Life cycle assessment of mid-range passenger cars powered by liquid and gaseous biofuels: Comparison with greenhouse gas emissions of electric vehicles and forecast to 2030

Ternel, Cyprien; Bouter, Anne; Melgar, Joris · 2021 · OpenAlex-citations

DOI: 10.1016/j.trd.2021.102897

archive: archived pipeline: cataloged verified

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

Summary

This study conducts a life cycle assessment (LCA) to compare the greenhouse gas (GHG) emissions of mid-range passenger cars powered by liquid and gaseous biofuels against electric vehicles (EVs), with forecasts extending to 2030. Motivated by the European Union’s stringent CO2 emission standards and the need to decarbonize the transport sector, the research addresses two primary gaps: the comprehensive evaluation of biofuels suitable for internal combustion engines (including bio-natural gas) and the projection of LCA results accounting for future improvements in vehicle weight, battery technology, and electricity grid carbon intensity. The authors aim to determine whether biofuels can complement electrified powertrains in achieving climate goals. The methodology employed a cradle-to-grave LCA approach compliant with ISO standards, utilizing SimaPro software and the Ecoinvent database. The study focused on "C segment" vehicles assembled and used in France, modeling various powertrain architectures: internal combustion engines (ICE), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Vehicle energy consumption was simulated using the Simcenter Amesim platform based on the World Harmonized Light Vehicles Test Procedure (WLTP). The analysis covered two time horizons: 2019 (current) and 2030 (prospective). For the 2030 forecast, the authors incorporated assumptions regarding technological advancements, including a 10% reduction in aerodynamic drag, a 20% reduction in rolling resistance, improved engine efficiencies, and increased battery energy density (from 150 Wh/kg to 200 Wh/kg). Fuel production emissions were modeled using default values from the Renewable Energy Directive II for biofuels and specific French electricity mix projections, which anticipated a shift toward higher renewable content but a slight increase in overall grid emission factors due to reduced nuclear share. The findings indicate that conventional engines powered by biofuels offer significant GHG emission benefits and can serve as a complementary solution to electrified powertrains. Specifically, PHEVs were identified as an ideal solution due to their limited battery size, which mitigates the high carbon footprint associated with battery manufacturing. The study highlights that the current industry trend of expanding battery sizes to increase EV range is detrimental to overall GHG impacts, as the emissions from producing larger batteries are not sufficiently offset by use-phase savings. Furthermore, biofuels provide a rapid decarbonization pathway because existing vehicle powertrains can accommodate high blends, such as 85% ethanol or 100% biodiesel, without requiring engine modifications. Bio-natural gas vehicles also demonstrated strong potential for emission reduction, particularly when utilizing biomethane from closed digestate systems. The significance of this research lies in its challenge to the prevailing assumption that large-battery electric vehicles are the sole optimal solution for transport decarbonization. By demonstrating that biofuels and PHEVs can achieve substantial climate benefits with lower upfront manufacturing impacts, the study suggests a diversified approach to energy transition. It implies that policy and industry strategies should consider the immediate availability and compatibility of biofuels alongside electrification, rather than relying exclusively on battery technology improvements that may inadvertently increase lifecycle emissions.

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 OpenAlex-citations 1 2026-06-19
archive success unpaywall 2 2026-06-26
extract success pdftotext 2 2026-06-26
clean success clean 1 2026-06-26
chunk success chunk 1 2026-06-26
embed success embed Qwen/Qwen3-Embedding-8B 1 2026-06-26
enrich success semantic_scholar 4 2026-06-26
promote success 1 2026-06-19
summarize success llm qwen3.6-27b-prismaquant summ-v5 1 2026-06-26
tag success vector_similarity 6 2026-06-26
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.