The Business Case for Sustainability Data
INTRODUCTION
The World has Changed
After the US presidential election results, many sustainability managers are rattled. Is there a rationale for their daily work? Are their jobs, teams and efforts at risk? This post lays out the business case for sustainability data, and the three key pillars of value: Energy and water cost savings; investor demands; and the value of highly accurate, enriched data for enterprise AI strategies. Each pillar delivers far more value than cost, and when summed together, the pillars make a compelling case for sustainability data.
Post-election, the role of sustainability management takes on a sharp, clear focus: Subject matter experts working under finance and compliance, speak the language of business impact and deliver quantifiable business wins to their employer. The new role is not about reporting, it is about building the business case for profitable investments and changes that are uniquely informed by sustainability data. The election results have accelerated a trend that was already underway.
This article is a bit long, so feel free to read just the headline sentences. And from just those snippets you’ll see a change in perspective: The sustainability agenda is set by large external forces that challenge business today. Accurate, reliable sustainability data is a key part of the solution. After all, “you can’t manage what you don’t measure.” Post-election, there will be a tight business frame on all decisions, pushing companies to produce sustainability data without an increase in headcount or costs. Sustainability data remains a must-have, and must be produced efficiently.
The Post-Election Landscape
The dateline above is less than one week after the U.S. elections, so the full impact of the 2024 election is not yet known. But the high-level components of the business landscape are clear:
- No proactive policy support for climate solutions, such as those in the Inflation Reduction Act
- No use of “ESG.” ESG functions, labels and certifications will disappear under the threat of shareholder lawsuits
- There will be rollbacks of current regulations regarding energy, water, pollution and toxins, coming from both the change in presidents and from the June 2024 Supreme Court decision regarding regulatory policy.
So why not stop sustainability data preparation and reporting? Because key business costs and risks can only be addressed with accurate, granular audited data on energy, water, waste and emissions. The value of making data-driven decisions with sustainability data far exceeds the cost of preparing that data.
In the post-election landscape, sustainability is no longer a siloed function, but one that supports progress on the three key challenges every company faces:
#1 Securing Low-Cost Energy and Water Supplies.
Fasten your seatbelts, local electricity and water prices are rising fast from higher demand, particularly from data centers that support digital technologies (AI, bitcoin mining, online streaming). For everyone else this means profits are at risk. Granular, accurate sustainability data by location is a must-have when spending to secure low-cost energy and water.
#2 Meeting Investor Demand for High-Quality Sustainability Data.
Investors have found that adding sustainability, climate risk and environmental data to financial data increases returns. The additional data improves investment selection. Companies that provide accurate, audited data will have higher valuations.
#3 Internal Data for Enterprise AI.
When we step out of the climate/sustainability silo, the number one corporate goal is adoption of enterprise-level AI. Because sustainability data is a lens into operations, it can be a hugely valuable accelerant to enterprise AI.
We will cover these three pillars more in-depth below, but in short, regardless of regulatory policies and the president, the demand for accurate, granular sustainability data remains strong in the post-election world. The reporting and audit standards have been set, so we know how to prepare the data. And while the regulations may change, the demand will persist. There is money to be made.
Why Focus on a Strong Business Case Now?
If the business case for sustainability data is strong, it is reasonable to ask why companies did not dig into it before. The answer lies in how quickly things have changed. Our past is different from our future, and most sustainability teams have been caught by surprise. Here’s why.
In the past, federal, state and local policies attempted to tip private decisions made by individuals and companies by creating new cost structure (a tax on carbon emissions, incentives for clean technologies, lower borrowing costs for green loans), or by restricting choices (fuel-efficiency standards for cars and trucks at federal and state levels, the renewable energy standards in 37 states and so on). These policy approaches make a public problem, such as too much carbon in the atmosphere, a private cost. Expect a rollback or elimination of these policies under the new presidential administration for the very same reason: they raise private costs. For sustainability managers, this means a reduction in policy acronyms and details, and a rise in focus on projects and insights that stand on their own economic legs. Scoop up any available incentives, but treat them as frosting on the cake. They are no longer the cake.
At the same time, we are at the arrival of a new era, one defined by a convergence of technology and sustainability considerations. This new landscape will shape business decisions for the next decade or more, and is driven by the three pillars listed above: rapidly rising energy and water costs; investor demands for sustainability data; and the fast-moving improvements in AI.
