Environmental Issues Relating to Artificial Intelligence and Emerging Technologies

Artificial Intelligence (AI) and emerging technologies are transforming industries, public services and everyday life. At the same time, these technologies are creating significant environmental concerns, including high energy consumption, electronic waste, water usage and greenhouse gas emissions. 

Understanding these environmental issues is essential because sustainable technological development requires balancing innovation with environmental protection through effective laws, responsible practices and international cooperation.

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How Does AI Affect the Environment?

Artificial Intelligence influences the environment in both positive and negative ways. Although AI can support environmental conservation and climate action, its development also consumes significant natural resources.

The major environmental impacts include:

High Electricity Consumption

Modern AI systems require powerful computing infrastructure.

Training advanced AI models involves processing massive datasets over several days or even weeks. This process consumes enormous quantities of electricity, particularly in large data centres equipped with thousands of specialised processors.

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As AI applications become more widespread, electricity demand is expected to continue increasing.

Greenhouse Gas Emissions

Electricity used by AI infrastructure often comes from fossil fuel-based power generation.

As a result, AI development indirectly contributes to carbon dioxide and other greenhouse gas emissions, especially in countries where renewable energy contributes only a small share of electricity production.

Apart from operational emissions, manufacturing computer hardware also produces significant carbon emissions.

Water Consumption

Water is an essential resource for AI infrastructure.

Large data centres generate substantial heat while operating. Cooling systems require large volumes of water to maintain safe operating temperatures.

Water is also consumed indirectly during electricity generation for powering AI infrastructure.

In regions already experiencing water scarcity, expanding data centres may increase pressure on local water resources.

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Electronic Waste

AI development depends upon frequent hardware upgrades.

Servers, graphics processing units (GPUs), processors and storage devices become obsolete rapidly due to technological advancements. Replacing this equipment generates increasing volumes of electronic waste.

Improper disposal of electronic waste can release hazardous substances such as:

• Lead
• Mercury
• Cadmium
• Brominated flame retardants

These substances may contaminate soil, groundwater and ecosystems if not properly recycled.

Mining of Critical Minerals

Modern AI hardware requires numerous critical minerals, including:

• Lithium
• Cobalt
• Nickel
• Copper
• Rare earth elements

Mining these minerals often causes:

• Deforestation
• Soil erosion
• Water pollution
• Biodiversity loss
• Habitat destruction

In some regions, mining activities have also raised concerns regarding labour rights and environmental justice.

Land Use and Infrastructure Development

The growing demand for AI has increased construction of:

• Data centres
• Manufacturing facilities
• Communication infrastructure
• Energy generation facilities

Large-scale infrastructure development may alter land use patterns, reduce green spaces and affect local biodiversity.

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Heat and Noise Pollution

Data centres operate continuously and generate significant heat.

Industrial cooling systems also produce considerable noise, affecting nearby communities and increasing local environmental stress.

Environmental Issues Created by Other Emerging Technologies

AI is not the only emerging technology with environmental implications.

Internet of Things (IoT)

IoT connects billions of smart devices through the internet.

Although IoT improves efficiency, it also results in:

• Increased electricity consumption
• Higher electronic waste generation
• Greater demand for semiconductor manufacturing
• Frequent replacement of connected devices

Blockchain Technology

Blockchain provides decentralised digital record keeping.

Certain blockchain networks require substantial computing power, resulting in high electricity consumption and increased carbon emissions.

However, newer blockchain systems use more energy-efficient mechanisms that significantly reduce environmental impacts.

Cryptocurrency Mining

Cryptocurrency mining remains one of the most energy-intensive digital activities.

Mining operations require specialised computers operating continuously, leading to:

• Massive electricity consumption
• High carbon emissions
• Electronic waste generation

Cloud Computing

Cloud computing enables businesses and individuals to store and process information remotely.

Although cloud services improve resource sharing, they depend on large data centres that consume significant amounts of electricity and water.

Robotics

Industrial robots increase production efficiency but require substantial resources for manufacturing and operation.

Their production involves extraction of metals, plastics and electronic components, contributing to resource depletion.

Autonomous Vehicles

Self-driving vehicles rely upon sensors, AI systems and continuous data processing.

While autonomous transport may improve fuel efficiency and traffic management, manufacturing advanced electronic systems increases demand for critical minerals and electronic components.

Quantum Computing

Quantum computing is still developing but is expected to require specialised infrastructure and sophisticated cooling systems.

