Digitise energy data to make carbon accounting verifiable

Submitted through the COP31 Sustainable Transformation Awards · SKD Türkiye (WBCSD Global Network Partner)

The company is a Turkish bank in the financial institutions sector, with more than 1,000 employees and a dispersed operational footprint whose energy consumption feeds its corporate carbon footprint.

Carbon accounting in a service organisation stands or falls on the quality of energy consumption data. Before the initiative, that data was collected, checked against invoices, chased where it was missing and validated largely by manual means. An average of 4,000 invoices a month passed through those steps, which made the process slow, difficult to audit and vulnerable to gaps and transcription errors.

The response was not to build a better emissions calculator. The company chose instead to work one level upstream, on the reliability of the data that the calculation consumes, on the basis that an emission figure is only as defensible as the consumption record behind it.

The model that emerged is called 3V1K, after its four Turkish elements: data, validation, efficiency and carbon. It was developed beyond the company's legal reporting obligations rather than in response to them.

The base year is 2025, and the results reported here compare the digitised process with the manual process that preceded it.

Location of the initiative: Türkiye — the company's own operations, where the energy consumption data originates

The model brings the collection, validation, reporting and calculation use of energy consumption data together under a single structure, replacing a set of separate manual routines.

Central data management is the first element. Consumption records that previously sat with different operational owners are held in one data structure, which gives a single reference point for carbon accounting, cost control and disclosure rather than several partial ones.

XML-based integrations connect that structure to the systems where the data originates, so that records arrive in a defined format rather than being re-keyed. This is what removes the transcription step that generated most of the earlier data gaps.

Automatic control mechanisms then apply the validation rules that were previously applied by eye. Records are checked for completeness and consistency as they arrive, and exceptions are raised rather than discovered later during reporting.

The outcome is that Scope 1 and Scope 2 emission calculations are performed on a data layer that is traceable and auditable end to end. The same records support the corporate carbon footprint, sustainability disclosure and external assurance without separate reconciliation.

What distinguishes the model from common practice is its point of focus. Most carbon management systems address the calculation and its outputs; this one addresses the data quality and validation process underneath, and joins data, validation, process efficiency and carbon management into one methodology that can be adapted to other sectors.

The project workflow is illustrated in the diagram below.

Figure 1: Consumption Data Generation Process

The workflow diagram prior to the process redesign is presented below. Before the development, all operations were performed manually, which increased the risk of errors and reduced process efficiency.

Figure 2: Consumption Data Entry Process Prior to the Project

The impact of the 3V1K model is measured through key performance indicators that demonstrate improvements in data processing efficiency, validation, operational productivity, and data quality.

Sustainability Impact

Climate

The initiative is an enabler for climate performance rather than an abatement measure in itself: it does not reduce energy consumption, it makes the record of that consumption reliable enough to manage and to disclose.

The energy consumption data used in Scope 1 and Scope 2 emission calculations is validated in line with the GHG Protocol approach, through the central data structure, the XML-based integrations and the automatic control mechanisms. The base year is 2025.

Data integrity and traceability have been raised across the process, which strengthens the reliability of the carbon emission calculations that depend on them. The company frames this as the practical basis for the shift from measuring to managing and then reducing emissions.

The resulting data feeds a disclosure chain that is externally tested: the company reports against emission reduction targets validated by the Science Based Targets initiative (1), discloses through CDP on climate, water and forests (2), reports in line with the ISSB standards IFRS S1 and S2 and the TCFD recommendations, and obtains independent third-party external assurance over the reported information (3).

Social

The efficiency gain is realised as released working time rather than as headcount reduction. Approximately 1,200 hours a year previously spent on invoice checking, chasing missing records and manual validation are returned to the sustainability and operational teams, who apply them to analysis rather than to data entry.

Transparency inside the organisation improves because consumption data is held once and can be traced to its source. Operational units, the sustainability function and external assurance providers work from the same record, which removes the reconciliation arguments that fragmented data tends to generate.

