: 3, Number of citations: 0.Annual scientific production and citation. Source: The authors
The horizontal axis of the grouped vertical bar graph is titled “Publication Year” and shows the years 2007, 2009, 2010 to 2025. The horizontal axis ranges from 0 to 1000, in increments of 200. For each year, two bars are shown for “Number of publications” and “Number of citations.” The legend at the top indicates that the green bar represents the “Number of publications” and the blue bar represents the “number of citations.” The data for the graph is as follows: 2007: Number of publications: 1, Number of citations: 3. 2009: Number of publications: 2, Number of citations: 50. 2010: Number of publications: 2, Number of citations: 4. 2011: Number of publications: 2, Number of citations: 54. 2012: Number of publications: 3, Number of citations: 121. 2013: Number of publications: 4, Number of citations: 14. 2014: Number of publications: 1, Number of citations: 61. 2015: Number of publications: 3, Number of citations: 131. 2016: Number of publications: 2, Number of citations: 10. 2017: Number of publications: 10, Number of citations: 278. 2018: Number of publications: 4, Number of citations: 21. 2019: Number of publications: 14, Number of citations: 406. 2020: Number of publications: 12, Number of citations: 230. 2021: Number of publications: 20, Number of citations: 422. 2022: Number of publications: 35, Number of citations: 792. 2023: Number of publications: 43, Number of citations: 548. 2024: Number of publications: 43, Number of citations: 121. 2025: Number of publications: 3, Number of citations: 0.Annual scientific production and citation. Source: The authors
Table 2 presents key contributors, including relevant articles, authors, citations, affiliations, and sources. Wang, Yang, and Li (2023) received 240 citations, while Kahia, Ben Jebli, and Belloumi (2019) had 202. The top ten articles (2015–2023) indicate an expanding field, with citations ranging from 240 to 70. Okonkwo, P.C. published 12 articles, followed by Barhoumi, E.M. and Al-Badi, A.H. with 10 each. Environmental Science and Pollution Research had the most articles (10), while Sultan Qaboos University led affiliations (60). Figures 3a and 3b shows “economics” (71) as the most frequent keyword, followed by “renewable energy” (55) and “engineering” (51).
Key contributors
| Most relevant articles | Most relevant authors | ||
|---|---|---|---|
| Authors/DOI | Citations | Authors | #Articles |
| Wang et al. (2023) 10.1016/j.envres.2022.114575 | 240 | Okonkwo, P.C. | 12 |
| Kahia et al. (2019) 10.1007/s10098-019-01676–2 | 202 | Barhoumi, E. M. | 10 |
| Al-Maamary et al. (2017) 10.1016/j.rser.2016.11.079 | 160 | Al-Badi, A. H. | 10 |
| Li et al. (2023) 10.1016/j.renene.2023.01.047 | 103 | Zghaibeh, M. | 8 |
| Shafiullah et al. (2021) 10.1016/j.renene.2021.07.092 | 103 | Malik, A. | 6 |
| Malik et al. (2019) 10.1007/s11356-019-05337-1 | 90 | Samour, A. | 6 |
| Okonkwo et al. (2022) 10.1016/j.ijhydene.2022.07.140 | 90 | Kazem, H. A. | 6 |
| Abdmouleh et al. (2015) 10.1016/j.rser.2015.05.057 | 89 | Ahshan, R. | 5 |
| Khan and Al-Ghamdi (2023) 10.1016/j.ijhydene.2022.12.033 | 79 | Belgacem, I. B. | 5 |
| Kazem and Khatib (2013) 10.1016/j.seta.2013.06.002 | 70 | Charabi, Y. | 4 |
| Most Relevant Sources | Most Relevant Affiliation | ||
| Sources | #Articles | Affiliation | #Articles |
| Environmental Science and Pollution Research International | 10 | Sultan Qaboos University | 60 |
| Environment, Development and Sustainability | 6 | Dhofar University | 31 |
| International Journal of Hydrogen Energy | 6 | Imperial College London | 7 |
| Renewable Energy | 6 | University of Exeter | 7 |
| Sustainability (Switzerland) | 6 | Swarnandra College of Engineering and Technology | 6 |
| Energies | 5 | University of Jeddah | 6 |
| Renewable and Sustainable Energy Reviews | 5 | A'sharqiyah University | 5 |
| International Journal of Sustainable Energy | 4 | Hamad Bin Khalifa University | 5 |
| Clean Technologies and Environmental Policy | 3 | National University of Science and Technology | 5 |
| Environmental Science and Pollution Research | 3 | Sohar University | 5 |
| International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences | 3 | University of Bahrain | 5 |
| International Journal of Renewable Energy Research | 3 | University of Nizwa | 5 |
| Sustainable Energy Technologies and Assessments | 3 | The University of Technology and Applied Sciences Al Musanna | 5 |
The figure has two parts labeled (a) and (b). Part (a) on the left is titled “Top 15 Keywords” and shows a horizontal bar graph. The horizontal axis of the horizontal bar graph is titled “Top 15 Keywords” and ranges from 0 to 100 in increments of 50. The vertical axis lists the top 15 keywords from top to bottom as: “hybrid systems,” “costs,” “alternative energy,” “law,” “energy policy,” “solar energy,” “sustainable development,” “investments,” “environmental science,” “wind power,” “oman,” “business,” “engineering,” “renewable energy,” and “economics.” Each keyword has a bar indicating its count. The data for the graph is as follows: hybrid systems: 10. costs: 13. alternative energy: 13. law: 18. energy policy: 21. solar energy: 23. sustainable development: 24. investments: 24. environmental science: 26. wind power: 36. oman: 44. business: 46. engineering: 51. renewable energy: 55. economics: 71. Part (b) on the right is titled “Trend Keyword Topics” and shows a horizontal timeline chart. The horizontal axis shows the years ranging from 2014 to 2024, in increments of 2 years. The vertical axis lists 16 keywords. The keywords listed from top to bottom are: “photovoltaic system”, “hybrid systems”, “costs”, “alternative energy”, “law”, “energy policy”, “solar energy”, “sustainable development”, “investments”, “environmental science”, “wind power”, “oman”, “business”, “engineering”, “renewable energy”, “economics”. Each keyword has colored horizontal lines or dots spanning different year ranges. The data from the chart is as follows: Photovoltaic system: Range: 2014 to 2022. Marker at 2018. Hybrid systems: Range: 2013 to 2022. Marker at 2018. Costs: Range: 2016 to 2022. Marker at 2021. Alternative energy: Range: 2020 to 2024. Marker at 2021. Law: Range: 2023 to 2024. Energy policy: Range: 2017 to 2021. Marker at 2020. Solar energy: Range: 2017 to 2022. Marker at 2019. Sustainable development: Range: 2020 to 2023. Marker at 2021. Investments: Range: 2013 to 2022. Marker at 2020. Environmental science: Range: 2021 to 2023. Marker at 2022. Wind power: Range: 2014 to 2022. Marker at 2020. Oman: Range: 2016 to 2023. Marker at 2022. Business: Range: 2022 to 2024. Marker at 2023. Engineering: Range: 2022 to 2024. Marker at 2023. Renewable energy: Range: 2020 to 2023. Marker at 2022. Economics: Range: 2021 to 2023.Keyword analysis. Source: The authors
The figure has two parts labeled (a) and (b). Part (a) on the left is titled “Top 15 Keywords” and shows a horizontal bar graph. The horizontal axis of the horizontal bar graph is titled “Top 15 Keywords” and ranges from 0 to 100 in increments of 50. The vertical axis lists the top 15 keywords from top to bottom as: “hybrid systems,” “costs,” “alternative energy,” “law,” “energy policy,” “solar energy,” “sustainable development,” “investments,” “environmental science,” “wind power,” “oman,” “business,” “engineering,” “renewable energy,” and “economics.” Each keyword has a bar indicating its count. The data for the graph is as follows: hybrid systems: 10. costs: 13. alternative energy: 13. law: 18. energy policy: 21. solar energy: 23. sustainable development: 24. investments: 24. environmental science: 26. wind power: 36. oman: 44. business: 46. engineering: 51. renewable energy: 55. economics: 71. Part (b) on the right is titled “Trend Keyword Topics” and shows a horizontal timeline chart. The horizontal axis shows the years ranging from 2014 to 2024, in increments of 2 years. The vertical axis lists 16 keywords. The keywords listed from top to bottom are: “photovoltaic system”, “hybrid systems”, “costs”, “alternative energy”, “law”, “energy policy”, “solar energy”, “sustainable development”, “investments”, “environmental science”, “wind power”, “oman”, “business”, “engineering”, “renewable energy”, “economics”. Each keyword has colored horizontal lines or dots spanning different year ranges. The data from the chart is as follows: Photovoltaic system: Range: 2014 to 2022. Marker at 2018. Hybrid systems: Range: 2013 to 2022. Marker at 2018. Costs: Range: 2016 to 2022. Marker at 2021. Alternative energy: Range: 2020 to 2024. Marker at 2021. Law: Range: 2023 to 2024. Energy policy: Range: 2017 to 2021. Marker at 2020. Solar energy: Range: 2017 to 2022. Marker at 2019. Sustainable development: Range: 2020 to 2023. Marker at 2021. Investments: Range: 2013 to 2022. Marker at 2020. Environmental science: Range: 2021 to 2023. Marker at 2022. Wind power: Range: 2014 to 2022. Marker at 2020. Oman: Range: 2016 to 2023. Marker at 2022. Business: Range: 2022 to 2024. Marker at 2023. Engineering: Range: 2022 to 2024. Marker at 2023. Renewable energy: Range: 2020 to 2023. Marker at 2022. Economics: Range: 2021 to 2023.Keyword analysis. Source: The authors
Figure 4 presents text analysis (Sri Hari, Porkodi, Saranya, & Vijayakumar, 2024). Figure 4(a) presents keyword analysis, highlighting RE's connections to wind, solar, and economic concepts. The green cluster links RE to wind and solar, the blue to economic aspects, and the purple to Oman and GCC nations. A word cloud (Figure 4b) identifies key themes, emphasizing RE's technical, economic, and environmental aspects.
