The Full Measure—Data and Decarbonization

Introduction: The full measure, data and decarbonisation. To reduce your company's greenhouse gas, or GHG, emissions, you first have to start with an accurate accounting of emissions across your enterprise. You can't manage what you don't measure. Yet our customers tell us getting accurate emissions measurement is a true pain point. Many companies rely on estimates, combining data collected in different ways from siloed parts of the business, and are trying to aggregate and normalise it. Plus, it's an ongoing struggle to keep track of the patchwork of emissions policies and regulations that are still taking shape, and that differ across regions, types, and size of businesses. Getting it wrong could have financial and reputational repercussions.

Software that automates data collection, monitoring, and reporting not only helps with those challenges, but allows you to develop decarbonisation strategies based on where you are today and where you have to go tomorrow, to identify and test the most impactful reduction scenarios. Hear from GE Vernova's Emission Management Software experts as they dive into this complex and critical issue in this audio blog.

Veerappan Muthaiah: Hi everyone, this is Veerappan, Engineering Director working for Electrification Software, a part of GE Vernova. I lead the Carbon Emissions Management Software development, and the software goes by the name, CERius.

Beth LaRose: Hi, this is Beth LaRose. I am an Executive Leader in our Consulting Services business. I'm focused on power system studies and analytics that are aiming to assist our customers with the energy transition and decarbonisation pathways.

Sofia Galas: Hi, everyone, my name is Sofia Galas. I'm leading EMEA strategy for our Decarbonisation Software Platform, called CERius, working with Veerappan. We work closely with heavy industries and energy companies to help them decarbonise and drive real climate impact.

Veerappan Muthaiah: One of the things that we have realised while working with our customers, I think there are real pain points in the industry, especially in terms of getting accurate emissions measurement. Now think about the scenario where a customer would like to kind of manage his decarbonisation objectives. And when he's planning for managing them, if accurate measurements aren't available, I think that kind of puts him off. So, we can only manage the emissions for what we can measure. It definitely remains a challenge in the industry, especially with power generation, especially with oil and gas, and also other continuous based process industries.

One of the things that I would add, which is kind of a pattern I have seen in the industry, is this. So, Scope 3 is one sort of emissions which helps report the indirect emissions; indirect emissions, which is through upstream and downstream, especially in terms of the consumption side of a particular enterprise or a corporate. And there are varying levels of how one could configure and report out the Scope 3 emissions as simple as someone could just do Scope 3 emissions calculation as simple, based out of dollars, especially in terms of purchases that they are making. And there are emission factors related to dollars, and then one could simply estimate them. It could be as simple as that.

Sofia Galas: Even if we're not talking about Scope 3 emissions, which is a universal challenge, let's say, and we take Scope 1, one of the topics that comes very often in my experience is, how do we deal with methane? And there's been a lot of initiatives to solve the problem. But still, when it comes to actually measuring it, more often than not, we come to the estimates that vary by 80%, or even more, just because the measurements are not done on a regular basis or they don't cover the whole system, be it upstream oil and gas, or even gas turbines. And this discrepancy basically shifts how green operations of a specific company are. And really, it's not just a data quality problem, it's also a compliance problem. Here, I wanted to ask Beth, what are the thoughts around it? And if you can tell us more about how reporting obligations are shifting now?

Beth LaRose: Yeah, certainly the regulations and requirements are ever-changing. There's definitely a patchwork of policy and regulations that are based on country or regions, and the requirements that actually shape this energy transition are things that we find often are changing and updating from year to year. So, for instance, with environmental, social, and governance reporting, this ESG reporting has several purposes that really include creating this transparency of operations and performance. It's used for risk management, as you mentioned, with the accurate data and reporting, and then being able to create continuous improvements against these metrics. So, that data acquisition accuracy is very important.

We're really focusing in this discussion, I think, around the environmental aspects, the E, and there's several different frameworks. And again, these vary based on country and region. So, for instance, you've already talked a little bit about Scope 3 with the greenhouse gas protocol. This is a widely used reporting and accounting standard covering Scope 1, which is a company's own or direct emissions that they're producing; Scope 2, which is more indirect or purchased energy emissions, so based on the energy purchases a company makes to power their operations; and then Scope 3, as you mentioned, which is the upstream and downstream value chain emissions. So, products that they're selling, those embedded emissions are an area that need to be tracked and reported accurately.

