CONSERVATION

The Environmental Reality of AI: Energy, Water, Carbon and Beyond

Artificial Intelligence (AI) doesn’t just exist in software — it runs on hardware, mostly housed in massive data centers around the world. Training and operating large AI systems is computationally intense, and that intensity translates directly into environmental footprints that are increasingly hard to ignore.

💡 Energy: Powering the AI Revolution

AI consumes significant electricity, and demand is rapidly growing:

Data centers — the physical backbone for AI — currently account for around 1–2% of global electricity use. AI workloads are a large and rising share of that.
Some estimates propose that global data center energy demand could nearly double by 2030, largely due to AI expansion.
A large LLM training run can consume hundreds to thousands of megawatt-hours of power — training older GPT-3 models emitted roughly the same as dozens of short-haul flights.
Certain future projections put AI server electricity use in the hundreds of terawatt-hours per year by the late 2020s.

To put it in context: data centers worldwide already rival small countries in total electricity use, and AI is the fastest-growing driver of that consumption.

💧 Water: The Hidden Thirst

Electricity isn’t the only resource at stake — data centers are also water-intensive:

Cooling equipment in these facilities often relies on water, and studies suggest billions of cubic meters of water withdrawals annually for AI infrastructure alone by mid-decade.
Large training jobs have been estimated to use hundreds of thousands of liters of freshwater.

The water footprint compounds in regions where freshwater is scarce, meaning AI infrastructure can compete with local agriculture and human needs.

🌍 Carbon: Emissions Still Loom Large

Even if data centers are increasingly powered by renewables, carbon intensity still matters:

Many companies offset emissions through carbon credits or claim “net-zero,” but absolute emissions can still rise as compute workloads expand. For instance, some major tech firms saw emissions increase by up to ~150% over a recent three-year span due to AI growth.
Industry forecasts suggest that if unchecked, AI-related emissions could climb into the tens or even hundreds of millions of tons of CO₂ equivalent annually by the 2030s.

🌱 Who Is Doing Real Work on Carbon Neutrality?

Several major AI players have made ambitious environmental pledges, though the depth and credibility of those vary.

🌿 Google

Google has been carbon neutral across operations since 2007 and aims to run on 24/7 carbon-free electricity by 2030.
Recent disclosures show efforts to measure and reduce the energy and water per query for Gemini AI, with dramatic efficiency improvements.
The company is also investing in nuclear energy projects to provide carbon-free baseload power for its data centers.

🌿 Microsoft

Microsoft aims to be carbon negative by 2030, and pursues “water positive” goals (meaning more water restored than used).
Its Azure cloud — a primary host for many AI systems, including some OpenAI workloads — has been carbon neutral since 2012 and is transitioning toward full renewable energy use.

🌿 Amazon

Amazon’s Climate Pledge targets net zero by 2040, and it is one of the largest corporate purchasers of renewable energy globally, but specific AI energy disclosures are limited.

🌿 Meta

Meta has contracted significant renewable energy capacity and reports high renewable percentages, but like others, sees absolute emissions rise with AI growth.

🌿 OpenAI

OpenAI doesn’t publish detailed emissions data itself, but works with Microsoft’s greener cloud and pursues software efficiency. Efficiency improvements have driven lower electricity per request over time.

🏆 Smaller Leaders and Innovators

Some niche tech and AI companies focus explicitly on sustainable AI, utilizing renewable energy-first data centers, advanced cooling, or waste-heat reuse — though these haven’t yet scaled to match the big tech footprint.

🧠 What We Can Do — Individually and Systemically

Even if much of the AI impact occurs at the corporate and infrastructure level, there are practical steps individuals, organizations, and policymakers can take to reduce harm.

🛠️ For Users and Businesses

Choose the right model for the task:
Using a smaller, efficient model for simple tasks instead of gigantic LLMs can dramatically cut energy use without sacrificing performance for the given job.

Batch requests and optimize workflows:
Sending fewer, larger batches of data instead of many tiny queries reduces idle compute and can cut total electricity usage.

