15 March 2026 3:03 am
How AI is transforming the future of the built environment

How AI is transforming the future of the built environment

Dec 11, 2025

Image : Rethinking The Future

How AI is transforming the future of the built environment

Vector Polygon dot connect line shaped AI. Concept for machine learning and artificial intelligence.
©undefined undefined | iStock, via Trimble

Artificial Intelligence has been part of our technological landscape for years, but its capabilities are rapidly advancing. The construction industry, in particular, is witnessing unprecedented changes driven by AI, with technology being used in ways unimaginable just a year ago. Benedict Wallbank, partnerships and digital construction strategy manager at Trimble, discusses further

Many of us already use AI assistants to some degree, such as ChatGPT. In fact, many are of the belief that we are at the start of a revolution that will profoundly reshape human society. For the construction industry, this transformation brings both immense opportunities and critical questions.

The vast bulk of AI applied in the construction industry today is “narrow AI”, which is trained to perform a single task, often better and faster than a human can. This is the main type of AI that is currently in use, powering everything from chatbots to workflow automation.

However, a more transformative change is on the horizon: AI with autonomous, agent-like behaviour that can plan, make decisions and execute complex tasks with less human input.

The next wave: Agentic AI and new business models

Greater change is on the way in the form of Artificial General Intelligence (AGI), also known as Agentic AI. Unlike narrow AI, AGI can apply previous learnings and skills to accomplish new tasks in a different context, without needing to be retrained by humans. This allows it to learn and potentially perform virtually any intellectual task a human can.

This evolution will have profound economic implications. According to Jari Heino, vice president & GM, BIM & Engineering at Trimble in Finland, our entire business model may be affected: “AI agents will eventually work somewhat independently, which opens up a whole new world. Which of our tasks can – and should – AI take over?”

The interest in AI within the construction sector is significant, with many seeking to understand its practical value. The true potential of AI lies not in replacing humans, but in eliminating the tasks that humans shouldn’t be doing in the first place.

Redirecting human potential

By automating repetitive, cognitively mundane and even dangerous work, AI frees up human resources to tackle more pressing and skilled challenges, allowing us to confront the labour shortage crisis head on.

In many ways, the time spent on repetitive tasks that could be easily automated represents wasted human potential. Instead, AI can redirect our skilled workforce towards the more pressing challenges and complex jobs, rather than consuming it with routine.

The result isn’t just about improving productivity and efficiency levels – it represents a fundamental shift in what construction professionals can accomplish.

Unlocking trapped data

Perhaps one of the industry’s most persistent challenges is fragmented data. A plethora of proprietary formats means that information can get trapped and value is lost at every project handover. While standards are important, forcing everyone to work the same way is not always a practical solution. Instead, AI can help to organise data behind the scenes, allowing teams to maintain flexible work practices while achieving data harmony.

Benedict Wallbank, who is also a non executive director at NIMA (formerly the UK BIM Alliance), elaborates on this potential: “I’ve been obsessed with the challenges of data interoperability and how we efficiently get to quality, whole-life asset data. At NIMA, so many of our current discussions are on how AI will help us achieve that goal. My personal view is that Agentic AI will provide much of the solution. Do we still need classification and standards? Yes – but AI offers the potential ability to identify and map data that is currently trapped within documents, drawings, models, scans and reality capture.”

Industry-native AI in action

While the world has seen great strides in general purpose AI, attention is turning to industry-native solutions that speak the language of construction. These specialised tools are focused on solving practical problems, understanding context and integrating with existing workflows.

Within Trimble, AI adoption is already widespread, from using it to speed up code writing to enhancing software solutions, all with the focus of streamlining design, modelling and field workflows.

AI enables users to modify 3D models with text prompts, automate geometry creation and classify models efficiently. It performs automated document classification, checks compliance in BIM models, analyses change orders, identifies road defects and runs energy simulations.

