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AI and machine learning are revolutionizing the real estate industry's path to decarbonization by enabling large-scale, cost-effective strategies that optimize energy efficiency and reduce emissions. Odyssey, Telesto’s machine learning-driven platform, exemplifies this transformation, offering tailored roadmaps for portfolios that prioritize both environmental impact and financial return.
Key takeaways
- Decarbonizing the built environment is crucial, as buildings contribute nearly 40% of global emissions, making significant emissions reductions essential for achieving net-zero goals.
- The diversity and uniqueness of each building make developing standardized decarbonization plans challenging, especially when trying to scale these efforts across large property portfolios.
- AI and machine learning, like the Odyssey platform, offer powerful tools for creating tailored, cost-effective decarbonization strategies, enabling real estate organizations to efficiently address the complexities of reducing emissions across diverse properties.
The built environment is a key lever for driving decarbonization. Buildings account for nearly 40% of global emissions, making decarbonizing the real estate sector essential for achieving net-zero goals. To be on track to reach global 2050 net-zero targets, a 50% reduction in greenhouse gas emissions from buildings is needed by 2030. However, the path toward decarbonization is fraught with complexities.
Each building is unique — a “snowflake,” as the industry expression goes — with distinct characteristics, functions, and local environmental factors that complicate the development of a one-size-fits-all decarbonization plan. From time-intensive site visits to local regulations, these variables not only make it difficult to create property-level decarbonization roadmaps at scale, but they also drive up the costs of decarbonizing large portfolios.
What’s more, many of the “low-hanging fruit decarb interventions,” such as energy-efficient lighting or EV chargers, have already been implemented. The remaining interventions are difficult to address at scale, requiring more upfront investment and specialized knowledge. Though more challenging to achieve, many of these interventions have increasingly large decarbonization and cost savings impacts.
Harnessing AI and machine learning for decarbonization
Machine learning, a subset of artificial intelligence, excels at analyzing large datasets and finding patterns that human analysis might miss. It is especially well-suited for the built environment, where the diversity of buildings creates an enormous range of data points and the optimal combination of interventions is far from clear. By using machine learning, real estate organizations can quickly assess the decarbonization potential of each property in their portfolio, generate bespoke strategies to optimize energy efficiency and reduce emissions, and understand how these property-level interventions ladder up to a portfolio strategy.
Enter Odyssey—a machine learning-driven platform designed to streamline this process by creating customized decarbonization roadmaps for each property. Odyssey’s power lies in its ability to model energy performance for buildings on a large scale, processing information about hundreds of properties simultaneously. This scale is a game-changer, allowing organizations to tackle the decarbonization challenge head-on without being bogged down by the high costs and time associated with traditional, manual approaches.
3 ways Odyssey drives decarbonization and cost savings for real estate portfolios
1. Detailed road-mapping: Odyssey creates detailed decarbonization roadmaps for each property, highlighting specific renovations and projected timelines. It also provides installation costs and expected ROI, both in CO2 reduction and financial potential. Every building may be a snowflake, but with Odyssey, customized roadmaps at the property-level are much easier to achieve.
2. Portfolio prioritization: Decarbonizing a large portfolio of properties can be daunting. Odyssey simplifies the process by identifying properties with the highest potential, giving companies a clear picture of where they’ll get the greatest “bang for the buck.” For example, Odyssey can recommend the most ROI-positive renovations or the most CO2e abated per dollar spent. Consequently, organizations are better able to plan proactively and accurately to reach emissions reduction targets.
3. Energy modeling & rapid business case creation: Odyssey can also help companies to prepare for capital planning. Energy modeling enables quick assessment of the impact (financial, CO2, energy intensity) of replacing a component at a given property, enabling capital planning teams to quickly understand the impact of replacing a more energy-efficient component for another.
As the real estate sector faces mounting pressure to decarbonize, the integration of AI and machine learning presents a powerful opportunity to leapfrog traditional methods. Leveraging platforms like Odyssey can help organizations navigate the complexities of building-specific decarbonization with greater efficiency, achieving substantial emissions reductions while unlocking significant cost savings. By embracing advanced technology, the real estate industry can turn the challenges of decarbonization into actionable steps, paving the way for a greener, more sustainable future.
Other Telesto resources: Learn more about property-level decarb roadmaps using the power of machine learning. Find additional information on how to get started with ESG and build topical familiarity with our ESG Glossary as well as Telesto’s ESG Maturity Model.