The diagram has two parts labeled (a) and (b). Part (a) on the top is labeled “Co-occurrence and network of Keywords” and shows a network diagram. The network diagram has a densely connected cluster of nodes of different sizes and colors. The various labels are as follows: The green cluster is present at the top left and has the nodes with the labels: “electric power generation,” “electricity,” “gas emissions,” “hydrogen storage,” “oman,” “investments,” “costs,” “levelized costs,” “cost benefit analysis,” “hybrid systems,” “wind,” “wind energy,” “wind power,” “solar energy,” “biogas,” and “wind turbines.” The blue cluster is present at the top right and has the nodes with the labels: “mathematics,” “population,” “macroeconomics,” “electrical engineering,” “sociology,” “market economy,” “business,” “epistemology,” “law,” “philosophy,” “marketing,” “politics,” “finance,” “environmental economics,” “renewable energy,” “energy transition,” “bioenergy,” and “marketing.” The pink cluster is present at the bottom and has the nodes with the labels: “economic growth,” “carbon dioxide “united arab emirates,” “gulf cooperation council,” and “Qatar.” Four yellow nodes are shown in the center without any labels. Part (b) on the top is labeled “Word Cloud for Titles of the Selected Articles,” and shows a word cloud with words in different colors and sizes. The text includes: “economic development,” “renewable energy resources,” “environmental economics,” “photovoltaic system,” “solar power generation,” “energy efficiency,” “wind power,” “greenhouse gas,” “solar energy,” “sustainability,” “business,” “philosophy,” “investments,” “fossil fuel,” “law,” “economic growth,” “carbon dioxide,” “finance,” “natural gas,” “ecology,” “economic analysis,” “energy policy,” “fossil fuels,” “costs,” “waste management,” “renewable energy,” “economics,” “political science,” “saudi arabia,” “biology,” “engineering,” “hydrogen production,” “energy consumption,” “computer science,” “renewable energies,” “cost benefit analysis,” “chemistry,” “environmental science,” “macroeconomics,” “alternative energy,” “oman,” “natural resource economics,” “electrical engineering,” “sustainable development,” “hybrid systems,” and “united arab emirates.”Text analysis on titles and keywords. Source: The authors
The diagram has two parts labeled (a) and (b). Part (a) on the top is labeled “Co-occurrence and network of Keywords” and shows a network diagram. The network diagram has a densely connected cluster of nodes of different sizes and colors. The various labels are as follows: The green cluster is present at the top left and has the nodes with the labels: “electric power generation,” “electricity,” “gas emissions,” “hydrogen storage,” “oman,” “investments,” “costs,” “levelized costs,” “cost benefit analysis,” “hybrid systems,” “wind,” “wind energy,” “wind power,” “solar energy,” “biogas,” and “wind turbines.” The blue cluster is present at the top right and has the nodes with the labels: “mathematics,” “population,” “macroeconomics,” “electrical engineering,” “sociology,” “market economy,” “business,” “epistemology,” “law,” “philosophy,” “marketing,” “politics,” “finance,” “environmental economics,” “renewable energy,” “energy transition,” “bioenergy,” and “marketing.” The pink cluster is present at the bottom and has the nodes with the labels: “economic growth,” “carbon dioxide “united arab emirates,” “gulf cooperation council,” and “Qatar.” Four yellow nodes are shown in the center without any labels. Part (b) on the top is labeled “Word Cloud for Titles of the Selected Articles,” and shows a word cloud with words in different colors and sizes. The text includes: “economic development,” “renewable energy resources,” “environmental economics,” “photovoltaic system,” “solar power generation,” “energy efficiency,” “wind power,” “greenhouse gas,” “solar energy,” “sustainability,” “business,” “philosophy,” “investments,” “fossil fuel,” “law,” “economic growth,” “carbon dioxide,” “finance,” “natural gas,” “ecology,” “economic analysis,” “energy policy,” “fossil fuels,” “costs,” “waste management,” “renewable energy,” “economics,” “political science,” “saudi arabia,” “biology,” “engineering,” “hydrogen production,” “energy consumption,” “computer science,” “renewable energies,” “cost benefit analysis,” “chemistry,” “environmental science,” “macroeconomics,” “alternative energy,” “oman,” “natural resource economics,” “electrical engineering,” “sustainable development,” “hybrid systems,” and “united arab emirates.”Text analysis on titles and keywords. Source: The authors
4.2 Findings from the selected studies
Oman is advancing green innovation and diversifying its economy to achieve net zero emissions by 2050. Research highlights its potential as a global green energy hub, particularly in hydrogen production, leveraging its terrain, solar irradiation, and wind speed. Figure 5(a) shows Oman as the most frequently studied country (110 times), with comparisons to the GCC, MENA, UAE, Asia, developed nations, and BRI countries. Figure 5(b) highlights RE sources in selected studies, with hybrid systems including solar-wind (7), solar-biomass (2), and other hybrid types (25), being prominent. Solar, renewable hydrogen, wind, biomass, and hydroelectric power are key areas, with solar and wind receiving the most attention.
The figure has two parts labeled (a) and (b). Part (a) on the left is labeled Country Assessment Distribution” and shows a horizontal bar graph. The horizontal axis of the horizontal bar graph is titled “number of articles” and ranges from 0 to 150 in increments of 50. The vertical axis lists the country categories from top to bottom as: “B R I Countries,” “Developed Countries,” “Asian Countries,” “U A E Countries,” “M E N A Countries,” “General,” “G C C Countries,” and “Oman.” Each country has a horizontal bar showing the number of articles published. The data from the graph is as follows: B R I Countries: 1. Developed Countries: 4. Countries: 5. U A E Countries: 7. M E N A Countries: 14. General: 27. G C C Countries: 36. Oman: 110. Part (b) on the right is labeled “R E Types Assessed” and shows a horizontal bar graph. The horizontal axis of the graph is titled “number of articles” and ranges from 0 to 120 in increments of 20. The vertical axis lists renewable energy (R E) types grouped as Primary and Hybrid. Each category has a horizontal bar showing the number of articles published for the R E type. The data from the graph is as follows: Primary type Solar Energy: 32. Wind Energy: 10. Green Hydrogen: 23. Biomass Energy: 7. Hydroelectric Energy: 1. Hybrid type Hybrid Type: 25. Solar, Biomass: 2. Solar, Wind: 7. The last bar shows the total of both groups as Renewable Energy: 97.Distribution of country and RE types. Source: The authors
The figure has two parts labeled (a) and (b). Part (a) on the left is labeled Country Assessment Distribution” and shows a horizontal bar graph. The horizontal axis of the horizontal bar graph is titled “number of articles” and ranges from 0 to 150 in increments of 50. The vertical axis lists the country categories from top to bottom as: “B R I Countries,” “Developed Countries,” “Asian Countries,” “U A E Countries,” “M E N A Countries,” “General,” “G C C Countries,” and “Oman.” Each country has a horizontal bar showing the number of articles published. The data from the graph is as follows: B R I Countries: 1. Developed Countries: 4. Countries: 5. U A E Countries: 7. M E N A Countries: 14. General: 27. G C C Countries: 36. Oman: 110. Part (b) on the right is labeled “R E Types Assessed” and shows a horizontal bar graph. The horizontal axis of the graph is titled “number of articles” and ranges from 0 to 120 in increments of 20. The vertical axis lists renewable energy (R E) types grouped as Primary and Hybrid. Each category has a horizontal bar showing the number of articles published for the R E type. The data from the graph is as follows: Primary type Solar Energy: 32. Wind Energy: 10. Green Hydrogen: 23. Biomass Energy: 7. Hydroelectric Energy: 1. Hybrid type Hybrid Type: 25. Solar, Biomass: 2. Solar, Wind: 7. The last bar shows the total of both groups as Renewable Energy: 97.Distribution of country and RE types. Source: The authors
4.3 VAR statistics
A forecasting model was applied to time-series data, and stationarity was tested using the ADF test. The results confirmed that GDP, IF, FDI, EC, and EG were stationary in the first difference with p < 0.05. This indicates that these variables are suitable for time-series modelling, as they do not have a unit root. In VAR modelling, determining the optimal number of lags for accurate forecasting and dynamic analysis is a crucial step known as lag order selection. The outcomes of applying different lag selection criteria to a dataset spanning 1985–2023 are shown in Table 3. The AIC indicates that model performance improves by selecting five historical observations. The HQIC recommends one lag, whereas the FPE criterion prefers three lags. The BIC, on the other hand, suggests an inclination toward a more efficient model and recommends selecting zero lags.
