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    Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies?
    (Basingstoke, Hampshire : Palgrave Macmillan, 2019) Kroll, Christian; Warchold, Anne; Pradhan, Prajal
    The Agenda 2030 with its 17 Sustainable Development Goals (SDGs) provides the framework that all United Nations (UN) member states have pledged to fulfill. The achievement of this agenda crucially depends on whether humankind will be able to maximize synergies and resolve existing trade-offs between the SDGs. We provide the first analysis of future interactions for projected SDG trends until 2030 within and between goals, and we analyze how trade-offs and synergies have evolved in the recent past globally. For certain goals, we find positive developments with notable synergies in our projections, especially for SDGs 1, 3, 7, 8, and 9: Poverty alleviation and strengthening the economy, rooted in innovation, and modern infrastructure, therefore continue to be the basis upon which many of the other SDGs can be achieved. However, especially SDGs 11, 13, 14, 16, and 17 will continue to have notable trade-offs, as well as non-associations with the other goals in the future, which emphasizes the need to foster innovations and policies that can make our cities and communities more sustainable, as well as strengthen institutions and spur climate action. We show examples of a successful transformation of trade-offs into synergies that should be emulated in other areas to create a virtuous cycle of SDG progress. The alarming inability to overcome certain persistent trade-offs we have found, and indeed the deterioration for some SDGs, can seriously threaten the achievement of the Agenda 2030.
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    A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon
    (Göttingen : Copernicus GmbH, 2017) Müller-Hansen, F.; Cardoso, M.F.; Dalla-Nora, E.L.; Donges, J.F.; Heitzig, J.; Kurths, J.; Thonicke, K.
    Changes in land-use systems in tropical regions, including deforestation, are a key challenge for global sustainability because of their huge impacts on green-house gas emissions, local climate and biodiversity. However, the dynamics of land-use and land-cover change in regions of frontier expansion such as the Brazilian Amazon are not yet well understood because of the complex interplay of ecological and socioeconomic drivers. In this paper, we combine Markov chain analysis and complex network methods to identify regimes of land-cover dynamics from land-cover maps (TerraClass) derived from high-resolution (30ĝ€m) satellite imagery. We estimate regional transition probabilities between different land-cover types and use clustering analysis and community detection algorithms on similarity networks to explore patterns of dominant land-cover transitions. We find that land-cover transition probabilities in the Brazilian Amazon are heterogeneous in space, and adjacent subregions tend to be assigned to the same clusters. When focusing on transitions from single land-cover types, we uncover patterns that reflect major regional differences in land-cover dynamics. Our method is able to summarize regional patterns and thus complements studies performed at the local scale.