Adapting global shared socio-economic pathways for national and local scenarios

Frame, B., Lawrence, J., Ausseil, A.-G., Reisinger, A., & Daigneault, A. (2018). Adapting global shared socio-economic pathways for national and local scenarios. Climate Risk Management. 


Socio-economic scenarios enable us to understand the extent to which global-, national- and local-scale societal developments can influence the nature and severity of climate changerisks and response options. Shared socio-economic pathways (SSPs) enable a systematic exploration of the challenges to adaptation and mitigation that alternative futures entail. However, SSPs are primarily defined for the global scale. If countries are to test their adaptation and mitigation options for robustness across plausible future socio-economic conditions, then SSPs require country-relevant detail to understand climate change risks at the national and local scales. New Zealand is used to illustrate how nationally relevant socio-economic scenarios, nested within SSPs can be developed to inform national- and local-scale studies of climate change impacts and their implications. Shared policy assumptions were developed, involving a mix of climate-specific and non-climate-specific policies, to demonstrate how international links and global-scale developments are critical locally—local choices may accelerate, reduce or even negate the impact of global trends for extended periods. The typology was then ‘tested’ by applying it in a local context. The research challenges observed in developing credible, salient and legitimate national-scale socio-economic scenarios include issues in developing scenarios across a multidisciplinary team. Finally, recommendations for adapting shared climate policy assumptions to produce national and local scenarios, and for assessing the feasibility and effectiveness of climate change adaptation options are presented. These include the need for: guidelines to embed national scenarios in global frameworks; a limit the number of plausible futures; inter-operability of models; an ability to work towards effective multi-disciplinary teams and integrative research; and the opportunity to involve participatory processes where feasible.

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