<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>5632</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-10-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A systematic map of machine learning in urban climate change mitigation</title>
    <abstract language="eng">To tackle the climate crisis, cities must reduce greenhouse gas (GHG) emissions rapidly. To aid these efforts, many cities are interested in leveraging artificial intelligence and machine learning (ML). Researchers and practitioners, however, only begin to understand how ML can contribute to achieving climate targets in urban contexts. To provide an overview of application areas, and the potential leverage of ML to reduce GHG emissions, we systematically map research conducted over the past three decades. We identify 1,206 relevant peer-reviewed records, and discover that research involving ML is expanding more rapidly than the literature on urban climate mitigation more broadly. The research focus largely aligns with urban mitigation options that the Intergovernmental Panel on Climate Change assessed as having high impact. We also find that research concentrates on the ML-strong regions Eastern Asia, Europe, and Northern America. This regional focus can influence research agendas, and we observed signs that this can lead to bias regarding which ML applications are pursued in urban climate action.</abstract>
    <identifier type="doi">10.21203/rs.3.rs-4242075/v1</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_doi_json">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:28:38Z","timestamp":1715214518785},"posted":{"date-parts":[[2024,5,8]]},"group-title":"In Review","reference-count":0,"publisher":"Research Square Platform LLC","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,4,9]]},"abstract":"&lt;title&gt;Abstract&lt;\/title&gt;\n        &lt;p&gt;To tackle the climate crisis, cities must reduce greenhouse gas (GHG) emissions rapidly. To aid these efforts, many cities are\ninterested in leveraging artificial intelligence and machine learning (ML). Researchers and practitioners, however, only begin to understand how ML can contribute to achieving climate targets in urban contexts. To provide an overview of application areas, and the potential leverage of ML to reduce GHG emissions, we systematically map research conducted over the past three\ndecades. We identify 1,206 relevant peer-reviewed records, and discover that research involving ML is expanding more rapidly\nthan the literature on urban climate mitigation more broadly. The research focus largely aligns with urban mitigation options\nthat the Intergovernmental Panel on Climate Change assessed as having high impact. We also find that research concentrates\non the ML-strong regions Eastern Asia, Europe, and Northern America. This regional focus can influence research agendas,\nand we observed signs that this can lead to bias regarding which ML applications are pursued in urban climate action.&lt;\/p&gt;","DOI":"10.21203\/rs.3.rs-4242075\/v1","type":"posted-content","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T05:18:19Z","timestamp":1715145499000},"source":"Crossref","is-referenced-by-count":0,"title":["A systematic map of machine learning in urban climate change mitigation"],"prefix":"10.21203","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2996-5976","authenticated-orcid":false,"given":"Marie Josefine","family":"Hintz","sequence":"first","affiliation":[{"name":"Technical University Berlin"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6641-1978","authenticated-orcid":false,"given":"Nikola","family":"Milojevic-Dupont","sequence":"additional","affiliation":[{"name":"Mercator Research Institute on Global Commons and Climate Change"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5710-3348","authenticated-orcid":false,"given":"Felix","family":"Creutzig","sequence":"additional","affiliation":[{"name":"Mercator Research Institute on Global Commons and Climate Change"}]},{"given":"Lynn","family":"Kaack","sequence":"additional","affiliation":[{"name":"Hertie School"}]}],"member":"8761","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-4242075\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-4242075\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T05:18:27Z","timestamp":1715145507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-4242075\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,8]]},"references-count":0,"URL":"http:\/\/dx.doi.org\/10.21203\/rs.3.rs-4242075\/v1","relation":{},"subject":[],"published":{"date-parts":[[2024,5,8]]},"subtype":"preprint"}}</enrichment>
    <enrichment key="opus_crossrefDocumentType">posted-content/preprint</enrichment>
    <enrichment key="opus_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="opus_import_origin">crossref</enrichment>
    <enrichment key="opus_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PublisherName,TitleMain_1,TitleAbstract_1,PublishedYear,Enrichmentopus_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Marie Josefine Hintz</author>
    <submitter>Alex Karras</submitter>
    <author>Nikola Milojevic-Dupont</author>
    <author>Felix Creutzig</author>
    <author>Lynn Kaack</author>
    <collection role="AY-23-24" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4940</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>479</pageFirst>
    <pageLast>509</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>47</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Digitalization and the Anthropocene</title>
    <abstract language="eng">Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: (a) planetary boundaries and stability, (b) equity within and between countries, and (c) human agency and governance, mediated via (i) increasing resource efficiency, (ii) accelerating consumption and scale effects, (iii) expanding political and economic control, and (iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability.</abstract>
    <parentTitle language="eng">Annual Review of Environment and Resources</parentTitle>
    <identifier type="doi">10.1146/annurev-environ-120920-100056</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <submitter>Alex Karras</submitter>
    <author>Felix Creutzig</author>
    <author>Daron Acemoglu</author>
    <author>Xuemei Bai</author>
    <author>Paul N. Edwards</author>
    <author>Marie Josefine Hintz</author>
    <author>Lynn Kaack</author>
    <author>Siir Kilkis</author>
    <author>Stefanie Kunkel</author>
    <author>Amy Luers</author>
    <author>Nikola Milojevic-Dupont</author>
    <author>Dave Rejeski</author>
    <author>Jürgen Renn</author>
    <author>David Rolnick</author>
    <author>Christoph Rosol</author>
