AI-Driven Sustainability: Transforming Data into Action

AI-Driven Sustainability: Transforming Data into Action

Digitalisation is one of the greatest transformation catalysts of our era. This transformation significantly affects businesses in complex and strategic areas such as sustainability management. Accurate and efficient collection, processing, and evaluation of sustainability data play a critical role in helping companies achieve their future sustainability goals. However, this data often comes from numerous sources, making its management challenging. Companies need a holistic approach to effectively collect, analyse, and report this data. Many companies process sustainability data manually, which is time-consuming and increases the risk of errors. Leveraging the power of technology to automate these processes will help companies make faster and more accurate decisions.

Sustainability managers and consultants spend a lot of time synthesising the necessary data for reporting. We know that some firms get stuck just in the data collection and reporting phase and cannot move on to defining their actions. In today’s digital world, some firms are forced to wait nearly 9-10 months to publish the previous year’s data. Yet, in this journey, data collection is only a part of the journey. It’s valuable for companies to extract meaningful insights from the data for their roadmaps, define their actions, assess the outcomes of these actions, and identify sustainability-linked risks and opportunities that could impact their business models. Particularly, I have observed that AI-powered software allows sustainability leaders to save time and undertake more valuable, transformative, and visionary roles.

I would like to start discussing the digitalisation developments in this area with one of the most critical sustainability issues: carbon footprint management. Managing the carbon footprint requires companies to collect and process various data, from energy consumption figures to air conditioning maintenance records, fuel consumption of company vehicles, and records of purchased products and services. Companies are now tracking this data from various sources like meters, ERP records, and Excel forms in real-time through a single digital application. This approach eliminates the need for days-long data consolidation and report writing, typical of traditional reporting processes. In these applications, data is automatically collected at desired frequencies by integrating with meters of thousands of facilities.

Over half of carbon footprint data is stored in unstructured sources like PDFs, reports, visual records, presentations, and notes, and processing this data can create a manual workload. Digital tools, however, support the faster collection of data buried deep within documents than ever before.

One of the chronic problems of this process is errors in manually collected data. Collecting data through applications minimises the possibility of manual errors. Additionally, smart algorithms can easily detect anomalies in thousands of lines of emission activity data, presenting you with reports of these points. This improves the processes of internal audits that would otherwise take days.

With these digitalisation solutions, you no longer must wait months to see your emission values and the results of your decarbonisation actions. Applications can perform real-time emission calculations with a single click and produce reports in PDF format ready for ISO 14064 greenhouse gas audits.

Measuring and reporting emissions is just the first step. Of course, actions must be defined to reduce emissions. The determination of these actions depends on various factors, such as the sector, the targeted reduction effect, budget, technological accessibility, etc. Some software applications considering all these variables support you in defining these actions and foreseeing the emission reduction impact that will result. Thus, you model your strategic plans through a digital application.

Not only the internal operational activities of companies but also the stakeholders in the supply chain are critical factor that directly affects their sustainability performance. In fact, they produce approximately 60% of all carbon emissions globally. Situations where suppliers’ environmental and social impact measurement, monitoring, and assessment systems are inadequate can also jeopardise the responsible purchasing goals of buyers. Communicating with thousands of suppliers and collecting data from them in a standard format is a challenging process. To manage this, some companies now include information collection processes from their suppliers in their sustainability software solutions. This allows them to collect data on  their suppliers’ emission-causing activities in a more reliable, transparent, verifiable, and standardised manner.

There are many standards related to sustainability reporting in the market, and these standards have many common metrics. Moreover, some companies need to report for multiple sustainability index evaluations, such as the Dow Jones Sustainability Index, FTSE4Good Index, etc., at the same time. This brings with it repetitive reporting workloads. Sustainability managers, seeking to alleviate the burden of duplicate reporting tasks, are increasingly adopting advanced technology solutions powered by artificial intelligence, particularly large language models (LLMs), to streamline and automate their reporting processes. These software match the metrics required in sustainability standards by utilising the power of artificial intelligence and allowing the answers written on the same subject to flow between the relevant reporting templates. This way, when you prepare data according to one standard, it becomes ready for the relevant other standards.

The use of artificial intelligence in managing sustainability performance emerges in various application areas. For example, AI models trained specifically for sustainability and energy management expertise can provide more accurate and up-to-date answers than chatbot applications trained with a broad knowledge base to the questions asked by users. Identifying the risks and opportunities affecting business models from the daily standard/regulation/guide/framework deluge for sustainability managers and consultants in businesses is becoming increasingly difficult. The number of qualified sustainability experts trained in this field is also quite limited compared to the need. Thanks to large language models of AI trained with reliable sources, co-pilots can offer correct insights to subject matter experts with very high success rates. AI can now interact with you by examining your data. For example, when you mention the features of your facility, it can give you an idea of whether you are subject to a regulation. Or, by examining your energy performance data, it can analyse your energy-intensive points. Energy consumption is the largest source of human-induced greenhouse gas emissions, accounting for 75.6% worldwide. It is important for businesses to increase energy efficiency, optimise energy consumption, and reduce greenhouse gas emissions by using AI-based energy performance analysis in this area.

Another area of application for interactive AI models is determining the environmental and social risks of banks in lending evaluation processes. Within the scope of sustainable financing practices, banks determine their risk levels by collecting data from customers during the lending stage. For these risk assessments, experts manually analyse dozens of documents from customers. AI-based applications now tell you the answer to your question as if talking to documents as soon as you upload them, making processes more efficient.

On the other hand, one of the topics that I think will start to take its place on the agenda soon in carbon emission management is the real-time tracking of scope 2 emissions. Conventional carbon footprint calculations based on electricity consumption use a single average annual emission factor, which is the lowest resolution emission factor. Innovative software now calculates the emission intensities of electric grids on an hourly basis. Thus, energy purchase decisions can be made according to the emission intensity of the grid. For example, environmentally friendly pricing policies can be applied in electric charging stations at hours with low emission intensity.

In summary, sustainability management can now be carried out more effectively, quickly, and accurately with the opportunities offered by digitalisation, especially artificial intelligence. Software providing critical insights to sustainability leaders helps them create more strategic and effective action plans.

To learn more about how Faradai can support you in digitising your sustainability journey, reach out to us at [email protected].

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