LIAISE Charter: Initiative for a Community of Practice on Impact Assessment Research for Sustainable Development

(09-04-2014) The LIAISE Charter lays out a vision on how to achieve the goal of a broad, open and inclusive centre of excellence on Impact Assessment (IA) for Sustainable Development (SD).

It consists of 3 main elements: 1) An outline of the gap between the existing IA knowledge reservoir and its actual use in IA for SD; 2) A vision on a future Community of Practice (CoP) that can address this gap; and 3) The implementation of this vision.

An evidence based approach is becoming increasingly important in policy development and societal decision making. LIAISE started in 2009 as a Network of 15 research institutes, funded through EC FP7 work programme. Its research focus is an improved use of Impact Assessment (IA) tools for polices targeted at Sustainable Development (SD) strategies, e.g. in the development of a green economy and in the field of resource efficiency. To this end, it is necessary to close the existing gap between the production (research side) and actual use (decision making side) of knowledge and methods on IA for SD.

This requires a transformation of the present consortium to a broad centre of excellence in research on IA for SD. It should generate added value for all stakeholders involved in a role as: 1) IA knowledge and information hub; 2) Networking and discussion forum; 3) Innovation and testing lab and 4) Tool identification and quality monitoring.

With the LIAISE Charter, we wish to share our vision on how to achieve the goal of a broad, open and inclusive centre of excellence, with parties that might be interested to become involved. It consists of 3 main elements: 1) An outline of the gap between the existing IA knowledge reservoir and its actual use in IA for SD; 2) Our vision on a future Community of Practice (CoP) that can address this gap; and 3) The implementation of this vision. A key element in the vision is the Charter itself: a document capturing the key notions and guiding principles among IA knowledge workers (researchers and practitioners) on the question how to improve IA research for SD.

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