Publications

The livelihoods of 130 to 270 million people depend on artisanal mining. Artisanal mining is a labour-intensive, often illegal, extractive activity. We combine geological knowledge and a source of exogenous temporal variation to construct the first proxy for artisanal gold mining in Africa—the main form of artisanal mining. We establish that an increase in the potential value of artisanal mining is a significant driver of deforestation. The historical increase in the gold price accounts for 8% of forest loss across the continent and, within the subset of gold-suitable areas, 28 %. In parallel, artisanal mining increases local economic wealth and may provide an alternative livelihood should a weather shock jeopardise agricultural output. Finally, mining-induced deforestation seems rooted more in the direct clearing of trees for the activity than in indirect deforestation triggered by increased local demand.

This study analyzes the impact of international climate agreements-specifically the Kyoto Protocol-on the trade in environmental goods over the period 1997-2021. Using entropy balancing and a sample of 112 countries, we show that the Kyoto Protocol led to an overall increase in the trade of environmental goods among Annex B countries. During this period, the protocol notably stimulated imports, although it did not significantly boost exports prior to 2016. The analysis also highlights heterogeneities linked to countries' technological capacities, economic structures, and institutional characteristics.

This article has two primary objectives: 1) to propose a new parametric model of volatility and 2) to identify the days with the most significant volatility shocks, while exploring the events that triggered these shocks. We analyze financial volatility during the global financial crisis and the COVID-19 pandemic, utilizing high-frequency financial data and an improved model, which incorporates asymmetry and handles outliers, to estimate volatility and determine the timing of the largest shocks within the day. For instance, for the global financial crisis, major volatility shocks coincide with events such as the bankruptcy of Lehman Brothers and the failure of the Emergency Economic Stabilization Act. However, the largest shock occurred on February 27, 2007, which, despite coinciding with market events like a Chinese stock market crash and Freddie Mac's tighter subprime loan policy, was primarily caused by a computer glitch. Our analysis underscores the value of high-frequency data in modeling financial volatility and identifying the key events driving volatility shocks.

Le Parlement néerlandais a approuvé une réforme majeure des régimes de retraite professionnelle en juin 2023. Jusqu’alors, ces fonds de pension étaient gérés en capitalisation, mais garantissaient aux affiliés une prestation définie, analogue aux annuités des régimes de retraite fonctionnant en répartition. Bien que les règles de calcul des prestations aient été progressivement durcies pour les salariés, la garantie d’une pension certaine pesait sur les employeurs qui supportaient in fine les risques de placement financier et de longévité des affiliés. La réforme contraint les régimes de retraite professionnelle à transformer les fonds à prestations définies en fonds collectifs à cotisations définies (collective defined contributions – CDC) d’ici le 1er janvier 2028. Ses principaux objectifs visent à réduire les risques financiers pour les employeurs, à mieux les répartir entre les générations et à assurer la soutenabilité des régimes dans un contexte de vieillissement de la population et d’incertitude économique. Notre article se propose d’analyser cette réforme sous l’angle de l’équité intergénérationnelle, dont l’objectif est de répartir équitablement les risques de marché et de longévité entre actifs et retraités, pour éviter que les fluctuations économiques ou l’allongement de l’espérance de vie ne pèsent uniquement sur une seule génération, en particulier sur les jeunes.

This study leverages high-resolution remote sensing and data processing advanced techniques to improve ambient urban temperature monitoring. Using Landsat 8 and Sentinel-2A satellite imagery, the methodology combines spectral harmonization and correction techniques with Convolutional Neural Network (CNN)-based super-resolution models to achieve a high spatial accuracy in Land Surface Temperature (LST) and Air Temperature (Ta) estimations. The analysis integrates key environmental indices such as the Normalized difference vegetation, water, and built-area indices, and corrects for atmospheric and surface effects to refine LST data. Results demonstrate that CNN models improve temperature spatial detail significantly to a resolution of 1 m with an R2 above 0.85, and with estimations of aerial temperature optimized using Météo-France and validated against Météociel data, showing errors within 2°C. Regression models further estimate Ta from LST with R2 values above 0.75, effectively mapping temperature distributions at fine resolutions for urban settings. This study bridges critical gaps in remote-sensing based temperature monitoring, hence offering a framework for high-resolution urban thermal analysis in regions with limited meteorological data.