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DTSTART:20190331T010000
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DTSTART:20191027T010000
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DTSTART;TZID=Europe/Helsinki:20190128T121500
DTEND;TZID=Europe/Helsinki:20190128T133000
DTSTAMP:20260825T002319
CREATED:20190125T142741Z
LAST-MODIFIED:20190125T142741Z
UID:12194-1548677700-1548682200@crest.science
SUMMARY:Mehdi BENATIYA ANDALOUSSI (Columbia University) - "Clearing the air: The role of technology adoption in the electricity generation sector" Polytechnique Recruitment
DESCRIPTION:Time: 12:15 pm – 1:30 pm\nDate: JANUARY 28\, 2019\nPlace: Room 3001\nMehdi BENATIYA ANDALOUSSI (Columbia University) – “Clearing the air: The role of technology adoption in the electricity generation sector” Polytechnique Recruitment\nAbstract:\nBetween 2005 and 2014\, the US electricity generation sector achieved unprecedented reductions in emissions of local air pollutants. This paper seeks to quantitatively uncover the factors that drove these sharp decreases in emissions and their connections to the cap-and-trade markets introduced by the Clean Air Interstate Rule. To that end\, I assemble a comprehensive dataset on power plant operations and costs. In a statistical decomposition of emission reductions at the power plant level\, I find that the adoption of capital-intensive abatement technologies constituted the primary factor influencing the emission reductions\, accounting for over 50% of the achieved reductions. Switching to cleaner fuel inputs and retiring dirty units also each contributed approximately 20% of the observed reductions. I further demonstrate that the costs incurred due to the adoption of abatement technologies amounted to $45 billion\, exceeding ex ante projections. I find that\, despite the high costs incurred by power plants\, these emission reductions generated net benefits to society. I estimate the health impacts of these emission reductions using novel satellite data to generate spatially continuous pollution measurements that I link to demographic information. A lower-bound estimate of the corresponding health impacts suggests that 19\,000 premature infant deaths were avoided during the period considered thanks to the achieved emission reductions. Finally\, I estimate the local demand for clean air by studying the impacts of power plant emission reductions on local housing markets. Matching micro-level housing transactions from a proprietary dataset to power plant locations\, I find that the emission reductions caused housing prices to increase in cleaned areas\, thereby appreciating house values by $8 billion. \n
URL:https://crest.science/event/mehdi-benatiya-andaloussi-columbia-university-clearing-the-air-the-role-of-technology-adoption-in-the-electricity-generation-sector-polytechnique-recruitment/
LOCATION:3001
CATEGORIES:Economics
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DTSTART;TZID=Europe/Paris:20190128T140000
DTEND;TZID=Europe/Paris:20190128T151500
DTSTAMP:20260825T002319
CREATED:20190110T130516Z
LAST-MODIFIED:20190110T130516Z
UID:12176-1548684000-1548688500@crest.science
SUMMARY:Mohamed NDAOUD (CREST) - "Interplay of minimax estimation and minimax support recovery under sparsity"
DESCRIPTION:\nThe Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 28 th of January 2019\nPlace: Room 1003. ⚠ salle 1003 !\nMohamed NDAOUD (CREST) – “Interplay of minimax estimation and minimax support recovery under sparsity” \nAbstract: We introduce the notion of scaled minimaxity for sparse estimation in high-dimensional linear regression model. Fixing the scale of the signal-to-noise ratio\, we prove that the estimation error can be much smaller than the global minimax error. Taking advantage of the interplay between estimation and support recovery we achieve optimal performance for both problems simultaneously under orthogonal designs. We also construct a new optimal estimator for scaled minimax sparse estimation. Sharp results for the classical minimax risk are recovered as a consequence of our study. For general designs\, we introduce a new framework based on algorithmic regularization where previous sharp results hold. Our analysis bridges the gap between optimization and statistical accuracy. The procedure we present achieves optimal statistical error faster than\, for instance\, classical algorithms for the Lasso. As a consequence\, we present a new iterative algorithm for high-dimensional linear regression that is scaled minimax optimal\, fast and adaptive. \nOrganizers:\nCristina BUTUCEA\, Alexandre TSYBAKOV\, Julie JOSSE\, Eric MOULINES\, Mathieu ROSENBAUM\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/jamal-najim-cnrs-upem-tba-2-2-3-5-2-2-2-2-2-2-3-2-2-2/
CATEGORIES:Statistics
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