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    <id>4128</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>768</pageFirst>
    <pageLast>771</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>4</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Digitizing a sustainable future</title>
    <abstract language="eng">Digital technologies have a crucial role in facilitating transitions toward a sustainable future. Yet there remain challenges to overcome and pitfalls to avoid. This Voices asks: how do we leverage the digital transformation to successfully support a sustainability transition?</abstract>
    <parentTitle language="eng">One Earth</parentTitle>
    <identifier type="doi">10.1016/j.oneear.2021.05.012</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <submitter>Alex Karras</submitter>
    <author>Lynn Kaack</author>
    <author>David Rolnick</author>
    <author>Lucia A. Reisch</author>
    <author>Lucas Joppa</author>
    <author>Peter Howson</author>
    <author>Artur Gil</author>
    <author>Panayiota Alevizou</author>
    <author>Nina Michaelidou</author>
    <author>Ruby Appiah-Campbell</author>
    <author>Tilman Santarius</author>
    <author>Susanne Köhler</author>
    <author>Massimo Pizzol</author>
    <author>Pia-Johanna Schweizer</author>
    <author>Dipti Srinivasan</author>
    <author>Lynn Kaack</author>
    <author>Priya L. Donti</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <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>
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    <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>4281</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>94</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>GPAI, Climate Change AI, Centre for AI &amp; Climate</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Climate Change and AI. Recommendations for Government Action</title>
    <abstract language="eng">The report, Climate Change and AI: Recommendations for Government, highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses.</abstract>
    <subTitle language="deu">Global Partnership on AI Report. In collaboration with Climate Change AI and the Centre for AI &amp; Climate</subTitle>
    <identifier type="url">https://www.gpai.ai/projects/responsible-ai/environment/climate-change-and-ai.pdf</identifier>
    <enrichment key="ConferenceName">2021 United Nations Climate Change Conference</enrichment>
    <enrichment key="ConferencePlace">Glasgow</enrichment>
    <enrichment key="ConferenceDate">31 October – 13 November 2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Peter Clutton-Brock</author>
    <submitter>Alex Karras</submitter>
    <author>David Rolnick</author>
    <author>Priya L. Donti</author>
    <author>Lynn Kaack</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY" 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>4777</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>518</pageFirst>
    <pageLast>527</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-02-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Aligning artificial intelligence with climate change mitigation</title>
    <abstract language="eng">There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation.</abstract>
    <parentTitle language="eng">Nature Climate Change</parentTitle>
    <identifier type="doi">10.1038/s41558-022-01377-7</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>Priya L. Donti</author>
    <author>Emma Strubell</author>
    <author>George Kamiya</author>
    <author>Felix Creutzig</author>
    <author>David Rolnick</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>4280</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>567</pageFirst>
    <pageLast>568</pageLast>
    <pageNumber/>
    <edition/>
    <issue>7882</issue>
    <volume>598</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine learning enables global solar-panel detection</title>
    <abstract language="eng">An inventory of the world’s solar-panel installations has been produced with the help of machine learning, revealing many more than had previously been recorded. The results will inform efforts to meet global targets for solar-energy use.</abstract>
    <parentTitle language="eng">Nature</parentTitle>
    <identifier type="doi">10.1038/d41586-021-02875-y</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY" number=""/>
    <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>5206</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>2023-12-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset</title>
    <abstract language="eng">Despite the importance of ambitious policy action for addressing climate change, large and systematic assessments of public policies and their design are lacking as analysing text manually is labour-intensive and costly. POLIANNA is a dataset of policy texts from the European Union (EU) that are annotated based on theoretical concepts of policy design, which can be used to develop supervised machine learning approaches for scaling policy analysis. The dataset consists of 20,577 annotated spans, drawn from 18 EU climate change mitigation and renewable energy policies. We developed a novel coding scheme translating existing taxonomies of policy design elements to a method for annotating text spans that consist of one or several words. Here, we provide the coding scheme, a description of the annotated corpus, and an analysis of inter-annotator agreement, and discuss potential applications. As understanding policy texts is still difficult for current text-processing algorithms, we envision this database to be used for building tools that help with manual coding of policy texts by automatically proposing paragraphs containing relevant information.</abstract>
