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Regional Sustainability >

2025 , Vol. 6 >Issue 3: 100231

DOI: https://doi.org/10.1016/j.regsus.2025.100231

Research article

Factors influencing greenhouse gas emissions from road transport and energy consumption in the Extended Bangkok Metropolitan Region, Thailand

  • Sutinee CHOOMANEE a ,
  • Vilas NITIVATTANANON , a, * ,
  • Kampanart SILVA b ,
  • Kunnawee KANITPONG c ,
  • Jai Govind SINGH d
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  • aUrban Innovation and Sustainability, Department of Development and Sustainability, School of Environment, Resources and Development, Asian Institute of Technology, Pathum Thani, 12120, Thailand
  • bNational Energy Technology Center, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
  • cTransportation Engineering, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, 12120, Thailand
  • dSustainable Energy Transition, Department of Energy and Climate Change, School of Environment, Resources and Development, Asian Institute of Technology, Pathum Thani, 12120, Thailand
* E-mail address: (Vilas NITIVATTANANON).

Received date: 2024-07-27

  Accepted date: 2025-06-17

  Online published: 2025-08-13

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Abstract

Road transport plays a crucial role in facilitating mobility and the movement of goods, particularly in the Extended Bangkok Metropolitan Region (EBMR), Thailand. This area is undergoing rapid industrialization and urbanization, resulting in significant energy consumption and greenhouse gas (GHG) emissions. This study examined the relationships among individual socioeconomic factors, travel characteristics, and energy consumption characteristics and their impacts on GHG emissions from road transport. The path analysis technique was applied to identify the key driving factors and their causal relationships. The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023. The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport. Among the drivers, factors such as income, age, education, and driving experience indirectly influenced travel characteristics and energy consumption characteristics, impacting GHG emissions. Similarly, individual socioeconomic factors affected the travel characteristics of tourists and personal travelers. Driving experience was a crucial factor for public road transport and freight vehicle drivers, influencing travel characteristics and contributing to GHG emissions. These findings highlight the importance of key policy recommendations, such as promoting the adoption of electric vehicles, optimizing public transport, incentivizing low-emission tourism, and modernizing freight transport with clean technologies, to enhance efficiency, reduce emissions, and support regional sustainability. This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors, revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics. The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.

Key words： Road transport; Energy consumption; Greenhouse gas (GHG) emissions; Travel characteristics; Path analysis; Extended Bangkok; Metropolitan Region (EBMR)

Cite this article

Sutinee CHOOMANEE , Vilas NITIVATTANANON , Kampanart SILVA , Kunnawee KANITPONG , Jai Govind SINGH . Factors influencing greenhouse gas emissions from road transport and energy consumption in the Extended Bangkok Metropolitan Region, Thailand[J]. Regional Sustainability, 2025 , 6(3) : 100231 . DOI: 10.1016/j.regsus.2025.100231

1. Introduction

The transport sector is a key contributor to global economic activity and energy consumption, but it is also a major contributor to greenhouse gas (GHG) emissions, intensifying climate change and contributing to extreme weather events (Zhang et al., 2018; Duy et al., 2019; Rodrigue, 2020). Global GHG emissions are continuously increasing due to fossil fuel use across energy, transport, industry, and agriculture (U.S. Environmental Protection Agency, 2022). Road transport is the dominant sector, accounting for 85.00% of total energy consumption in developed economies and generating 37.00% of end-use sector emissions (Rodrigue, 2020). While the global pandemic briefly reduced transport emissions by 10.00% in 2020, emissions have subsequently rebounded with economic recovery and rising mobility (IEA, 2023a). This trend is particularly pronounced in urbanizing regions, where economic growth and motorization drive high fuel demand. As of 2023, China, the United States, and India remain the largest GHG emitters (Wisevoter, 2024). In Thailand, road transport emissions have risen, with over 4.00×107 fossil fuel vehicles registered from 2010 to 2021 (Department of Land Transport, 2022). Diesel makes up 63.00% of national fuel consumption, while gasoline accounts for 30.00% (Department of Alternative Energy Development and Efficiency, 2020). Thailand has promoted electric vehicle (EV) adoption to achieve net-zero emissions by 2050, but road transport still contributes 38.40% of final energy consumption (Department of Alternative Energy Development and Efficiency, 2020). The Extended Bangkok Metropolitan Region (EBMR), as Thailand’s economic hub, has experienced increased road transport activity and energy consumption, significantly impacting national emissions (Ratanavaraha and Jomnonkwao, 2015; Misila et al., 2017; Cheewaphongphan et al., 2017, 2020).
Given the urgent need to reduce transport emissions, international frameworks such as the Paris Agreement and the United Nations Sustainable Development Goals emphasize mitigating GHG emissions and transitioning to sustainable transport. Achieving net-zero emissions by 2050 is a core objective (Tang et al., 2019; Bu et al., 2021). The Intergovernmental Panel on Climate Change (IPCC) supports sustainable development pathways integrating climate actions to enhance resilience and reduce disaster risks (IPCC, 2022). At the national level, Thailand’s Nationally Determined Contributions (NDCs) prioritize emission reductions in the transport sector, which is currently the second-largest source (24.00%) of energy-related emissions. In 2012, road transport accounted for 97.59% of the sector’s emissions, while air, waterborne, and rail transport accounted for much smaller shares (Thailand Greenhouse Gas Management Organization, 2022). As of 2023, Thailand ranked the 23rd globally in GHG emissions at 2.57×108 t, with road transport emissions projected to reach 5.55×1011 t CO2 equivalent by 2030 (Pita et al., 2017; Wisevoter, 2024). Despite the growing focus on transport emissions, knowledge gaps persist in understanding how individual socioeconomic factors influence emissions across driver groups. Most studies have focused on general travel characteristics and energy consumption characteristics without fully considering socioeconomic variations among private, public transport, and freight drivers (Chen and Lei, 2017; Misila et al., 2017; Pita et al., 2020). Bridging this gap is essential for developing targeted strategies that account for the diverse range of socioeconomic and behavioral influences on emissions.
This study aimed to fill this research gap by examining individual socioeconomic factors, travel characteristics, and energy consumption characteristics influencing GHG emissions from road transport in the EBMR. The EBMR was selected as the study area due to its high fuel demand, significant contribution to national GHG emissions, and diverse driver groups, making it an ideal location for examining the impacts of individual socioeconomic factors on GHG emissions. This study addressed the following questions: what are the key factors contributing to GHG emissions and energy consumption in the EBMR, and how do they vary across different driver groups? Using a quantitative analysis combined data from questionnaire surveys conducted from December 2022 to May 2023, this study conducted a comprehensive examination of the factors influencing GHG emissions to support evidence-based policy-making and transport planning. By providing new insights into the relationships among individual socioeconomic factors, travel characteristics, and energy consumption characteristics, this study contributes to the existing literature and establishes a foundation for developing targeted strategies to reduce road transport GHG emissions in Thailand and other rapidly urbanizing regions.

