{"paper_id":"1e3ca2cb-0a62-4717-af6a-5c2c3bc405b3","body_text":"Blue Economy and Sustainable Economic Growth in Saudi Arabia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blue Economy and Sustainable Economic Growth in Saudi Arabia omar Al-kasasbeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7535978/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the role of the blue economy in promoting sustainable economic growth in Saudi Arabia, utilizing the Autoregressive Distributed Lag (ARDL) framework to analyze both short- and long-term dynamics. The findings reveal that key blue economy elements, including fisheries production, aquaculture, and the combined outputs of agriculture, forestry, and fisheries, contribute positively to economic growth, while inflation exerts a negative effect. Trade emerges as a significant driver of growth, emphasizing the importance of trade openness in fostering economic development. The study highlights the potential of Saudi Arabia to leverage its marine resources, given its strategic geographic location and abundant blue economy opportunities, to stimulate infrastructure development and generate employment. However, achieving these outcomes requires strong political commitment and investment in research. The study acknowledges limitations related to data availability and the complexity of the blue economy, suggesting that future research should adopt comprehensive data collection methods and consider the social and environmental impacts of blue economy initiatives. By addressing these dimensions, Saudi Arabia can effectively harness its blue resources to advance sustainable economic development and contribute to the achievement of Sustainable Development Goal 14. Blue Economy Economic Growth Sustainable Development ARDL Model KSA Introduction Throughout the ages, the sea has been one of humanity’s most important crutches, supplying food and transport, commerce and more. More recently the concept of the Blue Economy has gained broad recognition as a key approach for sustainable economic growth involving the sustainable use of the sea's resources. Circular Material Flow: This is in strong correlation to the concept of the Circular Economy, where resource optimization, waste reduction and circularity play an important role. The nexus between BE and CE provides a promising but also complicated trajectory for sustainable ocean management. (Ahammed et al., 2024 ). The Blue Economy seeks to promote economic growth, social inclusion, and environmental sustainability at the same time. Given the increasing anthropogenic pressures and limited capacity of marine ecosystems, sustainable management of ocean resources is imperative. The term Blue Economy was formally introduced in 2009 during a U.S. Senate Committee hearing, where it was linked to economic advancement and climate change mitigation. Since then, it has gained traction in global discourse, notably at the 2012 United Nations Conference on Sustainable Development (Rio + 20), where the ocean was identified as a thematic priority (Alzghoul et al., 2024 ). The Blue Economy encompasses key sectors such as aquaculture, fisheries, renewable energy, marine tourism, biotechnology, and shipping, all of which contribute significantly to the global GDP. Nevertheless, the concept suffers from definitional inconsistencies, often overlapping with similar constructs like the Ocean Economy, Marine Economy, and Blue Growth. While these paradigms share goals of economic development and ecosystem protection, they diverge in scope and emphasis. Central to this discussion is the tension between unbridled economic growth and the need to preserve marine ecosystems. While growth may drive employment and raise incomes, unchecked development risks causing ecological degradation and displacing coastal communities (Akinlo, 2022 ). Equity concerns are increasingly being raised, particularly regarding the marginalization of vulnerable groups. Small Island Developing States (SIDS) were among the earliest proponents of an inclusive BE model grounded in social justice and sustainability. However, current policy discourse often prioritizes industrial expansion over equity, leading to phenomena like “ocean grabbing,” where access to marine resources is monopolized by elites. The global fishing industry, for instance, has been criticized for exploitative labor practices and exclusionary policies that hinder small-scale fishers. Similar concerns arise in aquaculture and offshore energy sectors. In response, academics have urged the need to incorporate the Blue Economy with the United Nations Sustainable Development Goals, particularly SDG 14. This orientation prioritizes the two poles of ecological conservation and economic development. Yet, uneven capabilities for governance capacities, basic economic conditions, and climate vulnerability impede such an integration. The unequal effects of marine degradation highlight the need for policy to support sustainability and equity. Although many studies have considered the effects of economic growth on individual marine components, relatively little research has focused on the direct contributions of fundamental Blue Economy elements to sustainable development, such as fisheries, maritime transportation, and tourism. Given that 80 per cent of global trade is carried by the maritime industry, and around 350 million people around the world work in fisheries, the sector’s contribution is significant, but not maximized. Saudi Arabia has a lower per capita income compared to some other Gulf Cooperation Council countries, and it is actively working to reform its economic structure and governance systems (Alshuwaikhat & Mohammed, 2017 ; Al-Kasasbeh et al., 2024 ). As part of these efforts, the Kingdom launched Vision 2030, an ambitious strategic framework aimed at achieving sustainable development. However, despite this important step, there is still a considerable way to go, particularly in place strategies that simultaneously drive economic growth and mitigate carbon emissions. Although there has been growing interest in understanding how blue economy sectors affect national economic performance, there are still notable gaps in the literature, particularly regarding their contribution to sustainable development in Saudi Arabia. This study seeks to fill that gap by providing one of the first empirical examinations of the ways in which essential blue economy sectors, such as fisheries, marine trade, and tourism, support sustainable economic growth. In addition, Saudi Arabia’s Vision 2030 is seen as more than just a roadmap for boosting the country’s own economy; it is also expected to create positive ripple effects throughout the region. This potential for wider impact makes it even more important to understand how the Kingdom’s key sectors contribute to sustainable growth. To shed light on these connections, this study uses the Autoregressive Distributed Lag (ARDL) model to explore how indicators of the blue economy relate to sustainable economic growth in Saudi Arabia, looking closely at both the long-term trends and the short-term shifts that shape these relationships. Literature Review The \"blue economy\" concept has gained considerable global traction since its initial emergence in Western discourse during the 1990s (Silver et al., 2015 ). Despite its growing popularity, the term remains somewhat ambiguous, often functioning more as a buzzword than a concrete policy directive (Bueger, 2015 ). Its interpretations vary widely, though most converge on aligning economic development with ecological sustainability. Scholars typically categorize perspectives on the blue economy into four primary frameworks: the ocean as a form of natural capital, as a source of livelihood, as a space for innovation, and as a platform for profitable business ventures (Cisneros-Montemayor, 2019 ; Voyer et al., 2018 ). Bakoben and Khan (2024) emphasize that the blue economy seeks to stimulate economic growth by utilizing ocean-based industries such as fisheries, aquaculture, tourism, and maritime transport. The main idea is to use marine resources to create value that benefits local communities, particularly those in coastal areas. Fishing stands out among these industries as one of the blue economy's core pillars. Fishing is one of the oldest maritime industries, predating contemporary sectors like shipping, and has historically been a vital source of income for numerous coastal societies, as noted by Johnson et al. ( 2018 ). Even though its economic contribution may have decreased when compared to high-revenue industries like offshore oil and tourism, fishing remains critically important for jobs and food security across many areas. In actuality, it is still the main source of jobs within the maritime industry. Additionally, there is an ongoing discussion in scholarly literature regarding the nomenclature used to describe ocean-based economic activities. According to Lee et al. ( 2020 ) the terms \"blue economy\" and ocean economy are commonly used interchangeably, a view echoed by Kwiatkowski and Zaucha ( 2023 ), who point out that blue economy, maritime economy, and ocean economy are frequently used synonymously in both academic research and policy documents to describe economic activities connected to marine and coastal environments. The ocean economy encompasses a broad spectrum of assets and resources that significantly contribute