In the field, AI can monitor site safety by identifying PPE compliance and hazard zones, as well as comparing scans with models in order to detect deviations. AI aids in finding content, creating materials and detailing designs, providing comprehensive support for various needs.

Navigating the future with trust and responsibility

As AI becomes more autonomous, questions of trust, accountability and regulation are critical.  Global approaches to regulation vary.  The UK has set out five key principles to be policed sector by sector, while the EU is taking a centralised approach, establishing a shared supervision and enforcement regime.

The US has opted for a lighter touch, leaving regulation to existing laws and individual states to encourage innovation.

The more we hand over tasks to autonomous systems, the more important it becomes to define when a human needs to be involved. Like all things, AI is not infallible. We build systems around the reality of human fallibility, and yet we expect near-infallibility from automated systems.

Regardless, it’s clear that the AI genie has escaped its bottle and is in the process of reshaping the industry. The firms that thrive won’t be those that race to implement every new innovation but those that ask deeper questions of it: which human capabilities should we amplify? How do we preserve the irreplaceable judgment that comes from years of experience in the field?

The organisations that navigate this transition with strategic clarity, understanding that AI serves the builder rather than replacing the craft, will forge the sustainable path forward.

Ben Wallbank

Digital Construction and Partnerships Manager
Trimble
+44 0800 048 8152
vp_uksales@trimble.com

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Learn more about Trimble, here: www.trimble.com/construction.

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How artificial intelligence is helping urban planners

How artificial intelligence is helping urban planners

Dec 6, 2025

Andrea Yu
Special to The Globe and Mail

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Planners and researchers are turning to artificial intelligence to better understand how people move, live and work – while keeping human judgment at the heart of city building.GETTY IMAGES

 

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From housing demand to traffic congestion, city planners have long relied on imperfect models to predict how people move and live. Now, artificial intelligence promises to make those forecasts more accurate.

Mohamad Khalil, a transportation engineering researcher who is currently a postdoctoral fellow at the University of Alberta, has been interested in machine learning long before it was trendy.

“I actually started working with machine learning in 2014,” he says. “To me, at the time, it seemed very appealing and very futuristic and an important next step to [urban planning] modelling.” Mr. Khalil says that most urban planning models, which were developed 50 or 60 years ago, are overly simplistic.

“It assumes that humans are 100-per-cent rational and will choose the best option for their own sake,” he says. “For example, you will choose the best route to go to work based on travel time. However, this is not 100-per-cent true. Sometimes, for some reason, you might choose another route.” In comparison, machine learning models are able to use countless data points collected from devices such as mobile phones and vehicle GPS systems to create more complex models, faster and with greater accuracy.

“Machine learning excels with complicated behaviour,” he says.

Mr. Khalil, who conducted his PhD thesis in transportation engineering at the University of British Columbia, built a “modelling suite” – a collection of tools that help simulate and visualize different land-use scenarios using AI to make better predictions that factor in changes across urban, transportation and demographic systems.

“If we’re implementing a policy, how is that policy going to affect a city?” Mr. Khalil says. “If 20 per cent of people are working online compared to 100-per-cent remote, maybe we’ll see less traffic on our roads, maybe people need bigger homes if both partners are working from home and maybe they don’t need two vehicles.”

He envisions his research and modelling tools being adopted by city planners and decision-makers.

“We present to them the different scenarios that could happen,” he says. In turn, planners can make decisions and recommendations to elected officials about infrastructure development such as zoning, building new transit lines and housing.

By considering multiple scenarios, which can be produced quickly and accurately, urban planners can take a more creative and flexible approach to their work by experimenting with different parameters and possibilities.

That ability to test real-world scenarios before making costly infrastructure decisions is already taking hold in Canadian cities.

Ryan Smith, divisional director of planning and development services for the City of Kelowna in the southern interior of British Columbia, has been using predictive modelling to make more informed decisions and recommendations for nearly a decade, although he says the technology has improved recently to analyze larger data sets.

