How Should a CIO Manage Energy, Water and Sustainability Risk?

For many CIOs, sustainability used to sit beside data center operations rather than inside it. Facilities teams tracked electricity and PUE, sustainability teams prepared emissions reports, and IT teams focused on compute capacity, reliability and service delivery. AI infrastructure is making those boundaries harder to hold.

Power availability can determine whether new compute capacity can be deployed. Cooling choices influence water consumption. Energy sourcing can materially change carbon emissions. Grid upgrades can affect local electricity prices, and large projects can face community opposition, permitting delays and legal challenges. An August 2026 report cited roughly $29 to $30 billion in grid capacity costs associated with AI data center demand across four recent capacity auctions, while a separate report described a proposed 7.65 GW natural gas generation project in Texas intended to support an Amazon data center. At the same time, discussions around Meta and Wonder Valley highlighted concerns about water, power and local acceptance. For the CIO, sustainability risk is becoming part of capacity risk: energy, water and community acceptance can decide whether future compute capacity is operationally possible, financially acceptable and politically sustainable.

Energy Is No Longer Just an Operating Expense

Electricity has always been one of the largest costs of running a data center, but AI is changing the scale of demand and the consequences of where that demand appears. Gadget Review cites Monitoring Analytics in reporting that AI data centers contributed roughly $29 to $30 billion in grid capacity costs across four recent auctions, about 46 percent of total capacity charges in that period. The article focuses on PJM, the regional grid covering 13 U.S. states and Washington, D.C., where growing data center demand is contributing to higher capacity costs that flow through to electricity customers. Large projects can affect the economics of the grid around them, which can influence regulation, public policy and community willingness to approve further expansion. A CIO planning new capacity needs to understand where the electricity will come from, what additional grid infrastructure is required, and who is expected to pay for it.

The Grid Connection Has Become Part of the Business Case

Gadget Review describes a growing political response to these costs, noting that 27 U.S. states were advancing legislation intended to require data centers to fund more of their own grid expansion. The cost of a data center can no longer be assessed only at the building boundary — a project may require a new substation, transmission upgrades, additional generation and backup capacity, and the allocation of those costs can materially change the business case. Future site selection should weigh expected grid obligations as seriously as land cost, taxation, network access and labor availability. A site with inexpensive land but a politically difficult power expansion may ultimately be less attractive than one where energy capacity already exists.

Behind-the-Meter Generation Changes the Sustainability Equation

Some operators are responding to grid constraints by bringing generation closer to the data center. Tom's Hardware reports that Amazon is planning a natural gas power plant in Pecos County, Texas, to supply a data center at the same site, with permits allowing up to 7.65 GW of generation across 35 gas turbines and up to 33 million tons of annual CO2 emissions — alongside Amazon's continuing commitment to reach net zero carbon emissions by 2040. That is an obvious tension between rapidly expanding AI capacity and long-term sustainability targets. Tom's Hardware calls this broader pattern behind-the-meter generation, where hyperscalers build or secure dedicated power partly because conventional grid connections take too long. Operators are evaluating gas, nuclear and renewable options, making energy sourcing a tradeoff between availability, reliability, speed, cost and emissions.

Sustainability Targets Need to Survive Contact With Capacity Planning

Corporate sustainability commitments are often set years before the infrastructure decisions that determine whether they are achievable. That gap matters more as AI demand rises, because a power-sourcing decision made today can shape the organization's emissions profile for years. If compute demand doubles and the additional infrastructure can only be deployed quickly using carbon-intensive generation, the CIO faces a direct conflict between capacity and sustainability objectives. The organization needs enough planning data to see the tradeoff before it becomes urgent: how much energy future workloads will require, what generation sources are available, how facility efficiency affects total consumption, and whether alternative locations could support the same demand with a different environmental profile.

Water Is Becoming a Capacity Constraint Too

Electricity gets most of the attention because its relationship with AI compute is easy to see, but water is more complicated because consumption depends heavily on cooling design, climate and facility architecture. One Reddit post links to reporting about protests against a Meta data center north of Edmonton over water and power concerns; another links to a court ruling involving Sturgeon Lake Cree Nation and the proposed Wonder Valley project, again centered on a water dispute. These threads do not provide enough detail to reconstruct every factual element, but the topics matter because water consumption has become visible enough to contribute to local opposition and legal challenges. For CIOs, the question is straightforward: how much water does the chosen cooling architecture require, and is that appropriate for the location? Water should be a site-specific infrastructure resource, not a metric that only appears in annual reporting.

