Despite substantial conservation efforts, the loss of ecosystems continues globally, along with related declines in species and nature's contributions to people. An effective ecosystem goal, supported by clear milestones, targets and indicators, is urgently needed for the post-2020 global biodiversity framework and beyond to support biodiversity conservation, the UN Sustainable Development Goals and efforts to abate climate change. Here, we describe the scientific foundations for an ecosystem goal and milestones, founded on a theory of change, and review available indicators to measure progress. An ecosystem goal should include three core components: area, integrity and risk of collapse. Targets-the actions that are necessary for the goals to be met-should address the pathways to ecosystem loss and recovery, including safeguarding remnants of threatened ecosystems, restoring their area and integrity to reduce risk of collapse and retaining intact areas. Multiple indicators are needed to capture the different dimensions of ecosystem area, integrity and risk of collapse across all ecosystem types, and should be selected for their fitness for purpose and relevance to goal components. Science-based goals, supported by well-formulated action targets and fit-for-purpose indicators, will provide the best foundation for reversing biodiversity loss and sustaining human well-being.

/pdf/scientific-foundations-for-an-ecosystem-goal-milestones-and-36njgka4gt.pdf

Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework.

Cet ouvrage est publié sous la responsabilité du Secrétaire général de l'OCDE et du Président de la CUA.Les opinions et les arguments exprimés ici ne reflètent pas nécessairement les vues officielles des Membres de l'OCDE ou de son Centre de développement, ou des pays membres de la Commission de l'Union africaine.Ce document, ainsi que les données et cartes qu'il peut comprendre, sont sans préjudice du statut de tout territoire, de la souveraineté s'exerçant sur ce dernier, du tracé des frontières et limites internationales, et du nom de tout territoire, ville ou région.Les noms des pays et territoires utilisés dans cette publication conjointe suivent la pratique de l'Union africaine.Avertissements territoriaux spécifiques applicables à l'OCDE : Note de la Turquie Les informations figurant dans ce document qui font référence à « Chypre » concernent la partie méridionale de l'Ile.Il n'y a pas d'autorité unique représentant à la fois les Chypriotes turcs et grecs sur l'Ile.La Turquie reconnaît la République Turque de Chypre Nord (RTCN).Jusqu'à ce qu'une solution durable et équitable soit trouvée dans le cadre des Nations Unies, la Turquie maintiendra sa position sur la « question chypriote ».Note de tous les États de l'Union européenne membres de l'OCDE et de l'Union européenne La République de Chypre est reconnue par tous les membres des Nations Unies sauf la Turquie.Les informations figurant dans ce document concernent la zone sous le contrôle effectif du gouvernement de la République de Chypre. 

Valeur totale de la participation en amont et en aval aux CVM par secteur en Afrique de l’Ouest, millions USD, 2015

Flux financiers entrants en Afrique du Nord en pourcentage du PIB, 2000-19

This chapter identifies priority policies to facilitate regional value chains in the context of the COVID-19 pandemic and the African Continental Free Trade Area (AfCFTA). First, it reviews past efforts at continental and regional levels to develop regional value chains and highlights key lessons for implementing the AfCFTA. Second, it offers recommendations on how African policy makers can work with the private sector to accelerate digital adoption and reduce the costs of cross-border trading and production. Third, the chapter reviews public policies to create stronger linkages within African industrial networks. It focuses on policies related to skills development, public procurement and investment. 

Strengthening regional value chains in the African Continental Free Trade Area

Central Africa’s most important trade partners broken down by volume of trade in intermediate, consumption, and capital goods

Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world’s second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests. A large dataset of 6 million trees from 193 taxa is used to map the floristic and functional composition of central African forests and predict their vulnerability to climate change.

Unveiling African rainforest composition and vulnerability to global change

We address the component-based regularization of a multivariate Generalized Linear Mixed Model (GLMM) in the framework of grouped data. A set Y of random responses is modelled with a multivariate G...

Component-Based Regularization of Multivariate Generalized Linear Mixed Models

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Unveiling African rainforest composition and vulnerability to global change

Component-Based Regularization of Multivariate Generalized Linear Mixed Models