{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T19:55:27Z","timestamp":1782330927174,"version":"3.54.5"},"reference-count":898,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["1803547"],"award-info":[{"award-number":["1803547"]}]},{"name":"Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University","award":["SES-00949710"],"award-info":[{"award-number":["SES-00949710"]}]},{"DOI":"10.13039\/100000015","name":"US Department of Energy","doi-asserted-by":"crossref","award":["DE-FG02-97ER25308"],"award-info":[{"award-number":["DE-FG02-97ER25308"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"MIT Media Lab Consortium"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML 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 ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.<\/jats:p>","DOI":"10.1145\/3485128","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T05:06:27Z","timestamp":1644296787000},"page":"1-96","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":744,"title":["Tackling Climate Change with Machine Learning"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2855-393X","authenticated-orcid":false,"given":"David","family":"Rolnick","sequence":"first","affiliation":[{"name":"McGill University and Mila - Quebec AI Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priya L.","family":"Donti","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lynn H.","family":"Kaack","sequence":"additional","affiliation":[{"name":"Hertie School and ETH Z\u00fcrich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly","family":"Kochanski","sequence":"additional","affiliation":[{"name":"University of Colorado Boulder"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Lacoste","sequence":"additional","affiliation":[{"name":"Element AI\/Service Now"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kris","family":"Sankaran","sequence":"additional","affiliation":[{"name":"University of Wisconsin - Madison and Universit\u00e9 de Montr\u00e9al"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew Slavin","family":"Ross","sequence":"additional","affiliation":[{"name":"New York University and Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikola","family":"Milojevic-Dupont","sequence":"additional","affiliation":[{"name":"Mercator Research Institute on Global Commonsand Climate Change and Technische Universit\u00e4t Berlin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natasha","family":"Jaques","sequence":"additional","affiliation":[{"name":"Google Brain and UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Waldman-Brown","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra Sasha","family":"Luccioni","sequence":"additional","affiliation":[{"name":"Mila - Quebec AI Institute and Universit\u00e9 de Montr\u00e9al"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tegan","family":"Maharaj","sequence":"additional","affiliation":[{"name":"Mila - Quebec AI Institute and Polytechnique Montr\u00e9al"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan D.","family":"Sherwin","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. Karthik","family":"Mukkavilli","sequence":"additional","affiliation":[{"name":"University of California and Lawrence Berkeley National Lab"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad P.","family":"Kording","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carla P.","family":"Gomes","sequence":"additional","affiliation":[{"name":"Cornell University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew Y.","family":"Ng","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demis","family":"Hassabis","sequence":"additional","affiliation":[{"name":"DeepMind"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John C.","family":"Platt","sequence":"additional","affiliation":[{"name":"Google AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Creutzig","sequence":"additional","affiliation":[{"name":"Mercator Research Institute on Global Commonsand Climate Change and Technische Universit\u00e4t Berlin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Chayes","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshua","family":"Bengio","sequence":"additional","affiliation":[{"name":"Mila - Quebec AI Institute and Universit\u00e9 de Montr\u00e9al"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ECC.2016.7810598"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.02.016"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504980"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.10.044"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.07.077"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1787\/9789264118461-en"},{"key":"e_1_3_3_8_2","first-page":"350","article-title":"Climate adaptation, local institutions and rural livelihoods","author":"Agrawal Arun","year":"2009","unstructured":"Arun Agrawal and Nicolas Perrin. 2009. Climate adaptation, local institutions and rural livelihoods. In Adapting to Climate Change: Thresholds, Values, Governance. Cambridge University Press, Cambridge. 350\u2013367.","journal-title":"Adapting to Climate Change: Thresholds, Values, Governance."},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2016.7588227"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.109792"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/7\/2\/024004"},{"key":"e_1_3_3_12_2","article-title":"Efficient multi-objective optimization through population-based parallel surrogate search","author":"Akhtar Taimoor","year":"2019","unstructured":"Taimoor Akhtar and Christine A. Shoemaker. 