to global economic activity, accounting for an estimated 3–5% of the world’s GDP. The marine environment offers significant economic potential for nations like India, whose vast coastline shares borders with six other countries. Possibilities include using marine minerals and energy resources for domestic development, boosting ocean-based industries, and promoting international trade (Llewellyn & English, 2016). A more balanced marine economic strategy has been fostered in recent years by the improvement in the integration of marine science and technological advancement with environmentally sustainable practices (Zhao et al., 2022 ). This widespread initiative shows that governments are becoming more aware of the blue economy and suggests a positive outlook for its future development. Recognizing this trend, some academics have suggested putting in place systems to guarantee that landlocked nations receive a portion of the gains from the exploitation of marine resources, encouraging a more equitable and inclusive global involvement in the blue economy (Schoolmeester et al., 2009 ). Academic literature has repeatedly highlighted the strategic significance of developing the blue economy, especially in light of the significant contributions that maritime sectors make to both national and global economies. In addition to being essential economic drivers, maritime industries from shipping and fisheries to offshore energy and marine biotechnology also hold the key to many countries' sustainable development paths. Methods of the study The Autoregressive Distributed Lag (ARDL) model, which is particularly suited for examining the short- and long-term dynamics of time-series data in a single-equation framework, was used in this study. The dependent variable can be affected by both the current and lagged values of a set of explanatory variables, as well as by its own lagged values, according to the ARDL approach. In this context, the model was applied to examine the relationship between Saudi Arabia's economic growth and key factors such as total fisheries production, agricultural output, aquaculture, capital formation, and trade. The specification of the model was grounded in the methodologies and theoretical foundations outlined in prior studies, including those by Mourougan and Sethuraman ( 2017 ), Emrouznejad et al. ( 2023 ), Abueid et al. ( 2018 ), and Uddin et al. ( 2022 ): GDP = f (FP, AFF, AP, INF, TRD) Definitions of these variables and their origins are detailed in Table 1 . Table 1 Variables description and data source Variable Abb. Unit Gross Domestic Product GDP GDP per capita (current USD) Fishery production FP Total Fishery production (metric tons) Agriculture, fishing and forestry Aquaculture production Trade Inflation AFF AP TRD INF stands for “Agriculture, fishing and forestry, value added (% of GDP) Aquaculture production (metric tons) as percent of GDP consumer index price (CPI) as a percentage The ARDL model is a useful econometric tool that offers several advantages. Its ability to simultaneously analyze short- and long-term dynamics is one of its main advantages, which makes it useful for identifying intricate relationships in time-series data. Additionally, it works well in the face of issues like endogeneity and bias from omitted variables, providing accurate estimates even in situations where conventional models might not work. The adaptability of the ARDL approach, which can handle variables that are integrated at various levels and is appropriate for non-stationary data, is another advantage. The model is a helpful tool for researchers looking at economic relationships over time because it successfully captures dynamic patterns by incorporating both lagged and differenced variables. Sadik-Zada ( 2022 ) developed the ARDL bounds testing technique to include cointegration analysis. This method was used in the current study to investigate how economic growth affected fisheries, aquaculture, agriculture, forestry, and other control variables. When some variables are level and others are first difference, the ARDL model is appropriate. It is also suitable for small sample sizes (Shin et al., 2014 ; Hossain et al., 2019 ; Abbasi et al., 2021 ; Rahman & Majumder, 2021 ; Abbasi & Erdebilli, 2023 ; Uddin et al., 2022 ; Arnaud et al., 2023 ; Voumik et al., 2023 ). Due to the mix of variable stationarity, this model was chosen for our investigation. Because some variables in our dataset were level stationary and others were stationary at the first difference, the ARDL model was appropriate. The model’s econometric formulation can be expressed as: $$\\:{GDP}_{t}=\\:{a}_{0}+{a}_{1}{FP}_{t}+{a}_{2}{AFF}_{t}\\:{+\\:{a}_{3}AP}_{t}{+\\:{a}_{4}INF}_{t}{+\\:{a}_{5}TRD}_{t}+\\:{\\epsilon\\:}_{t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$ After estimating Eq. (2), the Wald test was applied to differentiate between the short-term and long-term relationships among the variables. Results estimation The descriptive statistics for the variables used in this investigation are shown in Table 2 . These consist of the observed minimum and maximum values for GDP, FP, AFF, AP, INF, and TRD, as well as measures of central tendency (mean) and dispersion (standard deviation). GDP has a mean of 114.1, while independent variables like FP have a mean of 11.1; AFF has a mean of 19.5; AP has a mean of 28.1; INF has a mean of 27.8; and TRD has a mean of 36.6. Table 2 Descriptive Statistics GDP FP AFF AP INF TRD Mean 114.160 11.186 19. 515 28.145 27. 882 36. 677 Maximum 216. 973 15.459 32. 149 62.556 34.362 71. 498 Minimum 98.951 3.529 7. 259 3.426 27.603 13.418 SD 92.035 3.180 8. 649 14.671 4.932 12.764 To determine the direction and strength of the relationships between variable pairs and to explore the possibility of multicollinearity in the dataset, correlation analysis was utilized. These associations are summarized in the correlation matrix, which is shown in Table 3 . The findings show that there is no reason to be concerned about multicollinearity in this analysis because the correlation coefficients between the variables are not overly high. Table 3 Correlation Analysis Variables GDP FP AFF AP INF TRD GDP 1.000 FP 0.289 1.000 AFF 0.561 0.259 1.000 AP 0.319 -0. 753 -0.493 1.000 INF -0.015 0. 113 -0.093 0. 302 1.000 TRD 0.227 -0. 425 0.173 -0. 302 -0.031 1.000 A unit root test is a crucial first step in figuring out the integration order of variables prior to using the Autoregressive Distributed Lag (ARDL) approach to cointegration, according to Pesaran et al. ( 2001 ). To determine whether the variables in this study are stationary at I(0) or I(1), the ARDL bounds testing method was used. For variables including GDP, FP, AFF, AP, INF, and TRD, Augmented Dickey–Fuller (ADF) tests were performed to evaluate the series' stationarity characteristics. The null hypothesis for the ADF test assumes the presence of a unit root, implying non-stationarity in the time series. Table 4 ADF and PP unit root tests. ADF PP Variables Level 1st Diff. Level 1st Diff. T Statistics T Statistics T Statistics T Statistics GDP 3.114** -3.382** 3.324* -3.424** FP -0.918 -2.326* -1.516 -2.623* AFF -1.339 -5.372* -1.868 -6.946* AP -2.836 -4.697* -3.747 -4.825* INF -3.129*** -4.547* -2.212 -4. 408* TRD -1.129 -4.564* -0.852 -4.549* Note: *, **, *** denotes 1%, 5% and 10% level of significance respectively, Schwarz Information Criteria (SIC) were used in lag selection. The variables in Table 4 show a mixed order of integration, according to the findings of the ADF and Phillips–Perron (PP) unit root tests. A few variables showed stationarity at both levels, while others were found to be stationary at level and first difference. The requirements for using the Autoregressive Distributed Lag (ARDL) model are satisfied by this combination of integration orders, in which no variable is integrated of order two or higher. The dynamic relationships between the time-series variables used in this study can therefore be investigated using the ARDL approach. Table 5 Optimal lag selection of the model. Lag LogL LR FPE AIC SC HQ 0 -108.3067 NA 0.003126 4.110303 4.213705 4.310836 1 89.76721 362.1742* 6.16e-07* -4.126473* -3.14638* -3.824317* 2 95.23408 10.82789 8.79e-07 -3.736240 -2.274149 -3.126619 The Autoregressive Distributed Lag (ARDL) technique is well known for being a reliable method to choose the optimal lag length and estimate models with varying degrees of stationarity. To guarantee the accuracy of the short-run and long-run estimates, the proper lag structure must be chosen during the first stage of the ARDL process. To determine the ideal lag length, several criteria are frequently employed, including the Schwarz Bayesian Criterion (SBC) and the Akaike Information Criterion (AIC Issues like serial correlation, which can skew the results, can be avoided by selecting the appropriate lag. The best lag structure was found in this study using the Akaike Information Criterion (AIC). The ideal lag length was determined to be one based on the AIC values calculated with statistical software. The detailed results of the lag selection procedure are shown in Table 5 . Table 6 Bounds Testing for Cointegration Estimated Models F -statistics 10.74431 \\(\\:GDP=f(FP,AFF,AP,INF,TRD\\) ) ‘Level of significant’ Lower Bounds Upper Bounds 10% level 2.21 3.29 5% level 2.46 3.81 1% level 3.26 4.17 The conditional ARDL model was used to assess the joint significance of the model coefficients using the bounds test for cointegration. This test determines whether the variables have a long-term equilibrium relationship. If the calculated F-statistic is exceeds the upper bound of the critical values, the bounds testing approach rejects the null hypothesis that there is no long-term relationship. The ARDL bounds test results are shown in Table 6 In this study, the computed F-statistic is 10.874431, exceeding the upper bound critical values at the significance levels of 1%, 5%, and 10%. Furthermore, this value surpasses the upper bound critical values that Pesaran et al. ( 2001 ) established. These findings provide strong proof of a long-term cointegration relationship between the model's independent and dependent variables. Table 7 Long Run and Short Run Estimates Dependent Variable: GDP Variable Coefficient Std. Error t-Statistic Prob. Short Run Coefficients D(FP) 0.005* 0.008 2.481 [0.012] D(AFF) 0.024* 0.048 2.481 [0.012] D(AP) 0.012* 0.021 2.045 [0.036] D(INF) -0. 