The monitoring model is also a capability-building one: results are reviewed monthly, and the feedback from operational users of the system is taken into regular system improvements.

Business Impact

Benefits

The efficiency result is direct and measured. In a process handling an average of 4,000 invoices a month, processing times improved by 50 per cent, producing approximately 1,200 hours of annual operational efficiency.

Data quality improvements carry a second benefit that is qualitative but material: fewer gaps and errors mean less rework at reporting time, and a consumption record that can be traced to source shortens the external assurance and disclosure cycle rather than lengthening it.

Because consumption data is held centrally and checked automatically, the same dataset serves carbon accounting, sustainability reporting and internal energy cost control without being rebuilt for each purpose.

The model was designed as a methodology rather than a one-off system, which means the process design can be reused as new data sources or new reporting requirements appear, and it is transferable to banking, finance, industrial and service organisations facing the same data problem.

Costs

The project was developed entirely using internal resources, with no external platform acquisition or implementation costs. The estimated internal development cost was TRY 942,588.94. No significant additional external operating costs were incurred after implementation, as the platform is maintained using existing internal resources. Based on the operational efficiency gains generated by the automation, including approximately 1,200 hours of annual time savings, the estimated payback period is approximately 7 months.

The cost base consists primarily of system development and integration efforts, including the design of the central data structure, XML-based integrations with source systems, the codification of validation rules as automated controls, and the IT resources required to operate and maintain the solution.

While the project significantly reduces manual checking and data processing effort, ongoing operating activities include data governance, integration maintenance, and exception handling, which are managed through existing internal resources.

Change management is a real cost consideration and is identified by the company as one of the principal risks in wider deployment, alongside gaps in data standardisation and differences in integration between organisations. These risks are managed through a common data standard approach, compatible integration infrastructure, and technical frameworks aligned with regulatory requirements.

Costs are contained by integrating the model into the company's existing digital infrastructure rather than developing a standalone application, and by embedding its outputs into corporate sustainability reporting rather than creating a parallel process.

Impact Beyond Sustainability And Business

Co-benefits

The most significant systemic effect is that carbon management stops being a reporting exercise carried out once a year and becomes a structure that works alongside data quality processes, operational routines and decision-making.

Standardisation of collection, validation and reporting spreads along the value chain, and stakeholder behaviour moves towards a more transparent, measurable and data-based pattern as the data standard is applied.

The model is deliberately sector-agnostic. Any organisation that calculates a carbon footprint from energy consumption data faces the same upstream problem, so the methodology can be integrated into sustainability and reporting processes in banking, finance, industry and services alike.

Potential side-effects

Automating validation can produce misplaced confidence. Controls confirm that a record is complete and internally consistent, not that the meter reading or the invoice behind it is correct, so exception handling and periodic manual sampling have to be retained rather than retired once the automation is trusted.

Centralisation creates a dependency. When one data structure serves carbon accounting, disclosure and assurance, an integration failure affects all three at once, which raises the importance of integration monitoring and continuity arrangements.

Organisations with fragmented data definitions will find that the harmonisation work precedes the benefit. The efficiency gain reported here assumes a common data standard is in place; without it the integration effort rises and the payback period lengthens.

Typical Business Profile

The model suits organisations that calculate a carbon footprint from energy consumption data collected across multiple sites and multiple suppliers, where invoice volume is high enough that manual checking becomes the constraint on reporting quality.

It is most relevant to banks, financial institutions, industrial companies and service organisations that already carry a sustainability reporting obligation or commitment, and that have an information technology function able to build and maintain integrations with source systems.

Delivery requires a sustainability function that owns the carbon accounting methodology and an information technology function that owns the data structure, working together from requirement analysis through to operation, with senior management ownership to make the data standard binding across operational units.

Approach

1. Map the existing data path end to end: Record how consumption data is currently received, checked, chased and validated, measure the volume passing through each step and the time it consumes, and identify where gaps and errors are actually created.