2. Literature review

The drivers of GHG emissions from road transport, including economic growth, vehicle technology, travel behaviors, fuel economy, and environmental considerations, were systematically reviewed. These factors were analyzed for both passenger and freight transport to understand their impact on energy consumption and GHG emissions (Du et al., 2019; Wang et al., 2019; Polloni-Silva et al., 2021; Jiang et al., 2022; Milewski and Milewska, 2023). Previous studies of road transport GHG emissions often consider macro factors such as population (Chen and Lei, 2017; Peng et al., 2020; Pita et al., 2020; Jiang et al., 2022), energy consumption (Chen and Lei, 2017; Talbi, 2017), motor vehicle statistics (Chen and Lei, 2017; Talbi, 2017), gross domestic product (GDP) per capita (Fan and Lei, 2016; Chen and Lei, 2017; Pita et al., 2020; Jiang et al., 2022), transportation energy intensity (Chen and Lei, 2017), transportation intensity (Mraihi et al., 2013; Chen and Lei, 2017; Guo and Meng, 2019), and energy intensity (Mraihi et al., 2013; Gambhir et al., 2015; Chen and Lei, 2017; Talbi, 2017; Jain and Rankavat, 2023). Most researchers typically rely on secondary sources, such as recorded and statistical data.
In addition, driving behavior is a key factor in understanding road transport GHG emissions (Shafaghat et al., 2016; Hu et al., 2021; Huang et al., 2021; Jiang et al., 2022). It is increasingly recognized as one of several micro-level factors, alongside individual socioeconomic factors, travel characteristics, and energy consumption characteristics, within the broader context of road transport. First, demographic and socioeconomic factors, such as age (Mwale et al., 2022; Lu, 2023), education level (Mwale et al., 2022; Lu, 2023), income (Mwale et al., 2022; Lu, 2023), family size (Mwale et al., 2022), and vehicle ownership (Sillaparcharn, 2007; Xu and Lin, 2015; Zhang et al., 2018; Lu, 2023), are key contributors to GHG emissions. Additionally, examining driving experiences can provide insights into driving behavior (Zacharof and Fontaras, 2016; Nocera et al., 2017). Second, travel characteristics are important for assessing road usage dynamics, safety implications, and transportation efficiency. These characteristics include trip frequency (Shabbir et al., 2010; Zhang et al., 2018), road type (Zacharof and Fontaras, 2016), speed (Zhu and Xiong, 2023), travel distance (Papagiannaki and Diakoulaki, 2009; Zhang et al., 2018; Zhu and Xiong, 2023), travel time (Mittal et al., 2017), and the number of passengers (Tang et al., 2019). Finally, optimizing fuel efficiency depends on understanding energy consumption characteristics, including fuel type (Papagiannaki and Diakoulaki, 2009; Pita et al., 2017, 2020; Jiang et al., 2022; Zhu and Xiong, 2023), refueling frequency (Goel et al., 2015), the amount of fuel/energy consumed (Zhu and Xiong, 2023), fuel consumption (Talbi, 2017; Yang et al., 2018; Li, 2019; Zhu et al., 2020; Jiang et al., 2022), engine size (Papagiannaki and Diakoulaki, 2009; Jiang et al., 2022), and vehicle loading (Wang et al., 2012; Li et al., 2013; Zacharof and Fontaras, 2016; Jiang et al., 2022; Zhu and Xiong, 2023). Thus, understanding the complex drivers of road transport GHG emissions necessitates analyzing these factors across various driver groups.
Previous studies mainly rely on secondary data sources, such as national statistics (Chen and Lei, 2017; Misila et al., 2017; Pita et al., 2020; Mwale et al., 2022; Zhao et al., 2022). These data provide an overview of emission trends but do not sufficiently incorporate the individual-level factors (micro factors) that can influence road transport GHG emissions. They do not capture the underlying behavioral and socioeconomic determinants influencing transport energy consumption and GHG emissions at a more granular level. This knowledge gap highlights the need for research integrating individual socioeconomic factors with travel characteristics and energy consumption characteristics. Empirical data from secondary sources, including the Organization for Economic Co-operation and Development (OECD), World Development Indicators (WDI), and International Energy Agency (IEA), have identified the key contributors to road transport emissions, such as fuel type, vehicle weight and size, and total distance traveled (OECD, 2020). Additionally, individual socioeconomic factors (e.g., age, gender, income, and education level) and national-level transport services (World Bank, 2024) also have a significant impact on GHG emissions. The IEA (2023b) states that fuel type, energy mix, transport demand, travel behavior, and energy efficiency are key determinants of road transport GHG emissions. These findings align with the systematic literature review, emphasizing the need to integrate individual socioeconomic factors, travel characteristics, and energy consumption characteristics to better understand the complex drivers behind GHG emissions. This study builds on previous research by offering an integrated analysis of individual socioeconomic factors, travel characteristics, and energy consumption characteristics across a diverse range of driver groups, i.e., local residents, tourists, public transport drivers, and freight vehicle drivers. It provides new insights into how these factors influence road transport GHG emissions. This study provides a comprehensive understanding of emission drivers to support evidence-based policy-making and targeted interventions, while addressing gaps in previous research.

3. Methods

3.1. Conceptual framework

The key drivers that influence GHG emissions from road transport activities, which ultimately contribute to climate change, are complex and multifaceted. Figure 1 shows the interconnectedness of these drivers, including individual socioeconomic factors, travel characteristics, and energy consumption characteristics. This study is based on the notion that individual socioeconomic factors considerably influence travel characteristics and energy consumption characteristics, while travel characteristics influence energy consumption characteristics, which in turn impact GHG emissions and climate change (Misila et al., 2017; Markolf et al., 2019; Serdar et al., 2022).
Fig. 1. Conceptual framework used in this study. GHG, greenhouse gas. This figure is adapted from Misila et al. (2017), Markolf et al. (2019), and Serdar et al. (2022).