    <author>Daniela Russ</author>
    <author>Thomas Turnbull</author>
    <author>Elena Verdolini</author>
    <author>Felix Wagner</author>
    <author>Charlie Wilson</author>
    <author>Aicha Zekar</author>
    <author>Marius Zumwald</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="Faculty" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>5954</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer Science and Business Media LLC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-10-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A systematic map of machine learning for urban climate change mitigation</title>
    <abstract language="eng">Many cities are interested in leveraging artificial intelligence and machine learning (ML) to help urban climate change mitigation (UCCM). Researchers and practitioners, however, are only beginning to understand how ML can contribute to achieving climate targets in cities. Here, we systematically map 2,300 peer-reviewed articles published between 1994 and 2024 that explore the use of ML in UCCM. We find that, despite fast growth in this research area, the use of generative artificial intelligence and large language models remains negligible, which contrasts to their increasing adoption in other urban domains. Among 40 identified application areas, ML research focuses predominantly on high-impact mitigation options denoted by the Intergovernmental Panel on Climate Change. This trend may partly be driven by data availability and commercial interest, which risk perpetuating geographic inequities and diverting efforts toward less impactful mitigation options. We therefore offer recommendations to guide the impactful deployment of ML solutions in UCCM.</abstract>
    <parentTitle language="eng">Nature Cities</parentTitle>
    <identifier type="doi">10.1038/s44284-025-00328-5</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_doi_json">x</enrichment>
    <enrichment key="opus_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Marie Josefine Hintz</author>
    <submitter>Alex Karras</submitter>
    <author>Nikola Milojevic-Dupont</author>
    <author>Felix Creutzig</author>
    <author>Tim Repke</author>
    <author>Lynn H. Kaack</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY-25-26" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4941</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>10</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings</title>
    <abstract language="eng">Building stock management is becoming a global societal and political issue, inter alia because of growing sustainability concerns. Comprehensive and openly accessible building stock data can enable impactful research exploring the most effective policy options. In Europe, efforts from citizen and governments generated numerous relevant datasets but these are fragmented and heterogeneous, thus hindering their usability. Here, we present EUBUCCO v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland. Three main attributes – building height, construction year and type – are included for respectively 73%, 24% and 46% of the buildings. We identify, collect and harmonize 50 open government datasets and OpenStreetMap, and perform extensive validation analyses to assess the quality, consistency and completeness of the data in every country. EUBUCCO v0.1 provides the basis for high-resolution urban sustainability studies across scales – continental, comparative or local studies – using a centralized source and is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments.</abstract>
    <parentTitle language="eng">Scientific Data</parentTitle>
    <identifier type="doi">10.1038/s41597-023-02040-2</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Nikola Milojevic-Dupont</author>
    <submitter>Alex Karras</submitter>
    <author>Felix Wagner</author>
    <author>Florian Nachtigall</author>
    <author>Jiawei Hu</author>
    <author>Geza Boi Brüser</author>
    <author>Marius Zumwald</author>
    <author>Filip Biljecki</author>
    <author>Niko Heeren</author>
    <author>Lynn Kaack</author>
    <author>Peter-Paul Pichler</author>
    <author>Felix Creutzig</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="Faculty" number=""/>
    <collection role="HertieResearch" number="">Data Science Lab</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4117</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>96</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>55</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>2022-02-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tackling Climate Change with Machine Learning</title>
    <abstract language="eng">Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.</abstract>
    <parentTitle language="eng">ACM Computing Surveys</parentTitle>
    <identifier type="url">https://dl.acm.org/doi/10.1145/3485128</identifier>
    <identifier type="doi">10.1145/3485128</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <submitter>Alex Karras</submitter>
    <author>Lynn Kaack</author>
    <author>David Rolnick</author>
    <author>Priya L. Donti</author>
    <author>Kelly Kochanski</author>
    <author>Alexandre Lacoste</author>
    <author>Kris Sankaran</author>
    <author>Andrew S. Ross</author>
    <author>Nikola Milojevic-Dupont</author>
    <author>Natasha Jaques</author>
    <author>Anna Waldman-Brown</author>
    <author>Alexandra S. Luccioni</author>
    <author>Tegan Maharaj</author>
    <author>Evan D. Sherwin</author>
    <author>Karthik Mukkavilli</author>
    <author>Konrad P. Kording</author>
    <author>Carla P. Gomes</author>
    <author>Andrew Y. Ng</author>
    <author>Demis Hassabis</author>
    <author>John C. Platt</author>
    <author>Felix Creutzig</author>
    <author>Jennifer Chayes</author>
    <author>Yoshua Bengio</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4123</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning from urban form to predict building heights</title>
    <abstract language="eng">Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.</abstract>
    <parentTitle language="eng">Plos one</parentTitle>
    <identifier type="doi">10.1371/journal.pone.0242010</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Nikola Milojevic-Dupont</author>
    <submitter>Alex Karras</submitter>
    <author>Nicolai Hans</author>
    <author>Lynn Kaack</author>
    <author>Marius Zumwald</author>
    <author>François Andrieux</author>
    <author>Daniel de Barros Soares</author>
    <author>Steffen Lohrey</author>
    <author>Peter-Paul Pichler</author>
    <author>Felix Creutzig</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
</export-example>
