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STEEP - Systems Thinking For Comprehensive City Efficient Energy Planning
The project partners are 3 cities with similar characteristics, experience and policies in the sustainable use and production of energy and very ambitious carbon and energy reduction targets, aligned with Europe’s targets. In each city, initiatives to reduce energy inefficiency are already in place; however each of these has been aimed at a specific sectoral objective, while overlooking the possible impact on other sectors. This is why all stakeholders and city systems are considered important when undertaking measures aimed at improving sustainability, to make this an integral part of their urban planning in order to achieve the best results in the least amount of time and at the lowest cost.
For this reason, these cities have decided to join together to improve efficiency along all the key aspects of their energy value chain, by applying smart city concepts in an integrated manner while learning from each other’s expertise and viewpoint in applying sustainable practices. The partners are experts on Energy (FSS, SANSEBASTIAN, TECNALIA and ACCIONA), ICT (BRISTOL, UNIBRISTOL, ARUP and CSE) and Mobility (FLORENCE, SPES and ATAF).
To achieve the proposed objectives, the STEEP project will create a process model of systems-based thinking for district energy master planning, which will be applied to 3 city districts to better understand the systems that impact upon energy use and the interventions that can be taken to meet the ambitious energy and carbon targets. These models will be enriched and validated through open innovation methodologies applied with the stakeholders. The learning obtained from the process model for district energy master planning will then be applied when defining a specific action plan (time line, costs and pay-back periods) at city level. With the knowledge gathered in this process, a replicable open source methodology and Key Performance Indicators will be produced for developing and screening integrated Smart City Plans.