The vast literature on the link between economic development and environmental degradation supports that the economy’s improvement is likely to accelerate environmental deterioration [11]. Nevertheless, countries plan to reduce unemployment, poverty, and social inequalities and to ensure quality education and environmental preservation. This contrasts with the Kuznet environmental curve hypothesis. Thus, increasing the economy while preserving the environment would amount to reducing GHG emissions while minimizing the effects of this reduction on economic growth. The Paris Agreement recommended a global response to climate change threats and strengthening efforts to eliminate poverty [12,13,14]. Among other things, the Paris Agreement encouraged the maintenance of the global average increase temperature below 2 °C compared to pre-industrial levels. This was done to prevent global warming from attaining 1.5 °C between 2030 and 2052 [21]. Would it, therefore, be preferable for all countries to adopt a commune energy policy? The answer is no. The economists’ conclusions agree that the association between economic development and emissions can vary depending on the study areas, implying several recommendations. It is, therefore, up to each area to conduct its investigations to propose sustainable development policies per the resolutions to which the country is a signatory.

Hussain et al. [22] conducted some analyses about the liaison between environmental deterioration and economic development in Pakistan with data on CO₂ emissions, GDP, and energy consumption from 1971 to 2006. They employed the Johansen cointegration, VECM, and Granger causality tests. The findings reported a long-term association between GDP, energy consumption, and CO₂ emissions. Moreover, there is an increasing curve between GDP and CO₂ emissions, rejecting the environmental Kuznet curve relationship. Their results align with Halicioglu [23] in Turkey, He and Sandberg [24] in Canada, and Fodha and Zaghdoud [25] in Tunisia. The authors insisted that a carbon reduction policy would harm economic growth, and if no strict air pollution control is taken, the population will suffer. They recommended network-monitoring policies based on developed countries’ policies.

Similarly, Mohapatra and Giri [26] employed ARDL and VECM on CO₂ emissions, trade openness, energy consumption, urbanization, and gross fixed capital formation data from 1971 to 2012 to show that economic growth adds to energy consumption, which contributes to carbon emissions in the long term in India. In addition, the result supports that urbanization harms environmental quality. Recognizing that the country suffers from an economic deficit and that economic growth and urbanization are deteriorating the environment, the authors had to choose economic development while formulating energy policies that could mitigate the environmental consequences. They recommended that the country invests in clean energy and research to find less-polluting energy sources.

In the words of Ali et al. [27], the EKC hypothesis is applicable in the long term but not in the short term in Pakistan. They reached this conclusion with data from 1980 to 2012 on economic development, CO₂ emissions, and energy consumption. They suggested policymakers consider economic growth impact while formulating energy policy. The main recommendation made is the gradual shift to renewable energy.

On the contrary, Bosah et al. [28] demonstrated that economic development provoked long-term environmental degradation in 15 economies from 1980 to 2017. Moreover, the results indicate that urbanization does not influence CO₂ emissions, while energy consumption enhances them. They reached these conclusions using data on urbanization, GDP, CO₂ emissions, and energy consumption. Technological innovation, population growth control, and public transport improvement were recommended. Their findings support the results of Sheraz et al. [29] in G20 countries and Tong et al. [30] in E7 countries.

Since several studies about economic growth and the environment recommend using clean energy, scholars must esteem renewable energy’s impact on development to formulate appropriate sustainable policies [31,32,33,34]. Even if the ultimate goal of energy policies is to reduce carbon emissions, the fact remains that policymakers manage what energy policy agrees with economic development. Somoye et al. [35] classified renewable energy’s effect on economic growth into three spectrums: positive impact, negative impact, or no impact. This leads to taking precautions while recommending renewable energy adoption in developing countries, as those countries urgently need to promote growth.

