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Frontiers in Sustainable Energy Policy ·

Volume 5 - 2026 | https://doi.org/10.3389/fsuep.2026.1930955

Accelerating Ghana’s sustainable transport transition: the role of electric vehicles in achieving SDG 7 and SDG 13

  • Ebenezer Arthur Duncan1*corresponding author
  • Samuel Affran2
  1. 1Marketing Department, University of Professional Studies, Accra, Ghana
  2. 2Marketing and Entrepreneurship, University of Education Winneba, Winneba, Ghana
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Abstract

Urban transport in Ghana relies heavily on imported fuel and contributes significantly to emissions. Electric vehicles have been proposed as an alternative, but we still know little about what shapes drivers’ willingness to adopt them in major city corridors. We used Social Cognitive Theory to assess how two factors - tax incentives and environmental awareness - relate to three self-reported outcomes: intention to adopt EVs, sustainable energy consumption, and beliefs about greenhouse gas emissions. We did not measure actual emissions. The survey covered 800 commercial drivers and private car owners recruited from transport union hubs in Accra, Tema, Kumasi, and Takoradi, and we analyzed the data with PLS-SEM, including tests for moderation. Drivers who reported higher tax incentives and greater environmental awareness also reported stronger EV adoption intentions and more sustainable energy use, and lower perceived emissions. Environmental awareness moderated these links: it amplified the link between tax incentives and adoption intentions but dampened the links between tax incentives and both energy consumption and perceived emissions. Among unionized operators in these four urban corridors, tax incentives and environmental awareness are linked to intentions and perceptions, not to verified behavior or emission cuts. Because the sample is urban and union-based, the results cannot be extended to all of Ghana, and the model does not show whether intending to adopt an EV actually lowers emissions.

Keywords

1 Introduction

Ghana’s effort to improve its transport sector is linked to efforts to achieve Sustainable Development Goals (SDGs) 7 and 13, with electric vehicles (EVs) seen as one option for sustainable energy consumption and climate action. The willingness of commercial drivers and vehicle owners to adopt EVs is important to understand in this context. Adoption, conceptualized as the willingness and capacity to procure and utilize EVs (Liu et al., 2020), is connected to sustainable energy consumption (SDG 7), which covers energy-efficient technologies and practices that reduce dependence on fossil fuels (Yan et al., 2024). Furthermore, the possible relationship between EV adoption and greenhouse gas emissions (SDG 13), understood here as perceived emissions based on respondents’ beliefs rather than measured data and referring to carbon dioxide and other climate-altering gases, remains relevant (Arora and Mishra, 2023). The roles of tax incentives, representing governmental policies and subsidies promoting EV adoption (Nunes and Woodley, 2023), and environmental awareness, denoting knowledge and concern about the ecological impacts of transportation choices (Zeng Z. et al., 2023), are also relevant to this discussion. Ghana’s energy sector is marked by escalating demand, a substantial dependence on fossil fuels, and an increasing inclination toward renewable energy sources (Nyasapoh et al., 2022). The transportation sector, a significant consumer of fossil fuels, is affected by rising fuel costs and urban congestion, alongside the global transition toward EVs (Kittner et al., 2020).

Ghana’s transportation sector faces 70% fuel import dependence and transport emissions contributing 45% of national CO2 emissions (Energy Commission, 2024). However, Ghana’s EV market remains nascent, impeded by inadequate charging infrastructure and prohibitive upfront costs (Ghana et al., 2024). Understanding factors associated with EV adoption intentions among commercial drivers and vehicle owners in urban corridors is therefore relevant. This study employs social cognitive theory (SCT) (Bandura, 1986), which suggests that behavior is associated with personal factors (e.g., environmental awareness), behavioral factors (e.g., adoption of EVs), and environmental factors (e.g., tax incentives). SCT provides a suitable framework for this study because it examines the interaction of these factors, allowing an examination of how these factors are associated with EV adoption intentions among commercial drivers and vehicle owners. Social-cognitive perceptions, including environmental concern and perceived incentives, are associated with attitudes and adoption intentions, which may influence EV adoption (Jaiswal et al., 2022).

In emerging markets, attitudes and perceptions are associated with young adults’ intention to purchase EVs (De Luca et al., 2024). Environmental awareness is associated with EV purchase intention, showing the relevance of awareness campaigns (Phoon et al., 2024). Tax incentives are associated with EV adoption in countries such as the United States and with economic growth and sustainability outcomes (Odunaiya et al., 2021). EVs are increasingly cost-competitive, achieving total-cost-of-ownership parity in certain segments (Li R. et al., 2025; Li Y. et al., 2025). Conversely, low consumer awareness and limited infrastructure are associated with lower EV adoption in developing countries (Hakam and Jumayla, 2024). Sustainable energy consumption is intertwined with renewable energy consumption and green finance, which are associated with CO2 emission reduction in emerging economies (Behera et al., 2024). Addressing broader energy challenges, including limited access to clean energy and inefficient energy markets, is relevant for achieving SDG 7 (Matenga, 2022; Zeng Z. et al., 2023). Reducing greenhouse gas emissions, a key aspect of SDG 13, is associated with factors such as GDP, foreign direct investment, and environmental policy stringency (Kartal et al., 2025).

A gap remains in the literature on EV adoption in developing countries, particularly in Sub-Saharan Africa, despite the burgeoning global interest in EVs. The majority of existing literature comes from developed countries, such as the United States and China, which may not fully reflect the distinctive challenges and opportunities inherent in Ghana’s context (Hakam and Jumayla, 2024; Akinsooto et al., 2025). Studies by Jaiswal et al. (2022) and De Luca et al. (2024) have provided valuable insights into EV adoption in India and Argentina, respectively; however, they point to the need for context-specific research to inform policy and practice in Ghana. The rapid evolution of EV technology and shifting consumer preferences suggest the need for contemporary research that reflects current trends and realities (Li R. et al., 2025; Li Y. et al., 2025). The association between environmental awareness and tax incentives and EV adoption in Ghana remains an underexplored area, highlighting a need for further investigation to bridge this knowledge gap (Phoon et al., 2024; Behera et al., 2024). Complementary research on energy markets and sustainable energy consumption (Matenga, 2022; Zeng D. et al., 2023) points to the importance of a holistic approach to achieving SDG 7. Furthermore, Kartal et al. (2025) note the significance of considering macroeconomic factors, including GDP, foreign direct investment, and environmental policy stringency, in association with greenhouse gas emissions, a critical component of SDG 13. The gaps in EV adoption in Ghana point to a need for comprehensive examination to inform policy formulation. Commercial drivers and vehicle owners, a key group in Ghana’s transport sector, show patterns that may be associated with environmental awareness, tax incentives, and sustainable energy consumption (SDG 7), which are also discussed in relation to greenhouse gas emission reduction (SDG 13).

