Place-based Net-Zero decision making – A generation behind but catching up fast?

Post Date
28 March 2024
Read Time
4 minutes
  • ESG advisory

This article has been written by guest contributor, Robert Harwood - Chief Executive Officer, Slingshot Simulations.

In a joint publication on ‘Accelerating Net Zero Delivery’[1] with PWC, Otley Energy, and the University of Leeds, the UK government revealed investments in regional decarbonisation are able to deliver reduced carbon emissions and have substantially greater social benefit when those investments are tailored to the characteristics of a specific location – i.e. they are ‘place-based’ interventions. As the report explains, these enable each region to adopt the most socially cost-effective combination of low carbon measures based on the specific characteristics, needs and opportunities of different locations. For example, a city-region with predominantly Victorian building stock could prioritise measures to promote improvements to glazing and insulation. The analysis found that these ‘place-based’ investments can deliver more than six times the wider social benefit compared with place agnostic interventions.

However, making equitable place-based decisions to accelerate decarbonisation and build climate resilience is challenging. Regional economic growth, regeneration and inclusivity must be balanced with the many interdependent and often competing mobility, energy and land and building use policy options. As was highlighted at this month’s Connected Places Catapult Summit in London, ‘systems thinking’ is a key requirement to enable holistic place-based decision making for Net-Zero by looking across policy options rather than the more siloed and fragmented policy approaches traditionally adopted.

Implementing a systems-thinking approach in a place-based policy context faces three critical challenges:

  • The data required to inform and support policy decision making is disconnected, voluminous, complex and being generated in ever increasing amounts.
  • The modelling and analysis based on these data sets is limited in scope and often restricted to narrow policy options. For example, traffic and housing stock regeneration are often considered in isolation, rather than taking a holistic, whole place perspective.
  • There is a data and digital skills gap that further hinders the ability to use data and models as efficiently as they could be.

The result, according to Ampersand[2], a management consultant, is that many decision makers are struggling to articulate meaningful carbon strategies, are confronted with ‘analysis paralysis’, and are stuck between high level political commitments and what practical action to take on the ground.

But these challenges are not new. A generation ago, engineers designing some of the most complex products on the planet (from space rockets to nuclear reactors) faced similar problems. Billions of data points were being developed during every product test. Their models only considered individual aspects of performance – such as aerodynamics – making it difficult to predict the performance of the whole system, often resulting in significant over design or sub optimal decisions. And transforming these data and models into insight was the realm of PhD level experts. The situation is very different today. Systems engineering practices that connect disconnected data and integrate multi-disciplinary models and analytics are commonplace. This ‘decision intelligence’ capability is supported by intuitive digital tools that are accessible even by graduate engineers and non-specialists.

Building on the lessons learned, the discipline of place-based decision intelligence is emerging, driven by the need to take an integrated ‘systems-level’ approach to deliver Net-Zero, decarbonisation, climate resilience and adaptation outcomes at the pace required by the climate emergency.

In this case the disconnected data is not engineering data, it is geospatial and includes socioeconomics, population, land and building use, energy system, building stock quality, traffic, transport, and more. The models and analytics are not related to aerodynamics. They are related to traffic movement, mode shift, EV uptake, energy grid transformation, domestic heating shifts, economics and more.

While it took a generation for advanced product engineering to transition to a systems decision-making capability, the rapid maturation of technologies like digital twins and AI promises to enable the transformation of place-based decision making to happen at a much more rapid pace.

What are we doing at SLR?

By combining the decision intelligence data science and analytic capabilities of Slingshot Simulations with the ESG domain expertise of SLR, we are revolutionising place-based decision-making to deliver decarbonisation and climate resilience solutions at the pace society needs.



[1] - IUK-090322-AcceleratingNetZeroDelivery-UnlockingBenefitsClimateActionUKCityRegions.pdf (

[2] - Ampersand:,predict%20changes%20in%20technology%2C%20policy%20and%20public%20opinion

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