Environment Analyst presents an expert interview with Rodrigo Fernandes, director of ES(D)G at the infrastructure engineering software specialist, Bentley Systems, focused on the role of AI and data-driven solutions in delivering resilient infrastructure.

 

EA: What are the critical steps to ensure that sustainability metrics are prioritized from the outset and across the asset’s full lifecycle – and to help close 'the gap' between environmental & social objectives and engineering design teams?

RF: I believe this transformation needs to happen on various levels.

On a governance level, it's essential to ensure that policymakers and investors align with the right policies and financial instruments that promote the reporting (and optimization) of sustainability metrics. It's also essential to define a general consensus regarding what sustainable infrastructure is and which sustainability metrics should be prioritized.

Infrastructure owners (especially the ones that are more mature in terms of sustainability) might adopt sustainability practices on their own, but without specific guidance and standards (on what sustainable infrastructure looks like), harmonization, appropriate regulatory frameworks, and dedicated investment programs, the process becomes uncertain, heterogeneous, and too subjective (which can result in different interpretations of sustainability priorities).

Moving further downstream in the infrastructure lifecycle, we must also think about the "how": even if all the above happens, how can infrastructure professionals ensure sustainability metrics are not only considered but properly measured or estimated – and how can they influence decisions?

Infrastructure must be sustainable, secure, and resilient. This requires thinking about cross-system effects and addressing future as well as present challenges. 

Data-driven solutions – such as intelligent digital twins – supported by an open, connected data environment make systems-based thinking and action more possible. 

Data-driven solutions can support better environmental intelligence by enabling the proper quantification of sustainability-related metrics – in all the various life cycle stages, including very early design stages. These approaches can integrate IoT, AI, and other numerical models – for instance, allowing the generation of what-if scenarios and "optioneering". One example is to use advanced data-driven solutions to compare the carbon footprint from different design alternatives.

However, the above solutions in infrastructure will fail to provide actionable insights if they cannot access the right data in the right format and at the right time.

Managing infrastructure projects (especially the larger, complex ones) is particularly challenging due to the variety of involved data formats, disciplines, stakeholders, and long-living assets. Thus, an open, connected data environment is critical to reduce data silos and to promote interoperability, collaboration, and transparency across different disciplines, lifecycle stages, vendors, and infrastructure stakeholders. Network Rail, for instance, adopted this connected data environment to properly understand embodied carbon footprint from both current and future assets associated with the development of three major rail projects.

Finally, chief digital officers and chief sustainability officers must work together towards the acceleration of a "double transition" (sustainable and digital transformation) inside their organizations, enabling and empowering the needed cultural shift and moving towards the adoption of data-driven solutions for sustainability delivery of infrastructure (this transformation process might also involve upskilling or reskilling workforce). 

 

EA: How is the growing application of the Envision sustainability framework and rise of social equity and environmental justice considerations changing the approach to infrastructure development?

RF: Infrastructure and society are becoming more connected at an ever-faster pace, where risks of failure can cascade faster and broader than ever before. Infrastructure is the physical fabric of our society. Infrastructure is an interconnected "system of systems" forming the physical bedrock of our society. Beyond supplying water, power, and transport services, it enhances city livability, improves quality of life, and bolsters productivity and prosperity, all while interacting with the natural environment. 

Infrastructure is indeed critical to achieving and influencing Sustainable Development Goals. Of the 169 sub-targets included in the 17 UN SDGs, 92% are directly or indirectly influenced by infrastructure provision. 

Recognizing the overarching purpose of infrastructure – to support and serve people and society – we must get better at delivering interconnected social, environmental, and economic outcomes that align with the SDGs.

Sustainable infrastructure challenges us to rethink our traditional approaches, asking us to build systems that are not only robust and efficient but also regenerative and inclusive.

As we face escalating climate crises, resource depletion, and urbanization pressures, the question of what constitutes sustainable infrastructure becomes more critical. The question is not just about how we build but what we build. The Envision framework is a rating system that is increasingly becoming a standard in infrastructure projects helping us to better understand not only if we are doing the project right, but also if we are doing the right project.

The increasing adoption of guidance and standards for measuring sustainable infrastructure increases transparency, objectivity, and harmonization, and can even facilitate sustainability investments. 

