Skip to main content

Cost to the Environment

Attribution

Original work: "Educators' guide to multimodal learning and Generative AI" — Tünde Varga-Atkins, Samuel Saunders, et al. (2024/25) — CC BY-NC 4.0
Adapted for UK Nursing Education by: Lincoln Gombedza, RN (LD)
Last Updated: December 2025

The environmental cost of GenAI is often invisible but significant. As nursing professionals committed to health and wellbeing, understanding and mitigating these environmental impacts is part of our professional responsibility.

The Hidden Energy Costs​

Training Large Language Models​

Massive Energy Consumption

  • Training GPT-3 consumed approximately 1,287 MWh of electricity
  • Equivalent to the annual energy use of ~120 US homes
  • Carbon footprint comparable to 5 cars' lifetime emissions
  • Each new model generation requires even more energy

Water Usage

  • Data centers require significant water for cooling
  • Training GPT-3 consumed an estimated 700,000 liters of water
  • Ongoing inference (generating responses) also requires cooling

Daily Usage Impact​

Per Query Costs

  • Each ChatGPT query uses ~10x more energy than a Google search
  • Image generation uses significantly more energy than text
  • Video generation has the highest energy cost
  • Cumulative impact grows with widespread adoption

Carbon Footprint​

Direct Emissions​

Data Center Operations

  • Servers running 24/7 to provide AI services
  • Cooling systems to prevent overheating
  • Backup power systems and redundancy
  • Geographic location affects carbon intensity

Infrastructure

  • Manufacturing of specialized AI hardware (GPUs, TPUs)
  • Transportation and installation of equipment
  • Ongoing maintenance and upgrades
  • End-of-life disposal and e-waste

Indirect Emissions​

Increased Device Usage

  • Students and educators using devices more frequently
  • Shorter device lifecycles due to AI requirements
  • Need for more powerful hardware
  • Network infrastructure expansion

E-Waste and Resource Depletion​

Hardware Lifecycle​

Rapid Obsolescence

  • AI advances drive demand for newer, more powerful devices
  • Older hardware becomes inadequate for AI applications
  • Shortened replacement cycles increase waste
  • Rare earth minerals and precious metals consumed

Disposal Challenges

  • Electronic waste contains toxic materials
  • Limited recycling infrastructure
  • Environmental contamination risks
  • Resource recovery is energy-intensive

Nursing's Environmental Responsibility​

Professional Duty​

NMC Code Alignment

  • Platform 2: Promoting health and preventing ill health
  • Environmental health is public health
  • Nurses have a duty to minimize harm
  • Sustainability is part of holistic care

NHS Sustainability

  • NHS committed to net zero by 2040
  • Healthcare sector contributes ~4-5% of UK emissions
  • Nursing education should model sustainable practices
  • Future nurses need environmental awareness

Health Implications​

Climate Change and Health

  • Environmental degradation affects patient health
  • Vulnerable populations disproportionately impacted
  • Nurses witness climate-related health issues firsthand
  • Professional responsibility to advocate for sustainability

Mitigation Strategies​

For Educators​

Conscious AI Use

  1. Evaluate Necessity

    • Ask: "Is AI the best tool for this task?"
    • Consider lower-impact alternatives
    • Use AI strategically, not habitually
    • Batch queries when possible

  2. Choose Efficient Tools

    • Smaller models for simpler tasks
    • Text over images when possible
    • Images over video generation
    • Local processing when available

  3. Optimize Usage

    • Craft effective prompts to reduce iterations
    • Reuse and adapt AI outputs
    • Share resources to avoid duplication
    • Archive useful responses

Institutional Actions

  1. Green AI Policies

    • Select providers with renewable energy commitments
    • Negotiate sustainable service agreements
    • Monitor and report AI-related emissions
    • Set institutional AI usage guidelines

  2. Infrastructure Choices

    • Prefer providers using renewable energy
    • Consider geographic location of data centers
    • Evaluate carbon offset programs
    • Support green technology initiatives

For Students​

Mindful Consumption

  1. Reduce Unnecessary Use

    • Use AI for complex tasks, not simple lookups
    • Try traditional methods first
    • Avoid using AI for tasks you can easily do yourself
    • Be intentional about each AI interaction

  2. Optimize Queries

    • Plan prompts before submitting
    • Be specific to reduce iterations
    • Learn from previous interactions
    • Don't regenerate unnecessarily

  3. Device Stewardship

    • Maintain devices to extend lifespan
    • Repair rather than replace when possible
    • Recycle electronics responsibly
    • Choose energy-efficient settings

Sustainable AI Practices in Nursing Education​

Alternative Approaches​

Low-Tech Solutions

  • Peer discussion and collaboration
  • Traditional library resources
  • Face-to-face mentoring
  • Hands-on clinical practice

Hybrid Approaches

  • Use AI for initial drafts, refine manually
  • Combine AI with traditional teaching methods
  • Balance digital and analog learning
  • Prioritize human interaction

Green AI Principles​

Efficiency

  • Use the smallest model that meets your needs
  • Batch similar queries together
  • Cache and reuse results
  • Avoid redundant processing

Transparency

  • Track and report AI usage
  • Educate about environmental costs
  • Share sustainable practices
  • Advocate for green AI

Innovation

  • Support development of efficient AI models
  • Participate in sustainable AI research
  • Share best practices
  • Contribute to policy development

Measuring and Monitoring​

Personal Carbon Footprint​

Awareness Tools

  • Some AI providers offer carbon impact data
  • Browser extensions can estimate query costs
  • Track your AI usage patterns
  • Set personal reduction goals

Institutional Metrics

  • Monitor aggregate AI usage
  • Calculate estimated carbon footprint
  • Compare against sustainability targets
  • Report progress transparently

The Bigger Picture​

Systemic Change Needed​

Industry Responsibility

  • AI companies must prioritize efficiency
  • Transparency in environmental reporting
  • Investment in renewable energy
  • Development of "green AI" models

Policy and Regulation

  • Government incentives for sustainable AI
  • Carbon pricing for data center operations
  • E-waste management requirements
  • International cooperation on standards

Nursing Leadership​

Advocacy Role

  • Nurses can influence institutional policies
  • Professional bodies should set standards
  • Education programs should include sustainability
  • Research into sustainable healthcare practices

Practical Examples for Nursing​

High-Impact Choices​

Instead of AI:

  • ✅ Discuss case studies in study groups
  • ✅ Use textbooks and peer-reviewed journals
  • ✅ Practice clinical skills hands-on
  • ✅ Seek mentor guidance for complex questions

Strategic AI Use:

  • ✅ Generate initial care plan frameworks
  • ✅ Create visual aids for patient education
  • ✅ Summarize lengthy research articles
  • ✅ Practice communication scenarios

Course Design​

Sustainable Integration

  • Limit AI assignments to high-value tasks
  • Provide clear guidance on appropriate use
  • Incorporate environmental awareness
  • Model sustainable practices

Reflection Questions​

  1. Awareness: How much energy does your daily AI use consume?
  2. Necessity: Which of your AI interactions could be replaced with lower-impact alternatives?
  3. Efficiency: Are you using AI as efficiently as possible?
  4. Advocacy: How can you promote sustainable AI use in your institution?
  5. Future: What role will you play in creating sustainable healthcare?

Next: Understand Cost to Knowledge and how AI affects learning and retention.