Will humans move towards renewable energies for the sake of the life of the planet and the fight against global climate change and the importance of humans for the environment and human survival? What will happen to the position of fossil and nuclear energies? Why should humans avoid fossil fuels such as oil and gas, etc., and should they no longer use oil and its derivatives for the sake of the Earth's atmosphere and the life of humans and their creatures?

Economic expansion has been the main goal of every country for many centuries. However, once scientists began to warn about the disastrous effects of uncontrolled growth on both natural resources and climate change, the concepts of green growth and sustainable development progressively took over. In fact, climate change is a global phenomenon threatening the lives of both present and future generations. According to the IPCC reports [1,2], greenhouse gasses (GHG), in particular CO2 emissions, are the main cause of this phenomenon. Since fossil fuels positively affect carbon emissions [3,4,5,6,7], combating climate change means, first of all, limiting the consumption of these polluting energies. As energy is necessary for economic growth, it is not possible to avoid its use. However, it is possible to gradually replace fossil fuels with a source that emits less GHG and which is more respectful of the environment. These are obviously renewable energies which are proven to be negatively linked to carbon emissions [8,9,10,11] and an efficient policy to attain sustainable development [12]. In recent decades, impressive headway has been generated in the sector of sustainable energies. In fact, even during the health crisis resulting from the COVID pandemic, investment in the sustainable energy sector expanded by 2 % in 2020 [13]. In addition, clean power represented 38 % of the global electricity supply in 2021 [14]. These advancements are mainly a consequence of the drastic lowering in solar and wind energy costs following the economies of scale and innovations. For instance, the levelized cost of electricity (LCOE) calculated for solar photovoltaics had declined by about 85 % throughout the decade 2010–2020 [15]. Although developed countries have emitted the most GHG into the atmosphere to achieve a high level of development, it is rather developing countries that are the most concerned with the phenomenon of changes in climate. In addition, they do not have either the financial means or the necessary expertise to deal with such a threat. For these reasons, we have chosen to deal with the case of Tunisia which is a developing country located in North Africa and which has an enormous potential of renewable energies but is still dependent on imports of natural gas [16]. In fact, the national energy balance was characterized by an amplification of the deficit over the period 2010–2019. This dynamic stems from the considerable decrease in production combined with an increase in consumption. The energy deficit has climbed to 55 % in 2019, while it was only 20 % in 2010 [17]. Furthermore, Tunisia is amongst the first developing countries which has started a global strategy of promotion of renewable energies since the 1980s and has even created an agency for these energies in 1985 which is among the first agencies in the countries of North Africa. The path of Tunisia in the diffusion of renewable energies has been characterized by peaks and troughs, especially in the case of solar energy which is well explained by Omri et al. [18]. On the basis of the last data posted by the Tunisian government, in 2022, the total installed capacity in renewable energies is only 472 megawatt (MW) [19]. In addition, during 2020, the production of electricity by STEG (which is the only state-owned electric and gas utility) was obtained up to 96.7 % from natural gas and only 3.2 % from renewable energies. As for the production of electricity from diesel, it remains very low (0.1 %) [20]. These achievements are very modest compared to other similar countries such as Morocco. According to Omri et al. [21], this situation in Tunisia is due to the different barriers facing the transition to renewable energies such as political and financial hazards. Given that the targets of Tunisia in the field of renewable energies are very ambitious but the realizations are far away from these objectives, it will be of great interest to analyze the drivers of green energies in order to set convenient strategies. Whereas the studies dealing with the impacts of renewable energies either on CO2 emissions or growth are multiple, there are just a very restricted number of papers investigating the drivers of green energy production or consumption. It is therefore important to study the determining factors of this transition to help policymakers orient their strategies in the right direction. Surprisingly, only two studies have recently dealt with the main drivers of renewable energies in Tunisia which are Saadaoui and Chtourou [22] and Saadaoui and Chtourou [23]. These studies have dealt with the institutional and financial drivers but have neglected the technological factors such as ICT and another important driver which is FDI. Hence, the impacts of TFP, FDI, and ICT have been totally missing from the literature concerned with the determinants of renewable energies in Tunisia. For this reason, the main objective of the current investigation is to complete the existing literature by investigating the impacts of the TFP, ICT, along with the FDI, and trade on renewable energies in Tunisia. To attain this objective, the ARDL cointegration approach elaborated by Pesaran et al. [24] is adopted. In fact, the FDI is a considerable pillar in the renewable energy diffusion