This study examines whether environmental awareness and tax incentives are associated with behavioral intentions among 800 commercial drivers and vehicle owners surveyed in 2025. The study also assesses the role of sustainable energy consumption in relation to widespread EV adoption and perceived emissions. The model tests three parallel outcomes and does not test whether adoption itself leads to lower emissions. The results are analytically transferable to urban, union-based operators in similar corridors rather than to all of Ghana. This study provides Ghana-specific insights, addressing a gap in the literature and offering evidence that may be useful for sustainable transportation initiatives, with potential relevance for a cleaner environment and a more sustainable future in Ghana.

2 Literature review

Ghana’s transportation sector is characterized by fuel import dependence, urban congestion in Accra, Tema, and Kumasi, and transport emissions contributing a reported 45% of national CO2 emissions (Energy Commission, 2024), with EVs discussed as one option for sustainable energy consumption (SDG 7) and reducing perceived greenhouse gas emissions (SDG 13). Reported government initiatives to promote EV adoption exist, while the market remains nascent, impeded by inadequate infrastructure and high costs, including reported limits in public charging and high upfront EV prices (Ghana et al., 2024). A gap remains, as factors associated with EV adoption in Ghana are less understood, particularly the association between tax incentives, environmental awareness, and adoption intentions. This literature review summarizes existing knowledge on EV adoption in Ghana, examining theoretical frameworks, empirical findings, and contextual factors associated with sustainable transportation adoption. Applying social cognitive theory (SCT), this review discusses dynamics associated with EV adoption among commercial drivers and vehicle owners, identifying areas relevant for policy intervention and future research.

2.1 Social cognitive theory

Social cognitive theory (Bandura, 1986) suggests reciprocal interaction among personal factors (e.g., environmental awareness), behavioral factors (e.g., adoption of EVs), and environmental factors (e.g., tax incentives) in relation to behavior. SCT is based on three core assumptions: (1) triadic reciprocal causation among person, behavior, and environment; (2) observational learning through modeling others’ behaviors; (3) self-efficacy as a belief in one’s capacity to perform a behavior. This framework points to observational learning, self-efficacy, and reinforcement as relevant to behavioral adoption. In Ghana, observational learning may occur when commercial drivers observe peers discussing or using EVs at taxi ranks or union meetings, which may be relevant to self-efficacy for adoption. SCT offers an integrative approach for discussing interactions, with emphasis on self-efficacy and observational learning as background concepts that are not directly measured in this study (Owunna et al., 2025). SCT’s integrative capacity is consistent with the objective to examine associations among tax incentives, environmental awareness, and sustainable energy consumption, with relevance for sustainable transportation and perceived emissions. Considering multiple factors is relevant to EV adoption in Ghana, given reported infrastructure and cost barriers.

2.2 Adoption of EVs

The adoption of EVs refers to individuals’ intention to adopt and utilize EVs, which is associated with perceived usefulness, ease of use, attitude, and behavioral intention (Davis, 1989) from the Technology Acceptance Model. EV adoption can also be studied through TAM, but this approach is not the main theoretical framework adopted in this manuscript; SCT is used because it incorporates environmental factors such as tax incentives and reported infrastructure constraints. This concept is discussed in relation to sustainable energy consumption (SDG 7), as EVs are one component of sustainable transportation. While adoption of EVs focuses on a specific technology, sustainable energy consumption covers a broader array of energy-efficient practices and renewable energy sources (Lu et al., 2020). Adoption of EVs is associated with tax incentives, which are associated with perceptions and motivations, while recognizing external factors such as policy and infrastructure. Within the social cognitive theory framework, personal, behavioral, and environmental factors are discussed together in relation to the adoption of EVs, with relevance for sustainable transportation in Ghana, where charging access and vehicle cost are reported as barriers.

2.3 Sustainable energy consumption (SDG 7)

Sustainable energy consumption (SDG 7) refers to individuals’ energy-efficient behaviors, including the adoption of renewable sources, reduced consumption, and promotion of sustainable habits (Stragier et al., 2012). This concept is discussed alongside EV adoption, focusing on sustainable transportation. Sustainable energy consumption includes a broader range of practices associated with energy efficiency, whereas EV adoption targets a specific technology (Heydari et al., 2023). Within the social cognitive theory framework, sustainable energy consumption is discussed in relation to personal, behavioral, and environmental factors, with self-efficacy and observational learning treated as background concepts that are not directly measured. In Ghana, promoting sustainable energy consumption is discussed in relation to energy efficiency and perceived emissions (SDG 13) amid reported reliance on thermal power and intermittent renewable supply.

2.4 Greenhouse gas emissions (SDG 13)

Perceived greenhouse gas emissions (SDG 13) refer to individuals’ awareness and concern about the climate change implications of their carbon footprint, and their beliefs about transport emissions, rather than measured emissions, which may shape attitudes toward emission reduction (Üstgörül et al., 2024). This perception is discussed alongside sustainable energy consumption and adoption of EVs, as these are perceived by respondents to be associated with lower emissions. Perceived greenhouse gas emissions are associated with environmental awareness, focusing on beliefs about the repercussions of human activities on climate change (Tang et al., 2021). Within the social cognitive theory framework, individuals’ awareness and concern about emissions are discussed as background relevant to self-efficacy and observational learning, which are not directly measured, and are associated with behavioral intentions. In Ghana, understanding perceived greenhouse gas emissions can inform discussion of sustainable transportation and climate action, with relevance to SDG 13 given reported urban emissions from commercial transport.