2. Define one central data structure: Replace distributed spreadsheets and local records with a single structure that holds consumption data for carbon accounting, cost control and disclosure, and agree the data standard before any integration work begins.

3. Build integrations in a defined format: Connect the source systems to the central structure through XML-based integrations so that records arrive in a fixed format and the manual re-keying step disappears.

4. Codify the validation rules as automatic controls: Convert the checks previously performed by eye into control mechanisms that test completeness and consistency on arrival, and route failures to an exception queue rather than to the end of the reporting cycle.

5. Anchor the calculation in a recognised methodology and a base year: Apply the GHG Protocol approach to the Scope 1 and Scope 2 calculations that consume the data, and fix the base year so that later results are comparable.

6. Instrument the process with a small set of indicators: Track the number of invoices processed, the automatic validation rate, the data gap and error rate, and the process completion time, which together show whether data quality and efficiency are moving in the same direction.

7. Move reporting onto the central system: Report the indicators monthly from the central structure and use the same records in corporate sustainability reporting, so that a single dataset serves operational management, disclosure and external assurance.

8. Place the model inside corporate governance: Assign committees and responsible units accountable for data quality, system integrations and the continuity of carbon reporting, and run the feedback from operational users, validation results and system performance data into a continuous improvement cycle.

Stakeholders Involved

• Project leads: The model is owned at senior management level as part of the company's sustainability and digital transformation strategies, and was made a corporate priority rather than a departmental project. The board and the relevant senior management units support the sustainability and data management objectives it serves, and designated committees and responsible units are accountable for data quality, system integrations and the continuity of carbon reporting.

• Company functions: Sustainability teams and information technology units took the active roles in project design and decision-making. Requirement analysis, process design and system development were shaped by those two functions working jointly, with the sustainability function owning the carbon accounting methodology and the technology function owning the data structure and integrations. Operational users of the system supply the feedback that drives subsequent improvements.

• Main providers: No external solution provider is named in the source material. The central data structure, the XML-based integrations and the automatic control mechanisms are described as developed within the company's own digital infrastructure.

• Other: The initiative does not involve direct external stakeholder interaction, but public regulation, sustainability reporting standards and sector practice were taken as design references, so regulatory alignment and sector expectations are built into the model rather than retrofitted. Independent assurance providers test the reported information, and disclosure bodies receive the resulting data.

Key Parameters To Consider

The base year is 2025, and the reported improvement compares the digitised process with the manual process it replaced. Volume matters to the result: the 50 per cent improvement is measured on a process handling an average of 4,000 invoices a month, so organisations with lower invoice volumes should expect a smaller absolute time saving.

Impact data is tracked monthly, reported through the central system and used in sustainability reporting, which means the monitoring routine is part of the existing reporting cycle rather than an additional exercise.

The model is positioned as a corporate system integrated into energy data management, sustainability reporting and carbon calculation infrastructure, not as a standalone application. That integration is what secures its medium and long-term continuity, and it is also the reason it cannot be lifted into another organisation without the equivalent governance in place.

The principal deployment constraints are data standardisation gaps, differences in integration between organisations, and change management.

Implementation And Operations Tips

Work upstream of the emission figure. The temptation is to invest in calculation and disclosure tooling; the constraint in most organisations is the consumption record that feeds it.

Agree the data standard before building integrations. Retrofitting a common standard onto integrations already in production is the most expensive way to discover that definitions differ between units.

Automate validation but keep an exception route and periodic manual sampling. Controls prove internal consistency, not external accuracy, and the distinction matters when the data is assured.

Use one dataset for several purposes. Serving carbon accounting, energy cost control, sustainability disclosure and external assurance from the same validated records is what converts a data project into an efficiency result.

External Links

• Interactive annual report

Source List

(1) Science Based Targets initiative validation

(2) CDP reporting

(3) Independent third-party external assurance