@article{KaackRolnickReischetal., author = {Kaack, Lynn and Rolnick, David and Reisch, Lucia A. and Joppa, Lucas and Howson, Peter and Gil, Artur and Alevizou, Panayiota and Michaelidou, Nina and Appiah-Campbell, Ruby and Santarius, Tilman and K{\"o}hler, Susanne and Pizzol, Massimo and Schweizer, Pia-Johanna and Srinivasan, Dipti and Kaack, Lynn and Donti, Priya L.}, title = {Digitizing a sustainable future}, series = {One Earth}, volume = {4}, journal = {One Earth}, number = {6}, doi = {10.1016/j.oneear.2021.05.012}, pages = {768 -- 771}, abstract = {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?}, language = {en} } @article{KaackRolnickDontietal., author = {Kaack, Lynn and Rolnick, David and Donti, Priya L. and Kochanski, Kelly and Lacoste, Alexandre and Sankaran, Kris and Ross, Andrew S. and Milojevic-Dupont, Nikola and Jaques, Natasha and Waldman-Brown, Anna and Luccioni, Alexandra S. and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, Karthik and Kording, Konrad P. and Gomes, Carla P. and Ng, Andrew Y. and Hassabis, Demis and Platt, John C. and Creutzig, Felix and Chayes, Jennifer and Bengio, Yoshua}, title = {Tackling Climate Change with Machine Learning}, series = {ACM Computing Surveys}, volume = {55}, journal = {ACM Computing Surveys}, number = {2}, doi = {10.1145/3485128}, pages = {1 -- 96}, abstract = {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.}, language = {en} } @article{KaackVaishnavGrangerMorganetal., author = {Kaack, Lynn and Vaishnav, Parth and Granger Morgan, M and Azevedo, In{\^e}s and Rai, Srijana}, title = {Decarbonizing intraregional freight systems with a focus on modal shift}, series = {Environmental Research Letters}, volume = {13}, journal = {Environmental Research Letters}, number = {8}, doi = {10.1088/1748-9326/aad56c}, abstract = {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.}, language = {en} } @article{KaackKatul, author = {Kaack, Lynn and Katul, Gabriel}, title = {Fifty years to prove Malthus right}, series = {Proceedings of the National Academy of Sciences}, journal = {Proceedings of the National Academy of Sciences}, doi = {10.1073/pnas.1301246110}, abstract = {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 …}, language = {en} } @article{KaackAptGrangerMorganetal., author = {Kaack, Lynn and Apt, Jay and Granger Morgan, M. and McSharry, Patrick}, title = {Empirical prediction intervals improve energy forecasting}, series = {Proceedings of the National Academy of Sciences}, volume = {114}, journal = {Proceedings of the National Academy of Sciences}, number = {33}, doi = {10.1073/pnas.1619938114}, pages = {8752 -- 8757}, abstract = {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.}, language = {en} } @incollection{KaackChenGrangerMorgan, author = {Kaack, Lynn and Chen, George and Granger Morgan, M}, title = {Truck traffic monitoring with satellite images}, series = {Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies}, booktitle = {Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies}, publisher = {Association for Computing Machinery}, address = {New York}, doi = {10.1145/3314344}, publisher = {Hertie School}, pages = {155 -- 164}, abstract = {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.}, language = {de} } @article{MilojevicDupontHansKaacketal., author = {Milojevic-Dupont, Nikola and Hans, Nicolai and Kaack, Lynn and Zumwald, Marius and Andrieux, Fran{\c{c}}ois and de Barros Soares, Daniel and Lohrey, Steffen and Pichler, Peter-Paul and Creutzig, Felix}, title = {Learning from urban form to predict building heights}, series = {Plos one}, volume = {15}, journal = {Plos one}, number = {12}, doi = {10.1371/journal.pone.0242010}, abstract = {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.}, language = {en} } @unpublished{KaackFriederichLuccionietal., author = {Kaack, Lynn and Friederich, David and Luccioni, Alexandra and Steffen, Bjarne}, title = {Automated Identification of Climate Risk Disclosures in Annual Corporate Reports}, abstract = {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.}, language = {en} } @techreport{KaackDontiStrubelletal., type = {Working Paper}, author = {Kaack, Lynn and Donti, Priya and Strubell, Emma and Rolnick, David}, title = {Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals}, abstract = {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.}, language = {en} } @article{Kaack, author = {Kaack, Lynn}, title = {Machine learning enables global solar-panel detection}, series = {Nature}, volume = {598}, journal = {Nature}, number = {7882}, doi = {10.1038/d41586-021-02875-y}, pages = {567 -- 568}, abstract = {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.}, language = {en} }