The nuts and bolts of automating emissions data disclosure
Compliance requirements for corporate greenhouse gas (GHG) emissions disclosure are coming if they’re not already here, depending on your jurisdiction of operation.
Ultimately, the aim of any emissions inventory baselining exercise is to decarbonize. That is why governments, security exchanges, and standards bodies don’t only stipulate requirements for emissions disclosures, but also for the plans and pathways each company is taking to reduce and eliminate them. The logic is simple—you need to know where you stand today to improve tomorrow.
Those tasked with GHG disclosure often refer to the complexity and resourcing burdens involved. The resources required to collect, calculate, verify, and report emissions can be daunting, especially when reporting standards are still maturing. This is magnified for large industrial companies—for whom disclosure scrutiny is higher owing to the materiality of their emissions—as the complexity is of a magnitude larger due to the number of facilities and equipment involved. However, we must persevere—as the cost of not knowing and not complying with incoming legislation is high.
To reduce the resources and consequent financial burden, and provide the insights required to decarbonize, you need to automate the process.
Data pain beneath the veneer of each emissions metric
At SLB our own experience, and discussions with other large industrial companies are quite telling:
“…we have hundreds of people running around collecting data”, group environment senior expert, O&G group
“…how can I make improvements if it takes me more than 6 months to find out what our situation is”, head of environment and sustainability, chemical group
Dig deeper and you will understand why it is such a struggle. Internal data resides in many systems—from different enterprise resource planning (ERP) implementations to operations data at facilities—hence people resort to manual entries. That alone is a huge challenge—how do you coordinate and error check thousands, if not hundreds of thousands, of rows of manual entries, all of which need to be auditable and verifiable?
The pain does not stop there. Each activity, purchase, and product must be matched with their equivalent emissions factor, which differs by region, bringing up many questions. Where do you get this information? Is it even accurate or representative, especially if it has a bearing on your reported metrics and thus competitiveness? What happens if part numbers, emission factors, emissions sources, operations change? How do you keep up?
The number of agencies—both compliance and voluntary—that you report to, each with its own unique nuances, further compounds the complexity. As consumer consciousness around the importance of disclosure has grown, customers might even have begun asking for specific emissions information—particularly in requests for proposals and quotations.
If you are senior executive and still believe your organization has this under control without automation—check with your team. Our discussions with people doing the job on the ground tells us otherwise.
“… I almost quit my job with all this manual work…I want to improve but am mired by menial data chasing and entry…”, sustainability expert, O&G company
“…I spend more time explaining why this is important, how emissions are calculated, and chasing data...than actually analyzing and figuring was how to decarbonize economically…”, global director, petrochemical group
Investing in emissions data automation—digital solutions
Companies must automate or face an ever-increasing resource burden with disclosures and associated risks of errors. We at SLB—with more than 800 facilities in over 100 countries and over 200 sold products—have learned many lessons since 2019. Today 70% of our emissions data collection, calculation, and reporting is automated.
In our journey towards automation we have found seven key areas—for efficiency and insight generation improvements.
1. Commitment
The first of these is the importance of a company’s leadership making a visible commitment to progress on emissions disclosure and reduction. This, translates into motivation and support for concrete actions that benefit customers, employees, and management.
As a technology company, we were naturally drawn to using digital technologies to resolve the many pain-points we’ve highlighted. Our senior leadership has put us on a path to always having the latest data at our fingertips, not only on a monthly or weekly basis, with minimal requirements for manual intervention.
2. Understanding the metrics
Understanding metrics is a central part of the early planning process for emissions data management. It is essential to clearly identify, define, and analyze key performance indicators (KPIs) to evaluate their efficiency and effectiveness. These should include a clear baseline, process time, productivity, completeness rate, and accuracy, as well as potential cost savings. With these metrics we can benchmark current performance, measure improvements post-automation, and ensure the automation process addresses efficiency, accuracy and real-time insights.
3. Holistic system requirements
Your emissions data management system should address all stakeholder needs. This includes security standards and measures, performance standards, scalability, interoperability with existing systems, well regulatory compliance, and maintenance requirements. A comprehensive approach ensures the system is robust, efficient, and sustainable. Even if emissions data is shared publicly through a sustainability report, the granularity of the data is still highly confidential.
We must also prepare for the unexpected and the future, building with the ability to integrate new business systems and evolve alongside regulations.
4. Automate data collection and data ontology
The goal for data collection is to use prebuilt APIs to efficiently connect various sources—as emissions data is often spread out in many data repositories such as ERP systems, operational databases, or even spreadsheets in many cases. This approach avoids duplication of data that is already available in other business systems, instead transferring this data into a structured framework, known as the ontology. This makes it easier for users to integrate and analyze the data. Data integration is further facilitated through strong data governance practices, that involve maintaining comprehensive metadata documentation for clarity and conducting regular audits to ensure data accuracy and integrity.
5. Data lineage and error checking with AI capabilities
For company auditing and compliance reporting, it is vital that GHG emissions data is accurate and complete. This means auditors can trace the flow of data from source to final report, ensuring transparency and traceability.
Given the volume of data involved, it is important to automate data quality monitoring and integrate it with the validation process. We do this by leveraging AI for continuous monitoring and error detection, meaning we can identify and rectify discrepancies at each stage.
6. Look beyond: evolve estimates to actuals
We must go beyond estimates to actuals and ensure we have a comprehensive approach when reporting on emissions data.
Depending on data availability and maturity, most companies early in their journey use a spend-based methodology, which relies on industry averages of emission levels and spending for a particular financial unit. However, as maturity and understanding grow within a company, most will move away from this methodology to obtain more accurate emission data and reduce potential uncertainty. For example, moving to activity-based calculation or even to installation and use of various types of sensors to obtain more precise emission values. For these more advanced methodologies it is essential that your digital solution is flexible enough to encompass any type of data calculation or analysis, be that linear, non-linear, or polynomial. For example, spend-based methods using secondary emission factor products use phase calculations or uncertainty analysis.
7. Use analytics to plan decarbonization pathways
A successful decarbonization pathway needs to start with validated data and have a continuous feed from your emission management system—so it always stays up to date.
Once data collection is done, an energy systems model and simulation are used to predict the outcomes of various decarbonization strategies. The exact mix that is right for each company can be determined by controlling for either minimum cost or maximum emissions reduction. This may involve energy efficiency measures, methane leak detection and repair (LDAR) roll out, renewables initiatives, and carbon capture and storage projects.
This may all sound daunting. Unfortunately, there are no shortcuts, but you can certainly simplify and smooth the process by leveraging the lessons and solutions of others.
In our own journey we performed a make vs. buy analysis. While there were many solutions available, none fit the bill for the scale and automation we required.
When we took the lessons and key requirements noted above into account, we recognized that a make strategy to automate these workflows may have risks, such as data quality or operational issues. However, we were fortunate to have our own digital division that we could leverage to tackle these risks. Given that many of the necessary building blocks were already available to us internally—from cyber-security to analytics engines, and data pipelines—we took the decision to make the solutions we and others in hard to abate industries need.
Data automation for disclosure and insights is an ongoing journey. As the reporting landscape and customer expectations mature further, your emissions data must be more accurate with higher fidelity.
Contact us to find out more about our journey and how the lessons we’ve learned can help support you on yours.
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