296** 0.018 6.756 [0.011] D(TRD) 0.447* 0.000 4.967 [0.005] ECM(-1) -0.012** 0.023 -7.591 [0.000] Long Run Coefficients FP 0.046* 0.005 5.790 [0.000] AFF 0.061* 0.048 3.359 [0.000] AP 0.152** 0.006 3.734 [0.000] INF -1.019** 0.005 4. 515 [0.000] TRD 0.828* 0.018 3. 659 [0.000] Note that * and ** denote levels of significance at 1% and 5% respectively. This study investigated the short-term dynamics affecting economic growth using the ARDL framework. The estimated short-run coefficients for each variable under consideration are shown in Table 7 . The findings show that FP, AFF, AP, and TRD all contribute positively to economic growth in the short run. In contrast, INF exhibits a negative correlation with short-run economic growth. An in-depth examination of the short-run coefficients shows that metric tons increase in fisheries production results in a 0.005-unit increase in GDP, demonstrating the sector's gradual but positive economic impact. Similarly, a 1% increase in the combined output from agriculture, forestry, and fisheries is linked to a 0.024-unit increase in GDP, highlighting the significance of these industries in sustaining economic activity. Another important factor is aquaculture production, which raises GDP by 0.012 units for every additional metric ton. On the other hand, inflation exerts a dampening effect on economic growth. Specifically reflected in the fact that a 1% increase in inflation as a percentage of GDP results in a 0.026-unit drop in GDP, reflecting the adverse impact of rising price levels on the economy. Conversely, trade emerges as a major contributor to short-term economic growth, as a 1% increase in trade with GDP is linked to a noteworthy 0.447-unit increase in GDP. To validate convergence towards long-run equilibrium, an expectation based on economic theory, the short-term dynamics confirm that the Error Correction Term (ECT) has a negative sign (Gujarati & Porter, 2009 ). The ECT coefficient in the estimated model is -0.012, which is appropriately signed and statistically significant. This implies that any temporary imbalance is eventually corrected, albeit at a slow pace. The existence of a long-term causal relationship between the independent variables and the dependent variable is further supported by the negative and significant ECT. The results confirm that all variables are cointegrated, demonstrating a steady and enduring relationship between them, as indicated in Table 6 . The robustness of this long-term association is further supported by the high statistical significance of the Error Correction Term (ECT) (Banerjee et al., 2003 ). According to the long-term findings, a 1% increase in the total output of agriculture, forestry, and fisheries (AFF) results in an approximate 0.6% increase in economic growth. According to these findings, Saudi Arabia has significant potential to use aquaculture as a driver of economic growth. The nation has a lot of potential to grow and develop its aquaculture sector because of its strategic location. In addition to enhancing the livelihoods of millions, fostering the development of fisheries and aquaculture can maximise regional economic growth. The results also highlight how crucial fisheries production (FP) and aquaculture production (AP) are to promoting economic growth. The ARDL estimates indicate that in Saudi Arabia, an increase of one unit in aquaculture production can result in a 0.152-unit increase in economic growth, whereas a corresponding increase in fisheries production can lead to a 0.046-unit increase in GDP. Conversely, the long-term analysis shows that inflation (INF) continues to have a negative correlation with economic growth, reflecting its long-term restraint on the economy. However, these results align with and support the results of previous studies, such as those conducted by Ayres and Warr ( 2009 ), Funge-Smith et al. ( 2012 ), Ngawi ( 2014 ), and Stuenkel ( 2017 ), highlighting the vital role that the agriculture and fisheries sectors play in promoting sustainable economic growth in Saudi Arabia. The results indicate a negative relationship between inflation and economic growth. This result is consistent with findings by Alharthi and Hanif ( 2020 ), Khan and Senhadji ( 2002 ), and Mario and Josipa ( 2017 ), who also observed a weak but negative relationship between inflation and economic growth. Additionally, the analysis reveals that trade has a major and favourable effect on economic expansion. This outcome is consistent with research by Rassekh ( 2007 ), Vamvakidis ( 2002 ), and Rigobon and Rodrik ( 2005 ), who similarly identified a positive correlation between trade openness and economic growth, highlighting the critical role that trade plays in fostering economic growth. The diagnostic test results are presented in Table 8. The normality test produced a p-value above the 5% significance threshold, indicating that the residuals of the model are normally distributed. Additionally, the LM test results confirm the absence of autocorrelation and demonstrate homoscedasticity within the model, supporting the reliability of the estimates. Furthermore, the results from the Ramsey-Reset test indicate that the model is correctly specified, reinforcing the validity of the model used in this analysis. Conclusion A well-managed blue economy holds considerable potential for driving economic growth, provided that a country effectively maps its marine resources and integrates them within a robust institutional framework supported by concrete policies and research. The development of a blue economy can also stimulate infrastructure growth while generating employment opportunities. The results of this study, which takes into account both short- and long-term dynamics, are consistent with previous literature from other nations and demonstrate that the components of the blue economy support Saudi Arabia's economic growth. However, realizing these advantages calls for significant political will, funding for research, raising public awareness, and a cultural shift towards the responsible use and appreciation of marine resources. Saudi Arabia can accelerate its economic development by efficiently utilizing its blue resources. In conclusion, the study finds that Saudi Arabia has the ability and potential to successfully adopt the blue economy concept. Strong political will, thorough research, cultural awareness, and a positive societal mindset that supports reliance on the blue economy are all necessary for long-term success. Policymakers will need to work together to manage ocean resources sustainably, which will help them better grasp the importance of the blue economy. By guaranteeing food security for communities, such initiatives will improve livelihoods, aid in the accomplishment of Sustainable Development Goal 14, and enhance economic growth. Limitations and further study It is critical to recognise the limitations of this study. The study's capacity to gather thorough and precise data on Saudi Arabia's blue economy may have been hampered by limitations in data availability and reliability. Furthermore, the analysis might have missed the subtle interactions between sectors and understated the intricacy and interdependencies present in the blue economy. It's also possible that external factors that could impede Saudi Arabia's way of achieving sustainable economic growth have not received enough attention. Future research could overcome these limitations by utilizing more reliable data collection techniques and including a wider range of variables to capture the complex nature of the blue economy. A more comprehensive understanding of the blue economy's effects could be obtained by conducting additional research on the social and environmental effects it has on nearby communities. Moreover, examining the regulatory and governance frameworks necessary for effectively managing and regulating the blue economy would provide insights into ensuring its sustainability and the equitable distribution of its benefits. By addressing these limitations and expanding the scope of analysis, future studies will be better positioned to understand the ways in which blue economy components influence Saudi Arabia’s sustainable economic development, thereby contributing to informed policymaking and effective implementation of blue economy strategies. Declarations Disclosure statement No potential conflict of interest was reported by the author Clinical Trial Number Not Applicable in this study. Funding The author received no financial support for the research, authorship, and/or publication of this article. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. Ethical approval This study was conducted in accordance with the ethical standards of and adhered to the principles outlined. Consent to participate Informed consent was obtained from participant included in the study. Each participant was informed about the nature of the study, and their participation was voluntary. 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(2009), Continental Shelf: The Last Maritime Zone, UNEP/GRID-Arendal, Norway. Silver JJ, Gray NJ, Campbell LM, Fairbanks LW, Gruby RL. Blue economy and competing discourses in international oceans governance. J Environ Dev. 2015;24(2):135–60. Stuenkel O. The BRICS leaders Xiamen declaration: An analysis. blog post-western world; 2017. Uddin H, Rahman MH, Majumder SC. The impact of agricultural production and remittance inflows on economic growth in Bangladesh using ARDL technique. SN Bus Econ. 2022;2(4):32. Vamvakidis A. ,How robust is the growth-openness connection? Historical evidence. J Econ Growth. 2002;7(1):57–80. Voumik LC, Naf SM, Majumder SC, Islam MA. The impact of tourism on the women employment in South American and Caribbean countries. Int J Contemp Hospitality Manage. 2023;35(9):3095–112. Voyer M, Quirk G, McIlgorm A, Azmi K. Shades of blue: What do competing interpretations of the blue economy mean for oceans governance? J Environ Policy Plann. 2018;20:595–616. Zhao W, Lee W, Zhai R. Study on the coupling and coordinated development of the coastal area’s fnancial development, scientifc and technological innovation, and marine economic system. J Nanjing Univ Aeronaut Astronaut. 2022;24:30–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7535978\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":556458056,\"identity\":\"571bc741-0d39-4cd4-aec7-a11ebbc7ad98\",\"order_by\":0,\"name\":\"omar Al-kasasbeh\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Jadara University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"omar\",\"middleName\":\"\",\"lastName\":\"Al-kasasbeh\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-04 12:08:38\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7535978/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7535978/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":97974964,\"identity\":\"0e876b96-636d-4cfc-8afb-4fa7e43fe5ef\",\"added_by\":\"auto\",\"created_at\":\"2025-12-11 11:38:10\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":56613,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Blueeconomic.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/07bed778464c1e9434a3f168.docx\"},{\"id\":98423382,\"identity\":\"f94d00c6-256e-4bfa-9459-922e721d60a5\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:32:11\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":3478,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"fc9634dcdbc248169f747ad6e2937d8d.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/4d3417b82684a90f74547cca.json\"},{\"id\":97974967,\"identity\":\"c273faf5-e611-4df1-82fc-2edbac87e401\",\"added_by\":\"auto\",\"created_at\":\"2025-12-11 11:38:12\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":102302,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"fc9634dcdbc248169f747ad6e2937d8d1enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/b3f687f01699b0ac6bd36bce.xml\"},{\"id\":97974968,\"identity\":\"460eabff-244b-4d8b-89bf-2de28f211f3f\",\"added_by\":\"auto\",\"created_at\":\"2025-12-11 11:38:12\",\"extension\":\"xml\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":100888,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"fc9634dcdbc248169f747ad6e2937d8d1structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/6691bd8c7487d180c3da1e80.xml\"},{\"id\":97974965,\"identity\":\"2531b31f-bcd9-4b3b-abe6-c1a6999cfe07\",\"added_by\":\"auto\",\"created_at\":\"2025-12-11 11:38:11\",\"extension\":\"html\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":105283,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/f5a779dc92c6babfa7f03513.html\"},{\"id\":102410939,\"identity\":\"2f2c0b5a-8448-47fe-b7ee-8a91260ab57b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-11 11:57:39\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":588036,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535978/v1/50bfd045-834c-4efc-9efc-64e0ebe150d9.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Blue Economy and Sustainable Economic Growth in Saudi Arabia\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThroughout the ages, the sea has been one of humanity\\u0026rsquo;s most important crutches, supplying food and transport, commerce and more. More recently the concept of the Blue Economy has gained broad recognition as a key approach for sustainable economic growth involving the sustainable use of the sea's resources. Circular Material Flow: This is in strong correlation to the concept of the Circular Economy, where resource optimization, waste reduction and circularity play an important role. The nexus between BE and CE provides a promising but also complicated trajectory for sustainable ocean management. (Ahammed et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe Blue Economy seeks to promote economic growth, social inclusion, and environmental sustainability at the same time. Given the increasing anthropogenic pressures and limited capacity of marine ecosystems, sustainable management of ocean resources is imperative. The term Blue Economy was formally introduced in 2009 during a U.S. Senate Committee hearing, where it was linked to economic advancement and climate change mitigation. Since then, it has gained traction in global discourse, notably at the 2012 United Nations Conference on Sustainable Development (Rio\\u0026thinsp;+\\u0026thinsp;20), where the ocean was identified as a thematic priority (Alzghoul et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The Blue Economy encompasses key sectors such as aquaculture, fisheries, renewable energy, marine tourism, biotechnology, and shipping, all of which contribute significantly to the global GDP.\\u003c/p\\u003e\\u003cp\\u003eNevertheless, the concept suffers from definitional inconsistencies, often overlapping with similar constructs like the Ocean Economy, Marine Economy, and Blue Growth. While these paradigms share goals of economic development and ecosystem protection, they diverge in scope and emphasis. Central to this discussion is the tension between unbridled economic growth and the need to preserve marine ecosystems. While growth may drive employment and raise incomes, unchecked development risks causing ecological degradation and displacing coastal communities (Akinlo, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEquity concerns are increasingly being raised, particularly regarding the marginalization of vulnerable groups. Small Island Developing States (SIDS) were among the earliest proponents of an inclusive BE model grounded in social justice and sustainability. However, current policy discourse often prioritizes industrial expansion over equity, leading to phenomena like \\u0026ldquo;ocean grabbing,\\u0026rdquo; where access to marine resources is monopolized by elites. The global fishing industry, for instance, has been criticized for exploitative labor practices and exclusionary policies that hinder small-scale fishers. Similar concerns arise in aquaculture and offshore energy sectors.\\u003c/p\\u003e\\u003cp\\u003eIn response, academics have urged the need to incorporate the Blue Economy with the United Nations Sustainable Development Goals, particularly SDG 14. This orientation prioritizes the two poles of ecological conservation and economic development. Yet, uneven capabilities for governance capacities, basic economic conditions, and climate vulnerability impede such an integration. The unequal effects of marine degradation highlight the need for policy to support sustainability and equity.\\u003c/p\\u003e\\u003cp\\u003eAlthough many studies have considered the effects of economic growth on individual marine components, relatively little research has focused on the direct contributions of fundamental Blue Economy elements to sustainable development, such as fisheries, maritime transportation, and tourism. Given that 80 per cent of global trade is carried by the maritime industry, and around 350\\u0026nbsp;million people around the world work in fisheries, the sector\\u0026rsquo;s contribution is significant, but not maximized.\\u003c/p\\u003e\\u003cp\\u003eSaudi Arabia has a lower per capita income compared to some other Gulf Cooperation Council countries, and it is actively working to reform its economic structure and governance systems (Alshuwaikhat \\u0026amp; Mohammed, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Al-Kasasbeh et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). As part of these efforts, the Kingdom launched Vision 2030, an ambitious strategic framework aimed at achieving sustainable development. However, despite this important step, there is still a considerable way to go, particularly in place strategies that simultaneously drive economic growth and mitigate carbon emissions.