Eco-Efficiency Tables

Total emissions are based on actual, estimated, or extrapolated data. All assumptions and calculation methodologies are in line with the ISO 14064 Standard Guidelines with supporting documentation. The most appropriate emission factors have been used for each activity data type, from internationally recognized sources, e.g. DEFRA (2016 and 2017), GHG Protocol, eGRID, and IEA (2016), RE-DISS (2016) or if more relevant, from country or contract specific sources. The factors include all GHGs where possible and the gases' Global Warming Potential as per the IPCC assessments.

Data reported under 2017 relates to the period Oct. 1, 2016 to Sept. 30, 2017. Q4 2016 is used to represent activity of Q4 2017, with an uncertainty of +/- 5% across all KPIs (excl. water +/-10%).

Previous-year data is reviewed annually and error corrections are applied if necessary. To better reflect the use of company-owned vehicles for business travel, the methodology was reviewed and updated historically, excl.distance traveled for private use.

Emissions from liquid fossil fuels decreased in 2017, largely driven by a reduction in diesel use in Germany.

Emissions from HFCs decreased in 2017. The decrease is in the expected range factoring in maintenance work conducted.

Emission factors from IEA for electricity were used for the countries where DB operates (except for the US where the eGRID factors were used). The former set of factors is only available in metric tons of CO₂, while eGRID factors are specified in CO₂e. However, as the proportion of non-CO₂ GHG emissions is minute compared to CO₂, we are reporting all emissions from electricity in CO₂e.

Emissions from rented vehicles and taxis has reduced, largely driven by a reduction in spending on taxis in Germany and India.

For 2017, carbon neutrality was accomplished by the purchase and retirement of verified emissions reduction units.

All floor area metrics use an annual average derived from Oct 2016 to Sept 2017 data (3.486 million m²).

All FTE metrics use an annual average for 2017 (98,262).

Calculated electricity and heating intensities are used to estimate electricity and heating demand where data is not available. Calculated intensities from refrigerant gas loss are also used to extrapolate where data is not available.

Total energy consumption in gigawatt hours comprises all sources used in Scope 1 and 2: natural gas, liquid fossil fuels (mobile and stationary), renewable and grid electricity, district heating, cooling, and steam. Standard joule to kWh conversion factors were used. There is no sale of electricity, heating, cooling, or steam.

Energy consumption reductions achieved in offices total 10.8 GWh from 149 initiatives (these saving are in-year, i.e. a saving completed in June only gets 6 months of saving towards 2017). In branches the reduction was 0.7 GWh from 39 initiatives (these saves are annualized, i.e. a saving completed in June counts for the entire 12 months of 2017). The types of energy included in the reductions are electricity, district cooling, district heat, natural gas. These totals exclude saving made in the Postbank portfolio.