Basis of reporting

The information presented in this report is collected from various online and offline, internal and external resources. In many cases, interviews with partners and employees took place in order to write the text. For the data, a variety of systems were used, including but not limited to our SAP systems and specific project data.

This information should be read in conjunction with the basis of preparation, describing the scope, materiality, boundaries, reliability & completeness and reporting process (Annex 2).

For all metrics included in the report, reporting risks and mitigating measures (controls) have been developed. Main reporting risks relate to underreporting or overreporting depending on the indicator, incompleteness of data and dependency on external data providers. Underreporting and overreporting risks are mitigated by robust data definitions with predefined scopes and boundaries, and applying the least favourable outcome principle. Incompleteness risks are mitigated by predefined scopes and boundaries and external data providers risks are mitigated by certification requirements. Every metric has a functional data owner and a data reviewer to reduce the risk of inaccuracy. We are setting up periodic reporting for all applicable sustainability metrics to enhance management of our material sustainability matters.

None of the sustainability KPIs presented in the report have been validated by an external body other than our assurance provider.

Environmental data

Our Environmental data includes the emissions related to our mobility, housing and purchased goods and services. For the allocation into the different scopes, we follow the Greenhouse Gas Protocol Corporate Accounting and Reporting Standard, using the operational control approach.

For buildings, we are tenants of our offices, meaning that we are for a large extent dependent on our landlord for the energy contracts. Where we do our own purchasing, GHG emissions resulting from energy usage are reported in our scopes 1 and 2, whereas those where we cannot influence the energy sources we report emissions in scope 3. Emissions from offices with an occupied surface <1,000 m2 are extrapolated on the basis of the emissions of offices above this threshold.

For conversion of energy consumption to CO₂e emissions we used the most up to date emissions based on www.co2emissiefactoren.nl :

Electricity (unknown): 1 kWh equals 0.328 kilogrammes CO₂e
Heat: 1 GJ equals 25.05 kilogrammes CO₂e
Gas: 1 m3 equals 2.134 kilogrammes CO₂e

For the conversion of natural gas consumption to MJ, we used the conversion factor from the GasUnie: caloric value per m3 is 35,17 MJ.

For lease cases, the data included are obtained from our fleet supplier. As we do not separately monitor business trips, commuting and private use of lease cars, our data includes all these elements. Our lease emissions are reported under Scope 1 and 2, since we do our own purchasing of fuels and electricity.

For conversion of these fuels to CO₂e emissions we used the most up to date emission factors based on www.co2emissiefactoren.nl :

Petrol: 1 litre equals 2.821 kilogrammes CO₂e
Diesel: 1 litre equals 3.256 kilogrammes CO₂e
LPG: 1 litre equals 1.802 kilogrammes CO₂e
Electricity: 1 kWh equals 0.328 kilogrammes CO₂e

For conversion of employee commuting kilometres to CO2 emissions we used the most up to date emission factors based on www.co2emissiefactoren.nl:

Bus: 1 kilometre per passenger equals 0.109 kilograms CO₂e
Metro: 1 kilometre per passenger equals 0.000 kilograms CO₂e
Tram: 1 kilometre per passenger equals 0.000 kilograms CO₂e
Car (Fuel type unknown): 1 kilometre per passenger equals 0.193 kilograms CO₂e
Public transport (general): 1 kilometre per passenger equals 0.020 kilograms CO₂e
Scooter: 1 kilometre per passenger equals 0.080 kilograms CO₂e
Train: 1 kilometre per passenger equals 0.003 kilograms CO₂e

Total kilometres travelled by plane are obtained from our travel agents. It is standing policy that we use the most recent conversion factors. Hence, for the calculation of the related CO₂e emissions, we have used the 2024 conversion factors as provided by DEFRA (www.defra.gov.uk) using a classification that distinguishes economy, premium economy, business class and first class and categorises air travel in domestic, short-haul international and long-haul international flights. For the various subgroups, the following CO₂e conversions are used:

Domestic average: 0.27257kg CO₂e /kilometre per passenger
Short-haul international average: 0.18592kg CO₂e /kilometre per passenger
Short-haul international economy class: 0.18287 kg CO₂e /kilometre per passenger
Short-haul international business class: 0.27430 kg CO₂e /kilometre per passenger Long-haul international average: 0.26128 kg CO₂e /kilometre per passenger
Long-haul international economy class: 0.20011 kg CO₂e /kilometre per passenger
Long-haul international premium economy class: 0.32015 CO₂e /kilometre per passenger
Long-haul international business class: 0.58028 kg CO₂e /kilometre per passenger
Long-haul-international first class: 0.80040 kg CO₂e /kilometre per passenger

The total kilometres travelled by train are obtained from our supplier Nederlandse Spoorwegen. For the calculation of related CO₂e emissions for national rail, we used a conversion factor of 0.003 kg CO₂e / kilometre per passenger as published bywww.co2emissiefactoren.nl .

For international rail, the total kilometres travelled are provided by our travel agency. For the calculation of related CO₂e emissions for international rail, we used a conversion factor of 0.0045 kg CO₂e / kilometre per passenger for intracontinental rail travel between different countries and of 0.0355 kg CO₂e / kilometre per passenger for intracontinental rail travel within the same country (taking an international train for a domestic journey).