2019. Efficient multi-objective optimization through population-based parallel surrogate search. arXiv preprint arXiv:1903.02167 (2019).","journal-title":"arXiv preprint arXiv:1903.02167"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/INISTA.2017.8001123"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolecon.2018.03.031"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.05.128"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11030476"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpubeco.2011.03.003"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1257\/aer.104.10.3003"},{"key":"e_1_3_3_19_2","first-page":"164","article-title":"Urban bus arrival time prediction: A review of computational models","volume":"2","author":"Altinkaya Mehmet","year":"2013","unstructured":"Mehmet Altinkaya and Metin Zontul. 2013. Urban bus arrival time prediction: A review of computational models. International Journal of Recent Technology and Engineering 2, 4 (2013), 164\u2013169.","journal-title":"International Journal of Recent Technology and Engineering"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.09.045"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.04.095"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.5555\/3327757.3327922"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0973408212475199"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018GL077049"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.23919\/PSCC.2018.8442521"},{"key":"e_1_3_3_26_2","volume-title":"Governing Micro-Mobility: A Nationwide Assessment of Electric Scooter Regulations","author":"Anderson-Hall Kirstin","year":"2019","unstructured":"Kirstin Anderson-Hall, Brandon Bordenkircher, Riley O\u2019Neil, and Smith C. Scott. 2019. Governing Micro-Mobility: A Nationwide Assessment of Electric Scooter Regulations. Technical Report."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v72.n3.4"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.06.097"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/2818498.2818512"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0608163103"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.109899"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1190\/tle37010058.1"},{"key":"e_1_3_3_33_2","volume-title":"The Climate Crisis: An Introductory Guide to Climate Change","author":"Archer David","year":"2010","unstructured":"David Archer and Stefan Rahmstorf. 2010. The Climate Crisis: An Introductory Guide to Climate Change. Cambridge University Press."},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2012.08.062"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2015.08.063"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/50\/4\/043001"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.2307\/2234208"},{"key":"e_1_3_3_38_2","unstructured":"Solomon Assefa. 2018. Hello Tractor Pilot Agriculture Digital Wallet based on AI and Blockchain. Retrieved from https:\/\/www.ibm.com\/blogs\/research\/2018\/12\/hello-tractor\/."},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.07.149"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19020309"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aal4321"},{"key":"e_1_3_3_42_2","article-title":"Machine learning methods that economists should know about","volume":"11","author":"Athey Susan","year":"2019","unstructured":"Susan Athey and Guido W. Imbens. 2019. Machine learning methods that economists should know about. Annual Review of Economics 11, 1 (2019), 685\u2013725.","journal-title":"Annual Review of Economics"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1994-5"},{"key":"e_1_3_3_44_2","unstructured":"Rockwell Automation. 2014. AkzoNobel Powder Coatings saves over 15 000 euros per month thanks to advanced energy monitoring solution from Rockwell Automation. Retrived from https:\/\/literature.rockwellautomation.com\/idc\/groups\/literature\/documents\/ap\/energy-ap009_-en-p.pdf."},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2019.02.012"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-environ-021913-153558"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1080\/15568318.2017.1346732"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.8b03834"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate1354"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v39i1.2785"},{"key":"e_1_3_3_51_2","doi-asserted-by":"crossref","unstructured":"Xuemei Bai Richard J. Dawson Diana \u00dcrge-Vorsatz Gian C. Delgado Aliyu Salisu Barau Shobhakar Dhakal David Dodman Lykke Leonardsen Val\u00e9rie Masson-Delmotte Debra C. Roberts and Seth Schultz. 2018. Six research priorities for cities and climate change. Nature 555 7964 (2018) 23\u201325.","DOI":"10.1038\/d41586-018-02409-z"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm.2011.6102321"},{"key":"e_1_3_3_53_2","first-page":"1","volume-title":"2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP\u201919)","author":"Baker Kyri","year":"2019","unstructured":"Kyri Baker. 2019. Learning warm-start points for AC optimal power flow. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP\u201919). IEEE, 1\u20136."},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-06645-7"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aao4408"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2016.07.002"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.05.019"},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2015.7171844"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87714-w"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274927"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aax0848"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate3255"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2017.12.204"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487166.2487175"},{"key":"e_1_3_3_65_2","doi-asserted-by":"crossref","unstructured":"Sara Beery Yang Liu Dan Morris Jim Piavis Ashish Kapoor Markus Meister and Pietro Perona. 