“We’re flying less blind now,” he says. Kelowna has been an early adopter of such technologies. One example is an AI-enabled predictive modelling tool Mr. Smith uses to see what neighbourhoods are likely to be redeveloped soon. These are typically areas with older homes and buildings that might be demolished soon and rebuilt with additional housing density.

“We know what year a house was built, whether or not the owner lives in the house, the improvement value on the property and how much the building is worth,” he says. “We can create a probability of redevelopment with that data and make smarter infrastructure decisions.”

That might look like curb, gutter, sidewalk and street tree improvements, replacing and upsizing sewer and watermains or improving electrical infrastructure in neighbourhoods that are likely to see a higher rate of redevelopment and therefore an increase in residents. These tools allow planners like Mr. Smith to “get ahead” of risks, such as ensuring neighbourhoods have sufficient infrastructure to support more residents.

But while some planners see clear benefits to integrating AI into city planning, others caution against letting the technology steer too much of the process.

Pamela Robinson, a professor at Toronto Metropolitan University’s School of Urban and Regional Planning, cautions against becoming too reliant on it.

“I would argue that AI could be an input into research and decision support, but it shouldn’t be making the decisions,” Ms. Robinson says. “Planners need to stay in charge and be the humans in the loop around the sound professional advice they offer.”

Ms. Robinson sees the potential for AI to improve city building in several different ways, from expediting the approvals process for issuing building permits to platforms for public engagement and consultation and design decisions, such as what types of cladding on a building have lower greenhouse gas emissions. However, she encourages urban planners, decision makers and elected officials to use these technologies with care.

“I think planners are appropriately curious and cautious, and I think that’s a good thing,” she says. “The planners that we’ve worked with want to deliver good outcomes for their residents and they’re committed to their work and the communities where they’re planners. There’s a lot of hype around these tools. It’s early days and I think this kind of curiosity and caution will serve Canadian cities well.”

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Urbanization is projected to increase local surface temperature

Urbanization is projected to increase local surface temperature

Dec 5, 2025

Image: ScienMag

Urbanization is projected to increase local surface temperature by 2100

Communications Earth & Environment volume 6, Article number: 988 (2025) Cite this article

 

Abstract

Future projection of global land surface temperature often emphasizes climate change while neglecting urbanization. Yet, urbanization-induced warming strongly influences heatwave-related health risks and energy demands. Here, we developed a 1-km resolution global land surface temperature dataset for 2020–2100 at five-year intervals, combing climate change-induced global warming and urbanization-driven local warming, which were estimated using multi-model ensemble projections, and a dynamic regression model linking impervious surface area and local temperature, respectively. Our dataset aligns closely with satellite observations, showing high spatial and temporal consistency. By 2100, urbanization contributes an average local warming of 0.1 °C, with approximately 10–16% of urban areas experience extreme warming exceeding 1 °C. Urban areas remain warmer than the global mean, whereas their warming rates are 0.5–8% lower than the global average under all scenarios. The derived dataset enables improved assessments of urban heat risks assessments and supports climate-resilient urban planning.

Introduction

Global rapid urbanization has intensified the urban heat island (UHI) effect, raising worldwide sustainability concerns. By altering urban land surface properties, urbanization amplifies UHI, a trend well-documented in recent studies. This intensification, in turn, threatens human health through heatwaves, increases energy consumption, and disrupts water–atmosphere interactions, thereby challenging future sustainable development. As a key indicator, surface UHI (SUHI) is particularly valuable due to its high spatiotemporal resolution and is widely applied in urban planning, climate research, public health, and environmental protection.

Satellite-derived land surface temperature (LST) is central to assessing SUHI and its dynamics. SUHI is typically quantified as the urban-rural LST difference. Moreover, LST provides consistent, repeatable observations across diverse spatial (local to global) and temporal (diurnal to interannual) scales, supporting analyses of thermal environments and the estimation of air temperature. With sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer, and the Land Remote-Sensing Satellite series, large-scale SUHI assessments are feasible owing to their complementary spatial and temporal resolutions. Looking ahead, as global warming and urbanization persist, SUHI is expected to intensify.

