[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118633-en":3,"doc-seo-118633-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118633,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Understanding Climate Legislation Decisions with Machine Learning","Effective climate action depends on rapidly adopting climate-positive legislation that supports both mitigation and adaptation. Legislation can have far-reaching effects through interactions between climate policy, technology, and market forces. Current approaches search for policy levers and obstacles, motivating the use of recent machine learning language models. The work proposes an NLP/ML pipeline to analyze climate legislation text, classify legislation, and predict policy voting outcomes. Explainability and decision modeling improve transparency for advocates and decision makers.","Clark, J. , Wan, M. , & Santos-Rodriguez, R. (2023) . Understanding Climate Legislation Decisions with Machine Learning. Paper presented at Tackling Climate Change with Machine Learning:  \nworkshop at NeurIPS 2023, New Orleans, Louisiana, United States.  \nPeer reviewed version  \nLink to publication record on the Bristol Research Portal  \nPDF-document  \nUniversity of Bristol – Bristol Research Portal  \nGeneral rights  \nThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: [http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/](http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/)  \nUnderstanding Climate Legislation Decisions with  \nMachine Learning  \nJeffrey N. Clark  \nUniversity of Bristol, UK [jeff.clark@bristol.ac.uk](jeff.clark@bristol.ac.uk)  \nMichelle W.L. Wan  \nUniversity of Bristol, UK [michelle.wan@bristol.ac.uk](michelle.wan@bristol.ac.uk)  \nRaúl Santos-Rodríguez  \nUniversity of Bristol, UK  \n[enrsr@bristol.ac.uk](enrsr@bristol.ac.uk)  \nAbstract  \nEffective action is crucial in order to avert climate disaster. Key in enacting change is the swift adoption of climate positive legislation which advocates for climate change mitigation and adaptation. This is because government legislation can result in far-reaching impact, due to the relationships between climate policy, technology, and market forces. To advocate for legislation, current strategies aim to identify potential levers and obstacles, presenting an opportunity for the application of recent advances in machine learning language models. Here we propose a machine learning pipeline to analyse climate legislation, aiming to investigate the feasibility of natural language processing for the classification of climate legislation texts, to predict policy voting outcomes. By providing a model of the decision making process, the proposed pipeline can enhance transparency and aid policy advocatesand decision makers in understanding legislative decisions, thereby providing a tool to monitor and understand legislative decisions towards climate positive impact.  \n1 Introduction  \nLegislation is a key lever in fighting climate change. While climate policies lay out a plan of action, climate legislation is enforceable law. Legislation therefore has the potential to both help and hinder climate-positive progress, impacting societal change, public infrastructure, and industry.  \nDespite recent commitments, current climate change mitigation efforts remain insufficient [11] . Existing solutions to fight climate change include low-carbon energy generation, tightening emissions regulations, limiting the destruction of carbon sequestering environments, and reducing agricultural emissions; these could halve global emissions, yet remain largely underutilised [12] . In large part, this is an issue of political will and legislative implementation [5] . However, history has shown that national efforts can be rapidly mobilised, such as during World War II, or the COVID-19 pandemic. Political decisions could therefore also rapidly utilise these existing solutions, and accelerate the development and deployment of new technologies, to fight climate change.  \nImproved transparency around legislative decisions can aid decision makers in progress towards climate change mitigation and adaptation. To this end, policy advocates already attempt to keep track of policy approvals and their driving factors. However, understanding and predicting legislative decisions is highly complex. Machine learning offers techniques towards achieving this, through analysis, modelling, and explaining these decisions.  \nRecent advances in machine learning have enabled the development of sophisticated tools to help tackle climate change [14], in domains from urban planning [10] to precision agriculture [9] . Of  \nTackling Climate Change with Machine Learning: worksho","cbCaivLjK8fjbOZx","https://ap.wps.com/l/cbCaivLjK8fjbOZx","pdf",264331,1,6,"English","en",105,"# Abstract\n# Introduction\n## Legislative role in climate action\n## Machine learning for transparency and prediction\n## NLP and large language models for climate policy\n# Proposed machine learning pipeline\n## Preprocessing and feature generation\n## Voting outcome prediction\n## Explainability techniques","[{\"question\":\"What is the main goal of the proposed machine learning pipeline?\",\"answer\":\"The pipeline aims to analyze climate legislation texts and assess the feasibility of NLP for classifying legislation while predicting policy voting outcomes.\"},{\"question\":\"How does the approach support transparency in legislative decision-making?\",\"answer\":\"By applying explainability techniques to model outputs, the method helps humans interpret predictions and understand drivers behind voting decisions.\"},{\"question\":\"Which model types are referenced for voting outcome prediction?\",\"answer\":\"The document describes fine-tuning a pre-trained ClimateBERT large language model for legislation classification and voting outcome prediction.\"}]","Understanding Climate Legislation Decisions with Machine Learning | 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is the main goal of the proposed machine learning pipeline?","Question",{"text":75,"@type":76},"The pipeline aims to analyze climate legislation texts and assess the feasibility of NLP for classifying legislation while predicting policy voting outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach support transparency in legislative decision-making?",{"text":80,"@type":76},"By applying explainability techniques to model outputs, the method helps humans interpret predictions and understand drivers behind voting decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model types are referenced for voting outcome prediction?",{"text":84,"@type":76},"The document describes fine-tuning a pre-trained ClimateBERT large language model for legislation classification and voting outcome 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