The built environment, which includes buildings, highways, bridges, and other infrastructure, accounts for about 40% of embodied carbon. These emissions are a major source of climate change, prompting politicians and business leaders to focus on developing more energy-efficient structures. However, Ming Hu, Associate Dean for Research at the University of Notre Dame’s School of Architecture, believes that lowering emissions through energy-efficient design alone is insufficient.

Hu and her team call for a larger approach, tackling not only building-related emissions but also the “embodied carbon” locked up in existing structures. Their study, which uses a revolutionary way to assess embodied carbon in over one million Chicago structures, provides a groundbreaking tool for visualizing and controlling these emissions.

What is Embodied Carbon?

Embodied carbon is defined as the total greenhouse gas emissions produced by a structure or product over its full life cycle. These emissions are caused by the mining and manufacture of raw materials, their transportation, as well as the building’s construction, maintenance, and eventual demolition.

Materials used in building, such as asphalt, concrete, and steel, contribute significantly to embodied carbon. While attempts to reduce operational carbon emissions—those emitted during a structure’s use—have progressed, the embodied carbon from building new buildings is sometimes disregarded. In many cases, the embedded carbon from these materials is significantly more harmful to the environment than the energy used throughout the building’s operational phase.

Understanding and reducing the impact of embedded carbon has been difficult due to a lack of comprehensive data. This is where Hu’s research becomes really significant. Her team’s tool provides unique visibility into buildings’ carbon footprints, with the potential to revolutionize urban planning and sustainability efforts.

Addressing the Data Gap with a New Analytical Tool

To fill a knowledge vacuum in embodied carbon, Hu and Siavash Ghorbany, a PhD student in civil and environmental engineering at Notre Dame, created a cutting-edge technology for analyzing embedded carbon in over one million Chicago structures. Their recently published study reveals 157 distinct architectural home types throughout the city.

This tool goes beyond academic inquiry by providing a visually engaging and interactive platform for policymakers and urban planners to investigate embodied carbon emissions at the granular level. For the first time, decision-makers may see the emissions associated with specific buildings or geographical areas. This type of precise data enables focused initiatives to cut carbon emissions.

The Importance of Visualizing Embodied Carbon

Traditionally, policymakers and the general public have struggled to understand the concept of embodied carbon. While operational emissions—those produced by heating, cooling, and lighting buildings—are more palpable, embedded carbon remains abstract. Hu believes that a visual tool will help overcome that gap by allowing stakeholders to clearly grasp the consequences of their decisions.

“If I were the mayor of Chicago,” Hu says, “I might look at this data and say, ‘Before I pull down this building, I have to think twice because there’s already a lot of carbon trapped in it. “Do I want to retrofit and reuse this building, or do I want to demolish it and build something new, which will increase the overall embodied carbon?”

Key Findings: Increasing Building Lifespan and Reducing Size

One of the key findings from Hu and Ghorbany’s research is that raising the average lifespan of buildings from 50 to 75 years while reducing their size by only 20% can reduce carbon emissions by up to two-thirds. This information is crucial for cities such as Chicago, where urban development frequently prioritizes the demolition of ancient structures in favor of new construction.

The researchers also discovered no scenario in which dismantling an existing structure and replacing it with a more energy-efficient one resulted in a net reduction in carbon emissions. Even if a new structure is more energy efficient, the embodied carbon in its construction is so large that it will take at least 20 years to offset the initial emissions.

The Reasons for Reusing Existing Buildings

Hu highlights that renovating and reusing existing structures, rather than demolishing and creating new ones, is nearly always the most environmentally responsible choice. This is true even for older buildings that need extensive repairs to satisfy modern efficiency standards. Cities can significantly cut their carbon footprints by prolonging the life of existing structures and improving their energy efficiency.

Renovation minimizes both operational and embodied carbon throughout a building’s life cycle. “We should always reuse existing buildings,” Hu argues. “The real question is just to what extent we want to renovate and retrofit them.” Reuse enables a significantly shorter “carbon payback period” and a more sustainable approach to urban development.

Chicago as a Case Study

Hu and Ghorbany chose Chicago as their primary focus for a variety of reasons. First, its proximity to Notre Dame aided collaboration and research. Second, Chicago has a long architectural history and a wide variety of building types, making it an ideal laboratory for their research. Third, the city is the world’s eighth greatest emitter of greenhouse emissions, making it a top priority for carbon-reduction measures.

Chicago’s building stock is diverse in terms of architectural styles and construction methods, ranging from old brownstones to modern skyscrapers. This variability enabled the researchers to investigate several archetypes and their contributions to carbon emissions. The study’s findings identify pollution hotspots throughout Chicago, giving useful data for future urban planning endeavors.

Function of Machine Learning and Artificial Intelligence

Hu and Ghorbany’s research relies heavily on machine learning and artificial intelligence (AI). To examine the massive quantity of data required for their study, the researchers drew from a variety of existing databases, including the National Structure Inventory and Cook County Open Data Chicago. They then utilized AI to geolocate the data and categorize it by key parameters like building materials, roof type, and construction era.

The AI-powered tool calculates a building’s total embodied carbon by multiplying the baseline emissions of a specific dwelling archetype by its footprint. This approach provides a highly accurate approximation of the embodied carbon for a wide range of structures, allowing users to evaluate various carbon reduction scenarios.

Conclusion

Ming Hu and Siavash Ghorbany’s ground-breaking research offers a critical new tool for analyzing and lowering embodied carbon in the built environment. They proved the necessity of reusing and extending the lifespans of existing buildings in Chicago by evaluating the carbon emissions of over one million structures. Their research provides politicians and urban planners with concrete information, allowing them to make better decisions about urban growth and carbon mitigation.