Blue Economy: Blessing of the sustainable growth in Tunisia

Blue Economy: Blessing of the sustainable growth in Tunisia | 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Research Article Blue Economy: Blessing of the sustainable growth in Tunisia Olfa Saghrouni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5763617/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 The Blue economy in Tunisia is an opportunity to achieve integrated and sustainable development. Fundamental relationships between different macroeconomic variables may follow certain common theories, but local preferences are also decisive in determining their unique behavior. Looking into Blue Economic growth nexus is, therefore, needed in Tunisia, so as to understand the long -run economic stance and to capture the short-run dynamics in the national economy. This paper aims to analyze the impact of Blue Economic and economic growth in Tunisia using time series data over the period 2002 to 2022. This study is based on the Auto-Regressive Distributive Lags (ARDL) approach that is proposed by Pesaran et al (2001). Blue Economic are important for economic growth and economic growth has an impact in Tunisia. A successful and sustained economic growth requires growth in Blue Economic. JEL classification: O4, Q56, R11 Blue economy sustainable ARDL 1. Introduction The term ‘blue economy’, first introduced in 2010, is the sustainable use of ocean resources for economic growth, jobs, ocean health, and to improve livelihoods. However, a sustainable blue economy faces various challenges in the form of global warming, ocean acidification, and lack of knowledge about the ocean; for example, 95% of the sea is still unexplored, making it more important to understand the blue economy and implement it on a global scale. Other challenges include harmful algal blooms (HABs), invasive species, coral bleaching, and thermohaline circulation. This review discusses various aspects of the blue economy like food, value-added products, offshore energy, oxygen source, mining, fisheries, carbon sequestration, and cloud seeding. The future aspects of blue economy, like sustainability, effective policies, and reducing carbon footprints and microplastics are also explored here. Blue Economy (BE) encompasses sustainable use of marine resources for economic growth, improved livelihoods and employment, while maintaining the health of the marine ecosystem. According to the United Nations, more than three billion people depend on marine and coastal biodiversity for their survival. BE is the use of marine resources in a sustainable manner to ensure economic growth, better living conditions and create employment opportunities as well as to ensure the health of marine ecosystem (World Bank 2013; Zhao, Guan, and Sun 2019 ; Li et al. 2021 ; Kedong et al. 2022 ; Xu and Gao 2022 ; Ozili 2022 ). The world counts numerous coastal and island countries with lower and lower-middle income levels, for whom oceans represent a significant jurisdictional area and a source of opportunity. In those countries, innovation and growth in the coastal, marine and maritime sectors could deliver food, energy, transport, among other products and services, and serve as a foundation for sustainable development. Diversifying countries’ economies beyond land- based activities and along their coasts is critical to achieving the Sustainable Development Goals and delivering smart, sustainable and inclusive growth globally. In Europe for example, the blue economy represents roughly 5.4 million jobs and generates a gross added value of almost 500€ billion a year.(World Bank Group). Indonesia's great maritime potential as a source of prosperity is one of the leading visions and missions in the 2005–2025 Long Term National Development Plan ( Araujo &All 2021) . As other countries, Indonesia has initiated BE programs since 2012 in Bali and West Nusa Tenggara, featuring an integrated upstream and downstream marine development. However, the targeted outcome of this effort has not yet achieved, due to several challenges, including coordination difficulties among stakeholders. As the biggest archipelago in the world with abundant ocean resources, the development of the BE implementation model will be an integral part of the Redesign of Indonesia's economic transformation and recovery after the Covid19 pandemic. The blue economy thus promotes economic growth, social inclusion, and preservation or improvement of livelihoods while ensuring the environmental sustainability of the sea and coastal areas. It should also provide the best conditions for greater resilience and adaptation to climate change. A sustainable blue economy provides essential benefits for current and future generations; restores, protects and maintains diverse, productive and resilient ecosystems; and is based on clean technologies, renewable energy and circular material flows. This paper aims to study growing the Blue Economy to Combat poverty and accelerate prosperity. The activities commonly understood to represent the blue economy include maritime shipping, fishing and aquaculture, coastal tourism, renewable energy, water desalination, undersea cabling, seabed extractive industries and deep sea mining, marine genetic resources, and biotechnology. Due to its privileged position in the center of the southern shore of the Mediterranean, Tunisia enjoys undeniable assets with its 1,300 km of continental coastline where 7.6 million people live (more than two thirds of its population), who are heavily dependent of the exploitation of coastal and marine resources for their livelihood means of subsistence. The blue economy, which refers to the sustainable use of maritime resources for economic growth, improved livelihoods and jobs and healthy maritime ecosystems, represents an opportunity for sustainable development and wealth creation for the Tunisia, particularly in the context of the post-COVID-19 recovery. Recognizing the importance of Tunisia's unique coastal and marine resources and seeking to develop an integrated and sustainable strategy to increase their economic contribution, the Tunisian government, represented by the Ministry of Environment and the General Secretariat of Maritime Affairs, in partnership with the World Bank, has been engaged since 2020 in the process of identifying opportunities for the development of the blue economy in Tunisia. This process has been based mainly on an expert diagnosis, using the most recent documentation and statistics, and enriched by a questionnaire and a broad consultation with the various factors involved in managing the country's coast and sea. The objective of the diagnosis was to make an inventory of the existing resources and to lay the foundations of the future national strategy for the blue economy This has helped define a strategic vision from which a set of key traditional or established activities, developed sustainably, along with emerging activities, will play a significant role in the development of a blue economy in Tunisia. At the same time, well thought-out and sustained activities related to the protection of the marine and coastal ecosystems will be required. (World Bank : 2023) . Political instability increases the uncertainty of the environment in which FDI takes place, and therefore decreases the incentive for transnational or multinational corporations to invest. As such, it reduces both the volume of FDI and its efficiency. In addition, the North African countries suffering from political instability are generally characterized by insufficient and deteriorated infrastructure and poor governance since 2011. On the other hand, good institutions characterized by a stable political climate could guarantee a more efficient allocation of factors, allow investing in higher-yielding activities, reducing uncertainty, promoting convergence between private and social returns and facilitating coordination between economic agents.(Saghrouni olfa, 2022 ). "The blue economy offers an opportunity for sustainable development and wealth creation for Tunisia through sustainable use of marine and coastal resources for economic growth, improved livelihoods and jobs, and healthy marine and coastal ecosystems," said Alexandre Arrobbio, World Bank Country Manager for Tunisia. "I welcome the Government’s commitment to developing the blue economy in Tunisia as part of its next development plan," he added. The report identifies three strategic objectives: (i) promotion of economic growth of maritime activities (ii) social inclusion and gender equality, and (iii) sustainability of natural resources and ecosystem services. To achieve these objectives, five areas of intervention are proposed: establishment of institutional governance; promotion of resources and financing mechanisms; support for job creation, poverty alleviation, the inclusion of vulnerable groups, and gender mainstreaming; development of knowledge of marine and coastal capital; and strengthening of resilience to climate change. Following the publication of this report, the Tunisian Government and the World Bank will continue their cooperation for the development of the blue economy in Tunisia. The World Bank has mobilized the PROBLUE Trust Fund to undertake the second phase of technical assistance, supporting a roadmap for the development of the blue economy in Tunisia. In the second phase of assistance to Tunisia, the Bank will conduct analyses and offer advice on institutional policies and promotion of public and private investment, in addition to providing support for strategic and operational dialogue with relevant stakeholders. The aim of this work is to empirically evaluate the effect of the blue economy in Tunisia is an opportunity to achieve integrated and sustainable development using annual data over the period 2005 to 2021. To address such a question, we organize our work into five sections. To reach this goal the paper is structured as follows. In section 2, we present the literature review concerning the nexus between the blue economy and sustainable developments. Secondly, we discuss the Methodology Model Specification and data used in this study in Section 3. Thirdly, Section 4 presents the empirical results as well as the analysis of the findings. Finally, Section 5 is conclusion. 