However, explicit projections of future urban heat patterns remain limited. While some studies employ Earth System Models and General Circulation Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to estimate temperature, these models lack detailed urban representation. Models such as the Community Earth System Model incorporate urban processes but remain constrained by coarse resolution and simplified parameterizations. In contrast, Regional Climate Models offer finer-scale detail through downscaling, yet they are computationally intensive and less applicable for global-scale analyses.

To address this gap, we developed a global 1 km LST dataset (2020–2100) that integrates the combined effects of both climate change and urbanization. First, we quantified global warming driven by climate change using a multi-model ensemble of CMIP6 surface temperature projections. Next, we estimated urbanization-induced warming by establishing dynamic regression models between MODIS-derived LST and impervious surface area (ISA). These models were iteratively updated and applied to projected ISA time series to characterize the urbanization-induced warming. Finally, by combining climate and urbanization components, we generated a 1 km LST dataset for 2020–2100, capturing both large-scale climate impacts and localized urban heat amplification to support advanced thermal analyses.

Results

Historical relationship between changes in ISA and LST

The response of LST change (ΔLST) to ISA change (ΔISA) from 2003 to 2020 exhibits pronounced global spatial heterogeneity (Fig. 1). The historical slopes predominantly range from −0.04 to 0.04 °C per %, with 68% of analyzed cities experiencing increased warming alongside ISA expansion. In  contrast, some cities show a negative ΔISA–ΔLST relationship, where  each unit increase in ΔISA corresponds to a decrease in ΔLST, resulting in slightly lower LST values (Fig. 1a). Cities with high slopes are concentrated in rapidly urbanizing regions, including parts of Africa, East and Southeast Asia, and northern South America, reflecting intensified urban heat effects in these areas. Statistically, more than half of the cities exhibit significant correlations (p < 0.1) (Fig. 1b), particularly those undergoing rapid urbanization (Supplementary Fig. 1). Across climate zones, LST consistently increases with ISA growth, though the magnitude of response varies substantially. Tropical and temperate regions show stronger warming, with average slopes of 0.0187 °C per % and 0.0125 °C per %, respectively (Supplementary Fig. 2), likely driven by rapid urbanization and local climatic conditions. For instance, Guangzhou, a representative rapidly urbanizing city, experienced a 100% increase in ISA, accompanied by a strong positive ΔISA–ΔLST  correlation from 2003 to 2020 (Supplementary Fig. 1). By contrast, despite being located in a temperate zone, Washington D.C. experienced less than half the warming observed in Guangzhou, likely due to its more mature stage of urban development (Supplementary Fig. 1). Arid and cold regions exhibit comparatively modest responses, with average slopes of 0.0022 °C per % and 0.0076 °C per % respectively (Supplementary Fig. 2), likely attributable to lower solar radiation, limited anthropogenic heat sources, and the high-albedo effects of vegetation or snow cover. For example, Helsinki shows only about one fifth of Guangzhou’s LST response, despite a 70% increase in ISA (Supplementary Fig. 1). This climatic variation underscores the crucial role of environmental context in modulating the thermal impacts of urbanization, highlighting the sensitivity of the ΔISA–ΔLST relationship across climatic zones.

Fig. 1: Linear relationship between ΔLST and ΔISA.
figure 1

a Slope derived from historical observations. b Statistical significance of the linearly fitted model. The analysis included 6359 level-2 administrative units from Database of Global Administrative Areas with urban areas larger than 100 km². Base map source: Esri ArcGIS Online (public domain).

