What does long-term value look like when the future is becoming harder to predict? As infrastructure leaders navigate accelerating change across energy, water, communities and industry, Nexus Magazine explores how strategic foresight can help organisations make better decisions today. From electrification and resource security to legacy assets and investment priorities, we examine why the most valuable infrastructure will be the infrastructure that continues to hold its value as the world changes around it.
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Edition 2
Innovation in infrastructure
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Contents Introduction: Embracing a new era of innovation — and it’s uncertainty ................................................................ 3 The next AI revolution won’t just optimise — it will control and build ........................................................ 4 From data overload to actionable insight: The case for integrated marine science ......................... 7 Flying taxis are coming to cities. Here’s what it takes to build for them. .......................... 12 Pipes and plants: How Vancouver’s asset management strategy is teaching cities to think green....................................... 16 A polluted data estate: How to fix AEC’s hidden productivity killer ............................................................... 20 E-fuels early movers can win, but need to tread carefully .............................................. 23 Emergency response: Building long-term trust through short-term flexibility ......................................................... 26 Quieter by design: Rethinking sound in infrastructure planning ...................................................... 29 Beyond minimising harm: The path to third generation sustainable design .............................................................. 33 About the authors .............................................................. 38
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Embracing a new era of innovation — and its uncertainty
Welcome to this second edition of Nexus Magazine. This quarter, we’re exploring the frontiers of innovation in sustainable development, ranging from flying electric taxis to advances in marine science to the evolution of fuel.
Anne Lynch Global General
Manager Technical Transformation and Innovation, GHD T here’s a common thread running through all these stories — innovation isn’t just accelerating, it’s fundamentally changing. New technologies — particularly AI — are expanding capabilities and enabling tasks that previously took weeks to be completed in minutes. AI is emerging as a powerful tool for deriving insights from complex datasets and using those insights to improve the modeling of environmental impacts and infrastructure performance. Sound engineering has always been defined by precision and execution. That won’t change. But the advance of AI demands a significant rethink of how the work is done. As AI takes on more of the initial design and analytical workload, the role of human engineers will remain essential but will need to evolve. Their value will increasingly lie in quality assurance and understanding client and community needs, or what I call “power-skills” engineering. Their role will shift to validating outputs and applying their expert judgment to make decisions in the context of local, social and environmental conditions. This shift raises a crucial consideration: finding the right balance between human judgment and machine capabilities. AI can get things wrong, of course. But the bigger risk is that it is used without integrating an understanding of real-world engineering, social and
environmental risks. That’s where engineers and other industry professionals will remain essential, and if anything, become more critical. In my experience, the most effective innovations happen when we bring together people with computational capabilities and those with unique engineering domain expertise on a project. This is the real “sweet spot” that can enable faster progress while still protecting people, environments and systems. At the same time, there are real constraints to this new form of innovation. Many questions around data ownership, security and governance remain unresolved. Traditional business models built on time-based billing struggle to capture the value created by automation. And we need to train and develop the next generation of engineers to equip them for this new world. The scale of the possibilities being unlocked by technology offers far more cause for hope than fear. The potential waiting to be unleashed when brilliant engineers can spend more time on creative work rather than mundane tasks is truly exciting. Together, the stories in this edition point to a future where engineering is more integrated, intelligent and aligned with communities and the environment. It’s time to embrace the uncertainty that comes with it.
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The next AI revolution won’t just optimise — it will control and build
Over the past two years, AI has been defined largely by large language models, systems that operate in the digital layer, generating text code and workflows. The next phase of AI matters not because it produces better text, code or workflows, but because it begins to shape physical systems directly.
Robert Casamento Guest author Global Strategy
Executive Across AI, Energy and Climate
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W e are moving from an era of digital analysis to one of physical execution, as AI shifts from the operational dashboard into the design, control and composition of infrastructure. Driving that transition is the emergence of physical AI and more deeply, physics-infused AI where machine learning is combined with physics, chemistry and materials science to alter not just how infrastructure is optimised, but the physical assumptions on which it is designed.¹ Leaders across the technology and scientific landscape are already pointing in this direction. Jensen Huang, CEO of NVIDIA, has described the next frontier as AI that understands the laws of physics,² while Demis Hassabis, CEO of Google DeepMind, has positioned AI as a driver of scientific discovery.³ The value of AI is no longer confined to what it can generate on a screen, but what it can alter in the world. In the energy sector, these approaches are being used to design entirely new polymeric materials for hydrogen fuel cell membranes, computationally generating and screening thousands of candidate structures for proton exchange and gas separation performance beyond what existing materials can achieve. In semiconductors, machine learning–based interaction models are being used to simulate atomic-level diffusion behavior in next-generation lithography systems, compressing materials design cycles from months to weeks and producing atom-level insights that guide the creation of new chamber materials. These are not simply efficiency gains. They represent a shift toward invention, a signal that the frontier of AI is moving from the digital layer into the molecular one. The shift from optimisation to discovery For decades, progress in industrial chemistry and materials science has been constrained by the pace of physical testing. New materials and chemical pathways had to be discovered through slow, iterative laboratory work. That is beginning to change. Hybrid approaches that combine AI with physics-based simulation now allow researchers to explore possible designs computationally at speeds and levels of accuracy previously out of reach.
