This study applies three different artificial intelligence algorithms (Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) to estimate CO₂ emissions in Türkiye’s transportation sector. The input parameters considered are Energy consumption (ENERGY), Vehicle Kilometers (VK), POPulation (POP), Year (Y), and Gross Domestic Product Per Capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO₂ emissions using statistical indicators (R², RMSE, MSE, and MAE). Across all scenarios and algorithms, R² values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful in estimating CO₂ emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer the potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO₂ emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector’s environmental impact. It also contributes to the understanding of AI-assisted CO₂ emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.

The imperative to decarbonize global energy systems and enhance energy security necessitates a transition towards ecofuels, broadly classified as biofuels, waste-derived fuels, and electrofuels (e-Fuels). The primary goal of this review is to provide a holistic and comparative evaluation of these three pivotal ecofuel pillars under a unified framework, identifying their strategic niches in the energy transition by critically assessing their interconnected technical, economic, and policy challenges. It offers a comparative dissection of inherent resource constraints, spanning biomass availability, the immense scale of renewable electricity required for e-Fuels, sustainable carbon dioxide (CO₂) sourcing, and the complexities of utilizing non-biodegradable wastes, identifying that true feedstock sustainability and holistic lifecycle management are paramount, cross-cutting limitations for all pathways. This review critically highlights how the current global reliance on fossil fuels for electricity production (approx. 60%) and the upstream emissions embodied in renewable energy infrastructure challenge the climate neutrality claims of ecofuels, particularly e-Fuels, underscoring the necessity for comprehensive well-to-wheels (WtW) lifecycle assessments (LCAs) over simpler tank-to-wheels (TtW) approaches. This perspective is crucial as emerging regulations demand significant greenhouse gas (GHG) emission reductions (70–100%) compared to fossil fuels. Ultimately, this synthesis argues for a nuanced, technologically neutral deployment strategy, prioritizing specific ecofuels for hard-to-abate sectors, and underscores the urgent need for stable, long-term policies coupled with robust and transparent LCA methodologies to guide a truly sustainable energy transition.

In recent years, exergy analysis method has been widely used in the design, simulation and performance assessment of various types of engines for identifying losses and efficiencies. In this study, the first and second Laws of thermodynamics are employed to analyze the quantity and quality of energy in a four-cylinder, direct injection diesel engine using petroleum diesel fuel and biodiesel fuel. The experimental data were collected using steady-state tests which enable accurate measurements of air, fuel and cooling water flow rates, engine load, and all the relevant temperatures. Balances of energy and exergy rates for the engine were determined and then various performance parameters and energy and exergy efficiencies were calculated for each fuel operation and compared with each other. The results of tested biodiesel offer similar energetic performance as petroleum diesel fuel. In addition to this, the exergetic performance parameters usually follow similar trends according to the energetic performance parameters.