Using data analytics to reduce your carbon footprint
Technology and industry titan Elon Musk is in the news for many reasons. His views on carbon emissions are particularly prescient: “We're running the most dangerous experiment in history right now," he says, “which is to see how much carbon dioxide the atmosphere can handle before there is an environmental catastrophe”.
The ill effects of loading our atmosphere with more and more carbon are undeniable. World governments and organisations have set ambitious targets to reduce emissions, whilst keeping human progress in mind. Industries are major contributors to the rising levels of carbon emissions. Mitigation plans are afoot in many large industries: the first step is to use data analytics to monitor their energy usage and next, devise ways and means to reduce their carbon footprint.
In this article, we will explore how some key ‘emitter’ industries, including BFSI (Banking, Financial Services, and Insurance), logistics/supply chain, and transportation, can use data analytics to reduce their carbon footprint.
Let us take a look at the BFSI Industry. This industry generates a significant carbon footprint due to the high energy consumption in their data centres and servers. Here’s a startling statistic from the world’s largest economy: the 18 largest US banks and asset managers were responsible in 2020 for generating almost 2 billion tons of carbon, making the US financial sector the 5th largest carbon emitter on the planet, behind four countries. Fossil fuel investments also underwrite much of the huge carbon footprint being generated by this sector.
Some solutions are obvious: data analytics can be used to optimise energy consumption in said data centres by identifying underutilised servers, improving cooling mechanisms, and reducing unnecessary energy usage. Others require more planning and nuance: using data analytics to understand baseline aggregate carbon usage, as many banks are doing, and then planning ways to meet emissions targets by a future date. Some banks have established carbon baselines for their loan books, and are looking at planned and sustained ways to bring the baseline down by specific amounts.
Let’s cast our gaze next on another major emissions culprit, the Logistics Industry. This industry is responsible for a significant portion of the carbon emissions due to the very nature of what they do: the transportation of goods. Here too, data analytics can help logistics companies optimise their transportation routes to reduce fuel consumption and emissions. Analytics of data such as traffic patterns, road conditions, and delivery locations can help companies identify the most efficient routes that minimise the distance travelled and reduce fuel consumption. This will not only reduce the carbon footprint of the industry but also help them harvest savings while increasing their efficiency.
Within logistics, let’s talk about Supply Chains. Here again, transportation of goods globally is a major activity that contributes to carbon emissions. Consider also the energy consumption of manufacturing facilities that produce these goods - the origin points of many supply chains. Here, analysts look at manufacturing process optimisation and energy consumption data to streamline supply chains. For example, a company can reduce emissions by sourcing materials locally, using renewable energy sources, and optimising transportation routes.
Emissions management software can be a huge enabler of drives to reduce carbon footprints. This is an umbrella term for a suite of products that help calculate, track and report greenhouse gas emissions (GHGs) at a very granular level.
How about the Transportation Industry itself? Here, we include both public and private transportation, both significant contributors to carbon emissions. Here, paradigms such as using analytics to optimise routes, reduce idling time, and promote the use of electric or hybrid vehicles are arguably game changers in carbon footprint mitigation. For example, a study conducted by a major ride-hailing company found that by optimising routes, it could reduce the distance travelled by 13.6% and decrease emissions by 12.8%.
What is good for the planet is good for the pocket. Harvesting insights from analytics to drive costs down usually translates to reduced carbon emissions. This often entails optimising processes, reducing weights, and integrating sustainable materials. The movement has gained momentum globally: for instance, the rising demand for electric cars among the public, renewable energy companies getting more support from government policies, and movements to promote lesser usage of plastics among people. As we usher in an era of smart homes, smart cities and grids, user carbon profiling and forest preservation, the awareness of the carbon damage being done to the planet is at the forefront of product and process innovation.
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