Google DeepMind’s GNoME system, for instance, identified millions of potential new crystal structures and offers a glimpse of how computational discovery can compress decades of materials research into scalable, model-driven workflows. This does not eliminate the need for testing, but it sharply narrows the search space for next-generation batteries, semiconductors and other high-performance materials.⁴ In energy storage, AI-driven digital twins are enabling the design of entirely new solid electrode materials for next- generation batteries, generating and evaluating candidate structures at a scale no conventional R&D process could match. In water treatment, the same approach is being applied to design novel sorbent materials for PFAS capture and removal — simulating thousands of candidate molecular structures for binding affinity rather than testing them sequentially in a laboratory.⁵ In each case, the starting point is not an existing material to be improved, but a performance requirement to be met. AI is working backwards through chemistry and physics to find structures that have never been synthesised before. Structural impacts on CAPEX and OPEX For those who design, finance and operate physical assets, this is an economic story as much as a technology one. Consider the energy sector. Industrial processes — from hydrogen production to sustainable fuels — remain constrained by the capital and operating costs of extreme heat, pressure and expensive catalysts. If AI can design catalysts that allow reactions to occur at lower temperatures with higher yields or under less extreme conditions, the effect is not marginal. It changes plant design, equipment requirements and in some cases, the commercial logic of the process itself.⁶ This marks a shift from optimising systems we inherited to engineering systems we can deliberately redesign. Over time, this could reshape supply chains as well, as raw inputs are redesigned for efficiency, availability and local suitability rather than remaining tied to the legacy chemistry of the last century. In that sense, the boundary of what is economically viable begins to move with the science.
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Validation and the engineering imperative
they may arrive faster than traditional infrastructure planning cycles expect. For infrastructure leaders, this is not a question of if, but of when and whether today’s investment decisions reflect that trajectory.⁶ That makes complacency risky, particularly for those planning large-scale projects today. The assumption that infrastructure will continue to be governed by the same material constraints that shaped the past century is becoming less secure and assets designed around today’s process assumptions may prove obsolete sooner than expected. The organisations that grasp this early will not just operate better assets; they will be better placed to shape the next cost curves of the physical economy. The boundary of what’s possible We are moving from an era in which AI augments decision-making to one in which it begins to reshape the physical systems those decisions act upon. The boundary between the digital and the physical is shifting — and with it the boundary of what is possible. In a century defined by pressure on energy, resources and resilience, that shift could prove as consequential as any digital revolution. AI is no longer just a software layer sitting on top of infrastructure. It is becoming part of the design, control and operation of the physical world itself.
None of this removes the hard part. Moving from computational discovery to physical deployment introduces a different order of complexity. Engineering is and should remain risk-aware. It is shaped by operational realities, safety standards and the long test of durability in the field. It also operates within liability frameworks that require designs to be understood, not just produced and that will not change simply because the tool generating them is more powerful. The core skill of engineering will not disappear. But it will change. The work will shift, in part, from solving within relatively fixed constraints to exercising judgment over a much wider design space — validating and taking responsibility for solutions generated computationally. That in turn will require new capabilities. Asset owners and engineering firms will need stronger validation frameworks, updated testing protocols and the ability to design and validate new industrial process architectures — alongside closer engagement with regulators as standards evolve for computationally generated materials and processes. There is also a question of timing. Because these advances scale first through computation, simulation and design tools before they scale through construction,
Physical AI, by the numbers 10–20 years
10x–100x faster Early-stage discovery workflows
Millions Billions
Traditional materials discovery
Materials identified computationally
Deployed and multiple billion-dollar valuations: capital is moving quickly into AI-for-materials and discovery platforms⁷
Selected references
1. Karniadakis, G. E. et al. (2021). “Physics-informed machine learning.” Nature Reviews Physics, 3(6), 422–440. https://www.nature.com/articles/s42254-021-00314-5 2. Huang, J. (2025). GTC 2025 Keynote Address. NVIDIA GTC San Jose. Transcript retrieved from Rev.com https://www.rev.com/transcripts/gtc-keynote-with-nvidia- ceo-jensen-huang 3. Hassabis, D. (2024). Accelerating Scientific Discovery with AI. Nobel Prize Lecture, December 8, 2024. Nobel Prize Committee. https://www.nobelprize.org/prizes/ chemistry/2024/hassabis/lecture/ 4. Merchant, A. et al. (2023). “Scaling deep learning for materials discovery.” Nature, 624, 80–85. https://www.nature.com/articles/s41586-023-06735-9
5. Aspuru-Guzik, A. et al. (2018). “The role of AI in chemical discovery.” ACS Central Science, 4(2), 144–152. https://pubs.acs.org/doi/10.1021/acscentsci.8b00338 6. International Energy Agency. World Energy Investment 2025. https://www.iea.org/ reports/world-energy-investment-2025 7. McKinsey & Company. The State of AI: Global Survey 2025.; Tech Funding News (2025), “Ex-OpenAI execs raise $200M at $1B valuation for AI materials science startup backed by a16z.” https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai https://techfundingnews.com/ex-openai-execs-raise-200m-at-1b-valuation-for-ai- materials-science-startup-backed-by-a16z/
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The case for integrated marine science From data overload to actionable insight
For decades, progress in marine science was constrained by what could be observed directly at sea. Surveys were expensive, time- limited and geographically narrow.
Madelaine Hooper Marine Science Lead NSW, GHD
Dr. Justin Meager Technical Director – Aquatic Ecology, GHD
T oday, that constraint has largely disappeared. But it’s been replaced by a new challenge: making sense of the sheer volume of information now being generated about marine environments. Satellites, sensors and monitoring platforms now produce ocean data at unprecedented scale. An estimated 8 exabytes of data are generated each year, yet roughly 80 percent of this data remains unexamined.¹ Research networks are accumulating petabyte-scale archives, prompting Ocean Networks Canada CEO Kate Moran to warn that “there aren’t enough data specialists” to keep pace with the volume.²
At the same time, human activity in the world’s oceans is accelerating. Offshore wind is expanding, ports and defence infrastructure are growing, desalination plants are multiplying and ageing energy assets are entering complex decommissioning phases. Regulatory pressure is rising too: governments have committed to protecting 30 percent of global marine areas by 2030, yet only 8.4 percent is protected today; at the current pace, the world is 83 years from meeting that target.³ Together, these forces are reshaping what marine science must deliver — and how quickly. Meeting that challenge depends less on any single breakthrough than on how effectively existing and emerging tools, methods and expertise are brought together. Nexus Magazine | GHD | 7
30×30 progress tracker Global marine protection is at 8.4% versus the 30% target for 2030; at the current pace, the target is still ~83 years away.
0%
30%
8.4%
Why integrated methods matter This integrated approach is becoming essential to reduce uncertainty, close data gaps and produce evidence that can keep pace with rising development and regulatory demands. A recent Scottish government programme illustrates the strength of this approach. Digital aerial surveys (DAS) provided wide spatial coverage of marine mammals across offshore areas. Passive acoustic monitoring (PAM) added continuous temporal data, detecting animals that might never be observed during aerial or vessel surveys. Together, these methods produced a far more reliable picture of distribution and movement, reducing geographic and seasonal bias.⁴ We use the same integrated philosophy for our work in Australia. Aerial surveys reveal distribution and migration patterns over large spatial scales. Vessel- based surveys add fine-scale behavioural detail to understand how areas are used by different species. We deploy PAM during all stages of projects, including noisy construction phases, to simultaneously monitor noise levels and track real-time responses from sensitive species. Environmental DNA (eDNA) sampling, which detects species from genetic traces in the water, further boosts species detection, with global trials showing the method can identify up to twice as many species as visual surveys alone.⁵ Insights from Indigenous and local communities add vital cultural and ecological understanding, strengthening site interpretation. Animal tracking is increasingly complementing these approaches on GHD projects, helping link species presence to movement patterns and habitat use across project lifecycles.
Each method offers unique strengths and limitations. Integration closes critical evidence gaps, but it also introduces new complexity. Combining aerial imagery, acoustic records, eDNA data and animal tracking insights can generate datasets of enormous size and diversity. As marine projects scale and project timelines tighten, the challenge is no longer just collecting better data — it’s analysing it quickly enough to inform real-world decisions. This is where AI is beginning to make a meaningful difference.
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AI accuracy across marine-analysis tasks
Reported AI performance in marine monitoring: accuracy exceeds 90 percent across multiple tasks, with some applications reaching 98–99 percent.
99%
Whale species classification Fish species identification
98%
94%
Plankton classifiers
90%
Camera-trap image labelling
Turning data volume into insight The volume of marine data now being generated makes manual analysis increasingly impractical. AI is helping close that gap. Across global studies, machine-learning models have demonstrated strong performance in tasks such as species identification and behavioural analysis. For example, deep-learning tools have classified whale species from imagery with 98 percent accuracy and estimated body length within 5 percent of manual measurements.⁶ Fish species have been identified in video datasets with more than 94 percent accuracy,⁷ and plankton classifiers routinely exceed 90 percent
accuracy.⁸ In large camera-trap studies, AI has automatically labelled more than 99 percent of images, reducing manual labelling by more than 17,000 hours.⁹ Acoustic analysis is improving as well. NOAA researchers found that blending machine learning with synthetic data increased detection precision from 86 percent to 90 percent and recall from 88 percent to 93 percent. 10 But high performance does not always require deep learning. For well-defined animal calls such as pygmy blue whale signals, conventional template-matching techniques remain highly effective. When deep learning is used, adaptation to local conditions is essential. Transfer learning allows pre-trained models