2. Review of literature Given the heterogeneity of adopters of the term, it is natural that the concept may represent different issues for each of them, in such a way that it is still possible to have dissonance in the way each author perceives the concept of Blue Economy (Silver, Gray, Campbell, Fairbanks, & Gruby, 2015). However, some previous researches have tried to answer some questions that can contribute to the understanding of the theme, such as which key concepts are used by geography researchers working in Blue Economy (Garland, Axon, Graziano, Morrissey, & Heidkamp, 2019 ); concepts related to sustainable innovations or theories on this topic (Cillo, Petruzelli, Ardito, & Del Giudice, 2019); studies that make a critical onto-epistemological review of the literature on marine space planning (Fairbanks, Boucquey, Campbell, & Wise, 2019 ); or sustainable growth based on the Gross Domestic Product (Raworth, 2019 ), as well as studies that analyze the theme from a post-structuralist perspective (Bear, 2017 ). Thus, although one cannot yet speak of consensus, a prudent step would be to understand the concept and, further, understand what Blue Economy is not (Garland et al., 2019 ). Industrialization has an impact statistically and economically significant on the inclusion of economic growth in Tunisia. Based on the literature. The VECM estimation, which is particularly well suited to the connection between short and long term dynamics. The coefficient of the error correction term is negative and statistically significant, thus confirming the existence of an error correction mechanism. This coefficient, which expresses the degree to which the inclusive growth variable will be recalled towards the long-term target. The coefficient of the industry index (IND) in terms of inclusive growth is positive and significant. The current crisis can be an excellent opportunity to “pay back better”, by Saghrouni Olfa ( 2024 ). The BE aims to promote economic growth, improve life and social inclusion without compromising the oceans’ environmental sustainability and coastal areas since the sea’s resources are limited and their physical conditions have been harmed by human actions (European Commission ( 2020 )). In 2013 the BG Strategy, “Scenarios for Selected Maritime Economic Functions Union,” appeared and examined the usages of the scenarios of the BG project. It aimed to develop the maritime dimension of the Europe 2020 strategy, with a 15-year horizon (2025–2030). In this regard, the scenarios were understood and developed in two ways: the micro-future scenarios and the general scenarios (Wolters HA, & all, (2013)). Discussion and research on the BE mostly are started from clarifying what the definition of the BE and the characteristics of BE. Pauli ( 2009 ) proposed a BE model based on technological innovation to supply affordable products, promote local job creation, preserving the environment but competitive in the markets. Hugh ( 2020 ), however, argues that there is a lack of clear definitions for BE despite of a clear emergence of the BE concept; while Lee et al. ( 2020 ) put forward that this unclarity is due to BE several disciplines root. Regardless of this argument, the leading determination of BE focuses on its sustainable dimension, as the sustainable aspect is intrinsically connected to the BE concept. The critical component of the BE lies on the requirement a key component of the blue economy. Goddard (2015) points out that the balance of ocean systems’ capacity and resilience supports the sustainable BE. Martínez-Vázquez et al. ( 2021 ) observes the similarities in definition of ocean economy, marine economy, and blue growth. Three aspects of sustainability (i.e., ecological, social, and economic) should be considered in boosting the blue growth by utilizing the resources from oceans for the society’s maximal benefit as argued by Niiranen et al. (2018). Lu et al. (2019) attempt to integrate various discourse of BE and provides broad definition of the BE by considering of sustainability, productivity, and integrated planning and development both coastal zone economic system and marine ecosystem. As expected, Upadhyay & Mishara (2020) find the role of BE in sustainable and inclusive development. The useful to explore the BE economic sectors to get a better perception of the BE from different angles. World Bank (2017) identifies BE sectors considering two characteristics, i.e. the living “renewable” as well as the non-living or “non-renewable” resources of the oceans, involving 19 related economic sectors, comprising from explicitly living fisheries to the non-living extractive industries, like offshore oil and gas, seabed mining, and dredging. Five activities relating to the BE are identified by Smith Godfrey (2016) by applying a value chain method on the oceans, from harvesting of living resources, extraction of non-living resources, generation of new resources in the upstream, to trade of resources and resource health in the downstream . Based on the interdependence and impact on coastal and marine areas, nine key industries are the driver of growing a BE including fishery and aquaculture; ports, shipping, and marine transport; tourism, resorts, and coastal development; oil and gas; coastal manufacturing; seabed mining; renewable energy; marine biotechnology; marine technology; and environmental services .Facing a diversity of industries in the BE, a comprehensive ecosystem approach is necessary to take advantage of industrial clusters for applying an agglomerative outcome on the BE. Tunisia has experienced a low rate of economic growth in recent years, but the COVID-19 pandemic has resulted in a contraction in economic growth of 4.2% in 2020. The sea-based sectors of the economy have been badly affected and tourism has been one of the hardest hits. Economic recovery after the COVID-19 pandemic through the BE is seen as a strong strategy to increase the resilience and sustainability of the sea-based sector to become a catalyst for long-term shared prosperity. In the implementation of the BE, World Bank ( 2021 ) categorizes Tunisia’s BE initiatives into: 1. Better fisheries management. 2. Preparation and integration of marine spatial plans. 3. Expansion of Marine Protected Areas (MPA). 4. A national action plan for waste management at sea. 5. Integrated and sustainable tourism development program (World Bank ( 2021 ) 3. Data description and empirical methodology 3.1. Econometrics model specification The present study investigates determinants of the size of the blue economy and blue economy activities in Tunisia using Vector Autoregressive Model (VAR). We have used a VAR method since several advantages over cross-sectional regression. Panel data provide more degrees of freedom and more sample variability than cross-sectional data and improve the efficiency of econometric estimates. The variables in a VAR are systematically treated endogenous by including for each variable an equation explaining its evolution based on its own lags and the lags of all the other variables in the model. Algebraically the VAR model can be written as: U (VAR) = (GDP, BE, REI, FDI, ELC, X, Trsm,TF). Following Lai, (2004), Wong (2008) and Chimobi and Uche (2010), the model could be specified as: GDP = f (BE, REI, FDI, ELC, X, Trsm,TF) (1) In an econometric form Eq. (1) can be stated as: $$\:\:{\varvec{L}\varvec{n}\varvec{G}\varvec{D}\varvec{P}}_{\varvec{i}\varvec{t}\:}={\varvec{\alpha\:}}_{0}\:+{\varvec{\alpha\:}}_{1}{\varvec{L}\varvec{n}\varvec{B}\varvec{E}}_{\varvec{i}\varvec{t}}+{\varvec{\alpha\:}}_{2}\varvec{L}\varvec{n}\:{\varvec{R}\varvec{E}\varvec{I}}_{\varvec{i}\varvec{t}}\:+\sum\:_{\varvec{j}=1}^{\varvec{k}}\varvec{\beta\:}{\varvec{Q}}_{\varvec{j}\varvec{i}\varvec{t}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}$$ 2 Where, GDP t is economic growth proxied by GDP per capita, BE t is Blue Economy Investment Behaviour, REI t is designates Investment in renewable energy, Q is the additional explanatory factors included in the specifications (as per Foreign direct investment (FDI) , Electricity production (ELC) , Exports (%GDP) (X) , Tourism (Trsm) , Technology Factors (TF )), j is the number the control factors; The variables are logged so that, the first differences can be interpreted as growth rates and to reduce variation in time series data sets. The coefficients are elasticity and ɛ is the white noise error term. The study used annual data over the period 2002 to 2022. The data are collected from (Table 1 ): Table 1 Definition and source of the used variables. Variable Codes Description Data sources Blue Economy Investment Behaviour BE Resource-Based View Investment in renewable energy REI EIA:EnergyInformationAdministration Technology Factors TF Technology,organization,Environment Electricity production Foreign direct investment Exports (%GDP) Tourism ELC FDI X Trsm WDI: World development Indicators 3.2. Estimation technique This sub-section proceeds as follows: First, Augmented Dickey- Fuller (1981) and the Phillips Perron (PP) (1988) unit root tests have been employed to know whether the series are stationary or otherwise. If the series are non-stationary and we used a classical method of estimation such as Ordinary Least Squares (OLS), we are mistakenly going to accept spurious relationships, in which their results would be meaningless. Moreover, if the series are found to be non-stationary, the common knowledge is differencing the series if they are DS. But, differencing has its own costs. It prevents detection of the long-run relationship that may be present in the data, that is, the long-run information is lost. The null hypothesis for the test states that the series has unit root. Whereas, the alternative hypothesis says the series is stationary. The unit root test is undertaken both at the intercept and intercept plus trend regression forms. Secondly, if the series are found to be stationary after differencing, Johansen cointegration technique (that has an ability to capture the properties of time series by estimating all of the possible cointegrating vectors along with the test statistics) is applied to test the long run relationship among the variables. Thirdly, once cointegration is examined vector error correction model is used to obtain both short run and long run information. Fourthly, Granger causality test is applied to could hold through error correction term. Finally, volatility test acquired to Forecast Error Variance Decomposition and Impulse Response Function have been used to detect out sample causality test. Diagnosis tests at each stage of estimation technique are performed to check parameter consistency. 