Data center expansion driven by demand for artificial intelligence is increasingly being constrained by environmental, social and governance (ESG) pressures and infrastructure limits, according to a new GlobalData report.
GlobalData estimates annual data center power consumption will rise from 590 terawatt-hours (TWh) in 2025 to 1,520TWh in 2030, lifting data centers’ share of global electricity use to 4.5% from 2.1% in 2025. The report argues that growth is being slowed by factors including rising water use in already water-stressed regions, public opposition, grid-connection delays, and shortages of materials and skilled labour.
The company’s Strategic Intelligence report, “The Constraints Reshaping Data Center Development,” said many announced projects remain in planning. GlobalData said projects that broke ground between Q3 2025 and Q3 2026 waited an average of just over two years from announcement to the start of construction, compared with about 18 months for projects that started between Q1 2020 and Q2 2025.
Martina Raveni, a senior analyst in GlobalData’s Strategic Intelligence team, said community pushback is increasing as concerns grow about emissions from both construction and the energy use associated with training and running large language models, as well as noise, land use and electricity costs.
“Data centers are increasingly located in water-stressed areas, and their construction, along with the energy-intensive training and running of large language models (LLMs) are adding new sources of emissions. Together with concerns over noise, land use, and rising electricity bills, these impacts are hardening public opposition. According to Data Center Watch, in Q1 2026 alone, at least 75 US data center projects worth roughly $130 billion were blocked or delayed, the largest single-quarter total on record. Similar backlash is emerging in countries including the UK, Chile, South Korea, and Japan.”
Grid access is also emerging as a limiting factor. GlobalData cited the International Energy Agency’s 2026 Electricity Outlook, which said more than 2,500GW of renewable, large-load and storage projects are in connection queues globally. In Europe’s main data center hubs, developers can face waits of seven to 10 years, the report said, while building grid infrastructure can take five to 15 years compared with the one to three years required to build a data center.
The report said congestion is pushing some operators toward on-site gas generation, which could increase emissions even as grids decarbonise.
Supply chain capacity and workforce shortages are also contributing to delays, according to GlobalData. Raveni said hyperscale sites can require up to 50,000 tonnes of copper and that AI data centers may need 10 to 20 times more fibre than traditional builds at a time when manufacturers are at capacity.
Raveni continues: “Data center buildout also depends on fragile supply chains. A single hyperscale facility can require up to 50,000 tons of copper, while AI data centers need 10 to 20 times more fiber than traditional builds, at a time when fiber manufacturers are already at capacity. On top of this, a shortage of electricians and other skilled trades is delaying projects.”
GlobalData said operators are responding with measures including closed-loop cooling, water stewardship commitments, diversification of power supplies through renewables, battery storage and fuel cells, and extending equipment lifespans through circular economy practices. It added that community engagement and workforce development are increasingly being treated as requirements for project delivery.
The report argues that the constraints are beginning to affect AI development through higher costs and tighter access to compute capacity.
Raveni concludes: “These constraints are already translating into real delays for AI infrastructure. As capacity tightens, compute becomes more expensive and harder to access, pushing AI developers toward smaller models and more limited use cases. Unless grid access, equipment supplies, and construction timelines become more predictable, AI progress will be shaped as much by power and hardware logistics as by algorithmic innovation.”
