Nov-2024

Power of carbon accounting in the low-carbon fuel industry

Why life-cycle assessments are essential for renewable diesel and sustainable aviation fuel producers to meet ESG and net-zero goals.

Kristine Klavers
EcoEngineers

Article Summary

In today’s carbon-conscious world, carbon accounting is essential for industries to measure, manage, and reduce greenhouse gas (GHG) emissions. It goes beyond compliance, helping organisations meet their environmental, social, and governance (ESG) and net-zero goals, optimise production processes, and tap into financial incentives that drive return on investment (ROI).

Emerging climate regulations, low-carbon fuel markets, subsidies, and tax credits offer significant opportunities to support companies and their stakeholders on their ESG journey, making literacy in carbon accounting and carbon markets all the more critical. Understanding carbon accounting and exploring the challenges involved is important for achieving compliance and strategic goals. As businesses market low-carbon products, they must be confident and transparent with the results they communicate to stakeholders, including the public, clients, and financial entities (see Figure 1).

A key to unlocking the power of carbon accounting is life-cycle assessments (LCAs). LCAs are particularly important in determining the carbon intensity (CI) of low-carbon renewable fuels such as renewable diesel (RD) and sustainable aviation fuel (SAF). Understanding carbon accounting enables businesses to communicate their low-carbon calculations effectively to stakeholders, positioning them for success in an increasingly regulated marketplace requiring minimum CI scores that are verified. This article is not meant to be a complete analysis of LCAs, but rather it is meant to reinforce their importance in the context of carbon accounting.

Foundations of carbon assessments
LCAs are the foundation of product-level carbon accounting. They are a systematic and comprehensive method for evaluating the environmental impact of a product, service, or system, from its inception to its end-of-life (cradle-to-grave). LCAs are a tool used to ensure that emissions are quantified across the life-cycle of a product, including raw material extraction, production, transportation, use, and disposal using available data and established models.

In 2006, the International Organization for Standardization (ISO) published ISO 14040 (ISO, 2006), a framework for developing LCAs to measure a product’s impact and facilitate environmental decision-making, helping to ensure consistency and the ability to compare products and processes.

ISO 14040 outlines four key phases to develop an LCA:
υ Goal and scope definition: This phase builds the LCA framework and, among many variables, involves establishing the functional units, objectives, and boundaries for the assessment, defining the purpose of the study and identifying the product system to be assessed.

Using the same units for comparing LCAs is crucial. For example, CI measured in kilograms of carbon dioxide equivalent (CO2e) per kg of product (kg CO2e/kg) is different from one measured in kg CO2e/MJ of product (megajoule). The main difference between these two units of measurement is that the former measures the CI based on the mass of the product, while the latter measures it based on the energy content.

The system boundaries phase requires the boundaries to be clearly defined (for example, gate-to-gate, gate-to-grave, and overall cradle-to-gate). As RD and SAF production chains expand, upstream processes like farming practices and fertiliser use might need to be included in the system boundaries, which again will significantly impact the CI score (see Figure 2).

ϖ Inventory analysis: During this phase, data is collected on all relevant inputs (such as raw materials, energy, and water) and outputs (such as emissions, waste, and byproducts) relating to the system boundary established in Phase 1. This inventory forms the foundation for the impact assessment, which will be described in Phase 3.

Data comes from sources such as process data, design data, industry data averages, public data, and/or set default values. All data and assumptions need to be referenced, and the collection process and timing of the data must be included. The better the data, the more reliable and accurate the calculations.

Depending on the model applied for determining the CI scores of RD and SAF, there are different default values available. This is especially clear when including the feedstock indirect land-use change (iLUC) value. Currently, the iLUC values of the different models available vary widely (see Figure 3).

ωLife-cycle impact assessment (LCIA): This phase uses the results of the earlier phases to determine and evaluate the potential environmental impacts associated with the product or system. This is typically defined in the goal and scope phase. Key categories include global warming potential (GWP), resource depletion, and water use. Different impact categories are identified and quantified, from climate change to acidification and land use.

ξ Interpretation: In this phase, the results are interpreted against the study’s original goals and scope. This includes identifying significant issues, drawing conclusions, and making recommendations for reducing environmental impacts. It is essential to fully describe assumptions, describe sources of data, and test sensitivity to different variables used to calculate CI scores. Highlighting uncertainty and variability without explaining its context in the study provides no inherent value and can expose companies to reputational and regulatory risks.

Challenges in carbon accounting: Data, methodologies, and regulatory evolution
While the benefits of carbon accounting are clear, its implementation across diverse industries and regions presents several challenges. One of the most significant challenges is data identification, selection, availability, and quality. Carbon accounting involves the meticulous management of information and relying on precise and comprehensive data, which can be difficult to acquire, particularly for first-of-a-kind, new processes and industries with complex global supply chains. Although the data might be available, decisions on the formatting, collection, and sharing of the data are a challenge.

For example, the agriculture sector, which provides feedstocks for biofuels like RD and SAF, involves multiple stakeholders across different regions, each with its unique data collection practices. Inconsistent or incomplete data can lead to a high range of uncertainty in the calculated CI scores, jeopardising a company’s compliance with regulatory programmes and its ability to participate in carbon markets.

Another challenge is the variability between carbon accounting models and methodologies. Models are the software tools used to calculate GHG emissions, while methodologies refer to the underlying frameworks that define how, when, where, and why data is assessed. Different industries and regions use various models, each with unique default values, units, and data requirements. For example, the Argonne GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies) model is commonly used in the US to calculate CI scores for transportation fuels, whereas regions like Canada may use models such as GHGenius or openLCA. The differences in methodologies make comparing CI scores across projects and geographies challenging.