4. Estimation and discussion of results According to Granger and Newbold (1974), the regression results may be spurious if the variables are non-stationary. In the result, it is very important to test the existence of unit root and examine the order of integration for each variable beforehand, so as to avoid the spurious correlation problem. Both Augmented Dickey- Fuller (1981) and the Phillips Perron (PP) (1988) unit root tests suggest that the variables under examination are a unit root process at levels and, hence, integrated of order one, I (1). Tables (2) and (3) present the result of both ADF (based on the automatic lag length selection by Akaike information criteria (1969) and PP test. The obtained results show that all the time series in levels are non-stationary, which means they are integrated at an order of 1, i.e. I(1). Thus, the null hypothesis cannot be rejected for any of the variables under examination at 1 and 5% level of significance. However, when differenced once, the tests strongly reject the unit root, saying that they are integrated at an order of zero. The results of the unit root test are consistent with the theoretical argument that most macroeconomic series are not stationary at their levels and become stationary at their first difference. Once, the series are found to be stationary in differencing once, no further tests are required. Although the individual series could be non-stationary, that is, they are individually I (1), as presented above; a linear combination of them might be stationary (Engle and Granger, 1987), which means a well-defined linear relationship exists among them in the long run. So, the subsequent discussion provides a test for cointegration between the variables under investigation. Table 2 Tests for unit root (ADF and PP). Level ADF Test PP Test Variables Constant Linear and Constant Constant Linear and Constant GDP (6.234114)*** [7.297074]*** (6.152758)*** [7.200567]*** (6.241357)*** [36.99159]*** (6.163636)*** [39.74327]*** BE (1.959479) [4.771720]*** (3.534197)** [4.702376]*** (1.130256) [4.771865]*** (2.320115) [4.702509]*** REI (2.304182) [6.459060]*** (3.075550) [6.346644]*** (2.323510) [7.236470]*** (3.166953) [7.044401]*** LX (2.509105) [5.610313]*** (2.802248) [5.600772]*** (2.532288) [5.808220]*** (2.941999) [5.835443]*** FID (4.368613)*** [10.31328]*** (4.560528)*** [10.18581]*** (4.309671)*** [17.01350]*** (4.635771)*** [17.02537]*** TF (0.016488) [4.188038]*** (1.733657) [4.260888]*** (0.368077) [3.920234]*** (1.422377) [3.923482]** ELC (2.169513) [6.917485]*** (2.601525) [6.837791]*** (2.148742) [7.406595]*** (2.601525) [7.127076]*** Trsm (2.292521) [5.020878]*** (2.732287) [5.517733]*** (2.391118) [4.53746]*** (2.484977) [4.606190]*** ***;** and * denote significances at 1%; 5% and 10% levels respectively ( ) denotes stationarity in level [ ] denotes stationarity in first difference In the second step, we will assess to experiment with the cointegration between the variables of the model by putting into practice the Bounds Test. If the bounds test indicates the existence of a cointegration relationship, the third step would be to estimate the relationship of equilibrium of long term using the ARDL model. The Fourth step consists to determine the relationship in the short run using WALD Test which is included in the ARDL Model. Where Log is the natural logarithm, ∆ indicates the variable in the first difference, is an intercept, t refers to the time period in years from 2002–2022, and ε t is a white-noise error term. Lags (m,n,o,p,q,r,s) are determined using the Akaike information criteria (AIC). Once Eq. ( 3 ) has been estimated, the attendance of a cointegration relationship between the variables has to be elaborate by involving the bounds test. Indeed, the cointegration test is constructed predominately on the Fisher test (F-stat) for the joint significance of the coefficients of the lagged level variables, i.e., \(\:H0:\:{\delta\:}_{1}\:=\:{\delta\:}_{2}\:=\:{\delta\:}_{3}=\:{\delta\:}_{4}=\:{\delta\:}_{5}\:=\:{\delta\:}_{6}={\delta\:}_{7}=\:0\) , which indicates no cointegration, against the alternative \(\:H1:\:{\delta\:}_{1}\#\:{\delta\:}_{2}\:\#\:{\delta\:}_{3}\:\#\:{\delta\:}_{4}\:\#\:{\delta\:}_{5}\:\#\:{\delta\:}_{6}\:\#{\delta\:}_{7}\#\:0\) , which indicates that there is integration. After comparing the F-stat value with asymptotic critical value bounds calculated by Pesaran et al. ( 2001 ), the null hypothesis of no cointegration is rejected when the value of the F test protrudes the higher critical bounds value, embroilment that there is a cointegration relationship between the elaborated variables. The final step is to ensure the goodness of fit. Due to we concluded that series are not integrated of order two; we employed the ARDL cointegration test. Based on the ARDL Bound test, we can reject the null hypothesis and conclude that GDP, BE, REI, FDI, ELC, X, Trsm and TF are moving together in the long-term. The results are reported in Table 3 . As seen from the Table 5 , the results of Bounds Test indicate that F statistic is higher than the upper critical value. Thus, we reject the null hypothes is \(\:\text{H}0:\:{{\delta\:}}_{1}\:=\:{{\delta\:}}_{2}\:=\:{{\delta\:}}_{3}=\:{{\delta\:}}_{4}=\:{{\delta\:}}_{5}\:=\:{{\delta\:}}_{6}={{\delta\:}}_{7}=\:0\:\:\) In Eq. ( 3 ). In other words, Table 3 indicates that there is a cointegration nexus among the series in Tunisia. Table 3 Bounds Test ARDL Bounds Test Test Statistic Value K F-statistic 7.002525 8 Critical Value Bounds Significance I(0) Bound I(1) Bound 10% 2.12 3.23 5% 2.45 3.61 2.5% 2.75 3.99 1% 3.15 4.43 Since the validity of a cointegration nexus among GDP, BE, REI, FDI, ELC, X, Trsm ,TF is approved; we examine the long-term and short-term impacts of BE, REI, FDI, ELC, X, Trsm ,TF on economic growth (GDP) in Tunisia. Table 4 reports the results of the estimation of ARDL model in the long run. According to the results of the estimation, we find that BE has a significant and negative effect on economic growth. Keeping other things constant, a 1% increase in BE is accompanied by a 0.0805% improvement in economic growth. Also we find that REI has a significant and negative effect on economic growth. Keeping other things constant, a 1% increase in REI is accompanied by a 0.2810% improvement in economic growth. For control variables, Table 4 points that FDI have a negative and significant effect on economic growth. These results mean that respectively, a 1% increase in FDI is accompanied by a 0.9242% improvement in economic growth. That ELC and Trsm have a negative and significant effect on economic growth. These results mean that respectively, a 1% increase in ELC is accompanied by a 0.9652% improvement in economic growth and a 1% increase in Trsm is accompanied by a 0.1466% improvement in economic growth. However, X and TF have a significant and positive effect on economic growth. Holding other things constant, a 1% increase in TF is accompanied by a 2.2748% increase in GDP; and a 1% increase in X is accompanied by a 0.4533% increase in GDP. Table 4 long-run relationship ARDL Cointegrating And Long Run Form Dependent Variable: GDP Selected Model: ARDL(1, 3, 2, 2, 2, 0, 3) Cointegrating Form Variable Coefficient Std. Error t-Statistic Prob. D(BE) 0.502356 0.267049 1.881140 0.0762 D(BE (-1)) -0.713845 0.357040 -1.999340 0.0609 D(BE (-2)) 0.423212 0.230020 1.839894 0.0823 D(REI) 0.105356 0.030811 0.204204 0.0468 D(REI (-1)) -0.302232 0.180308 0.142204 0.0113 D(REI (-2)) -0.195456 0.172345 0.072145 0.0073 D(FDI) -0.103425 0.207057 -0.499499 0.6235 D(FDI (-1)) 0.403876 0.257174 1.570436 0.1337 D(ELC (2)) -0.592068 0.173252 -3.417372 0.0031 D(ELC (-1)(2) 0.312862 0.165018 1.895931 0.0741 D(X (2)) 0.438596 0.206344 2.125559 0.0476 D(X (-1),2) -0.369852 0.204984 -1.804299 0.0879 D(Trsm (2)) -0.166625 0.182764 -0.911695 0.3740 D(TF (2)) 1.054198 0.259995 4.054681 0.0007 D(TF (-1) (2) ) -0.350104 0.208039 -1.682875 0.1097 D(TF (-2) (2)) -0.285722 0.150967 -1.892616 0.0746 CointEq²(-1) -1.136231 0.167393 -6.787792 0.0000 Coint Eq = GDP - (-0.0805* BE -0.2810* (REI ) + 0.4533*D(X) -0.9652*D(FDI) − 0.1466*D(Trsm) + 2.2748*D(TF) + 5.7873) Table 5 reports empirical results of the short run analysis. We find that BE, REI and X cause GDP. Also, we find that FDI, ELC, Trsm and TF are statistically insignificant which mean that they don’t have any effect on economic growth. Table 5 Short run / Wald tests Variables Value df Probability BE 8.542112 3 0.0360 REI 4.581327 2 0.1012 X 8.643328 2 0.0133 FID 2.720076 2 0.2567 TF 0.831188 1 0.3619 ELC 0.185643 1 0.6600 Trsm 9.35873 3 0.0002 ***;** and * denote significances at 1% ; 5% and 10% levels respectively In Table 6 , diagnostic tests point out that the global indentation adopted is satisfying and reasonable. Tests performed to detect the presence of Breusch-Pagan-Godfrey, Harvey, Glejser, and ARCH in the estimated equation did not reveal any problem of heteroskedasticity at the 5% threshold. The R² determination coefficients are close to or greater than 50% and the Jarque–Bera test shows that the residues follow the normality law. Otherwise the probability of Fisher is less than 5%, which indicates that our model is well treated. Table 6 Diagnostics Tests Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.282238 Prob. F(19,18) 0.9956 Obs*R-squared 8.722348 Prob. Chi-Square(19) 0.9778 Scaled explained SS 1.546533 Prob. Chi-Square(19) 1.0000 Heteroskedasticity Test: Harvey F-statistic 0.333990 Prob. F(19,18) 0.9889 Obs*R-squared 9.904809 Prob. Chi-Square(19) 0.9553 Scaled explained SS 7.673741 Prob. Chi-Square(19) 0.9897 Heteroskedasticity Test: Glejser F-statistic 0.308335 Prob. F(19,18) 0.9928 Obs*R-squared 9.330821 Prob. Chi-Square(19) 0.9676 Scaled explained SS 3.664893 Prob. Chi-Square(19) 0.9999 Heteroskedasticity Test: ARCH F-statistic 0.103296 Prob. F(1,35) 0.7498 Obs*R-squared 0.108878 Prob. Chi-Square(1) 0.7414 Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.379384 Prob. F(2,16) 0.6903 Obs*R-squared 1.720482 Prob. Chi-Square(2) 0.4231 Tests of Quality R-squared 0.823097 F-statistic 4.407933 Adjusted R-squared 0.636366 Prob(F-statistic) 0.001362 Test of Normality Jarque-Bera 0.359624 Probability 0.835427 5. Conclusion and Policy Implications The positive effects of Blue Economy Investment Behaviour and Investment in renewable energy on economic growth are hugely tempted on the theoretical levels as well as on the empirical levels. However they turn out minus clear when uniqueness is exercised in a case of a developing country that suffers from many problems. This paper aims to trace the Blue Economy: Blessing of the sustainable growth in Tunisia using the ADF unit root test, PP unit root test, Bounds test and ARDL Model for the period 2002–2022. According to the result of the analysis, it was determined that Blue Economy Investment Behaviour and Investment in renewable energy have a negative effect on economic growth in the long run. These results provide evidence that Blue Economy Investment Behaviour and Investment in renewable energy, thus, are not seen as the fountain of economic growth in Tunisia during this considerable period and pain a lot troubles and inferior economic organization. The results obtained lead us to make the following recommendations in order to promote economic growth in Tunisia: First, the economic growth in Tunisia is not mainly linked to Foreign Direct Investment in long run. Second, the government should pay more attention to the nature of Blue Economy Investment Behaviour. Third, the government should orient the Blue Economy Investment Behaviour and Investment in renewable energy to more productive projects in order to enhance economic growth. Additionally, it’s also important for government to improve good governance policies and the business climate in order to reduce institutional inefficiencies. Fourth, it’s more important to reduce the risks and uncertainty associated with Blue Economy Investment Behaviour and Investment in renewable energy. Finally, the efforts should be directed to speed up the administrative procedures to attract more investments. Declarations Author Contribution olfa saghrouni : Conceptualization, Formal analysis, Writing, original draft, Data curation, Methodology, Software.olfa saghrouni,All authors reviewed the manuscript References Araujo, B., Akhir, K., Farand, F., & Nahumariri, M. (2021). Designing and Mapping the Blue Economy Company Index (BECdex) to the Sustainable Development Goals (SDGs) for Maritime Companies in the Coastal States. International Journal of Innovative Science and Research Technology , 6 (9), 417–426. Bear, C. (2017). Assembling ocean life: More-than-human entanglements in the blue economy. Dialogues in Human Geography , 7 (1), 27–31. https://doi.org/10.1177/2043820617691635 European Commission (2020). The EU blue economy report. 