3.2. Study area

The EBMR (13°04′23′′-15°04′16′′N and 99°05′59′′-101°59′20′′E, based on the outermost points of the region) is the major hub of economic activities in Thailand, driven by rapid urbanization, a flourishing tourism industry, and industrial development. It comprises 1 city and 12 provinces: Bangkok City (Thailand’s capital city and special local administrative area), Pathum Thani Province, Nonthaburi Province, Samut Prakarn Province, Samut Sakhon Province, Nakhon Pathom Province, Sara Buri Province, Phra Nakhon Si Ayutthaya Province, Chachoengsao Province, Chon Buri Province, Rayong Province, Phetchaburi Province, and Ratchaburi Province. Based on the 20-a national strategy development (2018-2037), we divided the EBMR into four zones: (1) Bangkok Metropolitan Region (BMR): the central economic hub in Thailand, including Bangkok City, and Pathum Thani, Nonthaburi, Samut Prakarn, Samut Sakhon, and Nakhon Pathom provinces, which is an important center of urban development and population growth; (2) Northern Zone: Phra Nakhon Si Ayutthaya and Saraburi provinces, known for historical sites and industrial estates; (3) Eastern Zone: Chachoengsao, Chon Buri, and Rayong provinces, forming the Eastern Economic Corridor, the key industrial zones, and a tourism hub; and (4) Western Zone: Ratchaburi and Phetchaburi provinces, part of the Western Economic Corridor, where the emphasis is on tourism and industrial expansion. The EBMR has grown beyond the BMR, incorporating the surrounding provinces to accommodate urban expansion, population growth, and industrialization. This has driven road infrastructure development, increased vehicle registrations, and escalated fuel consumption, making road transport a major energy consumer. This study focuses on Bangkok City and Phra Nakhon Si Ayutthaya, Ratchaburi, and Chon Buri provinces, which were selected for their high traffic volumes and significant energy consumption.

3.3. Methodological process

As shown in Figure 2, the methodological process consists of five steps as follows. Step 1: factors influencing GHG emissions from road transport (passenger and freight) were identified through a systematic review. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram (Moher et al., 2009; Page et al., 2021) guided the selection process by identifying relevant studies. Step 2: we developed a questionnaire to assess the factors based on input from actual road drivers. The questionnaire was reviewed by experts for reliability and validity and tested with 30 respondents, achieving a reliability score of 0.707. Step 3: the sample size was determined to be 1600 respondents (Yamane, 1967) with a precision of 10.00% and the representative areas are Bangkok City and Phra Nakhon Si Ayutthaya, Ratchaburi, and Chon Buri provinces. Step 4: the survey covered approximately 400 individuals per representative area, divided into sub-samples by traffic volume and vehicle types, based on 2022 data from the Thailand Ministry of Transport (Department of Land Transport, 2022). Step 5: descriptive statistical analysis and path analysis were applied to examine the causal relationships of the factors influencing GHG emissions from road transport and energy consumption, with the results aligning with the hypotheses inferred from the regression analysis (Randolph and Laura, 2013; Chen and Lei, 2017; Gui et al., 2017; Lu, 2023).
Fig. 2. Overall methodological process. PRISMA, preferred reporting items for systematic reviews and meta-analyses.
To identify the key factors influencing GHG emissions from road transport and energy consumption, literature searches were conducted using Web of Science, Scopus, and ScienceDirect. The search was conducted using the keywords: factors, influencing, emissions, road transport, energy, and consumption, with a selected publication period from 2018 to 2022. A total of 120 articles were found in Web of Science, 21 in Scopus, and 16 in ScienceDirect. Furthermore, an additional 11 articles from Google Scholar were obtained through manual searching. As a result, a total of 168 articles were included in this study. After removing duplicates (14 articles), 82 articles were excluded based on abstracts (63 articles are not relevant, 9 articles are review articles, 3 articles are early access, and 7 articles are conference papers). The total articles of this stage were 72 articles, and 39 articles were selected for full-text review. We conducted the selection process based on the PRISMA flow diagram (Fig. 3).
Fig. 3. Flow diagram of the systematic review process based on PRISMA approach. n is the number of articles.

3.4. Data collection

3.4.1. Primary data

This study used data obtained from a questionnaire survey designed to understand individual socioeconomic factors, travel characteristics, energy consumption characteristics, and behaviors influencing GHG emissions from road transport and energy consumption. It also explored participants’ perceptions of GHG emission sources, alternative reduction approaches, and energy adaptation in road transport. The survey targeted four primary driver groups and various vehicle types, collecting data from December 2022 to May 2023, with a sample size of 1600 participants. This comprehensive survey gathered information regarding the complexities of road transport, energy consumption, and GHG emissions, providing valuable data for analysis and interpretation.

3.4.2. Secondary data

A comprehensive literature review was conducted to identify the key factors influencing GHG emissions from road transport and energy consumption. By thoroughly examining research publications from 2018 to 2022 in academic databases, precise keyword operators and Boolean conditions were employed, resulting in a compilation of crucial elements. These elements were subsequently used to create questionnaires to gather primary data. These questionnaires were designed to capture the relevant variables identified in the literature review, enabling a structured investigation of the factors influencing GHG emissions from road transport.

3.5. Data analysis

3.5.1. Estimation framework of greenhouse gas (GHG) emissions

Based on the questionnaire data, we calculated GHG emissions from road transport. Activity data reflect the extent of the effect of road transport on GHG emissions. Emission factors, as defined by the IPCC (2006), represent the amount of GHG emissions per unit of activity.
$\text{GH}{{\text{G}}_{\text{total}}}=\sum\limits_{i=1}^{m}{\left( {{f}_{1}}S{{E}_{i}}+{{f}_{2}}T{{C}_{i}}+{{f}_{3}}F{{C}_{i}} \right)}\times E{{F}_{i}}$,
where GHGtotal is the total GHG emissions for all individuals in the sample (kg CO2 equivalent); m is the sample size; i is the summation index (from i=1 to i=1600 respondents); f1 is a linear function applied to the calculation of individual socioeconomic factors; SEi is the individual socioeconomic factor, i.e., age (years old), education level, income (USD/month), family size (persons/household), vehicle ownership (vehicles/person), and driving experience (a), for individual i; f2 is a linear function applied to calculate the travel characteristics; TCi is the travel characteristic, i.e., trip frequency (trips/week), travel distance (km/trip), travel time (h/trip), road type preference (based on speed control in km/h), speed (km/h), and the number of passengers (persons/trip), for individual i; f3 is a linear function applied to the calculation of energy consumption characteristics; FCi is the energy consumption characteristic, i.e., fuel type, refueling frequency (times/week), the amount of energy from refueling (L, kg, or kW•h), fuel economy (km/L or kW•h), engine size (cm3), and vehicle loading (kg), for individual i; and EFi is an emission factor representing the ratio of GHG emissions to a unit of fuel consumption (kg CO2 equivalent/L), by mobile combustion (on the road), based on the IPCC (2006). In addition, all individual socioeconomic factors, travel characteristics, and energy consumption characteristics were converted into a standardized Likert Scale (1-5) for a comparative analysis. Because these factors represent categorical and ordinal data, they are dimensionless. This equation provides a framework for understanding how individual socioeconomic factors, travel characteristics, and energy consumption characteristics contribute to GHG emissions. The equation serves as a conceptual reference to illustrate how different factors influence GHG emissions, but was not used for the calculation of direct emissions. Instead, the relationships between these factors were analyzed using a path analysis.

3.5.2. Path analysis

We applied a path analysis to assess the factors influencing GHG emissions from road transport and energy consumption, based on a hypothesis inferred from a regression analysis. This approach evaluated the total, direct, and indirect effects, clarifying variable interactions. The model, developed in SPSS AMOS 28 (version 28, IBM, New York, the United States), incorporated observational variables to quantify causal associations. The path analysis model defines the relationships of each of individual socioeconomic factors, travel characteristics, and energy consumption characteristics with GHG emissions:
\[{{Y}_{j}}={{\alpha }_{j}}+\sum\limits_{l}{{{\beta }_{jl}}{{Y}_{l}}}+\sum\limits_{i'}{{{\gamma }_{ji'}}{{X}_{i'}}}+{{\varepsilon }_{j}}\],
where Yj is an endogenous (dependent) variable influenced by exogenous (independent) variables and other endogenous variables, the meaning of Yj varies by equation, and it can represent either travel characteristics, energy consumption characteristics, or GHG emissions; αj is the intercept term of the baseline value of the endogenous variable when all influencing variables are zero; l is the index of the endogenous variables; βjl is a path coefficient, indicating the strength and direction of the relationships between the endogenous variables, such as the effect of travel characteristics on energy consumption or energy consumption on GHG emissions; Yl is the endogenous variable, including travel characteristics or energy consumption; i' is the index of exogenous variables; γji' is the path coefficient, indicating the strength and direction of the relationships between exogenous and endogenous variables, such as individual socioeconomic factors influencing travel characteristics or individual socioeconomic factors influencing energy consumption characteristics; Xi' is the exogenous variable, including individual socioeconomic factors; and εj is the error term for the residual error in the model. Because all variables are standardized on a Likert Scale (1-5), they are dimensionless. Unlike traditional emissions models, this approach focuses on behavioral and individual socioeconomic influences rather than an absolute emissions estimation.

4. Results

4.1. Key factors influencing GHG emissions from road transport and energy consumption

A systematic literature review was conducted adhering to the PRISMA guidelines. The evaluation procedure involved a comprehensive search of prominent academic databases, including Web of Science, Scopus, and ScienceDirect. Manual searches were also conducted to expand the database findings. The list of key factors included individual socioeconomic factors, travel characteristics, and energy consumption characteristics (Table 1). The individual socioeconomic factors were age, educational level, income, family size, vehicle ownership, and driving experience. Travel characteristic factors included trip frequency, travel distance, travel time, road type, speed, and the number of passengers. Energy consumption characteristics included fuel type, refueling frequency, the amount of fuel/energy refueling, and fuel economy, which refers to fuel efficiency, engine size, and vehicle loading. GHG emissions resulted from the interaction between road transport activities and energy consumption, and were primarily caused by human activities.
Table 1 Factors influencing greenhouse gas (GHG) emissions from road transport and energy consumption.
Group Conceptual factor Definition Unit of
conceptual factor		 Measurement scale Abbreviation
Individual socioeconomic factors Age The age of individual respondents was categorized into the following groups: 18-25, 26-35, 36-45, 46-55, and >55 years. years 18-25 years=1;
26-35 years=2;
36-45 years=3;
46-55 years=4;
and >55 years=5		 Age
Education
level		 The highest level of formal education attained by individuals, categorized into levels of below high school, high school, associated degree, bachelor’s degree, and master’s degree or higher. - Below high school=1;
high school=2;
associated degree=3;
bachelor’s degree=4;
and master’s degree or higher=5		 EDU
Income The average monthly income of individuals, categorized into ranges of below 460, 461-615, 616-765, 766-920, and >920 USD/month. USD/month Below 460 USD/month=1;
461-615 USD/month=2;
616-765 USD/month=3;
766-920 USD/month=4;
and >920 USD/month=5		 INC
Family size The number of individuals residing in the respondent’s household, categorized into ranges of 1-2, 3-4, 5-6, 7-8, and >8 persons/household. persons/household 1-2 persons/household=1;
3-4 persons/household=2;
5-6 persons/household=3;
7-8 persons/household=4;
and >8 persons/household=5		 FS
Vehicle ownership The number of vehicles owned by an individual, categorized into six groups: no vehicle, 1, 2-3, 4-5, 6-7, and >7 vehicles/person. vehicles/person No vehicle=0;
1 vehicle/person=1;
2-3 vehicles/person=2;
4-5 vehicles/person=3;
6-7 vehicles/person=4;
and >7 vehicles/person=5		 VO
Driving experience The number of years an individual has been driving, categorized into below 1, 1-3, 4-6, 7-9, and >9 a. a Below 1 a=1;
1-3 a=2;
4-6 a=3;
7-9 a=4;
and >9 a=5		 DE
Travel characteristics Trip
frequency		 The number of trips per week that an individual makes, categorized into 1-10, 11-20, 21-30, 31-40, and >40 trips/week. trips/week 1-10 trips/week=1;
11-20 trips/week=2;
21-30 trips/week=3;
31-40 trips/week=4;
and >40 trips/week=5		 TF
Travel
distance		 The average distance traveled per trip by an individual, categorized into below 50, 50-100, 101-150, 151-200, and >200 km/trip. km/trip Below 50 km/trip=1;
50-100 km/trip=2;
101-150 km/trip=3;
151-200 km/trip= 4;
and >200 km/trip=5		 TD
Travel time The average duration of a trip, categorized into below 1, 1-2, 3-4, 5-6, and >6 h/trip. h/trip Below 1 h/trip=1;
1-2 h/trip=2;
3-4 h/trip=3;
5-6 h/trip=4;
and >6 h/trip=5		 TT
Road type The category of road type preference that used by the individual as local highway, rural road, main highway, concession highway and motorway. - Local highway=1;
rural road=2;
main highway=3;
concession highway=4;
and motorway=5		 RT
Speed The average speed at which a vehicle is driven, categorized into below 40, 40-60, 60-80, 80-100, and >100 km/h. km/h Below 40 km/h=1;
40-60 km/h=2;
60-80 km/h=3;
80-100 km/h=4;
and >100 km/h=5		 SP
Number of
passengers		 The number of passengers in the vehicle per trip, categorized into 1-5, 6-10, 11-15, 16-20, and >20 persons/trip. persons/trip 1-5 persons/trip=1;
6-10 persons/trip=2;
11-15 persons/trip=3;
16-20 persons/trip=4;
and >20 persons/trip=5		 NP
Energy consumption characteristics Fuel type The type of fuel or energy used by a vehicle, such as diesel, gasoline, liquefied petroleum gas (LPG), compressed natural gas (CNG), or electricity. - Diesel=1;
gasohol=2;
LPG=3;
NGV=4;
and electric=5		 FT
Energy consumption characteristics Refueling frequency The number of times a vehicle is refueled per week, categorized as 1-2, 3-4, 5-6, 7-8, and >8 times/week. times/week 1-2 times/week=1;
3-4 times/week=2;
5-6 times/week=3;
7-8 times/week=4;
and >8 times/week=5		 FR
Amount of
fuel/energy
refueling		 The average amount of fuel/energy refueled per refill, categorized by quantity, as less than 40, 40-60, 60-80, 80-100, and >100 L. L, kg, or kW•h (depending on the fuel/energy type) Less than 40 L=1;
40-60 L=2;
60-80 L=3;
80-100 L=4;
and >100 L=5		 AE
Fuel
economy		 The average distance a vehicle can travel per liter of fuel or per kilowatt-hour of energy, categorized as below 8, 8-10, 11-13, 14-16, and >16 km/L. km/L or kW•h Below 8 km/L=1;
8-10 km/L=2;
11-13 km/L=3;
14-16 km/L=4;
and >16 km/L=5		 FE
Engine size The engine capacity of the vehicle, categorized as below 1000, 1000-1400, 1401-1800, 1801-2200, and >2200 cm3. cm3 Below 1000 cm3=1;
1000-1400 cm3=2;
1401-1800 cm3=3;
1801-2200 cm3=4;
and >2200 cm3=5		 ES
Vehicle
loading		 The total weight of the vehicle when fully loaded, including cargo or passengers, categorized as below 1000, 1001−1300, 1301−1500, 1501−1800, and >1800 kg. kg Below 1000 kg=1;
1001-1300 kg=2;
1301-1500 kg=3;
1501-1800 kg=4;
and >1800 kg=5		 VL

