Roberts, Emma and Spruce, Jonathan (2019) ‘In-Progress Reporting: Development of China’s Creative Economy Through Participatory Design Research in Post-Industrial Regeneration’. In: International Association of Societies of Design Research Conference 2019 (IASDR 2019), 02 September 2019 - 05 September 2019, Manchester, UK.
|
Available under License In Copyright. Download (400kB) | Preview |
Abstract
This paper reports on work-in-progress as part of a project funded in October 2018 by the U.K. Arts and Humanities Research Council and Newton Fund. It details the project’s early activities following the establishment of a multi-disciplinary research network of British and Chinese academics pursuing research through engaging with cultural organisations, commercial stakeholders and other constituent groups in Europe and China. The investigation focuses on generating alternative strategies for sustainable urban renewal of China's post-industrial areas. In the search for new drivers of growth, China is moving from a model of expansion, to one of revitalising its urban areas. This is as a result of the Chinese government’s recognition of the increasing importance of the creative industries in China in stimulating the country’s future economic growth. Since October 2018 the UK-China research team have worked on preliminary research activities that will become the foundation for the later stages of the project. The research has focused on the three Chinese cities of Shenyang, Dalian, and Wushan. each of these areas offers particular histories, social demographics, economic characteristics, and cultural identities that afford different opportunities for regeneration though a diversity of creative economy activities. Post-industrial sites within each area have been identified for potential re-generation and provide a focus for the investigations in each location. Although the project is still in its early stages, this paper defines the context for the project and documents the initial findings.
Impact and Reach
Statistics
Additional statistics for this dataset are available via IRStats2.