Research article Special Issues

The mediating effect of transport energy consumption on the relationship between nonrenewable energy consumption and CO2 emissions in Africa

  • Received: 17 November 2024 Revised: 12 February 2025 Accepted: 17 February 2025 Published: 26 February 2025
  • Energy is a vital tool in economic growth and development. However, the world continues to experience the effects of climate change due to high greenhouse gas emission levels mainly derived from fossil fuel consumption and human activities. The need for energy and effective transportation increases with economic expansion. Clean energy has the potential to mitigate climate change by reducing reliance on fossil fuels. This study examined the mediating effect of transport energy consumption on the relationship between nonrenewable energy consumption and CO2 emissions in 22 African countries from 2001 to 2020. The findings suggest that a 1% increase in nonrenewable energy increases CO2 emissions by 0.34%. The mediating effect regression shows a direct effect of 0.184, an indirect effect of 0.168, and a total effect of 0.352. The findings reveal that nonrenewable energy increases transport energy consumption by 0.93%. Transport energy is a significant mediator, which is stronger in resource-intensive countries. Clean energy reduces the adverse effects of nonrenewable energy usage. When clean energy increases, there is a reduction in CO2 emissions. Therefore, stakeholders should implement stringent environmental measures, develop efficient transportation and energy systems, and increase investment in clean energy to mitigate greenhouse gas emissions.

    Citation: Margaret Jane Sylva. The mediating effect of transport energy consumption on the relationship between nonrenewable energy consumption and CO2 emissions in Africa[J]. AIMS Environmental Science, 2025, 12(2): 193-222. doi: 10.3934/environsci.2025009

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  • Energy is a vital tool in economic growth and development. However, the world continues to experience the effects of climate change due to high greenhouse gas emission levels mainly derived from fossil fuel consumption and human activities. The need for energy and effective transportation increases with economic expansion. Clean energy has the potential to mitigate climate change by reducing reliance on fossil fuels. This study examined the mediating effect of transport energy consumption on the relationship between nonrenewable energy consumption and CO2 emissions in 22 African countries from 2001 to 2020. The findings suggest that a 1% increase in nonrenewable energy increases CO2 emissions by 0.34%. The mediating effect regression shows a direct effect of 0.184, an indirect effect of 0.168, and a total effect of 0.352. The findings reveal that nonrenewable energy increases transport energy consumption by 0.93%. Transport energy is a significant mediator, which is stronger in resource-intensive countries. Clean energy reduces the adverse effects of nonrenewable energy usage. When clean energy increases, there is a reduction in CO2 emissions. Therefore, stakeholders should implement stringent environmental measures, develop efficient transportation and energy systems, and increase investment in clean energy to mitigate greenhouse gas emissions.



    Iron oxide nanoparticles (NPs) have found wide application in medicine [1] and biotechnology [2,3], and their intensive production [4] creates the conditions for NPs penetration into the human body. NPs can partially dissolve and undergo agglomeration in biological media. These effects were found even for NPs of chemically inert γ-Al2O3 [5]. Despite high medical and biological potential, the penetration of iron oxide NPs into the human body can cause their dissolution with subsequent accumulation of highly toxic iron compounds. Therefore, the dissolution and agglomeration of iron-containing NPs in biological fluids are of great issue.

    The agglomeration of iron oxide NPs is actively studied in the literature [6,7,8]. However, little attention is paid to the solubility of iron-containing NPs in aqueous solutions [9] compared to colloidal particles γ-FeOOH [10], oxalate-coated α-Fe2O3 [11], α-FeOOH [12], and freshly formed amorphous ferric oxides [13]. The lack of data on the behavior of iron oxide NPs in biological media does not allow the prediction of the degree of accumulation of iron-contained products in the human body.

    The purpose of the paper is to show how the dispersion medium and the size of NPs affect the solubility and colloidal properties of iron oxide NPs in the simplest biological solutions.

    In the experiments, three engineered hematite NPs with an average surface particle size of 12, 32 nm (Nanografi Co. Ltd., Turkey), and 115 nm (Advanced Powder Technologies LLC, Russia), accordingly marked as Fe2O3-12, Fe2O3-32, and Fe2O3-115 were used. The morphology of the NPs was studied by a transmission electron microscopy (TEM, Jeol JEM-1400 microscope, Jeol, Japan, NUST "MISiS", resolution up to 0.24 nm, accelerating voltage 120 kV, copper grids with an amorphous carbon substrate) and a scanning electron microscopy (SEM, Vega 3 SBN, Tescan, Czech Republic, NUST "MISiS", resolution up to 8 nm, accelerating voltage 30 kV, without spraying any conductive coating). The TEM and SEM images were processed by the pallet method (at least 100 particles and 40 aggregates for each sample were measured) to find a particle size distribution and the average particle size. The specific surface area of the NPs was measured by the method of low-temperature nitrogen adsorption (SorbiPrep, Meta, Russia, TPU Center for Sharing Use "Nanomaterials and Nanotechnologies"). The average surface particle size was calculated from the specific surface measurement data. It was assumed that the particles had a spherical shape [14]. The phase composition was determined by an X-ray phase analysis (XRD, diffractometer 7000S, Shimadzu, Japan, TPU Center for Sharing Use "Nanomaterials and Nanotechnologies", Tomsk, CuКβ irradiation, voltage 40 kV, the current flowing through the tube 30 mA, the speed of the meter was 1.5 degrees/min in the range from 10 to 120 degrees).

    The suspensions of the 100 mg/L concentration of NPs were prepared on the base of the simplest pulmonary fluid (SPF, pH = 3.0 ± 0.2, 20 wt% C6H8O7, China) and the simplest sweat fluid (SSF, pH = 6.5 ± 0.2, 0.9 wt% NaCl, Grotex LLC, Russia), water solutions mimicking respiratory and skin penetration into the human body [15,16]. The NPs concentration was chosen based on the preliminary study to receive reproducible results of the colloidal study [17]. We used the composition of SPF instead of pulmonary fluid [15] since the degree of nickel NPs dissolution in SPF and in pulmonary fluid reached the same values [18]. The solutions were prepared in distilled water (pH = 6.5 ± 0.6, conductivity 0.2 µS/cm, aqua distillator DE-4 TZMOI, Tyumen Medico, Russia). The weighing was carried out on a scale ALC-210D4 (Acculab, Germany, ±0.0001 g).

