One Platform to Benchmark them All

Reliable data on investment costs, energy savings and financial performance play a crucial role in implementing the energy transition in the industrial and building sectors. To make risks and profitability transparent and assessable for financial institutions and policymakers, the De-risking Energy Efficiency Platform (DEEP) was established as the largest open-source European database for energy efficiency investments. Hosted by the European Energy Efficiency Financing Coalition and funded by the European Commission, the platform now contains data from more than 36,000 projects. But how meaningful are these comparative benchmarks when projects are evaluated across different countries and policy landscapes?

In the peer-reviewed paper "Return of the DEEP: One platform to benchmark them all", researchers of the Fraunhofer Institute for Systems and Innovation Research ISI, the Wuppertal Institute, Viegand Maagøe and the National Technical University of Athens analyses the significantly expanded dataset and provides a systematic re-evaluation of its key economic indicators. Head of the Future Energy and Industry Systems Division at the Wuppertal Institute and co-author of the study, summarises the key findings of the research: "Our updated evaluations confirm that cross-cutting technologies continue to offer short payback periods, whereas building envelope and system-level measures naturally require higher capital intensity. The underlying structural patterns of these financial indicators remain highly consistent even with a substantially larger database."

A key focus of the study is a structured cross-country comparison between German and US projects represented in the database. At first glance, the data reveals notable differences: the US project portfolio generally features lower investment levels and shorter payback periods.
The underlying reasons for these variances: Direct benchmark comparisons can easily misrepresent actual technological performance. The observed differences between countries are primarily driven by dataset provenance, project scale and the specific design of national support programmes rather than differences in engineering performance, adds Rohde.
The findings highlight the need to always take into account the data source and the underlying programme design when using benchmark indicators for policy instruments or funding decisions.

The peer-reviewed article is available via the link below.