Cooling Design Is Becoming a Strategic Decision

Cooling used to be mostly a facilities engineering choice, but high-density AI racks are pushing it closer to CIO strategy, since cooling architecture affects rack density, electricity consumption, water use, hardware compatibility, maintenance and how much future capacity a site can support. The CIO should understand the basic operating characteristics of the cooling system even if detailed engineering stays with specialist teams. Two facilities with the same electrical capacity can have very different AI deployment potential if only one supports high-density liquid-cooled racks; two cooling systems capable of the same compute load may have very different water requirements. The planning question should move from whether there is enough cooling to what environmental resources the cooling architecture consumes, and how that scales as density increases.

Water Should Be Measured Alongside Power

A CIO dashboard commonly includes server utilization, storage capacity and sometimes energy consumption, but water is rarely connected to the technology operating model. That needs to change wherever cooling creates meaningful water consumption. At minimum, organizations should track total facility water consumption, water use by cooling system, seasonal variation, water source, expected consumption under future expansion, and water required per unit of useful computing output. Today's utilization matters, but committed future demand matters more — a facility can have an acceptable water profile at today's load while becoming problematic after several phases of AI expansion, so planning should show both current and expected future demand.

Community Acceptance Is Becoming an Infrastructure Dependency

The Meta protest discussion and the Wonder Valley court dispute point to a risk traditional dashboards cannot measure easily: a technically viable project can still face social and political resistance, especially when local communities believe they carry infrastructure or environmental costs without receiving enough benefit. The Meta-related discussion includes opinions characterizing large developers as poor neighbors when local ecosystems bear the impact, while another commenter notes that communities' ability to resist development varies with local economic conditions — individual opinions rather than objective findings, but indicative of the sentiment infrastructure planners increasingly need to consider. CIOs are already familiar with technical approvals such as power connection, construction permits, security compliance, environmental permits and network availability. Hyperscale and AI data centers increasingly need another form of permission too: continued social acceptance. A project that consumes a visible share of local power or water can become politically sensitive even while meeting its technical and regulatory requirements, risking more restrictive zoning, additional taxes, grid-funding requirements, tighter environmental rules, delayed approvals or direct opposition. These risks can lengthen the time required to add capacity, and belong in the same strategic conversation as transformer lead times and GPU availability.

Sustainability Risk Is Increasingly Location-Specific

There is no universal answer to whether a data center consumes too much energy or water — the same consumption profile can have very different consequences depending on location, available generation, grid strength, water availability, regulatory conditions and community attitudes. A large electrical load in a region with abundant generation has a different effect than the same load on a constrained grid; a cooling system that consumes significant water in a water-rich region creates a different risk profile than the same design in a water-stressed one. The CIO needs to evaluate sustainability at the site level rather than rely only on global corporate averages, which can hide the local constraints that actually determine whether expansion is viable.

Efficiency Needs to Be Connected to Useful Output

Traditional sustainability discussions focus heavily on PUE, which remains useful because it shows how much facility energy is required beyond what IT equipment itself consumes. But AI infrastructure raises another question: what useful output is the electricity and water actually producing? CIOs need to connect facility efficiency with compute utilization — for AI environments, that could eventually mean GPU utilization, jobs completed, training throughput or inference output relative to energy consumed. Improving utilization delivers several benefits at once: better return on hardware investment, less need for additional capacity, delayed electrical expansion, lower energy consumption per unit of useful compute, and potentially reduced cooling and water requirements. Sustainability and capacity optimization increasingly overlap.

Build an Environmental Capacity Model

Traditional capacity planning asks how many racks, servers, GPUs or megawatts remain. A more complete model recognizes that a site's practical capacity is limited by whichever of the following dimensions reaches its boundary first:

This approach makes sustainability operational rather than purely report-oriented, and lets CIOs compare expansion options using the same framework they already use for technical and financial constraints.

