2016
DOI: 10.1016/j.envsoft.2016.05.008
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Multi-factor, multi-state, multi-model scenarios: Exploring food and climate futures for Southeast Asia

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Cited by 56 publications
(42 citation statements)
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“…In principle, underlying variables of demographic and economic development are correlated across regions (fertility rate, mortality, investment, technological adoption), however, some regional deviations are plausible, as political context also strongly influences the evolution of such variables. For easier comparability of the direct impacts of the regional drivers, we intentionally varied only the West Africa parameters, changing other parameters over time but keeping them constant across the scenarios for other regions of the world, similar to what was done in the CCAFS Southeast Asian Scenarios (Mason-D’Croz et al, 2016). The rest of the world follows the SSP2 population and economic development trajectory where, by 2050, the global population reaches 9.2 billion people (Kc and Lutz, 2014) and global average GDP per capita doubles to around 16,000 USD (Dellink et al, 2015).…”
Section: Resultsmentioning
confidence: 99%
“…In principle, underlying variables of demographic and economic development are correlated across regions (fertility rate, mortality, investment, technological adoption), however, some regional deviations are plausible, as political context also strongly influences the evolution of such variables. For easier comparability of the direct impacts of the regional drivers, we intentionally varied only the West Africa parameters, changing other parameters over time but keeping them constant across the scenarios for other regions of the world, similar to what was done in the CCAFS Southeast Asian Scenarios (Mason-D’Croz et al, 2016). The rest of the world follows the SSP2 population and economic development trajectory where, by 2050, the global population reaches 9.2 billion people (Kc and Lutz, 2014) and global average GDP per capita doubles to around 16,000 USD (Dellink et al, 2015).…”
Section: Resultsmentioning
confidence: 99%
“…Components of the modeling framework ( Figure 1) include the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT; Robinson et al, 2015), the spatially explicit land use model LandSHIFT , and two empirical models for the analysis of land use change effects on biodiversity (Alkemade et al, 2009(Alkemade et al, , 2013Biggs et al, 2008;Jenkins et al, 2013;Scholes & Biggs, 2005) and carbon stock changes in soils and vegetation (European Commission, 2010;European Parliament and Council, 2009;IPCC, 2006;JRC, 2010;Ruesch & Gibbs, 2008). This type of IMPACT-LandSHIFT model 10.1029/2019EF001287 Earth's Future coupling was already successfully implemented and applied for other scenario studies in Southeast Asia and East Africa (e.g., Mason-D'Croz et al, 2016;van Soesbergen et al, 2017). Both models are driven by exogenous climate and socioeconomic scenario data.…”
Section: Modeling Frameworkmentioning
confidence: 99%
“…Experienced shortcomings of qualitative approaches were the poor spatial explicitness and the difficulties of addressing climate change adequately, which hampers the qualitative assessment of management options. Similar to other studies, a difficulty in dealing with nonenvironmental drivers or factors such as governance was their translation from narratives into model input due to the required reduction in complexity (Walz et al 2007, Mason-D'Croz et al 2016) and the lack of time-series data to calibrate and validate the models in this regard.…”
Section: Implementing the Research Challenges In The Scenario Approachesmentioning
confidence: 97%