Transport infrastructure, economic growth, and transport CO₂ emissions nexus: Does green energy consumption in the transport sector matter?

Abstract

Attaining Sustainable Development Goals (SDGs) is important to control the adverse impacts of climate change and achieve sustainable development. Among the 17 SDGs, target 13 emphasizes enhancing urgent actions to combat climate-related changes. This target is also dependent on target 7, which advocates enhancing access to cheap alternative sustainable energy. To accomplish these targets, it is vital to curb the transport CO₂ emissions (TCO₂) which increased by approximately 80% from 1990 to 2019. Thus, this study assesses the role of transport renewable energy consumption (TRN) in TCO₂ by taking into consideration transport fossil fuel consumption (TTF) and road infrastructure (RF) from 1970 to 2019 for the United States (US) with the intention to suggest some suitable mitigation policies. Also, this study assessed the presence of transport environmental Kuznets curve (EKC) to assess the direction of transport-induced growth. The study used the Bayer-Hanck cointegration test which utilizes four different cointegration techniques to decide cointegration along with the Gradual Shift causality test which considers structural shift and fractional integration in time series data. The long-run findings of the Dynamic Ordinary Least Squares (DOLS) test, which counters endogeneity and serial correlation, revealed that the transport renewable energy use mitigates as well as Granger causes TCO₂. However, transport fossil fuel usage and road infrastructure enhance TCO₂. Surprisingly, the transport EKC is invalid in the case of the US, and increased growth levels are harmful to the environment. The association between TCO₂ and economic growth is similar to a U-shaped curve. The Spectral Causality test revealed the growth hypothesis regarding transport fossil fuel use and economic growth connection, which suggests that policymakers should be cautious while decreasing the usage of transport fossil fuels because it may hamper economic progress. These findings call for revisiting growth strategies and increasing green energy utilization in the transport sector to mitigate transport emissions.

Introduction

Transportation is necessary for the growth of every sector of the economy since the movement of goods and people is dependent on transportation (Wang et al. 2019). TCO₂ reached unprecedented levels of 8.25 (billion) metric tons in 2019 recording a massive increase of approximately 80% from 1990 to 2019. Even though TCO₂ was reduced by 12% due to the COVID-19 outbreak in 2020, transport emissions levels are expected to rebound in the upcoming years (STATISTA 2022a). Every year, there are more freight and people to move than in the preceding years; consequently, an increasing number of vehicles emit hazardous emissions into the environment leading to climate change. Thus, understanding the dynamics of TCO₂ is essential for achieving climate-related targets and reducing CO₂ emissions.

The transportation sector is a major driver of fossil fuel combustion since various modes of transportation, particularly road transport, consume petroleum and gas as the main sources of energy (IEA 2019). Along with the economic growth, the demand for transportation has escalated, which in turn increased passenger and freight transportation (Sharif et al. 2019b). Hence, passenger and freight transport accounts for approximately 50% of the growth in global travel (Ahmed et al. 2020). Since these modes of transport are enormously dependent on fossil energy use, road sector transportation activities generate around 74% of TCO₂ by consuming approximately 93% of petroleum for its energy needs (IEA 2019).

Considering the increasing contribution of transportation in fossil fuel usage, it is important to move towards green energy usage in the transport sector to achieve the Sustainable Development Goals (SDGs). SDG 13 (climate action) is unlikely to achieve without a transition to clean energy in the transport sector and accelerating actions regarding SDG 7 (clean and sustainable energy). Recently, in the COP26 held in Glasgow, world leaders admitted that net zero emissions in the transport sector are a necessity since road transport is responsible for almost 50% of global oil consumption. Moreover, net zero emissions in the transport sector are impossible without an energy transition in the transport sector and the introduction of new vehicle technologies (UN 2021).

Despite the necessity to decarbonize the transport sector through the energy transition, previous literature mostly neglects the long-run impacts of transport green energy utilization on TCO₂ and the mutual causal dynamics between TCO₂ and transport sector renewable energy usage. Understanding the association between the aforesaid factors is necessary for effective TCO₂ mitigation policies. In addition, it is also relevant to understand whether the future growth patterns align with the environmental commitment and targets of the transport sector. In this context, generally, the connection between economic growth and CO₂ is analyzed using the EKC concept, which indicates that growth beyond a threshold level promotes environmental quality by encouraging innovation, modern technology, effective environmental regulations, and green energy transition (Sinha et al. 2020; Ahmed et al. 2022; Caglar and Mert 2022a; Caglar and Ulug 2022b). Thus, economic growth is subject to deterioration in environmental quality only at a lower level of growth due to the priority for production at the cost of the environment and weak environmental laws (Ali et al. 2022; Sharif et al. 2022; Sofuoğlu and Kirikkaleli 2022). The favorable impacts of growth at higher levels of growth compared to the adverse environmental impacts of growth at lower levels of growth form the EKC (inverted U-shaped relation) between environmental deterioration and growth (Khan et al. 2020; Caglar et al. 2022a; Jian and Afshan 2022).