Lag order selection for VAR statistics
| Samples: 1985–2023 No. of observations: 39 | ||||
|---|---|---|---|---|
| Lag | AIC | BIC | FPE | HQIC |
| 0 | 6.296 | 6.523* | 542.4 | 6.372 |
| 1 | 5.510 | 6.870 | 252.2 | 5.967* |
| 2 | 6.130 | 8.624 | 524.6 | 6.969 |
| 3 | 5.063 | 8.691 | 246.6* | 6.284 |
| 4 | 5.387 | 10.15 | 694.6 | 6.989 |
| 5 | 4.480* | 10.38 | 1421.0 | 6.464 |
Note(s): * highlights the minimums
The GC test determines the relationships between the economic variables and assesses the predictive ability of a time series. Table 4 presents the results with five lags, as determined by AIC. The results show that IF, FDI, and EG have a statistically significant effect on GDP (p < 0.05), which shows that they can be used to predict economic growth. GDP and EG Granger-cause FDI, suggesting that energy production and economic performance impact foreign investment. Similarly, FDI and IF Granger-cause EG, highlighting their roles in electricity generation and energy demand. Additionally, GDP and FDI Granger-cause IF, implying that FDI trends may affect IF. However, EC did not strongly predict GDP or IF, as no significant causal relationship was found between them during the test period.
Granger causality test results
| Variables | GDP | IF | FDI | EC | EG |
|---|---|---|---|---|---|
| χ2 statistics (probability value) | |||||
| GDP | – | 16.2398 (0.0062) | 13.3671 (0.0202) | 2.5966 (0.7619) | 18.0124 (0.0029) |
| IF | 11.1911 (0.0477) | – | 11.1443 (0.0486) | 2.5557 (0.7681) | 7.6464 (0.1768) |
| FDI | 19.6189 (0.0015) | 9.5032 (0.0906) | – | 6.8051 (0.2355) | 17.9191 (0.0030) |
| EC | 7.2570 (0.2022) | 5.2624 (0.3847) | 3.5739 (0.6122) | – | 3.1616 (0.6751) |
| EG | 1.2040 (0.9445) | 24.7152 (0.0002) | 23.4288 (0.0003) | 2.6315 (0.7566) | – |
Note(s): The values in the brackets indicate a probability value with 5 lags determined through AIC
The JC test confirmed strong cointegration among GDP, IF, EC, FDI, and EG, despite short-term fluctuations. Test statistics exceeded critical values at 95% and 99% confidence levels, confirming a stable long-term relationship between energy and economic variables.
The VAR model, estimated using OLS, revealed key relationships in Table 5. The VAR results for the EG equation highlight the key relationships. The impact of past IF levels on GDP growth is evident in the GDP equation, where the IF at lag 1 has a strong positive effect (t = 3.747, p = 0.000). Over time, changes in EC affect IF because EC at lag 5 has a positive effect on IF (β = 0.3603, p = 0.029) for the IF equation. The FDI equation indicates that prior economic growth promotes FDI inflows, with GDP at lag 2 having a strong positive impact on FDI (β = 0.8679, p = 0.000). Additionally, IF at lag 3 negatively affects FDI (p = 0.000), suggesting that IF discourages investment.
Vector auto regression
| Samples: 1985–2023 | Lags: 5 | No. of observations: 39 | ||
|---|---|---|---|---|
| Log-likelihood: −178.043 | AIC: 4.47991 | BIC: 10.3752 | ||
| Det(Omega_mle): 77.7978 | HQIC: 6.46351 | FPE: 1421.21 | ||
| Variables | coefficient (β) | std. error | t-stat | prob |
| GDP | ||||
| L1.IF | 0.000000 | 0.000000 | 3.747 | 0.000 |
| IF | ||||
| L5.EC | 0.360330 | 0.165535 | 2.177 | 0.029 |
| FDI | ||||
| L2.GDP | 0.867925 | 0.174481 | 4.974 | 0.000 |
| L3.IF | 0.000000 | 0.000000 | −4.569 | 0.000 |
Also, the fact that the residuals of the Durbin-Watson test do not show any severe autocorrelation (GDP: 2.6411, IF: 2.5795, FDI: 2.2221, EC: 1.4620, EG: 2.2407) supports the model's stability, and an analysis of its eigenvalues also shows that it is stable. The GC test evaluates the predictive abilities of GDP, IF, FDI, EC, and EG. There was a 4.219 test statistic for EG, IF, EC, and FDI as causes of GDP, and 2.315 tests showed that the same was true for FDI. These values confirm strong statistical significance at the 5% level, with a p-value of 0.000 and a significant difference from the critical value (1.878).
Significant changes in economic indicators were predicted based on the projected statistics as plotted in Figure 6 with solid lines represents historical values and dashed lines represent predicted values. IF is expected to drop by 58.84% in 2024, whereas GDP is predicted to rise by six times, indicating increased price stability. EC is projected to increase by 37.21%, thereby strengthening the development of the energy sector. FDI is expected to grow by 29.62%, attracting more foreign capital, whereas power production may expand by 8%. Additionally, the GDP is forecasted to increase by 30%, FDI by 37%, and EC by four times by 2025. The sharp rise in EC suggests potential economic challenges, highlighting the need for strategic measures to maintain stability.
The horizontal axis of the line chart is labeled “Year” and ranges from 1985 to 2025 in increments of 2 years. The vertical axis of the graph is labeled “Value” and ranges from negative 20 to 40 in increments of 10 units. The graph contains five colored lines: The green line starts at (1985, 19.1), decreases in a concave-up manner to (1989, 5.5), then rises steeply to (1991, 31.62). The line shows constant fluctuations with major peaks at (1995, 18.21) and (2000, 42.54), and smaller peaks at (2008, 17.42), (2010, 19.78), and (2018, 7). It ends at (2023, 3.62). The line extrapolates in a dotted manner, increasing upward to (2026, 13.62). The orange line starts at (1985, 0.053) and shows sharp fluctuations, with high peaks at (1990, 43.15), (2000, 18.36), (2008, 33.68), and (2022, 17.57). It also shows deep drops at (1998, negative 10.21), (2015, negative 18.26), (2019, negative 24.26), and (2020, negative 9.57). The solid line ends at (2023, 4.97), and the dotted extrapolation decreases to (2026, negative 0.57). The blue line starts at (1985, 18.37), decreases to (1987, negative 2.63), and then increases to (1989, 12.36). With minor fluctuations, it reaches (2008, 8.89). The line continues with fluctuations and ends at (2023, 1.78). The dotted extrapolation increases to (2026, 10.78). The red line starts at (1985, 1.94), stays almost parallel to the horizontal axis until (2004, 0.84), then shows slight fluctuations and increases to (2007, 7.47). It decreases to (2015, negative 2.15), then trends upward and ends at (2023, 12.21). The dotted extrapolation continues upward to (2026, 23.89). The purple line starts at (1985, 3.52) and continuously increases, ending at (2023, 45.05). The dotted extrapolation stays almost constant and ends at (2026, 45.23). A vertical dashed line is shown at the year 2023. Note: All numerical values are approximated.Historical and predicted values using VAR model. Source: The authors
The horizontal axis of the line chart is labeled “Year” and ranges from 1985 to 2025 in increments of 2 years. The vertical axis of the graph is labeled “Value” and ranges from negative 20 to 40 in increments of 10 units. The graph contains five colored lines: The green line starts at (1985, 19.1), decreases in a concave-up manner to (1989, 5.5), then rises steeply to (1991, 31.62). The line shows constant fluctuations with major peaks at (1995, 18.21) and (2000, 42.54), and smaller peaks at (2008, 17.42), (2010, 19.78), and (2018, 7). It ends at (2023, 3.62). The line extrapolates in a dotted manner, increasing upward to (2026, 13.62). The orange line starts at (1985, 0.053) and shows sharp fluctuations, with high peaks at (1990, 43.15), (2000, 18.36), (2008, 33.68), and (2022, 17.57). It also shows deep drops at (1998, negative 10.21), (2015, negative 18.26), (2019, negative 24.26), and (2020, negative 9.57). The solid line ends at (2023, 4.97), and the dotted extrapolation decreases to (2026, negative 0.57). The blue line starts at (1985, 18.37), decreases to (1987, negative 2.63), and then increases to (1989, 12.36). With minor fluctuations, it reaches (2008, 8.89). The line continues with fluctuations and ends at (2023, 1.78). The dotted extrapolation increases to (2026, 10.78). The red line starts at (1985, 1.94), stays almost parallel to the horizontal axis until (2004, 0.84), then shows slight fluctuations and increases to (2007, 7.47). It decreases to (2015, negative 2.15), then trends upward and ends at (2023, 12.21). The dotted extrapolation continues upward to (2026, 23.89). The purple line starts at (1985, 3.52) and continuously increases, ending at (2023, 45.05). The dotted extrapolation stays almost constant and ends at (2026, 45.23). A vertical dashed line is shown at the year 2023. Note: All numerical values are approximated.Historical and predicted values using VAR model. Source: The authors
Every variable in an IRF analysis functions as an impulse, prompting responses from the other variables. The IRF plot in Figure 7 displays these responses, where the dotted lines indicate 95% confidence intervals and the blue lines indicate orthogonalized values. Figure 7(a) shows that a GDP shock reduces IF, causing brief fluctuations until the tenth period reaches stability, after which there is a decline. The EC initially declined, peaked in the fifth period, and then experienced brief oscillations. FDI exhibits temporary fluctuations, peaking in the second period before stabilizing after the sixth period. EG first decreases in the second period before rising steadily from the third period onwards. The IF shock in Figure 7(b) causes short-term fluctuations before GDP stabilizes after the eighth period. The EC initially declined and fluctuated near zero between positive and negative values. FDI shows temporary increases, with positive values in the second, fourth, sixth, and ninth periods before stabilizing. EG initially increases in the first period, remains close to zero until the eighth period, and then stabilizes with a positive effect.
