Assessing freight structure and its effect on transport CO2 emissions: heterogeneous and mediating effect analysis

Understanding the effect of freight structure (FS) on carbon dioxide (CO₂) emissions is significant for policymakers in the transport sector. However, few studies have investigated fully how the freight structure influences transport CO₂ emissions. In the paper, a comprehensive freight structure index is proposed to assess the freight structure in 16 provinces of China during 2005-2019. Then, the heterogeneous effect of the freight structure and other factors on CO₂ emissions are quantified by the quantile regression model developed. The mediating effect between freight structure and CO₂ emissions is further examined. And the panel causality test is used to explore the directions of causality among variables. The main results indicate that (1) the level of freight structure shows a distribution of increase from western and central regions to eastern regions in 16 provinces. (2) Freight structure has a positive effect on transport CO₂ emissions, and its effect is significantly heterogeneous in all quantiles. (3) Freight structure has an indirect impact on CO₂ emissions through the scale effect. (4) The bidirectional causality is confirmed between economic growth, industrial structure, and information and communication technology with carbon emissions. A unidirectional causality from CO₂ emissions to freight structure is also found. Finally, some policy recommendations are proposed for sustainable transport development in China. The results facilitate the formulation of the regionally tailored carbon emission reduction strategy in the transport sector.

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

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

Determinants investigation and peak prediction of CO2 emissions in China's transport sector utilizing bio-inspired extreme learning machine

The transport sector is recognized as one of the largest carbon emitters. To achieve China's carbon peak commitment in the Paris Agreement on schedule, it is indispensable to explore the peak carbon emissions and mitigation strategies in the transport sector. Many researches in the past have contextualized in China's total emissions peak, while the study about forecasting China's transport CO₂ emissions peak seldom appeared, especially the application of intelligent prediction model. To further investigate the determinants and forecast the peak of transport CO₂ emissions in China accurately, a novel bio-inspired prediction model is proposed in this paper, namely, the extreme learning machine (ELM) optimized by manta rays foraging optimization (MRFO), hereafter referred as MRFO-ELM. Adhering to this hybrid model, the mean impact value (MIV) method is then employed to evaluate and differentiate the importance of thirteen influencing factors. Additionally, three scenarios are set to conduct prediction of China's transport CO₂ emissions. The empirical results indicate that the proposed MRFO-ELM has excellent performance in terms of the optimization searching velocity and prediction accuracy. Simultaneously the level of vehicle electrification is verified to be one of the emerging major factors affecting China's transport CO₂ emissions. The transport CO₂ emissions in China would peak in 2039 under the baseline model scenario, while the plateau would occur in 2035 or 2043 under sustainable development mode and high growth mode, respectively. The peak years imply much pressure on China's transport carbon emissions abatement currently, whereas active policy adjustments can effectively urge the earlier occurrence of the emission peak. These new findings suggest that it is essential for China to improve the energy mix and encourage the electric energy replacement in line with urbanization pace, so as to achieve CO₂ emissions mitigation in the transport industry.