Against this discussion, this work explores the role of transport green energy consumption in TCO₂ in the US including transport fossil fuel consumption, trade openness, and road infrastructure following the EKC framework. The reason for selecting the US transport sector for this study is based on the fact that the US is the country with the biggest TCO₂ emissions followed by China and India. The US produced a total TCO₂ of 1.56 gigaton (Gt) in 2020 while the pre-pandemic average TCO₂ was over 1.7 Gt per year (STATISTA 2022a). In 2018, the US road transportation generated around 1.76 metric tons (mt) of TCO₂ which was higher than the combined road sector TCO₂ of India, Russia, China, and Japan (STATISTA 2022b). The US predominantly uses fossil energy (approximately 95%) in its transportation sector and the contribution of renewables is just around 5% of total transport energy as indicated in Fig. 1 (EIA 2022).

Fig. 1.

Transport fossil fuel and renewable energy usage (Trillion Btu) in 2019

Despite the high proportion of fossil energy in transportation, the USA is increasing the green energy in the transport sector over the year to reduce TCO₂ (Fig. 2). However, the current use of renewables is not sufficient to curb the growing TCO₂ levels (Caglar et al. 2022b); thus, it becomes essential to understand the relationship between transport sector variables and TCO₂ as well as the causal dynamics of transport variables with economic growth and TCO₂ for effective sustainable transportation policies.

Fig. 2.

Trends of transport renewable energy consumption in the USA

Against this background, this paper studies the impacts of transport green energy consumption on transport sector CO₂ emissions. This is an important contribution because the effects of transport green energy consumption on TCO₂ are largely unexplored in the past transportation literature. This research also explores the transport EKC and focuses on directing policies in the context of SDGs. Evidently, studies on transport EKC are very limited, and also the SDG focus of the study will enhance the generalizability of results and policies for other economies since all countries are required to follow the SDGs. Most of the previous studies on TCO₂ do not focus on SDGs, particularly SDG 7. Moreover, this investigation considers important factors, such as transport fossil fuel consumption, trade openness, and road infrastructure in the model for interesting results and comprehensive policies. The study aims to explore the cointegration, causality, and long-run relationship among the selected series. As it is immensely important to choose a suitable methodology for reliable results, this study uses the Fourier Toda-Yamamoto causality since this test considers structural shift and fractional integration, which can significantly impact the time series data. The Bayer and Hanck (2013) test used in the study is a reliable cointegration test as it combines four different cointegration tests and provides reliable outcomes. Besides, the long-run coefficients are computed by using the DOLS technique, which produces long-run elasticities accounting for endogeneity and serial correlation, addressing small sample bias, and accommodating variables with uniform or mixed order of integration. Thus, the idea of the paper along with reliable methodology will enable it to provide trustworthy outcomes and impactful environmental policies.

Literature review

There is an extensive body of literature on the transportation-CO₂ emissions nexus. Previous works mostly suggest that growth in the transport sector is highly correlated with transportation energy consumption, TCO₂, and environmental deterioration. Numerous macro- and micro-level studies have investigated drivers of TCO₂; for instance, Xu and Lin (2015) re-examined China’s TCO₂ drivers by taking provincial-level data. The results supported the EKC hypothesis by finding a nonlinear association between CO₂ and growth, whereas private vehicles and energy consumption revealed a U-shaped connection with TCO₂.

In contrast, Azlina et al. (2014) studied the influence of growth and TFF on CO₂ in Malaysia. The results showed the invalidity of the EKC hypothesis. Furthermore, they noted a significant positive impact of TFF, but a negative effect of industry value-added on CO₂ emission. Likewise, Ben Abdallah et al. (2013) use data from 1980 to 2010 to investigate the relationship between RF, transport value-added, fuel prices, road sector energy, and emissions in Tunisia. The results revealed a causality from fuel prices to road transport energy, mutual causality between transport emissions, transport value-added, and RF, and the absence of the EKC hypothesis. Their study further suggested that the policymakers can manage the issue of road transport emissions in Tunisia by focusing on the reinforcement of legislation, relocating production units, urban transport, and improving vehicles’ fuel efficiency in the long run. Ahmed et al. (2020) established that fuel prices are not correlated with Indian TCO₂; however, industrialization, RF, economic growth, and energy use in the road sector boost TCO₂. Likewise, in an early work, Wang et al. (2019) revealed that urbanization, RF, and road transport energy usage increase TCO₂ in Pakistan.