The diagram has five sections labeled from (a) to (e). Each section has 4 line graphs arranged horizontally. The details of each graph are as follows: Section (a) is labeled I R F for G D P. The first graph in this section shows the influence of G D P on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5 units. The graph displays three lines. The blue line starts at (0, 1.32), shows constant fluctuations, passing through the values (3.87, 0.25), (6.72, negative 0.54), (11.74, negative 0.92) and ends at (15, negative 0.75). The black line above starts at (0, 2.5), shows fluctuations, passes through (4.73, 3.62), (11.68, 1.54), and ends at (15, 1.60). The black line below starts at (0, 0.), falls to (1.66, negative 2.94), shows fluctuations through (6.8, negative 3.00), (10, negative 2.05), and ends at (15, negative 3.00). The second graph in this section shows the influence of G D P on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 10 to 10, in increments of 10 units. The graph displays three lines. The blue line starts at (0, 2.79), decreases to (1.79, negative 0.61), fluctuates slightly through (6.91, negative 2.76), (10.86, 2.15), and ends at (15, negative 0.93). The black line above starts at (0, 5.69), rises with fluctuations to (4.87, 9.53), and falls with fluctuations to (7.84, 7.53), (10.80, 8), and ends at (15, 4.46). The black line below starts at (0, negative 0.92), decreases to (3.95, negative 7.53), shows fluctuations through (7.90, negative 5.07), (10.92, negative 2.92), and ends at (15, −5.23). The third graph in this section shows the influence of G D P on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5 units. The graph displays three lines. The blue line starts at (0, negative 0.63), rises to (2.13, 4.89), fluctuates slightly, and ends at (15, negative 4.26). The black line above starts at (0, 0.42), increases to 2.01, 7.23), and decreases with fluctuations to (7.07, 4.78), and ends at (15, 2.66). The black line below starts at (0, negative 1.38), increases with fluctuations to (1.95, 2.44), and ends at (15, negative 4.04). The fourth graph in this section shows the influence of G D P on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0.00 to 0.05, in increments of 0.05. The graph displays three lines. The blue line starts at (0, 0.18), decreases to (2.03, negative 0.005), increases gradually to (8.14, 0.016), and ends at (15, 0.004). The black line above starts at (0, 0.31), rises with fluctuations to (5.06, 0.051), (11.04, 0.05), and ends at (15, 0.04). The black line below starts at (0, 0.001), decreases to (1.97, negative 0.037), increases with fluctuations to (5.12, negative 0.018), (11.04, negative 0.019), and ends at (15, −0.029). Section (b) is labeled I R F for I F. The first graph in this section shows the influence of I F on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.00), shows constant fluctuations, and passes through (0.62, 1.11), (6.56, 0.69), and ends at (15, 0.16). The black line above starts at (0, 0.00), increases with constant fluctuations through (0.62, 3.29), (6.56, 2.92), and ends at (15, 1.38). The black line below starts at (0, 0.00), decreases to (2.56, negative 2.87), shows constant fluctuations, passing through (7.62, negative 2.60), and ends at (15, −0.95). The second graph in this section shows the influence of I F on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 4.39), drops to (1.75, negative 1.56), fluctuates slightly through (8.82, 0.96), and ends at (15, 0.85). The black line above starts at (0, 6.71), decreases to (1.85, 2.47), continues with fluctuations to (8.95, 4.79), and ends at (15, 3.78). The black line below starts at (0, 1.56), decreases to (1.91, negative 5.90), and continues with fluctuations through (9.01, negative 3.08) and ends at (15, negative 1.97). The third graph in this section shows the influence of I F on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, negative 0.16), fluctuates continuously through (2.96, 1.38), (8.76,0.95) and ends at (15, negative 0.053). The black line above starts at (0, 0.95), increases with fluctuations to (3.02, 3.88), (8.76, 3.35) and ends at (15, 1.48). The black line below starts at (0, negative 1.11), decreases with fluctuations through (3.75, negative 2.55), (9.87, negative 2.66), and ends at (15, −1.64). The fourth graph in this section shows the influence of I F on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 0.025 to 0.025, in increments of 0.025. The graph displays three lines. The blue line starts at (0, negative 0.017), rises to (0.92, 0.005), shows small fluctuations, and passes through (9.01, 0.007), and ends at (15, 0.007). The black line above starts at (0, negative 0.003), rises to (1.17, 0.025, shows fluctuations, and passes through (9.01, 0.027), and ends at (15, 0.021). The black line below starts at (0, negative 0.032), increases to (0.92, negative 0.016), increases with fluctuations to (8.91, negative 0.013), and ends at (15, −0.009). Section (c) is labeled I R F for E C. The first graph in this section shows the influence of E C on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0 to 5 in increments of 5 units. The graph displays three lines. The blue line starts at (0, 0.00), rises to (3.39, 1.39), fluctuates slightly through (11.80, 0.88), and ends at (15, 0.44). The black line above starts at (0, 0.00), rises to (0.98, 4.11), shows fluctuations through (6.76, 5.07), and ends at (15, 2.20). The black line below starts at (0, 0.00), decreases to (1.10, negative 2.79), increases with fluctuations to (6.72, negative 2.13), and ends at (15, negative 1.25). The second graph in this section shows the influence of E C on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.00), rises to (0.98, 0.88), fluctuates with small variations through (9.77, 0.57), and ends at (15, 0.104). The black line above starts at (0, 0.00), rises to (0.92, 3.28), shows fluctuations through (9.77, 2.44), and ends at (15, 1.97). The black line below starts at (0, 0.00), decreases to (1.88, negative 1.97), shows fluctuations through (6.76, negative 3.02), and ends at (15, negative 1.40). The third graph in this section shows the influence of E C on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, negative 1.33), rises to (0.86, 0.64), fluctuates slightly through (8.92, 1.26), and ends at (15, 0.11). The black line above starts at (0, negative 0.64), increases to (0.99, 2.63), shows fluctuations through (8.74, 4.92), and ends at (15, 2.40). The black line below starts at (0, negative 2.25), decreases with fluctuations to (2.85, negative 3.39), (8.98, negative 2.32), and ends at (15, negative 2.09). The fourth graph in this section shows the influence of E C on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0.00 to 0.05, in increments of 0.05. The graph displays three lines. The blue line starts at (0, negative 0.12), rises to (1.45, 0.026), shows slight fluctuations through (11.98, 0.008), and ends at (15, 0.004). The black line above starts at (0, 0.00), rises to (1.04, 058), decreases gradually with slight fluctuations through (11.88, 0.034), and ends at (15, 0.027). The black line below starts at (0, negative 0.022), increases to (0.922, negative 0.007), shows fluctuations, and decreases to (11.86, negative .0.017), and ends at (15, negative 0.012). Section (d) is labeled I R F for F D I. The first graph in this section shows the influence of F D I on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.0), increases to (0.97, 1.79), fluctuates around zero, passing through (5, 0.70), (10.8, negative 0.31), and ends at (15, 0.01). The black line above starts at (0, 0.0), increases to (0.97, 4.84), decreases with fluctuations to (5.85, 2.91), (12.82, 1.95), and ends at (15, 1.85). The black line starts at (0, 0.0), and decreases to (2.1, negative 3.12), increases with constant fluctuations to (9.97, negative 1.48), and ends at (15, negative 1.17). The second graph in this section shows the influence of F D I on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2 to 2, in increments of 2. The graph displays three lines. The blue line starts at (0,0).0, increases to (1.96, 0.71), fluctuates around zero, passing through (7.83, 0.36), (9.95, negative 0.40), and ends at (15, 0.02). The black line above starts at (0, 0.0, increases to (1.73, 2.60), shows fluctuations, and decreases to (7.76, 1.87), (11.75, 1.57), and ends at (15, 1.26). The black line below starts at (0, 0.0), decreases to (0.90, negative 1.44), shows constant fluctuations, and passes through (6.48, negative 1.30), (12.88, negative 1.72), and ends at (15, negative 1.18). The third graph in this section shows the influence of F D I on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), increases to (2.72, 2.76), fluctuates around zero, passing through (5.90, 1.36), (10.90, negative 0.26), and ends at (15, 0.032). The black line above starts at (0, 0.0), increases to (3.03, 7.89), decreases with fluctuations to (8.93, 4.60), (11.97, 4.21), and ends at (15, 3.55). The black line below starts at (0, 0.0), decreases to (0.83, negative 5.13), shows constant fluctuations through (5.90, negative 3.15), (11.97, negative 2.23), and ends at (15, negative 2.10). The fourth graph in this section shows the influence of F D I on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 0.025 to 0.025, in increments of 0.025. The graph displays three lines. The blue line starts at (0, 0.0), decreases to (0.76, negative 0.01), fluctuates around zero, passing through (6.04, 0.016), (9.94, 0.013), and ends at (15, 0.002). The black line above starts at (0, 0.019), shows fluctuations through (2.98, 0.031), (9.64, 0.034), and ends at (15, 0.018). The black line below starts at (0, negative 0.008), shows constant fluctuations, and passes through (2.98, negative 0.012), (10.10, negative 0.012), and ends at (15, negative 0.010). Section (e) is labeled I R F for E G. The first graph in this section shows the influence of E G on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (1.58, 1.88), (4.74, negative 1.03), and ends at (15, 0.12). The black line above starts at (0, 0.0), increases to (1.77, 5.57), constantly decreases with fluctuations through (5.61, 3.13), (9.90, 2.69), and ends at (15, 1.73). The black line below starts at (0, 0.0), decreases to (0.88, negative 4.135), and increases with constant fluctuations through (6.42, negative 2.69), (9.90, negative 1.53), and ends at (15, negative 0.76). The second graph in this section shows the influence of E G on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2 to 2, in increments of 2. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (2.86, 0.12), (8.44, 0.26) and ends at (15, negative 0.16). The black line above starts at (0, 0.0), increases to (0.82, 1.66), shows fluctuations, and passes through (4.82, 2.62), (12.96, 1.85), and ends at (15, 1.42). The black line below starts at (0, 0.0), decreases to (090, negative 2.048), shows constant fluctuations, and passes through (6.85, negative 2.14), (12.58, negative 1.47), and ends at (15, negative 1.51). The third graph in this section shows the influence of E G on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (4.90, negative 0.75), (10.76, 0.61), and ends at (15, 0.10). The black line above starts at (0, 0.0), increases to (2.97, 7.19), shows fluctuations through (7.23, 6.09), and ends at (15, 3.76). The black line below starts at (0, 0.00), decreases to (0.89, negative 5.27), shows constant fluctuations through (7.42, negative 4.75), ends at (15, negative 3.49). The fourth graph in this section shows the influence of E G on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (3.91, 1.60), (7.75, 0.43), and ends at (15, 0.20). The black line above starts at (0, 0.0), increases to (1.92, 2.37) shows fluctuations through (3.91, 5.19), (10.71, 2.84), and ends at (15, 2.37). The black line below starts at (0, 0.0), decreases to (3.03, negative 3.93), shows constant fluctuations through (7.98, negative 3.15), and ends at (15, negative 1.51). Note: All numerical values are approximated.Impulse response function analysis. Source: The authors
The diagram has five sections labeled from (a) to (e). Each section has 4 line graphs arranged horizontally. The details of each graph are as follows: Section (a) is labeled I R F for G D P. The first graph in this section shows the influence of G D P on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5 units. The graph displays three lines. The blue line starts at (0, 1.32), shows constant fluctuations, passing through the values (3.87, 0.25), (6.72, negative 0.54), (11.74, negative 0.92) and ends at (15, negative 0.75). The black line above starts at (0, 2.5), shows fluctuations, passes through (4.73, 3.62), (11.68, 1.54), and ends at (15, 1.60). The black line below starts at (0, 0.), falls to (1.66, negative 2.94), shows fluctuations through (6.8, negative 3.00), (10, negative 2.05), and ends at (15, negative 3.00). The second graph in this section shows the influence of G D P on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 10 to 10, in increments of 10 units. The graph displays three lines. The blue line starts at (0, 2.79), decreases to (1.79, negative 0.61), fluctuates slightly through (6.91, negative 2.76), (10.86, 2.15), and ends at (15, negative 0.93). The black line above starts at (0, 5.69), rises with fluctuations to (4.87, 9.53), and falls with fluctuations to (7.84, 7.53), (10.80, 8), and ends at (15, 4.46). The black line below starts at (0, negative 0.92), decreases to (3.95, negative 7.53), shows fluctuations through (7.90, negative 5.07), (10.92, negative 2.92), and ends at (15, −5.23). The third graph in this section shows the influence of G D P on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5 units. The graph displays three lines. The blue line starts at (0, negative 0.63), rises to (2.13, 4.89), fluctuates slightly, and ends at (15, negative 4.26). The black line above starts at (0, 0.42), increases to 2.01, 7.23), and decreases with fluctuations to (7.07, 4.78), and ends at (15, 2.66). The black line below starts at (0, negative 1.38), increases with fluctuations to (1.95, 2.44), and ends at (15, negative 4.04). The fourth graph in this section shows the influence of G D P on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0.00 to 0.05, in increments of 0.05. The graph displays three lines. The blue line starts at (0, 0.18), decreases to (2.03, negative 0.005), increases gradually to (8.14, 0.016), and ends at (15, 0.004). The black line above starts at (0, 0.31), rises with fluctuations to (5.06, 0.051), (11.04, 0.05), and ends at (15, 0.04). The black line below starts at (0, 0.001), decreases to (1.97, negative 0.037), increases with fluctuations to (5.12, negative 0.018), (11.04, negative 0.019), and ends at (15, −0.029). Section (b) is labeled I R F for I F. The first graph in this section shows the influence of I F on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.00), shows constant fluctuations, and passes through (0.62, 1.11), (6.56, 0.69), and ends at (15, 0.16). The black line above starts at (0, 0.00), increases with constant fluctuations through (0.62, 3.29), (6.56, 2.92), and ends at (15, 1.38). The black line below starts at (0, 0.00), decreases to (2.56, negative 2.87), shows constant fluctuations, passing through (7.62, negative 2.60), and ends at (15, −0.95). The second graph in this section shows the influence of I F on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 4.39), drops to (1.75, negative 1.56), fluctuates slightly through (8.82, 0.96), and ends at (15, 0.85). The black line above starts at (0, 6.71), decreases to (1.85, 2.47), continues with fluctuations to (8.95, 4.79), and ends at (15, 3.78). The black line below starts at (0, 1.56), decreases to (1.91, negative 5.90), and continues with fluctuations through (9.01, negative 3.08) and ends at (15, negative 1.97). The third graph in this section shows the influence of I F on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, negative 0.16), fluctuates continuously through (2.96, 1.38), (8.76,0.95) and ends at (15, negative 0.053). The black line above starts at (0, 0.95), increases with fluctuations to (3.02, 3.88), (8.76, 3.35) and ends at (15, 1.48). The black line below starts at (0, negative 1.11), decreases with fluctuations through (3.75, negative 2.55), (9.87, negative 2.66), and ends at (15, −1.64). The fourth graph in this section shows the influence of I F on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 0.025 to 0.025, in increments of 0.025. The graph displays three lines. The blue line starts at (0, negative 0.017), rises to (0.92, 0.005), shows small fluctuations, and passes through (9.01, 0.007), and ends at (15, 0.007). The black line above starts at (0, negative 0.003), rises to (1.17, 0.025, shows fluctuations, and passes through (9.01, 0.027), and ends at (15, 0.021). The black line below starts at (0, negative 0.032), increases to (0.92, negative 0.016), increases with fluctuations to (8.91, negative 0.013), and ends at (15, −0.009). Section (c) is labeled I R F for E C. The first graph in this section shows the influence of E C on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0 to 5 in increments of 5 units. The graph displays three lines. The blue line starts at (0, 0.00), rises to (3.39, 1.39), fluctuates slightly through (11.80, 0.88), and ends at (15, 0.44). The black line above starts at (0, 0.00), rises to (0.98, 4.11), shows fluctuations through (6.76, 5.07), and ends at (15, 2.20). The black line below starts at (0, 0.00), decreases to (1.10, negative 2.79), increases with fluctuations to (6.72, negative 2.13), and ends at (15, negative 1.25). The second graph in this section shows the influence of E C on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.00), rises to (0.98, 0.88), fluctuates with small variations through (9.77, 0.57), and ends at (15, 0.104). The black line above starts at (0, 0.00), rises to (0.92, 3.28), shows fluctuations through (9.77, 2.44), and ends at (15, 1.97). The black line below starts at (0, 0.00), decreases to (1.88, negative 1.97), shows fluctuations through (6.76, negative 3.02), and ends at (15, negative 1.40). The third graph in this section shows the influence of E C on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, negative 1.33), rises to (0.86, 0.64), fluctuates slightly through (8.92, 1.26), and ends at (15, 0.11). The black line above starts at (0, negative 0.64), increases to (0.99, 2.63), shows fluctuations through (8.74, 4.92), and ends at (15, 2.40). The black line below starts at (0, negative 2.25), decreases with fluctuations to (2.85, negative 3.39), (8.98, negative 2.32), and ends at (15, negative 2.09). The fourth graph in this section shows the influence of E C on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0.00 to 0.05, in increments of 0.05. The graph displays three lines. The blue line starts at (0, negative 0.12), rises to (1.45, 0.026), shows slight fluctuations through (11.98, 0.008), and ends at (15, 0.004). The black line above starts at (0, 0.00), rises to (1.04, 058), decreases gradually with slight fluctuations through (11.88, 0.034), and ends at (15, 0.027). The black line below starts at (0, negative 0.022), increases to (0.922, negative 0.007), shows fluctuations, and decreases to (11.86, negative .0.017), and ends at (15, negative 0.012). Section (d) is labeled I R F for F D I. The first graph in this section shows the influence of F D I on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2.5 to 2.5, in increments of 2.5. The graph displays three lines. The blue line starts at (0, 0.0), increases to (0.97, 1.79), fluctuates around zero, passing through (5, 0.70), (10.8, negative 0.31), and ends at (15, 0.01). The black line above starts at (0, 0.0), increases to (0.97, 4.84), decreases with fluctuations to (5.85, 2.91), (12.82, 1.95), and ends at (15, 1.85). The black line starts at (0, 0.0), and decreases to (2.1, negative 3.12), increases with constant fluctuations to (9.97, negative 1.48), and ends at (15, negative 1.17). The second graph in this section shows the influence of F D I on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2 to 2, in increments of 2. The graph displays three lines. The blue line starts at (0,0).0, increases to (1.96, 0.71), fluctuates around zero, passing through (7.83, 0.36), (9.95, negative 0.40), and ends at (15, 0.02). The black line above starts at (0, 0.0, increases to (1.73, 2.60), shows fluctuations, and decreases to (7.76, 1.87), (11.75, 1.57), and ends at (15, 1.26). The black line below starts at (0, 0.0), decreases to (0.90, negative 1.44), shows constant fluctuations, and passes through (6.48, negative 1.30), (12.88, negative 1.72), and ends at (15, negative 1.18). The third graph in this section shows the influence of F D I on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), increases to (2.72, 2.76), fluctuates around zero, passing through (5.90, 1.36), (10.90, negative 0.26), and ends at (15, 0.032). The black line above starts at (0, 0.0), increases to (3.03, 7.89), decreases with fluctuations to (8.93, 4.60), (11.97, 4.21), and ends at (15, 3.55). The black line below starts at (0, 0.0), decreases to (0.83, negative 5.13), shows constant fluctuations through (5.90, negative 3.15), (11.97, negative 2.23), and ends at (15, negative 2.10). The fourth graph in this section shows the influence of F D I on E G. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 0.025 to 0.025, in increments of 0.025. The graph displays three lines. The blue line starts at (0, 0.0), decreases to (0.76, negative 0.01), fluctuates around zero, passing through (6.04, 0.016), (9.94, 0.013), and ends at (15, 0.002). The black line above starts at (0, 0.019), shows fluctuations through (2.98, 0.031), (9.64, 0.034), and ends at (15, 0.018). The black line below starts at (0, negative 0.008), shows constant fluctuations, and passes through (2.98, negative 0.012), (10.10, negative 0.012), and ends at (15, negative 0.010). Section (e) is labeled I R F for E G. The first graph in this section shows the influence of E G on G D P. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from 0 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (1.58, 1.88), (4.74, negative 1.03), and ends at (15, 0.12). The black line above starts at (0, 0.0), increases to (1.77, 5.57), constantly decreases with fluctuations through (5.61, 3.13), (9.90, 2.69), and ends at (15, 1.73). The black line below starts at (0, 0.0), decreases to (0.88, negative 4.135), and increases with constant fluctuations through (6.42, negative 2.69), (9.90, negative 1.53), and ends at (15, negative 0.76). The second graph in this section shows the influence of E G on I F. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 2 to 2, in increments of 2. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (2.86, 0.12), (8.44, 0.26) and ends at (15, negative 0.16). The black line above starts at (0, 0.0), increases to (0.82, 1.66), shows fluctuations, and passes through (4.82, 2.62), (12.96, 1.85), and ends at (15, 1.42). The black line below starts at (0, 0.0), decreases to (090, negative 2.048), shows constant fluctuations, and passes through (6.85, negative 2.14), (12.58, negative 1.47), and ends at (15, negative 1.51). The third graph in this section shows the influence of E G on E C. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (4.90, negative 0.75), (10.76, 0.61), and ends at (15, 0.10). The black line above starts at (0, 0.0), increases to (2.97, 7.19), shows fluctuations through (7.23, 6.09), and ends at (15, 3.76). The black line below starts at (0, 0.00), decreases to (0.89, negative 5.27), shows constant fluctuations through (7.42, negative 4.75), ends at (15, negative 3.49). The fourth graph in this section shows the influence of E G on F D I. The horizontal axis ranges from 0 to 15, in increments of 5 units. The vertical axis ranges from negative 5 to 5, in increments of 5. The graph displays three lines. The blue line starts at (0, 0.0), fluctuates around zero, passing through (3.91, 1.60), (7.75, 0.43), and ends at (15, 0.20). The black line above starts at (0, 0.0), increases to (1.92, 2.37) shows fluctuations through (3.91, 5.19), (10.71, 2.84), and ends at (15, 2.37). The black line below starts at (0, 0.0), decreases to (3.03, negative 3.93), shows constant fluctuations through (7.98, negative 3.15), and ends at (15, negative 1.51). Note: All numerical values are approximated.Impulse response function analysis. Source: The authors
Figure 7(c) shows that the GDP remains stable following an EC shock, with notable increases in the fourth and seventh periods. IF initially reacts positively, briefly turns negative in the seventh period, and then rises after the eighth period before declining in the eleventh period. FDI rises briefly, falls in the second period, stabilizes positively in the ninth, and declines in the tenth period. The EG first increased peaks in the second period and maintained a consistent influence after the sixth period. As shown in Figure 7(d), an FDI shock causes GDP to peak in the second period and then fluctuates between positive and negative regions before stabilizing after the thirteenth period. The IF briefly rises before reaching a negative peak between the tenth and thirteenth periods. The EC initially falls into a negative region, reaches a positive peak in the third period, and then fluctuates between negative and positive values. EG first declines and rises in the third period and then fluctuates within positive ranges. Figure 7(e) illustrates the response of the EG to shock. GDP initially peaked in the third period before declining into negative regions in the fourth and fifth periods, eventually stabilizing. IF and EC exhibited brief fluctuations in the negative region. FDI declines in the first and third periods, peaks in the fourth period, and stabilizes after the tenth period.
Moreover, the heatmap in Figure 8 displays the peak impulse response values between each pair of variables. Using a log scale emphasizes differences: high impacts, such as FDI's effect on EG (47.91), stand out in darker colours due to its higher values than the other variables, while low interactions appear lighter. This visual clearly identifies which shocks strongly influence which responses in the VAR model.
The matrix heatmap is labeled “Response” on the vertical axis and “Shock” on the horizontal axis. The horizontal labels are “E G,” “G D P,” “I F,” “E C,” and “F D I.” The same labels are also shown on the vertical axis. Each cell contains a numerical value: A vertical color bar on the right ranges from light yellow at 0.0 to dark red above 3.5. The data from the matrix is as follows: Row 1, Column 1: E G, E G: 0.69. Row 1, Column 2: E G, G D P: 0.00. Row 1, Column 3: E G, I F: 0.20. Row 1, Column 4: E G, E C: 0.65. Row 1, Column 5: E G, F D I: 3.89. Row 2, Column 1: G D P, E G: 0.13. Row 2, Column 2: G D P, G D P: 0.69. Row 2, Column 3: G D P, I F: 0.16. Row 2, Column 4: G D P, E C: 0.33. Row 2, Column 5: G D P, F D I: 1.45. Row 3, Column 1: I F, E G: 0.31. Row 3, Column 2: I F, G D P: 0.00. Row 3, Column 3: I F, I F: 0.69. Row 3, Column 4: I F, E C: 0.89. Row 3, Column 5: I F, F D I: 2.87. Row 4, Column 1: E C, E G: 0.65. Row 4, Column 2: E C, G D P: 0.00. Row 4, Column 3: E C, I F: 0.20. Row 4, Column 4: E C, E C: 0.73. Row 4, Column 5: E C, F D I: 3.68. Row 5, Column 1: F D I, E G: 0.00. Row 5, Column 2: F D I, G D P: 0.00. Row 5, Column 3: F D I, I F: 0.00. Row 5, Column 4: F D I, E C: 0.01. Row 5, Column 5: F D I, F D I: 0.69. The cells for “Row 1, Column 5: E G, F D I: 3.89” and “Row 4, Column 5: E C, F D I: 3.68” are shown in dark red color. The cell “Row 3, Column 5: I F, F D I: 2.87” is shown in a light red color, and the cell “Row 2, Column 5: G D P, F D I: 1.45” is shown in a light orange color. All other cells have varying shades of yellow according to the values as shown in the color bar on the right.Peak impulse response heatmap. Source: The authors
The matrix heatmap is labeled “Response” on the vertical axis and “Shock” on the horizontal axis. The horizontal labels are “E G,” “G D P,” “I F,” “E C,” and “F D I.” The same labels are also shown on the vertical axis. Each cell contains a numerical value: A vertical color bar on the right ranges from light yellow at 0.0 to dark red above 3.5. The data from the matrix is as follows: Row 1, Column 1: E G, E G: 0.69. Row 1, Column 2: E G, G D P: 0.00. Row 1, Column 3: E G, I F: 0.20. Row 1, Column 4: E G, E C: 0.65. Row 1, Column 5: E G, F D I: 3.89. Row 2, Column 1: G D P, E G: 0.13. Row 2, Column 2: G D P, G D P: 0.69. Row 2, Column 3: G D P, I F: 0.16. Row 2, Column 4: G D P, E C: 0.33. Row 2, Column 5: G D P, F D I: 1.45. Row 3, Column 1: I F, E G: 0.31. Row 3, Column 2: I F, G D P: 0.00. Row 3, Column 3: I F, I F: 0.69. Row 3, Column 4: I F, E C: 0.89. Row 3, Column 5: I F, F D I: 2.87. Row 4, Column 1: E C, E G: 0.65. Row 4, Column 2: E C, G D P: 0.00. Row 4, Column 3: E C, I F: 0.20. Row 4, Column 4: E C, E C: 0.73. Row 4, Column 5: E C, F D I: 3.68. Row 5, Column 1: F D I, E G: 0.00. Row 5, Column 2: F D I, G D P: 0.00. Row 5, Column 3: F D I, I F: 0.00. Row 5, Column 4: F D I, E C: 0.01. Row 5, Column 5: F D I, F D I: 0.69. The cells for “Row 1, Column 5: E G, F D I: 3.89” and “Row 4, Column 5: E C, F D I: 3.68” are shown in dark red color. The cell “Row 3, Column 5: I F, F D I: 2.87” is shown in a light red color, and the cell “Row 2, Column 5: G D P, F D I: 1.45” is shown in a light orange color. All other cells have varying shades of yellow according to the values as shown in the color bar on the right.Peak impulse response heatmap. Source: The authors
These results highlight the critical role of stable policy frameworks in sustaining renewable energy growth, particularly in relation to energy pricing and investment regulation. Policymakers in Oman should focus on achieving a balance between price stability and economic growth, while creating incentives to attract foreign investment into the renewable energy sector. Strengthening institutional and regulatory frameworks will ensure that the benefits of renewable energy contribute not only to energy security but also to long-term economic resilience.