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to be retrained on relatively small regional datasets, substantially reducing data demands. Synthetic data can further enhance results by blending animal signals with local background noise to represent new environments. AI can dramatically accelerate analysis, but its value depends on the systems around it. Models are only as effective as the data pipelines, validation processes and organisational structures that support them. As marine science becomes more automated and data-intensive, building the right tools and workflows is just as important as deploying the right algorithms. Designing systems that scale Innovation in marine science isn’t only about new sensors or smarter algorithms. Much of the progress comes from having the right tools, workflows and organisational systems to allow these technologies to scale. Underwater noise modelling is a clear example. GHD research has found that many commercial software packages lack transparency, struggle with regulatory requirements or cannot easily handle large data volumes. 11 To address this, our teams developed an in-house acoustics modelling toolbox using the data analysis-focused programming language R, designed to improve repeatability and make assumptions explicit. Scripted workflows also allow us to integrate commercial tools where useful while maintaining full control over data processing, visualisation and reporting. Custom tooling also supports advanced modelling needs. For a project commissioned by the Norwegian government, researchers developed bespoke scripts to analyse fish movement and behavioural responses to seismic noise. 12, 13 Linking acoustics, movement data and statistical modelling would not have been possible using packaged solutions alone. Emerging technologies will further expand these
capabilities. Autonomous underwater vehicles (AUVs) are already filling the gap between ship-based surveys and stationary monitoring systems. Swarm robotics is beginning to show potential for large-scale monitoring tasks. 14 Next-generation animal-borne tags are becoming smaller and more capable, with “grain-of-rice” sensors now collecting depth, temperature and acceleration data for up to 40 days, and transmitting across hundreds of metres. 15 Tools only deliver value when organisations know how to use them. Cross-regional knowledge hubs and communities of practice, like GHD’s 50-member marine science network, are essential for sharing methods, refining workflows and ensuring consistent delivery across diverse regulatory settings. A hybrid future for marine science Marine science is entering a period defined by scale: more activity offshore, more environmental commitments and more data than ever before. In response to this challenge, the most meaningful innovations are emerging not from just chasing the newest tool, but from connecting existing and emerging technologies. Digital aerial surveys, PAM systems, eDNA sampling, advanced modelling and machine learning all contribute different pieces of insight. Together, they form faster, clearer and more defensible evidence for complex marine decisions. As governments pursue global biodiversity targets and offshore development accelerates, this hybrid model will become increasingly essential. Organisations that can integrate methods, share knowledge and apply technology pragmatically will be well-placed to lead. For marine science, the future is not just digital or automated; it is connected.
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References 1. Terradepth and World Economic Forum. 2023. “11 Innovations Deepening Our Understanding of the Ocean through Data.” World Economic Forum, January. https://www.weforum.org/stories/2023/01/davos23-11-innovations-deepening- our-understanding-of-the-ocean-through-data/ 2. Ocean Networks Canada. n.d. “Advancing AI for Ocean Research and Climate Solutions.” Accessed [ADD ACCESS DATE]. https://www.oceannetworks.ca/news-and-stories/stories/advancing-ai-for- ocean-research-and-climate-solutions/ 3. Greenpeace International. 2023. From Commitment to Action: Achieving the 30×30 Target through the Global Ocean Treaty. https://www.greenpeace.org/static/planet4-international-stateless/2024/10/ b53a2f62-from-commitment-to-action-achieving-the-30x30-target-through- the-global-ocean-treaty.pdf 4. Scottish Government. 2020. Methodology for Combining Digital Aerial Survey Data with Passive Acoustic Baseline Data. https://www.gov.scot/publications/methodology-combining-digital-aerial- survey-data-passive-acoustic-baseline-data/ 5. Deiner, Kristy, Holly M. Bik, Evan Mächler, Miya Seymour, Albin Lacoursière- Roussel, Franziska Altermatt, Stefan Creer, et al. 2017. “Environmental DNA Metabarcoding: Transforming How We Survey Animal and Plant Communities.” Molecular Ecology 26 (21): 5872–95. https://onlinelibrary.wiley.com/doi/10.1111/mec.14350 6. Gray, Paul C., Alex C. Bierlich, Andrew S. Friedlaender, Leigh G. Johnston, Clive R. McMahon, and David W. Johnston. 2019. “Drones and Convolutional Neural Networks Facilitate Automated and Accurate Cetacean Species Identification and Photogrammetry.” Methods in Ecology and Evolution 10 (12): 2044–54. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13246 7. Siddiqui, S. A., A. Salman, M. I. Malik, F. Shafait, A. Mian, M. R. Shortis, and E. S. Harvey. 2017. “Automatic Fish Species Classification in Underwater Videos: Exploiting Pre-Trained Deep Neural Network Models to Compensate for Limited Labelled Data.” ICES Journal of Marine Science 75 (1): 374–89. https://doi.org/10.1093/icesjms/fsx109 8. Luo, Tao, Kun-Shan Cheng, and Chih-Wei Chang. 2018. “Automated Plankton Image Classification Using Deep Learning.” Limnology and Oceanography: Methods 16 (12): 814–27. https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lom3.10285 9. Norouzzadeh, Mohammad S., Anh Nguyen, Margaret Kosmala, Ali Swanson, Meredith S. Palmer, Craig Packer, and Jeff Clune. 