In another study, using data from 1990 to 2006, Mraihi et al. (2013) explored the causes of high TFF in Tunisia. The finding of the study portrayed that income, vehicle intensity, fuel intensity, road transportation, and urbanization are the main drivers of energy use in Tunisia. However, in Pakistan’s context, Danish et al. (2018) studied the linkages between GDP, urbanization, foreign direct investments, TFF, and TCO₂ by applying the ARDL and VECM approaches from 1990 to 2015. Interestingly, they disclosed that both urbanization and GDP do not affect transport CO₂ emissions, while transport emissions were significantly influenced by energy consumption and FDI. Using similar methods, Danish and Baloch (2017) revealed that transport energy use does not affect sulfur dioxide (SO₂); however, road infrastructure and urbanization levels expand the emissions of SO₂ in Pakistan. Shahbaz et al. (2015) studied transport energy, RF, and CO₂ emissions nexus in Tunisia. The results indicated that both road transport energy and transport infrastructures degrade environmental quality, while fuel prices improve the environment. However, in a recent panel study, Danish et al. (2019) conclude that RF does not affect TCO₂ in OECD countries, while income and population density are major contributors to TCO₂ in OECD countries.

By using the ARDL approach on data from 1971 to 2014, Rasool et al. (2019) examined the relationship between economic growth, population density, oil prices, road transport energy, and TCO₂ in Pakistan. The finding indicates that oil prices mitigate TCO₂. However, the increases in energy intensity, RF, and population concentration raise TCO₂. Surprisingly, they noted a negative relationship between income and TCO₂. In Malaysia’s setting, Chandran and Tang's (2013) empirical work found that road transportation causes high energy consumption and CO₂ emissions. For Europe, Cary and Ahmed (2022) established that passenger and heavy-duty vehicle standards for vehicle emissions are effective in controlling the oxides of nitrogen (NO_x). Thus, emissions standards for vehicles can be modified to reduce NO_x and improve human health.

In China’s context, Cai et al. (2012) estimated China’s transport sector CO₂ emissions at national and regional levels, and the finding of their work disclosed that Chinese TCO₂ increased higher than the IEA estimates. China’s TCO₂ emissions were 7% higher and road sector CO₂ emissions were 37% higher than the estimated value of IEA. He et al. (2005) probed the oil consumption-CO₂ emissions nexus in China’s road transport context during the period 1997–2002, and the results portrayed that road transport is one of the biggest oil consumers and high emitter in China. Furthermore, they added that China needs to introduce vehicle fuel economy standards to control the significant increase in oil consumption in the transport sector. Using the Wavelet method, Umar et al. (2020) indicated that innovation and transportation exacerbate environmental degradation in China, while financial development tends to mitigate emissions. Using quantile ARDL, Sharif et al. (2020a) disclosed that transportation and economic growth escalate CO₂ emissions in Malaysia. However, the study of Umar et al. (2020) and Sharif et al. (2020a) focused on overall country-level emissions. In another study, Sharif et al. (2019b) found a strong correlation between transport service and economic growth in the US. Nevertheless, they did not focus on the environmental effects of transportation.

In the Indian context, Schipper et al. (2009) estimated CO₂ from the land transport sector of India using four different scenarios. The estimated results revealed that the sustainable urban transport (SUT) scenario will more likely mitigate CO₂ emissions by 30% compared to the BAU (Business as Usual) scenario in the year 2030. In another scenario analysis, Das and Parikh (2004) scrutinized the association between CO₂ emissions and energy use and economic growth in two different sectors of Mumbai and Delhi transportation. The LEAP model’s results showed almost 1.7 times higher growth in transport energy compared to the Business as Usual (BAU) scenario. Furthermore, the estimated results show that there is an increase in emissions by almost 325 percent during the period from 1997 to 2020. Also, Jain and Tiwari (2016) conducted a scenario analysis in three medium-sized cities in India and disclosed that CO₂ emissions can be reduced by improving non-motorized and public transport infrastructure. Besides, Ramachandra and Shwetmala (2009) computed the emissions for different modes of transportation in India, while Singh (2006) and Singh et al. (2008) examined the trend of greenhouse gases in the Indian road transport sector.

The impact of transport renewables on transport emissions is overlooked in the past literature. However, many works revealed the environmental benefits of renewable energy in the context of overall emissions and other environmental indicators. Also, there are some studies relating to the impacts of transport biomass energy use on emissions. For instance, Caglar et al. (2022b) provided that green energy is beneficial to reducing the environmental footprints of the US. The study of Umar et al. (2021) described that fossil energy sources tend to escalate emissions while biomass energy lessens emissions generated from transportation in the USA. Nevertheless, the USA is using green electricity in the transportation sector to a large extent and this study ignored the impact of green energy on TCO₂. Using the data from 74 nations, Sharif et al. (2019a) established that green energy is useful in mitigating pollutant emissions. Likewise, Sharif et al. (2020b) depicted that Turkey’s footprint reduces due to the consumption of green energy. In Asian emerging economies, Khan et al. (2019) found that green logistics boost economic growth and the quality of the environment.