    The suspensions were aged for 24 h at 25 ± 2 ℃ with periodic 5 min sonication (ultrasonic bath GRAD 28-35, Grade Technology, Russia, power 100 W). The aliquot of 1 mL was periodically taken for colloidal study and 15 mL was centrifuged in a conical closed tube (Centrifuge 5702, Eppendorf, Germany, 4400 rpm, 15 min). The supernatant was carefully poured off the tube, acidified by adding 0.5 mL of 1 M HNO3, and stored for not more than 72 h. The concentration of iron ions in the supernatant was determined by the change in the light transmission coefficient (T, %) in a colored solution of iron sulfosalicylate (spectrophotometer PD-303 Apel, Japan, 430 nm, cylindrical glass bulb with a diameter of 12 mm). The calibration graph was plotted in the range of iron ion concentrations C = 2–10 mg/L (T, % = 96.02 − 3.2С). The dissolution rate was estimated as the amount of dissolved iron from the powder sample (α, %). The experiment was repeated 3 times.

    The colloidal properties of particles (particle size distribution and zeta potential) of suspensions were studied using the method of dynamic light scattering in a U-shaped polystyrene cuvette at 25 ℃ (Zeta sizer Nano ZS laser analyzer, Malvern, USA, NUST "MISIS", He-Ne laser, 4 mW, 633 nm). The obtained quantitative particle size distribution was used to calculate the average particle size (dav).

    According to the XRD data, the Fe2O3-12 and Fe2O3-32 NPs were observed to have hematite structure (α-Fe2О3) with peaks appearing at 24.2, 33.2; 35.7, 41, and 50 degrees and corresponded to lattice planes indexes (012), (104), (110), (113) and (024), respectively [19]. The Fe2O3-115 sample mainly consisted of hematite with residual amounts of Fe3O4 (at 45 (400) and 69 (620) [20]) and Fe (at 62 (214) and 64 (300) [21] (Figure 1).

    Figure 1.  XRD spectra of the particles.

    According to TEM and SEM data, the particles were agglomerated (Table 1) and mostly bound by inter-particle (coagulation, not phase) interaction (Figure 2). Fe2O3-12 NPs exhibit needle-like morphology (Figure 2a), while bigger NPs have a near-spherical shape (Figure 2b, c).

    Table 1.  Dispersion and morphology of the studied powders.
    Sample BET TEM and SEM
    Specific surface area/Average particle size Size distribution/average particle (aggregates) size Shape
    Fe2O3-12 93.5 m2/g/12.2 nm 10–119/49.6 nm (64.6–257.7/126 nm) Needle like
    Fe2O3-32 35.3 m2/g/32.4 nm 11.42–57.2/25.6 nm (426–770/570 nm) Near spherical
    Fe2O3-115 9.8 m2/g/114.5 nm 94–492/271 nm (0.9–6.5 μ/2.5 μm)

     | Show Table
    DownLoad: CSV
    Figure 2.  Morphology of (a) Fe2O3-12, (b) Fe2O3-32 and (c) Fe2O3-115 particles.

    It was shown that in the selected media, NPs underwent severe agglomeration, which intensified within an hour regardless of the particle size and medium composition (Figure 3) and was accompanied by the change of the surface charge (Figure 4). After that, the agglomeration reduced, followed by some kind of equilibrium. For example, the average size (dav) of Fe2O3-32 agglomerates exposed to the SPF medium for 0.5, 1, 3, and 24 h was 256,290,290, and 290 nm, respectively (Figure 3a). Meanwhile, the rate of establishing equilibrium on the surface of the particles was higher in SSF compared to SPF medium: for instance, the rates of charge change (Δξ-change of charge between adjacent measurements) for Fe2O3-115 during 3 h were 27 and 36% in SPF (Figure 4a) and SSF media (Figure 4b), respectively. Apparently, the adsorption-desorption processes occurred at the interface of the phases in line with the active dissolution of the surface layer. At further exposure, an equilibrium state was attained on the surface, since the zeta potential of NPs fluctuated within ±1% (Figure 4).

    Figure 3.  Changes of the average size of aggregates (dav, nm) in (a) SPF and (b) SSF media.
    Figure 4.  Change of zeta potential of NPs in (a) SPF and (b) SSF media.

    It was also observed that the particles were positively charged in SPF at the equilibrium state (≥3 h), while all particles in the SSF except Fe2O3-12 had a negative charge. For example, in 3 h zeta potential of Fe2O3-32 was +9 and −2 mV in SPF (Figure 4a) and SSF (Figure 4b), respectively. In general, more stable suspensions are formed in the SPF. The more positive charge at lower pH is proved with DLS measurements of differently sized hematite suspensions (0.5 g/L) in 25 mM NaCl equilibrated for several hours prior to analysis: at pH = 6.5, 8, and 40 nm, NPs had zeta potential of −8 and −26 mV, respectively, while at pH = 3 both particles had of +30 mV zeta potential [22].

    It was shown that smaller particles had lower colloidal stability in suspensions. For example, in SPF the equilibrium zeta potential was +2, +9, and +18 mV for the particles with a size of 12, 32, and 115 nm, respectively (Figure 4a). At the same time, the degree of particle agglomeration in fresh (<3 h) suspensions increased in both media with a decrease in the particle size. For example, the dav value was 253,222, and 166 nm in 15 min SPF solutions for particles with sizes of 12, 32, and 115 nm, respectively (Figure 3a).

    Our results about weakening aggregation in acidified medium (SPF, рН = 3) are agreed with the data obtained on α-Fe2O3 particles with a size of < 50 nm. With the decreasing pH from 6 to 3, the average particle size went down from 611 to 318 nm, and zeta potential increased from +5 to +35 mV in water [14], while hydrodynamic diameter (zeta potential) of 40 nm α-Fe2O3 NPs was ~1500 (−35 mV) and ~750 (+30 mV) in 25 mM NaCl, respectively at the pH of 3 and 7 [22].

    For bigger Fe2O3 particles (<200 nm) рН reduction from 6 to 3 caused the growth of electrophoretic mobility from 3.0 to 3.5 and the size decreased from ~2.3 µm to ~200 nm in water [6]. The charge of both, pristine Fe3O4 (51 nm) and citric acid coated Fe3O4 particles (58 nm) increased at the рН of 3 compared to 6 [8].

    The dissolution study showed that all Fe2O3 NPs dissolved in selected solutions: the degree of dissolution (α) reached 1.5% after 15 min (Figure 5). However, the graphs of dissolution degree did not exhibit incremental behavior within 24 h. The curves may be conditionally divided into three stages. Stage 1 is characterized by a rapid increase in the iron concentration in a supernatant. The maximum degree of particle dissolution (αmax) is reached in 30 min in SPF (Figure 5a) and after 1 h in SSF (Figure 5b). It can be seen that the dissolution degree in all media for particles with a size < 100 nm is greater than for bigger particles: for instance, in SSF αmax values are 0.68 and 0.33% for Fe2O3-32 and Fe2O3-115 particles, respectively (Figure 5b). After reaching the maximum (between 15 and 60 min), the iron content in the supernatant may stay constant (Figure 5a) or decrease (Figure 5b), and a brown precipitate was visually observed mainly in SSF at the stage 2. It was probably iron hydroxide having limited solubility in an aqueous solution. At stage 3, the concentration of iron ions did not change in SSF, although in SPF it increased: αmax values were 0.59, 0.31, and 0.23%, respectively, for particles with the size of 12, 32, and 115 nm (Figure 5a).

    Figure 5.  Kinetics of changes in the dissolution degree (α, %) of the particles in (a) SPF and (b) SSF.

    In general, Fe2O3 particles dissolved better in SPF: for example, the dissolution degree of Fe2O3-12 NPs after 1 h exposure was 1.29 and 0.78% in SPF and SSF, respectively. A decade ago, it was concluded that reduction of hematite by 10 mM ascorbic acid yielded enhanced rates of Fe(Ⅱ) production in 25 mM NaCl suspensions of 8 nm α-Fe2O3 NPs relative to 40 nm NPs: from pH 2 to 6, reductive dissolution of 8 nm hematite was between 3.3 and 6.5 times faster than 40 nm hematite on the basis of mass [22].

    We see that Fe2O3 particles dissolve worse than pure 45 nm Fe3O4 particles in phosphate-buffered saline after 24 h exposure, where Fe release percentages were between 7 and 12% [23].

    Different kinetics of particles dissolution at stage 2 of the dissolution curve in SPF (α did not change for Fe2O3-12 and Fe2O3-115, while it decreased for the Fe2O3-32, Figure 5a) may indicate different dissolution mechanisms. The dissolution of heavy metal oxides may include oxidation or hydrolysis with the formation of hydroxides [24]. The oxidation of iron with an oxidation state of +3 in Fe2O3 oxide by O2 molecules is considered not to be thermodynamically possible. Therefore, we will consider the hydrolysis reaction according to the Eqs 1 and 2:

    Fe2O3+3H2O2Fe(OH)3,ΔG298=51kJ/mol (1)
    Fe2O3+3H2O2Fe3++6OH,ΔG298=518kJ/mol (2)

    The change in ΔG at 25 ℃ will be 51 and 518 kJ/mol for the Eqs 1 and 2, respectively [25]. Therefore, it is more likely that the reaction proceeds according to Eq 1. According to the calculated value (ΔG > 0) the Eq 1 cannot proceed at normal conditions. However, it can be assumed that the excess surface energy, so-called "stored energy" (~100 kJ/mol [26]), can be achieved on the surface of particles with a size < 100 nm, which may be sufficient to create a barrier.

    Fe(OH)3 formed by the Eq 1 has a very low solubility (solubility limit = 6.3 × 10−38 [27]) and should precipitate and, therefore, it would be separated during centrifugation. However, before the formation of the precipitate, the hydroxide is likely to participate via ion exchange after a short time in the solutions: indeed, FeСit is formed in SPF, while FeCl3 is arisen in SSF prior to giving complexes of [FeCit6]3– and [FeCl6]3–. The precipitation of iron hydroxide was observed visually on the particles at the stage 2 of the dissolution curve (Figure 5). Therefore, the concentration of iron ions in SSF solution did not change after 3 h of holding bigger particles. Nevertheless, available theories cannot explain the accumulated experimental data on the dissolution of iron oxides currently. According to the kinetic and electrochemical regularities of dissolution [28], the process of iron oxide dissolution is determined by the concentration of hydrogen ions and a potential jump that occurs at the oxide/electrolyte interface. The potential of oxide electrodes in slightly acidic media (SSF with рН = 6.5) is determined by the Eq 3:

    MeOx+2xH++2(x0.5z)e=Me++xH2O (3)

    The reaction for acidic media (SPF with pH = 3):

    Men+1L+e=Me+L (4)

    where Me—metal, e—electron, MeO—metal oxide, and L—acid anion.

    In terms of probability, the accumulation of metal compounds in a body is stipulated by both the penetration of metal-containing particles into the body [29] as well as their subsequent dissolution in the physiological environment [16]. For example, particles with the size of 12, 32, and 115 nm can enter the alveolar region of the lungs (SPF) with a degree (probability P1) of 50, 43, and 7%, respectively [30], while, the degree of their dissolution (probability P2) in SPF (after 24 h) is 2.1, 1.4, and 2.3% (Figure 5a). Imagining that NPs penetration into the medium occurs prior to their dissolution, we get that the potential accumulation degree of iron compounds when penetrating with breathing Р1·Р2 maybe 1.05, 0.60, and 0.16%, respectively, for 12, 32, and 115 nm NPs. Thus, the degree of accumulation of iron compounds is obvious to increase with a decrease in inhaled Fe2O3 NPs size.

    This work demonstrated the agglomeration and dissolution behavior of three engineered α-Fe2O3 nanoparticles with an average surface particle size of 12, 32, and 115 nm in two physiological solutions—the simplest sweat and pulmonary fluids. Within 60 min exposure, the particle size and concentration of iron released increased in the suspensions, accompanied by an intensive change of the surface charge. After an hour, the colloidal properties did not change significantly, although the degree of dissolution ambiguously fluctuated.

    It was shown that the agglomeration of the particles in the simplest pulmonary fluid was lower than in the simplest sweat fluid, compared to the degree of dissolution, which was much higher in the pulmonary fluid than in the sweat. In the simplest pulmonary fluid the colloidal stability of suspensions reduced with a decrease in size of NPs, e.g., the average size of particles was 315,289,248 nm, while zeta potential was 2, 9, and 17 mV, respectively for 12, 32, and 115 nm NPs in 3 h suspensions. It was found that 24 h dissolution degree of α-Fe2O3 NPs reached 2.33% and 0.41% in the simplest pulmonary and sweat fluids, respectively.

    It was assumed that the degree of accumulation of iron compounds increased with the decrease of inhaled Fe2O3 NPs size. The mechanism of dissolution of hematite NPs in the slightly acidic and acidic mediums is proposed.

    The authors express sincere appreciation to Mr. Evgeny Kolesnikov from NUST MISiS for his help with TEM and SEM images. X-Ray and BET analysis was carried out in the framework of Tomsk Polytechnic University Competitiveness Enhancement Program grant, Russia.

    The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.