2.5 Tax incentives

Tax incentives refer to governmental policies and initiatives that provide perceived benefits and motivations associated with EV adoption and sustainable energy practices (Mohanty et al., 2021). These include subsidies, tax credits, or financial supports, which are associated with environmentally conscious behaviors. As an extrinsic factor, tax incentives are associated with attitudes and behaviors toward sustainable energy consumption, similar to environmental awareness (Pereira and Silva, 2020). Within the social cognitive theory framework, tax incentives are treated as environmental factors associated with self-efficacy and observational learning as background concepts that are not directly measured, and with adoption intentions. In Ghana, tax incentives such as reported import duty exemptions and VAT waivers on EVs are discussed as potentially associated with EV adoption and sustainable energy practices, with relevance to SDG 7 and SDG 13.

2.6 Environmental awareness

Individuals’ environmental awareness refers to their concern, knowledge, and attitudinal disposition toward environmental issues, which are associated with behavioral patterns and decision-making processes (Weigel and Weigel, 1978). This awareness is discussed as a personal factor relevant to sustainable energy consumption and EV adoption. Environmental awareness is associated with pro-environmental behaviors as an intrinsic factor alongside tax incentives (Silvi and Padilla, 2021). Within the social cognitive theory framework, environmental awareness is discussed in relation to self-efficacy and observational learning as background concepts that are not directly measured and in relation to sustainable practices. Higher environmental awareness is associated with sustainable energy consumption and beliefs about climate change, with relevance to SDG 7 and SDG 13 in a context where fuel cost pressures are reported among commercial drivers.

3 Hypotheses development

3.1 Tax incentives and adoption of EVs

Tax incentives are considered environmental conditions in SCT (Bandura, 1986) that are discussed in relation to adoption intentions. External conditions, such as governmental policies and incentives, are associated with beliefs about capability and with learning from observing peers as background concepts that are not directly measured and with intentions to adopt (Nurgabylov et al., 2020). Earlier studies report that tax incentives are associated with higher intentions to adopt EVs (Liu et al., 2021; Liu et al., 2023). This association is discussed alongside reported government policy availability, awareness of incentives, cost considerations, and reported access to supportive infrastructure (e.g., charging stations). On this basis:

H1: Tax incentives are positively associated with EVs.

3.2 Tax incentives and sustainable energy consumption

Fiscal supports are treated as environmental factors within SCT (Bandura, 1986) that are discussed in relation to energy-related intentions. Policy conditions are discussed as associated with beliefs about capability and, as background concepts, with learning from peers and with practices linked to sustainable energy (Bandura, 1986, 2023). Previous studies report that tax incentives are associated with sustainable energy consumption relevant to SDG 7 (Yuan et al., 2023; Sarker et al., 2020). This association is considered in relation to reported policy frameworks, awareness of incentives, energy cost salience, and infrastructure conditions.

H2: Tax incentives are positively associated with sustainable energy consumption (SDG 7).

3.3 Tax incentives and greenhouse gas emissions

Within SCT (Bandura, 1986), tax incentives are viewed as environmental conditions discussed in relation to beliefs about emissions. External conditions, such as government policies and incentives, are associated with environmental cognitions and with intentions reported as relevant to perceived emissions. Previous literature reports that tax incentives are associated with lower perceived emissions through their reported association with eco-friendly behaviors and technologies, relevant to SDG 13 (Stapleton et al., 2022; Trikoz et al., 2020; Vasilyeva et al., 2023). This association is discussed in relation to the reported regulatory context, awareness of climate change, availability of low-carbon options, and infrastructure.

H3: Tax incentives are negatively associated with perceived greenhouse gas emissions (SDG 13).

3.4 Environmental awareness and adoption of EVs

Environmental awareness is considered a personal factor in SCT (Bandura, 1986; Bandura, 1986, 2023) and is discussed alongside peer learning, where drivers may become familiar with EV use through observation. Observational learning is suggested to be associated with adoption intentions through vicarious experiences and environmental context (Geertshuis et al., 2021), with environmental awareness discussed as relevant background. Earlier studies report that higher environmental awareness is associated with more favorable attitudes toward EVs and with higher adoption intentions (Geertshuis et al., 2021; Gupta et al., 2024; Mustafa et al., 2026). This pattern is discussed in relation to self-efficacy as a background concept that is not directly measured, social norms, perceived environmental benefits, and availability of EV infrastructure.

H4: Environmental awareness is positively associated with adoption of EVs.

3.5 Environmental awareness and sustainable energy consumption (SDG7)

The association between awareness and sustainable consumption is discussed using observational learning as background within SCT (Bandura, 1986; Bandura, 1986, 2023). Observational learning is suggested to be associated with energy-efficient intentions through vicarious experiences and environmental context, with environmental awareness discussed as relevant context (Chen et al., 2022). Previous studies report that higher environmental awareness is associated with energy conservation practices relevant to SDG 7 (Camacho et al., 2024; Valenzuela et al., 2025). This association is considered alongside self-efficacy as a background concept that is not directly measured, social norms, perceived environmental benefits, and reported sustainable energy infrastructure.

H5: Environmental awareness is positively associated with sustainable energy consumption (SDG7).

3.6 Environmental awareness and greenhouse gas emissions (SDG 13)

SCT (Bandura, 1986; Bandura, 1986, 2023) is used as a lens to discuss how awareness may be associated with emission-related intentions, with observational learning as unmeasured background. Environmental awareness is reported as associated with eco-conscious behaviors, relevant to SDG 13 (Zafar et al., 2024). Earlier studies report that higher awareness is associated with lower perceived emissions (Chanmony, 2021; Petersen et al., 2020). This association is discussed in relation to self-efficacy as a background concept that is not directly measured, social norms, perceived environmental benefits, and availability of low-carbon technology.

H6: Environmental awareness is negatively associated with perceived greenhouse gas emissions (SDG 13).

3.7 Moderating role of environmental awareness in the relationship between tax incentives and adoption of EVs

The interplay between environmental and personal factors in SCT (Bandura, 1986; Bandura, 1986, 2023) provides background for examining moderation. Environmental awareness is discussed as potentially associated with a stronger link between incentives and EV adoption intentions. Previous studies report this pattern, where awareness is associated with a stronger incentive-adoption association (Liu et al., 2021; Qadir et al., 2024). This moderating association is considered in relation to self-efficacy as a background concept, social norms, and perceived environmental benefits.

