The Impact of Public–Private Partnership Investments in Transport on CO₂ Emissions in East Asian and Pacific Regions: A VAR Model

Abstract

This paper investigates investments in public–private partnerships within the transport sector and their impact on CO₂ emissions from transportation in the East Asian and Pacific regions. It explores the relationships between CO₂ emissions from transportation, investment in public–private partnerships, renewable energy, research and development, and economic growth. The study employs a unit root test, a cointegration model, and a vector autoregressive model, covering the period from 1990 to 2023. The findings suggest that public–private partnerships in the transport sector can reduce CO₂ emissions through adequate investment. Furthermore, renewable energy diminishes CO₂ emissions by regulating them and concurrently producing fewer emissions. Environmental research and development help reduce CO₂ emissions in the transport sector, while economic growth contributes to an increase in transport sector CO₂ emissions. These findings provide critical insights for policymakers, underscoring the necessity for a systematic reallocation of public–private partnership investments in the transport sector to reduce CO₂ emissions and enhance environmental sustainability in East Asian and Pacific regions.

1. Introduction

Public–private partnerships (PPPs) have emerged as a prominent global standard for financing and managing transport infrastructure, signaling a pivotal shift in how nations approach their transit systems [1]. Countries worldwide increasingly rely on private sector investments to enhance and modernize critical components such as roads, rail networks, and mass transit systems. These collaborations often promise a range of practical and economic benefits, including improved efficiency and innovation in infrastructure development. However, a significant and unresolved concern persists: the effectiveness of these partnerships in reducing CO₂ emissions. The transport sector is highly recognized as a significant contributor to CO₂ emissions due to its substantial dependence on fossil fuels for energy needs [2]. This reliance poses considerable risks to the environment and human health [3]. As a result, international organizations have adopted proactive strategies to manage and reduce CO₂ emissions, aiming to protect environmental integrity. The United Nations established the Sustainable Development Goals (SDGs) to be achieved by 2030. Specifically, SDGs 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action) directly address the need for sustainable transport infrastructure and emission reductions [4].

On one side of the debate, some scholars posit that PPPs can serve as a sustainable transport model. They argue that these collaborations have the potential to integrate cutting-edge green technologies and renewable energy systems, thereby fostering a more environmentally friendly approach to transport infrastructure [2,5,6,7]. These proponents emphasize the importance of sustainability in urban development and the role of private investment in driving greener innovations. The adoption of environmentally friendly energy sources is essential for any nation seeking to attain sustainable long-term economic growth, particularly due to the rising levels of CO₂ emissions [8]. Renewable energy sources serve as an alternative to fossil fuels since they generate much reduced CO₂ emissions and enhance environmental quality [5]. This enhances ecological integrity; hence, renewable energy is essential for achieving sustainable development objectives.

Conversely, a faction of researchers, including [9,10,11], raises critical points regarding the fundamental design and objectives of PPPs. They caution that, in practice, these partnerships can inadvertently contribute to increased CO₂ emissions. Factors such as heightened car dependency and the expansion of new suburban developments often exacerbate environmental concerns. This dichotomy highlights the complex nature of PPPs, underscoring the need for further investigation into their actual impact on sustainability and emissions in the realm of transport infrastructure.

Figure 1 illustrates investments in PPPs in the transport sector for the East Asian and Pacific regions from 1990 to 2023 (East Asian and Pacific regions include American Samoa; Australia; Brunei Darussalam; Cambodia; China; Fiji; French Polynesia; Guam; Hong Kong SAR, China; Indonesia; Japan; Kiribati; Korea, Dem. People’s Rep.; Korea, Rep.; Lao PDR; Macao SAR, China; Malaysia; Marshall Islands; Micronesia, Fed. Sts.; Mongolia; Myanmar; Nauru; New Caledonia; New Zealand; Northern Mariana Islands; Palau; Papua New Guinea; Philippines; Samoa; Singapore; Solomon Islands; Thailand; Timor-Leste; Tonga; Tuvalu; Vanuatu; and Vietnam). The figure shows fluctuations from 1990 to 2015. However, starting in 2016, investment in PPPs increased sharply until 2023, with only one noticeable drop in 2021, which may be attributed to the impact of the COVID-19 pandemic. This rising trend shows increased reliance on PPPs for funding transport infrastructure, reflecting the government’s openness to private sector involvement and investors’ growing trust in the long-term viability of these initiatives.

Multiple factors must be evaluated in light of the preceding authors’ assertions. What is the role of PPP investment in the CO₂ emissions of the transport sector? Prior research has assessed public–private partnership spending concerning total CO₂ emissions. Limited research [6,7] has evaluated the impact of PPP investment on CO₂ emissions from the perspective of the transportation sector. Secondly, does the application of environmental technology aid in reducing CO₂ emissions from the transport sector in East Asian and Pacific regions? The current research lacks a definitive comprehension of the relationship between environmentally related technologies and CO₂ emissions from transport. One cohort of researchers [3,13] identified a positive correlation, whereas another cohort [14,15,16] identified a negative correlation. Third, the East Asian and Pacific regions are emerging leaders in renewable energy. What is the significance of economic policy uncertainty and renewable energy in the East Asian and Pacific regions’ pursuit of sustainable development goals?

This paper makes several contributions to the existing literature. First, it provides an empirical analysis of the relationship between PPP investments and CO₂ emissions in the transport sector, filling a critical gap. While prior studies have examined transport-related CO₂ emissions, limited research has specifically investigated how PPP investments influence transport sector CO₂ emissions, particularly in rapidly developing regions [6,7]. This absence of sectoral and regional analysis represents a significant gap in the literature, which this paper aims to address. Second, this paper employs an innovative econometric approach, utilizing unit root tests, cointegration analysis, and vector autoregressive (VAR) modeling. This analysis explores relationships and interactions between PPP investments, renewable energy, R&D, and transport emissions, covering the period from 1990 to 2023. Renewable energy and technological innovation are crucial factors in mitigating emissions within the transportation sector and can contribute to achieving sustainable development objectives. Consequently, economic growth assumes a more pivotal role in formulating effective policies, especially in environmental, financial, and energy perspectives.

