AI environmental impact and sustainability

The world is making progress when it comes to curbing climate change, but not enough progress, with recent reports saying we need to move at a faster pace to avoid dangerous ecological ramifications. Experts warn that the fires, floods, and other climate disasters that frequently made headlines in 2023 will become commonplace if our efforts do not improve.

As we seek solutions, artificial intelligence can play a defining role. AI excels at performing the type of analysis scientists regularly look to for insights on how to enhance environmental sustainability. Luckily, there are many areas where AI can provide guidance on how to improve the impact of sustainability initiatives.

Using AI to Optimize Energy Efficiency

Improving energy efficiency is a critical component of increasing environmental sustainability. Statistics from 2023 show energy consumption increasing, with more than 80% of global energy supplies coming from fossil fuels. Increasing efficiency means both reducing energy needs and transitioning to renewable sources.

The potential AI has for improving energy efficiency is virtually unlimited. Optimizing energy consumption is a primary example of the role AI could play in this area, with potential applications such as smart building management to optimize the use of lighting and cooling, and industrial process optimization.

The lack of energy efficiency in transportation is a key sustainability concern. Despite the growing availability of electric vehicles, transportation is still responsible for approximately 19% of global energy use and approximately 20% of global carbon dioxide emissions.

However, leveraging AI to drive route optimization can significantly contribute to energy efficiency in the transportation space. AI can also be used to enhance the performance of vehicles by analyzing data to identify optimal maintenance strategies.

Using AI to Guide Precision Conservation

Conservation initiatives that support environmental sustainability — while well-intended — are often complex, costly, and rarely guarantee success. Precision conservation, which relies heavily on AI-driven assistance, is a data-driven approach to conservation that maximizes benefits while minimizing costs. Essentially, precision conservation seeks to determine the right time, place, and scale for conservation.

The process of precision conservation involves analyzing detailed data on soil, weather, and other influential factors. Using AI-driven platforms, it identifies the areas of greatest need and the responses most likely to improve conditions. AI also supports adaptive management in conservation efforts, which involves monitoring and analyzing conservation results to determine any adjustments needed to minimize otherwise wasted efforts and optimize impact.

Using AI to Improve Urban Sustainability

Urban environments present an immense challenge to fostering environmental sustainability because they are innately resource-intensive, often relying on outdated infrastructures that are not optimized for efficiency. Unfortunately, these regions are also typically key contributors to both air and water pollution.

Smart resource allocation, which relies on AI for data analytics and automation, has the potential to reduce environmental impacts in urban environments in several ways. For instance, AI-driven automation can empower smart grids to optimize energy distribution and drive water management systems that detect leaks and other inefficiencies to minimize waste.

Smart resource allocation can also increase the sustainability impact of urban planning and development. This involves leveraging AI to identify projects that will have the greatest long-term impact, such as enhanced stormwater management or wetlands restoration, and ensuring that they are properly resourced and prioritized.

Addressing AI’s Potential for Negative Environmental Impact

AI’s potential to negatively impact environmental sustainability must also be considered by those seeking to improve climate progress. For example, the processes required for training and running AI platforms can be energy-intensive. Making AI more sustainable requires developing more energy-efficient processes or powering AI platforms with renewable energy sources.

As scientists and other stakeholders increase their reliance on AI for analysis and guidance, they also increase the risk of AI biases undermining the optimization of sustainability initiatives, as these biases can result in suggestions that ignore or challenge reliable scientific findings. To avoid biased outcomes, AI platforms must be subjected to rigorous and regular testing.

While utilizing AI to assist in directing sustainability initiatives has its risks, its overall potential cannot be ignored. It provides those seeking to improve our climate progress with the power to both refine and amplify their efforts. Nevertheless, if trained, tested and deployed properly, AI could provide the key needed to reverse the tide of climate change.

Ed Watal is an AI Thought Leader and Technology Investor. One of his key projects includes BigParser (an Ethical AI Platform and Data Commons for the World). He is also the founder of Intellibus, an INC 5000 “Top 100 Fastest Growing Software Firm” in the USA, and the lead faculty of AI Masterclass — a joint operation between NYU SPS and Intellibus. Forbes Books is collaborating with Ed on a seminal book on our AI Future. Board Members and C-level executives at the World’s Largest Financial Institutions rely on him for strategic transformational advice. Ed has been featured on Fox News, QR Calgary Radio, and Medical Device News.