2020. Pub lications Ofce of the European Union, Luxembourg. https://doi.org/10.2771/073370 Fairbanks, L., Boucquey, N., Campbell, L. M., & Wise, S. (2019). Remaking oceans governance: Critical perspectives on marine spatial planning. Environment and Society , 10 (1), 122–140. https://doi.org/10.3167/ares.2019.100108 Garland, M., Axon, S., Graziano, M., Morrissey, J., & Heidkamp, C. P. (2019). The blue economy: Identifying geographic concepts and sensitivities. Geography Compass , 13 (7), e12445. https://doi.org/10.1111/gec3.12445 Hugh, G. (2020). Preconditions for a Blue Economy . DAWN Informs, Development Alternatives with Women for a New Era. Kedong, Y., Liu, Z., Zhang, C., Huang, S., Li, J., Lv, L., Su, X., & Zhang, R. (2022). Analysis and Forecast of Marine Economy Development in China. Marine Economics and Management , 5 (1), 1–33. 10.1108/MAEM-10-2021-0009 Lee, K. H., Noh, J., & Khim, J. S. (2020). The Blue Economy and the United Nations’ sustainable development goals: Challenges and opportunities (Vol. 137, p. 105528). Environment International. Li, X., Zhou, S., Yin, K., & Liu, H. (2021). Measurement of the HighQuality Development Level of China’s Marine Economy. Marine Economics and Management , 4 (1), 23–41. 10.1108/MAEM-10-2020-0004 Martínez-Vázquez, R. M., & Milán-García, J. (2021). & de Pablo Valenciano, J. Challenges of the blue economy: Evidence and research trends. Environmental Sciences Europe, 33(1). https://doi.org/10.1186/s12302-021-00502-1 Smith-Godfrey, S. (2016). Defining the blue economy. Maritime Affairs: Journal of the National Maritime Foundation of India , 12 (1), 58–64. https://doi.org/10.1080/09733159.2016.1175131 Ozili, P. K. (2022). Assessing Global Interest in Decentralized Finance, Embedded Finance, Open Finance, Ocean Finance and Sustainable Finance. Asian Journal of Economics and Banking . 10.1108/AJEB-03-2022-0029 Pauli, G. (2009). The Blue Economy, A report to the Club of Rome 2009 . UNEP. PEMSEA. (2015). Blue Economy for Business in East Asia: Towards an integrated understanding of Blue Economy. Partnerships in Environmental Management for the Seas of East Asia . Quezon City. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics , 16 (3), 289–326. Raworth, K. (2019). Economia Donut: uma alternativa ao crescimento a qualquer custo . Editora Schwarcz-Companhia das Letras. Saghrouni olfa (2024). Industrialization, Inclusive Economic Growth: Tunisia in the Era of the Current Crisis. Journal of Economics, Finance and Management Studies Volume 07 Issue 01 January 2024 Article 10.47191/jefms/v7-i1-75 , Page No: 745–755. Saghrouni olfa (2022). Foreign Direct Investment and Economic Growth: The Role of Corruption Control in North Africa. Journal of Economics, Finance and Management Studies 5 Issue 07 July 2022 Page No. 1961–1966. Silver, J. J., Gray, N. J., Campbell, L. M., Fairbanks, Luke, W., & Gruby, R. L. (2015). Blue economy and competing discourses in international oceans governance. Journal of environment & development: a review of international policy. - Thousand Oaks, Calif. [u.a.] : Sage, ISSN 1552–5465, ZDB-ID 2011500-3. - Vol. 24.2015, 2, pp. 135–160. Upadhyay, D. K., & Mishra, M. (2020). Blue Economy: Emerging Global Trends and India’s multilateral cooperation. Maritime Affairs: Journal of the National Maritime Foundation of India , 16 (1), 30–45. https://doi.org/10.1080/09733159.2020.1785087 Valentina Cillo & Antonio Messeni Petruzzelli & Lorenzo Ardito & Manlio Del Giudice. (2019). Understanding sustainable innovation: A systematic literature review, Corporate Social Responsibility and Environmental Management (Vol. 26, pp. 1012–1025). Wiley. 5. Vázquez, R. M., García, J. M., & de Valenciano, J. (2021). Challenges of the blue economy: Evidence and research trends; https://doi.org/10.21203/rs.3.rs-212565/v1 Wenhai, L., Cusack, C., Baker, M., Tao, W., Mingbao, C., Paige, K., Xiaofan, Z., Levin, L., Escobar, E., Amon, D., Yue, Y., Reitz, A., Neves, A. A., O’Rourke, E., Mannarini, G., Pearlman, J., Tinker, J., Horsburgh, K. J., Lehodey, P., & Yufeng, Y. (2019). Successful blue economy examples with an emphasis on international perspectives. Frontiers in Marine Science , 6. https://doi.org/10.3389/fmars.2019.00261 World Bank. (2021). Oceans for Prosperity: Reforms for a Blue Economy in Tunisia . The World Bank. World Bank (2023). 2023/01/18, Report Number :176299, Volume No : 1. World Bank 2013 Fish to 2030—prospects for Fisheries and Aquaculture. World Bank Report Number 83177-GLB. Washington, DC. Wolters, H. A., Gille, J., De Vet, J. M., & Molemaker, R. J. (2013). Scenarios for selected maritime economic functions. Eur J Futures Res , 1 (1), 11. Whisnant, R., & Vandeweerd, V. (2019). Investing in the new Blue Economy: The Changing Role of international development organizations in catalyzing private sector investment in support of regional strategic action programmes for the sustainable development of coasts and Oceans. Journal of Ocean and Coastal Economics , 6 (1). https://doi.org/10.15351/2373- 8456.1116 Xu, S., & Gao, K. (2022). Green Finance and High-Quality Development of Marine Economy. Marine Economics and Management . 10.1108/MAEM-01-2022-0001 Zhao, A., Guan, H., & Sun, Z. (2019). Understanding High-Quality Development of Marine Economy in China: A Literature Review. Marine Economics and Management , 2 (2), 124–130. 10.1108/MAEM-10-2019-0011 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. 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Introduction","content":"\u003cp\u003eThe term \u0026lsquo;blue economy\u0026rsquo;, first introduced in 2010, is the sustainable use of ocean resources for economic growth, jobs, ocean health, and to improve livelihoods. However, a sustainable blue economy faces various challenges in the form of global warming, ocean acidification, and lack of knowledge about the ocean; for example, 95% of the sea is still unexplored, making it more important to understand the blue economy and implement it on a global scale. Other challenges include harmful algal blooms (HABs), invasive species, coral bleaching, and thermohaline circulation. This review discusses various aspects of the blue economy like food, value-added products, offshore energy, oxygen source, mining, fisheries, carbon sequestration, and cloud seeding. The future aspects of blue economy, like sustainability, effective policies, and reducing carbon footprints and microplastics are also explored here.\u003c/p\u003e \u003cp\u003eBlue Economy (BE) encompasses sustainable use of marine resources for economic growth, improved livelihoods and employment, while maintaining the health of the marine ecosystem. According to the United Nations, more than three billion people depend on marine and coastal biodiversity for their survival. BE is the use of marine resources in a sustainable manner to ensure economic growth, better living conditions and create employment opportunities as well as to ensure the health of marine ecosystem (World Bank 2013; Zhao, Guan, and Sun \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kedong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu and Gao \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ozili \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe world counts numerous coastal and island countries with lower and lower-middle income levels, for whom oceans represent a significant jurisdictional area and a source of opportunity. In those countries, innovation and growth in the coastal, marine and maritime sectors could deliver food, energy, transport, among other products and services, and serve as a foundation for sustainable development. Diversifying countries\u0026rsquo; economies beyond land- based activities and along their coasts is critical to achieving the Sustainable Development Goals and delivering smart, sustainable and inclusive growth globally. In Europe for example, the blue economy represents roughly 5.4\u0026nbsp;million jobs and generates a gross added value of almost 500\u0026euro; billion a year.(World Bank Group).\u003c/p\u003e \u003cp\u003eIndonesia's great maritime potential as a source of prosperity is one of the leading visions and missions in the 2005\u0026ndash;2025 Long Term National Development Plan (\u003cb\u003eAraujo \u0026amp;All 2021)\u003c/b\u003e. As other countries, Indonesia has initiated BE programs since 2012 in Bali and West Nusa Tenggara, featuring an integrated upstream and downstream marine development. However, the targeted outcome of this effort has not yet achieved, due to several challenges, including coordination difficulties among stakeholders. As the biggest archipelago in the world with abundant ocean resources, the development of the BE implementation model will be an integral part of the Redesign of Indonesia's economic transformation and recovery after the Covid19 pandemic.\u003c/p\u003e \u003cp\u003eThe blue economy thus promotes economic growth, social inclusion, and preservation or improvement of livelihoods while ensuring the environmental sustainability of the sea and coastal areas. It should also provide the best conditions for greater resilience and adaptation to climate change. A sustainable blue economy provides essential benefits for current and future generations; restores, protects and maintains diverse, productive and resilient ecosystems; and is based on clean technologies, renewable energy and circular material flows.