2018. “Automatically Identifying, Counting, and Describing Wild Animals in Camera-Trap Images with Deep Learning.” Proceedings of the National Academy of Sciences 115 (25): E5716–25. https://www.pnas.org/doi/10.1073/pnas.1719367115 10. Clarke, Lisa A. 2021. North Atlantic Right Whale (Eubalaena glacialis) 2017– 2021: Technical Report on Acoustic Detection Analyses. NOAA Central Library. https://doi.org/10.25923/w39g-m842 11. GHD. n.d. “Analysis of Limitations in Commercial Underwater Acoustics Modelling Tools.” Unpublished internal report. 12. McQueen, K., J. J. Meager, D. Nyqvist, J. E. Skjæraasen, E. M. Olsen, Ø. Karlsen, P. H. Kvadsheim, N. O. Handegard, T. N. Forland, and L. D. Sivle. 2022. “Spawning Atlantic Cod (Gadus morhua L.) Exposed to Noise from Seismic Airguns Do Not Abandon Their Spawning Site.” ICES Journal of Marine Science 79 (10): 2697– 2708. https://academic.oup.com/icesjms/article/79/10/2697/6827581 13. McQueen, K., J. E. Skjæraasen, D. Nyqvist, E. M. Olsen, Ø. Karlsen, J. J. Meager, P. H. Kvadsheim, N. O. Handegard, T. N. Forland, and K. de Jong. 2023. “Behavioural Responses of Wild, Spawning Atlantic Cod (Gadus morhua L.) to Seismic Airgun Exposure.” ICES Journal of Marine Science 80 (4): 1052–1065. https://academic.oup.com/icesjms/article/80/4/1052/7076237 14. Brambilla, M., E. Ferrante, M. Birattari, and M. Dorigo. 2013. “Swarm Robotics: A Review from the Swarm Engineering Perspective.” Swarm Intelligence 7 (1): 1–41. https://doi.org/10.1007/s11721-012-0075-2 15. 2025. “A New Generation of Tiny Tracking Tags Offers a Fresh Look at the Lives of Little Fish.” Smithsonian Magazine, July 18. [NOTE: Original reference cited June 2022 — search returns July 2025. Confirm date and author name with writer before publication.] https://www.smithsonianmag.com/innovation/a-new-generation-of-tiny- tracking-tags-offers-a-fresh-look-at-the-lives-of-little-fish-180987011/
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Flying taxis are coming to cities Here’s what it takes to build for them Byron Tabet Senior Design Manager, GHD C onvenient and environmentally friendly, the Emirates’ most populous city. Once the custom-built vertiport — designed by our world’s first fleet of electric air taxis is set to transform urban mobility in the United Arab team — opens at Dubai International Airport, its two pads will be able to handle 10 landings an hour and transport 170,000 people a year.² The air taxis, or electric vertical take-off and landing (eVTOL) aircraft, are fast — carrying four passengers at speeds up to 320 kmh (200 mph). They will fly — far more quietly than traditional aircraft — to four locations around the city, producing zero direct emissions.³ The world needs better commuting options. A driver in the United States spends, on average, about 43 hours a year stuck in traffic jams. That’s equivalent to an entire week of work and nearly $800 in lost productivity.⁴ European cities tell a similar story. Drivers in Düsseldorf and Munich are spending 20 percent more time in traffic, while Londoners put up with an average of 101 hours of gridlock a year. Across the United Kingdom more broadly, hours and fuel wasted in traffic jams added up to an estimated 7.7 billion pounds ($10.2 billion) in 2024 alone.⁵ A compelling need to ease urban gridlock
Flying taxis will soon take to the skies over Dubai, transporting passengers between the city’s airport and downtown in as little as 10 minutes. And the fare? Potentially about what you would pay for a premium taxi.¹
Dubai’s planned launch of its air-taxi service in 2026 is just the beginning. More cities are starting to pay attention. Many urban-dwellers around the world may soon be able to enjoy a safe, sustainable and rapid commute high above the noise and gridlock below. From niche idea to fast-growing industry Los Angeles, among the world’s most congested urban areas, is one of a growing number of cities pinning their hopes on innovative transport. It’s racing to roll out air taxis in time for the 2028 Olympics.⁶ With local commuters already spending hours trapped in traffic, officials are hoping a fleet of eVTOL aircraft will help both residents and tourists avoid the roads with quick 10- to 20-minute flights between vertiports located throughout the city — from hubs at SoFi Stadium and LAX to others further afield in Santa Monica and Orange County.⁷
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Urban congestion burden: hours lost in traffic Work week = 40 hours Time lost to congestion is already massive: London drivers lose more than twice as many hours annually as the average US driver.
Similar plans are underway across the world: – Florida, US, is moving faster than most, with two test sites under construction and up to 24 vertiports planned.⁷ – China is looking to begin mass production of flying taxis, with commercial services starting in multiple cities within three years.⁸ – Globally, more than 1,500 vertiports are now on the drawing board — up sharply from about 1,000 just a year ago.⁹ These rapidly expanding plans for urban aerial commutes are transforming a once-niche idea into a valuable industry. Valued at just $1.4 billion in 2023, the eVTOL market is growing at a combined annual growth rate of more than 54 percent and is expected to reach $29 billion by 2030.¹⁰ The future of flying taxis seems bright. But as with any new developments in the aviation industry, there are significant headwinds.
101
43
US average London
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Air taxi service: What the vertiport can handle
Dubai’s first vertiport is designed for high-frequency operations: two pads handling up to 10 landings per hour and an estimated 170,000 passengers annually.