    [1] Filonchyk M, Peterson MP, Yan H, et al. (2024) Greenhouse gas emissions and reduction strategies for the world's largest greenhouse gas emitters. Sci Total Environ 944: 173895. https://doi.org/10.1016/j.scitotenv.2024.173895 doi: 10.1016/j.scitotenv.2024.173895
    [2] Intergovernmental Panel for Climate Change (2023) Climate change 2023: Synthesis report, contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change, Geneva, Switzerland. Available from: https://doi:10.59327/IPCC/AR6-9789291691647
    [3] Alves MR, Moutinho V, Macedo P (2015) A new frontier approach to model the eco-efficiency in European countries. J Clean Prod 103: 562–573. https://doi:10.1016/j.jclepro.2015.01.038 doi: 10.1016/j.jclepro.2015.01.038
    [4] Álvarez MAG, Montañés A (2023) CO2 emissions, energy consumption, and economic growth: Determining the stability of the 3E relationship. Econ Model 121: 106195. https://doi.org/10.1016/j.econmod.2023.106195 doi: 10.1016/j.econmod.2023.106195
    [5] Agoundedemba M, Kim CK, Kim HG (2023) Energy status in Africa: Challenges, progress and sustainable pathways. Energies 16: 7708. https://doi:10.3390/en16237708 doi: 10.3390/en16237708
    [6] İnal V, Addi HM, Çakmak EE, et al. (2022) The nexus between renewable energy, CO2 emissions, and economic growth: Empirical evidence from African oil-producing countries. Energy Rep 8: 1634–1643. https://doi.org/10.1016/j.egyr.2021.12.051 doi: 10.1016/j.egyr.2021.12.051
    [7] Ouki M (2023) Prospects for a potential African gas renaissance en route to a "just energy transition", The Oxford Institute for Energy Studies, 185.
    [8] Giwa SO, Taziwa RT (2024) Adoption of advanced coal gasification: A panacea to carbon footprint reduction and hydrogen economy transition in South Africa. Int J Hydrogen Energ 77,301–323. https://doi.org/10.1016/J.IJHYDENE.2024.06.190
    [9] Mirzania P, Gordon JA, Ozkan NB, et al. (2023) Barriers to powering past coal: Implications for a just energy transition in South Africa. Energy Res Soc Sci 101: 103122. https://doi.org/10.1016/j.erss.2023.103122 doi: 10.1016/j.erss.2023.103122
    [10] Muttitt G, Price J, Pye S, et al. (2023) Socio-political feasibility of coal power phase-out and its role in mitigation pathways. Nat Clim Change 13: 140–147. https://doi.org/10.1038/s41558-022-01576-2 doi: 10.1038/s41558-022-01576-2
    [11] IEA (2023) Fossil fuels consumption subsidies 2022, Paris. Available from: https://www.iea.org/reports/fossil-fuels-consumption-subsidies-2022.
    [12] UNFCCC (2021) Conference of the Parties serving as the meeting of the Parties to the Paris Agreement. Third Session Glasgow 31.
    [13] Udeh BA, Kidak R (2019) The excessive use of fossil fuel and its impact to climate change in Africa. Curr J Appl Sci Technol 32: 1–4. https://doi:10.9734/cjast/2019/41680 doi: 10.9734/cjast/2019/41680
    [14] Egana-delSol PA (2021) Energy consumption: Strategies to foster sustainable energy consumption, In: Filho WL, Azul AM, Brandli L, et al. Eds., Affordable and Clean Energy, Cham: Springer, 1–10. https://doi.org/10.1007/978-3-319-95864-4_35
    [15] Noussan M, Hafner M, Tagliapietra S (2020) The evolution of transport across world regions, In: The Future of Transport between Digitalization and Decarbonization: Trends, Strategies and Effects on Energy Consumption, Cham: Springer, 1–28. https://doi.org/10.1007/978-3-030-37966-7_1
    [16] IEA (2025) International Energy Agency. Available from: https://www.iea.org/energy-system/transport.
    [17] Mutezo G, Mulopo J (2021) A review of Africa's transition from fossil fuels to renewable energy using circular economy principles. Renew Sust Energ Rev 137: 110609. https://doi:10.1016/j.rser.2020.110609 doi: 10.1016/j.rser.2020.110609
    [18] Kelly AM, Radler RDNN (2024) Does energy consumption matter for climate change in Africa? New insights from panel data analysis. Innov Green Dev 3: 100132. https://doi:10.1016/j.igd.2024.100132 doi: 10.1016/j.igd.2024.100132
    [19] The Renewable Energy Transition in Africa. Available from: https://www.irena.org/publications/2021/March/The-Renewable-Energy-Transition-in-Africa.
    [20] Climate Resilience and a Just Energy Transition in Africa. African Development Bank, 2022. Available from: https://www.afdb.org/sites/default/files/2022/05/25/aeo22_chapter2_eng.pdf.
    [21] Hanto J, Schroth A, Krawielicki L, et al. (2022) South Africa's energy transition–Unraveling its political economy. Energy Sustain Dev 69: 164–178. https://doi:10.1016/j.esd.2022.06.006 doi: 10.1016/j.esd.2022.06.006
    [22] Li B, Haneklaus N (2022) The role of clean energy, fossil fuel consumption and trade openness for carbon neutrality in China. Energy Rep 8: 1090–1098. https://doi.org/10.1016/j.egyr.2022.02.092 doi: 10.1016/j.egyr.2022.02.092
    [23] Nwaiwu F (2021) Digitalisation and sustainable energy transitions in Africa: Assessing the impact of policy and regulatory environments on the energy sector in Nigeria and South Africa. Energy Sustain Soc 11: 48. https://doi:10.1186/s13705-021-00325-1 doi: 10.1186/s13705-021-00325-1
    [24] Wang Q, Li Y, Li R (2025) Integrating artificial intelligence in energy transition: A comprehensive review. Energy Strateg Rev 57: 101600. https://doi.org/10.1016/J.ESR.2024.101600 doi: 10.1016/J.ESR.2024.101600
    [25] Wang Q, Sun T, Li R (2025) Does Artificial Intelligence (AI) enhance green economy efficiency? The role of green finance, trade openness, and R & D investment. Hum Soc Sci Commun 12: 1–22. https://doi.org/10.1057/s41599-024-04319-0 doi: 10.1057/s41599-024-04319-0
    [26] Jing H, Chen Y, Ma M, et al. (2024) Global carbon transition in the passenger transportation sector over 2000–2021. Sustain Prod Consump 51,556–571. https://doi.org/10.1016/J.SPC.2024.10.006
    [27] Kurramovich KK, Abro AA, Vaseer AI, et al. (2022) Roadmap for carbon neutrality: the mediating role of clean energy development-related investments. Environ Sci Pollut R 29: 34055–34074. https://doi.org/10.1007/s11356-021-17985-3 doi: 10.1007/s11356-021-17985-3
    [28] IEA, World Energy Outlooks special report Africa energy outlook 2022. International Energy Agency, 2022. Available from: https://www.iea.org/reports/africa-energy-outlook-2022.