There's a couple of other regulations I'll just mention that are mandatory reporting in the EU, one being Corporate Sustainability Reporting Directive, the CSRD. These are for companies operating in the EU, and as of this February, the European Commission adopted some rules in their omnibus to simplify this package, which basically limits some of the mandatory reporting, which is non-financial sustainability reporting, to larger companies with more than 1,000 employees. This includes reporting on greenhouse gas emissions, water, and energy consumption. So, it's a very broad-based reporting mechanism in the EU that's gone into effect.

At the same time, there's another reporting mechanism around the EU Cross-Border Adjustment Mechanism, known as CBAM, which really aims to level the playing field in the EU by imposing a carbon tax on the embedded carbon and other greenhouse gas emissions, like nitrous oxide, on imported goods, that is the difference between EU ETS price for carbon and the carbon price of the country of origin for the imported goods. And this is currently impacting six different industrial sectors, including iron and steel, aluminium, cement, fertiliser, hydrogen, and the import of electricity into the EU. So, reporting under this mechanism started in October of '23, and the carbon tax will actually be assessed on the carbon and greenhouse gases on the 2026 imports. So, this is being phased in over the next couple of years.

Sofia Galas: Yeah, and I feel like also in this region, in the Middle East, countries are catching up with introducing the carbon limit, with Saudi Arabia introducing more and more policies around carbon. From customers, we usually hear that this annual reporting, or its GEO sustainability reports, is kind of not enough to catch up with changing regulations, maybe.

Beth LaRose: Yes.

Sofia Galas: And they are really, especially when the company is multinational, it's really hard to adjust quickly to changing policies and regulatory environment. And of course, there is a risk associated with not hitting the targets, not complying for the companies. And I think on that one, we wanted to touch upon the difference between voluntary and mandatory reporting, as you mentioned before, Beth.

Beth LaRose: Right. Yeah, there's a number of different mechanisms. I mentioned a couple of the mandatory reporting mechanisms. And certainly, misreporting there or not reporting in a timely manner can result in not only actual fines for the company, but reputational risk, you know, missing stakeholder or investor goals, missing opportunities potentially to deploy new technologies that could significantly reduce carbon, create other growth platforms. There's a lot of regional differences, as you mentioned, with some voluntary reporting and also voluntary carbon markets around the globe, that just adds to the complexity. The EU CBAM is actually meant to force more uniform reporting and compliance across countries, given that a number of countries and business from those countries import technology into the EU. So, what we're expecting over time is to see convergence around compliance mechanisms versus just the voluntary level of reporting or carbon pricing.

Sofia Galas: Yeah, and I guess that's what we see in North Africa and in partners like trading partners of the European Union. But if you go to regions like Sub-Saharan Africa, there's another risk, which is not being able to attract more investment, right, just because many companies don't even have an ESG score, and energy security is still a much, much bigger priority for African countries and companies rather than decarbonisation. So, they have to balance between attracting finance and getting the energy they need, and also being green.

Beth LaRose: Right, correct.

Veerappan Muthaiah: Just to add to that, if you think about what is the biggest driver for some of the organisations out there for emissions reporting, I think number one stands the regulation still, and followed by investors, and followed by public relations, and followed by possibly some of the corporate mandates for the public disclosure, and so on and so forth. So, the fourth one is corporate, to kind of state the obvious here. And the thing is, sometimes I also feel that some of the barriers that some of the organisations may have could just be the internal investments that they need for gathering the required data, and also taking the data to the next level where they can actually go and strategize and plan for the decarbonisation in the coming years. So, I think markets and regulations are the primary driver for decarbonisation, as I see.

Sofia Galas: I absolutely agree that the regulatory is always the number one driver for all the implementation of any kind of decarbonisation strategy or divested from fossil-fuel-based businesses. And of course, we cannot not mention cost of compliance here. So, companies are usually forced into that. And as soon as there's a price that's put on carbon in one or another way, let's say the CBAM or ETS or other mechanisms, companies start comparing what is their cost of inaction versus cost of investing in decarbonisation and actually deploying those green technologies that seemed absolutely not profitable years ago. Which probably brings us to the customer pains and what we usually hear as key pain points for our clients that think about, or are pressured into, creating decarbonisation strategies or progressing them in the changing and very uncertain world. So, maybe, Beth, you can comment something on that, what you've seen in your experience.

