Reading Science, Writing to Society: 2025

Can Engineering Help Cities Choose the Right Kind of Green?

The pressure of climate change and population increase is growing and is becoming a threat to urban infrastructure constantly. Urban water management has had the backbone of traditional “gray” infrastructure for decades. These engineered networks of sewers and pipes have been a solution but have often come with high costs, environmental trade-offs, and little to no flexibility. On the more recent side there is “green” infrastructure consisting of rain gardens, green roofs, and even parks which can offer multi-purpose land use, along with multiple benefits. The improvements to public health, and air quality have driven cities to invest more but eventually these benefits plateau and costs increase or are difficult to implement and maintain (Zhou 2014). Luckily researchers have begun to look into more options than just the two. Enter “blue-green” infrastructure, a new approach to managing stormwater that is hybrid, blending both engineering and natural elements to get the best of both worlds. The hybrid model allows for the environmental and even social benefits while managing stormwater more effectively.

The debate about which system is better can be very complex. Some public agencies such as the U.S EPA promote and support green infrastructure as a more sustainable and effective solution (US EPA 2015 Apr 24). But as they promote green infrastructure designers and engineers have many aspects to consider like flood control, public space, public disturbance, and biodiversity. There are layers to the decision and the uncertainty of future climate conditions, population growth, and systems lifespance makes it more complex.

Researchers have begun to look at using Bayesian Networks to help see a new side to the solutions of stormwater management. This model helps evaluate trade-offs, gives transparency into the designs, helps deal with the uncertainties, and design for what is needed in each location. This post will explore the Bayesian Network, discuss more complexities with urban planning, why blue-green infrastructure is the best option for designing systems, and what complications may arise from using these new tools for planning.

The Debate on Infrastructure: The Competing Visions on Stormwater Management

The urban infrastructure debate is often looked at as green versus gray. Those for green infrastructure emphasize environmental services such as improved air quality, temperature regulation, and recreation that parks and natural stormwater systems provide (Zhou 2014). Green infrastructure can improve biodiversity, public health, and neighborhood livability, and reduce urban heat island effects (Wu et al. 2022). Critics point out challenges that come along with the green infrastructure such as high installation and maintenance costs, limited available space in dense cities, and inconsistent performance across sites (Zhou et al., 2014).

On the other end there is gray infrastructure; it more frequently has the ability to handle more severe storms and manage stormwater efficiently, as well as a more predictable hydraulic performance. Although it is less flexible, efforts to maintain these systems are disruptive to communities, and have the potential to increase environmental degradation (Diaz-Sarachaga et al. 2016). Gray systems are usually underground so they are “out of sight out of mind” which means they have no ability to serve as multipurpose spaces, as well as not having the ability to adapt. This is a huge issue due to the unpredictable climate conditions that cities are currently experiencing, and the fact that they were designed for historical patterns of rainfall. Gray systems are able to deal with what they are designed for and not much else, and they do not offer multiple benefits.

Blue-green infrastructure is the option that combines these gray and green infrastructures. It is made to incorporate engineered systems designed to mimic natural processes for example, permeable pavements, bioswales, and constructed wetlands while keeping the benefits of green spaces. The idea of BGI is to enhance flood resilience, improve urban ecology, and social benefits. However, there is no one size fits all plan because BGI systems are challenging due to their performance depending heavily on local hydrology, urban setting, and social context.

These challenges are exactly why science based tools are critical to planning. The traditional planning typically has many trade offs and these usually mean not being able to find a full solution and leaves gaps in what is needed. The science based modeling tools help highlight the complexities and help planners make more informed, transparent, and adaptive infrastructure investments.

Blue-green infrastructure: from a single measure to city-wide network - Wetlands International

Figure 1: Informative Table of Green, Grey, and Hybrid (Blue-Green) Infrastructure adapted from Wetlands International (Blue-green infrastructure 2019)

Why Bayesian Networks Belong in This Conversation

Cities today have to plan for uncertain futures like climate change, changing land use, and new community needs. To manage this uncertainty, decision makers need tools that integrate all different kinds of data to represent uncertainty. Bayesian networks fit the bill.

Bayesian networks are probabilistic graphical models that combine statistics with expert opinions to estimate outcomes when data are incomplete or uncertain. Rather than giving one solution, they provide probabilities of different outcomes under varying scenarios. This lets planners weigh risks and benefits across multiple criteria like cost, resilience, and social impact. Orak and Smail’s application of Bayesian networks to urban infrastructure planning shows the potential of the plan. Their model combines hydrological performance data, cost estimates, and social objectives to simulate how different blue-green infrastructure systems perform under future climate scenarios. The model accounts for variability in site conditions and stakeholder priorities, recognizing that what works in one city or neighborhood may not elsewhere (Dai et al. 2023).

This precision and flexibility sets Bayesian networks apart from traditional tools. They help move the debate beyond green versus gray to a nuanced understanding of trade-offs, uncertainties, and values. In a world of limited resources and challenges, such tools are essential for smarter, more sustainable urban infrastructure.

How Bayesian Networks Reveal Hidden Trade-offs in Urban Planning

One of the main obstacles to BGI adoption is the difficulty of comparing benefits and costs. Green infrastructure offers public health and biodiversity gains but may require more space and maintenance, while gray infrastructure is more familiar and predictable but less sustainable (Zhou 2014). Blue-green infrastructure can blend these benefits but adds complexity to decision making.

