The importance of data in decarbonizing cities

The decarbonization of our cities and existing infrastructure is one of the biggest challenges we face in terms of reaching a net zero economy by 2050.

Existing buildings are a big part of the problem, with the built environment contributing around 40 percent of global carbon emissions and the fact that most of the buildings that will exist in 2050 are already built.

With both private and public sectors looking at ways to reduce their carbon emissions at pace, the challenge is how to scale up interventions to meet that demand and embrace technology and digital tools to enable decarbonization to take place at a large portfolio level.

Data-driven strategies for success

In many ways building new infrastructure, particularly buildings, to achieve net zero status is relatively easy, because clearly you control the construction process. What is more difficult is decarbonizing existing city infrastructure that was built long before energy efficiency was a key driver.

That makes building performance data so crucial in enabling a clear understanding of the presence of carbon across a large portfolio of buildings. For an organization with hundreds of buildings that can be a challenge— it won’t always be the case that they have comprehensive records or data. 

The data can then be used to develop your benchmark, targets and plan for interventions. The benchmark, or baselines, must be data-driven and target science-based so that cities can have a high degree of confidence that their plans will actually deliver net zero outcomes. 

By using behavioral and spatial data, among other sets, the process allows those involved to understand how a building is performing, which then feeds into decision making around investment and building the business case. 

This helps to provide sensible options, even if based on limited data sets, no matter the size of the portfolio involved; something that has never been possible. The result is a range of viable solutions, financial options and timeframes all culminating in the creation of a meaningful roadmap that can be used at any scale.

I think the key message here is that data in all forms covering asset, cost and carbon, will be critical on the journey to decarbonizing the built environment.

How to get good data

When helping organizations de-risk their journey through informed investment decision making, I strongly advocate for a focus on data-driven solutions. However, data maturity is all too often poor. Without good data, even the smartest data-driven solutions will underperform. 

The fact is, getting good data can be challenging, timely and costly—something we pay close attention to having developed our own Decarbonomics^TM service platform. 

We can source data to support the decarbonization journey and help secure funding in three key areas: building management systems and sensors, building surveys and open source benchmark data without analytics. 

Each of these come with varying costs and provide different levels of output accuracy. This is important as the accuracy of your outputs, whether a benchmark or costed roadmap, can have a material impact on the viability of your investment case, business case or funding application. 

So, for instance, understanding your cost-per-ton of carbon saved by intervention, at both building and portfolio level, will be critical to knowing if you qualify for specific funding. In addition, if you are above a funding threshold, you can identify other intervention models to make an application stack up and likely succeed. 

Taking a systems-based approach

Taking a systems-based approach provides the best chance of securing funding and ensures optimized delivery in the long run. This could be achieved through continuous improvement, proactively closing costly performance gaps that may threaten any long-term financing or funding agreements or arrangements you have in place. 

Moving forward with systems-based thinking also provides greater performance opportunities, such as establishing data standards that can flow through the supply chain and support the establishment and tracking of KPIs and other incentivized approaches.  

Making the data work hard

When looking at our own Decarbonomics^TM offering, one of the key drivers has been how we can accurately benchmark an organization’s estate with very little building survey data and still deliver robust and highly precise outputs. 

The mission is to help clients understand when enough data is enough and to minimize upfront costs, shrink pre-delivery program durations and enable them to move to delivery as quickly and efficiently as possible, without compromising long-term delivery performance.  

The more data you have or have access to (such as open-source benchmarks which are becoming increasingly available) the more powerful data-driven solutions will be. 

However, data alone is just the fuel. You still need the engine and, in this case, that engine is the intelligent analytics—algorithms, AI, and machine learning—that we apply to the data for robust and accurate outputs that can be visualized and interrogated by a diverse mix of users, from technical delivery teams to finance leads. 

Ultimately, the true power of smart, data-driven solutions is how they combine the best human expertise, data and technology. No matter how many clever digital and data systems we apply to these problems, it’s people who must drive the system and guide the outputs.

With the scale of the challenge ahead, we must deliver achievable and cost-effective net zero plans for today.  A theoretical net zero future, based on wishful thinking around behavior changes and new technologies isn’t going to help us reach our goal that’s only 28 short years away.