Beth LaRose: Yeah, I think to summarise what's already been mentioned, that there is potential to have some reputational damage or reputational risk that would be caused by missing specific stakeholder or investor goals, missing opportunities to deploy technology that could be furthering decarbonisation goals; and/or including goals also around energy security, such as using wind technology in countries that have a lot of wind resource, could be meeting a number of criteria for their business and their investor goals. Having to potentially pay fines or to have other regulatory compliance challenges, if they're not doing the reporting in a timely manner or doing it in an incomplete way, are also risks that companies face. And certainly, this is on a regional basis, given that regulations and compliance rules change from time to time in different parts of the world. And it's something that, as you mentioned, companies are trying to keep up with on a kind of day-to-day basis at this point.

Sofia Galas: And then, they have to deal with real life, which is gathering data from sensors or non-existent sensors up to the ERP systems, and all the corporate processes related to that. So, Veerappan, any thoughts about what you've seen as key pains of the customer, as a software lead, in terms of getting things done in the decarbonisation space and making sure the emissions baseline are in place and there's visibility and connectivity in all those things?

Veerappan Muthaiah: If we look at current demography, right, if we kind of think about an organisation or an enterprise, how much of a comprehensive view they have for an enterprise-wide emissions, and today that's possibly at 40% to 50%. That's the level that a particular organisation would have a comprehensive view of all their emissions. And that kind of points out to the challenges related to the data collection itself. So, some of the sustainability-related metrics that's been collected today, a lot of them actually exist in Excel spreadsheets, which is managed manually, and then there comes the challenges of doing version control over that, and so on and so forth. Now, that's not it, right? Like, let's say 70% of the data exists in an Excel spreadsheet, but that's not it. Those data still could be fragmented and those data, there are a few missing data that could be found even in multiple spreadsheets. So, the thing is that just becomes a big pain point for an enterprise or an organisation, especially in terms of getting the data to an auditing quality. So, there is a lack of real time and a granular visibility as stands today for the data, to get it to the quality for the auditing needs. That's a lot of work for someone to do.

The other one is that even after the data collection, there are multiple other processes, right? Like the processes include data cleaning. And once the data is cleaning, then we have to do data reconciliation. And then, there are multiple data aspects that somebody would have to go through. And all of them, if being done manually without standard processes being set, could become much more time-consuming. It could just be error prone and person-dependent who handles the data. And sometimes, handling Scope 3 methods and then having a clarity at the org level how each of the facilities are going to kind of report the methods, along with the data, that becomes a challenge, because there could be inconsistencies. That kind of leads into the place where there is a data provider within the organisation and then there is a data consumer. In this particular case, a plant or sites or a facility operations team would be the data providers, and the consumers are the sustainability team.

Now, how some organisations set automation goals for themselves and how they kind of plan through the entire strategy of going through the data connections and connectivity, and how they kind of leverage the existing ecosystem, especially in terms of multiple data connectivity and software providers being available, and how they kind of undertake those challenges based on what's available, that would be the key for them to come back and be most responsible, especially in terms of declaring their emission numbers.

Sofia Galas: Usually, the data is the biggest challenge when it comes to making this decision of investing into another project, because if data discrepancy is high, you cannot know for sure how much CapEx it needs or where exactly you need to put this project. So, this is another thing that I've heard from customers is a big struggle, is being not certain of how to choose an actual decarbonisation strategy or even a project, just because there's no certainty on the data, and there's also this uncertainty that comes with markets and regulations. And Beth, what are you thinking?

Beth LaRose: Yeah, I think that the biggest area too that I've heard feedback on, in addition to the ones you've mentioned, is where data is missing or incomplete and that they can't make a good decision because there's big data gaps. And so, that's an area where understanding how to resolve that and make sure that those systems are well connected and the data is complete, I think, is one of the bigger challenges.

Sofia Galas: Yeah, I 100% agree. And I think that brings us to the AI, as everything brings us to the AI these days, how can AI actually help to provide the complete data and also make effective strategies? And I think Veerappan, you're the first person to answer that.

Veerappan Muthaiah: First thing first, right? We were talking about missing data, fragmented data, and we were talking about data completeness for an organisation. One of the things today with the advancements that's happening in the AI and the machine learning, once we kind of understand what are the current emissions and its activities of an enterprise, then what we can do is we can use the latest of the ML models to forecast, especially in terms of what that incorrect or missing data could be. Now, the fun is this. The data could be from everywhere. The data could be from, to start with, IoT sensors; it could be from your OT system, which is your typical time-series-based system; and then, it could also be from ERP systems; or the data could be just the supplier inputs on the emission factors; it could sometimes be satellite inputs as well for fugitive emissions monitoring. So, there are all kinds of data that could be used for Scope 1, Scope 2, and Scope 3 emissions and its monitoring.