\u003c/p\u003e \u003cp\u003eThis paper aims to study growing the Blue Economy to Combat poverty and accelerate prosperity. The activities commonly understood to represent the blue economy include maritime shipping, fishing and aquaculture, coastal tourism, renewable energy, water desalination, undersea cabling, seabed extractive industries and deep sea mining, marine genetic resources, and biotechnology.\u003c/p\u003e \u003cp\u003eDue to its privileged position in the center of the southern shore of the Mediterranean, Tunisia enjoys undeniable assets with its 1,300 km of continental coastline where 7.6\u0026nbsp;million people live (more than two thirds of its population), who are heavily dependent of the exploitation of coastal and marine resources for their livelihood means of subsistence. The blue economy, which refers to the sustainable use of maritime resources for economic growth, improved livelihoods and jobs and healthy maritime ecosystems, represents an opportunity for sustainable development and wealth creation for the Tunisia, particularly in the context of the post-COVID-19 recovery. Recognizing the importance of Tunisia's unique coastal and marine resources and seeking to develop an integrated and sustainable strategy to increase their economic contribution, the Tunisian government, represented by the Ministry of Environment and the General Secretariat of Maritime Affairs, in partnership with the World Bank, has been engaged since 2020 in the process of identifying opportunities for the development of the blue economy in Tunisia. This process has been based mainly on an expert diagnosis, using the most recent documentation and statistics, and enriched by a questionnaire and a broad consultation with the various factors involved in managing the country's coast and sea. The objective of the diagnosis was to make an inventory of the existing resources and to lay the foundations of the future national strategy for the blue economy This has helped define a strategic vision from which a set of key traditional or established activities, developed sustainably, along with emerging activities, will play a significant role in the development of a blue economy in Tunisia. At the same time, well thought-out and sustained activities related to the protection of the marine and coastal ecosystems will be required. (World Bank : 2023) .\u003c/p\u003e \u003cp\u003ePolitical instability increases the uncertainty of the environment in which FDI takes place, and therefore decreases the incentive for transnational or multinational corporations to invest. As such, it reduces both the volume of FDI and its efficiency. In addition, the North African countries suffering from political instability are generally characterized by insufficient and deteriorated infrastructure and poor governance since 2011. On the other hand, good institutions characterized by a stable political climate could guarantee a more efficient allocation of factors, allow investing in higher-yielding activities, reducing uncertainty, promoting convergence between private and social returns and facilitating coordination between economic agents.(Saghrouni olfa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\"The blue economy offers an opportunity for sustainable development and wealth creation for Tunisia through sustainable use of marine and coastal resources for economic growth, improved livelihoods and jobs, and healthy marine and coastal ecosystems,\" said Alexandre Arrobbio, World Bank Country Manager for Tunisia. \"I welcome the Government\u0026rsquo;s commitment to developing the blue economy in Tunisia as part of its next development plan,\" he added.\u003c/p\u003e \u003cp\u003eThe report identifies three strategic objectives: (i) promotion of economic growth of maritime activities (ii) social inclusion and gender equality, and (iii) sustainability of natural resources and ecosystem services. To achieve these objectives, five areas of intervention are proposed: establishment of institutional governance; promotion of resources and financing mechanisms; support for job creation, poverty alleviation, the inclusion of vulnerable groups, and gender mainstreaming; development of knowledge of marine and coastal capital; and strengthening of resilience to climate change.\u003c/p\u003e \u003cp\u003eFollowing the publication of this report, the Tunisian Government and the World Bank will continue their cooperation for the development of the blue economy in Tunisia. The World Bank has mobilized the PROBLUE Trust Fund to undertake the second phase of technical assistance, supporting a roadmap for the development of the blue economy in Tunisia. In the second phase of assistance to Tunisia, the Bank will conduct analyses and offer advice on institutional policies and promotion of public and private investment, in addition to providing support for strategic and operational dialogue with relevant stakeholders.\u003c/p\u003e \u003cp\u003eThe aim of this work is to empirically evaluate the effect of the blue economy in Tunisia is an opportunity to achieve integrated and sustainable development using annual data over the period 2005 to 2021. To address such a question, we organize our work into five sections. To reach this goal the paper is structured as follows. In section 2, we present the literature review concerning the nexus between the blue economy and sustainable developments. Secondly, we discuss the Methodology Model Specification and data used in this study in Section 3. Thirdly, Section 4 presents the empirical results as well as the analysis of the findings. Finally, Section 5 is conclusion.\u003c/p\u003e"},{"header":"2. Review of literature","content":"\u003cp\u003eGiven the heterogeneity of adopters of the term, it is natural that the concept may represent different issues for each of them, in such a way that it is still possible to have dissonance in the way each author perceives the concept of Blue Economy (Silver, Gray, Campbell, Fairbanks, \u0026amp; Gruby, 2015). However, some previous researches have tried to answer some questions that can contribute to the understanding of the theme, such as which key concepts are used by geography researchers working in Blue Economy (Garland, Axon, Graziano, Morrissey, \u0026amp; Heidkamp, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); concepts related to sustainable innovations or theories on this topic (Cillo, Petruzelli, Ardito, \u0026amp; Del Giudice, 2019); studies that make a critical onto-epistemological review of the literature on marine space planning (Fairbanks, Boucquey, Campbell, \u0026amp; Wise, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); or sustainable growth based on the Gross Domestic Product (Raworth, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as studies that analyze the theme from a post-structuralist perspective (Bear, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, although one cannot yet speak of consensus, a prudent step would be to understand the concept and, further, understand what Blue Economy is not (Garland et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndustrialization has an impact statistically and economically significant on the inclusion of economic growth in Tunisia. Based on the literature. The VECM estimation, which is particularly well suited to the connection between short and long term dynamics. The coefficient of the error correction term is negative and statistically significant, thus confirming the existence of an error correction mechanism. This coefficient, which expresses the degree to which the inclusive growth variable will be recalled towards the long-term target. The coefficient of the industry index (IND) in terms of inclusive growth is positive and significant. The current crisis can be an excellent opportunity to “pay back better”, by Saghrouni Olfa (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe BE aims to promote economic growth, improve life and social inclusion without compromising the oceans’ environmental sustainability and coastal areas since the sea’s resources are limited and their physical conditions have been harmed by human actions (European Commission (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eIn 2013 the BG Strategy, “Scenarios for Selected Maritime Economic Functions Union,” appeared and examined the usages of the scenarios of the BG project. It aimed to develop the maritime dimension of the Europe 2020 strategy, with a 15-year horizon (2025–2030). In this regard, the scenarios were understood and developed in two ways: the micro-future scenarios and the general scenarios (Wolters HA, \u0026amp; all, (2013)).\u003c/p\u003e \u003cp\u003eDiscussion and research on the BE mostly are started from clarifying what the definition of the BE and the characteristics of BE. Pauli (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) proposed a BE model based on technological innovation to supply affordable products, promote local job creation, preserving the environment but competitive in the markets. Hugh (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however, argues that there is a lack of clear definitions for BE despite of a clear emergence of the BE concept; while Lee et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) put forward that this unclarity is due to BE several disciplines root.\u003c/p\u003e \u003cp\u003eRegardless of this argument, the leading determination of BE focuses on its sustainable dimension, as the sustainable aspect is intrinsically connected to the BE concept. The critical component of the BE lies on the requirement a key component of the blue economy. Goddard (2015) points out that the balance of ocean systems’ capacity and resilience supports the sustainable BE. Martínez-Vázquez et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observes the similarities in definition of ocean economy, marine economy, and blue growth. Three aspects of sustainability (i.e., ecological, social, and economic) should be considered in boosting the blue growth by utilizing the resources from oceans for the society’s maximal benefit as argued by Niiranen et al. (2018). Lu et al. (2019) attempt to integrate various discourse of BE and provides broad definition of the BE by considering of sustainability, productivity, and integrated planning and development both coastal zone economic system and marine ecosystem. As expected, Upadhyay \u0026amp; Mishara (2020) find the role of BE in sustainable and inclusive development.