\\u003c/p\\u003e\\u003cp\\u003eAlthough there has been growing interest in understanding how blue economy sectors affect national economic performance, there are still notable gaps in the literature, particularly regarding their contribution to sustainable development in Saudi Arabia. This study seeks to fill that gap by providing one of the first empirical examinations of the ways in which essential blue economy sectors, such as fisheries, marine trade, and tourism, support sustainable economic growth.\\u003c/p\\u003e\\u003cp\\u003eIn addition, Saudi Arabia\\u0026rsquo;s Vision 2030 is seen as more than just a roadmap for boosting the country\\u0026rsquo;s own economy; it is also expected to create positive ripple effects throughout the region. This potential for wider impact makes it even more important to understand how the Kingdom\\u0026rsquo;s key sectors contribute to sustainable growth. To shed light on these connections, this study uses the Autoregressive Distributed Lag (ARDL) model to explore how indicators of the blue economy relate to sustainable economic growth in Saudi Arabia, looking closely at both the long-term trends and the short-term shifts that shape these relationships.\\u003c/p\\u003e\"},{\"header\":\"Literature Review\",\"content\":\"\\u003cp\\u003eThe \\\"blue economy\\\" concept has gained considerable global traction since its initial emergence in Western discourse during the 1990s (Silver et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Despite its growing popularity, the term remains somewhat ambiguous, often functioning more as a buzzword than a concrete policy directive (Bueger, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Its interpretations vary widely, though most converge on aligning economic development with ecological sustainability. Scholars typically categorize perspectives on the blue economy into four primary frameworks: the ocean as a form of natural capital, as a source of livelihood, as a space for innovation, and as a platform for profitable business ventures (Cisneros-Montemayor, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Voyer et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Bakoben and Khan (2024) emphasize that the blue economy seeks to stimulate economic growth by utilizing ocean-based industries such as fisheries, aquaculture, tourism, and maritime transport.\\u003c/p\\u003e\\u003cp\\u003eThe main idea is to use marine resources to create value that benefits local communities, particularly those in coastal areas. Fishing stands out among these industries as one of the blue economy's core pillars. Fishing is one of the oldest maritime industries, predating contemporary sectors like shipping, and has historically been a vital source of income for numerous coastal societies, as noted by Johnson et al. (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Even though its economic contribution may have decreased when compared to high-revenue industries like offshore oil and tourism, fishing remains critically important for jobs and food security across many areas. In actuality, it is still the main source of jobs within the maritime industry.\\u003c/p\\u003e\\u003cp\\u003eAdditionally, there is an ongoing discussion in scholarly literature regarding the nomenclature used to describe ocean-based economic activities. According to Lee et al. (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) the terms \\\"blue economy\\\" and ocean economy are commonly used interchangeably, a view echoed by Kwiatkowski and Zaucha (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), who point out that blue economy, maritime economy, and ocean economy are frequently used synonymously in both academic research and policy documents to describe economic activities connected to marine and coastal environments.\\u003c/p\\u003e\\u003cp\\u003eThe ocean economy encompasses a broad spectrum of assets and resources that significantly contribute to global economic activity, accounting for an estimated 3–5% of the world’s GDP. The marine environment offers significant economic potential for nations like India, whose vast coastline shares borders with six other countries. Possibilities include using marine minerals and energy resources for domestic development, boosting ocean-based industries, and promoting international trade (Llewellyn \\u0026amp; English, 2016). A more balanced marine economic strategy has been fostered in recent years by the improvement in the integration of marine science and technological advancement with environmentally sustainable practices (Zhao et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This widespread initiative shows that governments are becoming more aware of the blue economy and suggests a positive outlook for its future development. Recognizing this trend, some academics have suggested putting in place systems to guarantee that landlocked nations receive a portion of the gains from the exploitation of marine resources, encouraging a more equitable and inclusive global involvement in the blue economy (Schoolmeester et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAcademic literature has repeatedly highlighted the strategic significance of developing the blue economy, especially in light of the significant contributions that maritime sectors make to both national and global economies. In addition to being essential economic drivers, maritime industries from shipping and fisheries to offshore energy and marine biotechnology also hold the key to many countries' sustainable development paths.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Methods of the study\",\"content\":\"\\u003cp\\u003eThe Autoregressive Distributed Lag (ARDL) model, which is particularly suited for examining the short- and long-term dynamics of time-series data in a single-equation framework, was used in this study. The dependent variable can be affected by both the current and lagged values of a set of explanatory variables, as well as by its own lagged values, according to the ARDL approach.\\u003c/p\\u003e\\u003cp\\u003eIn this context, the model was applied to examine the relationship between Saudi Arabia's economic growth and key factors such as total fisheries production, agricultural output, aquaculture, capital formation, and trade. The specification of the model was grounded in the methodologies and theoretical foundations outlined in prior studies, including those by Mourougan and Sethuraman (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), Emrouznejad et al. (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), Abueid et al. (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and Uddin et al. (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e): GDP = \\u003cem\\u003ef\\u003c/em\\u003e (FP, AFF, AP, INF, TRD)\\u003c/p\\u003e\\u003cp\\u003eDefinitions of these variables and their origins are detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eVariables description and data source\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAbb.\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eUnit\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGross Domestic Product\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGDP per capita (current USD)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFishery production\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTotal Fishery production (metric tons)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAgriculture, fishing and forestry\\u003c/p\\u003e\\u003cp\\u003eAquaculture production\\u003c/p\\u003e\\u003cp\\u003eTrade\\u003c/p\\u003e\\u003cp\\u003eInflation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003estands for “Agriculture, fishing and forestry, value added (% of GDP)\\u003c/p\\u003e\\u003cp\\u003eAquaculture production (metric tons)\\u003c/p\\u003e\\u003cp\\u003eas percent of GDP\\u003c/p\\u003e\\u003cp\\u003econsumer index price (CPI) as a percentage\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003cp\\u003eThe ARDL model is a useful econometric tool that offers several advantages. Its ability to simultaneously analyze short- and long-term dynamics is one of its main advantages, which makes it useful for identifying intricate relationships in time-series data. Additionally, it works well in the face of issues like endogeneity and bias from omitted variables, providing accurate estimates even in situations where conventional models might not work. The adaptability of the ARDL approach, which can handle variables that are integrated at various levels and is appropriate for non-stationary data, is another advantage. The model is a helpful tool for researchers looking at economic relationships over time because it successfully captures dynamic patterns by incorporating both lagged and differenced variables.