Wherever you are on your sustainability journey, data is a critical component to guiding your path. To truly understand our environmental impact and craft effective decarbonization strategies, we must embrace a data-driven approach. While the significance of data in measuring carbon footprints, managing energy, and mitigating environmental impact is clear, the path to automation, though desirable, is riddled with challenges. Herein, we will delve into the intricacies of data management in sustainability, specifically as it relates to carbon and energy, and explore the road to automation, including potential roadblocks.
Data Types:
Quantifying your company’s carbon footprint requires a comprehensive array of data sources tailored to your organization’s operations. From asset data to fuel and energy consumption records, utility bills to travel data, and supplier information, the breadth of data necessary is extensive and varied. Each piece contributes to the holistic understanding of your carbon emissions profile, facilitating informed decision-making in your sustainability endeavors. What gets measured gets managed, and accurate measurement requires a wide array of data. Detailed understanding of the types of data required to quantify carbon emissions and a comprehensive data needs can be important first steps towards data and process mapping.
Control over Data:
The accessibility and manageability of data often hinge on asset ownership and/ or operational control. Data within your purview is likely to be more readily accessible and can be efficiently managed. However, reliance on external parties, such as data from leased assets or suppliers, poses challenges. You may not be able to dictate provision of data more frequently than monthly or annually. It is also essential to have clear boundaries of your data and to document what will be included within your Scope 1, 2, and 3 greenhouse gas (GHG) emissions footprint. Guidance such as the GHG Protocol can support in defining boundaries and handling of leased assets. Nonetheless, establishing clear data requirements and formats within contractual agreements can streamline data acquisition from external sources.
Data Stakeholders:
Collaboration among various stakeholders is paramount for effective data management. Environmental, Social, and Governance (ESG) professionals, Environmental, Health, and Safety (EHS) professionals, site-level operations, finance departments, information technology (IT), legal, risk, and leadership are all potential internal stakeholders when it comes to gathering and processing sustainability data. External entities like utilities and consultants may also constitute essential stakeholders in the carbon accounting process. While the ESG team may ultimately have ownership over utilizing energy and carbon data for carbon accounting and disclosures, they are reliant on many teams and individuals both internally and externally to get both voluntary and mandatory reporting completed ahead of deadlines. Effective communication and collaboration among these parties ensure timely reporting and data validation, essential components of sustainable business practices.
It is essential that clear expectations and time frames are included when making data requests. Data gathering is often an iterative process and after an initial request is fulfilled, there may be follow up questions. Providing context about the “why” behind your data request, such as a regulatory report or tracking progress towards a decarbonization goal, can help get buy-in and participation from stakeholders.
Data Cadence & Granularity:
Reporting frequency plays a crucial role in monitoring and managing environmental performance. While annual reporting is standard, quarterly or even monthly reporting may be necessary in certain contexts, such as corporate reporting or reporting to investors. Waiting until year-end to gather data is inefficient; instead, a proactive approach, with data collection throughout the year, facilitates more real-time tracking of progress toward sustainability goals. If you wait til year end and measure emissions and progress towards goals in arrears, it can be difficult to navigate when a target is missed or there are data anomalies.
The level of detail in data collection directly impacts the efficacy of sustainability efforts. Granular data, such as meter-level electricity consumption or minute-by-minute energy usage, offers nuanced insights into operational efficiency and environmental impact. Granular data can also enable more frequent cadence of emissions assessment and better tracking of and reporting on progress towards goals with time to adjust if performance is off track. As organizations strive for greater accuracy and precision in their sustainability initiatives, access to granular data becomes increasingly imperative.
Increased Expectations for High Quality Data:
With increasing regulatory demands and scrutiny of green claims, customers are increasingly requesting more granular data and disclosures from their suppliers. If you are an energy supplier, such as a utility, it is safe to expect that your more advanced customers will soon be looking for more data, such as residual grid mix data, and in higher frequency from you. Some large corporations, such as Microsoft and Walmart, are requiring disclosures of Scope 1 and 2 emissions through CDP from their suppliers. For companies subject to California’s climate regulations, Senate Bills (SB) 253 and 261, which has requirements for Scope 3 emissions disclosures, it is anticipated that subject parties will lean on their suppliers for data to enable accurately meeting this disclosure requirement, particularly when faced with assurance and verification requirements. Reducing emissions throughout the value chain will require collaboration between suppliers and customers, and it starts with accurate data and accurate measurement.
Working Towards Automation:
At Cleartrace, we recognize the pivotal role of data in driving sustainability initiatives forward. Our data management platform empowers organizations to track energy and carbon data at the most granular level with transparency and traceability. As we navigate the complexities of sustainability, prioritizing data integrity and granularity is paramount. While reaching data automation is a journey and can require planning, effort, and collaboration – it is made easier by SaaS platforms such as the Cleartrace platform, coupled with APIs and data mapping support. The peace of mind knowing you have audit worthy data that is streamlined for any reporting cadence is a valuable tool in the face of emerging and increased regulatory drivers.
