AI vs Climate Change 2026: How Artificial Intelligence Is Saving Forests and Wildlife While Raising New Environmental Concerns
NEWS BG TODAY | 7 November 2026 | World News, Artificial Intelligence & Climate Change
Environmental Organisations Embrace AI Despite Growing Concerns About Its Environmental Footprint
Artificial intelligence is becoming an increasingly important tool in the global fight against climate change and biodiversity loss, but its rapidly growing energy and water requirements are creating a difficult dilemma for environmental organisations.
A Reuters investigation published on 7 November 2026 found that leading conservation groups are using AI to monitor forests, identify wildfire threats, count endangered animals and analyse scientific records.
Reuters interviewed 15 major environmental, conservation and development organisations.
All 15 reported using artificial intelligence in some form, while also expressing concern about the environmental consequences of the data centres supporting the technology.
The findings highlight a growing contradiction.
AI can help scientists and conservationists process enormous quantities of information much faster than traditional methods.
However, the computers required to develop and operate advanced AI systems can consume substantial amounts of electricity and water.
The issue is attracting international attention ahead of the COP31 United Nations climate conference in Türkiye.
Governments and environmental organisations are now considering how AI can deliver measurable benefits without creating unnecessary environmental damage.
AI and Climate Change 2026: Key Facts
| Category | Reported findings |
|---|---|
| Reuters investigation | 7 November 2026 |
| Organisations interviewed | 15 |
| Organisations using AI | All 15 |
| Main applications | Wildfire detection, wildlife monitoring, scientific research |
| Cambodia project | AI-assisted satellite wildfire alerts |
| Wildlife conservation | AI analysis of aerial elephant surveys |
| Chimpanzee research | Digitisation of historical handwritten records |
| Major environmental concerns | Electricity, water and greenhouse gas emissions |
| Data centre electricity outlook | Could double by 2030 |
| Data centre water demand | Could more than double by 2030 |
| International discussion | COP31 climate summit |
| Main policy question | How to develop and use AI sustainably |
The findings describe the 15 organisations interviewed by Reuters and should not be interpreted as a survey of every environmental organisation worldwide.
Why Are Environmental Groups Using Artificial Intelligence?
Conservation organisations collect large quantities of information.
This includes satellite images, photographs, wildlife surveys, climate measurements and historical research records.
Analysing these materials manually can take considerable time.
Artificial intelligence can help identify patterns and organise information more efficiently.
For example, AI systems can analyse images to locate animals or identify unusual environmental conditions.
They can also assist researchers in searching large collections of scientific documents.
These capabilities may allow conservation teams to respond more quickly to emerging threats.
However, AI results still require appropriate human oversight.
Errors in automated analysis could lead to incorrect decisions if findings are not checked.
AI Helps Detect Wildfire Risks in Cambodia
One of the most practical examples comes from Conservation International.
The organisation is using artificial intelligence to analyse satellite information about potential wildfire hotspots in Cambodia.
The system helps identify areas where fire risks may require attention.
Alerts can then be sent to community forest patrols operating in remote and environmentally sensitive locations.
This approach may allow teams to prioritise areas that need investigation.
Early detection is particularly valuable because wildfires can spread rapidly under dry and windy conditions.
However, an AI-generated alert is not the same as confirmation that a dangerous wildfire is occurring.
Ground observations and other evidence remain important.
The project illustrates how satellite technology and AI can support local conservation efforts.
Artificial Intelligence Helps Count Elephants
Monitoring wildlife populations is essential for conservation planning.
Researchers need reliable information about animal numbers and their distribution.
However, counting animals across large landscapes can be difficult.
Traditional surveys may involve teams manually examining aerial photographs.
AI can help automate parts of this process.
Save the Elephants has used artificial intelligence to analyse aerial wildlife surveys.
According to Reuters, the organisation found that automated analysis could complete work in hours that would otherwise require much longer manual processing.
The technology also helped identify animals missed during manual counts.
This demonstrates how AI can support researchers by processing visual information at scale.
Nevertheless, automated counts require validation to ensure accuracy.
Chimpanzee Research Enters the Digital Age
Artificial intelligence is also helping scientists preserve historical research.