Carbon Needs Operational Visibility, Not Only Annual Reporting

Tom's Hardware's Amazon example shows why emissions can become closely tied to infrastructure architecture: a dedicated gas plant creates a very different emissions profile than a data center supplied primarily through lower-carbon generation. Annual sustainability reports are useful for external disclosure, but CIOs making infrastructure decisions need more immediate information — which facilities consume the most energy, which workloads drive growth, how energy sourcing differs between locations, how utilization affects total consumption, and how planned capacity additions change the emissions trajectory. That turns carbon from an annual accounting exercise into an infrastructure planning variable, so the impact is visible while there is still time to choose a different design or location.

Who Pays for Growth — and Who Owns Sustainability Inside the Organization

Gadget Review raises one of the more politically important questions around AI infrastructure: who pays for the generation and grid upgrades required to support rapidly growing demand? The article reports both voluntary industry pledges and legislative efforts aimed at preventing ordinary ratepayers from bearing too much of those costs. Regardless of how individual jurisdictions resolve that debate, CIOs should assume large energy-consuming projects will face increasing scrutiny over cost allocation, and a responsible business case should identify what grid infrastructure must be added, who finances it, and how future regulation could change the arrangement. A related problem is that sustainability targets often sit with one part of the organization while infrastructure decisions sit with another — the sustainability team sets carbon targets, facilities manages energy, engineering buys GPUs, finance evaluates capex, and the CIO approves the program. Each team optimizing its own objective can leave the overall result inconsistent. A stronger operating model gives sustainability metrics clear owners and connects them to infrastructure decisions, so power, cooling, water and emissions implications are considered whenever new AI capacity is proposed.

What the CIO Should Measure — and Where Workloads Should Run

The CIO does not need hundreds of sustainability metrics — the most useful ones connect directly to operating decisions: facility energy consumption, available and committed MW, energy source mix, electricity cost, PUE, water consumption where relevant, cooling efficiency, compute utilization, estimated carbon impact and future capacity requirements. Platforms such as Sensaka can help bring infrastructure, power, cooling and utilization information into a common operational context so these relationships are easier to understand; the objective is not another sustainability dashboard, but making environmental resource consumption visible during infrastructure decisions. Energy and water also add a dimension to whether workloads should run in cloud, colocation or owned infrastructure. If one facility is power-constrained while another has substantial lower-carbon capacity, placement can affect both sustainability and expansion requirements: a training workload that doesn't need very low latency might run where energy conditions are more favorable, a facility nearing a water constraint might push future expansion elsewhere, and a region requiring major grid upgrades might make colocation or cloud capacity elsewhere more attractive. This doesn't mean sustainability always overrides cost or performance — it becomes one more variable in the placement decision.

Three Questions Before Approving the Next Expansion

Before approving significant new capacity, the CIO should be able to answer three questions covering power, cooling, water, grid expansion, emissions, community concerns and the time required to remove future constraints:

The recent examples in Texas, PJM and Alberta show how quickly these issues can move from technical planning into public policy, protests and legal disputes. Understanding them early creates more options for site selection, architecture and workload placement.

Sustainability Is Becoming Part of Operational Resilience

Energy, water and carbon are often discussed as environmental topics, but for a modern data center they are increasingly connected to continuity and growth. Insufficient power can prevent infrastructure deployment, cooling constraints can limit rack density, water restrictions can affect cooling strategy, carbon-intensive generation can conflict with corporate commitments, and community opposition can delay or prevent expansion — these are operational risks. The examples above make that shift visible: AI infrastructure can affect electricity pricing, dedicated generation can solve one capacity problem while creating a larger emissions challenge, and water and power concerns can become part of local opposition to proposed projects. For the CIO, sustainable data center operations come down to a practical principle: understand the full resource cost of every unit of new compute before committing to it. The best infrastructure strategy will increasingly be the one that can deliver enough compute, enough reliability and enough room for future growth while remaining economically, environmentally and socially viable over the life of the facility.

Sources: Gadget Review, Tom's Hardware, r/datacenter (Sturgeon Lake Cree Nation ruling), r/datacenter (Meta data center protest).

Related resources: explore our guide to Data Center Energy Management, review Liquid Cooling Monitoring, and read our overview of Data Center Capacity Management.