big data solutions for climate change

Big data and machine learning are two of the hottest topics in tech right now. Big data is a term used for data sets that are so large and complex that traditional data processing techniques are inadequate. Machine learning is a method of teaching computers to learn from data without explicitly programming them.

Big data and machine learning are being used to solve various ecological problems. For example, they are being used to develop better methods of prediction and detection for environmental disasters such as oil spills.

They are also being used to track the spread of invasive species and to understand the impact of climate change on ecosystems. The potential applications of big data and machine learning are vast, and they are already beginning to transform the field of ecology.

These cutting-edge technologies can (and will) play an increasingly critical role in protecting and preserving our planet while helping us adapt to living with climate change. The thing is, individual efforts to slash our carbon footprint will only get us so far. Plus, educating and changing people’s habits to more sustainable options takes a while. Tech might be our saving grace (most likely).

Here are a few ways big data and machine learning are being used to positively impact the environment.

 

Smarter Agriculture

smart agriculture

The world population is projected to reach 8.5 billion by 2030 and 9.7 billion by 2050. With more mouths to feed, there is a heightened need for food production.

The United Nations, Food and Agriculture Organization estimates that food production must increase by 70% to keep up with demand.

The increased demand is attributed to several factors, such as a growing population, changing diets, and loss of agricultural land.

Meeting the needs of the world’s population will require a concerted effort from farmers, policymakers, and consumers alike. And that’s where technology steps in to lend a hand.

Farmers are already using big data and machine learning to develop more efficient and sustainable farming operations. And efficiency is critical here. Food production (animal and crops) is one of the leading contributors to greenhouse gases. That’s besides the stretched water resources used in food and meat production.

By collecting data on weather conditions, soil quality, and crop yields, farmers better understand their land and how to best care for it. Farmers can then use this info to develop more efficient irrigation systems, optimize pesticide use, and predict crop yields—all of which can lead to reduced water and chemical usage and decreased greenhouse gas emissions.

Wait, there is more—Moore’s Law.

Attributed to Gordon E. Moore, the co-founder of Intel, Moore’s Law states:

“The number of transistors on a microchip double about every two years, though the cost of computers is halved.”

In short, computing power gets cheaper every year. Just think about your smartphone. It’s packed with tech, which would have cost you an arm, leg, and probably one of your kidneys only a few years back.

Now, imagine all that nice, powerful tech in the hands of farmers. And not just well-heeled ones (those are on some more advanced stuff); I’m talking about the ordinary farmer in rural America, paddies in Asia, or the African highlands.

The applications and opportunities are endless.

With time, the tech gets better and cheaper helping farmers everywhere practice more climate-smart farming. Communities become more resilient to drought and famine. Crop yields shoot while still maintaining biodiversity. Water usage drops, meaning more of the stretched resource goes a long way.

 

Tackling Food Waste

Big data and machine learning are providing new ways to tackle the problem of food waste. According to a recent study, food waste accounts for approximately 8% of global greenhouse gas emissions.

It’s estimated that one-third of all food produced is never consumed.

This represents a massive opportunity for businesses and organizations to reduce their environmental impact and save money. Big data and machine learning are used to identify food waste patterns and develop strategies for reducing them.

For example, one study used machine learning to analyze over 2,000 recipes and found that we could replace many common ingredients with less wasteful alternatives. This type of research is helping to identify new ways to reduce food waste and make the food system more efficient.

 

Better Battery Storage Solutions

how solar energy benefits from machine learning

The next stop is energy – the grand-daddy of progress and innovation. There are others but cut electricity to any place now, and things grind to a halt pretty fast.

The World Energy Council projects that energy demand will rise in tandem with a rise in the world population to about 9.7 billion in 2050. The WEC estimates that primary energy demand will grow by approximately 1.8% annually between now and 2030.

While this growth rate is slower than in previous decades, it still represents a significant increase in absolute terms. Of course, Meeting this increased demand will require a substantial expansion of energy production.

In this, too, we’re fortunate. The solution is right there…literally in our faces: the sun and other renewables like wind.

Renewables account for just over 30% of the global primary energy supply, but this is projected to rise to almost 50% by 2030 and nearly 90% by 2050.