Hotel stays are calculated on the basis of the data of our travel provider and our expense system. To calculate the carbon emissions caused by hotel stays by Deloitte partners and employees, we have multiplied the total number of hotel nights with 33.23 kg CO₂e. This conversion factor has been developed by DTTL on the basis of the Cornell University Hotel Benchmarking tool.

To calculate emissions related to homeworking, a CO₂e conversion factor of 0.33378 kg of CO₂e per FTE working hour is used, based on the DEFRA set of emission factors for 2024. The emission factor considers emissions related to both office equipment and heating. Homeworking data is obtained from the Engage for Change survey, and extrapolated to the total number of FTEs. The total number of hours worked per FTE in FY25 was set at 1,824 – lastly, the final total number of hours worked for the total number of FTEs was corrected for the number of sickness hours in FY25. This resulted in the following formula:

Emissions related to homeworking: EF × ((survey percentage working from home × total number of FTEs × 1,824) – (total sickness days × 8))

As of 2024/2025, we have also calculated emissions related to waste. To calculate these emissions, we have taken the figure provided to us by our waste processor Renewi for our waste emissions. We note that several of the waste streams and emissions have been verified by TNO. In addition, we note that the emission factors currently available for waste (such as from DEFRA) are still generic. Using the data provided by Renewi allows us to report a more specific figure, based on our own waste data.

The Purchased Goods and Services methodology is based on our procurement spend data. We apply a number of assumptions to the spend data, including how we allocate spend into procurement categories, how we treat our suppliers’ reported Scope 3 emissions, the CDP sector emission factors we apply to each spend category, and the extrapolation factors. We continually review our approach to reduce the risks inherent in these assumptions and the impacts of year-on-year fluctuations.

In 2023/2024, significant data quality improvements took place, allowing for better determination of actual cost incurred for PG&S. Notably, Deloitte has transitioned to an activity-based emissions calculation methodology for contingent labour, focusing on the carbon-generating activities of contractors, such as business travel, commuting, and working from home, instead of using spend (which also included the hourly rate charged for services delivered) as a proxy.

We will continue to review our approach to Scope 3 reporting in the future, aiming to continually improve the accuracy of our disclosures. When these enhancements lead to a material change in a reported figure, we are committed to explaining the nature of the change, our reasoning for its appropriateness, and the percentage variance compared to previous methodologies. As the reported data for Purchased Goods & Services highly depends on spend data, CDP sector emission factors, and other assumptions, we believe the reported figures to have a high level of measurement uncertainty.

Carbon emission reduction: the % by which our mobility and housing related carbon emissions were increased or decreased compared to comparable emissions in the base year 2018/2019 (30,477 tonnes). This year was chosen as reference year by DTTL for the global WorldClimate programme and hence is also applied by Deloitte Netherlands.

Social data

Unless otherwise indicated, our Social data excludes interns as inclusion would distort insights provided by the indicators used (e.g. on important areas such as % of employees receiving regular performance & career development reviews, and employee turnover). Also, only active employees on the payroll (excluding contractors/ externals/WIA/WAO, among others) are included in the talent data. No assumptions are made for the social metrics.

# employer of choice in relevant ranking: ranking in the benchmark study performed by Universum. For the business ranking, the following universities are included: Rijksuniversiteit Groningen, Erasmus Universiteit Rotterdam, Vrije Universiteit Amsterdam, Universiteit van Amsterdam and Universiteit van Tilburg. For STEM, the following universities are included: TU/Eindhoven, TU/Delft, Vrije Universiteit Amsterdam and Universiteit van Amsterdam.

Diversity of age, all employees: the distribution of all employees by age group, based on average headcount.

Female positions in top management: # women in Supervisory Board, Executive Board and Executive Committee divided by total membership of Supervisory Board, Executive Board and Executive Committee as per the end of our fiscal year (May 31).

Sickness leave: total number of sick days divided by the total number of scheduled days.

Wellbeing: The % of employees entitled to take family-related leave by gender and the % of entitled employees that took family-related leave, by gender. Family-related leave consists of additional birth leave, birth leave, care leave long lasting, flexible maternity leave, maternity leave, paid parental leave, short term care week(s), care leave paid week(s), care leave unpaid, bereavement leave and urgent leave family care. The family-related leave provisions of Deloitte are applicable to all employees who have an employment agreement with Deloitte Netherlands. Participation in the scheme is not possible for self-employed persons, interns, secondees/temporary workers. The scheme also does not apply to expat outbounds sent on host package to a foreign member Firm and for expat inbounds sent on home package to Deloitte Netherlands.

Female partners as % of total partners: # of female partners divided by total # of partners (headcount on May 31).

Total number of employees: # of employees, per gender and per region, based on average headcount.

Permanent employees and temporary employees: # of permanent and temporary employees, and breakdown by gender, based on average headcount.

Total turnover: # of employee turnover and % of employee turnover in the reporting period, based on headcount.