5. Discussion
RE is essential for national development, particularly for reducing carbon emissions, fostering environmental sustainability, and increasing economic growth. Numerous studies have examined its effects in various regions, focusing on carbon emissions (Mai & Tran, 2023), environmental sustainability (Samour et al., 2024), and country groups (Lahrech et al., 2024). However, there is limited research on country-specific analyses, especially in Oman, where sustainability and economic diversification depend heavily on RE. Unlike existing studies that emphasise on regional or multi-country analyses (Kebede, Kalogiannis, Van Mierlo, & Berecibar, 2022), this study provides a country-specific assessment of Oman's RE sector, offering insights that are directly relevant to local policy and investment strategies. This study addresses this gap by analysing the state of RE adoption in Oman from three perspectives, identifying key trends, challenges, and opportunities to guide future investment and policy decisions using energy economics theory.
First, with a 6.3% annual growth rate, the bibliometric analysis of 204 studies demonstrated strong production performance in the study field. The reliability of the analysis was reinforced by the prevalence of peer-reviewed journal publications. While citation impact varied, with earlier works receiving more attention, annual production steadily increased, with a sharp rise since 2020 (see Figure 2). Okonkwo, P.C. is the most relevant author with 12 articles, whereas Wang et al. (2023) has the most cited article with 240 citations. This reflects the varying levels of impact, highlighting the contributions of key scholars and publications to RE research (see Table 2). The presence of diverse journals and affiliations suggests the need for a multidisciplinary and international collaborative approach. Sultan Qaboos University was the most frequent contributor, while Environmental Science and Pollution Research was the top journal in this field (see Table 2). Keyword and trend analyses indicate the evolution of RE research with a growing emphasis on regional, technical, and economic aspects (see Figure 3). The recent rise in keywords such as 'law' signals an increasing interest in regulatory frameworks. The keyword network highlights the importance of RE research in Oman for GCC energy and economic development, associating it with regional economic and environmental factors (see Figures 4 and 5). These bibliometric findings extend prior research conducted by Wang et al. (2024) and Mentel et al. (2023) by revealing country-specific trends and emerging themes in Oman that were not previously highlighted in global studies.
Second, the analysis of selected studies highlights Oman's focus on economic diversification and net-zero emissions, with green innovation efforts centred on solar, wind, and hydrogen energy (Barghash, Al Farsi, Okedu, & Al-Wahaibi, 2022). Beitelmal, Okonkwo, Al Housni, Alruqi, and Alruwaythi (2020) and Okonkwo, Belgacem, Zghaibeh, and Tlili (2022) confirmed the financial feasibility of solar PV and hybrid energy solutions. However, infrastructure constraints, regulatory hurdles, and reliance on fossil fuels persist (Umar et al., 2020; Farajzadeh et al., 2022). While Oman prioritizes solar and wind, global research explores broader renewable technologies, grid integration, and storage solutions (Alharbi & Csala, 2020; Al-Badi, Al Wahaibi, Ahshan, & Malik, 2022). A smoother transition requires technological investment, financial incentives, and policy reforms (Li et al., 2023). Further acceleration of Oman's RE sector requires energy storage, smart grids, and research-driven policymaking (Zghaibeh et al., 2024). These studies indicate that Oman is advancing sustainable energy through legislative change, financial incentives, and technological progress. Unlike previous analysis of the GCC region conducted by Alharbi and Csala (2020) and Khan and Al-Ghamdi (2023), this study highlights the combination of technological, regulatory, and financial factors that are uniquely shaping Oman's RE adoption, thus providing actionable insights for local stakeholders.
Third, the findings of the VAR model highlight the key economic interactions. The ADF test confirms stationarity, making the variables suitable for time-series modelling. The lag selection suggests different optimal lags, although AIC favours five lags. The GC test reveals economic interdependencies, showing that GDP and FDI influence IF, whereas IF, FDI, and EG significantly impact GDP. The JC confirms the long-term relationships between variables. VAR estimation indicates that EC affects IF, GDP influences FDI, and historical IF affects GDP. The causal pathways in Oman's economic dynamics are significant, with a strong correlation between GDP and FDI. Inflationary trends are sensitive to domestic growth changes, and the impact of IF on GDP shocks is moderately large but noteworthy. The influence of EC on IF is less noticeable, suggesting that pressures on energy demand contribute to price instability gradually. These differences help decision-makers understand Oman's economic dynamics and identify the most impactful levers.
Forecasts predict economic growth during 2024–2025, with significant increases in GDP, FDI, and EC. The IRF analysis illustrates initial fluctuations before stabilization, demonstrating the long-term effects of shocks on the variables. While FDI and EG have distinct impacts on the economic variables, GDP shocks lower IF. These findings suggest that IF, FDI, and EC are critical to economic performance. Thus, to maintain economic stability, policymakers should focus on managing IF, regulating energy demand, and promoting investments. The predicted rise in EC necessitates strategic measures to mitigate potential challenges. These VAR model results confirm the findings of earlier studies such as Al-Badi et al. (2022) and Al-Sarihi & Cherni (2018) in Oman while also extending them by quantifying the dynamic interrelationships between macroeconomic and energy variables, offering empirical evidence to guide both theory and practice in the context of Oman.
Thus, the bibliometric and VAR analyses provide a comprehensive understanding of Oman's RE sector, highlighting both research trends and economic dynamics. These insights help identify practical implications, offering evidence-based guidance for policymakers, investors, and future research in the RE sector, while also providing country-specific evidence that extends prior regional and global studies.
The results have significant ramifications for upcoming plans and policies in renewable energy in Oman. Strong bibliometric growth indicates increased scholarly and policy interest, which can be harnessed to support collaborative innovation and research-driven policymaking. Given that price volatility may jeopardize long-term energy security, the VAR analysis findings highlight the necessity of policies that stabilize inflation while simultaneously promoting investments in renewable energy. Furthermore, the benefits of FDI underscore the importance of creating incentives to attract foreign investment for solar, wind, and hydrogen projects. To accelerate RE adoption, policymakers should prioritize strengthening institutional frameworks, simplifying regulations, and promoting infrastructure development. At the same time, strategies should focus on energy storage technologies and economic diversification to ensure that renewable energy contributes not only to emission reduction but also to sustainable and resilient economic growth in Oman.
5.1 Study implications
5.1.1 Theoretical implications
This study offers significant insights into the development of research on RE, particularly in Oman. Bibliometric analysis highlights the increasing significance of economic, technical, and regulatory dimensions, providing a theoretical framework for understanding the multifaceted nature of RE research. Identifying pivotal authors, articles, and emerging trends, including the growing emphasis on legal frameworks, suggests potential directions for further exploration of RE transition regulations. Furthermore, the VAR model highlights the intricate interrelationships among economic variables, thus enhancing theories on the economic effects of EC, FDI, and IF on GDP growth.
5.1.2 Practical implications
The findings highlight the need for policymakers and stakeholders in Oman and the wider GCC region to prioritise strategic initiatives, including infrastructure investment, policy reforms, and technological advancements, to drive RE development. The emphasis on solar, wind, and hydrogen energy underscores the importance of diversification to reduce dependence on fossil fuels. Establishing regulatory frameworks that encourage private investment and technological innovation is essential. As this study forecasts notable increases in GDP, FDI, and EC, ensuring effective energy demand management, inflation control, and a favourable investment climate is vital for maintaining economic stability. The key strategic policy recommendations based on the forecasting model results are:
Policymakers should focus on economic expansion to attract FDI through investment-friendly policies, tax incentives, and infrastructure development.
High IF discourages FDI. Implementing sound monetary policies can sustain investor confidence and support long-term economic stability.
Increasing EC requires proactive investments in RE projects, enhanced grid infrastructure, and well-defined energy transition policies.
Strengthening institutional capacity to provide financial support for RE projects is crucial. Policy incentives, public-private partnerships, and regulatory reforms can attract long-term investments.
Integrated economic and energy policies, along with industry-academia-government collaboration, can reduce volatility and ensure sustained growth.
Strengthening environmental policies can accelerate RE adoption, support economic growth, and create a stable investment environment.
In addition to domestic considerations, external dynamics such as global oil price fluctuations and international renewable energy policies play a decisive role in shaping Oman's energy sector. These externalities influence long-term RE adoption, energy costs, and investment flows. Therefore, policy strategies informed by VAR results should explicitly account for these external shocks to provide policymakers with more reliable forecasts and resilient planning tools for energy security and sustainable growth in Oman. To illustrate the interaction between these challenges and the potential policy responses, Figure 9 presents a framework in which the inner circle represents the difficulties and the outer circle outlines the solutions.