process [25]. Actually, FDI can be one of the main ways by which developing countries like Tunisia could integrate the global green economy. Indeed, FDI is an important channel for the transfer of capital needed to facilitate the transition to a greener economy by being directed, for example, to the renewable energy sector. Despite the importance of FDI for the Tunisian economy in general and for the transition to renewable energies, in particular, the statistics are disappointing. Indeed, in 2018, FDI flows in Tunisia represented only 5 % of the total FDI flows of the entire MENA region and Tunisia was ranked sixth among the beneficiary MENA countries of FDI flows [26]. Hence, analyzing the impact of FDI on renewable energies will be of great importance in order to direct government policies towards efficient ways to boost this diffusion through FDI. Concerning the effect of ICT, it was proven that it is a positive driving factor for economic growth [27] and a contributor to the improvement of climate quality [28,29,30]. Indeed, improving renewable energy consumption is among the most prominent channels through which ICT negatively affects CO2 emissions [31]. Actually, digitalization affects all aspects of the energy system [32]. Surprisingly, the consideration of the repercussions of ICT on the sustainable energy sector was neglected and the studies dealing with this recent issue are rare such as Lv et al. [33]. The current paper complements precedent studies in the field of green energy drivers by analyzing the interactions of ICT with FDI and trade. Hence, the predominant contributions of this research are threefold. First, this paper investigates the relationship between digital transformation and renewable energy transition in Tunisia, which presents the first trial to explore the so-called twin transition in this context. To that aim, the purpose of this article is to identify the role of digital structure in achieving the Sustainable Development Goals (SDG) through a sustainable energy mix. Actually, ICT is a very important factor to consider in the case of Tunisia. In fact, since the early eighties, Tunisia has employed ICT and developed programs to implement them in its development plan [34]. It is therefore interesting to analyze whether this effort has resulted in positive repercussions in the field of renewable energies or whether there is still a long path to pursue. For this reason, recent papers interested in the case study of Tunisia have emerged, by considering the role of ICT on CO2 emissions [35] and their effective role in economic growth [36]. However, no study has looked into the current issue. It is important to note that the examination of ICT is crucial especially in light of the COVID 19 pandemic which has revealed a fragile ICT structure in developing countries and demonstrated that these countries have difficulties in transitioning to a digital economy. Second, by analyzing the effect of FDI, this study provides an opportunity to verify the relationship between developed and developing countries in terms of funding and exchange. In reality, this study allows us to determine whether the green transition in Tunisia is a beneficiary or victim of technology transfer and foreign investments from developed countries. More specifically, by addressing the links between trade openness, FDI, and renewable energy, effective political suggestions could be formulated with regard to energy transition goals. Third, empirically, this paper presents the first essay using the frequency test of causality of Breitung and Candelon [37] in the analysis of key drivers of the clean energy transition in Tunisia. The current analysis will be organized into four sections. Section 1 and Section 2 will be devoted to the introduction and literature review, respectively. Section 3 will deal with the empirical approach. The resultant findings and discussions will be presented in Section 4. Finally, Section 5 will contain conclusions and main strategies. Literature review Economic literature has shown a strong link between the spread of renewable energies and economic factors. In this regard, Sadorsky [38] presented one of the first studies that investigated the macroeconomic factors that can determine the evolution of renewable energies in the Group of Seven (G7) countries. He confirmed that income and CO2 emissions are considered the most important drivers of sustainable energies. Since then, several other papers have investigated this issue. The literature reveals that economic factors tend to enhance the level of renewable resources in several cases. In fact, various studies report that improved income can lead to higher investment and consumption of renewable energy. For instance, Saadaoui [39] indicated that the enhancement of income per capita can generate an increase in renewable energies in MENA countries between 1990 and 2019. Moreover, Gyimah et al. [40] indicated that economic growth causes the diffusion of renewable energies in Ghana from 1990 to 2015. On the other hand, Das et al. [41] demonstrated the existence of one direction causality from renewable sources to economic expansion in India. In addition, Li and Lee [42] determined the existence of a positive consequence of economic growth in the context of twenty European countries from 1993 to 2018. However, income may not influence the development of renewable energies. In particular, Mukhtarov et al. [43] evaluated the impact of oil prices, pollutant emissions, and economic expansion on clean energy in Iran, using the General to Specific (Gets) approach. They found that the economic variable impact is insignificant on renewable energy diffusion. Additionally, Ergun et al. [44] argued that income per capita