The findings of this paper have significant policy implications for achieving sustainable transport systems. As governments in the East Asian and Pacific regions continue to rely heavily on PPP financing for infrastructure development, understanding the environmental consequences of these investments is crucial for designing effective climate policies. The research will inform policymakers on how to structure PPP contracts to incentivize green technology adoption, establish environmental performance standards, and create mechanisms for monitoring and reducing transport emissions.

The next sections of this paper are structured as follows: Section 2 offers a succinct summary of the relevant literature. Section 3 of the research includes data collection and the methodology. Section 4 presents an overview of the empirical findings and their subsequent analysis. Section 5 of the paper delineates the closing observations and underscores the policy ramifications.

2. Literature Review

PPPs have emerged as a major instrument of transport infrastructure funding all around the globe. PPPs offer the potential to finance transportation projects without placing financial pressure on governments while also utilizing the network of private capital and expertise. However, their CO₂ emission impacts are also debatable. PPPs have been shown to help the world transition to less carbon-intensive transport by using mass transit, electricity, and renewables [1,11]; however, there is also evidence of road-based PPPs causing an increase in emissions [7,9].

Adding to the PPP frameworks, larger structural pressures play a crucial role in influencing transport emissions. Emissions tend to increase in the early stages of development, but later emissions and economic growth become decoupled according to the Environmental Kuznets Curve (EKC) [17,18]. The third factor is demographic change, which influences transportation demand and carbon intensity and is linked to population growth, urbanization, and aging [19,20]. In the meantime, technological innovation and renewable energy are able to mitigate this set of drivers for energy transition, and recent research has demonstrated the use of PPPs in advancing energy transition through charging infrastructure, solar hubs, and digital transport technologies [21,22].

The analysis is based on the Environmental Kuznets Curve EKC hypothesis; however, the mechanism by which PPP expenditures affect CO₂ emissions necessitates further conceptual clarification. Public–private partnerships can influence emissions via multiple interrelated mechanisms. Initially, enhancing the financial framework of PPPs draws private capital and fosters cost-effectiveness in infrastructure development, facilitating more investment in energy-efficient transportation systems [1,23]. Secondly, PPPs promote the adoption of technology by incorporating private sector innovation and managerial proficiency, resulting in the implementation of greener technologies such as electric buses, intelligent logistics, and renewable-energy-powered transit [24,25]. Third, improved institutional quality and governance frameworks within PPP agreements can boost project accountability, regulatory adherence, and environmental performance. These channels collectively influence the conversion of PPP investments into reduced emissions in the transport industry [6,7].

Figure 2 elucidates these interactions by depicting a conceptual framework that outlines the causal sequence from PPP investment to emission outcomes via financing, technology, and institutional channels, along with their interplay with renewable energy, research and development, and economic growth. This paradigm facilitates the integration of theoretical foundations with empirical investigation by demonstrating how PPPs function as a multidimensional mechanism within the EKC environment.

The studies synthesized in this review are divided into three parts: PPPs, economic growth, and renewable energy. The section ends with an extended research gap, with each section finishing with a hypothesis that directly informs our multi-regression time series analytical approach.

2.1. Public–Private Partnerships

The environmental implications of PPPs depend on the design of a specific project and the current policy context, and they differ greatly. In the study of PPP-based transport investments in Pakistan from 1990 to 2020 by Ali, Hashmi, Habib, and Kirikkaleli [11], the Autoregressive Distributed Lag (ARDL) methodology is applied, and the results suggest that this kind of investment is supported by renewable-energy-based, well-regulated projects, and the emission rate reduces significantly. This finding supports the financing structure mechanism, where PPP contract design determines environmental outcomes. While Raghutla and Kolati [26] found that PPP investments in India improved renewable energy production efficiency, they noted that emissions could increase when applied to the road transport sector. According to Shahbaz, Raghutla, Song, Zameer, and Jiao [9], PPP energy investment led to increased CO₂ emissions in China unless accompanied by technological innovation. This demonstrates the technology adoption pathway mechanism within our theoretical framework. Likewise, Guo, Chen, and Feng [1] observed that investment in low-carbon transport, and specifically PPP transport investment, enhanced low-carbon development in major cities of China, but the effect differed with institutional quality. This variation illustrates the institutional quality effects mechanism proposed in our study.

In analyzing U.S. highway PPPs, Rouhani and Niemeier [27] stated that PPPs behave differently when property rights and regulatory frameworks vary, and indicated that PPPs reduce emissions under some regulatory regimes but increase emissions when it is otherwise. Liu, Anwar, Irmak, and Pelit [6] confirmed asymmetric impacts that when PPPs are used in isolation, they have an increasing effect on emissions but are negative when combined with environmental innovation in terms of reducing emissions. In the case of South Africa, as shown by Udeagha and Ngepah [28], renewable PPP helped to decrease emissions, whereas fossil fuel PPP investment aggravated them. Based on these empirical findings demonstrating heterogeneous PPP impacts across different transport modes and regulatory contexts, the hypothesis states that there are heterogeneous impacts of PPPs in transport infrastructures on CO₂ emissions.

2.2. Economic Growth

Adebayo [29] established in Japan that globalization and energy use stimulated growth at the expense of rising CO₂ emissions. Minh et al. [30] have studied Vietnam, showing that FDI, urban citizens, and renewable energy have developed a growth–emissions nexus. In terms of the decoupling progress of China, Han et al. [31] demonstrated that it was not complete yet, but getting better in two dimensions. The growth of the economies of 284 Chinese cities, as identified by Zhao et al. [32], resulted in a rise in transport emissions; however, each region had a relative decoupling level.