    [29] Driscoll JC, Kraay AC (1998) Consistent covariance matrix estimation with spatially dependent panel data. Rev Econ Stat 80: 549–560. https://doi.org/10.1162/003465398557825 doi: 10.1162/003465398557825
    [30] Shah WUH, Hao G, Yan H, et al. (2024) Role of renewable, non-renewable energy consumption and carbon emission in energy efficiency and productivity change: Evidence from G20 economies. Geosci Front 15: 101631. https://doi:10.1016/j.gsf.2023.101631 doi: 10.1016/j.gsf.2023.101631
    [31] Chen C, Pinar M, Stengos T (2022) Renewable energy and CO2 emissions: New evidence with the panel threshold model. Renew Energ 194: 117–128. https://doi:10.1016/j.renene.2022.05.095 doi: 10.1016/j.renene.2022.05.095
    [32] AlNemer HA, Hkiri B, Tissaoui K (2023) Dynamic impact of renewable and non-renewable energy consumption on CO2 emission and economic growth in Saudi Arabia: Fresh evidence from wavelet coherence analysis. Renew Energ 209: 340–356. https://doi:10.1016/j.renene.2023.03.084 doi: 10.1016/j.renene.2023.03.084
    [33] Wang Q, Li L, Li R (2022) The asymmetric impact of renewable and non-renewable energy on total factor carbon productivity in 114 countries: Do urbanization and income inequality matter? Energy Strateg Rev 44: 100942. https://doi:10.1016/j.esr.2022.100942 doi: 10.1016/j.esr.2022.100942
    [34] Hu K, Raghutla C, Chittedi KR, et al. (2021) The effect of energy resources on economic growth and carbon emissions: A way forward to carbon neutrality in an emerging economy. J Environ Manag 298: 113448. https://doi:10.1016/j.jenvman.2021.113448 doi: 10.1016/j.jenvman.2021.113448
    [35] Destek MA, Sinha A (2020) Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic co-operation and development countries. J Clean Prod 242: 118537. https://doi:10.1016/j.jclepro.2019.118537 doi: 10.1016/j.jclepro.2019.118537
    [36] Santos G (2017) Road transport and CO2 emissions: What are the challenges? Transport Policy 59: 71–74. https://doi:10.1016/j.tranpol.2017.06.007 doi: 10.1016/j.tranpol.2017.06.007
    [37] Wang H, Ou X, Zhang X (2017) Mode, technology, energy consumption, and resulting CO2 emissions in China's transport sector up to 2050. Energ Policy 109: 719–733. https://doi:10.1016/j.enpol.2017.07.010 doi: 10.1016/j.enpol.2017.07.010
    [38] Yin X, Chen W, Eom J, et al. (2015) China's transportation energy consumption and CO2 emissions from a global perspective. Energ Policy 82: 233–248. https://doi:10.1016/j.enpol.2015.03.021 doi: 10.1016/j.enpol.2015.03.021
    [39] Ağbulut Ü (2022) Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain Prod Consump 29: 141–157. https://doi:10.1016/j.spc.2021.10.001 doi: 10.1016/j.spc.2021.10.001
    [40] Chandran VGR, Tang CF (2013) The impacts of transport energy consumption, foreign direct investment and income on CO2 emissions in ASEAN-5 economies. Renew Sust Energ Rev 24: 445–453. https://doi:10.1016/j.rser.2013.03.054 doi: 10.1016/j.rser.2013.03.054
    [41] Mraihi R, Abdallah KB, Abid M (2013) Road transport-related energy consumption: Analysis of driving factors in Tunisia. Energ Policy 62: 247–253. https://doi.org/10.1016/j.enpol.2013.07.007 doi: 10.1016/j.enpol.2013.07.007
    [42] Peng Z, Wu Q (2020) Evaluation of the relationship between energy consumption, economic growth, and CO2 emissions in China' transport sector: The FMOLS and VECM approaches. Environ Dev Sustain 22: 6537–6561. https://doi.org/10.1007/s10668-019-00498-y doi: 10.1007/s10668-019-00498-y
    [43] Satrovic E, Cetindas A, Akben I (2024) Do natural resource dependence, economic growth and transport energy consumption accelerate ecological footprint in the most innovative countries? The moderating role of technological innovation. Gondwana Res 127: 116–130. https://doi.org/10.1016/j.gr.2023.04.008 doi: 10.1016/j.gr.2023.04.008
    [44] Wu Y, Zhu Q, Zhong L, et al. (2019) Energy consumption in the transportation sectors in China and the United States: A longitudinal comparative study. Struct Change Econ D 51: 349–360. https://doi.org/10.1016/j.strueco.2018.12.003 doi: 10.1016/j.strueco.2018.12.003
    [45] Yadav P, Davies PJ, Sarkodie SA (2021) Fuel choice and tradition: Why fuel stacking and the energy ladder are out of step? Sol Energy 214: 491–501. https://doi.org/10.1016/j.solener.2020.11.077 doi: 10.1016/j.solener.2020.11.077
    [46] Li S, Wang B, Zhou H (2024) Decarbonizing passenger transportation in developing countries: Lessons and perspectives1. Reg Sci Urban Econ 107: 103977. https://doi.org/10.1016/J.REGSCIURBECO.2024.103977 doi: 10.1016/J.REGSCIURBECO.2024.103977
    [47] ITF, ITF Transport Outlook. International Transport Forum, 2021. Available from: https://www.oecd.org/en/publications/itf-transport-outlook-2021_16826a30-en.html.
    [48] SLOCAT, Tracking Trends in a Time of Change: The Need for Radical Action Towards Sustainable Transport Decarbonisation. Transport and Climate Change Global Status Report, 2Eds., 2021. Available from: https://www.tcc-gsr.com.
    [49] Saidi S (2021) Freight transport and energy consumption: What impact on carbon dioxide emissions and environmental quality in MENA countries? Econ Chang Restruct 54: 1119–1145. https://doi.org/10.1007/s10644-020-09296-3 doi: 10.1007/s10644-020-09296-3
    [50] Lopez NS, Chiu ASF, Biona JBM (2018) Decomposing drivers of transportation energy consumption and carbon dioxide emissions for the Philippines: The case of developing countries. Front Energy 12: 389–399. https://doi.org/10.1007/s11708-018-0578-7 doi: 10.1007/s11708-018-0578-7
    [51] IEA (2024) Access and Affordability. Available from: https://www.iea.org/topics/access-and-affordability.
    [52] Chen L, Ma R (2024) Clean energy synergy with electric vehicles: Insights into carbon footprint. Energy Strateg Rev 53: 101394. https://doi.org/10.1016/j.esr.2024.101394 doi: 10.1016/j.esr.2024.101394
    [53] Cai Y, Sam CY, Chang T (2018) Nexus between clean energy consumption, economic growth and CO2 emissions. J Clean Prod 182: 1001–1011. https://doi:10.1016/j.jclepro.2018.02.035 doi: 10.1016/j.jclepro.2018.02.035