In other words, renewables are expected to play an increasingly important role in meeting the world’s energy needs over the next few decades. But, there’s a problem.

While the sun continuously churns out 173,000 terawatts of energy (that’s trillions of watts), enough to power all our needs 10,000 times over, capturing and storing that energy is still an issue.

We’ve really gotten better at capturing solar energy and the technology keeps improving. (Check out the latest developments and breakthroughs in solar here)

One of the biggest challenges facing renewable energy remains storage.

When the sun isn’t shining or the wind isn’t blowing, there needs to be another way to store energy so we can use it later. That’s where big data and machine learning comes knocking.

By analyzing large data sets, scientists and engineers can develop better battery technologies and discover new materials that can store more energy for longer periods.

These advancements are essential to making renewable energy a viable long-term solution for powering our world.

 

Automated Recycling Systems

Recycling has come a long way in recent years, thanks partly to advances in big data and machine learning.

According to a study by the University of Nebraska-Lincoln, these technologies are helping to streamline the sorting process and improve the overall efficiency of recycling facilities. By analyzing data from sensors and cameras, machine learning algorithms can identify different types of materials and sort them accordingly.

This saves time and labor costs, helps reduce contamination levels, and improves the quality of recycled materials.

Additionally, big data is being used to track global trends in recycling and develop new ways of reducing waste. For example, a recent study by IBM found that analyzing large datasets makes it possible to identify the materials most likely to be reused or recycled. This information is then used to create more efficient recycling programs that waste less material and cost less to operate.

 

Improved Public Transit Options

better public transit options

Transportation accounts for more than 25% of greenhouse gas emissions. If we’re to, luckily, avert the worst of climate change, we must rethink and reshape transportation.

We’ve argued for public transportation here. But, as noted in that post, public transit has issues that make it less desirable to commuters—predictability, convenience, cost, etc.

With the help of big data and machine learning, public transit is becoming smarter and more efficient than ever before.

By collecting data on traffic patterns and commuter behavior, transit agencies can make real-time adjustments to routes and schedules to help get people where they need to go in the most efficient way possible—resulting in fewer cars on the road and less pollution in the air.

As more smart cities come online, we’re beginning to taste the edges of possibility. A city that just works, and you’re guaranteed to get to where you need to be sustainably, affordably, and conveniently. And on time!

 

Reducing Waste in Manufacturing     

The manufacturing industry is responsible for a large percentage of global greenhouse gas emissions. But, using big data and machine learning, manufacturers are developing efficient processes that result in less waste and pollution.

For example, by analyzing data on material usage and production timelines, manufacturers can find ways to cut unnecessary steps, reduce material waste, and streamline production—all of which lead to a smaller environmental footprint.

Manufacturers gain insights into areas such as product quality, production line efficiency, and maintenance requirements by harnessing the vast amounts of data generated by industrial equipment and processes. Machine learning is then used to automate decision-making and actions in response to these insights, resulting in improved performance and reduced costs.

In addition, manufacturers are using big data and machine learning to develop predictive maintenance models that identify potential issues before they cause more significant problems.

 

Protecting Endangered Species

red panda - an endangered animal species

The world is facing an extinction crisis, with species disappearing at an alarming rate. (Birds, for example.)

One of the greatest challenges in protecting endangered species is collecting reliable data on their populations and habits. Traditional methods like surveys and censuses are time-consuming and expensive and often provide inaccurate results.

However, big data and machine learning have provided new ways to collect and analyze data on endangered species. For example, sensors are used to automatically count animals as they pass through an area, and GPS tags can track their movements.

The data is then processed using machine learning algorithms to identify patterns and trends that would be difficult to detect using traditional methods. As a result, big data and machine learning play a vital role in conservation efforts, helping us better understand and protect endangered species.

 

Conclusion

Big data and machine learning are changing the world as we know it—and not just developing new consumer technologies or improving business processes. These cutting-edge technologies are also playing a significant role in helping to save our planet.

From automating recycling processes to developing more efficient agricultural practices, big data and machine learning are being used in various ways to positively impact the environment.

And this is only the beginning; as these technologies continue to evolve, so too will their ability to help us create a cleaner, brighter future for our planet.

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