[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123646-en":3,"doc-seo-123646-105":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},123646,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","A Causal Discovery Approach to Learn How Urban Form Shapes Sustainable Mobility Across Continents","Global sustainability depends on low-carbon urban transport shaped by appropriate infrastructure, the deployment of low-carbon travel modes, and shifts in travel behavior. Implementing infrastructure changes requires location-specific understanding of cause-and-effect links between the built environment and mobility. Current studies inadequately represent causal relations among “6D” urban form variables, generalize across regions, and model effects at high spatial resolution. This work applies causal discovery and explainable machine learning to detect urban form effects on intra-city travel using high-resolution mobility data from six cities on three continents, identifying key determinants of travel-related emissions.","A CAUSAL DISCOVERY APPROACH TO LEARN HOW URBAN FORM SHAPES SUSTAINABLE MOBILITY ACROSS CONTINENTS  \nFelix Wagner1,2.*, Florian Nachtigall1,2, Lukas Franken3, Nikola Milojevic-Dupont1,2, Rafael H.M. Pereira4, Nicolas Koch1, Jakob Runge2,5, Marta Gonzalez6, Felix Creutzig1,2  \n1Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany 2Technical University Berlin, Berlin, Germany 3School of Engineering, The University of Edinburgh, Edinburgh, UK 4Institute for Applied Economic Research Brasília, Brazil 5Institute of Data Science German Aerospace Center, Jena, Germany 6Department of Civil and Environmental Engineering, UC Berkeley, California, US  \n∗To whom correspondence should be addressed. E-mail: wagner[at][mcc-berlin.net](mcc-berlin.net)  \nAugust 31, 2023  \nABSTRACT  \nGlobal sustainability requires low-carbon urban transport systems, shaped by adequate infrastructure, deployment of low-carbon transport modes and shifts in travel behavior. To adequately implement alterations in infrastructure, it’s essential to grasp the location-specific cause-and-effect mechanisms that the constructed environment has on travel. Yet, current research falls short in representing causal relationships between the \"6D\" urban form variables and travel, generalizing across different regions, and modeling urban form effects at high spatial resolution. Here, we address all three gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel based on high-resolution mobility data of six cities across three continents. We show that both distance to city center, demographics and density indirectly affect other urban form features. By considering the causal relationships, we find that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Differences in urban form effects across the cities call for a more holistic definition of 6D measures. Our work is a starting point for location-specific analysis of urban form effects on mobility behavior using causal discovery approaches, which is highly relevant for city planners and municipalities across continents.  \n1 Introduction and Background  \nCities are currently responsible for 70% of the world’s carbon emissions [1] and by 2050 nearly 70% of the earth’s population will live in urban areas [2] . Thus they have a pivotal role in defining how humanity will respond to the climate crisis. The largest CO2 emitter in cities next to the building sector is urban transport, being responsible for 3 Gt CO2-eq per year [3] . For sustainable urban transport, physical infrastructure is widely accepted to be a key leverage point, with stronger influence than personal or social factors [4] . To combat these grand challenges, governments around the globe are heavily investing in redesigning urban infrastructure, as demonstrated, for example, by the $ 1.2 trillion Bipartisan Infrastructure Bill, signed by the president of the United States Joe Biden in 2021 [5] . However, to effectively implement necessary changes, it is important to determine which infrastructure investments will be most effective where. Deriving these insights is challenging as random control trials are hardly feasible in complex socio-environmental contexts, such as cities. Fortunately, novel causal discovery and inference methods allow to approximate these cause and effect relationships from purely observational data [6] . For planning infrastructure interventions this is particularly relevant, due to the longevity and resulting lock-in effects of urban planning decisions.  \nPrevious studies have shown that compact urban development is associated with shorter vehicle kilometer traveled (VKT) which significantly contributes to lower trav","cbCaisxkMFhf8qj3","https://ap.wps.com/l/cbCaisxkMFhf8qj3","pdf",1975975,1,22,"English","en",105,"# Introduction and Background\n## Urban emissions and the role of urban transport\n## Compact development and the 6Ds framework\n## Limitations in existing research on causal mechanisms\n## Prior causal and graph-discovery studies","[{\"question\":\"Why is causal discovery important for understanding urban transport emissions?\",\"answer\":\"Urban planning decisions operate in complex socio-environmental systems where randomized control trials are rarely feasible. Causal discovery methods help infer cause-and-effect mechanisms from observational data to better target infrastructure investments.\"},{\"question\":\"What is the role of the “6D” urban form variables in this study?\",\"answer\":\"The study focuses on causal relationships among the “6D” variables that describe aspects of compact development. It addresses gaps where prior research often generalized beyond regions and did not model these relationships at high spatial resolution.\"},{\"question\":\"What urban form characteristics most strongly determine travel-related emissions?\",\"answer\":\"Across the analyzed cities, the spread of the city and the coverage of jobs across the city emerge as the strongest determinants of travel-related emissions. Results also support benefits of compact development.\"}]","A Causal Discovery Approach to Learn How Urban Form Shapes Sustainable Mobility Across Continents | PDF",1785817821,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-causal-discovery-approach-to-learn-how-urban-form-shapes-sustainable-mobility-across-continents","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-causal-discovery-approach-to-learn-how-urban-form-shapes-sustainable-mobility-across-continents/123646/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is causal discovery important for understanding urban transport emissions?","Question",{"text":75,"@type":76},"Urban planning decisions operate in complex socio-environmental systems where randomized control trials are rarely feasible. Causal discovery methods help infer cause-and-effect mechanisms from observational data to better target infrastructure investments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of the “6D” urban form variables in this study?",{"text":80,"@type":76},"The study focuses on causal relationships among the “6D” variables that describe aspects of compact development. It addresses gaps where prior research often generalized beyond regions and did not model these relationships at high spatial resolution.",{"name":82,"@type":73,"acceptedAnswer":83},"What urban form characteristics most strongly determine travel-related emissions?",{"text":84,"@type":76},"Across the analyzed cities, the spread of the city and the coverage of jobs across the city emerge as the strongest determinants of travel-related emissions. Results also support benefits of compact development.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]