Life cycle sustainability assessment of alternative fuels for heavy-duty vehicles: a systematic review

ElAgouz, Noura; Tamim, Samah; Alenazi, Ahad; Hoblos, Jalal; Alsharqwi, Yazan; Shaarawy, Mostafa; Onat, Nuri C.; Al-Quradaghi, Shimaa Ali; Kucukvar, Murat · 2026 · Crossref

DOI: 10.1007/s11367-026-02587-3

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This systematic review and meta-analysis evaluates the sustainability performance of alternative fuels (AFs) for heavy-duty vehicles (HDVs), addressing the urgent need to decarbonize the freight and public transit sectors. Motivated by the disproportionate contribution of HDVs to global greenhouse gas (GHG) emissions and traffic-related air pollution, the study aims to identify research gaps in existing literature and benchmark the environmental, economic, and social impacts of various fuel technologies. The authors specifically investigate how current studies cover fuel types and vehicle technologies, the methodological approaches adopted, and the integration of sustainability indicators, while also distinguishing between findings from developed and developing regions. The researchers conducted a comprehensive systematic literature review of 44 peer-reviewed articles retrieved from the Scopus database, covering publications from 2000 to 2024. The methodology involved a three-phase process: framing the review protocol, systematic filtering and categorization, and data analysis. Studies were categorized based on LCA methodological approaches (Input-Output, Process, or Hybrid), system boundaries (e.g., well-to-wheel vs. full life cycle), functional units, and regional context. A meta-analysis was performed to benchmark environmental impact categories, such as GHG emissions and energy consumption, across the reviewed studies. The results reveal significant disparities in research coverage and methodological rigor. Electric and diesel fuels are the most frequently analyzed, with research disproportionately focusing on buses (68%) rather than trucks (32%). Methodologically, Hybrid Life Cycle Assessment (LCA) dominates (56%), and well-to-wheel assessments account for 84% of studies, often neglecting upstream processes like raw material extraction and downstream phases like end-of-life disposal. The meta-analysis indicates that hydrogen and electric HDVs powered by renewable energy achieve the lowest GHG emissions in full LCA scenarios, whereas their fossil-based counterparts perform poorly. Economically, diesel, compressed natural gas (CNG), and liquefied natural gas (LNG) consistently demonstrate life-cycle cost advantages. Conversely, social sustainability remains the least explored dimension, and decision support tools like Multi-Criteria Decision Making are used in 93% of the studies. The study concludes that while alternative fuels offer significant environmental potential, particularly when paired with renewable energy, they face challenges related to high life-cycle costs and dependence on upstream energy sources. The authors emphasize the urgent need for methodological standardization, particularly in expanding system boundaries to include full life-cycle stages. Furthermore, the findings highlight a critical gap in social sustainability assessment and the necessity of explicitly considering regional contexts, as infrastructure and economic viability differ significantly between developed and developing nations. Addressing these gaps is essential for generating balanced, policy-relevant assessments to guide the transition toward sustainable heavy-duty transportation.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

StageOutcomeToolModelPromptAttemptsCompleted
discover success Crossref 1 2026-06-19
archive success canonical_url 1 2026-06-26
extract success cached 2 2026-06-26
clean success clean 1 2026-06-20
chunk success chunk 1 2026-06-20
embed success embed Qwen/Qwen3-Embedding-8B 1 2026-06-20
promote success 1 2026-06-19
summarize success llm qwen3.6-27b-prismaquant summ-v5 1 2026-06-26
tag success vector_similarity 6 2026-06-20
verify success 1 2026-06-26

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.