The age of data-driven environmentalism

The success of environmental policymaking is inextricably linked to the deployment of high-frequency data and artificial intelligence (AI). Geoff McGrath, Managing Director of CKDelta, discusses the role of holistic, data-driven decision making in responding to the climate crisis and driving down carbon emissions.

There is no end to the number of organisations and individuals clamouring to state that their incremental ‘green’ initiatives are delivering reductions in carbon emissions, boosting their calculated energy efficiency rating, and in turn, contributing to the pursuit of net zero. However, energy efficiency is not equivalent to net zero. It is also not, in itself, sufficient to keep global warming below 1.5°C – as called for in the Paris Agreement. In the same vein, incremental policy changes are unlikely to help us reduce emissions by 45 per cent by 2030 and to reach net zero by 2050.

Holistic planning

How we respond to the climate crisis today will have long term ramifications for decades to come. The time for getting ahead of the challenge has passed, our role as industry leaders is to think holistically – at a macro level – and take the radical steps required to reach our climate goals before it is too late. Truly ambitious innovation, centred around data-driven design and operation, should be used to inform policymaking. This will help deliver the most suitable political and economic solutions – backed up by real-time, objective measured data and not opinion-led responses – realising our desired environmental outcomes.

Deployment of data

The challenges involved in effectively utilising data will be particularly hard on those developing or deploying infrastructure programmes and operations where their Key Performance Indicators (KPIs) are, in effect, in tension with one another.

Take the utilities sector, for instance. They are guided by the need to:

1) Maintain a positive customer measure of experience (C-MeX) score;

2) Deliver a positive Return on Investment (ROI) for investors, and;

3) Meet regulatory environmental targets.

These three drivers do not necessarily complement one another. The KPI for sustainability cannot – and should not – be compromised if net zero emissions as a goal is to be realised.

We can reconcile some of these differences through the use of high-frequency data and AI. Embedded intelligence in smart infrastructure is key to ensuring decisions are data-driven at both the design and operational stages. It provides fuller and richer insights into the effect of design and/or operational interventions on future sustainability outcomes.

Deploying integrated simulation modelling into our energy networks is one such example of how we are already reconciling such differences, helping to deliver for investors, regulators, and customers. Open-platform data gathered from surveying networks and monitoring devices can be modelled to simulate how power is distributed through existing systems – enabling the optimisation of existing systems, reducing the need to physically upgrade the network to release capacity. A whole system model can help facilitate this by quickly and automatically incorporating data across the whole value

chain into existing network models. In essence, these tools can directly draw from utilisation data to bring cost benefits to consumers and deliver on our shared net zero goals.

Adaptive planning

Predictive analytics powered by high-frequency data empowers organisations to deliver the most suitable response to planning and optimisation challenges. Machine learning models can be used for planning purposes and decision support during the operations stage to help meet net zero targets. With the deployment of electric vehicle (EV) charge points, for example, analytical data can be used by operators to understand which charge points will best be utilised and therefore guarantee a ROI.

Designing systems that capture the data we need to optimally manage the network and operations, with machine learning embedded within them from the outset, would allow us to maximise our contributions towards delivering net zero. Network owners and operators, and other investors in infrastructure, have an opportunity to establish adaptive intelligence as the de facto mechanism for optimal management. This means that they would be able to adjust their setup for optimum operations in the future, based on real-time, high-frequency data. Decisions would be driven not by intuition or outdated insight, but by objective, measured data.

Digital twins

To say our world is increasingly digital would be an understatement. We live in the digital age and it is only right that we plan, deliver, and invest in sustainable solutions based on our digital reality.

The water sector is a prime example of an industry that is ripe for a digital revolution as we pursue net zero. The adoption of a digital twin in this sector can enable operators to create a human-in-the-loop simulator that will advance understanding of ‘what if’ moments in the planning of new assets and then provide decision support tools during system operation. Forecasting new patterns of consumption against challenges posed by scarcity of supply can help reduce waste and therefore limit the need to reduce household consumption as markedly.

Digital twins are best integrated with operational application systems. By combining the technologies’ capabilities with high-frequency data, planning and asset management outputs can be displayed in a more accessible user interface that can be referred to at all stages of the value chain, empowering organisations with the data they need to plan holistically.

Data-driven environmentalism

Since the first Earth Day on April 22nd 1970, organisations, businesses, and individuals have made great strides in environmental protection and sustainable decision making. As 2050 looms on the horizon and net zero carbon emissions remains a mere regulatory target, we must all look at our industries to understand what more we can and should be doing to respond to the climate crisis – and learn from sectors that are already leading the way.

Our decisions should be data-driven, with investments targeting the types of energy infrastructure and networks where we know we can make a genuine difference, such as hydrogen, and electricity. These alternatives to fossil fuels have a long future ahead of them, but only if we take the bold steps needed to plan and prepare for their adoption today.

Forecast and simulation modelling based on geospatial data is not a panacea, but they afford us a long-term view based on real-time patterns of behaviour. This allows us to extract actionable insights from the rollout of EVs and deployment of hydrogen in our gas networks, and empower other sectors accordingly.

We are standing on the precipice – our decisions and actions today will be felt for generations to come.

It is time to embrace the age of data-driven environmentalism and make use of the digital tools we have at our disposal to unlock the success of environmental policymaking and cut carbon emissions across the globe. To find out more, please click here. Alternatively, please visit:

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