SearcharxivSearch

arXiv · 2502.07201

Well-to-Tank Carbon Intensity Variability of Fossil Marine Fuels: A Country-Level Assessment

Abstract

The transition toward a low-carbon maritime transportation requires understanding lifecycle carbon intensity (CI) of marine fuels. While well-to-tank emissions significantly contribute to total greenhouse gas emissions, many studies lack global perspective in accounting for upstream operations, transportation, refining, and distribution. This study evaluates well-to-tank CI of High Sulphur Fuel Oil (HSFO) and well-to-refinery exit CI of Liquefied Petroleum Gas (LPG) worldwide at asset level. HSFO represents traditional marine fuel, while LPG serves as potential transition fuel due to lower tank-to-wake emissions and compatibility with low-carbon fuels. Using OPGEE and PRELIM tools with R-based geospatial methods, we derive country-level CI values for 72 countries (HSFO) and 74 countries (LPG), covering 98% of global production. Results show significant variation in climate impacts globally. HSFO upstream CI ranges 1-22.7 gCO2e/MJ, refining CI 1.2-12.6 gCO2e/MJ, with global volume-weighted-average well-to-tank CI of 12.4 gCO2e/MJ. Upstream and refining account for 55% and 32% of HSFO well-to-tank CI, with large exporters and intensive refining practices showing higher emissions. For LPG, upstream CI ranges 0.9-22.7 gCO2e/MJ, refining CI 2.8-13.9 gCO2e/MJ, with volume-weighted-average well-to-refinery CI of 15.6 gCO2e/MJ. Refining comprises 49% of LPG well-to-refinery CI, while upstream and transport represent 44% and 6%. Major players include China, United States and Russia. These findings reveal significant CI variability across countries and supply chains, offering opportunities for targeted emission reduction policies.

Explore related subjects

Keep this discovery

BibTeXRIS

Wennan Long, Diego Moya, Zemin Eitan Liu, Zhenlin Chen, Liang Jing, Muhammad Yousuf Jabbar, Dimitrios Orfanidis, Mohammad S. Masnadi. 2025-02-11. Well-to-Tank Carbon Intensity Variability of Fossil Marine Fuels: A Country-Level Assessment. https://arxiv.org/abs/2502.07201

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph