An examination of the activity, outcomes and cost effectiveness of resource efficiency clusters, focusing on five case studies, secondary research and stakeholder/expert interviews. In the 2017 Clean Growth Strategy the Government committed to work towards a zero avoidable waste economy by 2050. This Strategy included an intention to explore the development of a network of resource efficiency clusters led by Local Enterprise Partnerships (LEPs). The Waste and Resources Action Programme (WRAP) commissioned Winning Moves to undertake research in line with this intention. WRAP had identified two major barriers to SME adoption of resource efficiency; capability and capacity. SMEs either did not have the knowledge, understanding and skills to adopt resource efficiency measures or did not have the time and resources to implement actions. It was hypothesised that these barriers could be addressed through resource efficiency clusters. The research aimed to: identify, recruit and conduct a detailed evaluation of both the activities and impacts of five resource efficiency clusters; understand what LEPs are doing to support resource efficiency; identify and compare different types of resource efficiency clusters and examine existing evidence about the impact of cluster activity.
Results and achievements
Although it was not possible to draw definitive conclusions about the most effective type or delivery model for resource efficiency clusters. The evidence suggests that:
- The creation of networks and links with waste processors and circular businesses is an important part of resource efficiency clusters.
- The industrial symbiosis model appears to be very effective and requires a cross sector approach.
- There may be a role for sector-based approaches, again this would probably require some impetus as it does not currently appear to be a high priority.
- IT solutions may be an important (and potentially cost saving) support for this resource efficiency clusters.
The findings of this analysis were published as a report, which can be found on the following link: