Global climate change has been recognized as the biggest threat to all living beings in the sea, on land, and in the atmosphere ^[1]. Unprecedented challenges, such as extreme weather, loss of species, shifting rainfall patterns, glaciers melting, and rising global mean sea level have affected the survival and growth of humanity globally ^[2]. According to the IPCC Fourth Assessment Report 2014, since 1750, the concentration of carbon dioxide (CO₂) in the atmosphere has increased by 40%, while the same measure was 31% in 2001 [2,3]^[2][3]. As of 2010, the transportation sector accounted for 14% of global greenhouse gas emissions ^[3].

The transportation sector plays an essential role in humanity’s activities and affects the global economy. For example, the transportation of people and goods provides people with mobility, sustainable daily lives, local and international merchandise trade, and economic development [4,5,6,7]^[4][5][6][7]. However, most activities in the transportation sector are fueled by fossil-based energy sources, which are not renewable ^[8]. This implies that, while contributing to the global economy, the transportation sector has negative impacts on global climate change.

Considering its scale and the growing speed in energy consumption, the transportation sector has become the second largest CO₂ emitter in the world ^[9]. The transportation of people and goods accounts for about 25% of total world energy consumption ^[10], and about 25% of greenhouse emissions in the European Union (EU) ^[11]. From 1990 to 2015, the share of CO₂ emissions from the transportation sector in EU countries increased from 32% to 45% ^[12]. Given the continued growth in fossil-based energy usage and transportation-based CO₂ emissions, building a sustainable transportation sector and reducing its CO₂ emissions have become critical, especially for the 196 parties that adopted the Paris Agreement on December 12, 2015 ^[13].

2. Exploring Patterns of Transportation-Related CO₂ Emissions

Existing studies have examined the impact of transportation activities on economic development. The causal relationship between logistics development and economic growth in both the short and long term was studied using a dynamic structural model [14,15,16]^[14][15][16]. The causal relationship between transportation and income was investigated using a panel dataset ^[17] that included the data of 15 EU countries from 1970 to 2008, and the reseauthorchers found an endogenous relationship between income and transportation. The impact of roadway and railway infrastructure on India’s economic growth was studied using the vector error correction model ^[18] and weak short-term effects were found. By examining data from India from 1970 to 2010, these reseauthorchers found unidirectional causality from railway transportation to economic growth. Another study investigating data from Turkey from 1970 to 2005 showed the impact of highway infrastructure on Turkey’s economic growth ^[19]. Similarly, the causal relationships between transportation infrastructure investment and economic growth were also studied in China using time series data from 1978 to 2008 ^[20]. However, unlike previous findings from other countries, these reseauthorchers found unidirectional Granger causality from economic growth to transportation sector infrastructure development at the national level. By grouping the 107 countries in the dataset into high-income, middle-income, and low-income countries, Liddle and Lung found Granger causality runs from GDP per capita to transportation energy consumption per capita by analyzing International Energy Agency data from 1971 to 2009 using panel methods ^[21]. These reseauthorchers also found sufficient evidence that many countries exhibited significant Granger causality running from transportation sector energy consumption to GDP. Although these results were not exactly consistent [22^[22][23],23], the existing literature suggests casual relationships between transportation sector infrastructure development and economic growth, and wresearchers included selected economic features in ourthe models. Therefore, weresearchers considered using socioeconomic features (including GDP, income-level, and GDP from different sectors) in ourthe prediction model. Another stream of existing literature has studied the connections between transportation sector activities and the related CO₂ emissions. Lakshmanan and Han suggested that the growth in people’s propensity to travel drove up U.S. transportation energy use and related CO₂ emissions from 1970 to 1991. Using a decomposition scheme analysis, the reseauthorchers also revealed that freight transportation played a more important role than passenger transportation in U.S. transportation energy use and CO₂ emissions ^[24]. Similarly, Scholl et al. used a comparative analysis approach and studied the changes in CO₂ emissions from passenger transportation activities in nine OECD countries ^[25]. By analyzing the data from 1973 to 1992, the reseauthorchers observed a sharp increase in travel-related energy use and CO₂ emissions from travel-related activities and discussed the impact of fuel shifts within the transportation sector on the increase of CO₂ emissions. In a study conducted by Lu et al., highway vehicle activity was identified as the major driving factor that increased transportation CO₂ emissions from 1990 to 2002 in Germany, Taiwan, South Korea, and Japan ^[26]. Similar studies of transportation sector activity in selected Asian countries or regions suggested that travel-related activity was one of the major potential factors increasing CO₂ emissions [27,28,29,30,31]^{[27][28][29][30][31]}. These studies all suggested that the transportation sector has a direct impact on CO₂ emissions and listed it as the key explanatory variable for CO₂ emissions at the national level. In outhis resear study, wech, the researchers included transportation related features (air, railroad, and vehicle transportation) in ourthe prediction models. To forecast CO₂ emissions, existing studies have adopted different approaches. Some have used time series analysis methods such as exponential smoothing models and ARIMA [32,33,34]^[32][33][34]. Similar studies used grey models to predict CO₂ emissions in China, Iran, and Turkey [35,36]^[35][36]. Many other studies used time series models to predict CO₂ emissions in China, the U.S., Malaysia, Iran, and Zimbabwe [37,38]^[37][38]. Some studies used neural network methods for CO₂ emission prediction ^[39]. The gradient boosting decision tree (GBDT) algorithm was also used in predicting CO₂ from envelope renovation projects in Taiwan ^[40]. The support vector machine model was also used in CO₂ emission prediction in the Chengdu area ^[41]. All these studies used a dataset from one single nation and did not employ cross-validation using another nation’s dataset to evaluate the model. To fill this gap, this restudyearch aims to predict transportation-related CO₂ emissions using socioeconomic features and transportation sector features. WResearchers deploy the support vector machine (SVM) model and the gradient boosting regression (GBR) model to compare to the baseline model, the ordinary least squares (OLS) model, in order to find the best model.