    <parentTitle language="eng">Scientific Data</parentTitle>
    <identifier type="doi">10.1038/s41597-023-02801-z</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Sebastian Sewerin</author>
    <submitter>Alex Karras</submitter>
    <author>Lynn Kaack</author>
    <author>Joel Küttel</author>
    <author>Fride Sigurdsson</author>
    <author>Onerva Martikainen</author>
    <author>Alisha Esshaki</author>
    <author>Fabian Hafner</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="HertieResearch" number="">Data Science Lab</collection>
    <collection role="AY-23-24" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>6004</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-12-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revealing the empirical flexibility of gas units through deep clustering</title>
    <abstract language="eng">The flexibility of a power generation unit determines how quickly and often it can ramp up or down. In energy models, it depends on assumptions on the technical characteristics of the unit, such as its installed capacity or turbine technology. In this paper, we learn the empirical flexibility of gas units from their electricity generation, revealing how real-world limitations can lead to substantial differences between units with similar technical characteristics. Using a novel deep clustering approach, we transform 5 years (2019-2023) of unit-level hourly generation data for 49 German units from 100 MWp of installed capacity into low-dimensional embeddings. Our unsupervised approach identifies two clusters of peaker units (high flexibility) and two clusters of non-peaker units (low flexibility). The estimated ramp rates of non-peakers, which constitute half of the sample, display a low empirical flexibility, comparable to coal units. Non-peakers, predominantly owned by industry and municipal utilities, show limited response to low residual load and negative prices, generating on average 1.3 GWh during those hours. As the transition to renewables increases market variability, regulatory changes will be needed to unlock this flexibility potential.</abstract>
    <identifier type="doi">10.48550/arXiv.2504.16943</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Chiara Fusar Bassini</author>
    <submitter>Bernardo Bock</submitter>
    <author>Alice Lixuan Xu</author>
    <author>Jorge Sánchez Canales</author>
    <author>Lion Hirth</author>
    <author>Lynn Kaack</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY-25-26" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4942</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Predicting cycling traffic in cities: Is bikesharing data representative of the cycling volume?</title>
    <abstract language="deu">A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations.</abstract>
    <identifier type="urn">urn:nbn:de:kobv:b1570-opus4-49429</identifier>
    <identifier type="doi">10.48462/opus4-4942</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - 4.0 International</licence>
    <author>Silke K. Kaiser</author>
    <submitter>Alex Karras</submitter>
    <author>Nadja Klein</author>
    <author>Lynn Kaack</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>
    <file>https://opus4.kobv.de/opus4-hsog/files/4942/Kaiser_Klein_Kaack_2023_Predicting_cycling.pdf</file>
  </doc>
  <doc>
    <id>4118</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>13</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Decarbonizing intraregional freight systems with a focus on modal shift</title>
    <abstract language="eng">Road freight transportation accounts for around 7% of total world energy-related carbon dioxide emissions. With the appropriate incentives, energy savings and emissions reductions can be achieved by shifting freight to rail or water modes, both of which are far more efficient than road. We briefly introduce five general strategies for decarbonizing freight transportation, and then focus on the literature and data relevant to estimating the global decarbonization potential through modal shift. We compare freight activity (in tonne-km) by mode for every country where data are available. We also describe major intraregional freight corridors, their modal structure, and their infrastructure needs. We find that the current world road and rail modal split is around 60:40. Most countries are experiencing strong growth in road freight and a shift from rail to road. Rail intermodal transportation holds great potential for replacing carbon-intense and fast-growing road freight, but it is essential to have a targeted design of freight systems, particularly in developing countries. Modal shift can be promoted by policies targeting infrastructure investments and internalizing external costs of road freight, but we find that not many countries have such policies in place. We identify research needs for decarbonizing the freight transportation sector both through improvements in the efficiency of individual modes and through new physical and institutional infrastructure that can support modal shift.</abstract>
    <parentTitle language="eng">Environmental Research Letters</parentTitle>
    <identifier type="doi">10.1088/1748-9326/aad56c</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>Parth Vaishnav</author>
    <author>M Granger Morgan</author>
    <author>Inês Azevedo</author>