To get a sense of how fast things have changed, consider this example of sharply rising electricity demand: In 2018 California regulators expected electricity demand to increase by -0.5% to 1.5% per year through 2030. In 2020 they put out the same demand forecast (1). But in November, 2024, just one week before the elections, Silicon Valley Clean Power – a California energy provider – came out with a forecast that shows a 200% demand increase by 2030, e.g. a 15% increase per year. Silicon Valley Power attributed this to more data centers and more electric vehicles in its service area (2). Silicon Valley is one of the most expensive places in the world to build a data center, so it is not getting all the growth. Nationwide the number of data centers is expected to grow by 50% during the same period, and as we’ll see below, every data center puts pressure on local electricity and water prices. Local utilities are only just recognizing the change in demand.
In sum, we’re at the end of the era in which policies shape specific decisions and at the start of an era where strong economic forces put sustainability considerations, and sustainability data, into top-priority corporate strategies. Only sustainability data that is prepared as rigorously as financial data, and can be used everyday and strategic decisions, will be ready.
Three Pillars of Value for Sustainability Data
This section reviews three business priorities, and the role of sustainability data. The priorities start in familiar territory, energy and water costs, but quickly extend out of the usual climate/carbon accounting/sustainability silo, and form the pillars of value. This reflects the normalization of sustainability data in everyday business practices.
#1 Securing Low-Cost Energy and Water
After decades of nominal 0–2% annual growth, the demand for electricity has skyrocketed and is expected to increase 150 to 200% by 2030. This is driven by electrification – increased use of electric vehicles and electric heating – and rising electricity use from online streaming, bitcoin mining and AI. The fast growing demand has caught most folks by surprise and local electric utilities work on a different timescale. They need years to grow electricity supply, which creates short-term shortages and drives up prices.
In general official price forecasts – from regulators and local utilities – have not kept up with the surge in demand, but recent market transactions in the US northeast (NY, PA, NJ, MD) show price increases of up to 800% starting in June 2025 (3). While it is reasonable to expect that the pace of electrification will slow down in the face of rising costs, the external drivers for digital technologies such as AI, and bitcoin remain strong. Rising electricity prices will not show them down much, just shape the build out.
This graphic of recent headlines tells the urgent and widespread story.
Rising Electricity Prices Across the U.S.
FLORIDA
- 17% increase in electricity rates in 2024
- 50% increase over the past 5 years
Source: Tampa Bay Times
CALIFORNIA
- 2X growth in long-term demand forecasted
- 12–25% rate increases per year, 2024 & 2025
Source: siliconvalley.com
TEXAS
- Rates rising up to 40% month over month
- Market has less long-term planning, more sudden increases
Source: Fox26 Houston
US NORTHEAST
- Up to 800% cost increases by June 2025
- Limits on supply growth due to reliance on imports from Canada
Source: Reuters
The two maps below show how fragmented the US electricity and water market is, which means every corporate asset faces a different electricity and water cost structure, a different trajectory of price increases, and even different access to expanded electricity and water capacity. It all depends on local policies, which other users are in the service territory, and which other users have secured supplies.
The maps also tell the story of the hyper-localization of rising cost trends. Microsoft, Amazon and Google have tried to lock in low-cost energy sources for their data centers through recent purchases of electricity supply from nuclear power plants, but these resources won’t be available until after 2030 (4). Meanwhile, electricity demand keeps growing, with the number of U.S. data centers expected to grow by 50%, and the amount of electricity used per data center is expected to grow by up to 500% per site as data centers become bigger and more dense. Water use grows too (5).
Without granular energy and water use data by location, corporations are flying blind against the big competitors for local energy and water. And, every utility has a different mix of fixed charges that don’t go away with changes in power sources, and variable charges that will. These charges have different names in different utilities, so harmonized, aggregated data that cuts through the noise and enables apples-to-apples comparison of cost savings opportunities across locations is a must-have.
The Large Number of Local US Electricity Utilities
The Large Number of Local US Water utilities
Macquarie, a global player in construction and finance for energy infrastructure sums up the size of this economic opportunity: “We’re at the heart of a massive opportunity with a lot of tailwinds and gigantic volumes that can be invested, but at the same time a very challenging environment…. Once upon a time if you could find a field, you’d talk to the farmer and connect solar to the grid. Now the conversation begins with a very sophisticated insight into what the grid needs and what the market dynamics associated with the grid in a particular location are.” (8)
For sustainability managers who have the habit of tipping project or investment decisions to meet corporate net zero goals: Don’t. In the new era shareholders might sue, claiming mismanagement due to the neglect of profit-maximization. Instead, keep scouring the landscape for cheaper, lower risk energy and water solutions. Today, solar and wind power produce electricity at just a fraction of the cost of coal. While rising electricity costs may keep coal plants in operation longer than before, the newer capacity won’t be as carbon intense. And pay attention to what can be financed. Nuclear power has stalled for decades because its construction is plagued with delays and cost overruns; nuclear power construction loans are highly risky and mostly non-existent. So look for nimble, low-cost, easy-to-finance energy and water sources. They might just be green and low-carbon too.
