On Wednesday, November 15, 2023, Jeremy Tamanini addressed the Milan International Building Alliance’s conference “Building Innovation Forum.” Moderated by Radio24’s Anna Migliorato, his speech addressed the value of data for sustainability management and AI tools that can enrich the building lifecycle. An edited transcript of his prepared remarks follow:
ANNA MIGLIORATO, Radio24: Jeremy, for over a decade you have been working in sustainability measurement. Can you briefly introduce your practice & how your work relates to promoting sustainability in the built environment?
Thank you, Ana, and to the Milan International Business Alliance organizers of this Building Innovation Forum. This conversation comes at a moment when the climate crisis is accelerating. 2023 is almost over and the signs of the crisis are surrounding us every day – extreme heat, floods, wildfires, and the system effects of these changes on people, economies, and our infrastructure. To address this crisis, we need businesses to do what they do best: innovate. And I hope that you will agree after my brief remarks that there is no space with more potential for this innovation than the intersection of sustainability with building and construction.
As you mentioned, I started this practice over 10 years ago, when the issues of climate crisis, sustainability and green infrastructure were less central to the conversation. Yet when I founded the practice I felt that these issues would become central to our economy and how we imagine our future shared environment. This has been confirmed by the work we do, partnering with government entities, private clients, investors and NGOs to help them better collect, analyze and leverage data for promoting sustainable development.
Scientific consensus tells us that around 2030 – in just 7 years – the entire carbon budget associated with the 1.5 degree warming scenario will be exhausted, if current rates of country emissions growth persist. The building sector contributes more than 1/3 of these global GHG emissions, so embedding sustainable design & construction principles is of paramount importance.
Data and measurement have proven to be powerful catalysts for climate action over the past decade. As the saying goes “we can’t manage what we don’t measure.” Collecting data related to GHG emissions, energy consumption and resource use are necessary in every economic sector, including buildings. When we look at the life cycle of buildings: design, production, construction, facility use and decommissioning, there are ample opportunities to promote resource efficiency and reduce GHG emissions. Data is a foundation for this transformation. Collecting data on these different phases of the life cycle allow us to learn over time, and to leverage new technology to optimize future projects.
ANNA MIGLIORATO, Radio24: Yes, the intersection of data & sustainability is a hot topic these days and we will discuss the AI applications that relate to that. But first, tell us about the Global Green Economy Index you created. What insight does this tool provide and what does it say about Italy’s green performance relative to its peers?
The Global Green Economy Index™ (GGEI) was first index of its kind published in 2010. It evolved somewhat organically in parallel with my practice. The 2010s were a decade of learning, and clients needed frameworks and tools to communicate what we mean by a “green economy” and what are approaches and tested methodologies for tracking it.
The GGEI has been tracking country performance in the green economy throughout the past decade, taking an integrated view of relative country performance around climate change, sector decarbonization, green markets, and the environment. The GGEI tracks 160 countries and is defined by 18 total indicators.
For each of the 18 GGEI indicators, we measure the change in performance between a reference year (usually 2005) and the most recent year (usually 2020). For example, what was the emission intensity of a Italy’s economy in 2005 compared to 2020? Is this change an improvement or a decline in performance? We also calculate its distance from globally accepted targets associated with emission reductions, SDGs and other environmental, social and governance goals. For example, what are the efficiency improvements in sectors like buildings, and how does this rate compare to what is required to keep on track to limit warming to 1,5 degrees Celsius?
These two measurement components – the change in performance over time and the distance from global targets – offer new insight to market actors prioritizing ESG-aligned investment and commercial opportunities. The rate of change indicates green market momentum. Markets that are rapidly evolving towards more sustainable models may offer greater green investment opportunities. And the distance of each country from globally established targets conveys just how genuinely each market is realizing green growth.
So let’s look at Italy’s performance on the Global Green Economy Index. I would like to alert the audience that I was honored to participate in the annual Stati Generali della Green Economy organized by the Italian government and the Fondazione Sviluppo sosteniblile. Overall, Italy ranks 20th out of the 160 countries tracked on the GGEI. When you look at the two vectors of measurement (progress since 2005 and distance from established sustainability targets), the story is a bit different: Italy ranks 45th of the progress vector and 19th in terms of distance from global targets). Italy ranks 15th compared to other EU states, where Sweden is the top ranked country.
In terms of Buildings, our data indicate that Italy’s building sector has improved its emission intensity (emissions per unit GDP) by about 30% since 2005. This compares to a 43% improvement around Electricity & Heat, 57% improvement in manufacturing & construction. What this tells us is that while Italy’s building sector is improving, it is lagging behind these other sectors and must accelerate efficiency gains in the coming years. To provide some context, recent modeling from the UN Environment suggests that emissions from the buildings sector will need to be halved by 2030 or reduced by 8% annually to align with the 1.5C threshold. This highlights the urgency for action around decarbonization.
Disaggregating these country-level data are critical for an economy like Italy, and this is something I work with partners to realize. While these GGEI data give us a high-level view of Italy’s green economy and what it looks like in sectors like buildings, further analysis is required. For example, what does Italy’s building sector look like in different regions, types of buildings (new versus old construction), commercial versus residential buildings? How do different climate conditions across Italy impact decisions around sustainable building strategies? Also, are there certain types of buildings that account for the majority of GHG emissions in a city or region? Where I live in NYC, a small percentage of buildings account for a disproportionate volume of GHG emissions. These data signal to policymakers that a targeted approach to emission reductions might be more efficient than trying to decarbonize the entire buildings sector.
So, this where the insights from the GGEI come alive: by providing a framework and starting point to more localized projects and data management initiatives.