impedes renewable energy development for the group of African countries for the period 1990–2013. Furthermore, Uzar [45] found that income hinders renewable energies in 38 countries between 1990 and 2015. In fact, the different studies mentioned above have applied various methodologies over different periods of time which explains the inconclusive results and impacts. Moreover, a limitation of these previous analyses is that they have focused only on GDP per capita as an economic indicator. In fact, this indicator is the most frequently used variable to measure the effect of an economic situation on sustainable energies [46]. We believe we could improve upon it, by using the TFP as a measure of economic productivity influence on the clean energy transition. In addition to the aforementioned driver which is the TFP, our study addresses the involvement of FDI and trade openness in green energy development. Based on the case of Ghana, Ankrah and Lin [47] investigated the importance of these two factors in the transition to renewable electricity for the period 1980 to 2015. The empirical study relied upon the vector correction method and Johanson’s cointegration. In fact, this study confirmed the important role of FDI and trade in the development of green electricity. Moreover, within a set of North African countries, Ibrahiem and Hanafy [48] studied the interaction between CO2 emissions and different macro-economic indicators including FDI, economic expansion, and trade on the diffusion of clean energies from 1971 to 2014. They applied the pooled mean group (PMG) ARDL approach and the Granger causality approach. The results revealed that FDI, income, carbon dioxide emissions, and trade stimulate the development of renewable energy. They also indicated that trade openness causes energy consumption and that energy consumption causes FDI. Another strand of the empirical literature, which takes into account panel data in their analysis, explores the relationship between these different factors. In this framework, Ergun et al. [44] scrutinized the determinants of the share of green energy relative to fossil energy in Africa using a random-effects generalized least squares regression method. The explanatory factors are income per capita, the human development index (HDI), the level of democracy, and FDI. The finding revealed that FDI improves the share of renewable energy in these African countries. In accordance with the precedent analysis, Dingru et al. [49] studied the effects of trade and FDI on the sustainability of Sub-Saharan African countries’ energy mix. In this regard, the study demonstrates the significance of these two drivers in diversifying Sub-Saharan Africa’s energy mix. Furthermore, Han et al. [50] examined the consequences of urbanization and trade on both clean and polluting energies from 1990 to 2018 in China by using a quantile regression method. In fact, the outcomes of this empirical study highlighted that trade contributes with a significant effect to the development of fossil energies, but this effect is considered weak in the case of clean energies. They pointed out that trade activities in the production and export of goods are significantly linked to fossil fuel inputs. For the same case study, Zhao et al. [51] explained the determinants of renewable and fossil fuels resources in China from 1980 to 2016. Specifically, they tested the impacts of trade openness by applying the Fully Modified Ordinary Least Squares (FMOLS) method. The research outcomes illustrated that trade negatively affects the development of green energies, but this variable increases the diffusion of fossil energy. Uzar [52] incorporated the trade variable as a control variable in his examination of the effect of income disparity on the spreading process of sustainable energies in the context of 43 countries for the period 2000–2015. In this framework, no significant link was detected between trade and clean energies. On the other hand, Zhang et al. [53] reviewed the association between trade and sustainable energy development in the organisation for Economic Cooperation and Development (OECD) countries from 1999 to 2018. This analysis follows the work of Shahbaz et al. [54] in using import, export, and trade. The composition of the trade openness is a way to give a general insight into the linkage between clean energies and trade. The outcomes indicated that all imports, exports, and trade are positively affecting sustainable energies. Regarding the role of ICT, the investigation of its effect on the development of clean energies is limited. It is rather adopted significantly to explain CO2 emissions. In fact, in the existing literature, many analyses have focused on the linkages between ICT and the environment such as Shobande and Asongu [55]. For example, ICT has a positive impingement on carbon emissions in African countries over the years 1996–2014 [56]. However, Zafar et al. [57] proved that ICT increases the protection of the environment in Asian countries over the period 1990–2018. Moreover, ICT has assisted in the upsurge of energy intensity in China [58]. Additionally, Khan et al. [59] mentioned that ICT can improve environmental quality in the case of Morocco for the period 1985 to 2020. Despite the important role of ICT in global economic and environmental conditions, few studies have explored the effect of this variable on the transition to clean energy. For example, Murshed [31] analyzed the impact of ICT trade on different energy diffusion variables. The purpose of the analysis is to explain the variation in overall consumption of clean energies and many energy indicators and the reduction in carbon dioxide emissions with non-linear variation in the ICT variable for 