In sum, electricity and water demand is rising everywhere, with prices increasing even faster due to the fragmentation of regulated utilities: Slow moving additions to supply; direct competition between users for scarce electricity and water access; and low financial reserves at small utilities limits forward investments. Companies that don’t face up to this urgent issue will face increased costs and increased risks.
This is Sustainability Data 2.0. If 1.0 was setting up data flows and getting a small set of data inputs by site for annual reporting, 2.0 is all about comprehensive, granular, financial-grade data on energy and water updated every month as invoices come in. The data will be used in the urgent priority of securing energy and water supplies: project prioritization, capital budgeting and financing.
#2 Meeting Investor Demands
We’ve all seen the climate risk maps, and it can be hard to know what to do with the information. For example, take a look at the U.S. map below that shows every climate event in 2024 that had $1B or more in losses. The number of such events has increased by nearly 300% in recent years, and the events shown are not just hurricanes on the coasts and wildfires in the mountains. As the map shows, the largest number of $1B disasters are from widespread weather events hitting the heartland of the U.S.
Investors have been digesting this type of information for years and from their perspective there are two salient facts: climate change is here and investors can make money from climate change (10).
Let’s start with the data available on a Bloomberg terminal. After all, Bloomberg and similar companies would not offer sustainability data subscriptions without first establishing there is a market for it.
Today, a Bloomberg terminal offers four streams of data on any company:
- Financial data, showing profits and loss, asset changes and announced spending plans.
- Sustainability data, showing levels of water and energy use, reduction plans, and carbon emissions intensity (e.g. tons of carbon emitted per dollar of revenue).
- Physical risk data, showing the likelihood of natural disaster risk by location and estimates of the associated impact. This extends to key corporate suppliers too.
- Nature-related data, showing environmental impacts and dependencies for business-nature interfaces, such as access to clean water, contaminated soil, air quality risk and so on (11).
Bloomberg has automated feeds from NZDPU and CDP, two global sources of company-reported sustainability data for over 75,000 public and private companies, and these data sets are applied to over 100 million financial instruments by ISS, Moody’s and others (10).
Investors take in the Bloomberg data and data from other sources, and then use AI to refine their investment decisions. AI models – and investors – don’t categorize the data, they treat each company as a unified data profile. It’s all math, no politics. It’s all about making money.
With data in hand, there are two key use cases for the unified company profile.
The first use case is that investors want to pick the best investment from a group of peers. Typically investors have asset allocation mandates. For example, an investor might be required to hold 20 – 30% of all assets in the automotive sector. The investor wants to find the best investments within the sector. Lower energy and water use demonstrates less risk and lower costs, as does lower waste generated. These data may also signal proactive management, and that signal can be further amplified by a lower carbon-intensity of revenue. Companies also release their energy, water and emissions reduction plans to investors, again demonstrating proactive management, capital efficiency and pace of innovation and change.
Note that investors are not looking for specific “green companies”, such as a solar company which makes its money from a business model that supports a climate change agenda. They are looking for the best performing stocks amongst a set of sector peers, where performance is measured by investor returns.
The Financial Times reports that the “green premium” on bonds is disappearing and that is a good thing. The premium was paid when there was a shortage of investments with sustainability data and support. But today, as more than 30% of issuers have adopted the reporting standards, the premium has disappeared. (12)
In the long run investors don’t want to hold carbon-intense stocks and assets, as these are seen as having a brittle, risky business model. These companies are expected to go through waves of consolidation, and not all will survive. As one investor puts it “no one wants to be left holding the bag of carbon (13).” To compensate for the slow and continual erosion of their value, carbon-intense companies may take actions to boost investor returns, such as stock buybacks or raising dividends. Of course this works in the short-run, but it simultaneously increases investor concerns about the long-run. So in the short run, the best performing stocks may be more carbon-intense, but this is not a stable situation. Returns will shift to investments that are better equipped to survive and thrive in a world with climate change.