2 10 170k 10 Pads Landings / hours Passengers / year Minutes airport / downtown
The regulatory and technological challenge In the run-up to the Paris Olympics in 2024, there was mounting excitement about the prospect of flying taxis transporting people between events. Those plans ultimately never left the ground after local authorities raised concerns about safety.¹¹ It’s hardly surprising. After all, we’re talking about a new technology moving people through the air above some of the world’s most densely populated cities. There’s no question that it will attract a lot of attention from regulators, who will set rigorous standards for safety, noise and pollution. Every regulator has their own interpretation of what these standards should be — from domestic agencies like the United States Federal Aviation Administration (FAA) and United Arab Emirates General Civil Aviation Authority (GCAA), to international bodies including the European Union Aviation Safety Agency (EASA) and International Civil Aviation Organization (ICAO). Then there’s the challenge of the technology itself — because an electric flying taxi depends on breakthroughs involving electric propulsion systems, high-density batteries, efficient charging infrastructure at vertiports and specialised maintenance tailored to electric powertrains. And all of these complex pieces — both regulatory and technical — must work together to deliver reliable performance, rapid turnaround times and reasonable costs that can help flying taxis be profitable and scalable. Fortunately, many of these challenges are beginning to resolve. Expanding innovation and use cases Imagine you’re hoping to launch a flying taxi service in a city. Let’s go through all the questions you need to answer. Where can flying taxis take off and land? How will they
load and unload passengers? What’s the best way to charge their batteries and conduct maintenance? And how will they help passengers have a smooth journey from start to finish? Thanks to the experience gained in places like Dubai, good solutions to these problems are emerging: – Vertiports that use retrofitted rooftops of airport terminals, multi-storey car parks, rail stations and office towers. – Vertiports that link up with airports, trains, buses, ride-shares and other forms of transport to minimise travel time and inconvenience. – Charging infrastructure placed beneath landing pads to create space while acoustic barriers and optimised flight paths reduce noise. – Digital check-ins and automated security screening, weight verification and pre-flight safety briefings that cut boarding times to 10 minutes or less. – Rooftop lounges open to the public that turn vertiports into stunning visitor attractions rather than sterile waiting areas. Success in Dubai will stimulate efforts to deploy eVTOL aircraft in other industries. For healthcare, they could transport patients to the hospital and time-sensitive organs and treatments to patients. For the logistics industry, there’s the tantalising prospect of quick, quiet deliveries high above streets where company trucks and vans are often stuck at lights or trapped in traffic. UPS, for example, has committed to buying up to 150 eVTOL aircraft to move packages between the company’s hubs, while a company in China started testing an eVTOL aircraft that may carry up to 400 kilograms as far as 200 km.¹² , ¹³ These initiatives signal growing confidence that eVTOL technology can solve some of the world’s most pressing problems around urban transport and last-mile deliveries. While early rollouts will serve niche markets, the improving technology, evolving regulations and falling costs should see flying taxis emerging at scale, making the world cleaner, safer and more connected.
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References
1. GHD. 2024. “GHD-Designed Vertiport to Launch in Dubai, Advancing the Future of Urban Mobility.” Press release, December 10, 2024. https://www.ghd.com/en/about-ghd/news/press-releases/10-12-2024-ghd- designed-vertiport-to-launch-in-dubai-advancing-the-future-of-urban-mobility 2. Ali, A. 2025. “Air Taxis: UAE Sets Stage to Pioneer eVTOL Commercial Services.” Gulf News, February 3, 2025. https://gulfnews.com/business/aviation/air-taxis-uae-sets-stage-to-pioneer-evtol- commercial-services-1.500125735 3. Ali, A. 2025. “Air Taxis: UAE Sets Stage to Pioneer eVTOL Commercial Services.” Gulf News, February 3, 2025. https://gulfnews.com/business/aviation/air-taxis-uae-sets-stage-to-pioneer-evtol- commercial-services-1.500125735 4. INRIX. 2024. “INRIX 2024 Global Traffic Scorecard.” INRIX. https://inrix.com/scorecard/ 5. INRIX. 2025. “Urban Congestion in 2024 & Beyond: What the INRIX Traffic Scorecard Tells Us and How Cities Can Adapt.” INRIX, February 10, 2025. https://inrix.com/blog/analyzing-urban-congestion-in-2024-and-understanding- how-cities-can-adapt/ 6. Propmodo. 2025. “L.A. Races to Build the First Network of Flying Taxi Vertiports.” Propmodo, October 7, 2025. https://propmodo.com/l-a-races-to-build-the-first-network-of-flying-taxi- vertiports/ 7. CBS Miami. 2024. “Flying Cabs Coming to Florida? Governor Ron DeSantis, FDOT Offer Test Site for ‘Veriports’.” CBS Miami, October 11, 2024.
https://www.cbsnews.com/miami/news/florida-flying-cars-veriports-airtaxis-polk- county-governor-desantis-fdot/ 8. CNBC. 2025. “China’s Cities May See ‘Flying Taxis’ as Soon as Three Years, Aviation Company Ehang Predicts.” CNBC, April 4, 2025. https://www.cnbc.com/2025/04/04/china-may-see-flying-taxis-in-three-years- ehang-predicts.html 9. Business Aviation. 2025. “1,504 Vertiports Planned Worldwide: Global Infrastructure Surge Signals Low Altitude Mobility Maturity.” Business Aviation, February 2025. https://businessaviation.aero/evtol-news-and-electric-aircraft-news/vertiport/1- 504-vertiports-planned-worldwide-global-infrastructure-surge-signals-low- altitude-mobility-maturity 10. Grand View Research. n.d. “eVTOL Aircraft Market Size, Share & Trends Report, 2030.” Grand View Research. https://www.grandviewresearch.com/industry-analysis/evtol-aircraft-market-report 11. France 24. 2024. “Paris Scraps Plans for Olympic ‘Flying Taxis’.” France 24, August 8, 2024. https://www.france24.com/en/live-news/20240808-paris-flying-taxi-test-flights- scrapped-during-olympics 12. Flying Cars Market. n.d. “eVTOL Industry in the US Analysis.” Flying Cars Market. March 2, 2025. https://flyingcarsmarket.com/evtol-industry-in-the-us-analysis/ 13. Kajal, K. 2024. “World’s First Two-Ton Vertical Takeoff Aircraft Set to Fly in China.” Interesting Engineering, October 15, 2024. https://interestingengineering.com/transportation/worlds-first-two-ton-vertical- takeoff-aircraft
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How Vancouver’s asset management strategy is teaching cities to think green Pipes and plants
A Canadian city’s reimagining of infrastructure management offers a practical blueprint for municipalities worldwide grappling with climate change, population growth and ageing assets.