6 Asian countries. Empirical outcomes acknowledged that ICT trade directly increases clean resources, promotes the use of cleaner cooking combustibles, decreases pollutant emissions, and reduces energy use intensity. Zheng and Wang [60], as well, studied the consequence of mobile ICT use on the diffusion of green energies in Canada, the United States, Germany, the United Kingdom, Italy, the Netherlands, and Poland. They emphasized the positive linkage between renewable resources and ICT. Moreover, Zobeidi et al. [61] explored the impact of Instagram use on the dissemination of clean energies in Iran, using the structural equation. The study emphasized the role that government agencies could play in improving the confidence of Instagram users to raise awareness through messages that explain the issues of polluting energy consumption.Lv et al. [33] studied the effect of ICT on the propagation of green energy resources concerning many countries during the period 2000–2014, employing a spatial econometric approach. The results of empirical investigations indicated that ICT development increases the consumption of sustainable energies. Likewise, Awijen et al. [62] detected the same positive action of ICT on the production of clean resources. In fact, they used ICT as a percentage of internet users in 9 MENA countries over the years 1984–2014. The outcomes revealed that the production of green energies was sensitive to the improvement of ICT. Moreover, Bano et al. [63] determined the explanatory factors of sustainable energies and tourism in BRICS countries. The authors demonstrated that renewable resources are stimulated by economic factors and also by industrial innovations. On the other hand, natural resources and ICT have an adverse influence on the diffusion of this type of energy in these countries. Li et al. [64] investigated the role of ICT in green energy development in China between 2000-Q1 and 2020-Q4. They demonstrated that ICT is assisting in the shift to renewable energies. Furthermore, Talan et al. [65] demonstrated that ICT can support this transformation in the context of the G7. Lee et al. [66] used the quantile regression method to examine the influence of information technologies in 126 countries between 2000 and 2019. They agreed that Internet use, secure network services, and high-tech exports had a considerable impact on speeding green energy output in the low quantile, whereas fixed broadband subscriptions have a considerable impact in the high quantile. For the case of Tunisia, Amri et al. [67] indicated that ICT has an insignificant influence on carbon dioxide emissions, using data from 1975 to 2014 to scrutinize the reaction of TFP, and ICT on CO2 emissions. Moreover, Ben Lahouel et al. [68] focused on the same case study, for the period 1970–2018, to argue that ICT is an important factor to improve TFP and CO2 emissions. In fact, the two previous papers emphasized the effective consequence of ICT in the economic and environmental structure of Tunisia, but no study has addressed its consequence on the development of clean energies in Tunisia. Also in the same framework, these studies have used different proxies for ICT, our contribution is to test the impact of a more general index of ICT. Despite the importance of identifying the channels of energy transition in Tunisia, few studies have been interested in this subject. For instance, Saadaoui and Chtourou [22] inspected the consequences of financial development, GDP per capita, and the quality of institutions on the diffusion of clean energies in Tunisia during the period 1984–2017. They applied as econometric tools the linear and non-linear ARDL model and non-linear causality. They also called for an improvement of the financing mechanisms since the financial development shows a non-significant impact in the linear model and a negative in the non-linear approach. Data and method of cointegration Data This study focuses on the determinants of the dissemination of green energy in Tunisia. The explanatory variables proposed, in this empirical research, are explained in the following lines. First, the dependent variable which is the national consumption of renewable energies in kilotonne of oil equivalent (ktoe) is from the information base of the International Energy Agency (IEA). In fact, several indicators are proposed by the recent empirical literature to design the renewable energy transition. For example, the investment in the sustainable energy sector [69], renewable electricity [47], production of renewable energies [62, 70], and contribution of clean energies to the global energy consumption [71,72,23]. Concerning the explanatory variables of renewable energy consumption (RE), there are as follows: the total factor productivity (TFP), the ICT investment (ICT), the foreign direct investment (FDI), and the trade openness (TR) in the case of Tunisia for the years 1984–2019. More precisely, an ICT index is determined, which is composed of four technological indicators. Therefore, the principal component analysis (PCA) is employed by using the following series: internet users, mobile phone users, telephone users, and broadband access per 100 inhabitants from the World Bank. All indicators of the ICT index are integrated into the natural logarithm. The use of this index affords more information on investment in ICT in Tunisia. In general, the use of an aggregation of many indicators provides a more general overview than a single variable, such as the use of mobile or telephone users. In addition, TFP data, which is an indicator of the country’s economic activity, expressed in constant prices, comes from the Federal Reserve database. Moreover, to examine the “technology transfer” hypothesis, the