Spanning both the ten biggest emitters, Chen et al. [33] found that structural drivers of decoupling seemingly are global, whereas Fu et al. [34] showed there were mixed findings in the Yangtze River Economic Belt in China. Wang and Su [18] used a sample of 192 countries and demonstrated the EKC dynamics. Wu, Tam, Shuai, Shen, Zhang, and Liao [17] presented evidence of the EKC at the provincial level in China. In a cross-nation study of 15 leading economies, Saidi and Omri [35] showed that renewables/growth were negatively intertwined in terms of emission production. Lastly, Shafique et al. [36], in the case of Hong Kong, Singapore, and South Korea, highlighted that freight transport was an important factor associated with the growth of emissions. Drawing from the extensive EKC literature showing initial emission increases followed by decoupling at higher income levels, the hypothesis suggests that transport-related CO₂ emissions increase with economic growth.

2.3. Renewable Energy

According to Sroufe and Gopalakrishna-Remani [37], U.S. transport PPPs combined with renewable energy put sustainability and firm performance in place. Research by Wang and Ke [38] examined a Chinese case of a PPP in the energy sector charging up electric vehicles, demonstrating that PPPs speed up the expansion of the infrastructure. Zhang, Zhao, Xin, Chai, and Wang [21] employed system dynamics in designing the pricing of the PPP charging infrastructure for EVs. Liu and Wei [39] used fuzzy TOPSIS in the assessment of risks in PPP charging projects, whereas Wu et al. [40] utilized a three-dimensional risk framework. Zhang et al. [41] utilized DEMATEL to formulate the risk to PPP charging, and Zhang et al. [42] used VIKOR to select partners in the private sector. Huang, Lin, Lim, Zhou, Ding, and Zhang [22] applied the evolutionary college to determine how incentives can be used to facilitate PPP cooperation in EV charging.

Wen et al. [43] examine South Asia and the Pacific and conclude that PPP investment increased the ecological footprint, and renewable energy mitigated part of these impacts. PPP energy investment was found by Cheng et al. [44] to aggravate emissions in China, and energy productivity mitigated the same. The authors found that renewables reduce emissions in India, too [10]. Shahbaz, Raghutla, Song, Zameer, and Jiao [9] demonstrated that energy projects based on the PPP approach in China increased emissions unless technological innovation was incorporated. In the case of Pakistan, Liu, Tang, and Liu [14] found that PPP energy investment amplified the ecological footprint, and technological innovation counteracted the results. Lastly, Anwar, Sharif, Fatima, Ahmad, Sinha, Khan, and Jermsittiparsert [7] also revealed that PPP transport investment in China boosted emissions by large margins in most quantiles. Consistent with studies showing renewable energy’s mitigating effects on PPP-related emissions, the hypothesis proposes that the share of renewable energy and technology in PPP transport projects decreases the number of CO₂ emissions.

2.4. Research Gap

In recent years, a growing body of evidence has emerged from studies on PPPs. However, there are still some issues of crucial missing information. First, the majority of research is country-specific (e.g., Pakistan, China, India, and South Africa), and generalizability is, therefore, limited [6,9,11,26]. Unlike these single-country analyses, this paper employs a unique approach for East Asia and the Pacific, providing a comprehensive sectoral and regional analysis of the impact of PPP investments on transport sector CO₂ emissions and addressing the limitations of single-country analyses. Comparative work is essential for capturing the full range of institutional, geographic, and socioeconomic factors that influence PPP outcomes. Secondly, the empirical record is primarily cross-sectional, with few scholars monitoring the long-term evolution of the environmental effects of PPPs over decades. This paper fills this critical gap by employing an innovative econometric approach, utilizing unit root tests, cointegration analysis, and VAR modeling from 1990 to 2023 to analyze the interactions between PPP investments, renewable energy, R&D, and transport emission trends. This oversight hinders our ability to assess whether PPPs can achieve lasting reductions in emissions.

Third, the aspect of technological innovation has been poorly researched. Most PPP studies emphasize financial risk and pricing [40,41] while neglecting the impact of digital technologies on emission outcomes. Although recent studies mention AI, ICT, and smart grids [8,45], they often treat these as secondary rather than critical factors, which hinders the scalability of innovations. This paper uniquely integrates R&D as a proxy for technological innovation, offering empirical evidence for the technology adoption pathway mechanism.

Considering these constraints, future studies should be cross-country and longitudinal in nature, as well as interdisciplinary in approach, to examine how technological innovation and policy development can work together to influence normative behavior. This holistic approach will provide a deeper insight into the overall effect of coordinating PPPs, economic growth, and the adoption of renewable energy on transport-related CO₂ emissions. Taken together, these gaps make it apparent that country-based studies are needed that examine PPP financing, demographics, the uptake of renewable energy, and digital innovation within a combined framework of CO₂ emissions in the transport sector.

3. Data and Methodology

3.1. Data

To analyze the impact of investments through public–private partnerships in transport on CO₂ emissions from transport, this study utilizes annual time series from the World Bank’s World Development Indicators (WDIs), covering the period from 1990 to 2023. The timeframe was selected based on data availability, providing a sufficiently long period to capture meaningful trends and facilitate thorough analysis. This paper assumes that carbon emissions from transport (CO₂) are influenced by public–private partnership investment in transport (PPP), renewable energy (RE), research and development (R&D), and economic growth (GDP). This analysis is based on the available annual time series data for the East Asia and Pacific region. A description of the dependent variable and the explanatory variables is provided in

Table 1. Variable explanations.

Variable	Variable Name	Definition
CO2	CO2 emissions	Carbon dioxide emissions from the transport sector (million tons)
PPP	Public–private partnerships	Public–private partnerships investment in the transport sector (current USD)
RE	Renewable energy consumption	Percentage of total final energy consumption
RD	Research and development expenditure	Percentage of GDP
GDP	Economic growth	Annual percentage growth

.

Before presenting the econometric analysis,

Table 2. Descriptive Statistics.