2019. Synthetic examples improve generalization for rare classes. In IEEE Winter Conference on Applications of Computer Vision (WACV\u201920) .","DOI":"10.1109\/WACV45572.2020.9093570"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-019-0414-z"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10584-018-2260-9"},{"key":"e_1_3_3_68_2","unstructured":"Wanda Bell Lewis Ahron Kaufman William Joseph Krajewski John J. McGillicuddy Paul Aloysius Scanlon Jr. Abhijit Dey Sharon Ameet Fanse Giridhar Holenarsipur Nagaraj Shyamli Rai Sunitha Sundaramurthy Gurpreet Chahil Jeetendra Chandwani Arham GuptaMangesh Ashok Karhadkar Vincent Francis La Padula Paul J. Murray Himanshu Shailesh Shah and Rasika Vartak. 2016. Systems and methods for automated data privacy compliance. US Patent No. 9 507 960."},{"key":"e_1_3_3_69_2","unstructured":"Asher Bender Brett Whelan and Salah Sukkarieh. 2019. Ladybird Cobbitty 2017 Brassica Dataset. The University of Sydney. https:\/\/doi.org\/10.25910\/5c941d0c8bccb"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.07.063"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1515\/pjbr-2019-0004"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2004.08.035"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1002\/2017GL075122"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/1791314.1791349"},{"key":"e_1_3_3_76_2","article-title":"Online mixed-integer optimization in milliseconds","author":"Bertsimas Dimitris","year":"2019","unstructured":"Dimitris Bertsimas and Bartolomeo Stellato. 2019. Online mixed-integer optimization in milliseconds. Preprint arXiv:1907.02206 (2019).","journal-title":"Preprint arXiv:1907.02206"},{"key":"e_1_3_3_77_2","article-title":"Deep fault analysis and subset selection in solar power grids","author":"Bhattacharya Biswarup","year":"2017","unstructured":"Biswarup Bhattacharya and Abhishek Sinha. 2017. Deep fault analysis and subset selection in solar power grids. Preprint arXiv:1711.02810 (2017).","journal-title":"Preprint arXiv:1711.02810"},{"key":"e_1_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2017.01.001"},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.5555\/1162264"},{"key":"e_1_3_3_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.346"},{"key":"e_1_3_3_81_2","article-title":"Blue River Technology","author":"Technology Blue River","year":"2021","unstructured":"Blue River Technology. 2021. Blue River Technology. Retrieved from https:\/\/bluerivertechnology.com\/.","journal-title":"Retrieved from https:\/\/bluerivertechnology.com\/"},{"key":"e_1_3_3_82_2","article-title":"Bluefield Technologies","author":"Technologies Bluefield","year":"2021","unstructured":"Bluefield Technologies. 2021. Bluefield Technologies. Retrieved from http:\/\/bluefield.co\/.","journal-title":"Retrieved from http:\/\/bluefield.co\/"},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1080\/02681102.2011.643209"},{"key":"e_1_3_3_84_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2018.01.008"},{"key":"e_1_3_3_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8618658"},{"key":"e_1_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-016-0075-3"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.1787\/fmt-2015-5jrrz76d5td5"},{"key":"e_1_3_3_88_2","article-title":"End to End Learning for Self-Driving Cars","author":"Bojarski Mariusz","year":"2016","unstructured":"Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. 2016. End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316 (2016).","journal-title":"arXiv preprint arXiv:1604.07316"},{"key":"e_1_3_3_89_2","doi-asserted-by":"publisher","DOI":"10.5555\/3216236.3216237"},{"key":"e_1_3_3_90_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5363-6"},{"key":"e_1_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2013.1839"},{"key":"e_1_3_3_92_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401993"},{"key":"e_1_3_3_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/PACRIM.2017.8121895"},{"key":"e_1_3_3_94_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10531-014-0810-7"},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018GL081108"},{"key":"e_1_3_3_96_2","first-page":"137","volume-title":"An Analysis of Possible Energy Impacts of Automated Vehicles","author":"Brown Austin","year":"2014","unstructured":"Austin Brown, Jeffrey Gonder, and Brittany Repac. 2014. An Analysis of Possible Energy Impacts of Automated Vehicles. Springer International Publishing, Cham, 137\u2013153."},{"key":"e_1_3_3_97_2","volume-title":"Energy Systems, in IPCC, Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2014: Mitigation of Climate Change, chapter 8. Geneva. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.)","author":"Bruckner T.","year":"2014","unstructured":"T. Bruckner, I. A. Bashmakov, Y. Mulugetta, H. Chum, A. de la Vega Navarro, J. Edmonds, A. Faaij, B. Fungtammasan, A. Garg, E. Hertwich, D. Honnery, D. Infield, M. Kainuma, S. Khennas, S. Kim, H. B. Nimir, K. Riahi, N. Strachan, R. Wiser, and X. Zhang. 2014. Energy Systems, in IPCC, Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2014: Mitigation of Climate Change, chapter 8. Geneva. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.). Cambridge University Press, Cambridge."},{"key":"e_1_3_3_98_2","volume-title":"2010 AAAI Spring Symposium Series","author":"Brunskill Emma","year":"2010","unstructured":"Emma Brunskill and Neal Lesh. 2010. Routing for rural health: Optimizing community health worker visit schedules. In 2010 AAAI Spring Symposium Series."