    [54] Gielen D, Boshell F, Saygin D, et al. (2019) The role of renewable energy in the global energy transformation. Energy Strateg Rev 24: 38–50. https://doi:10.1016/j.esr.2019.01.006 doi: 10.1016/j.esr.2019.01.006
    [55] Li B, Haneklaus N (2022) Reducing CO2 emissions in G7 countries: The role of clean energy consumption, trade openness and urbanization. Energy Rep 8: 704–713. https://doi:10.1016/j.egyr.2022.01.238 doi: 10.1016/j.egyr.2022.01.238
    [56] Nkolo JC, Motel PC, Roux LL (2019) Stacking up the ladder: A panel data analysis of Tanzanian household energy choices. World Dev 115: 222–235. https://doi:10.1016/j.worlddev.2018.11.016 doi: 10.1016/j.worlddev.2018.11.016
    [57] Monserrate MAZ (2024) Clean energy production index and CO2 emissions in OECD countries. Sci Total Environ 907: 167852. https://doi.org/10.1016/j.scitotenv.2023.167852 doi: 10.1016/j.scitotenv.2023.167852
    [58] Xue C, Shahbaz M, Ahmed Z, et al. (2022) Clean energy consumption, economic growth, and environmental sustainability: What is the role of economic policy uncertainty? Renew Energ 184: 899–907. https://doi.org/10.1016/j.renene.2021.12.006 doi: 10.1016/j.renene.2021.12.006
    [59] Yang F, Wang C (2023) Clean energy, emission trading policy, and CO2 emissions: Evidence from China. Energy Environ 34: 1657–1673. https://doi.org/10.1177/0958305X221094581 doi: 10.1177/0958305X221094581
    [60] Wang Q, Li Y, Li R (2024) Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI). Hum Soc Sci Commun 11: 1–18. https://doi.org/10.1057/s41599-024-03520-5 doi: 10.1057/s41599-024-03520-5
    [61] Ahmed K, Apergis N, Bhattacharya M, et al. (2021) Electricity consumption in Australia: The role of clean energy in reducing CO2 emissions. Appl Econ 53: 5535–5548. https://doi.org/10.1080/00036846.2021.1925080 doi: 10.1080/00036846.2021.1925080
    [62] Tangato KF (2024) The impact of clean technology adoption on carbon emissions: A global perspective. Clean Technol Envir 1–18. https://doi.org/10.1007/s10098-024-03066-9
    [63] Bo X, You Q, Sang M, et al. (2023) The impacts of clean energy policies on air pollutants and CO2 emission reduction in Shaanxi, China. Atmos Pollut Res 14: 101937. https://doi.org/10.1016/j.apr.2023.101937 doi: 10.1016/j.apr.2023.101937
    [64] Ummalla M, Goyari P (2021) The impact of clean energy consumption on economic growth and CO2 emissions in BRICS countries: Does the environmental Kuznets curve exist? J Public Aff 21: e2126. https://doi.org/10.1002/pa.2126 doi: 10.1002/pa.2126
    [65] IEA, Clean Energy Investment for Development in Africa. International Energy Agency, 2024. Available from: https://www.iea.org/reports/clean-energy-investment-for-development-in-africa.
    [66] World Bank, DataBank. World Bank, 2024. Available from: https://databank.worldbank.org/home.aspx.
    [67] IEA, Data and Statistics. International Energy Agency, 2024. Available from: https://www.iea.org/data-and-statistics/.
    [68] Li R, Wang Q, Li L (2023) Does renewable energy reduce per capita carbon emissions and per capita ecological footprint? New evidence from 130 countries. Energy Strateg Rev 49: 101121. https://doi:10.1016/j.esr.2023.101121 doi: 10.1016/j.esr.2023.101121
    [69] Bekun FV, Gyamfi BA, Onifade ST, et al. (2021) Beyond the environmental Kuznets Curve in E7 economies: Accounting for the combined impacts of institutional quality and renewables. J Clean Prod 314: 127924. https://doi.org/10.1016/j.jclepro.2021.127924 doi: 10.1016/j.jclepro.2021.127924
    [70] Sarkodie SA, Adams S (2018) Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci Total Environ 643: 1590–1601. https://doi.org/10.1016/j.scitotenv.2018.06.320 doi: 10.1016/j.scitotenv.2018.06.320
    [71] Selcuk M, Gormus S, Guven M (2021) Do agriculture activities matter for environmental Kuznets curve in the next eleven countries? Environ Sci Pollut R 28: 55623–55633. https://doi.org/10.1007/s11356-021-14825-2 doi: 10.1007/s11356-021-14825-2
    [72] Espoir DK, Sunge R (2021) CO2 emissions and economic development in Africa: Evidence from a dynamic spatial panel model. J Environ Manage 300: 113617. https://doi.org/10.1016/j.jenvman.2021.113617 doi: 10.1016/j.jenvman.2021.113617
    [73] Mentel G, Tarczyński W, Dylewski M, et al. (2022) Does renewable energy sector affect industrialization-CO2 emissions Nexus in Europe and Central Asia? Energies 15: 5877. https://doi.org/10.3390/en15165877 doi: 10.3390/en15165877
    [74] Amoah A, Kwablah E, Korle K, et al. (2020) Renewable energy consumption in Africa: The role of economic well-being and economic freedom. Energy Sustain Soc 10: 32. https://doi.org/10.1186/s13705-020-00264-3 doi: 10.1186/s13705-020-00264-3
    [75] Magazzino C, Cerulli G, Shahzad U, et al. (2023) The nexus between agricultural land use, urbanization, and greenhouse gas emissions: Novel implications from different stages of income levels. Atmos Pollut Res 14: 101846. https://doi.org/10.1016/j.apr.2023.101846 doi: 10.1016/j.apr.2023.101846
    [76] Raihan A, Begum RA, Nizam M, et al. (2022) Dynamic impacts of energy use, agricultural land expansion, and deforestation on CO2 emissions in Malaysia. Environ Ecol Stat 29: 477–507. https://doi.org/10.1007/s10651-022-00532-9 doi: 10.1007/s10651-022-00532-9
    [77] Haug AA, Ucal N (2019) The role of trade and FDI for CO2 emissions in Turkey: Nonlinear relationships. Energy Econ 81: 297–307. https://doi.org/10.1016/j.eneco.2019.04.006 doi: 10.1016/j.eneco.2019.04.006
    [78] Rehman E, Rehman S (2022) Modeling the nexus between carbon emissions, urbanization, population growth, energy consumption, and economic development in Asia: Evidence from grey relational analysis. Energy Rep 8: 5430–5442. https://doi.org/10.1016/j.egyr.2022.03.179 doi: 10.1016/j.egyr.2022.03.179
    [79] Baron RM, Kenny DA (1986) The Moderator-Mediator variable distinction in social psychological research. Conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51: 1173. https://doi:10.1037/0022-3514.51.6.1173
    [80] Breusch TS, Pagan AR (1980) The lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47: 239–253. https://doi.org/10.2307/2297111 doi: 10.2307/2297111