Variable	Obs	Mean	Std. Dev.	Skewness	Kurtosis	Min	Max	Jarque–Bera *
Ln CO2	33	3.0347	0.16666	−0.2724	1.7904	2.7275	3.2498	0.2875
Ln PPP	33	9.6791	0.4487	0.5683	2.4085	8.9320	10.5178	0.3125
Ln RE	33	1.2394	0.1152	0.2862	1.3821	1.1060	1.4221	0.1655
Ln RD	33	0.3666	0.0221	0.7660	3.2687	0.3355	0.4281	0.1803
Ln GDP	33	0.6505	0.2069	−3.5712	18.0523	−0.3532	0.8813	0.7652

Source: Author calculations. * Estimated p-value.

summarizes the descriptive statistics and normality diagnostics, including skewness, kurtosis, and the Jarque–Bera tests. Each variable consists of 33 time series observations from 1990 to 2023 for countries in the East Asia and Pacific region. The near-zero skewness values indicate that the data are approximately symmetric, broadly supporting the normality assumption. According to the Jarque–Bera test findings shown in Table 2, the null hypothesis of normality cannot be rejected for any series since all variables have p-values higher than the 5% significance level. This suggests that all the variable distributions are roughly normal. This conclusion is further supported by the mild kurtosis and near-zero skewness readings, which show that the data are reasonably symmetric.

Table 3. Correlation matrix.

Variable	Ln CO2	Ln PPP	Ln RE	Ln RD	Ln DPG
Ln CO2	1.0000
Ln PPP	0.6878	1.0000
Ln RE	−0.9480	−0.5669	1.0000
Ln RD	0.7827	0.5407	−0.7417	1.0000
Ln GDP	0.1406	0.0173	−0.2003	0.1878	1.0000

Source: Author calculations.

presents the Pearson correlation matrix employed to evaluate potential multicollinearity among the variables. Most correlation coefficients are below ±0.80, suggesting no serious concern in the VAR estimation. As simple correlations may not fully capture multicollinearity, especially in VAR models, variance inflation factors (VIFs) are also examined in

Table 4. Variance inflation factors.

Variable	VIF	1/VIF
Ln PPP	1.56	0.639148
Ln RE	2.42	0.413465
Ln RD	2.41	0.414520
Ln GDP	1.19	0.839503
Mean VIF	1.90

Source: Author calculations.

. The VIF values range from 1.19 to 2.42, with a mean of 1.90, all well below the conventional thresholds of 5 or 10. These results confirm that multicollinearity is not a significant issue in the model estimation.

3.2. Methodology

This paper investigates the relationships between CO₂ emissions from transport, investment in public–private partnerships, renewable energy, research and development, and economic growth in East Asia and the Pacific regions for the period 1990 to 2023.

$${{CO}_2}_t=f\left({PPP}_t,{RE}_t.{RD}_t,{GDP}_t\right)$$

(1)

$${{CO}_2}_t={\beta }_0+{\beta }_1{PPP}_t+{\beta }_2{RE}_t+{\beta }_3{RD}_t{+\beta }_4{GDP}_t+{\epsilon }_t$$

(2)

In Equation (2), CO₂, PPP, RE, RD, and GDP illustrate CO₂ emissions and public–private partnerships in transport, renewable energy, research and development, and economic growth. Furthermore, ${\beta }_0$ shows that the constant term, ${\beta }_i$ (i = 1,…,4), displays the coefficient estimates for the explanatory variables, while t and ε represent time and error terms, respectively.

This paper applies various time series techniques to investigate the relationships between CO₂ emissions from transport, investment in public–private partnerships, renewable energy, research and development, and economic growth in East Asia and the Pacific regions. The logarithmic form of the model from Equation (2) is

$$ln{{CO}_2}_t={\beta }_0+{\beta }_1{lnPPP}_t+{\beta }_2{lnRE}_t+{\beta }_3{lnRD}_t{+\beta }_{4}ln{GDP}_t+{\epsilon }_t$$

(3)

This study performed diagnostic tests, including correlation analysis, the ARDL bounds test, and unit root testing, prior to applying the ARDL model. First, the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests are applied to determine whether the time series data are stationary or non-stationary [46].

ADF is presented as follows:

$$∆Y_t={\alpha }_0+{\beta }_1{Y}_{t-1}+{\sum }_{j=1}^{pj}{\gamma }_j{∆Y}_{t-\mathrm{j}}$$

(4)

The test reveals a mix of stationary and non-stationary variables. Thus, an Autoregressive Distributed Lag (ARDL) bounds test is conducted to assess cointegration among the variables Pesaran and Shin [47] and Pesaran et al. [48]. ARDL is presented as follows:

$$\begin{array}{c}\Delta ln{{CO}_2}_t=\alpha +{\sum }_{i=1}^p{\gamma }_i{∆{CO}_2}_{t-\mathrm{i}}+{\sum }_{i=0}^q{\varnothing }_i{∆\mathrm{P}\mathrm{P}\mathrm{P}}_{t-\mathrm{i}}+\hfill \\ { {\sum }_{i=1}^r{\theta }_i{∆\mathrm{R}\mathrm{E}}_{t-\mathrm{i}}{\sum }_{i=1}^s{\omega }_i{∆\mathrm{R}\mathrm{D}}_{t-\mathrm{i}} {\sum }_{i=1}^u{\delta }_i{∆\mathrm{G}\mathrm{D}\mathrm{P}}_{t-\mathrm{i}}+{\mathsf{\lambda }}_1{CO}_2}_{t-1}{+ {\mathsf{\lambda }}_{2}PPP}_{t-1}{+ {\mathsf{\lambda }}_{3}RE}_{t-1}+\hfill \\ { {\mathsf{\lambda }}_{4}RD}_{t-1}{+ {\mathsf{\lambda }}_{5}GDP}_{t-1}+{\epsilon }_t\hfill \end{array}$$