\\u003c/p\\u003e\\u003cp\\u003eSadik-Zada (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) developed the ARDL bounds testing technique to include cointegration analysis. This method was used in the current study to investigate how economic growth affected fisheries, aquaculture, agriculture, forestry, and other control variables. When some variables are level and others are first difference, the ARDL model is appropriate. It is also suitable for small sample sizes (Shin et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Abbasi et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Rahman \\u0026amp; Majumder, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Abbasi \\u0026amp; Erdebilli, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Uddin et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Arnaud et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Voumik et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Due to the mix of variable stationarity, this model was chosen for our investigation. Because some variables in our dataset were level stationary and others were stationary at the first difference, the ARDL model was appropriate. The model’s econometric formulation can be expressed as:\\u003c/p\\u003e\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{GDP}_{t}=\\\\:{a}_{0}+{a}_{1}{FP}_{t}+{a}_{2}{AFF}_{t}\\\\:{+\\\\:{a}_{3}AP}_{t}{+\\\\:{a}_{4}INF}_{t}{+\\\\:{a}_{5}TRD}_{t}+\\\\:{\\\\epsilon\\\\:}_{t}\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\:\\\\left(2\\\\right)$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cp\\u003eAfter estimating Eq.\\u0026nbsp;(2), the Wald test was applied to differentiate between the short-term and long-term relationships among the variables.\\u003c/p\\u003e\"},{\"header\":\"Results estimation\",\"content\":\"\\u003cp\\u003eThe descriptive statistics for the variables used in this investigation are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. These consist of the observed minimum and maximum values for GDP, FP, AFF, AP, INF, and TRD, as well as measures of central tendency (mean) and dispersion (standard deviation). GDP has a mean of 114.1, while independent variables like FP have a mean of 11.1; AFF has a mean of 19.5; AP has a mean of 28.1; INF has a mean of 27.8; and TRD has a mean of 36.6.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDescriptive Statistics\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e114.160\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.186\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19. 515\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28.145\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e27. 882\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e36. 677\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMaximum\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e216. 973\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.459\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e32. 149\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e62.556\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e34.362\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e71. 498\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMinimum\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e98.951\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.529\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7. 259\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.426\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e27.603\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e13.418\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e92.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.180\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8. 649\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.671\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4.932\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e12.764\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo determine the direction and strength of the relationships between variable pairs and to explore the possibility of multicollinearity in the dataset, correlation analysis was utilized. These associations are summarized in the correlation matrix, which is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The findings show that there is no reason to be concerned about multicollinearity in this analysis because the correlation coefficients between the variables are not overly high.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCorrelation Analysis\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"10\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.289\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.561\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.259\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.319\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0. 753\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.493\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0. 113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.093\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0. 302\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.227\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0. 425\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.173\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0. 302\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eA unit root test is a crucial first step in figuring out the integration order of variables prior to using the Autoregressive Distributed Lag (ARDL) approach to cointegration, according to Pesaran et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). To determine whether the variables in this study are stationary at I(0) or I(1), the ARDL bounds testing method was used. For variables including GDP, FP, AFF, AP, INF, and TRD, Augmented Dickey\\u0026ndash;Fuller (ADF) tests were performed to evaluate the series' stationarity characteristics. The null hypothesis for the ADF test assumes the presence of a unit root, implying non-stationarity in the time series.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eADF and PP unit root tests.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eADF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLevel\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1st Diff.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLevel\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1st Diff.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT Statistics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eT Statistics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eT Statistics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eT Statistics\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.114**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-3.382**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.324*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-3.424**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.918\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-2.326*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-1.516\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-2.623*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-1.339\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-5.372*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-1.868\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-6.946*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-2.836\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-4.697*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-3.747\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-4.825*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-3.129***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-4.547*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-2.212\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-4. 408*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-1.129\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-4.564*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.852\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-4.549*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eNote: *, **, *** denotes 1%, 5% and 10% level of significance respectively, Schwarz Information Criteria (SIC) were used in lag selection.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe variables in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e show a mixed order of integration, according to the findings of the ADF and Phillips\\u0026ndash;Perron (PP) unit root tests. A few variables showed stationarity at both levels, while others were found to be stationary at level and first difference. The requirements for using the Autoregressive Distributed Lag (ARDL) model are satisfied by this combination of integration orders, in which no variable is integrated of order two or higher. The dynamic relationships between the time-series variables used in this study can therefore be investigated using the ARDL approach.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eOptimal lag selection of the model.