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Spatial consistency of historical and future LST

Our estimated LST under diverse Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP-RCP) scenarios show strong spatial consistency with MODIS-observed LST during the historical period and preserves these spatial patterns in future projections (Fig. 2 and Supplementary Fig. 3). Globally, projected LST by 2100 under the SSP5-RCP8.5 scenario indicates a substantial increase, with high warming zones concentrated in northern and southern Africa, Australia, and the Middle East, where values may exceed 40 °C (Fig. 2a). Climate change-driven warming dominates this pattern, affecting nearly all regions in accordance with the overall LST distribution (Fig. 2b). In urban contexts, rapid urbanization further amplifies warming, particularly in suburban areas of cities such as Beijing, Indianapolis, Bangalore, and Accra, where warming gradients intensify toward the periphery (Fig. 2b). Although smaller than the effects of climate change, urbanization-induced warming remains pronounced in these expanding regions. At the global scale, estimated LSTs correlate well with MODIS-observed LST in 2020, achieving an R2 exceeding 0.9 (Supplementary Fig. 4). At the city scale, ~70% of cities exhibit R² values above 0.7, with representative cases such as Chicago (0.89), New Delhi (0.90), and Beijing (0.82) under the SSP1-RCP2.6 scenario (Supplementary Fig. 5). This strong spatial coherence underscores the robustness of our projections for future thermal patterns.

Fig. 2: Spatial patterns of future LST in 2020 and 2100.
figure 2

a Spatial patterns of future LST globally under the compounded warming effect of climate change and urbanization in 2100 under the SSP5–RCP8.5 scenario. b Detailed information of four representative cities in Beijing, Indianapolis, Bangalore and Accra. Note: the terms “Compounded”, “Urbanization” and “Climate change” refer to future LST under the compounded warming effect of climate change and urbanization, warming solely from climate change, and warming exclusively from urbanization, respectively. This nomenclature will be maintained throughout. Same base map source as Fig. 1.

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Read more on Nature text and related references (deleted here for ease of reading).

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Iran’s record drought and cheap fuel have sparked an air pollution crisis

Iran’s record drought and cheap fuel have sparked an air pollution crisis

Dec 5, 2025

Iran’s record drought and cheap fuel have sparked an air pollution crisis – but the real causes run much deeper

Sanam Mahoozi, City St George’s, University of London; Nima Shokri, United Nations University, and Salome M. S. Shokri-Kuehni, United Nations University; Technical University of Hamburg

Air pollution is the latest environmental crisis causing havoc across Iran. Large parts of the country are already suffering from a drought, one of the worst in decades. Its wetlands are dry, and its land is subsiding at alarming rates.

Now the fallout is also affecting the air that the country’s more than 85 million people breathe. As lakes, wetlands, and riverbeds dry out, their exposed surfaces turn into major sources of dust. Strong winds can lift this dust and transport it across cities and even distant regions.

The extremely dry conditions have worsened Iran’s already high levels of air pollution. In recent weeks, the capital Tehran was ranked as the most polluted city in the world, according to global air quality monitors. In November, its air quality index hit 200 – a level classified as “very unhealthy”.

The terrible air quality has forced authorities to close schools, universities and offices to reduce exposure. Hospitals are reporting rising numbers of cases of respiratory and cardiac complications across the country.

Local media have reported more than 350 deaths within ten days linked to worsening air quality in recent weeks. Demand for emergency services in the capital has also increased by more than 30% during November 2025, according to local statistics.

Other major Iranian cities, including Tabriz, Mashhad and Isfahan, have recorded readings above 150 in the last few weeks. These levels are considered dangerous for all age groups. In Ahvaz and Zabol, air pollution from sand and dust storms has blanketed the southern cities, putting lives and livelihoods at risk.

Studies indicate more than 59,000 Iranians die prematurely every year from air pollution-related illnesses.

As well as dust rising from dried out lakes and wetlands, ageing cars and low-quality fuel in Iran’s major cities are contributing to the air pollution.

But focusing on these causes misses the bigger picture.

Iran’s air-pollution emergency is caused by the same governance failures that have destabilised the nation’s water systems, emptied its aquifers, dried out its wetlands, and accelerated land subsidence.

Just as Iran’s water crisis is not simply the result of drought, Iran’s polluted air is not simply the product of traffic.