(5)

where the coefficient λ captures long-run relationships. In the analysis, this test fails to reject the null of no long-run relationship among the variables (the computed F-statistic falls below the lower critical bound). The results show no evidence of cointegration. Therefore, a variance autoregressive (VAR) model is utilized to explain the interdependence and dynamic relationships among carbon emissions from transport, which are influenced by public–private partnership investment in transport, renewable energy, research and development, and economic growth [49]. The dependent variable is CO₂, while the explanatory variables are PPP, RE, R&D, and GDP. Furthermore, the Granger causality test is employed to examine the direction of causality among the variables. The VAR model is expressed as follows:

$$\begin{array}{c}{∆Ln{CO}_2}_t={\mathsf{\beta }}_0+{\sum }_{i=1}^{\mathrm{n}}{\mathsf{\beta }}_1{Ln{CO}_2}_{t-i}+{\sum }_{i=1}^{\mathrm{n}}{\mathsf{\beta }}_{2}Ln{\mathrm{P}\mathrm{P}\mathrm{P}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{i=1}^{\mathrm{n}}{\mathsf{\beta }}_{3}Ln{\mathrm{R}\mathrm{E}}_{\mathrm{t}-\mathrm{i}}+\hfill \\ {\sum }_{i=1}^{\mathrm{n}}{\mathsf{\beta }}_{4}Ln{\mathrm{R}\mathrm{D}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{i=1}^{\mathrm{n}}{\mathsf{\beta }}_{5}Ln{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{t}-\mathrm{i}}+{\epsilon }_t \hfill \end{array}$$

(6)

The VAR framework is especially advantageous for this paper as it encapsulates the dynamic interaction among economic growth, energy investment, and CO₂ emissions without necessitating stringent exogeneity of regressors. It allows us to track the transmission of shocks to PPP investment, renewable energy, and R&D through transport emissions over time. The ensuing study of the variance decompositions elucidates the short-term and feedback effects within the system. This paper concentrates on region-level time series instead of a panel of countries, thus streamlining the modeling process into a singular multivariate time series framework [50]. Figure 3 depicts the flowchart of the analytical methodologies employed in the study to assess the dynamic impacts of carbon emissions from the transport sector and public–private partnership investments in transport, renewable energy, research and development, and economic growth in East Asia and the Pacific.

4. Results and Discussion

4.1. Results

The first set of results examined the time series stationarity of each variable by using a unit root test [46]. The Augmented Dickey–Fuller and Phillips–Perron tests indicate the presence of a unit root at the level and stationarity in initial differences, while GDP was stationary at the level. Ultimately, the unit root tests indicate that the majority of variables exhibit stationarity in first differences. The findings on the stationarity of the variable are presented in

Table 5. Unit root tests.

Variable	ADF		PP
	I (0)	I (1)	I (0)	I (1)
Ln CO2	−2.208	−4.635 ***	−2.502	−4.560 ***
Ln PPP	−1.154	−6.264 ***	−0.924	−6.463 ***
Ln RE	−1.447	−2.653 *	−1.248	−2.669 *
Ln RD	−2.396	−10.740 ***	−2.190	−11.477 ***
Ln GDP	−4.864 ***	−7.645 ***	−4.846 ***	−9.718 ***

Source: Author calculations. Note: ***, and * denote 1%, and 10% significant levels, respectively.

.

Consequently, the Autoregressive Distributed Lag (ARDL) bound test has been employed to investigate the existence of cointegration among the time series variables. This test facilitates the estimation of a cointegrating relationship between the mixed-order integrated variables. The paper utilizes the ARDL bound testing approach, as proposed by Pesaran and Shin [47] and Pesaran, Shin, and Smith [48], to examine the long-term equilibrium between CO₂, PPP, RE, RD, and GDP. The results of the ARDL bound test are presented in

Table 6. ARDL bound test.

F-Bounds Test		Null Hypothesis: No Level Relationship
Test statistic	Value	Significance	I0	I1
Value of F statistic	1.348	At 10%	2.45	3.52
K	5	At 5%	2.86	4.01
		At 2.5%	3.25	4.49
		At 1%	3.74	5.06

Source: Author calculations.

. The findings indicate that the projected F-statistic value of 1.348 is lower than the upper bound critical value of 5.06. This signifies the absence of the cointegration relationship among the variables at a 1% significance level.

To conduct the vector autoregressive model and then analyze the direction of causation, it is essential first to determine the lag duration. The technique for selecting the proper lag duration is predicated on the following criteria: LR—sequential modified LR test statistic; Hannan–Quinn information criterion; SC—Schwarz information criterion, with each test at the 5% significance level. This method is essential, as the findings may become biased if an unsuitable lag time is used [51]. The selection result demonstrates that the optimal lag time is three, as seen in

Table 7. VAR lag order selection criteria.

Lag	LogL	LR	FPE	AIC	HQ	SC
0	151.829		1.3 × 10−11	−10.8763	−10.8049	−10.6363
1	274.581	245.5	9.7 × 10−15	−18.1171	−17.689	−16.6773 *
2	303.671	58.181	8.8 × 10−15	−18.4201	−17.6352	−15.7804
3	343.65	79.957 *	5.5 × 10−15 *	−19.5296 *	−18.3879 *	−15.6901

Source: Author calculations. Note: * the lag order selected by the criterion.

.

Upon determining the suitable lag length, the results of the VAR model are reported in

Table 8. VAR model.