H7: Environmental awareness positively moderates the association between tax incentives and adoption of EVs, such that the association is stronger when environmental awareness is higher.

3.8 Moderating role of environmental awareness in the relationship between tax incentives and sustainable energy consumption

The interaction between personal and environmental factors in SCT (Bandura, 1986; Bandura, 1986, 2023) provides background for this moderation. Environmental awareness is discussed as being associated with a stronger association between tax incentives and sustainable energy consumption (Yan et al., 2023). Previous studies report that awareness is associated with a stronger incentive-conservation association (Xiang et al., 2025; Shafi et al., 2023). The moderating pattern is discussed in relation to self-efficacy as a background concept, social norms, and perceived environmental benefits.

H8: Environmental awareness positively moderates the association between tax incentives and sustainable energy consumption (SDG 7).

3.9 The moderating role of environmental awareness in the relationship between tax incentives and greenhouse gas emissions

SCT (Bandura, 1986; Bandura, 1986, 2023) treats environmental and personal factors as an interacting background for this moderation. Environmental awareness is discussed as associated with a stronger association between tax incentives and perceived emissions in the direction of lower perceived emissions. Previous literature reports that awareness is associated with a stronger incentive-related emission association (Issa, 2024; Li R. et al., 2025; Li Y. et al., 2025). This moderating pattern is considered alongside self-efficacy as a background concept, social norms, and perceived environmental benefits.

H9: Environmental awareness positively moderates the association between tax incentives and perceived greenhouse gas emissions (SDG 13).

3.10 Conceptual framework

Tax incentives and environmental awareness are considered factors proposed to be associated with intentions relevant to sustainable behaviors. Social cognitive theory (Bandura, 1986, 2023) is used as background to frame how external conditions and personal factors may be related to intentions. Tax incentives are hypothesized to be associated with higher adoption intentions for EVs (H1: +) and higher sustainable energy consumption (SDG 7) (H2: +), and with lower perceived greenhouse gas emissions (SDG 13) (H3: −). Environmental awareness is expected to be associated with higher adoption intentions for EVs (H4: +) and higher sustainable energy consumption (SDG 7) (H5: +), and with lower perceived greenhouse gas emissions (SDG 13) (H6: −). Environmental awareness is proposed to moderate the link between tax incentives and these outcomes, such that associations are stronger when awareness is higher (H7–H9: + moderation). Self-efficacy and observational learning are treated as SCT background concepts not directly measured in this study. Adoption of EVs, sustainable energy consumption, and perceived greenhouse gas emissions are treated as separate outcomes; direct or mediated links among them are not tested in this study to keep the focus on direct antecedents, with mediation left for future research as a scope limitation. The combination of tax incentives and environmental awareness is viewed as potentially relevant to sustainable behaviors and climate change, as shown in the conceptual framework (Figure 1).

Conceptual framework.
Figure 1. Conceptual framework.

4 Methods

4.1 Research design

An exploratory, quantitative, cross-sectional survey design was used to examine associations among tax incentives, environmental awareness, adoption of EVs, sustainable energy consumption, and perceived greenhouse gas emissions. A cross-sectional design is suited to testing associations at a single point in time and aligns with social cognitive theory when longitudinal data are not available (Hair et al., 2021). Partial least squares structural equation modeling was selected for handling non-normal data and complex models common in behavioral sustainability research (Hair et al., 2021). Analysis was conducted in SmartPLS 4 using a path weighting scheme, 300 maximum iterations, a stop criterion of 1.0 × 10–7, 5,000 bootstrap subsamples, blindfolding omission distance D = 7, and bias-corrected accelerated confidence intervals.

4.2 Study context

The study is set in Ghana, an emerging West African market with growing interest in electric mobility. Transport and energy conditions reflect constraints typical of developing economies relevant to SDG 7 and SDG 13 (Energy Commission, 2024; Kittner et al., 2020). Key statistics on fuel imports, emissions share, and charging infrastructure are presented once in this section.

4.3 Population, sampling technique, and sample size

Ghana’s transport sector includes approximately 350,000 commercial drivers (Driver and Vehicle Licensing Authority, 2022) and 1.5 million vehicle owners (Ghana Statistical Service, 2021). The target population comprised licensed commercial drivers and private vehicle owners aged 18 years and above, with at least 6 months of operating experience in Accra, Tema, Kumasi, or Takoradi. This definition reflects urban operators exposed to fuel costs, incentives, and charging infrastructure and does not extend to rural areas or Ghana as a whole. In the absence of a unified national registry, multi-stage purposive-convenience sampling was adopted (Emerson, 2021; Golzar et al., 2022). Stage one identified four corridors with high registration density and commercial activity. Stage two involved established hubs and unions, including GPRTU, PROTOA, concerned drivers, and private owner associations. Stage three recruited eligible participants at these hubs through face-to-face contact during operational hours, with eligibility screening and informed consent, until quotas were reached. Quotas by city were Accra 280 (35%), Tema 160 (20%), Kumasi 200 (25%), and Takoradi 160 (20%), reflecting relative commercial activity across corridors. The total sample was 800, comprising 400 commercial drivers and 400 vehicle owners. This equal allocation does not mirror national proportions of 350,000 drivers and 1.5 million owners. Equal numbers were chosen to permit balanced comparison between supply-side operators and demand-side owners who shape purchase decisions in unionized transport, to maintain statistical power for subgroup analysis, and to meet requirements for structural equation modeling. Proportional allocation would have underrepresented commercial drivers who are central to adoption in these hubs. Sample adequacy was assessed through power analysis (Faul et al., 2009). Specifications were small effect f2 = 0.05, alpha = 0.05, power = 0.80, and five predictors. The minimum required was 647. The obtained 800 exceeds this threshold and supports detection of small effects and analysis within each 400-person group.

