Data is key if we are to address climate change

Recently, EiD had a post that Canada needed better data on GHG emissions if it was to meet its long-term objectives.  Now Stefan Hogendoorn, CTO at Cloud Technology Solutions, writes on the Data Economy website of the importance of good data in addressing climate change.


Data will lead the fight against climate change

This year we’ve seen climate emergencies declared locally by 265 different local authorities across the UK. And the alarm has been raised at the supranational level too, with the EU Parliament declaring a global climate emergency.

At the same time, blazing bush fires in Australia, toxic levels of air pollution in India and severe flooding here in the UK have dominated news headlines – demonstrating the dangers climate change is already creating.

With concern rising about the impact of climate change, we need to find ways of tackling its causes and mitigating its effects. Here, data has a crucial role to play.

Whether it’s information on how the world’s climate has changed over time or key sources of carbon emissions, the data we collect holds crucial insight.

This insight is already shaping our responses to climate change and will become even more important in 2020.

Driving sustainability through data

Earlier this year, Google launched its Environmental Insight Explorer (EIE). A free online tool that uses mapping data to estimate the carbon emissions of buildings and transport in cities across the globe.

Its purpose is to allow urban planners to recognise key sources of pollution and reduce emissions by planning cities more sustainably.

Tackling carbon emissions generated by the environment is crucial to combating climate change – so this is a significant development.

Buildings and construction are responsible for 39 per cent of carbon emissions worldwide and 28 per cent of those emissions resulting from the energy used to heat, cool and light buildings (World Green Building Council, Bringing Embodied Carbon Upfront 2019).

And the EIE initiative puts data centre stage in trying to address the sustainability of our towns and cities.

This is not the first time Google has used data to highlight carbon emissions that comes from the built environment.

In fact, it has already used data to create models to improve the sustainability of its data storage centres themselves – significantly reducing its carbon footprint.

Google used its data to challenge the perceived wisdom that data storage facilities needed to be cooled to 18 degrees to operate properly – with higher temperatures leading to poor performance.

By using real-time data analysis to measure the performance of its data centres Google found this was not true.

Its analysis found that’s its data centres could operate in temperatures up to 27 degrees without any reduction in performance. The energy required to cool the centres was completely redundant.

At St Vincent’ Hospital in Australia, installing a data-led predictive model within the building’s HVAC system has led to a 20 per cent reduction in energy consumption.

Here, software is used to monitor data points including the weather conditions, building occupancy, energy prices and tariffs in real time.

This information is then used by the software to usage of the HVAC system – generating energy savings of 20 per cent.

Building on these examples, collecting and analysing data on energy consumption will become increasingly common in 2020.

And the falling cost of collecting and storing data will be a key driver behind this. Rather than having to invest in on-premise IT systems, businesses can harness the power of cloud infrastructure on a subscription basis.

This means they can benefit from far greater computing power and are able to store large volumes of data at a much lower cost.

Much of the data that can help improve sustainability and improve energy efficiency would have previously been lost – but this will soon cease to be the case.

And next year we’ll also see more businesses integrating their data collection strategy with IoT technology – allowing them to capture real-time data from across their workplaces.

Doing so will allow them to optimise a whole range of business operations – from heating and cooling schedules to more accurately predicting demand to reduce wastage in supply chains.

All of which will foster greater sustainability and help mitigate the environmental impact of business activity.

Forecasting climate change

Data can also help us deal with the effects of a changing climate by enabling machine learning to uncover huge learnings in how we best respond to climate emergencies.

Take agriculture for example. Farmers need to know when wet and dry seasons will arrive, so that they can plant their crops at the right time of the year to maximise yields.

However, changes in the climate and weather patterns mean that models based on previous recorded averages could become useless.

By using machine learning to analyse real-time data, farmers can identify changes in climate that mean they need to change the time of year at which crops are planted.

Farmers in the Indian states of Andrha Pradesh and Karnataka have been able to achieve a 30 per cent increase in crop yields by using ML.

By analysing 30 years of crop sowing and climate data ML can make accurate predictions about the best days for land preparation and sowing, providing this information to farmers via a mobile app.

There are other ways ML can confront the challenges faced by climate change too.

It can be used to identify new areas of flood risk too, combining data on flooding patterns and rising sea levels.

And by doing so it can provide crucial information to help safeguard people in at-risk and inform future plans for urban development. ML can also be used to combat deforestation.

It can be trained to detect the sound of chainsaws and integrate into an alarm system which alerts the authorities if it detects their use in protected areas.

And it can integrate with satellites to monitor tree-felling in the rainforest distinguish between sanctioned selective cutting on a sustainable level and unsustainable clear-cutting practices.

As we can see, data holds massive potential to help tackle climate change. And from reducing energy consumption to helping us adapt to a changing weather patterns, data is already playing a part.

But its use is still not as widespread as it could be – and much of its potential remains untapped.

Next year, falling data storage costs combined with an increasing awareness of the value of data could see this change.

And if that happens, 2020 could be the year data makes a decisive intervention in the fight against climate change.

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