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLag\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLogL\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFPE\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAIC\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eSC\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eHQ\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-108.3067\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.003126\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.110303\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4.213705\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4.310836\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e89.76721\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e362.1742*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.16e-07*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-4.126473*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-3.14638*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-3.824317*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e95.23408\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.82789\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.79e-07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-3.736240\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-2.274149\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-3.126619\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe Autoregressive Distributed Lag (ARDL) technique is well known for being a reliable method to choose the optimal lag length and estimate models with varying degrees of stationarity. To guarantee the accuracy of the short-run and long-run estimates, the proper lag structure must be chosen during the first stage of the ARDL process. To determine the ideal lag length, several criteria are frequently employed, including the Schwarz Bayesian Criterion (SBC) and the Akaike Information Criterion (AIC Issues like serial correlation, which can skew the results, can be avoided by selecting the appropriate lag. The best lag structure was found in this study using the Akaike Information Criterion (AIC). The ideal lag length was determined to be one based on the AIC values calculated with statistical software. The detailed results of the lag selection procedure are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBounds Testing for Cointegration\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eEstimated Models\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eF\\u003c/em\\u003e-statistics 10.74431\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:GDP=f(FP,AFF,AP,INF,TRD\\\\)\\u003c/span\\u003e\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lsquo;Level of significant\\u0026rsquo;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eLower Bounds\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eUpper Bounds\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e10% level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e5% level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1% level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe conditional ARDL model was used to assess the joint significance of the model coefficients using the bounds test for cointegration. This test determines whether the variables have a long-term equilibrium relationship. If the calculated F-statistic is exceeds the upper bound of the critical values, the bounds testing approach rejects the null hypothesis that there is no long-term relationship. The ARDL bounds test results are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e In this study, the computed F-statistic is 10.874431, exceeding the upper bound critical values at the significance levels of 1%, 5%, and 10%. Furthermore, this value surpasses the upper bound critical values that Pesaran et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e) established. These findings provide strong proof of a long-term cointegration relationship between the model's independent and dependent variables.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eLong Run and Short Run Estimates\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eDependent Variable: GDP\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStd. Error\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003et-Statistic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eProb.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eShort Run Coefficients\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD(FP)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.005*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.012]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD(AFF)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.024*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.012]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD(AP)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.012*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.045\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.036]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD(INF)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0. 296**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.756\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.011]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eD(TRD)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.447*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.967\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.005]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eECM(-1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.012**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-7.591\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eLong Run Coefficients\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.046*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.790\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAFF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.061*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.359\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.152**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.734\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-1.019**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4. 515\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTRD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.828*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3. 659\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.000]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eNote that * and ** denote levels of significance at 1% and 5% respectively.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis study investigated the short-term dynamics affecting economic growth using the ARDL framework. The estimated short-run coefficients for each variable under consideration are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e. The findings show that FP, AFF, AP, and TRD all contribute positively to economic growth in the short run. In contrast, INF exhibits a negative correlation with short-run economic growth. An in-depth examination of the short-run coefficients shows that metric tons increase in fisheries production results in a 0.005-unit increase in GDP, demonstrating the sector's gradual but positive economic impact. Similarly, a 1% increase in the combined output from agriculture, forestry, and fisheries is linked to a 0.024-unit increase in GDP, highlighting the significance of these industries in sustaining economic activity. Another important factor is aquaculture production, which raises GDP by 0.012 units for every additional metric ton. On the other hand, inflation exerts a dampening effect on economic growth. Specifically reflected in the fact that a 1% increase in inflation as a percentage of GDP results in a 0.026-unit drop in GDP, reflecting the adverse impact of rising price levels on the economy. Conversely, trade emerges as a major contributor to short-term economic growth, as a 1% increase in trade with GDP is linked to a noteworthy 0.447-unit increase in GDP.\\u003c/p\\u003e\\u003cp\\u003eTo validate convergence towards long-run equilibrium, an expectation based on economic theory, the short-term dynamics confirm that the Error Correction Term (ECT) has a negative sign (Gujarati \\u0026amp; Porter, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). The ECT coefficient in the estimated model is -0.012, which is appropriately signed and statistically significant. This implies that any temporary imbalance is eventually corrected, albeit at a slow pace. The existence of a long-term causal relationship between the independent variables and the dependent variable is further supported by the negative and significant ECT. The results confirm that all variables are cointegrated, demonstrating a steady and enduring relationship between them, as indicated in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. The robustness of this long-term association is further supported by the high statistical significance of the Error Correction Term (ECT) (Banerjee et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAccording to the long-term findings, a 1% increase in the total output of agriculture, forestry, and fisheries (AFF) results in an approximate 0.6% increase in economic growth. According to these findings, Saudi Arabia has significant potential to use aquaculture as a driver of economic growth. The nation has a lot of potential to grow and develop its aquaculture sector because of its strategic location. In addition to enhancing the livelihoods of millions, fostering the development of fisheries and aquaculture can maximise regional economic growth. The results also highlight how crucial fisheries production (FP) and aquaculture production (AP) are to promoting economic growth. The ARDL estimates indicate that in Saudi Arabia, an increase of one unit in aquaculture production can result in a 0.152-unit increase in economic growth, whereas a corresponding increase in fisheries production can lead to a 0.046-unit increase in GDP.\\u003c/p\\u003e\\u003cp\\u003eConversely, the long-term analysis shows that inflation (INF) continues to have a negative correlation with economic growth, reflecting its long-term restraint on the economy. However, these results align with and support the results of previous studies, such as those conducted by Ayres and Warr (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), Funge-Smith et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e), Ngawi (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), and Stuenkel (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), highlighting the vital role that the agriculture and fisheries sectors play in promoting sustainable economic growth in Saudi Arabia.