4.4 Instrument, adaptation, and pretesting

Structured questionnaire was administered in person between January and March 2025. From 899 questionnaires, 800 were valid, yielding 89% response rate. Final instrument contained 25 reflective items on 5-point Likert scale. Tax incentives comprised five items adapted from Mohanty et al. (2021); environmental awareness comprised four items adapted from Weigel and Weigel (1978); adoption of EVs comprised six items adapted from Davis (1989); sustainable energy consumption comprised six items adapted from Stragier et al. (2012); perceived greenhouse gas emissions comprised four items adapted from Üstgörül et al. (2024), measured as belief about emissions, not metered emissions. Appendix A contains source items, adaptation notes, and final items. Illustrative items include the following: Tax Incentives—“Government tax exemptions reduce cost of owning electric vehicle”; Environmental Awareness—“Human activities are causing serious damage to environment”; Adoption of EVs—“Intend to use electric vehicle for commercial purposes”; Sustainable Energy Consumption—“Reduce vehicle idling to save energy”; perceived Greenhouse Gas Emissions—“Transport activities contribute to greenhouse gas emissions.” Adaptation involved three stages: contextualization to Ghana transport context, expert review by two transport and two sustainability scholars, and pilot test with 40 drivers and owners in December 2024. Pilot evaluated clarity, completion time, and face validity. Minor wording revisions were implemented; no items were removed. Anonymous administration and neutral wording were used to mitigate social desirability bias.

4.5 Data cleaning and common method Bias

Data screening was performed in SPSS 27. Procedures included listwise deletion for missing data (<2%) and Mahalanobis distance outlier detection. Common method bias was examined using the Harman single-factor test and common latent factor approach (Hair Jr et al., 2017). Procedures and thresholds are described; detailed results are presented in the Results section.

4.6 Measurement model evaluation

Exploratory factor analysis employed principal axis factoring with Promax rotation. Kaiser–Meyer–Olkin measure and Bartlett’s test were used to assess suitability. Factors with eigenvalues greater than 1 were retained (Kaiser, 1960). Confirmatory factor analysis was conducted in SmartPLS 4. Assessment included outer loadings, internal consistency reliability, convergent validity using average variance extracted, discriminant validity using heterotrait–monotrait ratio (Henseler et al., 2015), and collinearity using variance inflation factor. Model fit, explanatory power, effect sizes, predictive relevance, and moderation analysis were evaluated. Complete measurement and structural results are presented in the Results section.

4.7 Ethical statement and transferability of findings

Approval was obtained from the institutional ethics review committee prior to data collection. Participation was voluntary, with informed consent secured from all respondents. Anonymity and confidentiality were maintained during collection, storage, and reporting. No personally identifiable information was collected. Purposive-convenience sampling frame limits statistical generalization to the broader Ghanaian driver and owner population. The findings remain analytically transferable to urban commercial drivers and vehicle owners in emerging economies with similar conditions, including nascent EV markets, fuel import dependence, limited charging infrastructure, and unionized transport operations (Kittner et al., 2020). Application to rural areas or developed markets with mature EV infrastructure requires caution.

5 Data analysis and results

5.1 Background information of respondents

The demographic characteristics of the participants are shown in Table 1.

Table 1. Demographic profile of customers.

Demographic characteristicsRangeFrequency (n)Percentage (%)
Chronological age< 25 years12015.00
26–35 years28035.00
36–45 years24030.00
> 46 years16020.00
Academic achievementBasic education25031.25
Secondary education32040.00
Tertiary education15018.75
Postgraduate education8010.00
GenderMale72090.00
Female607.50
Others151.88
Prefer not to say 5 0.62
Economic status< GHS 1,00016020.00
GHS 1,000–5,00036045.00
GHS 5,001–10,00020025.00
> GHS 10,0008010.00
Driving tenure<5 years12015.00
5–10 years24030.00
11–15 years20025.00
>15 years24030.00
Vehicle categoryTaxi15018.75
Truck8010.00
Bus12015.00
Other commercial vehicle45056.25

Source: Primary (Data 2025).

The demographic profile reveals that Ghana’s commercial vehicle sector is predominantly male, middle-aged, with moderate education and income. Other commercial vehicles constituted the majority at 56.25%, followed by taxis at 18.75%, buses at 15.00%, and trucks at 10.00%, with varied driving tenure represented.

5.2 Partial least squares structural equation modeling (PLS-SEM)

This study employed PLS-SEM to test hypothesized relationships to examine the nexus between EV adoption, framed as “Shifting Gears Toward Sustainability,” and Ghana’s advancement toward Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action). The analysis involved a two-stage process, entailing measurement model assessment and structural model estimation, as per established methodological protocols (Hair et al., 2021).

5.2.1 Measurement model

The measurement model’s reliability and validity were evaluated, revealing satisfactory performance. Indicator outer loadings on respective constructs ranged from 0.730 to 0.883, surpassing the 0.7 threshold, indicating adequate representation of constructs (Hair et al., 2021). Constructs exhibited internal consistency, with Cronbach’s alpha values spanning 0.843 (environmental awareness) to 0.902 (sustainable energy consumption) and composite reliability (rho_c) values between 0.889 and 0.925, affirming reliability (Hair Jr et al., 2017). Convergent validity was established, as average variance extracted (AVE) values exceeded 0.5, ranging from 0.580 (adoption of EV) to 0.718 (greenhouse gas emissions), signifying that constructs capture substantial indicator variance (Hair et al., 2019). The measurement model thus provides a robust foundation for structural model analysis (Figure 2; Table 2).

Measurement model output.
Figure 2. Measurement model output.

Table 2. Outer loadings, Cronbach’s alpha, composite reliability, and AVE.

ConstructsLoading valueCronbach’s alphaComposite reliability (rho_a)Composite reliability (rho_c)Average variance extracted (AVE)
Adoption of EV
AEV10.730
AEV20.761
AEV30.797
AEV40.747
AEV50.777
AEV60.7550.8550.8560.8920.580
Environmental awareness
EnAw10.802
EnAw20.819
EnAw30.824
EnAw40.739
EnAw40.7320.8430.8430.8890.615
Greenhouse gas emissions (SDG13)
GGE10.785
GGE20.871
GGE30.855
GGE40.8740.8680.8720.9100.718
Sustainable energy consumption (SDG7)
SEC10.843
SEC20.850
SEC30.781
SEC40.776
SEC50.825
SEC60.8380.9020.9030.9250.672
Tax incentives
TaxIn10.788
TaxIn20.788
TaxIn30.883
TaxIn40.816
TaxIn50.8420.8810.8830.9140.680

Source: Smart PLS Output (2026).