    <author>Srijana Rai</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4119</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fifty years to prove Malthus right</title>
    <abstract language="eng">A major question confronting sustainability research today is to what extent our planet, with a finite environmental resource base, can accommodate the faster than exponentially growing human population. Although these concerns are generally attributed to Malthus (1766–1834), early attempts to estimate the maximum sustainable population (ergo, the carrying capacity K) were reported by van Leeuwenhoek (1632–1723) to be at 13 billion people (1). Since then, the concept of carrying capacity has evolved to accommodate many resource limitations originating from available water, energy, and other ecosystem goods and services (1, 2). In PNAS, Suweis et al.(3) apply the concept of carrying capacity using fresh water availability on a national scale as the limiting resource to infer the global K. They estimate a decline in global human population by the middle of this century. We ask to what extent models that are …</abstract>
    <parentTitle language="eng">Proceedings of the National Academy of Sciences</parentTitle>
    <identifier type="url">https://www.pnas.org/content/110/11/4161</identifier>
    <identifier type="doi">10.1073/pnas.1301246110</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>Gabriel Katul</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4120</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>8752</pageFirst>
    <pageLast>8757</pageLast>
    <pageNumber/>
    <edition/>
    <issue>33</issue>
    <volume>114</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Empirical prediction intervals improve energy forecasting</title>
    <abstract language="deu">Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks.</abstract>
    <parentTitle language="eng">Proceedings of the National Academy of Sciences</parentTitle>
    <identifier type="doi">10.1073/pnas.1619938114</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>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>Jay Apt</author>
    <author>M. Granger Morgan</author>
    <author>Patrick McSharry</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4122</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>155</pageFirst>
    <pageLast>164</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Association for Computing Machinery</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Truck traffic monitoring with satellite images</title>
    <abstract language="deu">The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low-and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.</abstract>
    <parentTitle language="deu">Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies</parentTitle>
    <identifier type="doi">10.1145/3314344</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>George Chen</author>
    <author>M Granger Morgan</author>
    <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>
  <doc>
    <id>4126</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Identification of Climate Risk Disclosures in Annual Corporate Reports</title>
    <abstract language="eng">It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure.</abstract>
    <identifier type="url">https://arxiv.org/abs/2108.01415</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>David Friederich</author>
    <author>Alexandra Luccioni</author>
    <author>Bjarne Steffen</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4129</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals</title>
    <abstract language="eng">With the increasing deployment of artificial intelligence (AI) technologies across society, it is important to understand in which ways AI may accelerate or impede climate progress, and how various stakeholders can guide those developments. On the one hand, AI can facilitate climate change mitigation and adaptation strategies within a variety of sectors, such as energy, manufacturing, agriculture, forestry, and disaster management. On the other hand, AI can also contribute to rising greenhouse gas emissions through applications that benefit high-emitting sectors or drive increases in consumer demand, as well as via energy use associated with AI itself. Here, we provide a brief overview of AI’s multi-faceted relationship with climate change, and recommend policy levers to align the use of AI with climate change mitigation and adaptation pathways.</abstract>
    <identifier type="url">https://eu.boell.org/en/2020/12/03/artificial-intelligence-and-climate-change</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Lynn Kaack</author>
    <submitter>Alex Karras</submitter>
    <author>Priya Donti</author>
    <author>Emma Strubell</author>
    <author>David Rolnick</author>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>5630</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>30</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-10-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation</title>
    <abstract language="eng">Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.</abstract>
    <identifier type="doi">10.48550/arXiv.2406.18454</identifier>
    <identifier type="url">https://arxiv.org/abs/2406.18454</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Silke K. Kaiser</author>
    <submitter>Alex Karras</submitter>
    <author>Nadja Klein</author>
    <author>Lynn Kaack</author>
    <collection role="HertieResearch" number="">Publications PhD Researchers</collection>
    <collection role="AY-23-24" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
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