Environmental Impact of Artificial Intelligence

Environmental Impact of Artificial Intelligence

The nations around the world are working hard to conserve our planet. There are endangered species, wildfires, melting glaciers, global warming, climate change etc., that are causes of concern with regard to the sustainability of the planet. Initiatives of governments and private firms across the world are evidence that AI in the current world, play a significant role in achieving not only environmental but all other sustainable development goal- from ending hunger and poverty to achieving sustainable energy and gender equality to protecting and preserving biodiversity. The environmental impact of AI in India is a multifaceted issue. On one hand, AI can contribute to environmental sustainability by optimizing resource usage, predicting climate-related risks, and enhancing renewable energy power grids. For instance, geospatial data platforms like Ambee's can provide accurate climate data to help companies make informed decisions.

On the other hand, the development and deployment of AI models can have significant environmental costs. Training large language models, for example, can consume enormous amounts of energy, leading to high carbon emissions. A study found that training a single AI model can emit up to 626,000 pounds of carbon dioxide equivalent.

In India, AI is being used to address environmental challenges, such as:

Geospatial data platform from Ambee: Gspatial.ai records information from millions of sensors and from a constellation of earth observatory satellites all the way from 1990. The AI comes in right at the start of the data pipeline in formatting scientific data, preprocessing, and extracting data from complex satellite images.

Monitoring and refilling water bodies: An AI-powered app is being used in Tamil Nadu to monitor and refill water bodies.renewable energy optimization: AI is being used to enhance renewable energy power grids and predict energy demand.

Renewable energy optimization: AI is being used to enhance renewable energy power grids and predict energy demand.

LifeLands (LiLa):   LifeLands is an innovative digital tool that uses satellite imagery, AI, and geographic information system (GIS)-based analytics and visualization to detect and create green-blue corridors and restore ecosystems rapidly.

AI-enabled monitoring system for forest conservation: The National Remote Sensing Centre (NRSC), a programme under the auspices of the Indian Space Research Organization (ISRO), has engineered a new monitoring system to observe forest cover change and combat deforestation.

Deep learning AI models can be used in a number of ways to tackle climate change. For example, you can train the model to analyse climate data and make predictions about future climate patterns

Boosting fossil fuel production

Many of the world’s leading oil and gas companies are investing heavily in AI.AI tools – such as automated submarines, cloud-based software, drones, and smart equipment – can help discover new oil and gas reserves, increase the life of oil wells, and increase oil and gas production.This can accelerate fossil fuel production and extraction and help fossil fuel companies remain profitable.

Increasing monoculture

While AI can help farmers produce more and better quality crops, it can also contribute to an increase in monoculture (growing a single crop in a certain area of land).

 However, there are also concerns about the negative environmental impacts of AI in India, including,

 Energy consumption: AI systems require significant computational power, leading to high energy consumption and greenhouse gas emissions.

 E-waste generation: The rapid development and deployment of AI models can result in significant electronic waste generation.

 Water usage: Data centers supporting AI operations often require large amounts of water for cooling systems.

To mitigate the negative environmental impact of AI in India, consider the following solutions:

Sustainable AI Practices

1. Energy-efficient AI models: Develop and deploy AI models that require less computational power and energy consumption.
2. Renewable energy sources: Power AI data centers and operations with renewable energy sources, such as solar or wind power.
3. Sustainable e-waste management: Implement responsible e-waste disposal and recycling practices.

Green AI Technologies

1. Green computing: Develop and use energy-efficient computing hardware and software.
2. Edge AI: Deploy AI models at the edge of the network, reducing the need for energy-intensive data transmission.
3. AI-powered sustainability: Leverage AI to optimize resource usage, predict climate-related risks, and enhance renewable energy power grids.

Policy and Regulatory Frameworks

1. Environmental regulations: Establish and enforce regulations to minimize the environmental impact of AI development and deployment.
2. Sustainable AI standards: Develop and adopt industry-wide standards for sustainable AI practices.
3. Incentives for sustainable AI: Offer incentives, such as tax breaks or subsidies, to encourage the adoption of sustainable AI practices.

Education and Awareness

1. AI literacy programs: Educate developers, users, and policymakers about the environmental impact of AI and the importance of sustainable AI practices.
2. Environmental awareness campaigns: Raise awareness about the environmental consequences of AI development and deployment.
3. Sustainable AI research initiatives: Encourage research and development of sustainable AI technologies and practices

Collaboration and Partnerships

1. Industry-academia partnerships: Foster collaboration between industry leaders, academia, and research institutions to develop sustainable AI solutions.
2. Government initiatives: Encourage government-led initiatives to promote sustainable AI practices and reduce the environmental impact of AI.
3. International cooperation: Collaborate with international organizations and governments to develop global standards and best practices for sustainable AI.

Since 2011, the total number of AI-related GitHub projects has steadily increased, from 1,536 in 2011 to 347,934 in 2022.

As of 2022, 24.2 percent of GitHub AI projects were contributed by developers in India. The next most represented geographic areas were the European Union and the United Kingdom (17.3%), and then the United States (14.0%). 

The share of American GitHub AI projects has been declining steadily since 2016.

Artificial Intelligence Needs Environmental Ethics

 Environmental ethicists can highlight the environmental dimensions of AI, such as its energy footprint and its potential application for environmental protection. Additionally, environmental ethicists can assess the ethics of novel situations that could be enabled by AI, such as computer-based artificial life and artificial ecosystems.

Conclusion:

In summary, artificial intelligence has an important role to play in our efforts to protect the environment. AI offers greater efficiency in agriculture, natural resource management and pollution reduction.It allows us to gain more information about the environment and make better decisions to protect it. Artificial intelligence is therefore an invaluable tool for improving conservation and environmental protection efforts.