Note: - represents dimensionless. Factors influencing GHG emissions from road transport and energy consumption were derived from a systematic literature review, adhering to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (Moher et al., 2009; Page et al., 2021), and developed into a questionnaire survey that collected individual-level data for the path analysis.

4.2. Causal relationships of factors influencing GHG emissions from road transport and energy consumption

4.2.1. Overview of respondents

The survey included 1600 respondents across four driver groups: residents, travelers/tourists, public transport drivers, and freight transport drivers, covering a diverse range of vehicle and fuel types from representative areas. The sample was 78.90% male and 21.10% female, with ages ranging from 18-25 years (9.30%) to >55 years (10.80%). Education levels were below high school (19.30%), high school (41.40%), associate degree (13.50%), bachelor’s degree (22.90%), and master’s degree or higher (2.90%). Income ranged from 0 to over 920 USD/month, averaging 461 USD/month (23.00%). Family sizes were from 1-2 persons/household (22.40%) to over 8 persons/household (1.50%). Vehicle ownership ranged from no vehicles (0.50%) to more than 7 vehicles/person (0.30%), and driving experience spanned less than 1 a (1.10%) to over 9 a (45.40%).

4.2.2. Causal relationships of factors influencing GHG emissions from road transport and energy consumption: Perspectives from local residents

Residents of the EBMR drive primarily within the urban core. The survey included 330 drivers using motorcycles (2-3 wheels) and personal passenger vehicles, including those with up to 7 seats and those with 8 to 13 seats. Most traveled for work, education, or routine daily tasks, with the average travel distance being lower than 50 km. In analyzing the cause-and-effect relationships, hypothesis testing confirmed significant links between individual socioeconomic factors, travel characteristics, and energy consumption characteristics. Individual socioeconomic factors influenced GHG emissions indirectly through travel characteristics and energy consumption characteristics. Travel characteristics impacted energy consumption characteristics both directly and indirectly, while energy consumption characteristics directly affected GHG emissions. The adjusted model (Fig. 4) removed the non-significant pathways, with statistically significant regression coefficients (P<0.05). Key factors influencing GHG emissions were identified, with path coefficients decomposed into direct, indirect, and total effects. Standardized estimates helped to prioritize the critical factors for local residents.
Fig. 4. Path diagram of the adjusted model for GHG emissions from road transport and energy consumption for an overview of local residents. e1-e19 represent error terms of each variable, the number 1 on the arrows represents fixed factor loading, and the values on the lines represent standardized direct effects. ***, P<0.001; *, P<0.05; df, degrees of freedom; RMSEA, root mean square error of approximation; CFI, comparative fit index; TLI, Tucker-Lewis index; GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; RMR, mean square residual; HOELTER, Hoelter’s Critical N.
Impact of individual socioeconomic factors on travel characteristics: age positively influenced speed, road type, travel distance, and trip frequency, but did not affect the number of passengers. Education level influenced road type, travel distance, travel time, and speed, but reduced trip frequency (total effect: -0.033). Income followed a similar pattern, increasing road type, travel distance, travel time, and speed while reducing trip frequency (total effect: -0.081). Family size and vehicle ownership both increased trip frequency, speed, travel time, and travel distance. Driving experience significantly increased speed, with an effect strength of 0.411. These individual socioeconomic factors influenced travel characteristics, impacting energy consumption characteristics and GHG emissions.
Impact of individual socioeconomic factors on energy consumption characteristics: income strongly influenced energy consumption characteristics, and increasing refueling frequency negatively impacted fuel economy (total effect: -0.037). Family size and vehicle ownership slightly increased fuel consumption. Although larger households refueled slightly more, it was the amount of fuel/energy refueling that negatively influenced fuel efficiency (total effect: -0.133). Higher levels of education led to more refueling frequency due to the increased income, but also lowered fuel efficiency (total effect: -0.015). Driving experience had no direct impact on energy consumption characteristics. Among individual socioeconomic factors, income had the most significant effect on energy consumption characteristics. The key drivers of GHG emissions were income, age, education level, driving experience, and vehicle ownership, while family size had a minor impact.
Impact of travel characteristics on energy consumption characteristics: trip frequency increased refueling frequency, but decreased fuel economy. Travel distance also increased the amount of fuel/energy refueling (total effect: 0.148), but decreased fuel economy (total effect: -0.019). More passengers increased vehicle loading, engine size, the amount of fuel/energy refueling, and refueling frequency, but lowered fuel efficiency (total effect: -0.011). Travel characteristics strongly impacted GHG emissions, with travel distance, the number of passengers, and road type contributing most to increased GHG emissions. Trip frequency, speed, and travel time had a smaller effect.
Impact of energy consumption characteristics on GHG emissions: vehicle loading, engine size, and the amount of fuel/energy refueling were the primary variables contributing to GHG emissions, with all having statistically significant effects. Conversely, factors such as refueling frequency, fuel economy, and fuel type had a lesser influence on GHG emissions.

4.2.3. Causal relationships of factors influencing GHG emissions from road transport and energy consumption: Perspectives from travelers and tourists

The survey included 247 travelers and tourists using personal passenger vehicles, including those with up to 7 seats and those with 8-13 seats, primarily in EBMR tourism hotspots. They travelled between destinations, averaging 100-150 km/trip. Hypotheses were tested to analyze cause-and-effect relationships. Most paths confirmed that individual socioeconomic factors, travel characteristics, and energy consumption characteristics were statistically significant. Individual socioeconomic factors impacted GHG emissions indirectly through travel characteristics and energy consumption characteristics. Travel characteristics affected GHG emissions, partly through energy consumption characteristics. Energy consumption characteristics have both direct and indirect effects. The path diagram was adjusted by removing non-significant paths (Fig. 5), with all direct path coefficients being statistically significant (P<0.05). Path coefficients highlighted direct, indirect, and total effects, helping to prioritize the key factors and providing insights into tourist and traveler behavior.
Fig. 5. Path diagram of the adjusted model for GHG emissions from road transport and energy consumption for an overview of travelers and tourists. ***, P<0.001; *, P<0.05.
Impact of individual socioeconomic factors on travel characteristics: age influenced road type and speed. Education level and income also impacted road type and speed. Family size, vehicle ownership, and driving experience did not significantly affect travel characteristics.
Impact of individual socioeconomic factors on energy consumption characteristics: age, income, family size, vehicle ownership, and driving experience had an influential positive relationship with both the amount of fuel/energy refueling and fuel economy. The higher levels of these factors corresponded to increased fuel refueling and an increased vehicle fuel consumption rate, indicating a lower fuel efficiency. However, education level only exhibited a positive correlation with fuel economy, implying that higher education levels may promote a higher vehicle fuel consumption rate and consequently lower fuel efficiency. All individual socioeconomic factors had a positive effect on GHG emissions. The main factors influencing GHG emissions were income (0.029), driving experience (0.027), and age (0.018). Meanwhile, education level, family size, and vehicle ownership had a relatively smaller influence on GHG emissions.
Impact of travel characteristics on energy consumption characteristics: trip frequency and travel distance positively impacted refueling frequency and fuel economy, indicating a higher fuel efficiency. Conversely, road type, speed, and travel time had no substantial effect on energy consumption characteristics. The number of passengers significantly increased refueling frequency and vehicle loading. Overall, travel characteristics positively impacted GHG emissions, with influential factors ranked as travel distance, trip frequency, the number of passengers, road type, travel time, and speed.
Impact of energy consumption characteristics on GHG emissions: the influential factors were ranked as energy refueling, vehicle loading, engine size, and refueling frequency. However, fuel type negatively affected GHG emissions (total effect: -0.066). This indicates that transitioning to cleaner fuel types would reduce GHG emissions.

4.2.4. Causal relationships of factors influencing GHG emissions from road transport and energy consumption: Perspectives from public transport

Public transport drivers used motorcycles (2-3 wheels) and public passenger vehicles with up to 7 seats and 8-13 seats, and mini, medium, and large buses. The survey of 451 drivers took place in major EBMR transport hubs. Most respondents operated public transport services, with an average travel distance of over 5 km. Hypotheses were tested to analyze cause-and-effect relationships. Most hypothesized paths confirmed statistically significant correlations among individual socioeconomic factors, travel characteristics, and energy consumption characteristics. Individual socioeconomic factors indirectly influenced GHG emissions by shaping travel characteristics and energy consumption characteristics, while travel characteristics directly impacted GHG emissions through energy consumption characteristics. Income, family size, and vehicle ownership were omitted due to their lack of a significant impact. The path diagram was adjusted by removing non-significant paths (Fig. 6). Each estimate of the path coefficient resulting from the direct effect of the regression coefficients was statistically significant (P<0.05). The decomposition of the path coefficients and the standardized total, direct, and indirect effects were used to prioritize the key factors influencing GHG emissions from road transport and energy consumption. The total standardized impact was used to identify the factors with the largest influence on GHG emissions, including individual socioeconomic factors, travel characteristics, and energy consumption characteristics, thus providing an overview of public transport.
Fig. 6. Path diagram of the adjusted model for GHG emissions from road transport and energy consumption for an overview of public transport. ***, P<0.001; *, P<0.05.
Impact of individual socioeconomic factors on travel characteristics: age positively impacted speed, travel time, and travel distance. Education level specifically affected speed, while driving experience had most influence on speed.
Impact of individual socioeconomic factors on energy consumption characteristics: age was correlated with fuel economy, indicating reduced fuel efficiency. Furthermore, an increase in driving experience may lead to an increase in refueling frequency, albeit with a decrease in fuel economy, implying diminished fuel efficiency over time.
Impact of individual socioeconomic factors on GHG emissions: driving experience, age, and education level were the primary factors, with driving experience emerging as the most influential factor (total effect: 0.028).
Impact of travel characteristics on energy consumption characteristics: factors, including trip frequency, travel distance, speed, and the number of passengers, led to an increase in refueling frequency. Additionally, the number of passengers influenced both vehicle loading and refueling frequency. Trip frequency, travel distance, the number of passengers, and speed cause decreased fuel economy, indicating reduced fuel efficiency. However, road type and travel time did not have a serious effect on energy consumption characteristics. Travel characteristics influence GHG emissions through factors such as road type, speed, travel time, and travel distance, which generally lead to increased GHG emissions. However, trip frequency had both a small direct effect on GHG emissions (0.078) and a negative indirect effect through its influence on other factors. Specifically, an increase in trip frequency was associated with more efficient road type selection (-0.338), shorter travel times (-0.116), and more moderate speeds (-0.204), all of which reduced GHG emissions. While trip frequency typically increased travel activity, these factors contributed to a net decrease in GHG emissions (-0.042), demonstrating how, under certain conditions, more frequent trips can lead to lower overall emissions.
Impact of energy consumption characteristics on GHG emissions: all variables had a positive total effect. This effect was observed across factors such as engine size, vehicle loading, the amount of fuel/energy refueling, fuel type, refueling frequency, and fuel economy.

