Subject and goals of the project
Up to now, urban redevelopment projects in Poland have mainly followed socio-economic objectives. Aspects of energy and resource efficiency have been largely ignored. As part of the project, a modular training concept was developed and tested for representatives from municipalities, urban planning, architectural firms, and civil society consulting institutions (e.g. consumer advice centers), which uses various dialog and training offerings to highlight resource and energy efficiency as relevant planning and decision-making criteria for neighborhood renewal. In order to achieve the greatest possible multiplication, the concept is being developed and implemented as a train-the-trainer approach.
Innovation and exemplary nature of the project
The project has succeeded in
Special aspects of the project
The topic attracted a lot of attention from local governments, non-governmental organizations, and representatives of national and regional state funding institutions (e.g., the National Fund for Environmental Protection and Water Management (NFOS)) in Poland. There were 3 times more applications for each of the workshops than planned. In total, there were 61 key actors trained with the REVIPOWER Model Training and almost 300 key actors directly sensitized to the paradigm of energy and resource efficient urban development by REVIPOWER experts during external conferences. According to statistics from Instytut Monitorowania Mediów, media outreach within the project reached up to 60,000 people.
The implementation process was ensured together with selected project partners. The comprehensive final report describes the project implementation process in detail.
Funding subject: Energy- and resource-saving district development and renewal
Applicant:
Associated partners:
Location: Poland (focus: Warsaw)
Funding period: September 2017 to March 2019, Download final report
Project costs: Total volume: 172 736 Euro, DBU funding: 102 445 Euro
DBU-AZ: 34058
Note: Translation of the German version with DeepL
Last updated: 15.11.2021