Four Main Data Challenges in Managing Carbon Inventories
Updated: Sep 2
Countless articles and books have espoused the importance of data-informed decision making. As the saying goes, ‘you cannot manage what you do not measure’. Therefore, it should come as no surprise that companies have developed their own carbon inventories to track and report on their carbon emissions. According to the Governance & Accountability Institute, Inc., 90% of S&P 500 companies published sustainability reports in 2019. Companies that keep track of their carbon emissions are able to use this data to improve operational efficiency, support sustainability efforts, and evaluate potential regulatory impacts. Given the multiple benefits to effective carbon inventories management, we want to consider current common practices, discuss data challenges, and propose potential tools to improve carbon management practices.
What is the state of current carbon inventories?
Picture a smattering of spreadsheets with multiple tabs, scanned copies of energy bills with file names that have nothing to do with the contents, and multiple system logins to different software systems. This is a small glimpse into the all-too-common current state of data collection for carbon inventories at companies. The lucky subset of teams that have a centralized software tool to manage all of these disparate sources of data have a relatively easier time managing all this data, but those are not the vast majority of teams burdened with collecting and managing a company’s carbon inventory.
Here are some of the most common data challenges In Building & Maintaining carbon inventories:
1. Data may be hard to obtain because of all the disparate sources as well as different collection cycles, which often leads to incomplete data for an annual reporting cycle.
For example, consider the collection of energy bills from all facilities. Not only do different people receive the bills for different facilities, but they have to remember to scan and send a copy of the bill (ideally, appropriately named) to the responsible team. Energy bills arrive at different times of the month for different utilities, and they are not necessarily standardized across regions. Most companies also have a combination of facilities where they pay for the energy (and therefore receive the energy bills) as well as facilities where the energy bills are paid by the landlord. Furthermore, if a centralized team is responsible for overall data collection, they may not know local conditions and thus, miss out on energy data that they do not even know exists. In addition to the potential mismatch between when data is made available as compared to common annual reporting cycles, the frequency of data collection can vary drastically from daily energy readings and monthly fuel consumption from fleet vehicles to annually estimated emissions from employee commutes or periodic emissions from major events such as company-sponsored conferences. Consider the difficulties collecting data within an organization, and then expand the data collection struggle to the company’s vendors and external partners who own pieces of an organization’s supply chain emissions data.
2. Once the designated person or team has all the data, they have to spend a significant amount of time organizing the data.
A central team may be receiving data from such disparate sources like spreadsheets, text messages, emails, physical notes and scanned documents. These become vastly difficult to track and manage, and cannot be easily audited by a third-party. The data sources need to be able to be attributed to the appropriate part of the business, and yet, the data is often not easily structured to deliver management insights down the road. Another concern with organizing the data is that not all data collected is of high quality. More specifically, the data may not be fully representative of real-world emissions and/or introduce random sampling errors. The data quality is also impacted by the completeness of data collection. While some sophisticated energy meters may provide interval data every 5 minutes or every hour, other energy bills can be hard to understand or easily discern the minimum necessary data. Some data may not be provided at a useful resolution to break out emissions sources to the appropriate business unit or processes, and this can be difficult for a centralized team to discern manually.
3. The consolidated data has to undergo a series of calculations for each emission category, which calls into question the data reliability and transparency.
The electricity grid in different regions has very different input energy sources, which lead to very different emissions factors. The emissions factors are a measure of how clean the electricity grid is in different regions and countries and impact the total calculated emissions. Additionally, some companies have customized agreements with their energy providers, which leads to further customized accounting of emissions factors. Separate from electricity-related emissions are several other categories of business activities and associated carbon emissions that require very different types of calculations to derive the associated emissions from the raw data provided. These calculation methodologies can be opaque and lead to questions of data reliability and transparency, as well as diminish the ability to effectively review or audit carbon inventories.
4. The combination of the data challenges and potentially inconsistent methodologies in deriving an organization’s carbon emissions inventory leads to concerns about uncertainty in the data.
For an in-depth discussion about uncertainties associated with annual emissions reporting and mitigation trends, see the IPCC report. The sheer volume and diversity of data and calculations that make up an organization’s carbon footprint make it hard to sift through the compiled data to truly grasp where emissions are coming from and gather actionable insights. Returning to the example above about facilities energy data, some facilities’ energy bills are not submetered, which leads to the need to estimate the energy for a single organization within a much larger facility. Similarly, emissions from different value chain categories may often require inference or estimates due to insufficient, or otherwise lacking, data sources. There are significant challenges to identifying reliable data versus knowing what data is likely to be erroneous.
Given the numerous data challenges outlined above, organizations that rely heavily on spreadsheets and manual data collection and calculations can benefit greatly from investing in a software-based inventory tool. The need for high-quality data not only supports better organizational decision making but is also highly desired by investors. Right from the start, a software inventory tool will simplify and help automate data collection processes. Instead of ad hoc data collection, a software tool makes collaboration easy and transparent while enabling accurate inventory calculations that can be reviewed and audited in detail. Software inventory tools have the benefit of pre-established data structures for categorizing emissions by Scope 1, 2, and 3, while also providing flexibility to reflect organizational structure in categorizing carbon emissions. Using a software tool enables a major shift from manual data work to refocus on analyzing trends and performance analytics. This will ultimately help organizations better prepare for everything that comes after accounting for emissions such as responding to evolving conditions involving regulations, customer demands, and investor needs.
Sinai Technologies is here to help your organization be better prepared for evaluating and executing your decarbonization strategy. Our goal is to provide you with the tools you need to make the best decisions around emissions possible. Our platform serves as a force multiplier so that your carbon inventory team can focus on actionable insights and help future-proof your organization. We can help address the data challenges discussed above as well as support your organization’s overall modeling and climate-related forecasting needs. To learn more, schedule a demo today: https://www.sinaitechnologies.com/request-a-demo.
GlobeNewswire. (July 16, 2020). “90% of S&P 500 Index Companies Publish Sustainability Reports in 2019…”. Governance & Accountability Institute, Inc. Retrieved from https://www.globenewswire.com/news-release/2020/07/16/2063434/0/en/90-of-S-P-500-Index-Companies-Publish-Sustainability-Reports-in-2019-G-A-Announces-in-its-Latest-Annual-2020-Flash-Report.html.
International Panel on Climate Change (IPCC). (2006). “Chapter 3: Uncertainties”. Retrieved from https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_3_Ch3_Uncertainties.pdf.
Bloomberg News. (April 22, 2019). “As demand for ESG investing grows, so too does the need for high-quality data”. Retrieved from https://www.bloomberg.com/professional/blog/demand-esg-investing-grows-need-high-quality-data/.