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Abstract: The automobile industry in China has rapidly developed in recent years which resulted in an increase in gasoline usage and greenhouse gas (GHG) emissions. Focus on climate change has also accelerated to grow pressure on reducing vehicle weight and improving fuel efficiency. Aluminum (Al) as a light metal has demonstrated a great potential for weight savings in applications such as engine blocks, cylinder heads, wheels, hoods, tailgates etc. However, primary Al production requires intensive energy and the cost of Al is more than traditional steel, which may affect the total benefits realized from using Al in automobiles. Therefore, it is very essential to conduct a study to quantify the life cycle GHG emissions and energy consumption if the plan is to achieve fleet-wide Al intensive vehicles. This paper describes a life cycle assessment (LCA) methodology and the general modeling assumptions used to evaluate the impact of Al intensive vehicle on GHG emissions and energy consumption. The results indicated that the reductions in life cycle GHG emissions and energy consumption were not significant when the maximum Al content in an automobile is 145 kg, which is the average level of Al usage in automobiles in North America. A neural network methodology was used to forecast the vehicle stock in China from 2010 to 2020 and a vehicle fleet model was established to track GHG emissions and energy consumption of the vehicle fleet. A material availability factor was also introduced into the LCA methodology to further assist decision makers in providing rational proposals for a widespread implementation of Al in automobiles. A sensitivity analysis was also conducted to study the impact of the Al content in a vehicle on the final outcomes. The GHG emissions and energy consumption could be further reduced when the Al content in an automobile increases. [ABSTRACT FROM AUTHOR]