4.2.5. Causal relationships of factors influencing GHG emissions from road transport and energy consumption: Perspectives from freight transport

Freight transport drivers operate various truck types in the EBMR logistic hubs and truck parks. The survey included 572 drivers, primarily providing freight services, with an average travel distance of over 200 km. Hypotheses were tested to analyze cause-and-effect relationships. For most hypothesized paths, significant correlations were confirmed among individual socioeconomic factors, travel characteristics, and energy consumption characteristics. Individual socioeconomic factors indirectly affected GHG emissions through travel characteristics and energy consumption characteristics, while travel characteristics directly influenced GHG emissions. GHG emissions also impacted energy consumption characteristics both directly and indirectly. Income, family size, vehicle ownership, and the number of passengers were excluded due to their lack of significance. The adjusted path diagram removed statistically insignificant paths (Fig. 7). All direct path coefficients were significant (P<0.05). The decomposed path coefficients highlighted the key factors influencing GHG emissions from road transport and energy consumption. The standardized total effects identified the most influential individual socioeconomic factors, travel characteristics, and energy consumption characteristics in freight transport.
Fig. 7. Path diagram of the adjusted model for GHG emissions from road transport and energy consumption for an overview of freight transport. ***, P<0.001; *, P<0.05.
Impact of individual socioeconomic factors on travel characteristics: older individuals preferred main roads with speed regulation, which affected travel distance, speed, and travel time. Higher education influenced road selection, reducing travel distance and travel time under controlled speeds. Driving experience increased travel distance, speed, and travel time, with main roads being preferred due to speed regulation.
Impact of individual socioeconomic factors on energy consumption characteristics: advancing age and increasing driving experience were associated with a greater amount of fuel/energy refueling and refueling frequency, which led to a decreased fuel economy, suggesting a lower fuel efficiency over time. Although education level may have some effect, its impact was insignificant compared to age and driving experience.
Impact of individual socioeconomic factors on GHG emissions: driving experience was the most influential factor, contributing significantly to increased GHG emissions. While age had only a minor effect on GHG emissions, education level was associated with reduced GHG emissions.
Impact of travel characteristics on energy consumption characteristics and GHG emissions: a higher trip frequency improved fuel economy but increased refueling frequency. This relationship was dependent on travel distance and engine size, which increased fuel consumption per trip. Despite the more frequent refueling, fuel efficiency decreased, indicating shorter travel distances per unit of fuel. Road type, speed, and travel time did not affect energy consumption characteristics. Among the travel characteristics, travel distance had the greatest impact on GHG emissions, followed by travel time, road type, speed, and trip frequency.
Impact of energy consumption characteristics on GHG emissions: each factor positively impacted GHG emissions. Engine size emerged as the most influential factor, followed by fuel economy, refueling frequency, fuel type, vehicle loading, and the amount of fuel/energy refueling. These findings underscore the importance of considering travel characteristics and energy consumption characteristics in mitigating GHG emissions from road freight transport.

4.3. Driver perceptions toward emission sources, alternative approaches to reducing GHG emissions, and adoption of alternative energy road transport

Over 75.00% of drivers believed that energy consumption for road transport contributed to GHG emissions and climate change. They acknowledged the impact of driving habits and vehicle technology. Furthermore, 76.00% of drivers understood how climate change, extreme weather, and urbanization affect travel characteristics and increase GHG emissions. Over 77.00% of drivers supported mitigation and adaptation strategies to reduce GHG emissions and address climate change impacts. Drivers had mixed views on adopting alternative energy sources and adaptation strategies, but increasingly favored shifting from fossil fuels to cleaner options, such as EVs. While tax credits and rebates would encourage the adoption of alternative energy vehicles, their widespread use depends on strong government support. Electric, hybrid, and hydrogen vehicles are eco-friendly alternatives, although safety concerns remain. To drive sustainability, regulatory measures such as stringent emission standards are essential. Public education and awareness campaigns are essential for promoting climate action. Integrating adaptation strategies into policy frameworks is crucial for climate change mitigation.

