Enhancing Decarbonisation Strategies: The Role of Data-Driven Decision-Making

Written By

Dimitri Tomanos
Head of Advanced Analytics

at

ENGIE Impact

With the increase in computing power and data availability over the past decade, corporations have worked to prioritise making data-driven decisions. Unfortunately, in many cases the enthusiasm around collecting the data has not been met with equal enthusiasm around data validation, prioritisation or analysis — which can be especially disadvantageous when building a global decarbonisation program. While not every company is going to be equipped to establish carbon data management as a core capability, every organisation needs a fundamental understanding of carbon data management tools and best practices.

Gathering the disparate carbon data

The overall trend to interconnect machinery, sensors and other equipment on a centralised network — sometimes referred to as the industrial internet of things (IIoT) — can facilitate gathering carbon data. But the ability to gather carbon data is not the same as gathering useful carbon data. This can be especially true when considering global enterprises needing to consolidate diverse information across regions.

Data capabilities in certain areas of the world may not be as mature as others when it comes to the level of detail or capability. Being able to gather more sophisticated data at one facility may not be useful if other locations can only provide more rudimentary data. Other details around how the data is formatted before being centralised, conversion rates between currencies — or even between metric and imperial measurements — are all potential roadblocks to being able to start with clean data.

While the potential for gathering of carbon data continues to increase, it is important to recognise that simply collecting data is not enough. For global enterprises with diverse operations, consolidating carbon data across regions can present challenges. To ensure that the gathered data is useful, it is important to address these potential roadblocks and strive for consistency in data collection and formatting. Ultimately, this will enable organisations to make informed decisions and take effective action to reduce their carbon footprint.

Analysing the data

Once a solid foundation of carbon data has been established, with at least a basic insight into overall carbon consumption, there are several methods that can be used to deliver advanced analysis. Some examples include:

  • Digital twins: By creating a virtual replica of a physical energy asset, a digital twin can mimic performance and be used to run simulations — predicting potential outcomes of various scenarios. It can help assess long-term impact not only to carbon, but also Capex and Opex with the necessary granularity.
  • Benchmarking: Especially when trying to extrapolate estimates for those regions that may have less robust data, benchmarking based on internal data or sector-wide trends can help an organisation compare their current carbon output across sites and with other organisations.
  • Consistent reporting: Whatever the cadence or level of detail, utilise reporting and digital dashboards to provide consistent visibility around what’s happening at the site-level and on a macro-level, allow stakeholders to identify opportunities and trends. 

These tools, and others, can be adapted to the level of detail necessary for each stakeholder. A site-level sustainability manager may benefit from certain benchmarks that a corporate officer may not, and the necessary analysis may even vary from site to site. Having the proper carbon data framework in place will provide the flexibility necessary to identify opportunities.

Making data-driven decisions

The first step of decarbonisation is to start using less energy — building targets around energy efficiency and conservation measures. Proper data analysis can not only identify opportunities for carbon reduction, but the potential impact of each approach as well.

For instance, organisations that have an overall understanding of their carbon consumption can typically identify several asset-light interventions that collectively would reduce their overall carbon use by 3-5%. This could mean changing all the lightbulbs to more energy efficient options or discussing behavioural changes employees can integrate into their everyday.

Detailed analysis will also uncover site-level opportunities. Those opportunities can then be examined to see which could potentially be rolled out at scale, which could be duplicated at specific sites, and which — if still economical — would be limited to one location. The priority is often to identify the most scalable projects, establishing a standard approach and streamlining it across an organisation. It’s rare that these types of efforts would be exactly replicable across facilities, so modelling what the variance in overall impact would be is also important.

Regardless of the scale or impact, change management must be a key part of data-driven decision making. Strategic roadmaps, established governance, and stakeholder buy-in are all built on top of a strong foundation of consistent data in order to most effectively transition into implementation efforts. And just as an organisation may not have carbon data management as a core capability, implementation efforts may require outside coordination among contractors, vendors, utilities, and other organisations beyond in-house expertise or capacity. A project management office to support or oversee those efforts can help ensure proper execution.

Good data drives good decisions

Taking climate action is an urgent priority, and the collection and analysis of carbon data is critical for companies looking to make data-driven decisions on decarbonisation. While the ability to gather carbon data has been facilitated by the interconnection of machinery and other equipment on a centralised network, consolidating this data across regions can present challenges.

It is important for companies to prioritise the validation, prioritisation, and analysis of carbon data to ensure it is useful for decision-making — implementation change management programs and coordinating with outside organisations as needed. By establishing a strong foundation of consistent data, companies can identify opportunities for carbon reduction and take effective action to reduce their carbon footprint, ultimately contributing to a more sustainable future.

About the Author:

Dimitri has more than 10 years of experience in the development of mathematical cores and digital applications. Currently, he is responsible for the Advanced Analytics activities that develop digital solutions for ENGIE Impact’s teams in order to boost the consulting activities and develops digital solutions for other ENGIE and external entities. Dimitri has a strong mathematical background, especially in mathematical modeling and operations research. Dimitri is also a certified Product owner, aligning the roadmap of digital products with the business needs.

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