According to IRENA, 12.7 million people were employed directly and indirectly by the renewable energy sector in 2021, with 4.3 million jobs coming from photovoltaic solar, 1.3 million from wind power, 2.4 million from hydropower, and 2.4 million from biofuels [36]. This supports the finding of Sari and Akkaya [37] and Proença and Fortes [38], who show that renewable energy adds to employment. Namahoro et al. [39] established that the impact of renewable energy on economic development varies according to regions and income levels in Africa. Their investigation used economic development, energy intensity, carbon emissions, and renewable energy data from over 50 African countries from 1980 to 2018 and Panel CCEMG and PMG. They suggested that investments in renewable energy projects associated with better economic activity management could ensure sustainable development. Additionally, they encouraged country-specific and regional studies with more factors leading to carbon emissions. According to Vural [40], renewable energy and non-renewable’s effect on economic growth energy are very close. This is consistent with Apergis and Payne [41]. This finding means renewable energy could valuably replace non-renewable energy, with the advantage of adding to the environmental quality. This study used renewable and non-renewable energy, capital, and labor from six Sub-Saharan African countries during 1990–2015 and FMOLS. They encouraged more investments in the clean energy sector, tax incentives to encourage the private sector to adopt renewable energy, and skills training.

Maji [42] examined clean energy’s impact on economic development in Nigeria by using the ARDL bounds test, clean energy (alternative and nuclear energy and electric power consumption), and GDP. Their results reveal a mixed impact on the economic development of clean energy. More precisely, alternative and nuclear energy were found to retard economic growth, while combustible renewables and waste favor economic growth. Therefore, particular attention to renewable energy was recommended.

This contrasts with Maji et al. [43], who demonstrated that using renewable energy lowers productivity in 15 West African countries. However, they recommended drawing inspiration from European countries by adopting advanced clean technologies and increasing the proportion of solar, wind, and geothermal energy in the renewable energy mix.

Tsaurai and Ngcobo [44] support that renewable energy use reduces economic development, while education favors reducing the harmful effect of renewable energy on development in BRICS countries. This conclusion was obtained using data on economic growth, renewable energy consumption, education, saving, infrastructural development, trade openness, financial development from 1994 to 2015, and FMOLS. Therefore, they suggested that the countries’ leaders invest more in education.

Alper and Oguz [45] found no renewable energy effect on economic development in Cyprus, Estonia, Hungary, Poland, and Slovenia. The ARDL model and annual data from 1990 to 2009 were used. This finding supports Bulut and Muratoglu [46] in Turkey. They explain their results by the feeble renewable energy proportion in total energy consumption. However, their main recommendation is to increase clean energy use and promote its production.

The recent development of an asymmetric ARDL cointegration methodology by Shin et al. [47] uses both positive and negative partial sum decompositions to detect both long- and short-run asymmetric effects. The NARDL model devolves into the conventional symmetric ARDL model if the impact of separated components of an explanatory variable is found to be the same [48]. The nonlinear ARDL was used by Katrakilidis and Trachanas [49] to explore the drivers of the housing price dynamic in Greece. Namahoro et al. [50] evaluated the asymmetric nexus of renewable energy and economic development and how agriculture and capital may improve growth using NARDL in Rwanda, with data from 1990–2015. The study outcome encourages prioritizing investments in the agriculture and renewable energy sectors. This extension of the ARDL model was also used by Somoye et al. [35], Bibi and Li [51], and Toumi and Toumi [52] to study the asymmetric effect of renewable energy use on the economy. The main point of their conclusion promotes the use of cleaner technologies while maximizing renewable energy advantages and minimizing its harmful effects.

However, photovoltaic installations, one of the fastest-growing renewable energy sources, have drawbacks [53]. They are dependent on local solar irradiance’s availability and amount, which varies with cloud cover and can increase the voltage in the power grid, affecting individual installations [54]. To overcome the failures of photovoltaic installations, Kut and Pietrucha-Urbanik [53] proposed a new Multiple-Criteria risk assessment method to support the process of managing photovoltaic systems by analyzing the reliability of installation operations, as reliability affects investments and operating costs’ profitability. Following Adachi [55], the improvement of the connection to grid networks in the shielding process and plans for a shift from a niche space to a socio-technical regime in an energy sector structure in an empowering process are the two main factors of renewable energy policies. He also pointed out that delaying the implementation of renewable energy policy can lead to energy policy failure, as is the case in Poland. Germes et al. [56] declared that the success of renewable energy policies is closely tied to skills, competencies, and installations’ places. Forootan et al. [57] reported that machine and deep learning could improve the response methods to energy demand since using the machine and deep learning algorithms has significantly improved models’ accuracy.