Regionalized Well-to-Wheel Mechanism Diagnosis of Low-Carbon Bus Transition Across Northwest China’s Electricity–Hydrogen Systems
中国西北部の電力・水素システムにおける低炭素バス移行の地域別Well-to-Wheelメカニズム診断 (AI 翻訳)
Wei Zhang, Xiwu Hu
🤖 gxceed AI 要約
日本語
本研究は中国北西部の6省と隣接するエネルギー生産地域を対象に、低炭素バス移行に伴う温室効果ガス排出、一次エネルギー消費、水負荷を地域別に評価。LMDI分解や感度分析により、電気バスと水素バスの最適な導入条件を明らかにした。
English
This study develops a regionalized well-to-wheel assessment for six provincial electricity-hydrogen-bus systems in Northwest China. Using GREET pathway intensities and LMDI decomposition, it shows fleet-level GHG reductions from 9.86% to 73.81% across provinces, with divergent impacts on energy and water. The findings support coordinated expansion of battery and hydrogen fuel cell buses with electricity decarbonization and renewable hydrogen.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも地域ごとに異なる電源構成や水素供給を考慮したバス移行計画が求められる。本手法は、地方自治体や交通事業者が再生可能エネルギーと水素の利用可能性を踏まえた低炭素バス導入判断を行う際の参考になる。
In the global GX context
This paper offers a replicable methodology for regionalized well-to-wheel analysis that integrates electricity and hydrogen pathways. It demonstrates how local conditions (grid mix, hydrogen production) drastically alter GHG and resource trade-offs, informing global transition planning for public transit fleets.
👥 読者別の含意
🔬研究者:Provides a multi-indicator regional framework combining GREET, LMDI, and sensitivity analysis for transit decarbonization assessment.
🏢実務担当者:Fleet planners can use the screening approach to identify optimal BEB/HFCB deployment based on local energy profiles.
🏛政策担当者:Highlights the need for region-specific decarbonization policies that account for electricity and hydrogen pathway interdependencies.
📄 Abstract（原文）
The low-carbon bus transition depends on the electricity and hydrogen pathways that support vehicle operation. This study develops a regionalized WTW mechanism diagnosis for six provincial electricity–hydrogen–bus systems in Northwest China and an adjacent energy-output region. Localized GREET pathway intensities, fleet-level aggregation, Logarithmic Mean Divisia Index (LMDI) decomposition, B0-upstream HFCB exposure tests, local A/B/C perturbations and HFCB intensity-sensitivity checks are combined to evaluate greenhouse gas (GHG) emissions, primary-energy consumption and primary-water burden. The results show that B0–S1 fleet-level GHG reductions range from 9.86% in Shaanxi to 73.81% in Qinghai, while Gansu records the largest absolute decrease, from 126.52 to 41.21 kg CO2-eq/hkm. GHG, primary-energy and primary-water responses diverge: in Qinghai, S1–S2 GHG intensity decreases by 28.76%, while primary-energy consumption and primary-water burden increase by 1.99% and 21.31%, respectively. LMDI results reveal different attribution mechanisms, including dual-driver reduction in Gansu and a counteracting composition effect in Shaanxi. Exposure, perturbation and sensitivity tests indicate that hydrogen-related outcomes depend on pathway intensity, fleet share and break-even margins. The findings support pathway-conditioned screening that coordinates BEB and HFCB expansion with electricity decarbonization, renewable-hydrogen availability and multi-indicator burden assessment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18146961first seen 2026-07-13 05:42:31
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