    [81] Hausman JA (1978) Specification tests in econometrics. Econometrica 46: 1251–1271. https://doi.org/10.2307/1913827 doi: 10.2307/1913827
    [82] Chovancová J, Petruška I, Rovňák M, et al. (2024) Investigating the drivers of CO2 emissions in the EU: Advanced estimation with common correlated effects and common factors models. Energy Rep 11: 937–950. https://doi.org/10.1016/j.egyr.2023.12.057 doi: 10.1016/j.egyr.2023.12.057
    [83] Baltagi BH, (2010) Fixed effects and random effects, In: Durlauf SN, Blume LE Eds., Microeconometrics, London: Palgrave Macmillan, 59–64. https://doi.org/10.1057/978-1-349-95121-5_2713-1
    [84] Bell A, Jones K (2015) Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Polit Sci Res Meth 3: 133–153. https://doi.org/10.1017/psrm.2014.7 doi: 10.1017/psrm.2014.7
    [85] Borenstein M, Hedges LV, Higgins JPT, et al. (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1: 97–111. https://doi.org/10.1002/jrsm.12 doi: 10.1002/jrsm.12
    [86] Apergis N, Kuziboev B, Abdullaev I, et al. (2023) Investigating the association among CO2 emissions, renewable and non-renewable energy consumption in Uzbekistan: an ARDL approach. Environ Sci Pollut R 30: 39666–39679. https://doi.org/10.1007/s11356-022-25023-z doi: 10.1007/s11356-022-25023-z
    [87] Bekun FV, Alola AA, Sarkodie SA (2019) Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci Total Environ 657: 1023–1029. https://doi:10.1016/j.scitotenv.2018.12.104 doi: 10.1016/j.scitotenv.2018.12.104
    [88] Mahalik MK, Mallick H, Padhan H (2021) Do educational levels influence the environmental quality? The role of renewable and non-renewable energy demand in selected BRICS countries with a new policy perspective. Renew Energy 164: 419–432. https://doi:10.1016/j.renene.2020.09.090 doi: 10.1016/j.renene.2020.09.090
    [89] Wang J, Azam W (2024) Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions in top emitting countries. Geosci Front 15: 101757. https://doi:10.1016/j.gsf.2023.101757 doi: 10.1016/j.gsf.2023.101757
    [90] UNEP, Emissions Gap Report 2024. United Nations Environment Programme.
    [91] Aslam B, Hu J, Shahab S, et al. (2021) The nexus of industrialization, GDP per capita and CO2 emission in China. Environ Technol Inno 23: 101674. https://doi:10.1016/j.eti.2021.101674 doi: 10.1016/j.eti.2021.101674
    [92] Nasreen S, Mbarek MB, Atiq-ur-Rehman M (2020) Long-run causal relationship between economic growth, transport energy consumption and environmental quality in Asian countries: Evidence from heterogeneous panel methods. Energy 192: 116628. https://doi:10.1016/j.energy.2019.116628 doi: 10.1016/j.energy.2019.116628
    [93] Neves SA, Marques AC, Fuinhas JA (2017) Is energy consumption in the transport sector hampering both economic growth and the reduction of CO2 emissions? A disaggregated energy consumption analysis. Transport Policy 59: 64–70. https://doi:10.1016/j.tranpol.2017.07.004 doi: 10.1016/j.tranpol.2017.07.004
    [94] Alola AA, Bekun FV, Sarkodie SA (2019) Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe. Sci Total Environ 685: 702–709. https://doi:10.1016/j.scitotenv.2019.05.139 doi: 10.1016/j.scitotenv.2019.05.139
    [95] Ulucak R, Khan SUD (2020) Determinants of the ecological footprint: Role of renewable energy, natural resources, and urbanization. Sustain Cities Soc 54: 101996. https://doi:10.1016/j.scs.2019.101996 doi: 10.1016/j.scs.2019.101996
    [96] Sharma R, Sinha A, Kautish P (2021) Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia. J Clean Prod 285: 124867. https://doi:10.1016/j.jclepro.2020.124867 doi: 10.1016/j.jclepro.2020.124867
    [97] Filonchyk M, Peterson MP, Zhang L, et al. (2024) Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O. Sci Total Environ 935: 173359. https://doi.org/10.1016/j.scitotenv.2024.173359 doi: 10.1016/j.scitotenv.2024.173359
    [98] Oladunni OJ, Mpofu K, Olanrewaju OA (2022) Greenhouse gas emissions and its driving forces in the transport sector of South Africa. Energy Rep 8: 2052–2061. https://doi.org/10.1016/j.egyr.2022.01.123 doi: 10.1016/j.egyr.2022.01.123
    [99] Wooldridge JM (2002) Econometric analysis of cross section and panel data, 1 Eds., The MIT Press, 108: 245–254.
    [100] Baum CF (2001) Residual diagnostics for cross-section time series regression models. Stata J 1: 101–104. https://doi.org/10.1177/1536867x0100100108 doi: 10.1177/1536867x0100100108
    [101] Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Empir Econ 13–50. https://doi.org/10.2139/ssrn.572504
    [102] Wang H, Zhang R (2022) Effects of environmental regulation on CO2 emissions: An empirical analysis of 282 cities in China. Sustainain Prod Consump 29: 259–272. https://doi.org/10.1016/j.spc.2021.10.016 doi: 10.1016/j.spc.2021.10.016
    [103] Shi Q, Liang Q, Huo T, et al. (2023) Evaluation of CO2 and SO2 synergistic emission reduction: The case of China. J Clean Prod 433: 139784. https://doi.org/10.1016/j.jclepro.2023.139784 doi: 10.1016/j.jclepro.2023.139784
    [104] Wang Q, Zhang F, Li R, et al. (2024) Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. J Clean Prod 447: 141298. https://doi.org/10.1016/j.jclepro.2024.141298 doi: 10.1016/j.jclepro.2024.141298
    [105] Rai P, Gupta P, Saini N, et al. (2023) Assessing the impact of renewable energy and non-renewable energy use on carbon emissions: Evidence from select developing and developed countries. Environ Dev Sustain 27: 3059–3080. https://doi.org/10.1007/s10668-023-04001-6 doi: 10.1007/s10668-023-04001-6
    [106] Caldera Y, Ranthilake T, Gunawardana H, et al. (2024) Understanding the interplay of GDP, renewable, and non-renewable energy on carbon emissions: Global wavelet coherence and Granger causality analysis. PLoS One 19: e0308780. https://doi.org/10.1371/journal.pone.0308780 doi: 10.1371/journal.pone.0308780
    [107] Merforth M, Wagner A, Winter C, et al. (2023) Fossil fuel dependency of urban transport systems: How can transport authorities and operators navigate through multiple risks and threats at times of global crisis? https://transformative-mobility.org/wp-content/uploads/2023/12/fossil-fuel-dependency-of-urban-transport-systems.pdf.