Variables	Ln CO2		Ln PPP		Ln RE		Ln RD		Ln GDP
Variables	Coefficient	p-Value	Coefficient	p-Value	Coefficient	p-Value	Coefficient	p-Value	Coefficient	p-Value
Ln CO2 (−1)	0.916 ***	0.000	−1.841	0.669	−0.084	0.658	−0.041	0.810	11.309 ***	0.000
Ln CO2 (−2)	−0.459 *	0.087	0.837	0.866	−0.472 **	0.030	0.264	0.178	18.163 ***	0.000
Ln CO2 (−3)	0.465 **	0.015	3.542	0.315	−0.497 ***	0.001	−0.127	0.364	7.456 ***	0.001
Ln PPP (−1)	−0.003	0.739	0.470 ***	0.002	0.015 **	0.023	0.006	0.349	0.235 **	0.012
Ln PPP (−2)	−0.007	0.509	−0.204	0.281	0.019 **	0.024	−0.002	0.782	−0.358 ***	0.002
Ln PPP (−3)	−0.025 *	0.049	0.131	0.570	0.012	0.235	0.003	0.724	−0.644 **	0.000
Ln RE (−1)	0.068	0.748	9.131 **	0.019	1.153 ***	0.000	−0.331 **	0.031	1.657	0.482
Ln RE (−2)	−0.179	0.590	−19.036 ***	0.002	−0.164	0.541	−0.056	0.817	−5.166	0.165
Ln RE (−3)	−0.049	0.819	9.195 **	0.020	−0.109	0.530	0.359 **	0.021	3.322	0.166
Ln RD (−1)	−0.084	0.731	−9.705 **	0.031	0.0318	0.876	−0.303 *	0.088	−4.710 *	0.084
Ln RD (−2)	−0.310	0.148	−12.545 ***	0.002	0.324	0.062	0.244	0.118	0.541	0.822
Ln RD (−3)	−0.015	0.953	−7.952 *	0.094	−0.183	0.379	−0.266	0.156	5.948 **	0.039
Ln GDP (−1)	0.008	0.632	0.647 **	0.026	−0.003	0.833	−0.010	0.381	−0.575 ***	0.001
Ln GDP (−2)	0.020	0.106	0.130	0.564	−0.017 *	0.082	−0.015 *	0.098	−0.100	0.467
Ln GDP (−3)	0.007	0.503	0.196	0.690	−0.009	0.284	−0.007	0.365	0.266 *	0.025
C	0.959	0.001	9.642	0.058	−0.0210	0.925	0.174	0.387	5.899	0.056
R2	0.99	-	0.82	-	0.99	-	0.83	-	0.81	-

Source: Author calculations. Note: ***, **, and * denote 1%, 5%, and 10% significant levels, respectively.

. All estimated coefficients can be interpreted as short-run elasticities, given that the variables are expressed in natural logarithms. A high value of the adjusted R² indicates that the model is a good fit. It indicates that 89% of the variation in the dependent variable is collectively influenced by the explanatory variables. The coefficients indicate that the lags in transport CO₂ emissions are significant at the 5% level, suggesting that the current value of CO₂ emissions is influenced by its historical values. PPP is shown to have a negative correlation with CO₂ emissions in the third lag, suggesting that CO₂ emissions are reduced by PPP investment in the transport sector. The empirical research conducted by Anwar, Sharif, Fatima, Ahmad, Sinha, Khan, and Jermsittiparsert [7] for China; Liu, Anwar, Irmak, and Pelit [6] for India; and the authors of [52] for different Asian countries corroborates this finding robustly. The short-run findings for RE reveal that an increase in RE would lead to a decrease in pollution emissions in East Asia. Renewable energy sources produce significantly less pollution than nonrenewable energy resources, such as fossil fuels. The utilization of renewable energy in the transport sector primarily contributes to a reduction in CO₂ emissions within the transport sector. These findings align with those of Saidi and Omri [35] for 15 OECD countries and Mehmood [53] for G-11 nations.

The RD has an adverse impact on CO₂ emissions, indicating that research and development related to the environment, including electric vehicles and intelligent transportation systems (ITSs), may substantially decrease CO₂ emissions from the transportation sector. Nonetheless, these results lack statistical significance. This suggests that environmentally related research in East Asia and the Pacific remains in the developmental stage and requires more time to confirm its positive effect on CO₂ emissions. The results align with Danish et al.’s [54] study on OECD countries and Kwilinski et al.’s [55] study involving 24 EU countries. Finally, the GDP exhibits a statistically significant positive correlation with CO₂ emissions, signifying that an increase in GDP corresponds with a rise in transportation emissions. The findings align with the assertions of Anwar, Sharif, Fatima, Ahmad, Sinha, Khan, and Jermsittiparsert [7]; Dai, Alvarado, Ali, Ahmed, and Meo [2]; and Alnour et al. [56], who argue that heightened economic growth leads to an escalation in CO₂ emissions from the transport sector.

Table 9. Granger causality analysis.

Variables	Ln CO2	Ln PPP	Ln RE	Ln RD	Ln GDP
Ln CO2	-	6.8778 *	14.708 ***	7.9514 **	37.52 ***
		(0.076)	(0.002)	(0.047)	(0.000)
Ln PPP	6.7409 *	-	20.354 ***	1.0218	48.898 ***
	(0.081)		(0.000)	(0.796)	(0.000)
Ln RE	6.8019 *	10.082 **	-	13.81 ***	2.2574
	(0.078)	(0.018)		(0.003)	(0.521)
Ln RD	2.2297	14.577 ***	6.5473	-	5.4888
	(0.526)	(0.002)	(0.088)		(0.139)
Ln GDP	3.0883	5.0229	4.0732	4.0049	-
	(0.378)	(0.170)	(0.254)	(0.261)
ALL	21.167 **	29.08 ***	37.836 ***	31.507 ***	110.89 ***
	(0.048)	(0.004)	(0.000)	(0.002)	(0.000)

Source: Author calculations. Note: ***, **, and * denote 1%, 5%, and 10% significant levels, respectively.

and Figure 4 below display the results of the Granger causality analysis, indicating that the null hypothesis indicates no causal relationship between the variables. A bidirectional correlation exists between PPP investment in the transport sector and CO₂ emissions, indicating that PPP not only alleviates CO₂ emissions, along with elevated transport emissions, but is also a contributing factor. These findings align with the research conducted by Liu, Anwar, Irmak, and Pelit [6] in India and Anwar, Sharif, Fatima, Ahmad, Sinha, Khan, and Jermsittiparsert [7] in China. This indicates that PPP investment in transport is a crucial factor influencing CO₂ emissions in the short run within the East Asia and Pacific areas. This is supported by the claim that PPP investment in transport is essential for domestic economic output and is also critical at the national level for addressing climate change [57]. Similarly, a two-way causal relationship between RE and transport CO₂ emissions is established. Electric and hybrid vehicles produce fewer emissions, and renewable energy can supply sufficient electricity. Consequently, transitioning from fossil fuels to renewable energy decreases CO₂ emissions in the transport sector. The findings of this analysis align with those reported by Godil et al. [58] concerning China and Mehmood [53] for G-11 countries.

