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Climate change is not as simple as just being an environmental problem. It also poses a complex data problem. Companies and individuals around the world are grappling with the complexities of quantifying carbon impacts and addressing the challenges people face when capturing and utilizing climate data. 

The problem with climate data is simple but profound—it often doesn’t exist in the first place. When it does, it is typically high-level data, not the granular details that can truly drive change. To track all of your carbon-generating activities is not a simple undertaking.

In this article, I will highlight:

  • the common hurdles in aggregating relevant micro-level data when taking action
  • current applications in developments—like AI and machine learning 
  • why the solution isn’t always easy even when equipped with the best technology
  • how the right partnerships can make meaningful climate action attainable for your organization 

The struggle to utilize climate data: Meet Sophia

Access to relevant and actionable climate data is a high hurdle for too many people. I like to illustrate this reality with Sophia: a hypothetical 30-something developer, struggling to understand the vast landscape of climate action but yearning to take meaningful action.

As a climate-aware individual, Sophia has learned through reputable outlets that many corporate carbon footprints are much bigger than expected—even those with prominent carbon offset programs. This is a stark revelation that leaves her questioning her own and her company’s carbon footprint.

Sophia embarks on a mission to quantify this new revelation and soon finds herself lost in a maze of corporate departments, each providing her only scant pieces of information on her and the company’s carbon footprints. The company has ample data on cost-impacting activities, yet little on their environmental impact. The result is sweeping holes in her data with no simple way to fill them.  

Trying to bootstrap the data problem, Sophia attempts to collect this data from her colleagues through a self-created Slack channel and a detailed questionnaire. This only yields partial results though, as only a few of her well-intentioned colleagues end up participating. Carbon accounting, as it stands within her company, is not utilized at the level necessary for Sophia to take a step towards her own carbon goals. 

Too much data, not enough action

In a world where data is everywhere, why are people like Sophia struggling with capturing climate data? The challenge lies in accessing and understanding the micro-level data that could make a significant difference once aggregated—such as the energy needed to power a computer or the carbon emissions generated by inter-office travel.

Here’s a hypothetical future in Sophia’s story: after her initial defeat, Sophia looked to external examples and case studies to learn what she could do for her own company. Armed with these tools, Sophia pushes her team to incorporate data-driven carbon strategies and lobbies for its inclusion in her company’s climate action plan.

Putting data to work

For Sophia and other professionals, machine learning can process vast amounts of data to gain a deeper understanding of large-scale environmental systems. The Chesapeake Bay wetlands mapping project is a perfect illustration: the group leveraged a convolutional neural network (CNN) model to analyze and map its 18 trillion gallons of water and 64,000 square miles at an accuracy of 94%.

Sophia sees this as an opportunity to use similar AI models to analyze data about her company’s carbon emissions and identify key areas where changes can be made. She understands that her company, by applying similar strategies, can take a data-driven approach to accurately and credibly reduce their energy use and, consequently, their carbon footprint.

For example, using machine learning to analyze company-wide data on heating and cooling energy usage identifies underperforming systems or processes that disproportionately contribute to the carbon footprint of Sophia’s company. These insights could be used to implement targeted improvements that not only reduce carbon emissions but also improve operational efficiency and employee comfort.

Furthermore, by measuring the impact of these interventions over time, companies can continuously refine their strategies and make iterative improvements based on real-world evidence. This approach fosters a culture of continuous learning, diligence, and sustained improvement, where every decision is informed by data and every action is carefully measured for its impact.

Leveraging technology to bridge the data gap

As Sophia’s experience demonstrates, machine learning and data analysis aren’t just buzzwords—they’re powerful tools that can drive meaningful climate action. By harnessing the power of these technologies, individuals and organizations can turn the tide on climate change and make a real difference for the future of our planet.

To ground the action of bringing data, AI, and climate action together in a real world example, I’d like to introduce you to TXI, one of Climate Vault’s Carbon Champions and a digital product company that recognized its carbon footprint extended beyond its employees to its partners.

TXI approached Climate Vault to calculate and neutralize their annual emissions, including those from their use of Amazon Web Services (AWS). When starting the conversation with Climate Vault, TXI—just like Sophia’s and so many other companies —lacked actionable data and didn’t know its carbon footprint. Using machine learning, Climate Vault estimated TXI’s carbon footprint and helped neutralize both its own corporate footprint and its portion of AWS’s carbon footprint.

At Climate Vault, we’re utilizing this same AI and machine learning to bridge the data gap and help businesses take immediate, meaningful action.

The path forward

To make a stronger, more credible climate action plan, organizations can recognize the challenges of dealing with carbon and the opportunities to advance solutions:

  • It’s not always clear how to leverage climate data. Collecting and utilizing data related to carbon emissions and environmental impact is complex. Organizations (and people within them) often cannot access enough high-level data, especially because carbon accounting and climate data is still in its infancy, and the challenge lies in gathering and understanding the micro-level details that can drive significant change.
  • Machine learning can put climate data to work. Machine learning and data-driven strategies can facilitate meaningful climate action. These technologies are invaluable in processing vast quantities of data, leading to a better understanding of environmental systems and informing effective carbon reduction strategies.
  • A shift in mindset is crucial for success. Technology alone cannot fix the problems and challenges posed by climate change. A fundamental shift in perspective is needed, where environmental impacts are considered alongside financial outcomes in decision-making processes.

While businesses are understandably focused on surviving economic downturns, a focus on an organization’s carbon creates a stronger business environment long-term. I believe in the power of technology to lead us toward a sustainable future. The future success of our planet depends on prioritizing and leveraging climate data into serious climate action.

This post was guest-written by Hillary Lovric, Climate Vault’s VP of Marketing. Connect with Hillary on LinkedIn here.

[This piece is derived from a keynote address I previously gave at a Python Web Conference. A recording of my full talk can be found here.]

Ready to put the power of data to work for your business? Learn how you can take control of your carbon footprint when you measure, manage, and act on your organization’s carbon footprint, all in one place, with the Climate Solutions Platform

About Hillary Lovric

Hillary Lovric is the Head of Marketing for Climate Vault where she brings a proven track record of success in launching new products and driving growth through go-to-market strategies. She is a strategic thinker with a passion for solving complex problems and delivering world-class customer experiences. Hillary has over 10 years of experience in the software industry, with a focus on product marketing and solution consulting. Prior to joining Chargify, she was the Director of Solution Consulting at NetSuite, and also held various management positions at Monexa Services.  Hillary received a Bachelor of Arts in Economics at The University of British Columbia. Connect with Hillary on LinkedIn here