Summing up, the above literature revealed diverse and inconclusive results regarding the impacts of various determinants of transport emissions, such as RF, TFF, urbanization, GDP, fuel prices, etc., on TCO₂. Also, many empirical studies used overall CO₂ for investigating the environmental impacts of transport variables. In past literature, studies on the investigation of transport Environmental Kuznets Curve (EKC) are scant, and renewable energy usage in the transport sector is also overlooked by most scholars. Thus, unlike past works, this study evaluates the impacts of the transport sector’s renewable energy usage along with transport fossil fuel usage, road infrastructure, and trade openness on transport CO₂ in the USA using the EKC framework.

Data and methods

This study assesses the impacts of transport renewable energy use, transport fossil fuel use, economic growth, trade, and road infrastructure on TCO₂ emissions in the US from 1970 to 2019. According to Lin et al. (2021), growth and environmental pollution exhibit an inverted U-shaped relationship (EKC) because unlike early stages of growth, high levels of growth induce green energy use, effective ecological laws, innovation, and advanced technology that lessen atmospheric pollution (Sharif et al. 2020a). An increase in economic growth upsurges transportation and stimulates transport emissions (Danish et al. 2019). In contrast, the rise in income level can reduce transport emissions by means of promoting the affordability of efficient technologies (Rasool et al. 2019). Thus, a quadratic relationship between TCO₂ and growth may hold in the USA. Road transport infrastructure and transport fossil fuel use degrade the environment (Shahbaz et al. 2015). However, reducing the consumption of petroleum products and using more green energy can decrease environmental pollution (Sharif et al. 2019a, 2020b). Trade can stimulate TCO₂ because the import and export of goods escalate transportation activities leading to higher petroleum consumption (Barut et al. 2022). Following these arguments, this study constructs the following models:

$$TC{O}_{2t}={\delta }_o+{\delta }_1{G}_t+{\delta }_2{(G)}_t^2+{\delta }_{3}TF{F}_t+{\delta }_{4}TR{N}_t+{\delta }_{5}R{F}_t+{\mu }_t$$ 1

$$TC{O}_{2t}={\delta }_o+{\delta }_1{G}_t+{\delta }_2{(G)}_t^2+{\delta }_{3}TF{F}_t+{\delta }_{4}TR{N}_t+{\delta }_{5}R{F}_t+{\delta }_{6}TR{D}_t+{\mu }_t$$ 2

Here in Eq. 1, G denotes economic growth (GDP 2015 constant US $ per person), G² depicts the nonlinear form of growth for computing the EKC, TFF denotes transport fossil fuel consumption (kg of oil equivalent per person), TRN depicts transport renewable energy use (kg of oil equivalent per person), and RF represents road infrastructure (km of roads per person). TCO₂ depicts transport CO₂ emissions (metric tons per person), µ is the error term, and δ is the constant. In Eq. 2, TRD (trade percent of GDP) is added for robustness purposes. We checked whether our model is robust to the addition of more variables. The variables are used after transforming into logarithm form except for TRD which is in percentage.

For this study, annual time series data from 1970 to 2019 is collected for the US. The data on TCO₂, TFF, and TRN came from EIA (2022). The datasets for G and TRD are collected from WDI (2021). The data on RF is collected from the United States Department of Transportation (BTS 2022). The period of the research (1970–2019) is constrained due to the availability and appropriateness of the data, particularly for TCO₂, which is our response variable.

Methodology

Structural breaks in time series data can adversely affect the findings of unit root tests, such as PP and ADF. Therefore, the current study used the structural break unit root test of Zivot and Andrews (1992). This technique produces consistent results even in the presence of one unknown structural break in the dataset. Thus, after confirming the I(1) stationary level, we checked cointegration among all variables.

The prior literature offers numerous cointegration tests, for instance, Johansen and Juselius (1990), Johansen (1988), and Engle and Granger (1987). But, varying estimates of these tests as a result of certain limitations cause indecisiveness for researchers pertaining to the selection of a suitable method for analysis (Ahmed and Wang 2019). The Bayer and Hanck (2013) cointegration approach offers a solution for such problems by combining four diverse cointegration approaches. Hence, the findings of this test are more reliable compared to the results generated by individual cointegration tests. The Fisher statistics of the test are calculated by combining the probability values of the Johansen (JOH), Banerjee (BDM), Boswijk (BO), and Engle and Granger (EG) tests as shown in Eqs. 3 and 4.

$$EG-JOH=-2[Ln(Pro{b}_{\mathit{EG}})+Ln(Pro{b}_{\mathit{JOH}})]$$ 3

$$EG-JOH-BO-BDM=-2[Ln(Pro{b}_{\mathit{EG}})+Ln(Pro{b}_{\mathit{JOH}})+Ln(Pro{b}_{\mathit{BO}})+Ln(Pro{b}_{\mathit{BDM}})]$$ 4

The decision on cointegration involves the comparison of Fisher statistics (FS) against the critical values (CV) of Bayer and Hanck (2013). The FS higher than the CV implies cointegration in the model and vice versa.

