Nature-based green infrastructure is transforming asset management, offering sustainable and cost-effective alternatives to traditional gray assets. With traditional infrastructure responsible for nearly 79% of global emissions and half of all resource consumption and waste, shifting to green solutions is critical.
Beyond environmental benefits, green infrastructure (GI) can create lasting social and economic value that often increases over time.
It is a common misconception that GI only refers to parks and gardens. Dr. Jacqueline Balston, Director of Sustainability at IPWEA, has defined GI as: “An interconnected network of nature-based assets that are intentionally managed to provide diverse services and benefits to the environment and human well-being.”
GI includes naturally formed assets such as managed forests and waterways; managed assets that have been planted or modified, such as parks or restored mangroves; and nature-based hybrid facilities supported by some gray components such as streetscapes, green roofs and constructed wetlands.
However, a problem remains: many of these assets lie outside traditional asset lists, performance metrics and renewal cycles. Closing this gap requires new asset management frameworks, different perspectives on value and technological capabilities.
Just as gray infrastructure has adopted technologies such as asset management systems, sensors for condition monitoring and predictive maintenance, and digital twins over the past few decades, GI is now entering a technology-enabled phase.
First, advances in sensor technology, data analytics, and the Internet of Things (IoT) are enabling more sophisticated monitoring and management of the gastrointestinal tract.
In Australia, we use Water Sensitive Urban Design (WSUD), which integrates stormwater management into the urban landscape and uses vegetation and permeable surfaces to manage runoff. Automated control systems can play a key role in optimizing green stormwater infrastructure.
For example, the City of Monash in Victoria has so far installed 71 sensors in its stormwater pits to monitor levels and conditions in real time to enable a more efficient maintenance schedule and faster incident response times.
In 2020, the City of Whittlesea in Victoria conducted a smart city pilot program to deploy IoT sensors to collect anonymous data on space and park usage, air quality, water levels, stormwater and waste volumes. This program helped the City improve reporting, automate processes and improve data-driven decision making to ensure better service delivery across a range of municipal facilities.
Internationally, machine learning (ML) models are used to analyze historical patterns, environmental conditions and asset behavior to predict degradation, carbon sequestration, vegetation health and stormwater performance.
Researchers at the University of Texas have integrated ML with data on physical, spatial and socioeconomic factors to evaluate approaches to mitigating “urban heat islands.” By applying various ML algorithms, the team was able to better understand the consistent and dominant predictors of urban heat islands and found that tree canopy cover was the most effective means of reducing heating effects in residential and commercial areas.
Similarly, scientists from Duy Tan University in Vietnam used ML to understand the variables affecting the cooling intensity of green infrastructure in Hue city. They found that the mixing of water bodies, parks, bushland and tree canopies led to extensive cooling areas across the city. The models also highlighted hotspots where urgent interventions are needed to reduce public health risk during extreme heat events.
Finally, the use of virtual modeling to create a digital twin of a natural asset or an integrated network of green/gray assets has enabled scenario modeling, simulating climate impacts or service trade-offs, and visualizing future renewal needs.
In recent years, when the town of Unley in South Australia committed to increasing its tree canopy by up to 20% by 2045, it implemented a digital twin for tree canopy cover. They used LiDAR data collected in 2018 and 2021 to accurately measure and monitor tree canopy changes over time.
Using the digital twin, the council found that tree canopy cover increased by 1.36% over the three-year period and they have also worked to develop an app that allows the community to visualize changes in tree canopy in their area and on their property.
Much of the technology already exists to help communities create an integrated picture of their green and gray assets.
Ultimately, incorporating new technologies into GI management can help local governments and asset owners collect the data they need to understand how green assets perform and degrade over time, enable smarter renewal or replacement decisions, and support optimized maintenance and renewal budgets.
When it comes to embedding green assets into existing asset management systems and financing models, the path ahead is not without hurdles. GI only becomes core business when we treat it as Infrastructure – planned, financed, monitored and renewed like any other asset class. However, through continued collaboration and innovation, technology can become the bridge that connects nature-based solutions with the long-term resilience of infrastructure.
IPWEA publishes the Green Infrastructure Management Manual. This free resource is available for download on the IPWEA website.