In most major cities, a key burden comes from pollutants (such as nitrogen oxides, sulfur dioxide, and fine particulates produced by burning low-quality fuel) as well as outdated engines, and heavy industrial fuels such as mazut.

These toxic emissions accumulate in cities and directly contribute to respiratory disease, and cardiovascular illness. Recent global satellite analyses, which are currently being reviewed by the journal Nature Cities, suggest that most mega cities (population more than 10 million) with significant levels of nitrogen dioxide pollution in the lower atmosphere (the layer of air we breath) have cut pollution levels in recent years.

How Tehran’s residents are coming with the drought.

However, Tehran is among the few large cities worldwide where these concentrations have increased between 2019 and 2024.

But combustion engines in old cars are only half the story. In many regions, a substantial share of PM₁₀ and PM₂.₅ (particles smaller than 10 micrometers and 2.5 micrometers which penetrate the lungs and bloodstream) now originates from dust and salt storms generated by shrinking lakes, rivers and wetlands. These particles can travel hundreds or even thousands of kilometres within hours, affecting cities far beyond their points of origin.

Our research, on Lake Urmia – once the Middle East’s largest saltwater lake – shows this clearly: as the lake bed dried, salt-laden dust plumes were capable of travelling hundreds of kilometres and even crossing national borders in less than 12 hours. This is a vivid illustration of how tightly Iran’s water crisis is intertwined with its air-pollution crisis.

Key causes of Iran’s air pollution

Iran’s air-pollution problem is not just a transport problem, a technological shortfall or a meteorological misfortune. It is fundamentally the predictable outcome of decades of government priorities, distorted incentives, and institutional inertia.

First, Iran’s government priorities have shaped a foreign policy that ultimately led to international sanctions and deepened the country’s economic and international isolation.

This isolation has had direct environmental consequences. It restricts access to modern air‑quality monitoring systems, industrial filtration technologies, and low‑emission engines, while deterring the foreign investment needed to upgrade transport and industry.

As a result, while other countries have reduced NO₂ and particulate pollution through cleaner technologies and tighter standards, Iran’s options remain severely limited by the political choices that produced its isolation.

Second, Iran’s extremely low fuel prices, sustained by immense subsidies, have made the national economy dependent on cheap energy, a key driver of the country’s inefficient electricity generation and excessive consumption. Vehicles with fuel inefficiencies unimaginable elsewhere remain commercially viable.

This is not an accidental policy outcome. It is part of a broader economic cycle in which subsidised fuel sustains outdated domestic car production and high-emitting industries, some of which are tied to powerful institutions whose financial interests depend on maintaining the status quo.

Nitrogen oxide levels in Iran (tonnes):

A graph showing NO2 levels in Iran.
World Bank data, CC BY

Resetting national priorities

Many countries have cut urban pollution through stricter emissions standards, cleaner transport, and integrated city planning, but Iran cannot do this without addressing the structural forces driving its emissions.

Reversing Iran’s air-quality crisis requires a fundamental shift in government priorities, placing environmental security and public health at the centre of policymaking. Iran’s challenge is not technical capacity but distorted incentives and national priorities. Only by reducing international isolation, strengthening transparency, and dismantling subsidy-driven distortions can Iran unlock the technologies and investment needed to clean its air.

Once these structural barriers are addressed, real progress becomes possible. This would include gradually changing fuel prices to curb high-emission vehicles, restoring access to global technology and finance to modernise the vehicle fleet and public transport, and reviving wetlands, lakes, and soils through water-governance reform to cut dust pollution.

Complementing these measures with advanced satellite monitoring, AI-based analysis, air monitoring stations, and better urban planing.

The air is not polluted because Iranians drive too much – it is polluted because the system that shapes the country’s priorities and choices is broken.

Don’t have time to read about climate change as much as you’d like?