},{"key":"e_1_3_3_99_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.05.288"},{"key":"e_1_3_3_100_2","doi-asserted-by":"publisher","DOI":"10.5555\/2008780.2008798"},{"key":"e_1_3_3_101_2","doi-asserted-by":"publisher","DOI":"10.2172\/959402"},{"key":"e_1_3_3_102_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature15725"},{"key":"e_1_3_3_103_2","doi-asserted-by":"publisher","DOI":"10.3386\/w23908"},{"key":"e_1_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.4033427"},{"key":"e_1_3_3_105_2","doi-asserted-by":"publisher","DOI":"10.1149\/2.1051908jes"},{"key":"e_1_3_3_106_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0337-2"},{"key":"e_1_3_3_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2014.08.005"},{"key":"e_1_3_3_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489130"},{"key":"e_1_3_3_109_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-018-0175-0"},{"key":"e_1_3_3_110_2","article-title":"Camus Energy","author":"Energy Camus","year":"2019","unstructured":"Camus Energy. 2019. Camus Energy. Retrieved from https:\/\/camus.energy\/.","journal-title":"Retrieved from https:\/\/camus.energy\/"},{"key":"e_1_3_3_111_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/44\/1\/008"},{"key":"e_1_3_3_112_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-018-0108-1"},{"key":"e_1_3_3_113_2","article-title":"Carbon Engineering","author":"Engineering Carbon","year":"2021","unstructured":"Carbon Engineering. 2021. Carbon Engineering. Retrieved from https:\/\/carbonengineering.com\/.","journal-title":"Retrieved from https:\/\/carbonengineering.com\/"},{"key":"e_1_3_3_114_2","article-title":"Carbon Mapper","author":"Mapper Carbon","year":"2021","unstructured":"Carbon Mapper. 2021. Carbon Mapper. Retrieved from https:\/\/carbonmapper.org\/.","journal-title":"Retrieved from https:\/\/carbonmapper.org\/"},{"key":"e_1_3_3_115_2","article-title":"Carbon Tracker to Measure World\u2019s Power Plant Emissions from Space with Support from Google.org","author":"Tracker Carbon","year":"2019","unstructured":"Carbon Tracker. 2019. Carbon Tracker to Measure World\u2019s Power Plant Emissions from Space with Support from Google.org. Retrieved from https:\/\/www.carbontracker.org\/carbon-tracker-to-measure-worlds-power-plant-emissions-from-space-with-support-from-google-org\/.","journal-title":"Retrieved from https:\/\/www.carbontracker.org\/carbon-tracker-to-measure-worlds-power-plant-emissions-from-space-with-support-from-google-org\/"},{"key":"e_1_3_3_116_2","volume-title":"Position paper on high performance computing needs in Earth system prediction. National Earth System Prediction Capability.","author":"Carman J.","year":"2017","unstructured":"J. Carman, T. Clune, F. Giraldo, M. Govett, B. Gross, A. Kamrathe, T. Lee, D. McCarren, J. Michalakes, S. Sandgathe, and T. Whitcomb. 2017. Position paper on high performance computing needs in Earth system prediction. National Earth System Prediction Capability.Technical Report. Retrived from https:\/\/doi.org\/10.7289\/V5862DH3"},{"key":"e_1_3_3_117_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10584-010-9964-9"},{"key":"e_1_3_3_118_2","doi-asserted-by":"publisher","DOI":"10.1039\/C7EW00322F"},{"key":"e_1_3_3_119_2","doi-asserted-by":"publisher","DOI":"10.5555\/3067045"},{"key":"e_1_3_3_120_2","doi-asserted-by":"publisher","DOI":"10.1002\/2015WR017609"},{"key":"e_1_3_3_121_2","doi-asserted-by":"publisher","DOI":"10.3390\/app8050749"},{"key":"e_1_3_3_122_2","article-title":"Global Peatland Database","author":"Centre Greifswald Mire","year":"2021","unstructured":"Greifswald Mire Centre. 2021. Global Peatland Database. Retrieved from https:\/\/greifswaldmoor.de\/global-peatland-database-en.html.","journal-title":"Retrieved from https:\/\/greifswaldmoor.de\/global-peatland-database-en.html"},{"key":"e_1_3_3_123_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2010.10.025"},{"key":"e_1_3_3_124_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-3059.2010.02411.x"},{"key":"e_1_3_3_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93843-1_4"},{"key":"e_1_3_3_126_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.05.044"},{"key":"e_1_3_3_127_2","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy9030142"},{"key":"e_1_3_3_128_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2005.08.018"},{"key":"e_1_3_3_129_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301493"},{"key":"e_1_3_3_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2764844"},{"key":"e_1_3_3_131_2","doi-asserted-by":"crossref","unstructured":"Jie Chen Kees de Hoogh Maciek Strak Jules Kerckhoffs Roel Vermeulen Bert Brunekreef and Gerard Hoek. 2018. OP III\u20134 Exposure assessment models for NO2 and PM2.5 in the elapse study: A comparison of supervised linear regression and machine learning approaches. Occupational and Environmental Medicine 75 Suppl 1 (2018) A6.","DOI":"10.1136\/oemed-2018-ISEEabstracts.14"},{"key":"e_1_3_3_132_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2016.08.020"},{"key":"e_1_3_3_133_2","first-page":"6571","volume-title":"Advances in Neural Information Processing Systems","author":"Chen Tian Qi","year":"2018","unstructured":"Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud. 2018. Neural ordinary differential equations. In Advances in Neural Information Processing Systems. 6571\u20136583."},{"key":"e_1_3_3_134_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.12.018"},{"key":"e_1_3_3_135_2","article-title":"Mobility Data Specification","author":"Angeles City of Los","year":"2018","unstructured":"City of Los Angeles. 2018. Mobility Data Specification. Retrieved from https:\/\/github.com\/CityOfLosAngeles\/mobility-data-specification.git.","journal-title":"Retrieved from https:\/\/github.com\/CityOfLosAngeles\/mobility-data-specification.git"},{"issue":"2","key":"e_1_3_3_136_2","first-page":"20","article-title":"Multi-armed bandits for intelligent tutoring systems","volume":"7","author":"Clement Benjamin","year":"2013","unstructured":"Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, and Manuel Lopes. 