FDI which is procured from the World Bank database is used. Finally, data on trade openness, presented in our case as the average ratio of imports plus exports in a percentage of GDP, is procured from the World Development Indicators (WDI) site. The various descriptive statistics and stochastic properties of the natural logarithm of the explanatory variables are displayed in Table 1. Moreover, it portrays the skewness statistic, which indicates that all variables are negatively skewed except the FDI variable. In addition, the Kurtosis statistic depicts that all series have distributions following platykurtic forms. Finally, the normality test performed with the JarqueBera statistic attests the normal distribution of all series as the p-value is above the critical thresholds. Fig. 1 portrays the evolutions followed by the different variables of the model (in natural logarithm). It is important to note that all variables have undergone changes and evolutions during the study period. The variable under explanation which is RE trends upward. Indeed, Tunisia has made many regulatory and institutional changes to incentivize renewable energy use, which can explain this upward trend. Methodology The empirical model is derived from the current economic literature on the transition to renewable energies. The renewable energy demand is analyzed by testing the role of the TFP, ICT, FDI, and TR. Within this framework, our model can be illustrated in the following form: lnREt = α0 + α1lnTFPt + α2lnICTt + α3lnFDIt + α4lnTRt + σt (1) The variables lnRE, lnTFP, lnICT, lnFDI, and lnTR denote the natural logarithms of the consumption of renewable energies, total factor productivity, information and communication technology diffusion, foreign direct investment, and trade, respectively. α0 signifies the constant coefficient in our model, in addition, α1, α2, α3, and α4 explain the coefficients in relation with the different explanatory series in the model, concerning “t” it represents the year, and σt explains the error term. The consecutive steps that will be accomplished in order to enrich our methodology are the following: unit root tests, cointegration tests, testing the long and short term connections by ARDL with breakpoints, the sensitivity analysis, and the causality test. Before testing the cointegration between renewable energy and its determinants, it is essential to test the stationarity of the series. Actually, the stationarity of data depends on its pursuit of a stationary course which means that there is no trend or seasonal variation. The stationarity tests of Phillips and Perron [73] along with that of Dickey and Fuller [74] have been applied with the aim of specifying the integration order, which will feed our decision of the choice of the model. On the other hand, Zivot and Andrews [75] developed a unit root test with a structural break introduced “endogenously”, i.e. the (unknown) change point is estimated rather than fixed. In addition, it is considered that the time series could have some structural breaks: some macroeconomic variables could be modified during certain periods. In reality, the diffusion of green energies in Tunisia has undergone changes which may be explained by government strategies. In the same way, the world economic crisis of 2009 and the outbreak of the Tunisian revolution in December 2010 could be also a source of discontinuity in the trend of macroeconomic series. Hence, we avail of the cointegration technique developed by Pesaran et al. [24] to evaluate the liaisons between ICT, trade, TFP, FDI, and renewable energies in Tunisia from 1984 to 2019. The ARDL approach introduced by the works of Pesaran et al. [24] and Pesaran and Shin [76] is applied. In our case, the ARDL model is used to get over the requirements of the usual cointegration models. In fact, this methodology provides a long and short term analysis for small samples and integrated variables of different order either I(0) or I(1). To summarize, the main advantages of using the ARDL methodology in conformity with Pesaran et al. [24] and Pesaran and Shin [76] are: i) this approach allows for an appropriate analysis of the dynamics between all variables. ii) It solves the problem of endogeneity in the model. iii) The bounds testing method is convenient as long as the variables are integrated of I(1) or I(0) or a mix of both. So, contrary to Johansen’s cointegration, the ARDL does not stipulate the variables being integrated in identical orders. iv) Lastly, the approach affords reliable estimations and consistent results even for small samples. Subsequently, the presentation of the ARDL cointegration equation that includes the different variables representative of the model is presented in Eq(2):Cointegration results Before analyzing the different coefficients of the model, in the short and long-term, it is necessary to check the presence of a long-term association by using the F-statistic (FPSS) of Pesaran et al. [24]. The cointegration results are presented in Table 4. The null assumption of the F-statistic is the missing of cointegration among the different variables, which means the absence of a long-term relationship. Our results indicate that the F-statistic is higher than the critical limits at the significance level of 1 %. This allows us to reject the null assumption and subsequently confirm the presence of long-term linkages. From Table 5, it is possible to identify the influences of explanatory variables on the dissemination of renewable energies in Tunisia from 1984 to 2019. The ARDL describes these reactions in the short and long term. First, the error correction mechanism (ECT) presents the pace of adjusting in the direction of the equilibrium in the long-term. In order