ANNA MIGLIORATO, Radio24: AI has been a hot topic in 2023. What are the ways you envision AI can enhance the GGEI? In terms of the building and construction sectors, can you explain how AI applications can accelerate decarbonization & improve sustainability performance?
Climate Week 2023 in New York featured a series of riveting conversations around new AI applications with potential to accelerate climate action. The intersection of artificial intelligence and sustainability has been on my mind since 2020, when we published an insight on the topic, authored by strategic advisor Karuna Ramakrishnan. To follow up, we convened a webinar with experts employing AI in the realm of waste management (Conor Riffle, Rubicon), finance (Faiz Sayed, Aquantix AI), and nature-based solutions (Nan Pond, formerly of NCX). In 2020, the foundation was being set for AI in the sustainability space, and our research identified multiple ways in which these new applications could accelerate green breakthroughs in the 2020s.
Regarding your first question, yes, we see exciting ways that AI can enhance the GGEI, and data products like it. Publishing the Global Green Economy Index brings me close to country-level data related to various aspects of the green economy. The traditional “bottom-up” methods for generating these data (e.g. country reporting, sector-based estimates, and modeled datasets) are not always as accurate as required, and lack timeliness and granularity. Recently, “top-down” methods for data collection from satellites, sensors and other technology-enabled tools introduced a new approach to collecting and analyzing these data. AI plays a central role in this process, automating systems for data capture and teaching machines to translate images and observations into datasets related to different green economy topics. These topics include GHG emissions, land-use patterns, and site-specific readings from company assets generating power, manufacturing goods, or extracting raw materials. New initiatives employing AI related to GHG emissions include Climate TRACE, Kayrros, CarbonMapper, and GHGSat; platforms focused on agriculture, land-use patterns, and biodiversity include SkyWatch, Planet, and Gro Intelligence.
Responding to the second part of your question, I would like to present four examples of how AI applications can enhance different phases of the building life cycle.
Planning & Design
Buildings must be designed not just for people, but in harmony with the communities and natural environments they occupy. Final design plans must consider the carbon footprint, energy and resource use of the building. But also how the building enhances – rather than disrupts or displaces – surrounding communities & natural environments.
Artificial Intelligence (AI) helps create energy-efficient designs that minimize environmental impact. For instance, AI algorithms can analyze a multitude of design variables such as building orientation, window placement, and insulation types to optimize energy efficiency. Over time, AI applications can reveal deeper insights for these data. For example, how did sustainable building designs perform in practice? What can we learn from this performance to enhance future design processes?
Autodesk Forma carves a niche for itself as an all-encompassing AI-powered planning tool that offers architects and urban planners the ability to design sustainable, livable cities with heightened precision. Autodesk harnesses the power of AI to simulate the implications of diverse design decisions on critical factors, such as energy consumption, traffic flow, and air quality, with an aim to help designers make more informed and sustainable design choices while enhancing the sustainability and livability of projects. Autodesk Forma is also equipt to help identify potential design flaws before implementation, circumventing costly future rectifications.
AI helps the material selection process in two ways. First, it is utilized to measure and reduce the embodied carbon in different materials being considered for a project. Second, it can analyze and predict material performance over time, highlighting the most sustainable options. This reinforces a point that I made earlier: the importance of staff members who can collect and analyzing data over time.
The embodied carbon of materials is not a fixed value. It changes over time as new innovations are introduced and suppliers prioritize sustainability. The performance of these materials in the built environment changes over time too. Capturing this dynamic view of both sourcing and monitoring building materials is important.
The Embodied Carbon in Construction Calculator (EC3) tool, is tool that allows benchmarking, assessment, and reductions in embodied carbon, focused on the upfront supply chain emissions of construction materials.
AI offers new tools to improve safety and increase labor efficiencies in the construction process. By analyzing data from equipment sensors, AI can help predict when breakdowns are likely to occur, preventing project interruptions and possible accidents.
AI-powered robots may perform some of the most dangerous construction tasks, or automate repetitive ones so that labor can be allocated elsewhere. Construction companies use self-driving machinery to automate tasks, such as pouring concrete, welding, bricklaying, and demolition. Similarly, they can use autonomous or semi-autonomous bulldozers to excavate and prep work. Once the exact specifications are fed into these machines, they complete the job exactly as per the specifications, freeing up the human workforce for actual construction work.
Construction projects contain a series of interconnected inputs – including labor, materials, machines, and technology. Optimizing these elements is important to project outcomes and profitability. AI-powered robots and sensors empower project managers to oversee real-time resource requirements on multiple job sites. The resulting insights, labor can be shifted to different parts of the project or even a different job site.
Spot the dog, created by a firm called Boston Dynamics, is one example of how AI-power robotics can add efficiency to construction projects. Spot is nimble, able to collect visual and numeric data from project sites. Capturing a high volume of data through each stage of construction gives project managers more visibility to where pitfalls may exist, and like in earlier examples, frees up human labor for data analysis and other more strategic functions. Spot also operates 24 hours a day.
This final phase of the building life cycle reveals many AI applications to manage energy consumption. In my experience operations managers are becoming quite familiar with these tools. Reporting requirements around scope 1 (directly controlled by an organization, from operations etc) and scope 2 (purchased electricity) emissions are becoming commonplace. Reducing energy consumption and the associated cost-savings are always a company priority, making these tools an easy sell to corporate decision-makers. Some examples include C3.ai (monitor & reduce scope 1 emissions) and Brainbox AI (monitor & reduce scope 2 emissions).
For further information related to this presentation, please contact Jeremy Tamanini here.
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