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4 ways AI helps business protect the environment

The environment is a hot topic, literally. As global temperatures have warmed since 1850, the discussion on what to do about it has heated up as well. Humanity is having an undeniable impact on the natural world. Our growing demand for resources is leading to land-use changes, loss of biodiversity and pollution. Climate change continues to disrupt weather patterns, temperatures and water availability, leading to impacts on human and natural ecosystems — even the forests are on the move.

The good news is that there is more information than ever before about the environment. Growing global attention is leading to increasing regulations, deeper research and deployment of advanced sensing and mapping technologies. However, connecting the dots for better insights and solutions is difficult because the relevant information is often siloed, and decision makers are reluctant to act without a high degree of certainty.

Today’s complex supply chains make this an even tougher puzzle to unravel. Cognitive technology, enabled by artificial intelligence, or AI, is uniquely adapted to helping with these challenges, from finding patterns and interconnections within macro datasets to providing local, personalized diagnosis and predictions that learn and improve over time. (More about IBM's research on this topic here.)

Cognitive technology, enabled by artificial intelligence, or AI, is uniquely adapted to helping with sustainability challenges.
With its ability to understand, reason and learn, cognitive technology is proving a great ally in protecting our planet in four key ways:

1. Better conservation of natural resources. By combining satellite imagery, sensors and machine learning, companies and governments are reducing water usage in their operations as well as pinpointing the variables that lead to better soil health. One winery created a cognitive irrigation system that can deliver water in a way that’s situational, hyper-local, automated and self-tuning, helping it cut water use by 25 percent over three years.

2. Earlier pollution detection. Advanced machine learning and self-organizing mesh networks are helping organizations pinpoint the sources of pollution faster and more accurately, whether air pollution or methane leaks. This enables more targeted mitigation actions that are better for business and the environment, such as improved natural gas operations with reduced emissions.

3. Accelerating sustainable options. Cognitive technology is accelerating more sustainable energy and product choices for consumers. One of the biggest barriers to widespread use of renewable energy has been forecast accuracy. Not only is it tough to predict how much renewable energy will be available at a given time on a given day but solar and wind farms are adding to the supply (while decreasing their own demand), making forecasting more difficult. By combining advanced weather forecasting models with cognitive self-learning capabilities, a Vermont-based power company is developing a more precise, automated renewable energy forecast for solar and wind power.

Cognitive technology can also assist with environmental regulation compliance — an important first step toward greater transparency and greener product choices for consumers.
Cognitive technology also can assist with environmental regulation compliance — an important first step toward greater transparency and greener product choices for consumers. Cognitive platforms equipped with natural language capabilities can read large blocks of regulatory text and extract essential obligations, such as a local requirement for a specific label on a product.

4. Learning from nature’s ecosystems. Policy makers and companies that manage natural resources face an increasingly tough challenge to develop those resources sustainably as they change over time. It’s not always clear how a single stressor, such as salt runoff from roads, affects a natural ecosystem, let alone multiple stressors. Environmental assessments are often manually collected over time, making it more difficult to pinpoint and monitor cause and effect.

One research project in upstate New York is working to advance knowledge in this area. Scientists are analyzing data from environmental sensors around Lake George to build and refine computer models of the lake’s ecosystem. As more data is collected, machine learning will provide a better understanding of what the norms and anomalies are, enabling decision makers to run what-if scenarios and tradeoff analyses for better insights. This can lead to important applications for real-time environmental monitoring and more targeted remediation — a critical need for the $7 trillion natural resource industry.

While dire predictions remain about the future of our natural world, indicators are strong that the green economy is here to stay. With the help of cognitive technology, business leaders are more empowered than ever before to build a brighter — and greener — future for their business and the world. By detecting and acting on environmental harm faster and providing more sustainable choices for consumers, they will grow their competitive advantage. It’s a win-win proposition.

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