to identify the long run result, the ECT coefficient should be significantly negative. In our case, ECT is equal to “− 0.217″ and significant at 1 % threshold level. This finding proves the appearance of a long term liaison between the variables. In what follows, the long-term results of the model will be detailed. First, the influence of TFP on the consumption of sustainable energies is positive at the 5 % significance level. In fact, a 1 % change in TFP participates in improving the level of renewable energy development by 4.962 %. This result allows us to distinguish the potential contribution of the improvement of TFP to the diffusion of green energies in Tunisia. This result could be explained by the fact that the level of technological progress disembodied with capital in the Tunisian renewable energy industry is sufficiently high to boost the development of the green energy industry. More precisely, our results prove that the efforts made in Tunisia in order to increase productivity in firms and the economy in general, has led to an upsurge in green energies. Accordingly, the improvement of innovation and technological advancements in the economy, which has a great impact on TFP, will enhance the transition to a sustainable energy sector in Tunisia. This outcome is compatible with Belaïd et al. [70] who indicated that TFP promotes the production of renewable energies in MENA countries. Moreover, it is interesting to compare this result with previous studies that examined the effect of economic circumstances on the diffusion of green energies. In fact, our finding is in compliance with the results of Saadaoui [39] who indicated that economic expansion positively affects the consumption of green energies in the MENA zone for the period 1990–2018. Moreover, for Tunisia, Saadaoui and Chtourou [22] confirm that GDP per capita can enhance the consumption of sustainable energies for the period 1984–2017. Second, the long run impact of ICT on the consumption of renewable energies is negative. It highlights that following a 1 % increment in ICT, clean energies will turn down by 0.167 %. This unfavorable influence of ICT on the dissemination of renewable energies shows that the policies in Tunisia are not trending towards the integration of technology in the diffusion of green energies. For example, the development of smart grids is not yet introduced. This fact may be explained by the economic recession, bad governance, and political instability which can delay the implementation of new technologies in vital sectors of the economy, specifically the energy sector. Actually, Tunisia has been implementing a strategy to promote the incorporation of ICT into the economic and social development processes, for many years. Indeed, Tunisia is making visible progress in this area, with Internet penetration rates among the highest in Africa, robust connectivity, and an early embrace of digital technologies. However, Tunisia has not yet fully realized the sector’s potential and possibilities [36]. This is supported by our research, which demonstrates that Tunisia’s clean energy sector is not benefiting yet from this potential. These findings are outlined by Bano et al. [63] for BRICS economies (Russia, South Africa, Brazil, India, and China). Meanwhile, the outcomes are not in conformity with those of Awijen et al. [62] who confirmed the crucial role of ICT in the production of green energies in the MENA zone. In fact, they approved that internet users, presenting the ICT in their paper, can boost renewable energy production. Furthermore, Zheng and Wang [60] pointed out that ICT positively impacts renewable energies based on panel data of the following countries: the UK, Netherlands, USA, Canada, Italy, Poland, and Germany. Third, the influence of FDI on renewable energies is positive at a 1 % significance level. Actually, a 1 % expansion in FDI will increase the level of green energies by 0.466 %. This effect indicates that FDI flows tend to be oriented towards sustainable development and to upgrade the contribution of renewable energies in the Tunisian energy mix. Actually, it is an indication that investors are attracted to investment in green energies. This outcome is consistent with Tunisia’s efforts to further liberalize and integrate its economy into the global economy. This finding may be explained by the fact that the investment incentives code in Tunisia has set for a few years specific procedures and incentives for investments in sectors related to the environment, whether national or foreign. However, this is not sufficient and the government should guarantee a stable political climate which will increase visibility and attract foreign investments. In fact, economic and political climates favorable to foreign investment would provide funds for large-scale renewable energy projects and permit to reach the objectives set in the field of renewable energies. The favorable influence of FDI on green energy deployment is affirmed by the findings of Tiwari et al. [77] in the case of Asian countries, Ankrah and Lin [47] in the case of Ghana, and Doytch and Narayan [78] for 74 countries. On the other hand, this finding is not consistent with that of Khan et al. [79] who demonstrated that FDI has a negative impact on sustainable energy in 69 countries from 2000 to 2014. Finally, our results confirm that trade openness hampers the renewable energy transition. In fact, it reduces green energy consumption by 1.577 % in the long term. This result shows that improved degrees of imports and exports do not allow the acquisition of renewable energy and also the knowledge related to the diffusion of these technologies. It means that trade openness in Tunisia has an adverse effect on clean