5. Discussion

This study investigated how individual socioeconomic factors, travel characteristics, and energy consumption characteristics impact road transport GHG emissions, yielding findings that were consistent with existing research (Zhang et al., 2018; Mwale et al., 2022). The results highlighted both the direct and indirect effects of the factors investigated. Income, age, and education level influenced emission-generating behavior, with higher-income individuals generating more emissions due to their longer trips and vehicle choices (Bel and Rosell, 2017; Pita et al., 2020; Hussain et al., 2022). Older and higher-educated individuals own more vehicles and have greater driving experience, resulting in their being responsible for more emissions than other drivers. Travel characteristics, i.e., speed, road type, and trip frequency, are influenced by age, education level, income, family size, and vehicle ownership (Sillaparcharn, 2007), all of which affect energy consumption characteristics. Older drivers prefer highways with higher speed limits, increasing their fuel consumption. Individual socioeconomic factors also impact refueling frequency and fuel economy. Targeted regulations and sustainable policies could therefore promote the adoption of cleaner vehicles, manage vehicle ownership, and encourage responsible driving, particularly among older drivers, leading to GHG emission reductions.
Tourists and travelers exhibited similar GHG emission patterns to local residents. Age influenced GHG emissions indirectly through income and road type, with higher-income individuals preferring highways with tolls and higher speed limits, increasing GHG emissions. Education level impacted GHG emissions via income and road type, while family size and vehicle loading also played a role in influence GHG emissions via vehicle ownership, driving experience, amount of fuel filling and number of passengers. Greater driving experience increased refueling frequency and GHG emissions, while carrying more passengers contributed to GHG emissions through fueling patterns and fuel economy. Experienced drivers tended to drive faster, further raising GHG emissions. Trip frequency, travel distance, and travel time, as well as vehicle loading, influenced GHG emissions through fueling behavior. These findings highlighted the links among individual socioeconomic factors, travel characteristics, and energy consumption characteristics that influence GHG emissions among tourists and travelers. Promoting sustainable travel for high-income earners, who prefer faster, energy-intensive routes, is essential. Policies addressing vehicle ownership (Chen and Lei, 2017), trip frequency (Zhang et al., 2018; Mwale et al., 2022), and driving behaviors (Shafaghat et al., 2016; Huang et al., 2021; Jiang et al., 2022) may help mitigate GHG emissions and enhance sustainability (Sillaparcharn, 2007; Papagiannaki and Diakoulaki, 2009; Zacharof and Fontaras, 2016; Wang et al., 2019).
In addition, key factors, including age, education level, and driving experience, influenced GHG emissions from public transport. Driving experience, the most significant factor, affects GHG emissions through speed and travel distance (AlKheder, 2021; Tarriño-Ortiz et al., 2022). Counterintuitively, increased trip frequency was found to reduce GHG emissions, although this effect was moderated by travel distance and travel time. A higher trip frequency also increased refueling frequency and GHG emissions, highlighting the need for sustainable interventions. The number of passengers significantly impacted GHG emissions by influencing speed, refueling frequency, and vehicle loading. These findings underscore the importance of promoting public transport by implementing measures such as speed regulations, eco-driving training, and route optimization. Engine size, vehicle loading, fuel type, refueling frequency, and fuel economy all contribute to GHG emissions. These factors highlight the need for comprehensive strategies to reduce GHG emissions and transition to cleaner energy in public transport.
For freight vehicle drivers, age, education level, and driving experience were the key factors contributing to GHG emissions. Driving experience significantly influenced GHG emissions through travel distance and road type. Travel characteristics, such as travel distance, travel time, road type, speed, and trip frequency collectively increased GHG emissions, with travel distance being a major factor. These findings followed worldwide trends (Mraihi et al., 2013; Zhang et al., 2024), in which these factors are positively correlated with GHG emissions. Larger engines and higher fuel consumption also generated GHG emissions, with fuel choice affecting vehicle efficiency. These findings extended the observations made by Wang et al. (2012), highlighting the role of vehicle loading in increasing freight transport emissions. These results highlight the importance of driving behavior, travel pattern, and vehicle engine size in determining GHG emission levels. Understanding the importance of these factors is essential for developing targeted interventions to transition from internal combustion engines to cleaner fuel types and cleaner vehicle technologies, which is crucial for reducing GHG emissions and achieving sustainability in freight transport.
The differences in the main influencing factors across various travel characteristics indicated the unique challenges and opportunities for reducing GHG emissions from each mode. For local residents, income, vehicle ownership, and driving experience were the primary drivers of GHG emissions, with higher-income individuals often owning more fuel-intensive vehicles. Tourists are influenced by travel distance and speed, with longer trips on unfamiliar routes typically leading to higher fuel consumption. In public transport, trip frequency can reduce GHG emissions, but only when balanced with efficient route planning and moderate speeds. Conversely, freight transport was heavily impacted by travel distance, engine size, and vehicle loading, which contribute to increased GHG emissions, particularly from frequent long-distance trips.
Based on the empirical findings, this study recommends tailored policies across transport modes to enhance regional sustainability and reduce GHG emissions. For local residents, promoting EV adoption and eco-driving training could mitigate the impact of income and driving behavior on energy consumption characteristics. Continuing government incentives, including tax credits and rebates, will likely encourage widespread EV use. For tourists and travelers, prioritizing low-emission travel options, such as electric buses and shared mobility services, through the use of incentives such as tax breaks and reduced fares, could foster sustainable tourism. Public transport policies should optimize routes, introduce dedicated bus lanes, and electrify fleets, which would be supported by tax breaks and subsidies for green vehicle adoption. Freight transport should be modernized through the adoption of cleaner engines, logistics optimization, and efficient vehicle loading to cut emissions. Technological advances, such as alternative fuel engines, are also key to reducing emissions. Achieving sustainability requires integrated low-emission transport networks, optimization of public transport, and regional collaboration. Raising public awareness through education and knowledge-sharing will accelerate the transition to cleaner energy. These observations are aligned with the conclusions of Pita et al. (2020), who noted a preference for private vehicles among commuters despite their higher GHG emissions, underscoring the need for policies that promote greener transport. Finally, incorporating adaptation strategies into transport policy ensures long-term sustainability and climate change mitigation.

6. Conclusions and recommendations

The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport. This study identified distinct emissions patterns across different driver groups, highlighting the role of individual socioeconomic factors, especially income, education level, age, and driving behavior, in influencing energy consumption and GHG emissions. Local residents with higher levels of income and vehicle ownership tend to emit more GHGs due to their longer trips and preferences for energy-intensive vehicles. Older individuals and those with higher levels of education are also responsible for higher GHG emissions, which is driven by greater levels of vehicle ownership and a preference for highways with higher speed limits. Travel characteristics, including speed and road type, are influenced by age, income, and education level, impacting energy consumption characteristics and GHG emissions. Similar trends were observed among tourists and travelers, with age and income affecting road type and GHG emissions. Public transport drivers with more driving experience and a greater trip frequency were responsible for high GHG emissions, despite their lower GHG emissions per trip compared to other driver groups. Additionally, the number of passengers significantly affected GHG emissions. For freight drivers, age, education level, and driving experience, together with travel distance and engine size, contributed to higher GHG emissions. Drivers’ awareness of energy consumption and driving habits highlighted the need for a transition to cleaner energy and environmentally friendly transportation.
This study made a significant contribution to academic knowledge and also has practical applications. In terms of academic knowledge, the study revealed how key individual socioeconomic factors, travel characteristics, and energy consumption characteristics influence GHG emissions from road transport. We used a path analysis technique based on primary data from road drivers to provide robust evidence of the causal relationships among the key influencing factors and GHG emissions. In terms of practical applications, this study provides practical guidance to policy-makers by identifying targeted strategies to reduce GHG emissions. These include promoting EV adoption, optimizing public transport, incentivizing low-emission tourism, modernizing freight transport with clean technologies to enhance efficiency, and addressing the specific needs of different driver groups. This study contributes to the development of sustainable and resilient transportation systems that align with the sustainability goals for the region.
Although this study provided valuable insights into the factors influencing road transport GHG emissions, certain limitations should be recognized. The cross-sectional nature of the data, which was collected during a specific period, offers only a snapshot of emission behavior, without accounting for potential changes over time. Future research should focus on longitudinal studies to observe the changes in GHG emission patterns and assess the impact of evolving policies and technological advances. Expanding the research to consider different travel contexts and driver groups will also help refine strategies for GHG emission reduction and support the broader sustainability goals.

Authorship contribution statement

Sutinee CHOOMANEE: conceptualization, methodology, software, writing - original draft, and writing - review & editing; Vilas NITIVATTANANON: supervision, conceptualization, and writing - review & editing; Kampanart SILVA: supervision and writing - review & editing; Kunnawee KANITPONG: supervision and writing - review & editing; and Jai Govind SINGH: supervision and writing - review & editing. All authors approved the manuscript.

Ethics statement

This study followed ethical research practices from the Asian Institute of Technology. In addition, the participants provided their informed consent in participating for this study.

Declaration of conflict of interest

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

Acknowledgements

The first author of this manuscript expresses gratitude that the Royal Thai Government (RTG) provided financing for this study, as well as a scholarship to assist PhD studies at the Asian Institute of Technology (AIT). The National Science and Technology Development Agency (NSTDA) of Thailand via the Development of High-Quality Research Graduates in Science and Technology Project, a collaboration between NSTDA and AIT, also offers a top-up scholarship for this study.

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