Daily CSR

Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping industries and daily life, yet this progress comes with a rising environmental cost. Large-scale data centers require immense amounts of electricity and water, and the production of advanced hardware demands significant natural resources.

To counter these impacts, materials scientists are developing new solutions that enhance durability, improve energy efficiency and increase the resilience of the infrastructure that supports AI. Dow, for instance, uses advanced materials science to create technologies that improve thermal management in data centers. These solutions allow cooling systems to run more efficiently than conventional air-cooled systems, reducing energy use and helping cut greenhouse gas (GHG) emissions.

These advancements demonstrate how focused innovation can mitigate AI’s environmental footprint and encourage a more responsible, sustainable digital landscape.

What Is the AI Ecosystem?
The AI ecosystem refers to the expanding, interconnected system of technologies, data centers, hardware, organizations, and operational processes that enable the development, deployment and use of artificial intelligence.

Key Points

AI and ML are revolutionizing countless sectors, but their growth is contributing to a rising environmental footprint.
Cooling systems account for as much as 40% of a data center’s total energy usage, making thermal efficiency vital for reducing emissions and operational costs.
Companies are implementing sustainable AI practices, including energy-efficient system design, the use of renewable power, and hardware developed with recycled materials.
Advanced nuclear technologies—clean, stable, and nearly net-zero in emissions—are being considered to meet the increasing energy needs of AI operations.
Dow provides essential materials for sustainable AI growth, such as thermal management systems for electronics, cooling solutions for data centers, and silicone-based components that support robust telecom infrastructure.

Environmental Impacts of AI
AI’s environmental effects span the entire lifecycle of its systems—from production to operation to disposal. Data centers, which supply the computing power for AI, consume enormous amounts of electricity. By 2026, these facilities may use around 1,050 terawatt-hours of power—enough to rank them as the world’s fifth-largest energy consumer if they were a country. When this energy comes from fossil fuels, it significantly increases GHG emissions.

Water consumption is another major concern. Cooling technologies used in data centers and power plants require vast amounts of water, with global usage projected to reach up to 6.6 billion cubic meters annually by 2027. The rapid development cycle of AI hardware also accelerates the generation of electronic waste; by 2030, as much as 5 million tons of e-waste could result from upgrading devices and components to support more advanced AI models.
Addressing these impacts is crucial to ensuring AI’s growth aligns with global sustainability goals.

Key Areas of Environmental Impact
Energy Use
Training and operating modern AI systems demands extensive electricity. Cooling alone can represent up to 40% of a data center’s consumption in dense computing environments. Solutions include developing more efficient algorithms, improving hardware, and relying on renewable or lower-carbon energy sources.

Water Use
Water is heavily used in cooling processes and in power generation that supports AI infrastructure. As AI adoption accelerates, reducing water usage and finding water-efficient cooling technologies will be increasingly important.

Land Use
Expanding data center infrastructure often requires large areas of land, potentially disrupting ecosystems and increasing resource extraction in previously undeveloped zones.

Climate Impact
AI can support climate resilience by helping optimize resource use and forecast environmental conditions. However, without sustainable development strategies, the energy demands of AI systems may contribute to global emissions.

Advancing Sustainable AI
As the demand for AI grows, so does the need to make its development more environmentally responsible. High-performance computing strains power grids and increases emissions unless efficient cooling, responsible sourcing, and circular design principles are used.

Manufacturing components like processors and semiconductors often involves intensive mining and large volumes of water and energy. Taking a full lifecycle approach—from material sourcing to end-of-life disposal—is essential to minimizing environmental harm.

According to Brendy Lange, President of Performance Materials & Coatings, customers increasingly recognize that long-term sustainability requires innovation at the materials level, motivating deeper collaboration to design solutions that can withstand the demands of advanced technologies, including AI.

Material Innovations Driving a Sustainable AI Future
Advanced materials are essential to supporting next-generation AI systems and communication networks.

Consumer Devices
AI-enabled devices—from smartphones to wearables—depend on materials designed to manage heat, enhance durability and reduce waste. Dow’s innovations help improve thermal performance and extend device lifespan. Reusable or recyclable materials, such as DOWSIL™ TC-3015 Re-workable Thermal Gel, support circularity in consumer electronics.

Cloud and Data Centers
Overheating reduces data center efficiency and accelerates equipment failure. Dow provides heat transfer fluids, thermal interface materials and silicone encapsulants that help manage heat effectively. These solutions support liquid cooling, immersion cooling and long-lasting thermal performance, enabling operators to reduce energy consumption and extend hardware life.

Telecom Infrastructure
Telecom systems must withstand extreme environmental conditions. Silicone-based adhesives, sealants, and protective coatings protect components from moisture, dust, and thermal stress. Dow also offers cable innovations—such as AXELERON™ and ENDURANCE™ compounds—that improve power delivery and data transmission while supporting circularity through materials like REVOLOOP™ recycled resins.

Additional Sustainable AI Strategies
Organizations committed to sustainability are implementing:

Energy-efficient algorithmic design and hardware architecture
Data centers powered by wind, solar, or other renewable sources
Hardware containing recycled or responsibly sourced materials
Lifecycle assessments and transparent supply chain monitoring

These practices create a foundation for an ethical and resilient AI-driven future.

Meeting AI’s Energy Demand With Advanced Nuclear Power
Major technology companies are beginning to invest in small modular nuclear reactors to supply clean, reliable energy for large data operations. These reactors offer near-zero emissions and fill energy gaps where renewables may not be sufficient. Dow is similarly exploring advanced nuclear technology with X-energy to reduce emissions at its Seadrift Operations site on the U.S. Gulf Coast.

Building a Sustainable AI Future
Sustainable development must be embedded throughout the AI ecosystem. By adopting clean energy sources, using efficient materials and designing systems with circular principles in mind, organizations can reduce AI’s environmental footprint while maintaining high performance.

As AI becomes integral to everything from personal electronics to communications networks and cloud computing, sustainable design will be essential to balancing technological advancement with planetary well-being.

These collective efforts highlight the critical role sustainability plays in shaping AI’s future—creating solutions that support both the environment and the accelerating digital world.