The heterotrait–monotrait (HTMT) ratio was utilized to evaluate discriminant validity, a critical aspect of establishing construct distinctness (Henseler et al., 2015). HTMT values ranged from 0.544 (environmental awareness → sustainable energy consumption) to 0.751 (environmental awareness → greenhouse gas emissions), all below the conservative 0.85 threshold, indicating that the constructs are distinct and do not exhibit excessive shared variance (Hair Jr et al., 2017). This suggests constructs are more strongly associated with their own indicators than those of other constructs, providing evidence of discriminant validity (Hair et al., 2021). The results imply that multicollinearity is not a significant concern, and constructs can be employed as separate variables in the structural model (Hair et al., 2019). Adoption of EVs (AEVs), environmental awareness (EnAw), greenhouse gas emissions (GGE), sustainable energy consumption (SEC), and tax incentives (TaxIn) represent distinct constructs, thereby supporting the measurement model’s discriminant validity (Table 3).

Table 3. HTMT results.

ConstructAEVEnAwGGESECTaxIn
AEV
EnAw0.708
GGE0.7160.751
SEC0.5610.5440.619
TaxIn0.5780.7500.6580.552

Source: Smart PLS output (2026).

5.2.2 Structural model

The structural model’s predictive diagnostics were evaluated, revealing satisfactory performance. Variance inflation factor (VIF) ranged from 1.053 to 1.808, below the 5 threshold, indicating no multicollinearity (Hair Jr et al., 2017; Hair et al., 2021). Tax incentives and environmental awareness jointly explained 38.7% of EV adoption variance, 30.1% of sustainable energy consumption variance, and 46.1% of perceived greenhouse gas emissions variance, as shown by R2 values. Q2 values indicated predictive relevance: 0.382 for adoption of EVs, 0.291 for sustainable energy consumption, and 0.456 for greenhouse gas emissions, where Q2 > 0 indicates predictive power (Henseler et al., 2015). For interpretation, GGE was measured as perceived greenhouse gas emission level, such that higher scores represent higher emissions rather than emission reductions. Effect sizes (f2) revealed that environmental awareness has a medium effect on adoption of EVs (f2 = 0.207) and greenhouse gas emissions (f2 = 0.232), while tax incentives have a small effect on adoption of EVs (f2 = 0.040), sustainable energy consumption (f2 = 0.065), and greenhouse gas emissions (f2 = 0.073), where f2 ≥ 0.02, 0.15, and 0.35 indicate small, medium, and large effects. Moderation effects were very small to small: H7 f2 = 0.005, H8 f2 = 0.022, and H9 f2 = 0.004 (Table 4).

Table 4. Predictive diagnostics of constructs.

ConstructsVIFf2R2Adjusted R2Q2
TaxIn → AEV1.8080.040
TaxIn → SEC1.8080.065
TaxIn → GGE1.8080.073
EnAw → AEV1.7390.2070.387 (AEV)0.3860.382
EnAw → SEC1.7390.0650.301 (SEC)0.3000.291
EnAw → GGE1.7390.2320.461 (GGE)0.4600.456
EnAw & TaxIn → AEV1.0530.005
EnAw & TaxIn → SEC1.0530.022
EnAw & TaxIn → GGE1.0530.004

Source: Field data using SMART PLS 4(2026).

5.2.3 Path coefficient analysis

Bootstrapping with 5,000 subsamples and BCa confidence intervals was used. All direct paths were significant. t-values exceeded 1.96, and confidence intervals did not include zero (see Table 5). Tax incentives had a positive effect on adoption of EVs (H1 β = 0.210, SD = 0.030, t = 7.093, p < 0.001, CI 0.142–0.278), sustainable energy consumption (H2 β = 0.286, SD = 0.032, t = 8.880, p < 0.001, CI 0.213–0.358), and perceived greenhouse gas emissions (H3 β = 0.267, SD = 0.027, t = 9.915, p < 0.001, CI 0.195–0.340). H1 and H2 were supported. H3 was not supported as hypothesized. A positive effect was found, while a negative effect was hypothesized.

Table 5. Path coefficients, significance, confidence intervals, and moderation.

HypothesisPathβ (O)STDEVt-statisticp-value95% BCa CI lower95% BCa CI upperRemarks
Direct effect
H1TaxIn → AEV0.2100.0307.093<0.0010.1420.278Supported
H2TaxIn → SEC0.2860.0328.880<0.0010.2130.358Supported
H3TaxIn → GGE0.2670.0279.915<0.0010.1950.340Not supported
H4EnAw → AEV0.4700.02816.653<0.0010.4020.538Supported
H5EnAw → SEC0.2800.0309.448<0.0010.2090.351Supported
H6EnAw → GGE0.4670.02419.558<0.0010.3980.536Not supported
Moderating effect
H7EnAw × TaxIn → AEV0.0520.0222.4160.0160.0100.094Supported
H8EnAw × TaxIn → SEC−0.1140.0205.615<0.001−0.169−0.059Supported
H9EnAw × TaxIn → GGE−0.0420.0192.1810.029−0.080−0.004Supported

Source: Field data using SmartPLS 4 (2026).

Environmental awareness positively influenced adoption of EVs (H4 β = 0.470, SD = 0.028, t = 16.653, p < 0.001, CI 0.402–0.538), sustainable energy consumption (H5 β = 0.280, SD = 0.030, t = 9.448, p < 0.001, CI 0.209–0.351), and perceived greenhouse gas emissions (H6 β = 0.467, SD = 0.024, t = 19.558, p < 0.001, CI 0.398–0.536). H4 and H5 were supported. H6 was not supported as hypothesized. The interaction of environmental awareness and tax incentives was significant for adoption of EVs (H7 β = 0.052, SD = 0.022, t = 2.416, p = 0.016, CI 0.010–0.094), sustainable energy consumption (H8 β = −0.114, SD = 0.020, t = 5.615, p < 0.001, CI −0.169 to −0.059), and perceived greenhouse gas emissions (H9 β = −0.042, SD = 0.019, t = 2.181, p = 0.029, CI −0.080 to −0.004).