2013. Multi-armed bandits for intelligent tutoring systems. Journal of Educational Data Mining 7, 2 (2013), 20\u201348.","journal-title":"Journal of Educational Data Mining"},{"key":"e_1_3_3_137_2","article-title":"Climeworks","year":"2021","unstructured":"Climeworks. 2021. Climeworks. Retrieved from https:\/\/www.climeworks.com\/.","journal-title":"Retrieved from https:\/\/www.climeworks.com\/"},{"key":"e_1_3_3_138_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2013.03.008"},{"key":"e_1_3_3_139_2","unstructured":"Brendan Coffey. 2019. Factory Records: GE Providing Procter & Gamble Greater Access To The Cloud For Analyzing Manufacturing Data. Retrived from https:\/\/www.ge.com\/reports\/factory-records-ge-providing-procter-gamble-greater-access-cloud-analyzing-manufacturing-data\/."},{"key":"e_1_3_3_140_2","article-title":"S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts","volume":"10","author":"Cohen Judah","year":"2018","unstructured":"Judah Cohen, Dim Coumou, Jessica Hwang, Lester Mackey, Paulo Orenstein, Sonja Totz, and Eli Tziperman. 2018. S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts. WIREs Climate Change 10, 2 (2018), e00567.","journal-title":"WIREs Climate Change"},{"key":"e_1_3_3_141_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04228D"},{"key":"e_1_3_3_142_2","article-title":"Energy Primer: A Handbook of Energy Market Basics","author":"Commission Federal Energy Regulatory","year":"2015","unstructured":"Federal Energy Regulatory Commission. 2015. Energy Primer: A Handbook of Energy Market Basics. Federal Energy Regulatory Commission, Washington, DC.","journal-title":"Federal Energy Regulatory Commission, Washington, DC"},{"key":"e_1_3_3_143_2","doi-asserted-by":"publisher","DOI":"10.1175\/2007BAMS2432.1"},{"key":"e_1_3_3_144_2","article-title":"Estimating the marginal carbon intensity of electricity with machine learning","author":"Corradi Olivier","year":"2018","unstructured":"Olivier Corradi. 2018. Estimating the marginal carbon intensity of electricity with machine learning. Retrieved from https:\/\/medium.com\/electricitymap\/using-machine-learning-to-estimate-the-hourly-marginal-carbon-intensity-of-electricity-49eade43b421.","journal-title":"Retrieved from https:\/\/medium.com\/electricitymap\/using-machine-learning-to-estimate-the-hourly-marginal-carbon-intensity-of-electricity-49eade43b421"},{"key":"e_1_3_3_145_2","doi-asserted-by":"publisher","DOI":"10.1177\/1527476418796632"},{"key":"e_1_3_3_146_2","article-title":"Geoengineering: Research is Prudent, But No Substitute for Carbon Pollution Cuts","author":"Council Natural Resources Defense","year":"2015","unstructured":"Natural Resources Defense Council. 2015. Geoengineering: Research is Prudent, But No Substitute for Carbon Pollution Cuts. Retrieved from https:\/\/www.nrdc.org\/media\/2015\/150210.","journal-title":"Retrieved from https:\/\/www.nrdc.org\/media\/2015\/150210"},{"key":"e_1_3_3_147_2","doi-asserted-by":"publisher","DOI":"10.1038\/nphys3719"},{"key":"e_1_3_3_148_2","article-title":"12,000-mile trip to have seafood shelled","author":"Cramb Auslan","year":"2006","unstructured":"Auslan Cramb. 2006. 12,000-mile trip to have seafood shelled. The Telegraph.","journal-title":"The Telegraph"},{"key":"e_1_3_3_149_2","volume-title":"AGU Fall Meeting Abstracts","author":"Crane-Droesch A.","year":"2018","unstructured":"A. Crane-Droesch, B. Kravitz, and J. T. Abatzoglou. 2018. Using deep learning to model potential impacts of geoengineering via solar radiation management on US agriculture. In AGU Fall Meeting Abstracts."},{"key":"e_1_3_3_150_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcbb.12235"},{"key":"e_1_3_3_151_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate3169"},{"key":"e_1_3_3_152_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1315545112"},{"key":"e_1_3_3_153_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8EE03682A"},{"key":"e_1_3_3_154_2","doi-asserted-by":"publisher","DOI":"10.1017\/sus.2019.11"},{"key":"e_1_3_3_155_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aac8033"},{"key":"e_1_3_3_156_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcbb.12205"},{"key":"e_1_3_3_157_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.11.021"},{"key":"e_1_3_3_158_2","doi-asserted-by":"publisher","DOI":"10.1002\/wcc.81"},{"key":"e_1_3_3_159_2","first-page":"1","article-title":"Short-term origin-destination based metro flow prediction with probabilistic model selection approach","volume":"2018","author":"Dai Xiaoqing","year":"2018","unstructured":"Xiaoqing Dai, Lijun Sun, and Yanyan Xu. 2018. Short-term origin-destination based metro flow prediction with probabilistic model selection approach. Journal of Advanced Transportation 2018, 2399 (2018), 1\u201315.","journal-title":"Journal of Advanced Transportation"},{"key":"e_1_3_3_160_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00277"},{"key":"e_1_3_3_161_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915587723"},{"key":"e_1_3_3_162_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.08.017"},{"key":"e_1_3_3_163_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aas9793"},{"key":"e_1_3_3_164_2","doi-asserted-by":"publisher","DOI":"10.1145\/3210548"},{"key":"e_1_3_3_165_2","doi-asserted-by":"publisher","DOI":"10.5555\/3327757.3327820"},{"key":"e_1_3_3_166_2","doi-asserted-by":"publisher","DOI":"10.1145\/3396851.3397681"},{"key":"e_1_3_3_167_2","first-page":"25","volume-title":"40th Annual Meeting of the Cognitive Science Society (CogSci\u201918)","author":"De La Maza Crist\u00f3bal","year":"2018","unstructured":"Crist\u00f3bal De La Maza, Alex Davis, Cleotilde Gonzalez, and In\u00eas Azevedo. 