How can AI support decision-making in the assessment of sustainable forest investment projects?<\/p>\n\n\n\n
This is what we\u2019re looking into in our new research paper, \u201cUsing Large Language Models for the Assessment of Sustainable Forest Investment Projects,\u201d co-authored by <\/a>Maria Letizia Jannibelli<\/a>, Jiayu Luo and <\/a>Kilian Sprenkamp<\/a>. Kilian will present it at HICSS 2025 in January.<\/p>\n\n\n\n The paper explores how LLMs can support the assessment of forest investment projects, which are crucial role in addressing climate change and biodiversity loss.<\/p>\n\n\n\n Key highlights: Thanks for the fruitful collaboration, Xilva<\/a>, <\/a>Tim Duehrkoop<\/a>, <\/a>Yeray Mart\u00ednez Montesdeoca<\/a>, <\/a>Jamie Micah Lawrence<\/a>, <\/a>N\u00faria Garcia-Forner<\/a> \u2013 hope we can continue! \ud83c\udf31\ud83c\udf0d \u2013 and Innosuisse<\/a> for the Innocheque funding.<\/p>\n","protected":false},"excerpt":{"rendered":" How can AI support decision-making in the assessment of sustainable forest investment projects? This is what we\u2019re looking into in our new research paper, \u201cUsing Large Language Models for the Assessment … <\/p>\n","protected":false},"author":1002891,"featured_media":399,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"","_seopress_redirections_param":"","_seopress_redirections_type":0,"_seopress_analysis_target_kw":"","_seopress_news_disabled":"","_seopress_video_disabled":"","_seopress_video":[],"_seopress_pro_schemas_manual":[],"_seopress_pro_rich_snippets_disable_all":"","_seopress_pro_rich_snippets_disable":[],"_seopress_pro_schemas":[],"footnotes":""},"categories":[1],"tags":[],"class_list":["post-398","post","type-post","status-publish","format-standard","has-post-thumbnail","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/posts\/398","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/users\/1002891"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/comments?post=398"}],"version-history":[{"count":1,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/posts\/398\/revisions"}],"predecessor-version":[{"id":400,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/posts\/398\/revisions\/400"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/media\/399"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/media?parent=398"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/categories?post=398"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.unil.ch\/dino\/wp-json\/wp\/v2\/tags?post=398"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u2014 We developed GRAIDY, which extracts and summarizes relevant data from complex project documents.
\u2014 A retrieval-augmented generation approach reduces errors and increases reliability.
\u2014 It offers potential to serve as a powerful assistant to human experts.<\/p>\n\n\n\n