Carbon intensity of global crude oil trading and market policy implications

The global transition to a cleaner energy mix necessitates more precise emissions reporting across the petroleum supply chain. Current carbon footprint assessments lack the granularity to capture the complexities of crude oil trading, hindering the development of effective climate policies. The lack of source crude traceability obscures the "well-to-refinery-entrance" carbon intensities along specific pathways between producers and consumers. Crude oil remains crucial for sectors like aviation and petrochemicals where alternatives are limited, making efficient emissions management vital. Existing regulations, like the Low Carbon Fuel Standard (LCFS) and the Fuel Quality Directive, aim to incentivize low-carbon crude oil, but are hampered by data gaps and insufficient supply chain transparency. Previous studies using models like OPGEE have shown the heterogeneity of crude production carbon intensity but haven't translated these findings to marketable crude blends, limiting their policy relevance. This study aims to address these limitations by conducting a high-resolution global assessment of crude oil carbon intensity, incorporating detailed supply chain traceability and leveraging bottom-up engineering-based methods. The goal is to provide data-driven insights to inform the design of market-based policies that incentivize the use of low-carbon crude oil, potentially influencing its traded price and contributing to a low-carbon future.

Existing literature highlights the need for improved carbon intensity (CI) estimations and reporting in the petroleum industry, especially concerning crude oil trade. Studies based on the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model have demonstrated the heterogeneity of crude production CI at the field level. However, these studies lack the resolution needed to translate findings to marketable crude oil blends, hindering the development of effective policies to incentivize demand for low-carbon crude. Furthermore, existing studies often rely on fixed and approximated baseline values for crude oil transportation emissions, limiting their accuracy and policy implications. Consequently, research has focused on overall emissions reductions in the oil and gas sector rather than introducing incentives for low-carbon practices in global crude oil trade, where carbon intensity could be a key specification in crude oil valuation, similar to sulfur content and API density.

This study employs a high-resolution life cycle assessment (LCA) to quantify the "well-to-refinery-entrance" greenhouse gas (GHG) emissions from the global petroleum supply chain. The LCA is built upon a network representing the global oil supply chain, where nodes represent oil fields, shipping terminals, pipeline stations, and refineries, and edges represent pipelines and shipping routes. A multi-objective optimization algorithm is used to estimate crude blending at the country level, integrating with data on crude demand at refineries. This enables the tracking of crude oil at the level of individual supply chain pathways. The "well-to-refinery-entrance" scope encompasses crude extraction (upstream) and crude transportation (midstream) emissions. Upstream emissions are estimated using OPGEE version 3.0c, while midstream emissions are calculated using mode-specific models: the Crude Oil Pipeline Transportation Emissions Model (COPTEM) for pipeline transport and a shipping emissions estimator based on AIS-tracking data. The blend estimation algorithm is a country-specific multi-objective optimization problem that considers distance, connectivity, volume, and API to estimate the relationship between oil fields and crude blends. A gradient-based technique coupled with an initialization algorithm (incorporating unsupervised learning and a genetic algorithm) solves this optimization problem. The resulting blend-level upstream carbon intensities and high-resolution mapping of crude barrels from sources to destinations serve as input for a barrel-tracking algorithm that identifies shortest paths in the global supply chain network, weighted by pipeline lengths and shipping route distances. Mode-specific bottom-up models then estimate emissions associated with pipeline and shipping transport. Data sources include public datasets (NASA MODIS, Shuttle Radar Topography Mission) and commercial data providers (Wood Mackenzie, GlobalData, Kpler, S&P Global).

The study reveals significant variability in global "well-to-refinery-entrance" carbon intensities, ranging from 4.2 to 214.1 kg-CO₂-equivalent/barrel with a volume-weighted average of 50.5 kg-CO₂-equivalent/barrel. Upstream emissions constitute the majority (~90%) of the total emissions. Significant inter-blend variability exists within and between countries, driven primarily by differences in field-level CI. For instance, Russia shows lower inter-blend variation compared to the global average due to its large-scale infrastructure and geographically clustered blends. Conversely, countries like Canada and Venezuela exhibit higher variability due to factors like the use of carbon-intensive operational practices. The Oil Sands Synthetic blend from Canada stands out with the highest CI (144.5 kg-CO₂eq/bbl). Midstream emissions (crude transportation) contribute approximately 10% to the total emissions but show significant variability across different supply chain pathways, especially for blends with a large export footprint. The volume-weighted midstream CI is 5.37 kg-CO₂eq/bbl. Regional differences in midstream CI are apparent, with higher values observed in North America due to extensive pipeline networks and longer distances, while the Middle East shows lower values due to the prevalence of large tankers and efficient shipping routes. Aggregating well-to-refinery-entrance CI at the consumer country level reveals substantial variation in the net carbon footprint attributed to different countries, reflecting both domestic upstream CI and transportation emissions. Countries with high domestic upstream CI, like Venezuela and Canada, show higher overall CIs. Importers heavily reliant on shipping tend to exhibit higher refining-attributed CI than those with access to pipeline networks.

The findings highlight the significant heterogeneity in the life-cycle carbon emissions associated with global crude oil trade. This variability represents a considerable untapped decarbonization opportunity. The high-resolution LCA, with its detailed supply chain traceability, addresses the data gaps that have hampered previous policy efforts. The results underscore the potential for market-based policies to incentivize low-carbon sourcing and supply chain pathway prioritization. Differentiation of crude blends at refinery intake, as suggested by the study, could significantly influence crude oil pricing, potentially driving a nonlinear shift in the global supply curve. The different levels of aggregation (pathway, blend trade, country) provide the flexibility to implement policies with regional and sectoral scopes, creating a multi-pronged approach to incentivize CI-based crude differentiation. The study's granular data and broad coverage provide a framework for bridging the gap between model-based approaches and reporting practices.

This study provides a high-resolution assessment of the carbon intensity of global crude oil trade, revealing significant variability and highlighting the potential for substantial CO2 emissions reductions through policy interventions that prioritize low-carbon pathways. The detailed methodology and data provide a robust framework for informing future decarbonization policies, emphasizing the need for improved supply chain traceability and emissions reporting. Future research could focus on incorporating temporal variability into the analysis, exploring the dynamics of inventory buffers and tanker patterns, and further refining the model to enhance its accuracy and applicability to diverse policy contexts.

The study's reliance on 2015 data means that the results may not fully reflect recent changes in the oil industry. The availability of data, particularly for intra-field pipelines and certain transportation modes, could also affect the accuracy of the estimates. The model's assumptions about crude blending and transportation could influence the overall results. Further research is needed to account for temporal variability in the supply chain, including changing operating conditions, inventory buffers, and dynamic tanker patterns.