2018. A graph-based model to discover preference structure from choice data. In 40th Annual Meeting of the Cognitive Science Society (CogSci\u201918). 25\u201328."},{"key":"e_1_3_3_168_2","volume-title":"Willingness to pay to avoid environmental impacts of electricity generation","author":"Guzm\u00e1n Crist\u00f3bal de la Maza","year":"2013","unstructured":"Crist\u00f3bal de la Maza Guzm\u00e1n. 2013. Willingness to pay to avoid environmental impacts of electricity generation. Technical Report. Latin American and Caribbean Environmental Economics Program."},{"key":"e_1_3_3_169_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-polisci-080812-191558"},{"key":"e_1_3_3_170_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.08.045"},{"key":"e_1_3_3_171_2","article-title":"Stratospheric aerosol injection as a deep reinforcement learning problem","author":"de Witt Christian Schroeder","year":"2019","unstructured":"Christian Schroeder de Witt and Thomas Hornigold. 2019. Stratospheric aerosol injection as a deep reinforcement learning problem. In ICML 2019 Workshop on Climate Change: How Can AI Help?","journal-title":"ICML 2019 Workshop on Climate Change: How Can AI Help?"},{"key":"e_1_3_3_172_2","article-title":"Estimating food consumption and poverty indices with mobile phone data","author":"Decuyper Adeline","year":"2014","unstructured":"Adeline Decuyper, Alex Rutherford, Amit Wadhwa, Jean-Martin Bauer, Gautier Krings, Thoralf Gutierrez, Vincent D. Blondel, and Miguel A. Luengo-Oroz. 2014. Estimating food consumption and poverty indices with mobile phone data. Preprint arXiv:1412.2595 (2014).","journal-title":"Preprint arXiv:1412.2595"},{"key":"e_1_3_3_173_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1167311"},{"key":"e_1_3_3_174_2","doi-asserted-by":"publisher","DOI":"10.1109\/SASO.2008.64"},{"key":"e_1_3_3_175_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2009.07.001"},{"key":"e_1_3_3_176_2","doi-asserted-by":"publisher","DOI":"10.1006\/jema.2001.0515"},{"key":"e_1_3_3_177_2","article-title":"Dendra Systems","author":"Systems Dendra","year":"2021","unstructured":"Dendra Systems. 2021. Dendra Systems. Retrieved from https:\/\/dendra.io\/.","journal-title":"Retrieved from https:\/\/dendra.io\/"},{"key":"e_1_3_3_178_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2017.2694890"},{"key":"e_1_3_3_179_2","doi-asserted-by":"publisher","DOI":"10.5555\/3020488.3020498"},{"key":"e_1_3_3_180_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.5b06121"},{"key":"e_1_3_3_181_2","doi-asserted-by":"publisher","DOI":"10.1289\/isee.2017.2017-389"},{"key":"e_1_3_3_182_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-05690-8"},{"key":"e_1_3_3_183_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10584-017-1985-1"},{"key":"e_1_3_3_184_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2013.05.009"},{"key":"e_1_3_3_185_2","doi-asserted-by":"publisher","DOI":"10.5555\/1661445.1661448"},{"key":"e_1_3_3_186_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate2972"},{"key":"e_1_3_3_187_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2013.84"},{"key":"e_1_3_3_188_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1816020116"},{"key":"e_1_3_3_189_2","doi-asserted-by":"crossref","unstructured":"Bistra Dilkina Jayant R. Kalagnanam and Elena Novakovskaia. 2015. Method for designing the layout of turbines in a windfarm. US Patent No. 9 189 570.","DOI":"10.1016\/B978-0-12-801575-9.00006-8"},{"key":"e_1_3_3_190_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2018.02.009"},{"key":"e_1_3_3_191_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24750-0_19"},{"key":"e_1_3_3_192_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295054"},{"key":"e_1_3_3_193_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2935711"},{"key":"e_1_3_3_194_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.11.065"},{"key":"e_1_3_3_195_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.295.10.1127"},{"key":"e_1_3_3_196_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.09.033"},{"key":"e_1_3_3_197_2","doi-asserted-by":"publisher","DOI":"10.5555\/3433701.3433784"},{"key":"e_1_3_3_198_2","article-title":"Introducing machine learning for power system operation support","author":"Donnot Benjamin","year":"2017","unstructured":"Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Patrick Panciatici, and Antoine Marot. 2017. Introducing machine learning for power system operation support. Preprint arXiv:1709.09527 (2017).","journal-title":"Preprint arXiv:1709.09527"},{"key":"e_1_3_3_199_2","first-page":"5484","volume-title":"Advances in Neural Information Processing Systems","author":"Donti Priya","year":"2017","unstructured":"Priya Donti, Brandon Amos, and J. Zico Kolter. 2017. Task-based end-to-end model learning in stochastic optimization. In Advances in Neural Information Processing Systems. 5484\u20135494."},{"key":"e_1_3_3_200_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2956906"},{"key":"e_1_3_3_201_2","article-title":"From satellite imagery to disaster insights","author":"Doshi Jigar","year":"2018","unstructured":"Jigar Doshi, Saikat Basu, and Guan Pang. 2018. From satellite imagery to disaster insights. Preprint arXiv:1812.07033 (2018).","journal-title":"Preprint arXiv:1812.07033"},{"key":"e_1_3_3_202_2","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2009.7074633"},{"key":"e_1_3_3_203_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.02.156"},{"key":"e_1_3_3_204_2","article-title":"Mapping Agricultural Supply Chains from Source to Shelf","year":"2019","unstructured":"DrivenData. 