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Machine learning-based prediction analysis for Australia sustainable transport: Optimal transportation mix equation for long-term policy planning and environmental impact mitigation

Author

Listed:
  • Abbas, Sohail
  • Kashif, Uzma
  • Kousar, Shazia
  • Noor, Shameen
  • Lu, Heli
  • Lu, Siqi

Abstract

This study delivers a comprehensive evaluation of Australia's transportation sector and its pathway towards sustainability through an optimized transportation mix, integrating electric vehicles (EVs), hydrogen fuel cells, biofuels, and renewable electricity. Leveraging predictive modeling, the research investigates the carbon mitigation potential of diverse energy scenarios from 2024 to 2050. The results reveal that an optimal energy mix can reduce transportation emissions by over 80 % by 2050. Key projections include EV adoption rates rising from 12 % in 2024 to 100 %, hydrogen usage increasing from 4 MtCO2e to 50 MtCO2e, biofuel usage expanding from 10 MtCO2e to 30 MtCO2e, and electricity-related CO2 emissions declining sharply from 140 MtCO2e to 10 MtCO2e. These outcomes highlight the urgent need for innovative policy frameworks, robust infrastructure investments, and regionally tailored strategies to address socio-economic and technical barriers. Supported by extensive data from national and international agencies, linear regression modeling underscores the critical interplay between clean energy technologies and emissions reductions, demonstrating that no single technology can drive decarbonization independently. The study introduces a novel energy mix equation, balancing environmental, economic, and social dimensions, aligning with the principles of the Triple Bottom Line framework. This pioneering research offers a strategic roadmap for policymakers and stakeholders, emphasizing actionable interventions to optimize resource utilization and achieve sustainable transportation goals. The findings underscore the transformative potential of a cohesive energy strategy, securing Australia's environmental resilience, energy security, and leadership in sustainable transportation.

Suggested Citation

  • Abbas, Sohail & Kashif, Uzma & Kousar, Shazia & Noor, Shameen & Lu, Heli & Lu, Siqi, 2025. "Machine learning-based prediction analysis for Australia sustainable transport: Optimal transportation mix equation for long-term policy planning and environmental impact mitigation," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225042264
    DOI: 10.1016/j.energy.2025.138584
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