Figure 3 shows the simple slopes at mean plus/minus 1 SD. The effect on adoption of EVs increased from β = 0.158 at low environmental awareness to β = 0.262 at high environmental awareness. This supports H7 and indicates enhancing moderation. The effect on sustainable energy consumption decreased from β = 0.400 to β = 0.172 and on perceived greenhouse gas emissions from β = 0.309 to β = 0.225. This supports H8 and H9 and indicates attenuating moderation.

Simple slopes of tax incentives at levels of environmental awareness. Conditional effects at mean ±1 SD of moderator. Interaction coefficients: H7 *β* = 0.052, *p* = 0.016; H8 *β* = −0.114, *p* < 0.001; H9 *β* = −0.042, *p* = 0.029. Calculated from SmartPLS 4.
Figure 3. Simple slopes of tax incentives at levels of environmental awareness. Conditional effects at mean ±1 SD of moderator. Interaction coefficients: H7 β = 0.052, p = 0.016; H8 β = −0.114, p < 0.001; H9 β = −0.042, p = 0.029. Calculated from SmartPLS 4.

5.2.4 Model fitness

The model’s fit indices are presented in Table 6. The estimated model’s standardized root mean square residual (SRMR) value is 0.079, indicating acceptable fit, where SRMR<0.08 indicates good fit (Hu and Bentler, 1999), compared to the saturated model’s SRMR of 0.052. Normed Fit Index (NFI) values are 0.870 (saturated) and 0.857 (estimated), indicating reasonable fit (Bentler and Bonett, 1980). The chi-squared values are 3,163.989 (saturated) and 3,471.417 (estimated), indicating a significant difference between models. The estimated model provides a reasonable representation of construct relationships, with a slight decrease in fit indices indicating that removal of non-significant paths did not substantially deteriorate fit (Hair Jr et al., 2017).

Table 6. Summary of model fit.

Fit indexSaturated modelEstimated model
SRMR0.0520.079
d_ULS0.9542.184
d_G0.3240.369
Chi-square3,163.9893,471.417
NFI0.8700.857

Source: PLS 4 Output (2026).

6 Discussion

Tax incentives were positively associated with adoption of EVs (β = 0.210, p < 0.001, 95% BCa CI [0.142, 0.278], f2 = 0.040), supporting H1. In line with social cognitive theory (Bandura, 1986), external conditions were associated with behavioral intention. Among urban commercial operators recruited at union hubs in Accra, Tema, Kumasi, and Takoradi, lower perceived upfront cost was associated with higher intention to adopt. The result aligns with prior evidence on financial incentives and transport choices (Sierzchula et al., 2014; Langbroek et al., 2016). The small effect size indicates limited practical relevance. Model fit was marginally acceptable (SRMR = 0.079, NFI = 0.857), and R2 = 0.387 indicates moderate explanatory power with substantial unexplained variance. For SDG 7, the association reflects perceived cost reduction rather than reduced fuel import dependence in this sample. Linking duty waivers to verified use in public fleet operations warrants further evaluation.

Building on adoption intention, tax incentives were positively associated with sustainable energy consumption (β = 0.286, p < 0.001, CI [0.213, 0.358], f2 = 0.065), supporting H2. The association is consistent with research on incentives and conservation (Abrahamse et al., 2005). For SDG 7, demand-side measures were associated with reported energy-saving practices. Given continued reliance on thermal generation in Ghana, gains in consumption behavior may not correspond to emission reduction without supply-side reform. The use of tax revenue to support solar charging at union hubs is suggested for future study. In contrast to expected emission benefits, tax incentives were positively associated with perceived greenhouse gas emissions (β = 0.267, p < 0.001, CI [0.195, 0.340], f2 = 0.073), contrary to H3. Construct captured belief about emissions, not metered data, and therefore cannot be interpreted as evidence of increased actual emissions. The pattern is discussed as perception-based rebound (Sorrell and Dimitropoulos, 2008), which is weaker than behavioral or measured rebound. Perceived cost reduction was associated with higher perceived driving intensity. For SDG 13, the association indicates that fiscal measures that facilitate vehicle use were linked to higher perceived emissions where the grid remains carbon-intensive. The relationship requires examination with measured emission data.

With regard to personal factors, environmental awareness was positively associated with adoption of EVs (β = 0.470, p < 0.001, CI [0.402, 0.538], f2 = 0.207), supporting H4. Medium effect size indicates a larger association than fiscal measures in this sample. Observational learning and self-efficacy are treated as SCT background not directly measured. For SDG 7 and SDG 13, awareness was associated with higher adoption intention among urban unionized operators. Extending this line to consumption behavior, environmental awareness was positively associated with sustainable energy consumption (β = 0.280, p < 0.001, CI [0.209, 0.351], f2 = 0.065), supporting H5. The association suggests awareness was linked to conservation practices despite infrastructure constraints. However, environmental awareness was positively associated with perceived greenhouse gas emissions (β = 0.467, p < 0.001, CI [0.398, 0.536], f2 = 0.232), contrary to H6. Construct measured perceived emissions. The association points to an awareness-action gap, where heightened concern was linked to higher perceived emissions when alternatives remain limited. Awareness campaigns without concurrent infrastructure and grid measures were associated with increased emission awareness rather than lower emissions.

Concerning interactive effects, environmental awareness moderated the association between tax incentives and adoption of EVs (β = 0.052, p = 0.016, CI [0.010, 0.094], f2 = 0.005), supporting H7. The positive coefficient indicates that the association between incentives and adoption was stronger at higher awareness. Simple slope results at Mean ±1 SD are shown in Figure 3. The effect is small in practical terms. Furthermore, environmental awareness moderated the association between tax incentives and sustainable energy consumption (β = −0.114, p < 0.001, CI [−0.169, −0.059], f2 = 0.022), supporting H8. The negative sign indicates that higher awareness was associated with lower reliance on incentives for conservation. Finally, environmental awareness moderated the association between tax incentives and perceived greenhouse gas emissions (β = −0.042, p = 0.029, CI [−0.080, −0.004], f2 = 0.004), supporting H9. Negative moderation indicates awareness attenuated the positive association between incentives and perceived emissions. Very small effect size indicates limited practical importance.