Gemma Dunn Water Market Leader, Western Canada, GHD
Heidi Horlacher Guest author Senior Geoscientist, Green Infrastructure Implementation, City of Vancouver
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V ancouver’s experience shows that the path to climate resilience With mounting regulatory pressures and rising community expectations, Vancouver’s engineers faced a familiar dilemma when assessing the city’s century-old sewer and stormwater systems: dig up streets to install larger pipes or try something different. Rather than relying solely on traditional upgrades, the city explored a complementary approach — one that could deliver resilience while unlocking a wide range of co-benefits. The solution did not emerge from a single capital project or regulatory mandate. Instead, it flowed from Vancouver’s Rain City Strategy,¹ which reframed asset management to fully integrate green infrastructure. By applying disciplined, lifecycle-based management principles to green infrastructure, defined as tree trenches, wetlands, bioswales and permeable pavement, the city has achieved measurable gains. A problem every city faces Vancouver’s challenge mirrors municipalities is more than new, greener infrastructure: it’s about changing how cities plan, govern and integrate what they already have. everywhere.² Storms are becoming more frequent and intense, while urban growth replaces natural ground with hard surfaces that pool, rather than absorb, rainfall. The traditional response of upgrading pipes and treatment plants, referred to as grey infrastructure, is expensive and disruptive. These projects can cost millions of dollars and affect communities for months at a time. Vancouver began asking a different question: how could green infrastructure work alongside existing systems to extend their lifespan and deliver benefits that pipes alone cannot?
From roads to rainways This approach was put to the test on a deteriorating local roadway that experienced persistent flooding. Working closely with residents, the city developed the St George Rainway, a blocks-long blue-green system designed to manage stormwater more naturally.³ A rainway is a park- like network of green rainwater infrastructure features, such as rain gardens that use plants, trees and soil to manage the rainfall. These living systems work with pipes, streets and other parts of the built environment to capture and clean rainwater before returning it to the ecosystem. Culverts, stepping-stones and boardwalks allow residents to move through the space, integrating everyday use with effective water management. A new shared asset management strategy Cities have experimented with rain gardens and bioswales for years. What sets Vancouver apart is the systematic, programmatic way these living systems are managed as municipal assets. The core challenge is governance, not technology. Traditional asset management models assume single ownership: one department is responsible for one type of infrastructure. Green infrastructure breaks that mould. A single bioswale, for example, may involve the water utility for drainage performance, transportation for street integration and parks for vegetation maintenance.
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Vancouver responded by creating structures to coordinate multiple stakeholders around shared asset management principles. The city established the Green Infrastructure Implementation branch to lead, coordinate and steward green infrastructure across departments, bringing clarity to ownership and responsibility. Today, Vancouver maintains more than 400 green infrastructure assets, ranging from small curb bulges that slow traffic and capture rainwater to larger systems like the St George Rainway. These assets are tracked, monitored and maintained using lifecycle planning that considers everything from soil health to plant replacement schedules. Beyond the business case Vancouver’s data-driven analysis of its green infrastructure portfolio has challenged long-held
assumptions about high operational costs. By tracking actual maintenance expenses over several years, the city found that costs were lower than early estimates. In part, this was because maintenance crews became more efficient as their experience and the asset inventory grew. Each green infrastructure project can defer significant investment in traditional pipe upgrades while delivering added community value. Beyond stormwater management, green infrastructure provides benefits that grey infrastructure cannot — beautification, biodiversity, improved community health, enhanced property value and urban cooling. Over time, cities will increasingly be able to assign monetary value to outcomes such as reduced hospital visits, energy savings from urban cooling and other positive impacts — further strengthening the case for integrating green infrastructure into asset management systems.
The implementation playbook For cities looking to replicate Vancouver’s approach, five prerequisites stand out:
Senior leadership and political support
Connect with peers
Clear policies
Integrated, multidisciplinary teams
Learn by doing
Vancouver’s progress was enabled by the creation of a dedicated branch focused solely on green infrastructure, providing the mandate, resources and continuity needed for systematic implementation.
Outcome-oriented framework such as the Rain City Strategy¹ and Healthy Waters Plan⁴ helped align projects with broader city objectives.
Success depends on collaboration among engineers, landscape architects, planners and other professionals, helping decisions
Rather than waiting for perfect solutions, Vancouver emphasised pilot projects. Starting small allows the city to test ideas, build confidence and refine approaches over time.