Bayesian networks allow planners to visualize these trade-offs. Orak and Smail’s model connects variables like rainfall intensity, land use, installation costs, and ecosystem services into a network where changes to one factor ripple through others (Dai et al. 2023). An example of this is showing that high upfront investments in permeable pavements may significantly reduce flood risk and heat stress in some neighborhoods but offer diminishing returns in others due to soil conditions or limited space.

Knowing this is so important because BGI elements perform variably across contexts. Some studies show that bioswales, green roofs, and permeable pavements differ in their hydrological effectiveness depending on site specific conditions, making the need for site specific solutions apparent (Congying Li et al. 2017). Bayesian networks incorporate this variability, allowing designers and engineers to identify different solutions that target multiple goals rather than relying on a single factor. By incorporating stakeholder preferences and weighing multiple objectives probabilistically, the models also then provide transparency and collaboration in planning. Instead of debates being based on assumptions or politics, cities gain a clearer picture of trade-offs and uncertainties enabling smarter, more lasting decisions.

Why Blue-Green Infrastructure Wins

When accounting for multiple variables in stormwater management like cost, equity, biodiversity, and resilience, blue-green infrastructure often outperforms gray or purely green alternatives. Studies find that BGI can reduce runoff volumes by 40-60%, improving urban flood resilience while providing green space benefits (Wang et al. 2025). BGI also is trying to help mitigate urban heat islands, improve air quality, and have positive effects on public health. Green infrastructure’s health advantages depend on equitable distribution and long-term maintenance, meaning planning must incorporate social factors (Blue-green infrastructure 2019)

Bayesian networks represent more than a modeling tool, they highlight shifts toward integrated, adaptive urban planning. By addressing uncertainty and multi-criteria trade-offs, these models foster resilient infrastructure made to fit evolving environmental and social needs. Beyond stormwater, this approach can inform planning for wildfire risk, drought resilience, and affordable housing. Adopting such scientific tools helps cities become smarter, more equitable, and better prepared for the future.

Orak and Smail’s Bayesian network effectively balances these multidimensional factors. It shows that BGI portfolios can be optimized for resilience, cost-efficiency, and co-benefits designed to fit local hydrological and social conditions (Orak and Smail 2025). This view is critical in complex urban settings where infrastructure decisions impact diverse communities differently.

Figure 2 Blue-Green Infrastructure in Action adapted from (Blue Green Infrastructure – Designing for a sustainable future | GHD - The Power of Commitment)

Conclusion: Reframing the Infrastructure Debate with Science

Orak and Smail’s use of Bayesian networks can make urban infrastructure planning better by revealing the trade-offs and uncertainties. Their work highlights blue-green infrastructure as the best choice in variable urban environments balancing resilience, cost, and social benefits better than green or gray alternatives alone. By bringing this model, multi-criteria decision making into planning, these tools will offer a valuable way to frame infrastructure debates; one grounded in science, transparency, and adaptability.

Infrastructure shapes the quality of life in every city impacting safety, equity, and the environment. This post invites readers, planners, and policymakers to embrace science driven decision making tools like Bayesian networks paired with blue-green infrastructure solutions. For cities to have an impact in sustainable futures, then it’s time we give them the smartest tools to build resilience, equity, and livability for generations to come.

References List (CSE Name-Year)

Blue Green Infrastructure – Designing for a sustainable future | GHD - The Power of Commitment. GHD. [accessed 2025 Jul 5]. https://www.ghd.com/en/about-ghd/events/blue-green-infrastructure-designing-for-a-sustainable-future.

Blue-green infrastructure: from a single measure to city-wide network. 2019. Wetlands International. [accessed 2025 Jun 24]. https://www.wetlands.org/blog/blue-green-infrastructure-from-a-single-measure-to-city-wide-network/.

Congying Li, Fletcher TD, Duncan HP, Burns MJ. 2017. Can stormwater control measures restore altered urban flow regimes at the catchment scale? Journal of Hydrology. 549(Copyright 2017, The Institution of Engineering and Technology):631–53. doi:10.1016/j.jhydrol.2017.03.037.

Dai J, Alvarado R, Ali S, Ahmed Z, Meo MS. 2023. Transport infrastructure, economic growth, and transport CO2 emissions nexus: Does green energy consumption in the transport sector matter? Environ Sci Pollut Res. 30(14):40094–40106. doi:10.1007/s11356-022-25100-3.

Diaz-Sarachaga JM, Jato-Espino D, Alsulami B, Castro-Fresno D. 2016. Evaluation of existing sustainable infrastructure rating systems for their application in developing countries. Ecological Indicators. 71:491–502. doi:10.1016/j.ecolind.2016.07.033.

Orak NH, Smail L. 2025. A Bayesian Network model to integrate blue-green and gray infrastructure systems for different urban conditions. Journal of Environmental Management. 375(Compendex). doi:10.1016/j.jenvman.2025.124293.

US EPA O. 2015 Apr 24. Green Infrastructure. [accessed 2025 Jun 13]. https://www.epa.gov/green-infrastructure.

Wang L, Zhao J, Xiong Z, Zhuang J, Wang M. 2025. Integrating Grey–Green Infrastructure in Urban Stormwater Management: A Multi–Objective Optimization Framework for Enhanced Resilience and Cost Efficiency. Applied Sciences (Switzerland). 15(Compendex). doi:10.3390/app15073852.

Wu W, Liu Y, Gou Z. 2022. Green infrastructure and urban wellbeing. Urban Forestry & Urban Greening. 68:127485. doi:10.1016/j.ufug.2022.127485.

Zhou Q. 2014. A review of sustainable urban drainage systems considering the climate change and urbanization impacts. Water (Switzerland). 6(Compendex):976–992. doi:10.3390/w6040976.