    [108] Neves SA, Marques AC, Fuinhas JA (2017) Is energy consumption in the transport sector hampering both economic growth and the reduction of CO2 emissions? A disaggregated energy consumption analysis. Transport Policy 59: 64–70. https://doi.org/10.1016/j.tranpol.2017.07.004 doi: 10.1016/j.tranpol.2017.07.004
    [109] Aba MM, Amado NB, Rodrigues AL, et al. (2023) Energy transition pathways for the Nigerian Road Transport: Implication for energy carrier, Powertrain technology, and CO2 emission. Sustain Prod Consump 38: 55–68. https://doi.org/10.1016/j.spc.2023.03.019 doi: 10.1016/j.spc.2023.03.019
    [110] Cinderby S, Haq G, Opiyo R, et al. (2024) Inclusive climate resilient transport challenges in Africa. Cities 146: 104740. https://doi.org/10.1016/j.cities.2023.104740 doi: 10.1016/j.cities.2023.104740
    [111] Akpolat AG, Bakırtaş T (2024) The nonlinear impact of renewable energy, fossil energy and CO2 emissions on human development index for the eight developing countries. Energy 312: 133466. https://doi.org/10.1016/j.energy.2024.133466 doi: 10.1016/j.energy.2024.133466
    [112] Ahmad M, Ahmed Z, Alvarado R, et al. (2024) Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter? J Environ Manage 351: 119824. https://doi.org/10.1016/j.jenvman.2023.119824 doi: 10.1016/j.jenvman.2023.119824
    [113] ESI Africa, What Africa can learn from Kenya about geothermal energy, 2024. Available from: https://www.esi-africa.com/news/what-africa-can-learn-from-kenya-about-geothermal-energy/.
    [114] Naeem MA, Appiah M, Taden J, et al. (2023) Transitioning to clean energy: Assessing the impact of renewable energy, bio-capacity and access to clean fuel on carbon emissions in OECD economies. Energy Econ 127: 107091. https://doi.org/10.1016/j.eneco.2023.107091 doi: 10.1016/j.eneco.2023.107091
    [115] Zhou Y, Haseeb M, Batool M, et al. (2024) Achieving carbon-neutrality goals in Asian emerging economies: Role of investment in clean energy, eco-regulations, and green finance. Gondwana Res. https://doi.org/10.1016/J.GR.2024.06.017
    [116] Department of Mineral Resources and Energy, Government Gazette Staatskoerant Republic of South Africa, 2024. Available from: www.gpwonline.co.za.
    [117] IRENA, Renewable Energy Market Analysis: Africa and its regions. International Renewable Energy Agency, 2022.
    [118] AfDB, NOORo: the largest concentrated solar power complex in Africa increases the share of renewable energy in electricity generation in Morocco. African Development Bank, 2019.
    [119] Nteranya JN, (2024). Natural resources use in the Democratic Republic of Congo. In: Brears R. Eds., The Palgrave Encyclopedia of Sustainable Resources and Ecosystem Resilience, Cham: Palgrave Macmillan, 1–22. https://doi.org/10.1007/978-3-030-67776-3_66-1
    [120] Filho WL, Gatto A, Sharifi A, et al. (2024) Energy poverty in African countries: An assessment of trends and policies. Energy Res Soc Sci 117: 103664. https://doi.org/10.1016/J.ERSS.2024.103664 doi: 10.1016/J.ERSS.2024.103664
    [121] Jayachandran M, Gatla RK, Rao KP, et al. (2022) Challenges in achieving sustainable development goal 7: Affordable and clean energy in light of nascent technologies. Sustain Energy Techn 53: 102692. https://doi.org/10.1016/j.seta.2022.102692 doi: 10.1016/j.seta.2022.102692
    [122] Mohsin M, Jamaani F (2023) Unfolding impact of natural resources, economic growth, and energy nexus on the sustainable environment: Guidelines for green finance goals in 10 Asian countries. Resour Policy 86: 104238. https://doi.org/10.1016/j.resourpol.2023.104238 doi: 10.1016/j.resourpol.2023.104238
    [123] Huang Z Ren X (2024) Impact of natural resources, resilient economic growth, and energy consumption on CO2 emissions. Resour Policy 90: 104714. https://doi.org/10.1016/j.resourpol.2024.104714 doi: 10.1016/j.resourpol.2024.104714
    [124] Chorev S, (2023) The Suez Canal: Forthcoming strategic and geopolitical challenges, In: Lutmar C, Rubinovitz Z Eds., The Suez Canal: Past Lessons and Future Challenges, Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-031-15670-0_1
    [125] Walters J, Pisa N (2023) Review of South Africa's public transport system. Res Transp Econ 100: 101322. https://doi.org/10.1016/j.retrec.2023.101322 doi: 10.1016/j.retrec.2023.101322
    [126] Munanga Y, Mafuku SH, (2021) Climate-resilient infrastructure for water and energy in greater Harare, In: Chirisa I, Chigudu A Eds., Resilience and Sustainability in Urban Africa, Singapore: Springer. https://doi.org/10.1007/978-981-16-3288-4_5
    [127] Aderibigbe OO, Fadare SO, Gumbo T (2024) Transport situation in the global south: Insights from Nigeria, South Africa and India, Emerging Technologies for Smart Cities, Cham: Springer. https://doi.org/10.1007/978-3-031-66943-9_3
    [128] Kwanya LM (2022) Impact of the standard gauge railway on the Kenyan economy. Int J Latest Technol Eng Manag Appl Sci 11: 1–7.
    [129] Bouraima MB, Alimo PK, Agyeman S, et al. (2023) Africa's railway renaissance and sustainability: Current knowledge, challenges, and prospects. J Transp Geogr 106: 103487. https://doi.org/10.1016/j.jtrangeo.2022.103487 doi: 10.1016/j.jtrangeo.2022.103487
    [130] U.S Energy Information Administration, Hydropower made up 66% of Brazil's electricity generation in 2020. U.S Energy Information Administration, 2021. Available from: https://www.eia.gov/todayinenergy/detail.php?id=49436.
    [131] Quiñones G, Felbol C, Valenzuela C, et al. (2020) Analyzing the potential for solar thermal energy utilization in the Chilean copper mining industry. Sol Energy 197: 292–310. https://doi.org/10.1016/j.solener.2020.01.009 doi: 10.1016/j.solener.2020.01.009
    [132] Tan KM, Yong JY, Ramachandaramurthy VK, et al. (2023) Factors influencing global transportation electrification: Comparative analysis of electric and internal combustion engine vehicles. Renew Sustain Energ Rev 184: 113582. https://doi.org/10.1016/j.rser.2023.113582 doi: 10.1016/j.rser.2023.113582
    [133] GWEC, Global Offshore Wind Report. Global Wind Energy Council, 2024. Available from: https://gwec.net/global-offshore-wind-report-2024/.
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