energy by supporting unsustainable exchange. In fact, the import and export in Tunisia are directed towards fossil energy. Actually, a very important part of imports in Tunisia is devoted to non-renewable energies [80]. This result could be explained by the fact that the proliferation of national fossil resources and the increase in energy demand have led to a surge in imports of gas and oil for several decades. The necessity of these two sources for the economic growth process and the massive subsidies accorded to fossil fuels have deepened the energy deficit. In fact, the abundance of imported fossil fuels with affordable prices due to subsidies granted by the State has significantly slowed the transition to renewable energy sources. Therefore, it is necessary for Tunisia to control its imports, strengthen its energy efficiency policies, and control subsidies to fossil energies in order to minimize imports of non-renewable energies. This finding endorses the statement of Zhao et al. [51] in the case of China and Tiwari et al. [77] in the case of Asian economies. Indeed, this result is contrary to the results of Omri and Nguyen [81] who found that trade positively influences the consumption of green energies for middle and low-income countries, while their effect is insignificant for the high-income panel countries. Our short-term results confirm those of the long-term except for the impact of ICT. In fact, our findings confirm that TFP positively impacts the consumption of renewable energies, since the short-term elasticity of TFP is equivalent to 1.297 %. Moreover, the FDI positively affects the energy transition process. In contrast, trade openness hampers this substitution. Finally, the effect of the ICT variable in the short term reveals a non-significant liaison with the diffusion of green technologies. The last phase of our investigation is to verify the validation of the model. The R2 value of the model shows that the 99.74 % difference in renewable energy is outlined by the explanatory variables. In addition, the Breusch-Godfrey test of serial correlation demonstrates that the pvalue of the serial correlation test is not significant at all significance levels, which results in the acceptance of the null hypothesis of lack of serial correlation of the error terms. Concerning the Autoregressive Conditional Heteroskedasticity (ARCH) test, it suggests the absence of a conditional heteroskedasticity problem since the P-value is higher than the different significance levels. In the same sense, the error of the specification test of the Ramsey regression equation (RESET) indicates that the functional form of the empirical model is well designed and confirmed at different levels of significance. In addition, the normality diagnosis (J-B) of the model provides a satisfactory conclusion because it shows the absence of normality. i.e. the null assumption of the absence of normality was not discarded. Consequently, these specific tests indicate that the estimated ARDL model with structural breaks is accurately specified. Finally, to verify the stability of the model, the CUSUM along with the squared CUSUM tests, which are presented in Fig. 2, are performed. In fact, the precedent tests confirm that the estimated parameters are stable from 1984 to 2019 since the lines estimated are within the critical bounds at the 5 % threshold.The spectral causality results This paper includes a test of frequency causality adopted by Breitung and Candelon [37] to highlight the potential links within renewable energy and its determinants in Tunisia. The advantage associated with this approach is that it allows the evaluation of links at different levels of frequency [82]. The relationships between the different data are identified at frequencies of 0–1, 1–2, and 2–3. These frequencies refer to long term, medium term and short term relationships, respectively. According to Fig. 3, the findings of causal relationships show that renewable energy causes ICT only in the long run which is non consistent with the earlier results of Shah et al. [83] for the BRICS countries, Batool et al. [84] in a sample of East and South Asia countries, and Dahmani et al. [36] for the MENA region. Moreover, Fig. 4 illustrates that a bi-directional causality within TFP and renewable energy is detected only in the long term, which is similar to the work of Rath et al. [85] who confirmed the feedback assumption in the long term in a sample of 36 countries. In the short and medium run, there are no effective links between the two variables, which is similar to the finding of Tugcu and Tiwari [86] for BRICS countries On the other hand, the causality in medium and short run confirms the interdependence of FDI to renewable energy as demonstrated in Fig. 5. Moreover, in the long term, the results prove no causal link between FDI and RE. This result of no causality confirms the finding of Naz et al. [87] and Ben Jebli et al. [88]. In addition, our findings are different from those of Mert et al. [89], Chen et al. [90], Fan and Hao [91], Khan et al. [79], and Ibrahiem and Hanefy [48]. According to Fig. 6, there is a causality from trade to RE in the short and medium run at 5 % significance level, but in the long run causal liaison is significant at the frequency ω є [0.8, 1]. This one way link causality is confirmed by Chen et al. [90] and Ibrahiem and Hanefy [48]. In contrast, it can be considered different from Ben Jebli and Ben Youssef [92], Ike et al. [93], Leit˜ ao [94], and Batool et al. [84].Conclusions and policy recommendations This research examined the influences of ICT, TFP, TR, and FDI on clean energy consumption in Tunisia, using the ARDL model with structural break based on the period 1984–2019. In addition, we