The Jane Goodall Institute holds extensive records relating to chimpanzee behaviour and conservation.
Some of these records were created by hand over many decades.
Converting handwritten documents into searchable digital information can be extremely time-consuming.
Reuters reported that AI has helped reduce a digitisation task previously estimated to take as long as 15 years to approximately two years.
This could make valuable scientific information easier for researchers to access.
Historical records may help scientists study changes in animal behaviour, habitats and environmental conditions.
However, handwritten documents can be difficult for AI systems to interpret accurately.
Human review remains necessary to identify transcription errors.
Why Is AI's Environmental Impact Controversial?
The growing use of artificial intelligence requires substantial computing infrastructure.
Many AI applications operate through large data centres containing specialised processors.
These facilities require electricity to run computing equipment.
They also need cooling systems to prevent hardware from overheating.
Depending on the technology and location, cooling can require significant water resources.
The environmental impact depends on factors including the electricity supply, cooling method, hardware efficiency and scale of use.
As AI demand grows, the total resource requirements of data centres may increase.
This creates a challenge for organisations whose mission is to protect the environment.
Data Centre Electricity Demand Could Double by 2030
The International Energy Agency has projected that global electricity demand from data centres could approximately double by 2030.
AI is an important contributor to this growth, although data centres also support many other digital services.
Expanding electricity demand can create pressure on power networks.
The climate consequences depend partly on how the additional electricity is generated.
Data centres supplied by fossil-fuel-based electricity can contribute to greenhouse gas emissions.
Facilities powered by low-carbon electricity may have a different emissions profile.
However, renewable electricity does not eliminate every environmental impact associated with manufacturing equipment or building infrastructure.
The challenge is to expand computing capacity while limiting unnecessary resource consumption.
Water Consumption Is Another Major Concern
Water is used in some data centre cooling systems.
The amount required varies considerably between facilities.
Climate, cooling technology and operational practices all influence consumption.
Researchers have warned that global data centre water demand could more than double by 2030.
This could create challenges in regions already experiencing water scarcity.
However, not every data centre uses the same amount of water.
Some facilities use alternative cooling systems or designs intended to reduce consumption.
Transparent reporting is important because it allows governments and communities to assess local environmental impacts.
Can AI Help Reduce Its Own Environmental Footprint?
Artificial intelligence can potentially improve the efficiency of some computing operations.
AI-based systems may help optimise cooling, manage electricity demand or identify inefficient equipment.
However, such improvements do not automatically reduce total resource consumption.
If demand for computing grows faster than efficiency improves, overall electricity and water use may still increase.
This is sometimes described as a rebound effect.
Organisations therefore need to consider both the efficiency of individual AI tasks and the total amount of computing they use.
Environmental Organisations Face Difficult Choices
Conservation groups are increasingly evaluating when AI provides enough value to justify its environmental costs.
A system that helps detect a dangerous wildfire early may deliver substantial benefits.
However, using a resource-intensive AI model for a simple task may offer little additional value.
Some organisations are reducing unnecessary computing by reusing previous results.
Others are considering smaller AI models or processing information directly on devices in the field.
These approaches may reduce reliance on distant data centres.
However, the effectiveness of each method depends on the application.
Greenpeace Raises Questions About AI Expansion
Not every environmental organisation views the expansion of artificial intelligence in the same way.
Greenpeace has questioned whether the scale of planned data centre development is necessary.
Its concerns reflect wider debates about energy demand, emissions and resource use.
Other conservation organisations argue that AI's benefits can justify its use when applied carefully.
These positions are not necessarily contradictory.
It is possible to recognise useful AI applications while also questioning unnecessary or inefficient computing.
The central issue is whether the environmental benefits of a particular application outweigh its costs.
What Is Sustainable AI?
Sustainable AI refers to approaches intended to reduce the environmental and social impacts of artificial intelligence.
This can involve improving computing efficiency, using lower-carbon electricity and reducing unnecessary processing.
It may also include more transparent reporting of energy consumption, water use and emissions.
Sustainability assessments should consider the full lifecycle of AI systems.
Manufacturing processors, constructing data centres and replacing equipment can all create environmental impacts.
Responsible deployment therefore requires more than examining electricity consumption during a single AI task.
