Data Paves the Path to Sustainability Transformation

Over the last year, we’ve seen organizations setting bolder sustainability targets than ever before. Microsoft was first out of the gate in 2020 announcing they would be carbon negative by 2030 across Scope 1, 2 and 3 emissions. By 2030, Intel has committed to achieving net-positive water use, 100% renewable power, zero total waste to landfill, and additional absolute carbon emissions reductions even as they grow manufacturing capacity.

While the volume and variety of bold ambitions is both compelling and encouraging, it does not reflect the progress that most organizations have made to-date.

As more organizations pursue bolder, wider-reaching sustainability goals, there’s one key point to remember: data is the lifeblood of every sustainability strategy and is critical for building a business case, informing goals, implementing projects, evaluating decisions, and tracking and reporting that the strategies are on pace.

Here are five ways organizations should leverage data to accelerate sustainability transformation over the next decade.

1. Evaluate Projects Efficiently

By leveraging data from stakeholder surveys, operational technologies, resource cost and consumption, emissions and market prices, organizations can develop these decision-supporting tools that minimize bias influence and drive value.

• Cost benefit analysis – A cost benefit analysis objectively assigns long-term values and helps companies objectively re-evaluate original plans if circumstances change, mitigating the risk of both present and plan continuation biases.
• Scenario analysis – Scenario analyses help organizations mitigate both exaggerated expectations and present biases by considering different possible outcomes that could impact a decision and how those different scenarios advance or hinder the organization’s goals and priorities.
• Risk assessments – Risk assessments help organizations evaluate the likelihood that an event will occur and its potential impact. When they are informed by data, it can help mitigate exaggerated expectations. For example, risk assessments can be an important tool when weighing the cost of inaction or estimating the long-term value of a PPA.

2. Establish a Shared Keystone Metric

The success of a sustainability program depends on cooperation and collaboration between multiple departments: CEOs, CFOs, facilities, finance, legal, procurement, operations, sustainability, and marketing. The challenge is that all of these different functional departments can be managed by different metrics and KPIs that may be misaligned or even in conflict.

For example, a facilities manager aiming to lower operational expenses may prioritize renewable energy projects or contracts that are structured as capital investments, whereas a finance team may prioritize those that are kept off-balance sheet. Alternatively, legal teams that are evaluated on the number of contracts that they process may prioritize simpler contracts over more complicated deals that take more time but can deliver more value.

Resource data questions to ask 10 teams across your organization. Read blog →

Executive teams that leverage data to establish a keystone metric make it much easier to align objectives, strategies and incentives across different departments with competing priorities.

3. Quantify Market-based GHG Emissions

The only way to quantify the environmental impact of an organization’s renewable energy program is to compare its market-based emissions to its location-based emissions – that is to compare the emissions that it generates as result of the decisions it makes (e.g., renewable energy production, renewable energy purchases, third party supply agreements, green tariffs, etc.) to grid averages.

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Calculating these market-based emissions, however, can be a daunting task. To do so, organizations must first identify which sites are in regulated markets vs. which are in competitive markets. In competitive markets, the organization must incorporate generation and emission factors related to distributed energy resources like gas and diesel generators, CHP systems and on-site solar, energy attribute certificates associated with third-party purchases, and the specific emission-factors associated with power purchased from retail suppliers. In regulated markets, the organization must leverage the grid average and any green tariffs that they have entered into.

4. Prioritize Sustainability Projects by their Cost to Reduce Emissions

Many organizations are behind their sustainability targets because they lack a common metric that compares the economic efficiency of different sustainability investments. Without an apples-to-apples comparison, prioritizing renewable energy purchases, efficiency projects, fuel switching, and electrification opportunities can be a real challenge.

By evaluating sustainability investments in terms of how much it will cost to reduce one metric ton of carbon dioxide equivalent (mtCO2e), organizations can normalize the influence of regional energy prices, production/operational differences across their portfolio, and differences in the “dirtiness” of local electricity grids.

5. Align Data to Strategy Design and Disclosure Frameworks

While we’ve seen great progress in recent years with thousands of organizations following environmental disclosure frameworks like the CDP, Global Real Estate Sustainability Benchmark (GRESB) and Sustainability Accounting Standards Board (SASB), many organizations still struggle to collect sustainability data, track KPIs and prepare disclosure documents.

It may sound overly simple but achieving sustainability targets depends on having easy access to complete, accurate, and reportable data. Companies that spend weeks or months collecting data from disparate parts of their organization are not investing their human capital in value-adding activities, and they are well positioned to identify or take advantage of new opportunities.

Having data-informed strategies, analytics and reporting is essential for an organization’s sustainability success, from the early stages of opportunity identification as well as after implementation begins. Continually collecting and analyzing data ensures organizations focus on the right projects, track performance, learn when to adjust, and implement more broadly to achieve the expected outcomes while reporting progress to stakeholders.

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