The second use case for sustainability data is to hedge out climate risk. This may sound quite ambitious as climate change is pervasive, but the data shows it can be done. A stream of academic research initiated by Nobel Prize winning economist Robert Engle shows how to form a portfolio by selecting two types of stocks (14). The first group of stocks rises in value when there is climate news and the second group falls in value with climate news. To hedge out climate risk, one holds a balanced portfolio that goes up and down in equal measure for each climate news event. Of course, the portfolio has to be constantly rebalanced to ensure minimal losses. Engle and others have shown how effective this hedge can be, and now it is now widely used.
Again, investors use the data available on the Bloomberg terminal and in other places, combine that with AI and select the financial instruments for their portfolio. Climate risk is not really just a single news event, such as “hurricane hits Florida”, it is a set of economic consequences. For a portfolio of investments, the climate risk by event is characterized by this set of constantly changing data:
- Physical event occurs (hurricanes, wildfire, tornado)
- Geographic area of economic consequence is revealed
- Size of the economic consequence of the physical event is revealed
- Intensity of economic consequence is revealed ($ cost/ household and so on)
- Burden on federal emergency funds, local utilities and local government revealed
- Local insurance rates updated
- Forecasts are updated for all items above
- Valuations of other similar investments are affected
Again, sustainability data is one of the data sets brought in. Climate risk hedges help to mitigate the huge, concentrated losses of any one event and their long tails of cost. For investors, the more data on the individual investments and their exposure, the better.
In 2019 PG&E, the large electric and natural gas utility in California declared bankruptcy, in what the Wall Street Journal called “The First Climate Change Bankruptcy.” In 2017 the company was worth $36B. In 2019, stock value was less than $3B, debt exceeded assets by $20B, the utility owed $30B in liabilities and faced another 750 lawsuits. Today, in 2024, customers still have a fee on each utility bill from the 2019 bankruptcy, as regulators passed some – but not all – of the cost of bankruptcy onto customers. And in 2019, Edison, the parent company of Southern California Edison which is another large electricity provider in California, experienced a 52% drop in stock price. It took over three years for the stock to regain its former value. (15)
Both of these investment strategies make money for investors today. The investment strategies depend on well-prepared sustainability data by companies. Companies that don’t report their sustainability data lose out as they will never be selected as the best amongst peers, nor as part of a climate hedge. This reduces investor demand and valuation. Companies that do report – and provide high-quality data– increase investor confidence in their risk/reward ratio and get selected more often. None of this depends on who is president of the U.S. Investors and financial market players are already profiting from sustainability data. They only want more.
If Sustainability Data 1.0 was voluntary reporting using lots of estimates and cherry picking of what to report, Sustainability Data 2.0 is actual data on energy and water use in operations and in the supply chain – not estimates. 2.0 is sustainability data that is as reliable and complete as financial data. It is accurate and audited. Sustainability Data 2.0 tells the company story to investors, positions the company as a leader amongst peers and raises corporate valuation.
#3 Preparing for Enterprise AI
When we step out of the climate/sustainability silo, the number one corporate goal is adoption of enterprise-level AI. Because sustainability data is a lens into operations, it can be a hugely valuable accelerant to enterprise AI.
Leading AI is improving fast and has consequences for how work gets done in every company. From simple tasks such as manual data entry for invoices, to complex workflows such three-way-match payment approvals, AI can provide a huge step up in efficiency. Here’s a thought experiment that demonstrates the size of the win: Today companies in the S&P 500 spend an average of 8% of revenues on General & Administrative costs. If enterprise AI can bring the average down to 5% – which is quite possible – then $27 trillion of additional corporate value is created. To put that into context, $27 trillion is about one and half months of US GDP (16). The key blocker to this huge gain is accurate, enriched enterprise-level data. Without better internal data sets, leading AI “hallucinates” (has low accuracy rates) for basic data related tasks.
Everyone sees the potential for enterprise AI, as it is increasingly easy to use (the name ‘Chat GPT’ tells us that!), increasingly cost effective and constantly getting more accurate for certain tasks. For example, Google reports that 25% of its software code is now written by AI and the vast majority of software developers plan to use AI-powered coding assistants in the coming year (17).
Coding is a natural place to start with enterprise AI, as previous code will be well-formatted and structured. But for many data-driven applications, leading AI is held back by poor quality data. For example, using AI to query an SQL database produces accuracy rates of 70 to 80% at best. And it fails with more complex queries. With this high level of error rates, employees are better off with current non-AI methods for these tasks. But, research has shown that a key way to reduce these error rates is through highly accurate data tagged with metadata. Think of building a spreadsheet on steroids, one that performs well on every pivot table with no outliers or oddities. This quality of data works in harmony with LLMs and other leading AI (18) .