In the next step, the long-run relationship is estimated using the DOLS method. The DOLS is popular for time series analysis due to its ability to correct issues regarding small sample bias, serial correlation, and endogeneity (Wang et al. 2019). The DOLS is among the very few techniques that offer a higher degree of flexibility and do not restrict the addition of regressors with I(0) integration levels (Masih and Masih 2000).

In the next step, the Gradual Shift causality, which is also known as the Fourier-Toda-Yamamoto causality, is applied. Notably, the Toda and Yamamoto (1995) test for causality was the very first test suitable even for variables with fractional integration, and stationarity testing was not needed for using this test. The Toda and Yamamoto (TY) method uses a vector autoregression (VAR) algorithm and adds the d_max (maximum stationary order) of variables to the K (optimum lag order) for causal analysis. However, the TY test overlooks the chances of structural shifts; therefore, the Gradual Shift test of Nazlioglu et al. (2016) is applied which accounts for structural shifts during causal estimation and considers smooth and gradual shifts. This technique is designed by adding single cumulative frequencies. This test used a modified WALD test that uses TY along with Fourier approximation (Adebayo et al. 2021a).

Results and discussion

The descriptive statistics of TCO₂ along with regressors are provided in Table 1. The maximum (60,836.77) and minimum (25,279.19) values of G indicate that the growth levels expanded during the selected duration. The average growth of 42,478.95 is impressive in the US. The minimum (3.8383) and maximum (120.5539) values of TRN in kg of oil equivalent (kgoe) per capita indicate substantial growth in the use of renewables in the transport sector. However, the mean value of TRN (35.8120 kgoe per person) is still at a very low level.

Table 1.

Descriptive statistics (before log transformation)

	TCO2	G	TFF	TRN	RF	TRD
Mean	6.1700	42,478.95	2185.003	35.8120	0.0242	21.6121
Median	6.1647	41,491.72	2190.019	14.3415	0.0238	21.6635
Maximum	6.7682	60,836.77	2376.828	120.5539	0.0293	30.9559
Minimum	5.5194	25,279.19	1973.944	3.8383	0.0204	10.7572
Std. Dev	0.3306	10,821.35	115.6202	41.4606	0.0030	5.3931
Jarque–Bera	2.3199	3.8589	3.0100	11.0053*	4.2786	1.2608
Probability	0.3135	0.1452	0.2220	0.0041	0.1177	0.5324
Jarq. Bera after taking natural logs
Jarque–Bera	2.2023	3.918455	3.0128	3.8606	4.1798	1.2607
Probability	0.3325	0.140967	0.2217	0.1451	0.1237	0.5324

Variables are converted into natural logs except for TRD which is in percentage form

TCO₂ did not show high fluctuation over the selected period with a minimum value of 5.5194 and a maximum value of 6.7682 metric tons per person. Therefore, the standard deviation of TCO₂ remained very low. Notably, the Jarque–Bera values indicate that all variables except for TRN had a normal distribution; however, after taking the logarithm form, even the TRN exhibited normal distribution. The consumption of fossil fuels in the transport sector of the USA is shown in Fig. 3. The descriptive statistics are also presented in Fig. 4. In the box plots, the dot indicates the mean, the horizontal line within the box shows the median and the upper and lower limits of data show minimum and maximum values.

Fig. 3.

Transport fossil fuel consumption in the USA

Fig. 4.

Box plots of variables (descriptive statistics)

The unit root tests are applied in Table 2. The findings from the PP test indicate that TCO₂, G, TFF, RF, and TRD are nonstationary at I(0). To confirm these outputs, the ADF test is also used. The results indicate that the series is nonstationary at I(0); however, differencing the series made all variables stationary. Thus, TCO₂, G, TFF, RF, and TRD are integrated at the I(1) level.

Table 2.

PP and ADF tests

	PP		ADF
	Levels	Difference	Levels	Difference
TCO2	− 2.4075	− 4.3302*	− 2.7542	− 4.4115*
G	− 1.7812	− 5.1485*	− 2.2393	− 5.2248*
TFF	− 2.4162	− 4.2738*	− 2.7685	− 4.3449*
TRN	− 2.3882	− 3.9456**	− 2.4325	− 3.9047**
RF	− 0.4661	− 8.6006*	0.8778	− 8.6579*
TRD	− 2.8298	− 7.5296*	− 2.7644	− 7.2586*

* and ** illustrate 1% and 5% significance.