\u003c/p\u003e \u003cp\u003eThe useful to explore the BE economic sectors to get a better perception of the BE from different angles. World Bank (2017) identifies BE sectors considering two characteristics, i.e. the living “renewable” as well as the non-living or “non-renewable” resources of the oceans, involving 19 related economic sectors, comprising from explicitly living fisheries to the non-living extractive industries, like offshore oil and gas, seabed mining, and dredging. Five activities relating to the BE are identified by Smith Godfrey (2016) by applying a value chain method on the oceans, from harvesting of living resources, extraction of non-living resources, generation of new resources in the upstream, to trade of resources and resource health in the downstream .\u003c/p\u003e \u003cp\u003eBased on the interdependence and impact on coastal and marine areas, nine key industries are the driver of growing a BE including fishery and aquaculture; ports, shipping, and marine transport; tourism, resorts, and coastal development; oil and gas; coastal manufacturing; seabed mining; renewable energy; marine biotechnology; marine technology; and environmental services .Facing a diversity of industries in the BE, a comprehensive ecosystem approach is necessary to take advantage of industrial clusters for applying an agglomerative outcome on the BE.\u003c/p\u003e \u003cp\u003eTunisia has experienced a low rate of economic growth in recent years, but the COVID-19 pandemic has resulted in a contraction in economic growth of 4.2% in 2020. The sea-based sectors of the economy have been badly affected and tourism has been one of the hardest hits. Economic recovery after the COVID-19 pandemic through the BE is seen as a strong strategy to increase the resilience and sustainability of the sea-based sector to become a catalyst for long-term shared prosperity. In the implementation of the BE, World Bank (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) categorizes Tunisia’s BE initiatives into:\u003c/p\u003e \u003cp\u003e1. Better fisheries management.\u003c/p\u003e\u003cp\u003e2. Preparation and integration of marine spatial plans.\u003c/p\u003e \u003cp\u003e3. Expansion of Marine Protected Areas (MPA).\u003c/p\u003e \u003cp\u003e4. A national action plan for waste management at sea.\u003c/p\u003e \u003cp\u003e5. Integrated and sustainable tourism development program (World Bank (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"3. Data description and empirical methodology","content":"\u003cp\u003e\u003c/p\u003e\u003ch2\u003e3.1. Econometrics model specification\u003c/h2\u003e\u003cp\u003eThe present study investigates determinants of the size of the blue economy and blue economy activities in Tunisia using Vector Autoregressive Model (VAR). We have used a VAR method since several advantages over cross-sectional regression. Panel data provide more degrees of freedom and more sample variability than cross-sectional data and improve the efficiency of econometric estimates. The variables in a VAR are systematically treated endogenous by including for each variable an equation explaining its evolution based on its own lags and the lags of all the other variables in the model.\u003c/p\u003e\u003cp\u003eAlgebraically the VAR model can be written as:\u003c/p\u003e\u003cp\u003eU (VAR) = (GDP, BE, REI, FDI, ELC, X, Trsm,TF).\u003c/p\u003e\u003cp\u003eFollowing Lai, (2004), Wong (2008) and Chimobi and Uche (2010), the model could be specified as: \u003cb\u003eGDP = f (BE, REI, FDI, ELC, X, Trsm,TF) (1)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn an econometric form Eq.\u0026nbsp;(1) can be stated as:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:{\\varvec{L}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{i}\\varvec{t}\\:}={\\varvec{\\alpha\\:}}_{0}\\:+{\\varvec{\\alpha\\:}}_{1}{\\varvec{L}\\varvec{n}\\varvec{B}\\varvec{E}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\alpha\\:}}_{2}\\varvec{L}\\varvec{n}\\:{\\varvec{R}\\varvec{E}\\varvec{I}}_{\\varvec{i}\\varvec{t}}\\:+\\sum\\:_{\\varvec{j}=1}^{\\varvec{k}}\\varvec{\\beta\\:}{\\varvec{Q}}_{\\varvec{j}\\varvec{i}\\varvec{t}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere, GDP \u003csub\u003et\u003c/sub\u003e is economic growth proxied by GDP per capita,\u003c/p\u003e\u003cp\u003e \u003cb\u003eBE\u003c/b\u003e \u003csub\u003et\u003c/sub\u003e is Blue Economy Investment Behaviour,\u003c/p\u003e\u003cp\u003e \u003cb\u003eREI\u003c/b\u003e \u003csub\u003et\u003c/sub\u003e is designates Investment in renewable energy,\u003c/p\u003e\u003cp\u003e \u003cb\u003eQ\u003c/b\u003e is the additional explanatory factors included in the specifications (as per Foreign direct investment \u003cb\u003e(FDI)\u003c/b\u003e, Electricity production \u003cb\u003e(ELC)\u003c/b\u003e, Exports (%GDP) \u003cb\u003e(X)\u003c/b\u003e, Tourism \u003cb\u003e(Trsm)\u003c/b\u003e, Technology Factors \u003cb\u003e(TF\u003c/b\u003e)), j is the number the control factors;\u003c/p\u003e\u003cp\u003eThe variables are logged so that, the first differences can be interpreted as growth rates and to reduce variation in time series data sets. The coefficients are elasticity and ɛ is the white noise error term. The study used annual data over the period 2002 to 2022. The data are collected from (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cp\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\u003eDefinition and source of the used variables.\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\u003eCodes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription Data sources\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue Economy Investment Behaviour\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource-Based View\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment in renewable energy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eREI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEIA:EnergyInformationAdministration\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology,organization,Environment\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity production\u003c/p\u003e \u003cp\u003eForeign direct investment\u003c/p\u003e \u003cp\u003eExports (%GDP)\u003c/p\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eELC\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFDI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eTrsm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI: World development Indicators\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e3.2. Estimation technique\u003c/h2\u003e\u003cp\u003eThis sub-section proceeds as follows: First, Augmented Dickey- Fuller (1981) and the Phillips Perron (PP) (1988) unit root tests have been employed to know whether the series are stationary or otherwise. If the series are non-stationary and we used a classical method of estimation such as Ordinary Least Squares (OLS), we are mistakenly going to accept spurious relationships, in which their results would be meaningless. Moreover, if the series are found to be non-stationary, the common knowledge is differencing the series if they are DS. But, differencing has its own costs. It prevents detection of the long-run relationship that may be present in the data, that is, the long-run information is lost. The null hypothesis for the test states that the series has unit root. Whereas, the alternative hypothesis says the series is stationary. The unit root test is undertaken both at the intercept and intercept plus trend regression forms.\u003c/p\u003e\u003cp\u003eSecondly, if the series are found to be stationary after differencing, Johansen cointegration technique (that has an ability to capture the properties of time series by estimating all of the possible cointegrating vectors along with the test statistics) is applied to test the long run relationship among the variables. Thirdly, once cointegration is examined vector error correction model is used to obtain both short run and long run information. Fourthly, Granger causality test is applied to could hold through error correction term. Finally, volatility test acquired to Forecast Error Variance Decomposition and Impulse Response Function have been used to detect out sample causality test. Diagnosis tests at each stage of estimation technique are performed to check parameter consistency.\u003c/p\u003e"},{"header":"4. Estimation and discussion of results","content":"\u003cp\u003eAccording to Granger and Newbold (1974), the regression results may be spurious if the variables are non-stationary. In the result, it is very important to test the existence of unit root and examine the order of integration for each variable beforehand, so as to avoid the spurious correlation problem. Both Augmented Dickey- Fuller (1981) and the Phillips Perron (PP) (1988) unit root tests suggest that the variables under examination are a unit root process at levels and, hence, integrated of order one, I (1).\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;(2) and (3) present the result of both ADF (based on the automatic lag length selection by Akaike information criteria (1969) and PP test. The obtained results show that all the time series in levels are non-stationary, which means they are integrated at an order of 1, i.e. I(1). Thus, the null hypothesis cannot be rejected for any of the variables under examination at 1 and 5% level of significance. However, when differenced once, the tests strongly reject the unit root, saying that they are integrated at an order of zero. The results of the unit root test are consistent with the theoretical argument that most macroeconomic series are not stationary at their levels and become stationary at their first difference. Once, the series are found to be stationary in differencing once, no further tests are required.\u003c/p\u003e \u003cp\u003eAlthough the individual series could be non-stationary, that is, they are individually I (1), as presented above; a linear combination of them might be stationary (Engle and Granger, 1987), which means a well-defined linear relationship exists among them in the long run. So, the subsequent discussion provides a test for cointegration between the variables under investigation.\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\u003eTests for unit root (ADF and PP).