Une th\u00e8se de la RAND School of Public Policy propose une nouvelle approche analytique pour int\u00e9grer de mani\u00e8re syst\u00e9matique la notion d\u2019\u00e9quit\u00e9 dans les mod\u00e8les quantitatifs d\u2019aide \u00e0 la d\u00e9cision en mati\u00e8re d\u2019environnement et de climat. Elle part du constat que les d\u00e9cisions environnementales et climatiques ont des effets importants en termes d\u2019\u00e9quit\u00e9, qui influencent le d\u00e9bat public ainsi que la l\u00e9gitimit\u00e9 et la p\u00e9rennit\u00e9 des politiques, alors que les mod\u00e8les utilis\u00e9s dans l\u2019appui \u00e0 la d\u00e9cision prennent rarement en compte l\u2019\u00e9quit\u00e9 de fa\u00e7on transparente et globale. Lorsqu\u2019ils le font, ils se limitent le plus souvent \u00e0 l\u2019\u00e9quit\u00e9 \u00ab distributionnelle \u00bb, au d\u00e9triment des dimensions contextuelles et proc\u00e9durales.<\/p>\n
La recherche d\u00e9veloppe une \u00ab architecture analytique \u00bb permettant d\u2019int\u00e9grer plusieurs formes d\u2019\u00e9quit\u00e9 dans ces mod\u00e8les, puis l\u2019applique \u00e0 l\u2019\u00e9laboration d\u2019un plan de fermeture progressive des 96 centrales \u00e9lectriques au gaz naturel de Californie, dans le cadre des objectifs environnementaux de cet \u00c9tat. Cette architecture mobilise un dispositif technique fond\u00e9 sur un grand mod\u00e8le de langage avec r\u00e9cup\u00e9ration de documents (retrieval-augmented), utilis\u00e9 pour extraire et synth\u00e9tiser, \u00e0 partir de la litt\u00e9rature, des jugements argument\u00e9s sur l\u2019importance d\u2019indicateurs et le type d\u2019\u00e9quit\u00e9 concern\u00e9. Les d\u00e9saccords entre mod\u00e8les sont conserv\u00e9s comme source d\u2019incertitude.<\/p>\n
Le mod\u00e8le d\u2019aide \u00e0 la d\u00e9cision ainsi construit \u00e9value les priorit\u00e9s de fermeture des centrales selon trois principes de justice \u2014 utilitariste, suffisanciariste et \u00e9galitariste \u2014 consid\u00e9r\u00e9s comme des proxys de diff\u00e9rentes valeurs et perspectives de parties prenantes. Les r\u00e9sultats montrent que la prise en compte \u00e9largie de l\u2019\u00e9quit\u00e9 produit des classements de priorit\u00e9s de fermeture sensiblement diff\u00e9rents de ceux obtenus lorsque l\u2019on ne consid\u00e8re que l\u2019\u00e9quit\u00e9 distributionnelle. Ils mettent \u00e9galement en \u00e9vidence que ces classements \u00ab inform\u00e9s par l\u2019\u00e9quit\u00e9 \u00bb permettent d\u2019identifier les d\u00e9cisions de fermeture relativement robustes \u00e0 la diversit\u00e9 des valeurs et points de vue, par opposition \u00e0 celles qui sont fortement sensibles \u00e0 ces divergences.<\/p>\n
Les convergences entre principes de justice sont les plus nettes en haut et en bas du classement des centrales \u00e0 fermer, et les divergences se concentrent au milieu du classement. Cette configuration permet de rep\u00e9rer les segments de d\u00e9cision o\u00f9 une d\u00e9lib\u00e9ration renforc\u00e9e avec les parties prenantes appara\u00eet la plus n\u00e9cessaire. L\u2019\u00e9tude, conduite dans le contexte californien, est produite dans le cadre de la s\u00e9rie de th\u00e8ses de la RAND School of Public Policy, institution doctorale sp\u00e9cialis\u00e9e en analyse de politiques publiques, int\u00e9gr\u00e9e \u00e0 RAND, organisme de recherche \u00e0 but non lucratif. RAND pr\u00e9cise que ses publications ne refl\u00e8tent pas n\u00e9cessairement les positions de ses clients et financeurs.(contenu r\u00e9sum\u00e9 en fran\u00e7ais par l’IA)\u00a0Lire la Suite<\/a><\/p>","protected":false},"excerpt":{"rendered":" Une th\u00e8se de la RAND School of Public Policy propose une nouvelle approche analytique pour int\u00e9grer de mani\u00e8re syst\u00e9matique la notion d\u2019\u00e9quit\u00e9 dans les mod\u00e8les quantitatifs d\u2019aide \u00e0 la d\u00e9cision […]<\/p>\n","protected":false},"author":1,"featured_media":10675,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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