Track your footprint:
Companies should measure energy and carbon per API call or per model inference — transparency drives accountability.

⚙️ At the Infrastructure Level

Invest in efficient hardware and cooling:
Next-gen cooling (e.g., liquid cooling) and optimized server designs can slash data center water and energy use by up to half.

Locate data centers in low-carbon grids:
Placing centers where the electricity mix is cleaner (e.g., hydro, solar, wind, nuclear) lowers lifecycle emissions.

Renewables + baseload clean power:
Pairing AI operations with dedicated renewable capacity and nuclear where appropriate reduces reliance on fossil-based grids.

🧠 Policy & Regulation

Mandatory disclosures:
Requiring standardized emissions reporting for AI operations would make it easier to compare companies and measure progress — a growing policy priority in several ecosystems.

Carbon pricing:
Economically internalizing the cost of emissions incentivizes efficiency and cleaner energy sourcing.

Data center siting and water regulation:
Guidelines for siting data centers in water-secure regions and requiring water restoration can protect local ecosystems.

🌍 The Bottom Line: AI Can Be Part of a Sustainable Future — But Not Automatically

AI is a resource-intensive technology, and its footprint spans electricity, water, and carbon emissions — both directly (powering servers) and indirectly (manufacturing chips, supply chains).

Yet the narrative isn’t purely doom and gloom:

Efficiency per prompt has improved significantly over time.
AI can help reduce emissions in other sectors — optimizing grids, logistics, buildings, and industrial processes.
With thoughtful design, transparent reporting, and renewable-first infrastructure, AI’s global footprint can be managed and even turned into a net sustainability benefit.

As with any powerful technology, responsible use and investment in cleaner infrastructure will determine whether AI becomes a climate ally or a looming contributor to future emissions. The future of sustainable AI depends not on rhetorical pledges alone, but on real investment in low-carbon power, efficient design, and full lifecycle accountability.

AI Usage and Environmental Transparency at My Green Planet

At My Green Planet, we are committed to sustainability—not just in the content we publish, but in how we create and deliver it. This includes being transparent about our use of artificial intelligence (AI) and the environmental impact associated with it.

How We Use AI

AI Image Generation: All AI-generated images on our website are created using Microsoft Designer, a tool provided by Microsoft, which is a carbon-neutral company.
- You can read Microsoft’s official statement on its sustainability efforts here: Reducing AI’s Carbon Footprint.
Editing and Formatting: Many of our articles are also edited and formatted with the help of OpenAI (ChatGPT), enabling us to communicate more clearly and efficiently. OpenAI uses Microsoft Azure infrastructure, which has been carbon-neutral since 2012.

Our Estimated Environmental Impact

We calculated the estimated electricity use, carbon emissions, and water consumption associated with the creation and maintenance of our website content, based on the highest estimated values available:

Per Article

Electricity: 132 watt-hours (Wh)
CO₂ Emissions: 58 grams
Water Usage: 18 liters

Cumulative Site-Wide Impact

Electricity: ~5,500 Wh
(Roughly equal to running two loads of laundry)
CO₂ Emissions: ~2,400 grams
(Comparable to driving a gasoline car for about 6 miles)
Water Usage: ~740 liters
(Approximately two standard kiddie pools full of water)

How We Offset Our Footprint

To ensure that our digital operations remain environmentally responsible, we:

Use carbon-neutral platforms such as Microsoft Designer for AI image generation, Microsoft Azure interfaced programs such as OpenAI / ChatGPT for editing, and Google Sites for our website services.
Offset water and carbon emissions related to our site through:
- Personal lifestyle changes (reduced car use, reduced water usage, and energy-efficient living practices).

- Donations and promotion of donations to nonprofit organizations working in:
  - Clean energy and climate action
  - Freshwater conservation
  - Carbon neutrality and reforestation

Our Promise

We believe that transparency leads to accountability—and accountability leads to action. That’s why we will continue to monitor, reduce, and disclose our digital environmental impact while advocating for a more sustainable internet and a healthier planet.

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