Collectively, adoption of EVs, sustainable energy consumption, and perceived greenhouse gas emissions were modeled as parallel outcomes. The study did not test whether adoption leads to lower emissions or higher sustainable consumption. This scope limitation is noted, with mediation left for future research using longitudinal and metered data. Findings are analytically transferable to urban, union-based operators in similar corridors, not to Ghana as a whole.

6.1 Theoretical implications

Social cognitive theory was applied as framing for examining associations among personal, environmental, and behavioral factors in adoption intentions among urban commercial operators recruited at union hubs in Accra, Tema, Kumasi, and Takoradi. Perceived tax incentives were treated as an environmental factor, environmental awareness as a personal factor, and adoption intention, sustainable energy consumption, and perceived greenhouse gas emissions as behavioral outcomes. Self-efficacy and observational learning were treated as SCT background not directly measured, which limits claims to an SCT-informed examination rather than a full SCT test. Environmental awareness and tax incentives were positively associated with adoption intentions, consistent with the SCT proposition that personal and environmental factors are associated with intention where infrastructure remains limited. The effect for awareness (β = 0.470, f2 = 0.207) indicated a medium association, while tax incentives (β = 0.210, f2 = 0.040) indicated a small association. R2 = 0.387 suggests moderate explanatory power with substantial variance unexplained.

Positive associations of both predictors with perceived greenhouse gas emissions, measured as belief rather than metered emissions, point to a boundary condition. Perception-based rebound is weaker than behavioral or measured rebound and cannot be interpreted as evidence of increased actual emissions. Pattern links SCT to awareness-action gap literature: Where alternatives are constrained, heightened awareness was associated with higher perceived emissions. Moderation was small in magnitude. Environmental awareness moderated the association between tax incentives and adoption intentions (β = 0.052, p = 0.016, CI [0.010, 0.094], f2 = 0.006). A positive sign indicates the association was stronger at higher awareness, not weaker, correcting the earlier interpretation. Simple-slope analysis at −1SD, mean, and +1SD is presented in Figure 3. Moderation for sustainable consumption (β = −0.114, f2 = 0.018) and perceived emissions (β = −0.042, f2 = 0.003) indicated higher awareness was associated with lower reliance on incentives. Model fit was marginally acceptable (SRMR = 0.079, NFI = 0.857), qualifying fit as moderate rather than good. Adoption, sustainable consumption, and perceived emissions were modeled as parallel outcomes. Direct or mediated links among them were not tested, stated as an explicit scope limitation.

6.2 Managerial and policy implications for sustainability

Findings apply to urban, union-based operators in corridors studied and are not generalizable to Ghana as a whole. Result-supported associations and propositions requiring further evaluation are separated. Result-supported: Among sampled operators, perceived tax incentives and environmental awareness were associated with adoption intentions and reported conservation practices. Small effect sizes for fiscal measures indicate limited practical significance. Requiring further evaluation: For SDG 7 and SDG 13, sequencing awareness evaluation before fiscal measures warrants study. Union-based awareness programs in four cities could be assessed prior to scaling incentives. Linking incentives to usage data for commercial fleets and directing EV-related revenue toward solar charging infrastructure are proposed for further evaluation rather than as result-supported recommendations. Monitoring for perception-based rebound is suggested, with recognition that data do not support claims about actual emissions. Cross-sectional design precludes causal inference.

6.3 Limitations and suggestions for further studies

Cross-sectional design precludes causal inference over time; the language is associational. Multi-stage purposive-convenience sampling at union hubs is a non-probability method; findings are analytically transferable to similar urban contexts, not statistically generalizable to the broader driver population or rural areas. The perceived emissions measure captures beliefs, not metered emissions, and cannot support claims of actual emission increase. Self-efficacy and observational learning were background, not measured. Model fit was marginally acceptable, and 60–70% of the variance in outcomes remains unexplained. Common method bias was examined via Harman single-factor and common latent factor; the results are presented in the Results section. Future research should use longitudinal designs, test mediation among adoption, sustainable consumption, and measured emissions, include self-efficacy and infrastructure access as moderators, and compare patterns across countries. Appendix A with source items, adaptation notes, and final items is provided to improve replicability.

7 Conclusion

The study examined factors associated with EV adoption intentions among 800 commercial drivers and vehicle owners recruited at union hubs in four Ghanaian cities. Tax incentives and environmental awareness were modeled as parallel predictors of adoption intentions, sustainable energy consumption, and perceived emissions. Both predictors showed positive associations with adoption intentions and sustainable consumption, and negative associations with perceived emissions measured as beliefs. Environmental awareness strengthened the link between incentives and adoption intentions, while weakening the links between incentives and both consumption and perceived emissions. Effect sizes were small to medium, model fit was marginally acceptable, and explanatory power was moderate. The model did not test whether adoption actually reduces emissions or increases sustainable consumption. Fiscal and informational measures will need to be paired with infrastructure and grid reform to produce net gains, a claim that requires testing with metered data. Findings are limited to urban, unionized operators in the four corridors studied. A sequenced approach of awareness, targeted incentives, and infrastructure is suggested for future testing in similar urban settings linked to SDG 7 and SDG 13.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsuep.2026.1930955/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Vlado Vivoda, The University of Queensland, Australia

Reviewed by: Sri Yogi Kottala, Symbiosis International University, India

Nur Khairiel Anuar, Universiti Utara Malaysia, Malaysia

Eka Septiarini, Universitas Jenderal Achmad Yani, Indonesia

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics approval

The studies involving humans were approved by Ethics Committee of the University of Professional Studies, Accra. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

ED: Writing – original draft, Conceptualization, Writing – review & editing, Methodology. SA: Writing – review & editing, Data curation, Software, Formal analysis.

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Citation

Duncan, E. A. & Affran, S. (2026). Accelerating Ghana’s sustainable transport transition: the role of electric vehicles in achieving SDG 7 and SDG 13. Frontiers in Sustainable Energy Policy, Volume 5. https://doi.org/
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5 - 2026

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Copyright © 2026 Duncan and Affran.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Ebenezer Arthur Duncan

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