\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\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eADF Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePP Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinear and Constant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinear and Constant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.234114)***\u003c/p\u003e \u003cp\u003e[7.297074]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.152758)***\u003c/p\u003e \u003cp\u003e[7.200567]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.241357)***\u003c/p\u003e \u003cp\u003e[36.99159]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.163636)***\u003c/p\u003e \u003cp\u003e[39.74327]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.959479)\u003c/p\u003e \u003cp\u003e[4.771720]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.534197)**\u003c/p\u003e \u003cp\u003e[4.702376]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.130256)\u003c/p\u003e \u003cp\u003e[4.771865]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.320115)\u003c/p\u003e \u003cp\u003e[4.702509]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.304182)\u003c/p\u003e \u003cp\u003e[6.459060]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.075550)\u003c/p\u003e \u003cp\u003e[6.346644]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.323510)\u003c/p\u003e \u003cp\u003e[7.236470]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.166953)\u003c/p\u003e \u003cp\u003e[7.044401]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.509105)\u003c/p\u003e \u003cp\u003e[5.610313]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.802248)\u003c/p\u003e \u003cp\u003e[5.600772]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.532288)\u003c/p\u003e \u003cp\u003e[5.808220]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.941999)\u003c/p\u003e \u003cp\u003e[5.835443]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.368613)***\u003c/p\u003e \u003cp\u003e[10.31328]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.560528)***\u003c/p\u003e \u003cp\u003e[10.18581]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.309671)***\u003c/p\u003e \u003cp\u003e[17.01350]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.635771)***\u003c/p\u003e \u003cp\u003e[17.02537]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.016488)\u003c/p\u003e \u003cp\u003e[4.188038]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.733657)\u003c/p\u003e \u003cp\u003e[4.260888]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.368077)\u003c/p\u003e \u003cp\u003e[3.920234]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.422377)\u003c/p\u003e \u003cp\u003e[3.923482]**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.169513)\u003c/p\u003e \u003cp\u003e[6.917485]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.601525)\u003c/p\u003e \u003cp\u003e[6.837791]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.148742)\u003c/p\u003e \u003cp\u003e[7.406595]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.601525)\u003c/p\u003e \u003cp\u003e[7.127076]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrsm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.292521) [5.020878]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.732287)\u003c/p\u003e \u003cp\u003e[5.517733]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.391118)\u003c/p\u003e \u003cp\u003e[4.53746]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.484977)\u003c/p\u003e \u003cp\u003e[4.606190]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e***;** and * denote significances at 1%; 5% and 10% levels respectively\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e( ) denotes stationarity in level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e[ ] denotes stationarity in first difference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the second step, we will assess to experiment with the cointegration between the variables of the model by putting into practice the Bounds Test. If the bounds test indicates the existence of a cointegration relationship, the third step would be to estimate the relationship of equilibrium of long term using the ARDL model. The Fourth step consists to determine the relationship in the short run using WALD Test which is included in the ARDL Model. \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1736746750.png\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eWhere Log is the natural logarithm, ∆ indicates the variable in the first difference, is an intercept, t refers to the time period in years from 2002\u0026ndash;2022, and ε\u003csub\u003et\u003c/sub\u003e is a white-noise error term. Lags (m,n,o,p,q,r,s) are determined using the Akaike information criteria (AIC). Once Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) has been estimated, the attendance of a cointegration relationship between the variables has to be elaborate by involving the bounds test. Indeed, the cointegration test is constructed predominately on the Fisher test (F-stat) for the joint significance of the coefficients of the lagged level variables, i.e.,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:H0:\\:{\\delta\\:}_{1}\\:=\\:{\\delta\\:}_{2}\\:=\\:{\\delta\\:}_{3}=\\:{\\delta\\:}_{4}=\\:{\\delta\\:}_{5}\\:=\\:{\\delta\\:}_{6}={\\delta\\:}_{7}=\\:0\\)\u003c/span\u003e\u003c/span\u003e, which indicates no cointegration, against the alternative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:H1:\\:{\\delta\\:}_{1}\\#\\:{\\delta\\:}_{2}\\:\\#\\:{\\delta\\:}_{3}\\:\\#\\:{\\delta\\:}_{4}\\:\\#\\:{\\delta\\:}_{5}\\:\\#\\:{\\delta\\:}_{6}\\:\\#{\\delta\\:}_{7}\\#\\:0\\)\u003c/span\u003e\u003c/span\u003e, which indicates that there is integration. After comparing the F-stat value with asymptotic critical value bounds calculated by Pesaran et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the null hypothesis of no cointegration is rejected when the value of the F test protrudes the higher critical bounds value, embroilment that there is a cointegration relationship between the elaborated variables. The final step is to ensure the goodness of fit.\u003c/p\u003e \u003cp\u003eDue to we concluded that series are not integrated of order two; we employed the ARDL cointegration test. Based on the ARDL Bound test, we can reject the null hypothesis and conclude that \u003cb\u003eGDP, BE, REI, FDI, ELC, X, Trsm\u003c/b\u003e and \u003cb\u003eTF\u003c/b\u003e are moving together in the long-term. The results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As seen from the Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the results of Bounds Test indicate that F statistic is higher than the upper critical value. Thus, we reject the null hypothes is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}0:\\:{{\\delta\\:}}_{1}\\:=\\:{{\\delta\\:}}_{2}\\:=\\:{{\\delta\\:}}_{3}=\\:{{\\delta\\:}}_{4}=\\:{{\\delta\\:}}_{5}\\:=\\:{{\\delta\\:}}_{6}={{\\delta\\:}}_{7}=\\:0\\:\\:\\)\u003c/span\u003e\u003c/span\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In other words, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that there is a cointegration nexus among the series in Tunisia.\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\u003eBounds Test\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\u003eARDL Bounds Test\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\u003eTest Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.002525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCritical Value Bounds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eI(0) Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI(1) Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.99\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\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43\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\u003eSince the validity of a cointegration nexus among GDP, BE, REI, FDI, ELC, X, Trsm ,TF is approved; we examine the long-term and short-term impacts of BE, REI, FDI, ELC, X, Trsm ,TF on economic growth (GDP) in Tunisia.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the results of the estimation of ARDL model in the long run. According to the results of the estimation, we find that BE has a significant and negative effect on economic growth. Keeping other things constant, a 1% increase in BE is accompanied by a 0.0805% improvement in economic growth.\u003c/p\u003e \u003cp\u003eAlso we find that REI has a significant and negative effect on economic growth. Keeping other things constant, a 1% increase in REI is accompanied by a 0.2810% improvement in economic growth.\u003c/p\u003e \u003cp\u003eFor control variables, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e points that FDI have a negative and significant effect on economic growth. These results mean that respectively, a 1% increase in FDI is accompanied by a 0.9242% improvement in economic growth. That ELC and Trsm have a negative and significant effect on economic growth. These results mean that respectively, a 1% increase in ELC is accompanied by a 0.9652% improvement in economic growth and a 1% increase in Trsm is accompanied by a 0.1466% improvement in economic growth. However, X and TF have a significant and positive effect on economic growth. Holding other things constant, a 1% increase in TF is accompanied by a 2.2748% increase in GDP; and a 1% increase in X is accompanied by a 0.4533% increase in GDP.\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\u003elong-run relationship\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\u003eARDL Cointegrating And Long Run Form\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: GDP\u003c/p\u003e \u003cp\u003eSelected Model: ARDL(1, 3, 2, 2, 2, 0, 3)\u003c/p\u003e \u003cp\u003eCointegrating Form\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoefficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStd. Error\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et-Statistic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(BE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.502356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.267049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.881140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(BE (-1))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.713845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.357040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.999340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(BE (-2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.423212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.230020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.839894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(REI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(REI (-1))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.302232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(REI (-2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.195456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(FDI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.103425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.499499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(FDI (-1))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.403876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.570436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(ELC (2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.592068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.417372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(ELC (-1)(2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.895931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(X (2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.125559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(X (-1),2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.369852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.804299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(Trsm (2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.166625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.182764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.911695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(TF (2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.054198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.054681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(TF (-1) (2) )\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.350104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.682875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD(TF (-2) (2))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.285722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.892616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCointEq\u0026sup2;(-1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.136231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.787792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoint Eq\u0026thinsp;=\u0026thinsp;GDP - (-0.0805* BE -0.2810* (REI )\u0026thinsp;+\u0026thinsp;0.4533*D(X) -0.9652*D(FDI) \u0026minus;\u0026thinsp;0.1466*D(Trsm)\u0026thinsp;+\u0026thinsp;2.2748*D(TF)\u0026thinsp;+\u0026thinsp;5.7873)\u003c/b\u003e\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports empirical results of the short run analysis. We find that BE, REI and X cause GDP. Also, we find that FDI, ELC, Trsm and TF are statistically insignificant which mean that they don\u0026rsquo;t have any effect on economic growth.\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\u003eShort run / Wald 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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.542112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.581327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.643328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.720076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.831188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrsm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.35873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e***;** and * denote significances at 1% ; 5% and 10% levels respectively\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\u003eIn Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, diagnostic tests point out that the global indentation adopted is satisfying and reasonable. Tests performed to detect the presence of Breusch-Pagan-Godfrey, Harvey, Glejser, and ARCH in the estimated equation did not reveal any problem of heteroskedasticity at the 5% threshold. The R\u0026sup2; determination coefficients are close to or greater than 50% and the Jarque\u0026ndash;Bera test shows that the residues follow the normality law. Otherwise the probability of Fisher is less than 5%, which indicates that our model is well treated.\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\u003eDiagnostics Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeteroskedasticity Test: Breusch-Pagan-Godfrey\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic 0.282238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. F(19,18) 0.9956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared 8.722348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 0.9778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaled explained SS 1.546533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeteroskedasticity Test: Harvey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic 0.333990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. F(19,18) 0.9889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared 9.904809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 0.9553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaled explained SS 7.673741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 0.9897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeteroskedasticity Test: Glejser\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic 0.308335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. F(19,18) 0.9928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared 9.330821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 0.9676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaled explained SS 3.664893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(19) 0.9999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeteroskedasticity Test: ARCH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic 0.103296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. F(1,35) 0.7498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared 0.108878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(1) 0.7414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreusch-Godfrey Serial Correlation LM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest: F-statistic 0.379384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. F(2,16) 0.6903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared 1.720482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. Chi-Square(2) 0.4231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTests of Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared 0.823097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-statistic 4.407933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared 0.636366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb(F-statistic) 0.001362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTest of Normality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera 0.359624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbability 0.835427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eThe positive effects of Blue Economy Investment Behaviour and Investment in renewable energy on economic growth are hugely tempted on the theoretical levels as well as on the empirical levels. However they turn out minus clear when uniqueness is exercised in a case of a developing country that suffers from many problems. This paper aims to trace the Blue Economy: Blessing of the sustainable growth in Tunisia using the ADF unit root test, PP unit root test, Bounds test and ARDL Model for the period 2002\u0026ndash;2022.\u003c/p\u003e \u003cp\u003eAccording to the result of the analysis, it was determined that Blue Economy Investment Behaviour and Investment in renewable energy have a negative effect on economic growth in the long run. These results provide evidence that Blue Economy Investment Behaviour and Investment in renewable energy, thus, are not seen as the fountain of economic growth in Tunisia during this considerable period and pain a lot troubles and inferior economic organization.\u003c/p\u003e \u003cp\u003eThe results obtained lead us to make the following recommendations in order to promote economic growth in Tunisia: First, the economic growth in Tunisia is not mainly linked to Foreign Direct Investment in long run. Second, the government should pay more attention to the nature of Blue Economy Investment Behaviour. Third, the government should orient the Blue Economy Investment Behaviour and Investment in renewable energy to more productive projects in order to enhance economic growth. Additionally, it\u0026rsquo;s also important for government to improve good governance policies and the business climate in order to reduce institutional inefficiencies. Fourth, it\u0026rsquo;s more important to reduce the risks and uncertainty associated with Blue Economy Investment Behaviour and Investment in renewable energy. Finally, the efforts should be directed to speed up the administrative procedures to attract more investments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eolfa saghrouni : Conceptualization, Formal analysis, Writing, original draft, Data curation, Methodology, Software.olfa saghrouni,All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAraujo, B., Akhir, K., Farand, F., \u0026amp; Nahumariri, M. (2021). 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Understanding High-Quality Development of Marine Economy in China: A Literature Review. \u003cem\u003eMarine Economics and Management\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 124\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/MAEM-10-2019-0011\u003c/span\u003e\u003cspan address=\"10.1108/MAEM-10-2019-0011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"[email protected]","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, sustainable, ARDL","lastPublishedDoi":"10.21203/rs.3.rs-5763617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5763617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Blue economy in Tunisia is an opportunity to achieve integrated and sustainable development. Fundamental relationships between different macroeconomic variables may follow certain common theories, but local preferences are also decisive in determining their unique behavior. Looking into Blue Economic growth nexus is, therefore, needed in Tunisia, so as to understand the long -run economic stance and to capture the short-run dynamics in the national economy. This paper aims to analyze the impact of Blue Economic and economic growth in Tunisia using time series data over the period 2002 to 2022. This study is based on the Auto-Regressive Distributive Lags (ARDL) approach that is proposed by Pesaran et al (2001). Blue Economic are important for economic growth and economic growth has an impact in Tunisia. A successful and sustained economic growth requires growth in Blue Economic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification: O4, Q56, R11\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Blue Economy: Blessing of the sustainable growth in Tunisia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 06:03:11","doi":"10.21203/rs.3.rs-5763617/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"cbd7138e-2f34-449c-9a45-4d24ab67d6ac","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-18T20:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 06:03:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5763617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5763617","identity":"rs-5763617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}