Furthermore, the causality values confirm a one-way causal relationship from environmental research and development to transport carbon emissions at a 5% significance level, indicating that environmental research is essential for mitigating CO₂ emissions. The results align with Udeagha and Ngepah [28] in South Africa. This suggests that the East Asia and Pacific regions must prioritize sophisticated environmental technologies while enhancing their technological innovation, and should encourage the advancement of technologies that promote environmental protection. Simultaneously, environmental protection concerns must be examined more thoroughly in relation to economic, political, and social globalization by conducting more research on advanced technology. The analysis identifies a unidirectional causality from GDP to transport CO₂ emissions at a 1% significance level, affirming that transport emissions are influenced by economic growth [7]. This suggests that policymakers and governments in the East Asia and Pacific region should consider the impact of economic growth when developing environmental sustainability policies.

The variance decomposition method was utilized to measure the degree to which changes in one variable affect variations in others [59]. Variance decomposition specifically quantifies the fraction of the forecast error variance of CO₂ emissions attributable to its own shocks and to shocks from other variables within the system.

Table 10. Variance decomposition analysis of the VAR model.

Step	CO2	PPP	RE	RD	GDP
0	1	0	0	0	0
1	0.915674	−0.002801	0.06767	−0.083822	−0.007507
2	0.297362	−0.011835	−0.049258	−0.29676	−0.023487
3	0.233386	−0.04044	−0.212431	0.022524	−0.018041
4	0.616634	−0.03603	−0.450506	0.373311	−0.025365
5	0.442141	−0.015605	−0.420069	0.690195	−0.025555
6	0.118232	−0.015934	−0.520339	0.448368	−0.018151
7	0.33617	−0.025227	−0.798505	0.264875	−0.021849
8	0.379058	−0.02972	−0.772391	0.167483	−0.016766

presents results derived from the orthogonalized impulse response coefficient matrices. The results indicate that advances in renewable energy and technical advancements contribute approximately 77% and 17% to the changes in CO₂ emissions, respectively. This indicates that disturbances in renewable energy and research and development significantly impact the trajectory of CO₂ emissions over time. It is crucial to recognize that these results do not signify actual growth or shifts in renewable energy utilization; instead, they illustrate the relative significance of each variable’s innovations in elucidating forecast uncertainty in emissions [45]. Conversely, PPP investment and economic growth account for a diminished portion of CO₂ variance, suggesting that their short-term fluctuations exert a more constrained influence on emission dynamics during the research period. These findings highlight the necessity of fortifying PPP mechanisms and technological innovation strategies to improve their enduring efficacy in reducing transport-related emissions [60].

The recent changes suggest that the impacts of PPP investment on transport and economic growth are minimal, with each showing limited explanatory power for the variable of interest. This suggests that economic growth in the East Asia and Pacific region is in its early stages. Similarly, the PPP investment in transport in the East Asia and Pacific region economies is marked by underdevelopment and requires the promotion of transportation PPPs in this region [61]. In this context, PPP investment could significantly enhance environmental quality in the region after a specific threshold of PPP investment is reached.

The effects of PPP investment and renewable energy, along with CO₂ emissions, are represented by the VAR model, which delineates potential relationships among multiple variables. A detrimental outcome is that the models may exhibit specification bias, lack homoscedasticity, or fail to conform to a typical normal distribution. Standard diagnostic tests are conducted to identify potential modeling errors and ascertain whether the model is appropriately described. First, the paper employs a Lagrange multiplier (LM) test to assess autocorrelation in the residuals of VAR models, as proposed by Johansen [62]. Additionally, tests for normality and stability are conducted. The outcomes of the Lagrange multiplier (LM) test indicate the absence of serial correlations in the model residuals [63]. The normality test indicates that the model residuals have a normal distribution based on the Jarque–Bera test, as shown in the diagnostic test findings in

Table 11. Diagnostic tests.

Diagnostic Tests	Coefficient	p-Value	Decision
Jarque–Bera test	3.908	0.95141	Normally distributed
Lagrange multiplier (LM) test	21.3843	0.67100	No serial correlation
Stability condition test	0.9432	-	VAR satisfies the stability condition

. The outcome of the stability test indicates that the model satisfies the stability condition. Consequently, the paper’s results are not influenced by inconsistencies, and the diagnostic tests conducted indicate that the results are not misleading.

4.2. Discussion

The findings indicate a lagged adverse impact of PPP investment on transport CO₂ emissions, suggesting that PPP initiatives ultimately decrease emissions. This aligns with actual behaviors in the Asia-Pacific region [64]. For instance, Asian Development Bank (ADB) initiatives are cultivating sustainable transit infrastructure via public–private partnerships. Significantly, China has established the world’s inaugural all-electric public bus fleet [65]. This initiative substitutes diesel buses with electric alternatives and enhances charging infrastructure, effectively reducing CO₂ emissions from transport. Instances such as electric bus fleets in China and India illustrate how public–private partnerships can attract investment in low-carbon transport. They support our conclusion that increased PPP expenditure results in quantifiable reductions in emissions.

The utilization of renewable energy, such as transport electrification and biofuels, reduces emissions in the East Asia-Pacific region. Renewable energy sources provide significantly lower emissions compared to fossil fuels; thus, transitioning automobiles to clean energy diminishes CO₂ output. The proportion of renewables in transportation energy consumption has increased. Each percentage point of renewable energy adoption displaces carbon-intensive fuels, indicating that increased renewable energy in transportation is associated with reduced pollution [66].