Engagement with global, national and regional networks such as the Green Infrastructure Leadership Exchange⁵ enables faster learning and problem solving through shared experience.
reflect diverse expertise and perspectives.
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Bioswale Green Infrastructure Implementation branch.
Water Utility (drainage performance)
Transportation (street integration)
Parks (vegetation maintenance)
As climate pressures mount and urban infrastructure costs rise, Vancouver’s experience suggests the conversation has moved on. The focus now is less on whether green infrastructure should be adopted, and more on how quickly cities can create the systems to manage it effectively. What makes Vancouver’s model compelling is its scalability. As green infrastructure networks grow, maintenance
demands change, influencing staffing, budgeting and long-term planning. Cities do not need to transform their entire approach overnight. They can begin by applying asset management principles to the green infrastructure they already have, gradually building the systems and expertise needed for larger-scale implementation.
References 1. City of Vancouver. “Rain City Strategy.” City of Vancouver. https://vancouver.ca/files/cov/rain-city-strategy.pdf
3. City of Vancouver. “St George Rainway.” Shape Your City (City of Vancouver). https://www.shapeyourcity.ca/st-george-rainway 4. City of Vancouver. “Healthy Waters Plan.” City of Vancouver. https://vancouver.ca/home-property-development/healthy-waters-plan.aspx 5. Green Infrastructure Leadership Exchange. “Green Infrastructure Leadership Exchange.” https://giexchange.org/
2. GHD. 2025 “State of Green Infrastructure Asset Management Benchmarking Report.” GHD. https://www.ghd.com/en/campaigns/state-of-green-infrastructure- asset-management-benchmarking-report/download-the-report
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How to fix AEC’s hidden productivity killer A polluted data estate Every industry today is wrestling with the complexities of data — how to organise it, make it more usable, and use it to drive insights and
productivity. Still, few sectors are weighed down by this data problem as heavily as the architecture, engineering and construction (AEC) industry.
Carlos A. Baldor Jr. Guest author President and CTO of BST Global
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I t’s no exaggeration to say that the industry faces a data crisis. The nature of their work means that AEC companies generate vast volumes of project data across design files, contracts, models, field reports, RFIs and more. Yet much of it remains scattered across siloed tools, inaccessible formats, and disconnected teams, acting as a major drag on productivity as workers at all levels waste time hunting for information and duplicating work. This isn’t just an anecdotal observation — research consistently ranks AEC as a data laggard. The construction industry has been among the slowest to digitise.¹ Research has found that poor data and miscommunication cause 52 percent of all rework in construction.² The sector’s data problem is a significant factor in its lagging productivity compared to other industries. All those lost hours chasing data and reworking projects add up. McKinsey found that construction productivity only increased by 10 percent between 2000 and 2022, compared to a 50 percent jump in total economic productivity.³ To understand how the industry can break this cycle and solve its data crisis, Nexus Magazine sat down with Carlos A. Baldor Jr., President and CTO of BST Global, a leading provider of enterprise resource planning and work management software tools for AEC firms. Our conversation explored what it will really take for companies in the industry to harness data as an engine of productivity and competitive advantage.
Q: The AEC industry is drowning in fragmented, siloed data. How are you addressing what many call a “polluted data estate”? A: It’s undeniable that the volume and rate of data being produced have grown exponentially, while the systems and techniques used to manage it are outdated. Much of the information we handle is still locked in formats such as design documents, contracts and models, which makes it inaccessible, hard to search, and often stripped of context or fidelity. Fixing the data estate starts by rethinking how that data is created, not only how it’s stored. Trying to “capture” the data at the end of a project without reshaping the upstream processes is just more of the same. Our approach is to work with clients from the start to understand the desired data outcomes and then redesign workflows around those goals. Take contracts, for example: they’re one of the richest sources of operational insight, yet most firms have no pathway to apply them effectively. Q: Chief Technology Officers (CTOs) everywhere are struggling with data democratisation. How do you balance accessibility with governance? A: Several methodologies have emerged that deal with this exact challenge, and they align with how our product teams evolved to ensure they are focused on their clients and users. Approaches such as data mesh decentralise ownership.⁴ Under a mesh model, the teams closest to the work, whether that is finance, operations or IT, create and manage their own data products. They curate them, govern access to them and define quality thresholds. They are in the best position to own and govern such data products, including who consumes them and how they are consumed. This approach marks a shift from centralised IT bottlenecks to domain-driven ownership. By empowering teams while still enforcing data-product standards, you get the benefits of democratisation without creating confusion. Q: What’s your view on the statement “data is the new oil”? A: It’s a catchy phrase, but overly simplistic. A lot of effort today goes into “mining” data. But data is only valuable when it is used collaboratively and intentionally. Data becomes valuable not by being extracted, but when every producer and consumer feels responsible for its clarity, context and purpose, and when it is actively applied to solve real-world construction and design problems. In knowledge-based industries like AEC, the biggest impediment is inconsistency: people rarely provide information with enough context to be usable across disciplines or levels of expertise. Take the logistics industry: sensors monitor every step of the process, making inefficiencies immediately visible and reinforcing outcomes. AEC could do something similar for asset operations, but this is harder in the design process because projects are so unique.
What drives construction rework
Over half of construction rework is linked to poor data and miscommunication (52 percent).
52%
Rework caused by other factors
48%
Rework caused by poor data and miscommunication
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