The circular diagram consists of two concentric circles divided into 38 segments. Each segment in the inner circle presents a challenge, while the corresponding segment in the outer circle provides a solution. The complete text in the diagram is as follows: Inner ring: “Aging energy networks,” Outer ring: “Modernise infrastructure.” Inner ring: “Behind renewable targets,” Outer ring: “Accelerate adoption efforts.” Inner ring: “Bureaucratic approval hurdles,” Outer ring: “Streamline approval processes.” Inner ring: “Extreme weather risks,” Outer ring: “Strengthen infrastructure.” Inner ring: “Fossil fuel dependency,” Outer ring: “Diversify energy sources.” Inner ring: “Fragmented energy policies,” Outer ring: “Institutional coordination.” Inner ring: “Grid integration issues,” Outer ring: “Upgrade grid infrastructure.” Inner ring: “Harsh climate impact,” Outer ring: “Adapt to climate risks.” Inner ring: “High adoption costs,” Outer ring: “Reduce technology costs.” Inner ring: “High investment costs,” Outer ring: “Increase financial incentives.” Inner ring: “Highly controlled market,” Outer ring: “Market liberalization.” Inner ring: “Hydrogen storage issues,” Outer ring: “Enhance storage conditions.” Inner ring: “Import dependence,” Outer ring: “Develop local manufacturing.” Inner ring: “Inconsistent regulations,” Outer ring: “Standardize energy policies.” Inner ring: “Insufficient infrastructure,” Outer ring: “Expand renewable projects.” Inner ring: “Intermittency of renewables,” Outer ring: “Develop energy storage.” Inner ring: “Lack of engagement,” Outer ring: “Strengthen outreach efforts.” Inner ring: “Limited advanced technologies,” Outer ring: “Invest in R and D.” Inner ring: “Limited energy awareness,” Outer ring: “Increase public campaigns.” Inner ring: “Limited financial incentives,” Outer ring: “Subsidise renewables.” Inner ring: “Limited government subsidies,” Outer ring: “Expand funding schemes.” Inner ring: “Limited land access,” Outer ring: “Optimize land use.” Inner ring: “Limited private participation,” Outer ring: “Attract private investments.” Inner ring: “Limited raw materials,” Outer ring: “Secure supply chains.” Inner ring: “Low investor confidence,” Outer ring: “Strengthen investment policies.” Inner ring: “Low technical efficiency,” Outer ring: “Optimize technology design.” Inner ring: “Misinformation spread,” Outer ring: “Ensure factual communication.” Inner ring: “Price volatility,” Outer ring: “Ensure pricing stability.” Inner ring: “Reliance on fossil fuels,” Outer ring: “Expand renewables mix.” Inner ring: “Resistance to change,” Outer ring: “Educate stakeholders.” Inner ring: “Skilled workforce shortage,” Outer ring: “Enhance technical training.” Inner ring: “Slow innovation pace,” Outer ring: “Encourage research funding.” Inner ring: “Slow investment returns,” Outer ring: “Offer tax incentives.” Inner ring: “Solar panel inefficiency,” Outer ring: “Enhance panel durability.” Inner ring: “Unclear government policies,” Outer ring: “Develop clear regulations.” Inner ring: “Uncompetitive energy markets,” Outer ring: “Improve market conditions.” Inner ring: “Water resource limitations,” Outer ring: “Improve water management.” Inner ring: “Weak business incentives,” Outer ring: “Promote investment benefits.”Potential challenges and recommendations for RE development. Source: The authors
The circular diagram consists of two concentric circles divided into 38 segments. Each segment in the inner circle presents a challenge, while the corresponding segment in the outer circle provides a solution. The complete text in the diagram is as follows: Inner ring: “Aging energy networks,” Outer ring: “Modernise infrastructure.” Inner ring: “Behind renewable targets,” Outer ring: “Accelerate adoption efforts.” Inner ring: “Bureaucratic approval hurdles,” Outer ring: “Streamline approval processes.” Inner ring: “Extreme weather risks,” Outer ring: “Strengthen infrastructure.” Inner ring: “Fossil fuel dependency,” Outer ring: “Diversify energy sources.” Inner ring: “Fragmented energy policies,” Outer ring: “Institutional coordination.” Inner ring: “Grid integration issues,” Outer ring: “Upgrade grid infrastructure.” Inner ring: “Harsh climate impact,” Outer ring: “Adapt to climate risks.” Inner ring: “High adoption costs,” Outer ring: “Reduce technology costs.” Inner ring: “High investment costs,” Outer ring: “Increase financial incentives.” Inner ring: “Highly controlled market,” Outer ring: “Market liberalization.” Inner ring: “Hydrogen storage issues,” Outer ring: “Enhance storage conditions.” Inner ring: “Import dependence,” Outer ring: “Develop local manufacturing.” Inner ring: “Inconsistent regulations,” Outer ring: “Standardize energy policies.” Inner ring: “Insufficient infrastructure,” Outer ring: “Expand renewable projects.” Inner ring: “Intermittency of renewables,” Outer ring: “Develop energy storage.” Inner ring: “Lack of engagement,” Outer ring: “Strengthen outreach efforts.” Inner ring: “Limited advanced technologies,” Outer ring: “Invest in R and D.” Inner ring: “Limited energy awareness,” Outer ring: “Increase public campaigns.” Inner ring: “Limited financial incentives,” Outer ring: “Subsidise renewables.” Inner ring: “Limited government subsidies,” Outer ring: “Expand funding schemes.” Inner ring: “Limited land access,” Outer ring: “Optimize land use.” Inner ring: “Limited private participation,” Outer ring: “Attract private investments.” Inner ring: “Limited raw materials,” Outer ring: “Secure supply chains.” Inner ring: “Low investor confidence,” Outer ring: “Strengthen investment policies.” Inner ring: “Low technical efficiency,” Outer ring: “Optimize technology design.” Inner ring: “Misinformation spread,” Outer ring: “Ensure factual communication.” Inner ring: “Price volatility,” Outer ring: “Ensure pricing stability.” Inner ring: “Reliance on fossil fuels,” Outer ring: “Expand renewables mix.” Inner ring: “Resistance to change,” Outer ring: “Educate stakeholders.” Inner ring: “Skilled workforce shortage,” Outer ring: “Enhance technical training.” Inner ring: “Slow innovation pace,” Outer ring: “Encourage research funding.” Inner ring: “Slow investment returns,” Outer ring: “Offer tax incentives.” Inner ring: “Solar panel inefficiency,” Outer ring: “Enhance panel durability.” Inner ring: “Unclear government policies,” Outer ring: “Develop clear regulations.” Inner ring: “Uncompetitive energy markets,” Outer ring: “Improve market conditions.” Inner ring: “Water resource limitations,” Outer ring: “Improve water management.” Inner ring: “Weak business incentives,” Outer ring: “Promote investment benefits.”Potential challenges and recommendations for RE development. Source: The authors
Beyond economic and policy impacts, the findings have important social implications. Expanding RE infrastructure and technologies can create employment opportunities and enhance technical skills through specialized training programs. Promoting public awareness and engagement in RE projects strengthens societal participation and acceptance of sustainable energy initiatives. Greater adoption of renewable energy can improve energy access in remote areas, reduce reliance on fossil fuels, lower energy costs, and contribute to better public health by minimizing pollution. These social benefits highlight the broader societal value of transitioning toward sustainable energy systems in Oman and the wider GCC region.
6. Conclusion
This study offers an in-depth examination of economic interactions, policy implications, and research trends related to RE with a focus on Oman. A bibliometric analysis of 204 studies highlights a noteworthy annual growth rate of 6.3%, suggesting a growing scholarly interest in RE. The wide range of journals and associations indicates a multidisciplinary and internationally cooperative research methodology. Key contributors have made significant contributions, reflecting the dynamic character of the field. Keyword analysis shows that policy frameworks play an increasingly important role in the adoption of RE sources, with emphasis on economic, technological, and regulatory considerations.
In pursuing economic diversification and net-zero emissions, Oman is reportedly leading green innovation, with a focus on solar, wind, and hydrogen power. However, regulatory hurdles, infrastructure constraints, and dependence on fossil fuels remain challenges, although solar PV and hybrid systems have proven cost-effective. For long-term sustainability, Oman must also consider research emphasizing the importance of grid integration and energy storage. The rapid adoption of RE sources requires substantial investment in technology, financial incentives, and policy reforms. Insights into the interplay between EC, FDI, IF, and GDP are provided through economic analysis using the VAR model. The results indicate that EC and FDI are key drivers of economic growth, while IF serves as a major factor influencing economic stability. JC and GC tests confirm that these variables have long been interdependent. Forecasts suggest that GDP, FDI, and EC will continue to increase in the coming years. To preserve economic stability, strategic measures are required to manage energy demand, control inflation, and stimulate investment.
Despite its contributions, this study has several limitations. It relies on secondary data from multiple sources. The bibliometric analysis is based on specific databases and may not cover all relevant studies. Moreover, the datasets used for the VAR analysis differ across sources, warranting further investigation. The findings are also influenced by the selected keywords and search parameters, which may exclude broader dimensions of RE research. The VAR model may not encompass all influencing variables, and external shocks or policy modifications could affect future forecasts. In addition, the interpretation of VAR results could be expanded to provide stronger connections with political and economic implications. Future studies could benefit from incorporating raw data or larger, multi-source datasets and applying advanced econometric or machine learning techniques to enhance the robustness of the analysis. They could also complement quantitative approaches with qualitative methods, such as expert interviews or detailed case studies, to capture deeper insights into the challenges and opportunities of Oman's RE sector. Although this study provides insights into Oman's RE sector, its applicability to other regions may be limited due to differences in policies, resources, and economic conditions.
Thus, this study makes an important contribution to the state of the art by being the first to jointly employ bibliometric and VAR analyses to examine Oman's RE sector. This integrated approach reveals both research trends and policy priorities while demonstrating the influence of economic drivers such as FDI, EC, and IF in shaping long-term sustainability. By combining methodological rigour with country-specific insights, the study provides a replicable framework for other emerging economies and establishes a foundation for future research and policymaking. Consequently, policymakers and scholars can clearly recognize the academic and practical significance of this work.
Future research should integrate diverse datasets to address data variability and exclusions. Expanding bibliometric analyses across multiple databases could offer a broader perspective. Additionally, incorporating expert opinions as primary data would allow for a more detailed examination of sectoral challenges. The adoption of advanced econometric methods and alternative modelling strategies may strengthen the robustness of VAR analyses. Future studies should also explore the impacts of external shocks, such as global oil price fluctuations, international renewable energy policies, and sector-specific energy regulations. Moreover, AI-driven energy management, intelligent grids, and infrastructure development could provide valuable insights. Cross-national comparisons and the challenges of policy implementation should be examined. Future work may also evaluate energy storage solutions, grid stability, public–private collaborations, and the role of financial incentives in accelerating RE adoption.
