From operational impacts and supply chains to reputation and employee retention, climate change has put businesses’ sustainability and resiliency strategies in the spotlight as they combat and adapt to global warming. As the world heats up, so does the urgency for companies to act on their pledges to reduce carbon emissions.

The demand for businesses to assume accountability for climate change has increased noticeably. Just recently, the Intergovernmental Panel on Climate Change reported that it is still possible to limit global temperature rise to 1.5 C, but only if countries and businesses take immediate action.

While climate change can create new risks for a business, such as the operational impacts of extreme weather events or reputation management for its response to global warming, it can also offer rewarding business opportunities. Companies can increase their resiliency and energy efficiency to improve productivity and conservation of resources, thereby potentially reducing operational costs. Climate change is spurring innovation across numerous industries, inspiring the creation of new products and services that are less carbon-intensive or enable carbon reduction for other businesses. All told, these measures can help bolster a company’s competitiveness and unlock new market opportunities for growth.

As businesses revisit their energy efficiency and resiliency strategies, many are studying the potential use of artificial intelligence and machine learning (AI/ML) technologies to shrink their carbon footprint. Boston Consulting Group (BCG) estimates that by 2030, AI could reduce greenhouse gas emissions by 5 to 10 percent globally, which translates to 2.6 to 5.3 fewer gigatons. Further, AI could generate between US$1 trillion to $3 trillion in value in terms of corporate sustainability.

Physical buildings are the first place companies should look to reduce their carbon footprint. According to the International Energy Agency (IEA), a commercial building’s entire life cycle is responsible directly and indirectly for approximately 37 percent of global energy- and process-related CO2 emissions – and for nearly 15 percent of direct CO2 emissions.

Companies can use advanced technologies to both reduce building emissions and improve resiliency. BCG also found that companies can use AI to monitor their emissions, predict their future emissions and – armed with that knowledge – make adjustments to reduce emissions. AI/ML can optimize logistics as well, reducing the materials required to build things. Before a facility manager can harness the power of AI/ML, it is crucial to understand its capabilities as well as its advantages and disadvantages.

Building automation technologies to reduce energy use

To use an advanced technology to the fullest potential for managing energy usage, companies must first consider its attributes and how it can best fit into operations. For example, FMs can use AI/ML to anticipate occupants’ needs while also improving the occupant experience, operational efficiency and reduction of carbon emissions. These technologies can track and address everything from ideal comfort settings for lighting and temperature to security and energy measures – for example, AI/ML can optimize power consumption or trigger an automatic shutdown during a perceived threat, like an extreme weather event.

Lighting makes up about 17 percent of all electricity consumed in U.S. commercial buildings. To reduce this energy consumption, AI/ML technologies can monitor which rooms are unused and automatically turn off those lights, and can also track room usage data to anticipate future occupancy. With HVAC operations, which are known to account for 35 percent of total energy consumption in commercial buildings, an AI-automated system can determine when and where occupants are not present. With this knowledge, the system can adjust HVAC operations in unused rooms, helping save energy costs. Once a person enters a room, the HVAC system can adjust to enhance occupant comfort and experience.

From the boiler room to the boardroom, AI/ML technologies can help companies better reach their sustainability and resiliency goals. They can also give FMs a clearer picture of how to optimize building performance, reduce carbon emissions and cut energy costs – all while enhancing occupant experience and creating a more resilient future.

Data analytics provide a holistic view of energy spend

As buildings come back online following the COVID-19 pandemic, companies are implementing AI-powered data analysis to provide FMs with insights to optimize building use. At the macro level, business leaders are also using AI/ML analytics to help adjust their building portfolios in response to fluctuating economic, environmental and regulatory conditions. Those that lease their buildings are looking to owners to focus on key performance indicators (KPIs) tied to reducing carbon footprint, improving energy savings and enhancing occupant safety.

By providing real-time tracking and analysis of KPIs, these advanced technologies can help optimize building operations to meet energy savings targets and even reduce maintenance costs. Data analytics can also provide FMs with actionable insights they can use to improve resiliency, reduce energy consumption, run predictive maintenance and increase overall efficiency.

When paired with other building technologies, such as upgraded Wi-Fi networks and building management systems (BMS), it can provide owners and FMs with a holistic view of their property's operations for even greater insights into carbon reduction and sustainable management.

BMS increase efficiency & drive sustainability

A recent study by the Pacific Northwest National Laboratory found that as much as 30 percent of a building’s energy consumption can be eliminated through more accurate sensing, more effective use of existing controls and deployment of advanced controls such as those offered by modern, AI-powered BMS.

A BMS monitors and controls a building's mechanical and electrical equipment (e.g., power systems, HVAC, lighting, life safety systems and security systems). When coupled with advanced software, a BMS can reveal intelligence on hidden energy waste and provide predictive information that can be used to optimize energy efficiency and maintenance while still supporting occupant safety and well-being.

For example, AI-driven algorithms can enable a BMS to track and predict energy usage, cost savings and which rooms within a building are getting used the most and for what purposes. An AI-enhanced BMS can also weigh conditions and demand in buildings against current occupancy, weather and utility pricing. It can identify issues before they occur, helping to prevent sudden equipment failures and unplanned downtime.

Advantages and considerations when using AI/ML technologies

While the intent of these technologies is to create less carbon, numerous business advantages also exist from cost savings, employee retention and reduced human error to desirable building certifications and market competitiveness.

Sustainability upgrades that improve energy and water efficiency are known to cut utility expenses, and they appeal to today’s environmentally conscious employees who pay considerable attention to a company’s values. By implementing sustainability strategies and communicating them to the public, an organization potentially increases its attractiveness to the workforce, opening the door to a more competitive talent pool.

AI/ML can also reduce the human error rate by taking over the heavy lifting involved in managing, analyzing and dissecting impossibly large volumes of data. It automates repetitive and mundane tasks, such as addressing comfort requests, and allows facility teams to focus on high-value activities. Unlike humans, this technology is available around the clock to provide FMs support and information.

In addition, the building itself may qualify for certifications, such as the Leadership in Energy and Environmental Design (LEED) certification. LEED sets a benchmark for healthy, efficient, carbon- and cost-saving green buildings and its certification is recognized globally as a symbol of sustainability achievement and leadership.

It is important to understand that advanced technologies can bring negative and positive impacts to the carbon equation, and it is critical for businesses to measure both. AI has the potential to accelerate environmental degradation, according to the World Economic Forum. The use of power-hungry graphics processing units (GPUs) to train AI algorithms has already been cited as a contributor to carbon emissions. The process of training a single AI model produces nearly 670,000 pounds of carbon-equivalent emissions. To put this in perspective, it roughly equals the lifetime emissions of five average cars in the U.S. Even so, the amazing power of AI/ML to automate tasks and improve operational and energy efficiencies may outweigh its negatives.

Climate change is an urgent issue that needs to be addressed. To forestall a potentially cataclysmic future, businesses must reinvent their operational strategies immediately to curb carbon emissions and support a viable future for the planet. As new tools and frameworks are emerging that will impart a fuller understanding of AI's potential to track, analyze and reduce carbon footprints, AI/ML will undoubtedly play an increasing role in mitigating the effects of global warming.