As commercial adoption expands, environmental sustainability will become an important regulatory consideration.

What Are the Positive Environmental Contributions of AI?

Despite its environmental challenges, AI also offers significant opportunities for environmental protection.

Climate Change Prediction

AI improves climate modelling by analysing enormous datasets.

This helps scientists predict:

• Temperature changes
• Extreme weather events
• Floods
• Cyclones
• Droughts

Better forecasting supports disaster preparedness and climate adaptation.

Renewable Energy Management

AI helps optimise renewable energy systems by:

• Predicting electricity demand
• Managing smart grids
• Improving solar energy efficiency
• Enhancing wind power generation

This contributes to more efficient use of clean energy.

Precision Agriculture

AI supports sustainable farming through:

• Crop monitoring
• Soil analysis
• Pest detection
• Water management
• Fertiliser optimisation

These applications reduce resource consumption while increasing agricultural productivity.

Wildlife Conservation

AI-powered cameras, drones and satellite imagery assist in:

• Wildlife monitoring
• Tracking endangered species
• Preventing illegal hunting
• Detecting deforestation

These technologies strengthen biodiversity conservation efforts.

Pollution Monitoring

AI systems analyse environmental data to identify:

• Air pollution
• Water pollution
• Industrial emissions
• Illegal waste disposal

Real-time monitoring helps authorities respond more effectively to environmental violations.

Waste Management

AI improves waste management by:

• Automating waste segregation
• Increasing recycling efficiency
• Identifying recyclable materials
• Optimising waste collection routes

These measures support circular economy principles.

What Is the Legal Framework Governing Environmental Issues Related to AI?

Although there is no dedicated global treaty regulating AI’s environmental impact, several international and national legal frameworks are relevant.

International Framework

Various international organisations are promoting responsible AI development.

Important initiatives include:

• UNESCO Recommendation on the Ethics of Artificial Intelligence
• United Nations Sustainable Development Goals (SDGs)
• United Nations Environment Programme (UNEP) initiatives
• OECD Principles on Artificial Intelligence
• International Telecommunication Union (ITU) sustainability initiatives
• Paris Agreement on Climate Change

These frameworks encourage environmentally responsible technological innovation and sustainable development.

Indian Legal Framework

India does not yet have legislation specifically regulating AI’s environmental footprint.

However, several environmental laws indirectly apply.

Constitution of India

Environmental protection finds constitutional support through:

• Article 21 – Right to life, interpreted to include the right to a clean and healthy environment.
• Article 48A – Directs the State to protect and improve the environment.
• Article 51A(g) – Imposes a fundamental duty upon citizens to protect the natural environment.

Environment (Protection) Act, 1986

This Act empowers the Central Government to take measures for protecting and improving environmental quality.

Industries establishing AI-related infrastructure must comply with applicable environmental standards.

E-Waste (Management) Rules, 2022

These Rules regulate:

• Collection of electronic waste
• Recycling
• Safe disposal
• Extended Producer Responsibility (EPR)

AI hardware manufacturers and electronic equipment producers fall within this regulatory framework.

National Green Tribunal Act, 2010

The National Green Tribunal provides an effective forum for adjudicating environmental disputes arising from industrial activities, pollution and environmental damage.

Which Environmental Principles Apply to AI Development?

Several established principles of environmental law can guide sustainable AI governance.

Sustainable Development

Technological advancement should meet present needs without compromising environmental resources for future generations.

Precautionary Principle

Where environmental risks are uncertain, preventive action should be preferred over waiting for scientific certainty.

Polluter Pays Principle

Organisations causing environmental damage should bear the cost of preventing and remedying that damage.

Intergenerational Equity

Natural resources should be conserved so that future generations also enjoy environmental benefits.

Public Trust Doctrine

Natural resources are held in trust by the State for public benefit and must be protected from excessive exploitation.

Circular Economy

AI industries should encourage:

• Repair
• Reuse
• Recycling
• Recovery of valuable materials

rather than relying upon continuous extraction of new resources.

Conclusion

Artificial Intelligence and emerging technologies have immense potential to improve economic growth, public services and environmental management. However, their rapid expansion also creates significant environmental challenges, including increased energy consumption, carbon emissions, electronic waste, water use and resource depletion. Existing environmental laws provide an important foundation, but dedicated regulatory frameworks for sustainable AI are increasingly necessary.