Get a weekly roundup in your inbox instead. Every Wednesday, The Conversation’s environment editor writes Imagine, a short email that goes a little deeper into just one climate issue. Join the 47,000+ readers who’ve subscribed so far.The Conversation

Sanam Mahoozi, Research Associate, City St George’s, University of London; Nima Shokri, Professor, Applied Engineering, United Nations University, and Salome M. S. Shokri-Kuehni, Lecturer in Environmental Engineering, United Nations University; Technical University of Hamburg

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The Conversation

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Earthen Architecture for Greener Construction

Earthen Architecture for Greener Construction

Dec 3, 2025

Image above: Oman’s Strong Development Gains: What Investors and Entrepreneurs Need to Know for Business Growth

 

Earthen Architecture for Greener Construction: How Sustainable Building Trends are Transforming Oman’s Real Estate Market

Earthen Architecture for Greener Construction: How Sustainable Building Trends are Transforming Oman’s Real Estate Market

MUSCAT, DECEMBER 2 — The Sultanate of Oman is actively exploring the use of traditional building materials, especially clay, to promote more sustainable construction practices amid a growing global emphasis on low-carbon alternatives in the built environment. This focus was underscored during LC3 Day 2025 — Oman, an international technical forum that convened experts, researchers, and industry stakeholders to share advancements in low-carbon building materials.

Eng Khalfan bin Masoud Al Naabi, Director General of Urban Planning at the Ministry of Housing and Urban Planning, stressed the increasing importance of adopting environmentally friendly materials as nations work to reduce the environmental impact of urban development. He emphasized that low-carbon materials are gaining worldwide attention for their ability to reduce emissions, enhance energy efficiency, and bolster the resilience of cities over the long term.

Highlighting Oman’s initiatives, Eng Al Naabi referenced the “Revival of Earthen Architecture” project, a collaboration between the Ministry of Housing and Urban Planning and the Ministry of Heritage and Tourism. This initiative aims to reintroduce clay—a material integral to Oman’s architectural heritage—using modern engineering techniques that improve performance while preserving its cultural and environmental significance.

“Clay is part of Oman’s architectural heritage, and today we are exploring ways to redevelop it through modern engineering methods that ensure efficiency while enhancing the cultural and environmental character of the urban landscape,” he stated.

According to Eng Al Naabi, earthen and other low-carbon materials present significant opportunities to lessen the environmental footprint of construction, particularly in arid climates where thermal performance and energy consumption are critical factors. Clay-based structures are noted for their natural insulation properties, which help regulate indoor temperatures and reduce cooling needs.

He also highlighted the importance of scientific research and international collaboration in promoting the broader use of alternative materials. Sharing global experiences and research findings is vital for understanding material properties and enhancing performance, especially as new technologies for processing and applying these materials continue to emerge.

Eng Al Naabi pointed out that the global construction sector is increasingly adopting low-carbon materials, driven by technological advancements and growing environmental awareness. He noted that technical forums like LC3 Day serve as important platforms for professional dialogue and knowledge exchange, rather than policy formulation.

“The key value of such scientific gatherings is bringing experts together to exchange knowledge and showcase experiences,” he said, adding that collaboration between the public sector, private industry, and academic institutions is crucial to addressing the environmental challenges facing the construction industry.

By hosting this forum, Oman created a space for international specialists to discuss both practical and research-driven strategies to reduce carbon emissions in construction, while also showcasing local initiatives such as earthen architecture. Organizers expect the discussions to foster broader professional learning and innovation in sustainable material use, both within Oman and globally.

Special Analysis by Omanet | Navigate Oman’s Market

Oman’s revival of traditional clay-based architecture integrated with modern engineering represents a strategic leap toward sustainable urban development, positioning the country as a pioneer in low-carbon construction in arid climates. For businesses, this shift opens opportunities in green building materials and eco-friendly construction technologies, while smart investors should consider backing innovations that enhance energy efficiency and cultural heritage preservation. This initiative also signals potential risks for conventional construction sectors lagging in sustainability adaptation, urging a forward-looking approach to environmental and market demands.

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