2019. Mapping Agricultural Supply Chains from Source to Shelf. Retrieved from http:\/\/drivendata.co\/case-studies\/mapping-agricultural-supply-chains-from-source-to-shelf\/.","journal-title":"Retrieved from http:\/\/drivendata.co\/case-studies\/mapping-agricultural-supply-chains-from-source-to-shelf\/"},{"key":"e_1_3_3_205_2","article-title":"DroneSeed","year":"2021","unstructured":"DroneSeed. 2021. DroneSeed. Retrieved from https:\/\/droneseed.com\/.","journal-title":"Retrieved from https:\/\/droneseed.com\/"},{"key":"e_1_3_3_206_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1123"},{"key":"e_1_3_3_207_2","doi-asserted-by":"crossref","unstructured":"Victor Duarte. 2018. Machine Learning for Continuous-Time Economics. (2018). Retrieved from https:\/\/doi.org\/10.2139\/ssrn.3012602","DOI":"10.2139\/ssrn.3012602"},{"key":"e_1_3_3_208_2","article-title":"Renewable Energy Will Be Consistently Cheaper Than Fossil Fuels By 2020, Report Claims","author":"Dudley Dominic","year":"2018","unstructured":"Dominic Dudley. 2018. Renewable Energy Will Be Consistently Cheaper Than Fossil Fuels By 2020, Report Claims. Retrieved from https:\/\/www.forbes.com\/sites\/dominicdudley\/2018\/01\/13\/renewable-energy-cost-effective-fossil-fuels-2020\/.","journal-title":"Retrieved from https:\/\/www.forbes.com\/sites\/dominicdudley\/2018\/01\/13\/renewable-energy-cost-effective-fossil-fuels-2020\/"},{"key":"e_1_3_3_209_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2011.2181683"},{"key":"e_1_3_3_210_2","doi-asserted-by":"publisher","DOI":"10.1002\/2016GL069258"},{"key":"e_1_3_3_211_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2018.05.047"},{"key":"e_1_3_3_212_2","article-title":"A vision for the development of benchmarks to bridge geoscience and data science","author":"Ebert-Uphoff Imme","year":"2017","unstructured":"Imme Ebert-Uphoff, David Thompson, Ibrahim Demir, Yulia Gel, Mary Hill, Anuj Karpatne, Mariana Guereque, Vipin Kumar, Enrique Cabal-Cano, and Padhraic Smyth. 2017. A vision for the development of benchmarks to bridge geoscience and data science. In17th International Workshop on Climate Informatics.","journal-title":"17th International Workshop on Climate Informatics"},{"key":"e_1_3_3_213_2","article-title":"ecoRobotix","year":"2021","unstructured":"ecoRobotix. 2021. ecoRobotix. Retrieved from https:\/\/www.ecorobotix.com\/en\/.","journal-title":"Retrieved from https:\/\/www.ecorobotix.com\/en\/"},{"key":"e_1_3_3_214_2","article-title":"How machine learning contributes to smarter pipeline maintenance","author":"Edward Tim","year":"2018","unstructured":"Tim Edward and Rob Salkowitz. 2018. How machine learning contributes to smarter pipeline maintenance. Retrieved from https:\/\/www.oilandgaseng.com\/articles\/how-machine-learning-contributes-to-smarter-pipeline- maintenance\/.","journal-title":"Retrieved from https:\/\/www.oilandgaseng.com\/articles\/how-machine-learning-contributes-to-smarter-pipeline- maintenance\/"},{"key":"e_1_3_3_215_2","first-page":"128","article-title":"History of climate modeling","volume":"2","author":"Edwards P. N.","year":"2010","unstructured":"P. N. Edwards. 2010. History of climate modeling. Wiley Interdisciplinary Reviews: Climate Change 2, 1 (2010), 128\u2013139. Issue 1.","journal-title":"Wiley Interdisciplinary Reviews: Climate Change"},{"key":"e_1_3_3_216_2","unstructured":"Karen Ehrhardt-Martinez Kat A. Donnelly and John A. Skip Laitner. 2010. Advanced metering initiatives and residential feedback programs: A meta-review for household electricity-saving opportunities. American Council for an Energy-Efficient Economy Washington DC."},{"key":"e_1_3_3_217_2","article-title":"Machine learning can boost the value of wind energy","author":"Elkin Carl","year":"2019","unstructured":"Carl Elkin and Sims Witherspoon. 2019. Machine learning can boost the value of wind energy. Retrieved from https:\/\/deepmind.com\/blog\/machine-learning-can-boost-value-wind-energy\/.","journal-title":"Retrieved from https:\/\/deepmind.com\/blog\/machine-learning-can-boost-value-wind-energy\/"},{"key":"e_1_3_3_218_2","volume-title":"Pricing Carbon: The European Union Emissions Trading Scheme","author":"Ellerman A. Denny","year":"2010","unstructured":"A. Denny Ellerman, Frank J. Convery, and Christian De Perthuis. 2010. Pricing Carbon: The European Union Emissions Trading Scheme. Cambridge University Press."},{"key":"e_1_3_3_219_2","doi-asserted-by":"publisher","DOI":"10.1149\/2.0861802jes"},{"key":"e_1_3_3_220_2","volume-title":"The reference electrification model: A computer model for planning rural electricity access","author":"Ellman Douglas Douglas Austin","year":"2015","unstructured":"Douglas Douglas Austin Ellman. 2015. The reference electrification model: A computer model for planning rural electricity access. Ph.D. Dissertation. Massachusetts Institute of Technology."},{"key":"e_1_3_3_221_2","article-title":"Smart \u201cPredict, then Optimize\u201d","author":"Elmachtoub Adam N.","year":"2021","unstructured":"Adam N. Elmachtoub and Paul Grigas. 2021. Smart \u201cPredict, then Optimize\u201d. Management Science (2021).","journal-title":"Management Science"},{"key":"e_1_3_3_222_2","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhz072"},{"key":"e_1_3_3_223_2","doi-asserted-by":"publisher","DOI":"10.5555\/1296126"},{"key":"e_1_3_3_224_2","doi-asserted-by":"publisher","DOI":"10.1080\/01441647.2018.1442887"},{"key":"e_1_3_3_225_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.10.012"},{"key":"e_1_3_3_226_2","doi-asserted-by":"publisher","DOI":"10.1109\/EEM.2017.7981877"},{"key":"e_1_3_3_227_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.03.048"},{"key":"e_1_3_3_228_2","unstructured":"Richard Evans and Jim Gao. 2016. DeepMind AI reduces Google data centre cooling bill by 40%. Retrieved from https:\/\/deepmind.com\/blog\/article\/deepmind-ai-reduces-google-data-centre-cooling-bill-40."