Then, to solve the problem, especially in terms of data substitution, what one would do is ML models could track all these data, right? When an ML model kind of tracks all these data with its current emissions performance, it can be used further for forecasting, especially when there is a missing data and when there is an incorrect data. Based on the current emission activities, one can forecast the business as usual. But one could also then make a wad of scenarios for, "Hey, if I introduce a decarbonisation lever", and what that actually does to the forecast, it actually lowers the curve. So, what are the group of decarbonisation levers that one could plan and could add into the wad of scenarios to say how they could control the emissions in the future? So, the ML models could play a major role there.

Now, I'm extending that a little further. So, we did talk about ML models helping with the data substitution; we now talked about the ML models helping with forecasting; but then, with the advent of LLMs, like what we have in the industry, what's called the Model Context Protocol, which can do the web search as well. So, with the change in market-related factors for cost of compliance or the factors related to cap and trade, one could dynamically change the strategy. So, this is almost like all the data are interconnected in a company, and then the ML model is listening through the web for change in policies and change in cost of compliance, carbon tax, etc. And based on those inputs, the ML models could kind of run further scenarios and it can advise the customer what's the right trajectory for him to go towards the credible path of decarbonisation, or the credible pathway towards decarbonisation. So, those three are the areas that I think AI software can play a major role in the future.

But that being said, I'm equally excited to hear what you think, Sofia first, and then followed by what Beth thinks about this on this area.

Sofia Galas: Yeah, honestly, I just want to double-down on what you said. One of the feedbacks we get with everybody basically being obsessed with Gen AI is this focus that shifting from anything else, like machine learning, for example, I think it's still important to remember about it, especially with the situation where data accuracy is still not on the highest levels, and companies are still struggling with it and still struggling to fill in those gaps, as Beth was saying. I think Gen AI is a great application when it comes to strategy and to actually making informed decisions with the current market situation, whilst ML is a really, really great tool to make sure there is no discrepancy and the data is accurate, and also to clean up whatever is missing from the Scope 3 accuracy, for example, etc. And, Beth, what about yourself maybe, anything to that?

Beth LaRose: Yeah, I think just to kind of build upon what you had mentioned, Veerappan, in terms of scenarios and sensitivities, I think that could be a very good use case with AI, not just for near-term operations, so looking at hour-ahead, day-ahead, week-ahead type operational situations, but looking further ahead to the three, five, ten, even longer timeframes out to take the existing data and then extrapolate using AI to future scenarios. I think that these could end up being broader and more inclusive, if you will, of different risk factors for a company, so that they're able to look at a robust solution that converges in a number of different scenarios. So, I do think that is another use case moving forward, to take the data and extrapolate it broader in a more kind of multiplicative way, if you will, like a lot of scenarios. I'm talking about thousands of scenarios now, not just looking at two or three kind of high, low, medium cases. But I think AI has the ability to help chart a path among a lot of different cases that result in a more robust solution longer term.

Sofia Galas: Yeah, especially with the ability to move lots of data and bring the external factors in.

Beth LaRose: Exactly, yeah.

Sofia Galas: And especially in the world where we have so much uncertainty and so many factors, it's almost incomprehensible for a human mind. So, I think we could maybe wrap this up with some final thoughts. For me, thinking that there's a lot of ambition in the climate space, given the urgency and the crisis we're all in, but still there's not enough precise actions that are driven by accurate data and clear paths from each company responsible for most of the emissions, I think there's a lot of focus on how we all can improve that and just make sure we're not falling behind all those shiny pledges that companies and countries made.

Beth LaRose: Yeah, I would agree with that, Sofia, and add that having the clarity with data and being able to make data-driven decisions, and being able to create robust plans for the future, will be super-important as we move forward.

Veerappan Muthaiah: Yeah, I think I agree with both of you. And I think just to add to that, especially the measurements; the measurements in the emissions is quite important for organisations, because I'll go back to how I started articulating what one could measure is what they could manage. For managing and to achieve the complexity of net zero for an organisation, it's most important that they do it with accurate data-based operations. And obviously, when that's taken at the right level, it'll lead to operational excellence, especially in terms of managing the decarbonisation. And obviously, that will not just do carbon reductions, which will do the good things for the earth, but also it will contribute to the bottom-line benefits from a long-term perspective.

So, I only wish that organisations and companies look at decarbonisation, not just as a way to comply to regulators, but they're taking a holistic approach, would always help for the greater good.