COP31 Could Put Sustainable AI on the Global Agenda
The COP31 United Nations climate conference is scheduled to take place in Antalya, Türkiye, from 9 to 20 November 2026.
Reuters reported that Türkiye plans to promote principles for the sustainable development and deployment of AI.
The initiative could encourage governments, companies and organisations to consider the technology's environmental impact more carefully.
However, the details and level of international support for any commitments will need to be assessed as the summit progresses.
A voluntary pledge would also differ from legally binding regulation.
The conference provides an opportunity to discuss standards, transparency and responsible technology development.
United Nations Calls for Greater Transparency
United Nations Secretary-General António Guterres has called for major AI companies to disclose information about the environmental impacts of their systems.
Such reporting could help policymakers and the public understand the resources required by different technologies.
Information about electricity use, water consumption and greenhouse gas emissions would be particularly relevant.
However, meaningful comparisons require consistent measurement methods.
Without common reporting standards, companies may disclose figures that are difficult to compare.
Greater transparency could support more informed decisions about AI infrastructure.
How Could AI Help Protect Biodiversity?
Biodiversity conservation involves protecting species, habitats and ecosystems.
AI can assist researchers in identifying changes that might otherwise be difficult to detect.
Satellite images can reveal changes in forest cover.
Camera-trap photographs can help monitor animal populations.
Audio recordings may provide information about birds and other wildlife.
Automated systems can organise this information and highlight potential problems.
However, technology cannot replace habitat protection, conservation funding or effective environmental policy.
AI is best understood as a tool that can support broader conservation efforts.
Could AI Improve Climate Disaster Preparedness?
Artificial intelligence may help analyse environmental information relevant to floods, droughts and wildfires.
For example, machine-learning systems can assist researchers in identifying patterns in weather and satellite data.
These insights may support risk assessments and early-warning systems.
However, accurate predictions require reliable information and appropriate scientific validation.
AI models can produce errors, especially when conditions differ from those represented in their training data.
Public safety decisions should therefore rely on established scientific methods and expert oversight.
Why Does This Matter to Developing Countries?
Many developing countries face serious environmental challenges.
These may include deforestation, biodiversity loss, extreme weather and limited conservation resources.
AI tools could help local organisations analyse information more efficiently.
However, access to computing infrastructure, internet connectivity and technical expertise varies widely.
Costs can also limit adoption.
Partnerships, training and affordable technology may help make useful tools more accessible.
At the same time, developing countries should not be expected to accept disproportionate environmental burdens from data centre expansion.
What Should Technology Companies Do?
Technology companies have an important role in reducing the environmental impact of AI.
Improving hardware efficiency can reduce the energy required for individual computing tasks.
Better cooling systems may lower water consumption.
Companies can also provide clearer information about the resource demands of their products.
However, efficiency improvements need to be considered alongside overall growth in computing demand.
Responsible development should account for the effects on electricity networks, water supplies and local communities.
What Happens Next?
Environmental organisations are likely to continue testing AI applications that support conservation.
Researchers will assess whether these systems deliver reliable and measurable benefits.
Technology companies will face growing pressure to explain the environmental costs of AI.
Governments may also examine reporting requirements and infrastructure planning.
COP31 could provide an important forum for discussing sustainable AI principles.
The longer-term challenge will be ensuring that technological progress supports environmental goals rather than undermining them.
Final Thoughts
Artificial intelligence is becoming a valuable tool for conservation organisations around the world.
It can help detect wildfire threats, analyse wildlife surveys and preserve decades of scientific research.
The Reuters investigation demonstrates that major environmental groups are already using these capabilities.
However, the expansion of AI also creates significant concerns about electricity consumption, water demand and greenhouse gas emissions.
These competing effects make responsible deployment essential.
The goal should not be to reject every use of artificial intelligence or assume that all applications are environmentally beneficial.
Instead, organisations need to evaluate the actual benefits, costs and alternatives.
As world leaders prepare for COP31, sustainable AI is emerging as an important part of the wider climate discussion.
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Editorial note: This original article is based on Reuters reporting published on 7 November 2026. Forecasts of future data centre electricity and water demand are projections, not confirmed outcomes. The environmental benefits of individual AI applications depend on their effectiveness and resource requirements.
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