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Analysing interactions among Sustainable Development Goals with Integrated Assessment Models

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TLDR
In this article, the authors compare the key interactions identified among the SDGs in an expert survey, with their current and planned representation in models as identified in a survey among modellers.
Abstract
To achieve all Sustainable Development Goals (SDGs) by 2030, it is necessary to understand how they interact with each other. Integrated Assessment Models (IAMs) represent many human–environment interactions and can inform policymakers about the synergies and trade-offs involved in meeting multiple goals simultaneously. We analyse how IAMs, originally developed to study interactions among energy, the economy, climate, and land, can contribute to a wider analysis of the SDGs in order to inform integrated policies. We compare the key interactions identified among the SDGs in an expert survey, with their current and planned representation in models as identified in a survey among modellers. We also use text mining to reveal past practices by extracting the themes discussed in the IAM literature, linking them to the SDGs, and identifying the interactions among them, thus corroborating our previous results. This combination of methods allowed us to discuss the role of modelling in informing policy coherence and stimulate discussions on future research. The analysis shows that IAMs cover the SDGs related to climate because of their design. It also shows that most IAMs cover several other areas that are related to resource use and the Earth system as well. Some other dimensions of the 2030 Agenda are also covered, but socio-political and equality goals, and others related to human development and governance, are not well represented. Some of these are difficult to capture in models. Therefore, it is necessary to facilitate a better representation of heterogeneity (greater geographical and sectoral detail) by using different types of models (e.g. national and global) and linking different disciplines (especially social sciences) together. Planned developments include increased coverage of human development goals and contribute to policy coherence.

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Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review

TL;DR: In this paper, the authors provide a first-of-its-kind overview on the SDGs and their nexus with digitalization, while unraveling policy implications and future research directions.
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Policy coherence to achieve the SDGs : using integrated simulation models to assess effective policies (SPECIAL FEATURE : Sustainability Science and Implementing the Sustainable Development Goals)

TL;DR: This paper presents and demonstrates the use of the new System Dynamics based iSDG family of models, and uses a national model for Tanzania to analyse impacts of substantial investments in photovoltaic capacity on three SDGs.
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Articulating the effect of food systems innovation on the Sustainable Development Goals

TL;DR: Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging.
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Cleaner production for achieving the sustainable development goals

TL;DR: The authors of this Virtual Special Issue as mentioned in this paper sought to introduce first-hand knowledge and discussions identifying and highlighting the ways cleaner production can contribute to reach the United Nations sustainable development targets, and provided an overview of the recent trends that cleaner production practitioners/researchers are following in the chase of the sustainable development goals.
References
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Transforming our world : The 2030 Agenda for Sustainable Development

Un Desa
TL;DR: The Scoping meeting on collaboration between Regional Seas Programmes and Regional Fisheries Bodies in the Southwest Indian Ocean is described in this article, where the authors propose a framework for collaboration between regional sea programmes and regional fisheries bodies in the Indian Ocean.
Journal ArticleDOI

Learning the parts of objects by non-negative matrix factorization

TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.

Learning parts of objects by non-negative matrix factorization

D. D. Lee
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
Proceedings Article

Algorithms for Non-negative Matrix Factorization

TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
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