Trend and intercept are added

However, this study has utilized time-series data which is often subject to structural breaks, and these unit root tests are not robust in the case of structural breaks. Hence, it is necessary to use a method that could consider potential breaks in data. Thus, the ZA test is also used in the study for reporting stationary levels along with structural break years. The application of the ZA test confirmed most of these findings. As indicated in Table 3, the series has an integration level of I(1) and only G (economic growth) is stationary at levels. ZA test revealed some structural breaks in data and most of the breaks overlap with the timing of the financial crises of 2007–2008. This structural break in TCO₂ is considered during the analysis by adding a dummy variable for 2008 in the long-run equation. This essential unit root inspection enabled us to continue towards the cointegration analysis for model 1 and model 2.

Table 3.

ZA test

	I(0)		I(1)
	t-stat	BR.Year	t-stat	BR.Year
TCO2	− 4.8059	2008	− 5.1817**	2008
G	− 4.8890***	2008	− 5.8732*	2008
TFF	− 5.5933*	2008	− 5.076**	2008
TRN	− 4.1615	2006	− 5.026**	2003
RF	− 3.2594	2005	− 9.8996*	1992
TRD	− 2.9861	2009	− 7.0995	1981

*, **, and *** show 1%, 5%, and 10% significance

Checking the cointegration is the prerequisite for the long-run investigation. Hence, the Bayer-Hanck test is utilized in Table 4 to analyze the possibility of cointegration in our models. The value for EG-JOH (fisher statistics) was 55.5273 for model 1 and 55.7393 for model 2. These values are higher than the 1% levels of critical values. Likewise, EG-JOH-BO-BDM statistics based on four tests revealed high values of 62.9489 and 111.0014 for models 1 and 2, respectively. These values are also more than the 1% critical values, and thus, this test shows strong evidence of cointegration in the estimated models.

Table 4.

Bayer-Hanck cointegration test

	Test stats
	EG-JOH	EG-JOH-BO-BDM	Cointegration
Model 1 (TCO2/G, G2, TFF, TRN, RF)	55.5273*	62.9489*	✓
Model 2 (TCO2/G, G2, TFF,TRN, RF, TRD)	55.7393*	111.0014*	✓
Critical values	EG-JOH	EG-JOH-BO-BDM
1% level (model 1)	15.701	29.85
1% level (model 2)	15.348	29.544

* illustrates 1% significance

The results in Table 5 illustrate that G poses a negative influence on TCO₂ while G² poses a positive influence on TCO₂. This finding discloses a 0.6771% reduction in TCO₂ associated with a 1% rise in G² while a 0.0366% uplift in TCO₂ connected with a 1% upsurge in growth. Thus, this association resembles a U-shaped curve rather than the EKC (inverted U-shaped connection). This output opposes the findings of Kharbach and Chfadi (2017) who conclude the validity of transport EKC in Morocco. This outcome is also against the result of Rasool et al. (2019) who suggest that rising growth levels limit TCO₂ by promoting efficient transport technology. Nevertheless, this result indicates the absence of the transport EKC in the context of the US and the investigations of Ben Abdallah et al. (2013) for Tunisia and Azlina et al. (2014) for Malaysia also disclosed that the transport EKC does not exist. This upshot unfolds that economic growth in the context of TCO₂ is not heading in a desirable direction. Thus, a rise in growth will not reduce emissions in the transport sector rather TCO₂ will intensify in the upcoming years due to the increase in growth and the related utilization of fossil fuels. This requires revisiting the growth strategies taking into consideration the overwhelming impacts of growth on the environment through transportation activities.

Table 5.

DOLS results (long-run) model 1

	Coeff	t-stat	Prob
G	− 0.6771*	− 2.9924	0.0075
G2	0.0366*	3.3888	0.0031
TFF	0.9660*	47.4878	0.0000
TRN	− 0.0043***	− 1.8133	0.0856
RF	0.0974**	2.3096	0.0323
C	− 2.1638***	− 1.7797	0.0911
D08	− 0.0043	− 1.6039	0.1252
R-squared	0.9994
Ad. R-squared	0.9987

* (1% significance),** (5% significance), and *** (10% significance)

The utilization of fossil fuels in the transport sector intensifies TCO₂ as indicated by the positive coefficient of TFF. As discussed before, transport fossil fuel combustion accounts for almost 95% of total transport energy use in the USA, and fossil fuel combustion deteriorates the environment by generating CO₂ (EIA 2022). Consequently, the US transport sector has become the leading sector in terms of CO₂ emissions due to the massive utilization of fossil fuels (STATISTA 2022b). This upshot aligns with the conclusion of Azlina et al. (2014), Ahmed et al. (2020), and Shahbaz et al. (2015) for Malaysia, India, and Tunisia, respectively. This result is also supported by the conclusions of Umar et al. (2021), who unfolded a positive connection between TFF and emissions from the transport sector in the USA.

The utilization of green energy in the transport sector is decreasing TCO₂. TCO₂ levels reduce by around 0.0043% as a result of a 1% uplift in the use of TRN in the US. Renewable energy in the transport sector of the US is mainly comprised of green electricity consumption and biomass energy, and according to our analysis, green energy is limiting the TCO₂ levels although its impact is lower than the increase in TCO₂ associated with the consumption of fossil fuels. The possible reason for this could be a comparatively lower use of TRN in the US. This is a unique outcome as previous studies have overlooked the long-run impacts of TRN in the transport sectors. However, this notion is supported by many studies that report the environmental benefits of renewable energy and its negative connection with the overall CO₂ (Apergis and Payne 2014; Shahbaz et al. 2019; Adebayo et al. 2021b; Tang et al. 2022). Based on this finding, the USA can design strategies for the energy transition in transport to move towards the achievement of SDGs 7 and 13. This outcome is sensible since green energy helps to reduce the overdependence on conventional sources of energy, such as coal, oil, and gas. In addition, green sources of energy do not or hardly ever generate environmental pollution. Thus, the harmful emissions generated from the transport sector can be reduced by enhancing the consumption of green electricity and biofuels including ethanol, methanol, bio-crude, biodiesel, and methane.

Road infrastructure is also stimulating TCO₂ in the USA. Around 0.0974% escalation in TCO₂ connected with a 1% rise in the RF is shown in Table 5. This is not unexpected because the US road transport sector generated 1.76 mt of TCO₂ in 2018 which was more than the combined road sector TCO₂ of India, Russia, China, and Japan. Road transport contributes the majority of TCO₂ in the US because vehicles for passengers and freight predominantly consume fossil fuels (STATISTA 2022b). This output matches with the verdicts of Shahbaz et al. (2015) and Ahmed et al. (2020) for Tunisia and India, respectively. Further, the dummy variable included for the structural break of 2008 in TCO₂ is insignificant. After this, it is important to conduct the robustness analysis by including trade openness in model 2.

As indicated in Table 6, the inclusion of TRD in the model did not change the main findings of the research. The negative impact of TRN and positive impacts of TFF, and RF on TCO₂ are consistent with the previous table. Also, the U-shaped connection between G and TCO₂ is consistent with the outcomes of model 1. Furthermore, TRD escalates TCO₂ in the US suggesting that the import and export of products increase transportation activities resulting in more energy use and higher TCO₂. This effect conforms to the findings of Barut et al. (2022) for E7 nations. In addition, the studies of Jawad et al. (2017) for Pakistan and Destek et al. (2016) for selected European countries disclosed positive impacts of TRD on overall CO₂. Trade involves buying and selling goods and services, which indeed increases transportation activities in a country. Thus, it is reasonable to expect an increase in the consumption of fossil fuels as a result of increased levels of trade. Although trade can bring green technology, the massive transportation activities from the trade of goods and services and resulting emissions surpass the possible technique effects of trade in the transport sector. Consequently, international trade escalates emissions in the transport sector. Overall, the output in Table 6 illustrates that the estimates of this research are robust.

Table 6.

Robustenss check (model 2)

Variables	Coeff	T-Stat	Prob
G	− 0.7554**	− 2.1700	0.0477
G2	0.0467**	2.8766	0.0122
TFF	0.8310*	11.91319	0.0000
TRN	− 0.0149**	− 2.2075	0.0445
RF	0.3656*	3.1438	0.0072
TRD	0.0022*	3.0061	0.0094
C	− 0.4539	− 0.2424	0.8120
D08	− 0.0102	− 1.7174	0.1079
R-squared	0.9992
Ad. R-squared	0.9974

* (1% significance),** (5% significance), and *** (10% significance)

Next, the Gradual Shift method is applied for estimating the causal directions. The results in Table 7 illustrate that G, TFF, RN, and TRD Granger cause TCO₂. Thus, strategies for these regressors can influence TCO₂ in the US. We also estimated the causalities of variables with economic growth for better policymaking. Regarding fossil fuel usage in the transport sector, the causality from TFF to G illustrates the growth hypothesis which implies that the US should be very careful while designing energy conservation plans because the growth of the US economy is dependent on fossil fuel usage in the transport sector. The causality from G to TRN suggests that increasing growth levels are favorable to renewable energy use in the transport sector. Also, growth Granger causes RF implying that the rise in growth level positively boosts road infrastructure. As there is no causality from RF to growth, it implies that the USA can concentrate on developing and extending other suitable transport infrastructures rather than road infrastructure keeping in view their environmental impacts without the fear of a negative influence on economic growth.

Table 7.