\\u003c/p\\u003e\\u003cp\\u003eThe results indicate a negative relationship between inflation and economic growth. This result is consistent with findings by Alharthi and Hanif (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), Khan and Senhadji (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), and Mario and Josipa (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), who also observed a weak but negative relationship between inflation and economic growth. Additionally, the analysis reveals that trade has a major and favourable effect on economic expansion. This outcome is consistent with research by Rassekh (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), Vamvakidis (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), and Rigobon and Rodrik (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e), who similarly identified a positive correlation between trade openness and economic growth, highlighting the critical role that trade plays in fostering economic growth.\\u003c/p\\u003e\\u003cp\\u003e\\u003cimg 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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe diagnostic test results are presented in Table\\u0026nbsp;8. The normality test produced a p-value above the 5% significance threshold, indicating that the residuals of the model are normally distributed. Additionally, the LM test results confirm the absence of autocorrelation and demonstrate homoscedasticity within the model, supporting the reliability of the estimates. Furthermore, the results from the Ramsey-Reset test indicate that the model is correctly specified, reinforcing the validity of the model used in this analysis.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eA well-managed blue economy holds considerable potential for driving economic growth, provided that a country effectively maps its marine resources and integrates them within a robust institutional framework supported by concrete policies and research. The development of a blue economy can also stimulate infrastructure growth while generating employment opportunities.\\u003c/p\\u003e\\u003cp\\u003eThe results of this study, which takes into account both short- and long-term dynamics, are consistent with previous literature from other nations and demonstrate that the components of the blue economy support Saudi Arabia's economic growth. However, realizing these advantages calls for significant political will, funding for research, raising public awareness, and a cultural shift towards the responsible use and appreciation of marine resources. Saudi Arabia can accelerate its economic development by efficiently utilizing its blue resources.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, the study finds that Saudi Arabia has the ability and potential to successfully adopt the blue economy concept. Strong political will, thorough research, cultural awareness, and a positive societal mindset that supports reliance on the blue economy are all necessary for long-term success. Policymakers will need to work together to manage ocean resources sustainably, which will help them better grasp the importance of the blue economy. By guaranteeing food security for communities, such initiatives will improve livelihoods, aid in the accomplishment of Sustainable Development Goal 14, and enhance economic growth.\\u003c/p\\u003e\"},{\"header\":\"Limitations and further study\",\"content\":\"\\u003cp\\u003eIt is critical to recognise the limitations of this study. The study's capacity to gather thorough and precise data on Saudi Arabia's blue economy may have been hampered by limitations in data availability and reliability. Furthermore, the analysis might have missed the subtle interactions between sectors and understated the intricacy and interdependencies present in the blue economy. It's also possible that external factors that could impede Saudi Arabia's way of achieving sustainable economic growth have not received enough attention.\\u003c/p\\u003e\\u003cp\\u003eFuture research could overcome these limitations by utilizing more reliable data collection techniques and including a wider range of variables to capture the complex nature of the blue economy. A more comprehensive understanding of the blue economy's effects could be obtained by conducting additional research on the social and environmental effects it has on nearby communities. Moreover, examining the regulatory and governance frameworks necessary for effectively managing and regulating the blue economy would provide insights into ensuring its sustainability and the equitable distribution of its benefits. By addressing these limitations and expanding the scope of analysis, future studies will be better positioned to understand the ways in which blue economy components influence Saudi Arabia\\u0026rsquo;s sustainable economic development, thereby contributing to informed policymaking and effective implementation of blue economy strategies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDisclosure statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo potential conflict of interest was reported by the author\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical Trial Number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot Applicable in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author received no financial support for the research, authorship, and/or publication of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics, Consent to Participate, and Consent to Publish declarations:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003enot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted in accordance with the ethical standards of and adhered to the principles outlined.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eConsent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eInformed consent was obtained from participant included in the study. Each participant was informed about the nature of the study, and their participation was voluntary.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author confirms that the participant provided written informed consent for the publication of any data derived from their involvement in the study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbbasi S, Erdebilli B. Green closed-loop supply chain networks\\u0026rsquo; response to various carbon policies during COVID-19. Sustainability. 2023;15(4):3677.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbbasi S, Daneshmand-Mehr M, Ghane Kanaf A. The sustainable supply chain of CO2 emissions during the coronavirus disease (COVID-19) pandemic. 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J Nanjing Univ Aeronaut Astronaut. 2022;24:30\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Blue Economy, Economic Growth, Sustainable Development, ARDL Model, KSA\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7535978/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7535978/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study investigates the role of the blue economy in promoting sustainable economic growth in Saudi Arabia, utilizing the Autoregressive Distributed Lag (ARDL) framework to analyze both short- and long-term dynamics. The findings reveal that key blue economy elements, including fisheries production, aquaculture, and the combined outputs of agriculture, forestry, and fisheries, contribute positively to economic growth, while inflation exerts a negative effect. Trade emerges as a significant driver of growth, emphasizing the importance of trade openness in fostering economic development. The study highlights the potential of Saudi Arabia to leverage its marine resources, given its strategic geographic location and abundant blue economy opportunities, to stimulate infrastructure development and generate employment. However, achieving these outcomes requires strong political commitment and investment in research. The study acknowledges limitations related to data availability and the complexity of the blue economy, suggesting that future research should adopt comprehensive data collection methods and consider the social and environmental impacts of blue economy initiatives. By addressing these dimensions, Saudi Arabia can effectively harness its blue resources to advance sustainable economic development and contribute to the achievement of Sustainable Development Goal 14.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Blue Economy and Sustainable Economic Growth in Saudi Arabia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-11 11:38:05\",\"doi\":\"10.21203/rs.3.rs-7535978/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":1}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"ffc775d5-0f0f-4603-827d-60476027ba8c\",\"owner\":[],\"postedDate\":\"December 11th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-11T11:57:12+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-11 11:38:05\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7535978\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7535978\",\"identity\":\"rs-7535978\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}