The model suggests that environment-related R&D exerts a negative albeit statistically insignificant influence on emissions. This certainly indicates that green transportation solutions are still developing in the region. For example, intelligent transport systems (ITSs) are in the initial stages of implementation. Although intelligent transportation systems (ITSs) can significantly diminish future CO₂ emissions, their current influence is constrained by scale and adoption rates. The results indicate that environmental innovation in the East Asia-Pacific region is in its nascent stage; additional time or improved measures may be required to identify substantial emissions gains from research and development.

Ultimately, GDP growth in the sample exhibits a positive and substantial correlation with increased transport CO₂, aligning with the notion that economic expansion stimulates greater travel and freight activity. The intensity of this association differs throughout the region. High-income economies, such as Japan, Korea, and Singapore, have predominantly decoupled transport emissions from GDP growth; their emissions have either stabilized or declined despite economic expansion. Conversely, the majority of developing East Asia-Pacific nations exhibit relative decoupling, as their transport emissions are increasing, albeit at a slower rate than GDP. The correlation between positive GDP and CO₂ emissions thus illustrates these fundamental trends [67,68]. This complex perspective indicates that authorities in underdeveloped nations should prioritize sustainable transportation to prevent uncontrolled emission increases, while rich economies may already be seeing advantages from cleaner technologies and energy efficiency.

5. Conclusions

This paper examines the relationship between PPP investment in transport and CO₂ emissions from transport, while adjusting for the influences of environmental innovations by research, economic growth, and renewable energy, using an innovative econometric approach combining unit root tests, cointegration analysis, and the VAR model. Building on the three-mechanism theoretical framework—financing structure effects, technology adoption pathways, and institutional quality effects—this study provides a comprehensive sectoral and regional analysis of PPP investments’ impact on transport sector CO₂ emissions in East Asia and the Pacific. This paper employs the VAR approach introduced by Sims [49] to estimate the joint dynamics and causal relationships between different variables. The empirical analysis validated the presence of a link between all the variables. This study contributes to the existing literature by providing empirical evidence for a three-mechanism theoretical framework that illustrates how PPP investments affect transport emissions through financing structures, technology adoption, and institutional quality in the EKC context.

The findings indicate that PPP investments lead to a decrease in CO₂ emissions after a specific lag period, implying that the environmental advantages of infrastructure projects materialize gradually. This effect may function through various routes, including the financial and managerial efficiency of PPP projects, the implementation of low-carbon and digital transportation technology, and the enhancement of regulatory and institutional quality. Instances from the region—such as eco-friendly metro systems in Singapore and Hong Kong, electric bus fleets in China, and renewable energy-powered transport infrastructure in South Korea—demonstrate how effectively structured public–private partnership projects can facilitate cleaner mobility transitions.

Renewable energy exhibits a substantial inverse correlation with emissions, suggesting that the proliferation of renewable energy sources aids in regulating and decarbonizing the transportation sector. Environmental research and development mitigate CO₂ emissions from the transportation sector; however, the coefficient values of this variable are statistically insignificant. This suggests that the East Asian and Pacific regions are in the nascent phase of implementing environmentally linked technology, and in the near future, RE will facilitate a reduction in CO₂ emissions. In addition, this may indicate the nascent phase of environmental innovation systems in several developing economies within the region. Future studies ought to investigate alternative or sector-specific innovation metrics—such as transportation-related R&D expenditures or patent statistics—and perform subgroup analysis to differentiate between advanced and emerging economies. Shifting from internal combustion engine cars to electric vehicles is among the most efficient methods to diminish CO₂ emissions [69].

Economic growth continues to correlate positively with CO₂ emissions, indicating that the region’s growth path remains energy-intensive and reliant on carbon. This outcome, however, likely conceals developmental variability over the East Asia and Pacific region. Developed economies, including Japan and Australia, have initiated the decoupling of growth from emissions, while emerging economies such as Indonesia, Vietnam, and the Philippines persist in their dependence on fossil-fuel-based growth [70]. Subsequent investigations could thus incorporate heterogeneity-based modeling (e.g., distinct VAR or ARDL models according to development level) to reveal differential dynamics aligned with the EK C concept.

The results indicate that the industrial sector must transition its energy practices to green and renewable energy sources [71,72]. It is essential to establish a unified forum to enhance R&D collaboration, improve communication and preparedness, promote joint initiatives for environmental innovation, and enable the transfer of sustainable technology. A robust and well-defined PPP policy is essential for developing a strong framework for initiating, procuring, and implementing infrastructure projects within the PPP framework. Collectively, these initiatives can mitigate emissions from the transport sector over the long term.

The findings highlight the necessity of developing environmentally focused PPP frameworks that explicitly incorporate ecological objectives into transportation infrastructure planning. Policymakers ought to incorporate carbon reduction and technology adoption standards into PPP contracts, facilitate the integration of renewable energy in transportation systems, and motivate private partners through green finance mechanisms, including tax credits and sustainability-linked bonds. To bolster innovation results, governments ought to augment R&D collaboration networks, facilitate the commercialization of low-carbon technologies, and customize policy incentives according to each nation’s developmental stage—with advanced economies prioritizing technological enhancement, while developing nations concentrate on institutional fortification and the implementation of clean infrastructure. A regionally tailored strategy will guarantee that PPPs not only draw investment but also expedite the shift toward sustainable, low-emission transportation systems throughout East Asia and the Pacific [73].

Despite employing econometric methodologies to establish this relationship, this paper nonetheless has several significant shortcomings. The current outcomes concentrate on a narrow selection of factors during a restricted timeframe, and the empirical assessment was conducted for the East Asian and Pacific regions. A subsequent study might investigate the impact of PPP investments on transport by utilizing the present dataset with varying parameter metrics. Moreover, it is essential to examine the impact of various transport modes on environmental quality in both developed and developing countries through the application of sophisticated econometric models. Future studies need to focus on designing resource efficiency and signing public–private partnership protocols that are economically beneficial and environmentally sound.