Data Science Helps Sustainability in These 6 Awesome Ways
By: Mohammad Ahmad
Tags: Data Science, Environment, Technology, Tech, Sustainability

1: Climate Change Prediction

The easiest way for machine learning and statistics to help climate change is by aiding prediction. Climate prediction reporting, weather models, and various other models are built using historical data and as many parts of our environment have been measured over a period of time, data exists for us to run models on.

The reports from these experiments are vital for various governments, charities and non-profits to best allocate resources so it’s imperative that the results are accurate. Data Science and Machine Learning specifically aim to model the mean and variance (amongst other things), so over the past 10 years as computational power and data storage/recording techniques have improved, there’s been a flood of new information about the environment.

2: Renewable Energy Optimisation

Solar, Wind and Water energies are fantastic options for renewable energies. They’re not costly and also not very difficult to harness. Using a number of data science techniques, different algorithms can be implemented so that processing a huge amount of data is easy and can lead to insights that can really make an impact on achieving sustainability goals.

Different Solar, Hydro and Wind Power Plants have been set up throughout the world and using data science, these plants are monitored and operated.

Bearing this in mind, the cost of producing renewable electricity is becoming cheaper every year and its only time for the optimisation models to prefer renewable energy over traditional sources.

3: Carbon Emission Measurement

Managing the amount of carbon a company or entity produces is a big issue. Firstly, it’s near impossible to measure it exactly (where do you draw the line?) but also, what constitutes your responsibility. If you take the tube to work, should you offset the carbon produced there, or, should the tube operator?

A number of data science methods exist now which help in this endeavour. The systems can now implement carbon trackers which gathers data on the amount of carbon emission from different factories.

Now any data you collect in this space will be slightly noisy so various methods exist that try to model the correct distribution and then aim to minimise the amount being produced.

4: Water Conservation

Despite the earth being mostly water, the amount we can actively drink and use is significantly less so we need to focus on the conservation of water. However, it’s apparent that capitalist ideals often don’t fall in line with what society needs.

Take the example of Avocados in Mexico. This ‘green gold’ requires a significant amount of fresh water and as such, farmers provide this. However, the towns near these farms end up going through periods of drought: farmers prefer their crops to be watered rather than other humans.

OK maybe that’s a bit facetious but the point stands — sustainable water provisions need to be more widely available and that’s where Data Science can come in.

A study has shown that an average American Household consumes about 320 gallons of water per day.

Using Smart Irrigation System, enabled by data science techniques, can save up to 8800 gallons water per home per year.

5: Heat Energy Conservation

NEST thermostats don’t just look cool — they offer a service to consumers that makes their lives both easier and more sustainable.

These thermostats are expensive but the use of Smart Thermostats can reduce minimum 10% energy on heating and 15% energy on cooling.

If that’s not a fantastic use of Data Science; I’m not sure what is.

6: Extreme Weather Forecasting and Preventive Measures

Extreme weather forecasting is notoriously difficult. Often, these events are mathematically so unlikely but even then, they occur in new ways that catches everyone off guard.

Given that, the use of data from highly affected areas can be used to model the boundaries within which extreme weather occurs. Is it dependent on X or Y?

The awesome thing is that now, we spend so much time measuring various parts of the environmental ecosystem that our understanding of what drives these events improves every day. This technology, as you guessed, can save thousands of lives (easily).

As an example, the Montreal Institute of Learning Algorithms have been using GAN’s (General Adversarial Network) to model what it would look like if severe weather storms effected previously ‘safe’ areas.