With leading AI hungry for enterprise-level data, sustainability managers can get the triple win of securing low-cost energy and water, meeting investor demands and accelerating enterprise AI. With modern data preparation systems, a company can leapfrog over legacy setups and deliver highly accurate, enriched sustainability data. This is data on water, energy, waste and emissions is a window into the core of the company’s operations. When enriched with cross-functional metadata from ERP systems, finance and procurement, sustainability data is a unique AI-ready window into efficiencies. With the incredibly rapid pace of improvement in AI, sustainability data acquires value far beyond its current use in reporting.
If Sustainability Data 1.0 was all about spreadsheets and human teams producing the small data sets needed for upcoming reporting, Sustainability Data 2.0 is about automated, accurate data at scale, with enrichment, ready for use by enterprise AI as a unique window into the company’s operations. Sustainability data accelerates AI, and AI accelerates insights for cost-effective energy and water management, as well as investor-ready integrated financial-sustainability profiles.
In Sum
We can’t stay this more strongly: The past is not our future, and our future needs accurate, reliable, complete, granular, audited financial-grade sustainability data. The value of the data in the three settings above far exceeds the costs of preparation. That does not relieve sustainability teams of meeting the demand for data in the most efficient manner; there will be tremendous pressure to get the job done without an increase in headcount. If AI is coming, and companies are evaluated by investors on AI-enabled operational efficiency, then sustainability data preparation must fit into that trajectory too.
The New To Do List for Sustainability Managers
- Gear up. It’s time to learn about finance, capital budgeting, data operations and compliance. Sustainability is coming out of the silo and is a normalized cross-functional business operation.
- Focus on quantifiable impact. Use sustainability data to identify locales and projects that secure low-cost energy and water suppliers.
- Think outside in. Talk to investor relations and external company analysts. See how they use your sustainability reports and data sets. Then improve your data and tell your story better.
- Race towards AI. Demonstrate how your sustainability data fits into the enterprise AI trajectory and delivers a unique, safe place to apply enterprise AI for operational wins. Don’t get left behind as the rest of your company turns to AI.
- And keep on reporting. Your customers and investors will demand it. Reporting software can make this job much easier. And the good news is that an efficient sustainability data preparation system can self-fund the cost of the reporting software through back office savings. The bundle of lower costs will slide through every budget approval.
Sources
Note that California uses a 12-year planning cycle so the forecast was not updated in 2020. In 2022 the state issued an updated forecast to 2035, showing 1.3% annual average growth.
California Energy Demand Update, 2022–2035, California Energy Commission
These stale forecasts show how quickly change has arrived for the electricity grid.
2. 2023 Integrated Resource Plan, Silicon Valley Power
3. PJM power auction results yield sharply higher prices, Reuters
4. Microsoft deal propels Three Mile Island restart, with key permits still needed, Reuters
5. How Many Data Centers Are There and Where Are They Being Built?, ABi Research
6. Electric Retail Service Territories, US. Energy Atlas
7. The Impacts of the Pandemic Remain for Small Water Systems and Customers In-Debt, Pacific Institute
8. Macquarie’s Quest to Become and Energy Transition Champion, Infrastructure Investor
9. Billion-Dollar Weather and Climate Disasters, National Centers for Environmental Information
10. See previous GLYNT.AI blog posts including: Is Your Sustainability Data Ready for Investors? and Follow the Money
11. Bloomberg Launches Solution to Help Investors Assess their Exposure to Nature and Biodiversity-related Risks, Bloomberg
12. Green Bonds: the Disappearing Greenium is a Welcome Development, Financial Times
13. See GLYNT.AI’s Follow the Money
14. See GLYNT.AI’s Follow the Money and Nobel Laureate Robert Engle: Hedging Future Risks Through Today’s Choices, NYU Shanghai
15. PG&E: The First Climate-Change Bankruptcy, Probably Not the Last, The Wall Street Journal, and Pacific Gas & Electric Market Cap 2010-2024 | PCG, Macrotrends
16. EV/EBITDA as of Nov 9, 2024 for the S&P 500 is 26; 20 was used in the calculation. The current market cap of the S&P 500 is $45T. 3% expense reduction x 20 EV/EBITDA x $45T = $27T.
17. Google CEO says over 25% of new Google code is generated by AI, Ars Technica
18. How an Old Google Search Trick is Solving AI Hallucinations, The Information, and Unleashing The First Forrester Wave™ For Vector Databases, Forrester
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