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Cornelis P. Balde

Researcher at United Nations University

Publications -  24
Citations -  2004

Cornelis P. Balde is an academic researcher from United Nations University. The author has contributed to research in topics: Hydrogen storage & Extended X-ray absorption fine structure. The author has an hindex of 12, co-authored 18 publications receiving 1633 citations. Previous affiliations of Cornelis P. Balde include Utrecht University & Statistics Netherlands.

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The Global E-waste Monitor 2014: Quantities, flows and resources

TL;DR: In this article, the authors present the first comprehensive assessment of e-waste volumes, their corresponding impacts and management status on a global scale using an internationally-adopted measuring framework that has been developed by the Partnership on Measuring ICT for Development.

The Global E-waste Monitor 2020: Quantities, flows and the circular economy potential

TL;DR: In 2019, the world generated a striking 536 Mt of e-waste, an average of 73 kg per capita, an increase of 92 Mt since 2014 and is projected to grow to 747 Mt by 2030 as discussed by the authors.

The Global E-waste Monitor 2017: Quantities, Flows and Resources

TL;DR: The most comprehensive overview of global e-waste statistics following the guidelines that were developed by the Partnership on Measuring ICT for Development is provided in this article. But the authors do not provide a detailed analysis of these guidelines.
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Sodium alanate nanoparticles--linking size to hydrogen storage properties.

TL;DR: By wet-chemical synthesis, carbon nanofiber-supported NaAlH 4 is prepared with discrete particle size ranges of 1-10 microm, 19-30 nm, and 2-10 nm, which may guide hydrogen storage research for a wide range of nanostructured light (metal) hydrides.
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Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

TL;DR: An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed, which links all three pillars in IOA to construct mathematical relationships between various data points to increase the reliability of e-waste estimates compared to the approach without data processing.