Gradual shift causality

	Fourier	Wald Stat	Prob
G to TCO2	4	14.0658*	0.0071
TCO2 to G	4	1.6608	0.7978
TFF to TCO2	1	8.8666***	0.0645
TCO2 to TFF	1	6.5949	0.1589
TRN to CO2	2	9.8089***	0.0809
TCO2 to TRN	2	4.4628	0.4849
RF to TCO2	3	20.7895*	0.0077
TCO2 to RF	3	7.6878	0.4645
TRD to TCO2	1	35.1304*	0.0000
TCO2 to TRD	1	4.6103	0.4653
TFF to G	4	13.8885*	0.0077
G to TFF	4	2.2529	0.6894
TRN to G	1	1.31085	0.9338
G to TRN	1	11.9837**	0.0350
RF to G	4	5.0650	0.4080
G to RF	4	9.4498***	0.0924
TRD to G	4	10.1978***	0.0698
G to TRD	4	2.5061	0.7756

*, **, and *** denote 1%, 5%, and 10% significance

Conclusion and policy suggestions

This study assessed the presence of transport environmental Kuznets curve and the long-run effects of TCO₂ by using the data from the USA from 1970 to 2019 including several important drivers of TCO₂, such as fossil fuel usage in transport, trade openness, and road infrastructure. The findings revealed that the EKC in the US transport sector does not exist. Increases in growth expand TCO₂ in the long run rather than reducing it and thus, a U-shaped curve between TCO₂ and growth holds. However, green energy consumption in transport limits TCO₂ and boosts environmental quality. The consumption of fossil fuels in the transport sector increases TCO₂ and decreases the quality of the environment. Likewise, road infrastructure and trade intensify TCO₂. In causal outcomes, TRN, TFF, and G Granger cause TCO₂. The growth hypothesis is found in the context of economic growth and TFF. In addition, economic growth Granger causes green energy without any feedback.

Three important findings require careful environmental strategies from the US government. First, the direction of economic growth should be corrected by modifying policies since higher growth has adverse repercussions for environmental quality. Second, the growth hypothesis entails that limiting TFF might hinder development in the USA. Third, even though TRN mitigates TCO₂, the casualty from growth to TCO₂ implies that growth should be further accelerated, and if the growth discourages, TRN investments might be affected. Thus, actions regarding climate change and mitigation (SDG 13) require boosting technology levels to enhance energy efficiency so that efficient technology could produce more output using less TFF. This will require less fossil fuels without interrupting the economic growth of the US economy. Notably, expansion in growth will facilitate investments in green energy because causality is from green energy to growth. Subsidies for green energy adoption, for instance, tax reliefs and lower prices of electric vehicles can stimulate the reduction of TCO₂ and limit emissions from transport. In addition, vehicles with hybrid engines may help to reduce the levels of TCO₂ in the USA by consuming less fossil fuels. Policymakers can offer low taxes and other benefits to facilitate the production of electric and hybrid engine vehicles.

Strategies should be formed and lucrative benefits should be offered to produce biomass-based transport fuels, such as methanol, ethanol, bio-crude, biodiesel, and methane, which can help to replace fossil energy in the long run. Current transport infrastructure boosts TCO₂ in the USA; in this context, rail and subway-based services should be extended which will be helpful to reduce the burden on freight and passenger vehicle that contribute heavily to TCO₂. In the vehicle categories, bus-based public transport should be improved further to decrease private vehicle ownership. In addition, non-motorized transport can be encouraged further so that people could prefer bicycles for short distances. To do so, companies should be encouraged to provide shared bike services by offering certain tax benefits. Also, media campaigns can be launched aiming at promoting the health and environmental benefits of non-motorized transport. This will help to make non-motorized transport more popular and attractive. Trade is stimulating TCO₂ in the USA, and thus, the government may focus on the trade of green products, that require fewer energy resources during their production and use, to overcome this problem. In addition, certain restrictions and duties on international trade keeping in view the environmental implications of trade will help to reduce the harmful effects of trade. The strategies discussed above will help to combat climate change and attain SDGs 13 and 7.

This study used a time series analysis on the role of green energy consumption in the transport sector and TCO₂ connection by using a relatively small sample consisting of data from 1970 to 2019. Hence, considering the small dataset, the study only included a few transport-related variables in the model. In addition, the aggregated fossil fuel consumption from the transport sector was used in the model. This presents a useful opportunity for future studies to collect data for developing or developed countries and conduct a panel study by considering some more transport-related variables in the model. Apart from this, the disaggregated transport fossil fuel energy can also be used in the model to assess the separate environmental impacts of each source of transport energy.

Author contribution

JD: Conceptualization; methodology; writing original manuscript; RA: Writing original manuscript; formal analysis SA: Writing, reviewing, and editing ZA: Writing; editing; reviewing; supervision; help in analysis; corrections; administration MSM: Review; conclusion; editing; data curation.

Data availability

Data analyzed in this study can be accessed free of cost from the links provided in the paper.

Declarations

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Competing interests

The authors declare no competing interests.