have tried to determine the causality links within variables by using the frequency causality test of Breitung and Candelon [37]. In fact, we believe that the outcomes of this study can afford strong policy guidelines in order to hasten the transformation of the energy mix to more green energy consumption. In what follows, the different results approved by this study will be explained. First, the TFP, which is influenced by the country’s economic level, significantly accelerates the energy transition as increased economic activity can raise the demand for renewable energy. The TFP impact is prominent in the short and long terms, with the prevalence of the long term effect over the short term. In fact, it is indicated that the evolution of 1 % in the TFP stimulates the consumption of sustainable energies by 4.962 % in the long run against 1.297 % in the short term. This result could be explained by the fact that the level of technological progress disembodied with capital in the Tunisian renewable energy industry is sufficiently high to boost the development of the green energy industry. Furthermore, it was demonstrated that the ICT index, which is representative of four technological variables, inhibits the deployment of renewable energies in Tunisia for the long-term. Specifically, the increase of ICT by 1 % decreases green energy consumption by 0.167 %. Hence, improving the ICT may increase the demand for other sources of fuels in the context of Tunisia. This result provides evidence that the ICT infrastructure in Tunisia has not been directed yet towards the development of renewable energies, which is expected for the case of developing countries. In fact, policymakers in Tunisia do not consider ICT to influence the choice of consumption of the citizens. Indeed, technologies can be oriented to stimulate and orient the choices of consumers, as they can be used to increase the confidence of citizens toward the use of renewable energies. More specifically, social networks such as Instagram that are based on the use of the internet could improve the confidence of the users in renewable energies [61]. This result calls for a better use of this social network by public decision-makers by disclosing more advertisements that encourage green technologies. Hence, policymakers should accelerate the implementation of ICT in the energy sector to facilitate the use of clean sources. Indeed, the rapid use of ICT increases the dematerialization and digitization processes in the Tunisian economy. Concerning the trade openness, it has a negative effect on the consumption of green energies. In fact, a 1 % upsurge in trade openness engenders a dwindling in clean energy consumption by 1.577 % in the long term. This result is due to the fact that Tunisia is a net importer of fossil fuels. In addition, customs duties on imported solar panels are among the highest rates in the MENA region. This finding allows us to suggest that Tunisia must orient its trade policies towards the exchange of green technologies by reducing, for example, customs duties and the value-added rate on imported solar panels, which can facilitate the transition towards renewable energies. Finally, we deduced from short and long term results that FDI boosts the level of diffusion of clean energy in Tunisia. In fact, a 1 % increment in FDI, in the long term augments by 0.466 % the consumption of renewable energies. Hence, it is very important that the spread of clean energies in Tunisia must pass from the phase where only the State is responsible to a phase of public-private partnership where the investors as well as Tunisian and foreigners take part in this transition. Hence, encouraging foreign investors by facilitating administrative procedures and offering tax advantages is very important. Indeed, Tunisia has made considerable efforts, in recent years, to improve the legislative and regulatory framework in order to boost domestic and foreign investments in areas that play an important role for inclusive and sustainable growth. In this context, we can cite Law no 2015–49 relating to Public-Private Partnership contracts as amended and supplemented by Law no 2019–47 on improving the investment climate. On the other hand, the main outcomes of the causality test have illustrated that there are one-way causal relations from renewable energies to ICT in the long term and from renewable energies to FDI in the short and medium term. In addition, there is a bi-directional causality between TFP and sustainable energy in the long term. Finally, there is evidence of causality running from trade to sustainable energies. In order to improve our research, we propose in future research to decompose the variable trade to emphasize the effect of imports and exports. In fact, we believe that by this decomposition, we can designate more effective policies. Moreover, in the present work, it was difficult to apply this idea because the existing data does not cover the sample period. Finally, we propose to analyze the effect of a more recent indicator which is the green total factor productivity as used by Xie et al. [95]. Given the significant potential of renewable energies in Tunisia, the transition to renewable energies is possible, but it is facing various barriers, in particular the carbon-lock-in, which is well explained by Omri et al. [21] and the dominance of natural gas. Hence, the strategy to phase out fossil fuels should be done smoothly, especially since fossil fuels are proven to be a driver of economic growth in Tunisia. In fact, despite all efforts made by both developed and developing countries to disseminate renewable energy sources, their growth remains under the settled goals due to a variety of barriers [96,97].