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EA: Can you provide some examples of innovative approaches to increase climate mitigation and embed resilience into infrastructure assets utilizing AI-driven and/or nature-based solutions?

RF: Today, infrastructure accounts for 79% of total greenhouse gas emissions and 88% of all adaptation costs. The adverse environmental effects of infrastructure in the industrial age are cumulative and can no longer be ignored. 

So, there is intense pressure on infrastructure professionals. They are being asked to lead a monumental effort to decarbonize and climate-proof our infrastructure on a scale and pace never seen before.

By combining engineering data with sensor and other data, we can recreate existing or future physical infrastructure in the digital world (which we call infrastructure digital twins) and make actionable decisions in both climate mitigation and adaptation. We can leverage artificial intelligence (AI), Internet of Things (IoT), modeling, simulation, and analysis to accelerate progress toward sustainability goals by mitigating, anticipating, preventing, and adapting to climate risks.

One example is decarbonizing existing infrastructure assets. By remotely monitoring existing assets — such as dams, bridges, water and electrical grids, district heating systems, or offshore wind platforms — with an infrastructure digital twin, engineers can reduce energy consumption or the frequency of physical inspections and maintenance and, thus, reduce the carbon footprint.

When building new infrastructure – both decarbonization goals, and de-risking projects must be properly taken into account. As industries strive to reach ambitious net-zero emissions goals due to huge project backlogs and talent gaps, optimizing capital expenditure investments and accelerating project execution is crucial.

With AI-powered digital twins, we can calculate and analyze the carbon and material footprint of any type of infrastructure asset in the design stage before it is even built, allowing engineers to optimize designs to ensure the lowest carbon and material outcome possible. The same digital twins can also be used to make simulations towards optimized resilience for assets and cities. Better information and the ability to collaborate around it virtually, also helps reduce the risks of budget overruns and delays, a severe threat to hyperscale green capital projects that are critical to meet net-zero emissions targets.

However, the industry needs to go beyond decarbonization and better adapt existing and future infrastructure assets to climate change effects, protect people, and save our limited natural resources, such as water.

There's a dangerous intersection of climate change and aging infrastructure – how our aging roads, bridges, dams, pipelines, and power grids become even more susceptible to floods, heat waves, and rising sea levels. In that sense, digital twins can not only help design more resilient future infrastructure, but they can also help manage existing assets. For instance, digital twins (supported by AI) can intelligently monitor natural systems, such as tree coverage, to address urban heat islands; anticipate structural problems in critical assets, such as dams or bridges, or detect and prevent water leaks, to help increase resilience.

 

EA: How can we utilize climate vulnerability assessments to help manage project risk and facilitate investment – and how is the approach evolving given the sharpening of climate-induced stresses we are seeing more and more of?   

RF: There's a consensus that climate change impacts how organizations respond to stakeholders, regulations, and compliance. There's also a strong emphasis on incorporating sustainability at the core of the business strategy to be more resilient to climate change effects and leverage future market opportunities. From a conceptual perspective, this is intuitive and understandable. This is also tangible, as more intense and frequent extreme weather events disrupt businesses and their supply chains. Potential impacts in infrastructure projects are no exception – they can also be affected by climate change directly (climate change effects can disrupt the project delivery in multiple ways), or indirectly (investments might also be at risk if the project is found to build an asset in a climate-vulnerable area).

Infrastructure professionals and all the companies operating in the sector currently need to face regulatory inefficiencies, consolidate disparate forms of data, and manage the multitude of projects piling up due to the engineering resource capacity gap.

Thus, in a world of uncertainties, how can we properly leverage climate vulnerability assessments to better analyze and quantify the potential and probable impact on specific infrastructure projects?

As mentioned previously, while more and more data are available today, data is usually in silos, in different formats, multiple repositories, and disciplines across the supply chains. Using digital twins built on open platforms to simulate, federate, visualize, and analyze the data will strongly facilitate these data-centric approaches and mitigate siloed challenges. 

Digital twins can indeed facilitate climate risk analyses with infrastructure intelligence to break down these barriers, accessing data from anywhere worldwide within an open, interoperable, and collaborative platform.

This means project owners have access to data and technologies that can help them generate robust climate-related scenario analysis. As a result, they can now make more informed decisions to confidently manage and reduce their project risks.

 

FURTHER INFORMATION: Bentley ES(D)G Sustainability Stories