Full-time and part-time employees: # of full-time and part-time employees, per gender and per region, based on average headcount.

Females in leadership roles: % of females in leadership roles, based on average headcount. This includes females within the manager and upper-level cohort, including partners. The Global Delivery Network (GDN) is excluded from this calculation.

Gender pay equity: The ratio of basic salary (fixed, monthly average paid to an employee) and remuneration of women to men as per December, 2024. KPI includes only active payroll employees with a base salary >€0, excluding contractors/ externals/WIA/WAO, Equity Partners along with Supervisory Board members among others.

Remuneration ratio: annual total remuneration ratio of the highest paid individual to the median annual total remuneration for all employees (excluding the highest-paid individual).

Percentage of employees receiving regular performance and career development reviews: eligible employees for snapshot divided by the number of employees that have at least one requested and completed snapshot excluding partners and Interns. Our interns (e.g. thesis intern or internships) are not part of our regular performance cycle, as the main purpose for these functions are education and learning, rather than performance. Our partners are not part of our regular performance cycle. Instead, we maintain a performance management system that is also used to determine their annual profit share and that takes into account such aspects as quality, integrity, inclusive leadership, commercial performance and relationship management.

Average hours of training per year headcount: The average hours of internal and external training that our employees have undertaken during the reporting period, broken down by gender, job grade and per business. External trainings are delivered by external vendors, for example Universities or professional education institutes, and are attended by employees during working hours. Internal trainings are instructor-led and/or digital trainings managed through our learning management system SABA, as well as self-reported learning hours spent on other learning activities, like professional study hours and facilitating peer learning sessions. Examples of internal trainings are Deloitte University programs, Performance Conversation trainings, and cyber security eLearnings.

Number of hours spent on DIF projects: This KPI represents the total hours dedicated by Deloitte from June 1, 2024, to May 31, 2025, on pro bono work under the Deloitte Impact Foundation (DIF). These hours relate not only to the hours recorded on projects but also to preparation hours, and ensure that we approve qualitative projects that fit within the DIF organization. This includes the contributions of change makers and members of the advisory board who play a vital role in advancing our societal impact objectives. Projects are evaluated based on their impact on society, which can be initiated both bottom-up (employees proposing a societal impact project) and top-down, including initiatives from Deloitte Global such as our WorldClass programme or efforts to support Financial Health.

Number of hours on DIF projects x average hourly rate: This KPI represents the hours spent by Deloitte during the period June 1, 2024 to May 31, 2025 on carrying out DIF projects and is multiplied by the internal average hourly rate. This internal hourly rate is the cost price for Deloitte for each hour worked (which therefore covers wages, social security contributions, lease car, laptops, licenses, etc.). This internal hourly rate is coordinated with the Control Department. In this way, the social value is made clear. The KPI consists of a deposition of the number of hours spent on social projects, including the monetary value of these hours.

Number of participating employees in DIF projects: This KPI monitors the total number of employees who have logged hours in in the internal system (Swift) on a DIF project in the period from June 1, 2024 to May 31, 2025. From Swift, it is easy to look at the number of different employees who have logged hours on the DIF project codes.

Number of DIF projects: This KPI monitors the number of different project codes that are created after approval of a DIF project. Only the project codes that are directly related to a DIF project.

Governance data

In calculating the value of Governance data, we have applied the following data definitions:

Regulatory quality: % of regulatory reviews (reviews issued by PCAOB, AFM, NBA, ADR, and Inspectie OCW), of which the results were communicated in the reporting year that are satisfactory as a percentage of all regulatory reviews on Deloitte Accountants B.V. issued in the reporting year.

NPS at C-level among strategic clients: the net promotor score as determined during the client service assessment conversations, in which we regard a score of 9 or higher (on a 1-10 scale) as active promotors minus detractors ( a 6 or lower). The NPS is calculated by subtracting the detractors from the promoters and dividing this number by the total number of respondents. Where clients indicate to be an active promotor and are considering a score between 8 and 9, the independent interviewer will seek confirmation with the client. When confirmed, these clients are also categorised as active promotors.

Client satisfaction (engagements): the average satisfaction score received from clients on post-engagement questionnaires sent out by the businesses during the financial year. The post-engagement questionnaire is sent out on discretion of the engagement manager or partner. The average satisfaction score received from clients (on a 1-10 scale) on post-engagement questionnaires sent out by the businesses during the financial year, multiplied by a factor 10. The post-engagement questionnaire is sent out on discretion of the engagement manager or partner.

Incident reporting includes the number of reported occurrences as registered in NAVEX, broken down into individual incident categories. Validation and follow-up on incidents reported lies with our ethics team and leader.

Total percentage of employees who have received training on anti-corruption: The percentage of individuals who have completed the training is calculated by dividing the number of people who have finished the training by the number of people who were assigned the training. The training is mandatory every two years for each employee, regardless of job level. Additionally, all new hires are required to complete the training upon joining the organisation.

Number of data leaks identified: corresponds to the total number of data leaks that were reported to the Privacy Office.

Substantiated complaints concerning breaches of customer privacy and losses of customer data are treated seperately.