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Cosme, I; Santos, R; O'Neill, DW (2017)
Publisher: Elsevier
Languages: English
Types: Article
Debates around ecological and social limits to economic growth and new ways to deal with resource scarcity without compromising human wellbeing have re-emerged in the last few years, especially with the increasing calls for a degrowth approach. In this paper, a framework is developed to support a systematic analysis of degrowth in the selected academic literature. This article attempts to present a clearer notion of what degrowth academic literature has been exploring, by identifying, organizing and analysing a set of proposals for action retrieved from a selection of articles. The framework is applied to classify these proposals according to their alignment to ecological economics policy objectives (sustainable scale, fair distribution, and efficient allocation), type of approach (top-down versus bottom-up), and geographical focus (local, national, or international). A total of 128 peer-reviewed articles focused on degrowth were reviewed, and 54 that include proposals for action were analysed. The proposals identified align with three broad goals: (1) Reduce the environmental impact of human activities; (2) Redistribute income and wealth both within and between countries; and (3) Promote the transition from a materialistic to a convivial and participatory society. The findings indicate that the majority of degrowth proposals are national top-down approaches, focusing on government as a major driver of change, rather than local bottom-up approaches, as advocated by many degrowth proponents. The most emphasised aspects in the degrowth literature are related to social equity, closely followed by environmental sustainability. Topics such as population growth and the implications of degrowth for developing nations are largely neglected, and represent an important area for future research. Moreover, there is a need for a deeper analysis of how degrowth proposals would act in combination.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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