The climate and data agendas are deeply linked.

Decarbonising our society will require systems that are automated, that balance resources (across energy, water, agriculture, transport and the built world) to maximise efficiency and reduce waste. Balancing our infrastructure means it will have to ‘self-heal’ — for example in a decentralised energy grid, if a local renewable source goes offline that’s powering a hospital (or, given vehicles will be electric, drive away), the lights need to stay on.

To enable this, we need to start sharing asset-level data (e.g. from sensors), ‘digital twins’, environmental and geospatial data (including earth observation data), climate models and how they will impact the world, and policy data that helps everyone drive change.

Given the time imperative (physics doesn’t care about our politics) there is an imperative to get the right regulatory and legislative environment to support the rapid digital transformation of our industrial sectors.

Sorting this out will bring huge efficiencies to the private sector, and unlock the data needed by our academic institutions to get the data they need for modelling (whether creating green finance or digital twins).

Three priorities for development include:

  1. A mandate to make data accessible (whether Shared Data or Open Data)
  2. A mandate for participation (to stimulate open market behaviours for the benefit of all actors)
  3. A mandate for evidence-based targets aligned with our national goals (demonstrable net-zero returns)

Equally, we must act to avoid price escalation of ‘higher risk’ approaches that can deliver net-zero outcomes: balancing addressing risks and controls to maintain stability in critical national infrastructure and derisking investment in innovation.

Smart systems need to be fed with good data, when and where it is needed. A vast diversity of solutions that span sectors and disciplines will be engaged. We must now embrace the innovation approach that led to the web: many parts, loosely joined. Creating standards that aid cohesion and interoperability—and then get out of the way—which is what we’re trying to achieve will, I believe help us all move forward with confidence.

Addenda: for example, there’s more work needed around data standardisation and harmonisation to make sure that points 1 and 2 have an intended effect. A good example is EU’s open data repositories, like EU Open Data Portal: their metadata quality tends to be high, but the data itself is not standardised and/or not documented well, so the usability of published datasets is low. So, common data standards are key.