},{"key":"e_1_3_3_229_2","doi-asserted-by":"publisher","DOI":"10.1080\/01944363.2016.1245112"},{"key":"e_1_3_3_230_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2013.04.039"},{"key":"e_1_3_3_231_2","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-9-1937-2016"},{"key":"e_1_3_3_232_2","doi-asserted-by":"publisher","DOI":"10.1089\/big.2014.0026"},{"key":"e_1_3_3_233_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mio.2016.04.003"},{"key":"e_1_3_3_234_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.02.041"},{"key":"e_1_3_3_235_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.12.001"},{"key":"e_1_3_3_236_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2011.101911.00087"},{"key":"e_1_3_3_237_2","doi-asserted-by":"publisher","DOI":"10.1088\/0741-3335\/54\/2\/025002"},{"key":"e_1_3_3_238_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/51\/8\/083052"},{"key":"e_1_3_3_239_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.13863"},{"key":"e_1_3_3_240_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139177245"},{"key":"e_1_3_3_241_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-environ-031913-100450"},{"key":"e_1_3_3_242_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat1203"},{"key":"e_1_3_3_243_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5403"},{"key":"e_1_3_3_244_2","volume-title":"Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change","author":"Fischedick Manfred","year":"2014","unstructured":"Manfred Fischedick, Joyashree Roy, Amr Abdel-Aziz, Adolf Acquaye, Julian Allwood, Jean-Paul Ceron, Yong Geng, Haroon Kheshgi, Alessandro Lanza, Daniel Perczyk, Lynn Price, Estela Santalla, Claudia Sheinbaum, and Kanako Tanaka. 2014. Industry. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.). Cambridge University Press."},{"key":"e_1_3_3_245_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.76"},{"key":"e_1_3_3_246_2","volume-title":"Peatland Fires and Carbon Emissions (Bulletin 50)","author":"Flannigan Mike","year":"2012","unstructured":"Mike Flannigan, Chelene Krezek-Hanes, Mike Wotton, Mike Waddington, Merritt Turetsky, and Brian Benscoter. 2012. Peatland Fires and Carbon Emissions (Bulletin 50). Technical Report."},{"key":"e_1_3_3_247_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202130"},{"key":"e_1_3_3_248_2","doi-asserted-by":"publisher","DOI":"10.5194\/acp-18-17529-2018"},{"key":"e_1_3_3_249_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09677-x"},{"key":"e_1_3_3_250_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2018.09.021"},{"key":"e_1_3_3_251_2","article-title":"Integrated Plasma Simulator (IPS) v2.1 documentation","author":"Foley Samantha","year":"2011","unstructured":"Samantha Foley. 2011. Integrated Plasma Simulator (IPS) v2.1 documentation. Retrieved from http:\/\/ipsframework.sourceforge.net\/doc\/html\/.","journal-title":"Retrieved from http:\/\/ipsframework.sourceforge.net\/doc\/html\/"},{"key":"e_1_3_3_252_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1614023113"},{"key":"e_1_3_3_253_2","doi-asserted-by":"publisher","DOI":"10.1145\/2346496.2346499"},{"key":"e_1_3_3_254_2","volume-title":"Workshop on Pervasive Urban Applications","author":"Frias-Martinez Vanessa","year":"2012","unstructured":"Vanessa Frias-Martinez, Victor Soto, Jesus Virseda, and Enrique Frias-Martinez. 2012. Computing cost-effective census maps from cell phone traces. In Workshop on Pervasive Urban Applications."},{"key":"e_1_3_3_255_2","article-title":"Automated identification of climate risk disclosures in annual corporate reports","author":"Friederich David","year":"2021","unstructured":"David Friederich, Lynn H. Kaack, Alexandra Luccioni, and Bjarne Steffen. 2021. Automated identification of climate risk disclosures in annual corporate reports. arXiv preprint arXiv:2108.01415 (2021).","journal-title":"arXiv preprint arXiv:2108.01415"},{"key":"e_1_3_3_256_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.01.180"},{"key":"e_1_3_3_257_2","article-title":"Intelligent packaging systems: Sensors and nanosensors to monitor food quality and safety","volume":"2016","author":"Fuertes Guillermo","year":"2016","unstructured":"Guillermo Fuertes, Ismael Soto, Ra\u00fal Carrasco, Manuel Vargas, Jorge Sabattin, and Carolina Lagos. 2016. Intelligent packaging systems: Sensors and nanosensors to monitor food quality and safety. Journal of Sensors 2016, 2 (2016), 1\u20138.","journal-title":"Journal of Sensors"},{"key":"e_1_3_3_258_2","doi-asserted-by":"publisher","DOI":"10.1002\/aenm.201300060"},{"key":"e_1_3_3_259_2","article-title":"Our position on geoengineering","author":"Fund Environmental Defense","year":"2019","unstructured":"Environmental Defense Fund. 2019. Our position on geoengineering. Retrieved from https:\/\/www.edf.org\/climate\/our-position-geoengineering.","journal-title":"Retrieved from https:\/\/www.edf.org\/climate\/our-position-geoengineering"},{"key":"e_1_3_3_260_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate2392"},{"key":"e_1_3_3_261_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aabf9f"},{"key":"e_1_3_3_262_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0158949"},{"key":"e_1_3_3_263_2","doi-asserted-by":"publisher","DOI":"10.1175\/WAF-D-17-0010.1"},{"key":"e_1_3_3_264_2","doi-asserted-by":"publisher","DOI":"10.1002\/2014GL062231"},{"key":"e_1_3_3_265_2","article-title":"GainForest","year":"2021","unstructured":"GainForest. 2021. GainForest. Retrieved from https:\/\/www.gainforest.app\/.","journal-title":"Retrieved from https:\/\/www.gainforest.app\/"},{"key":"e_1_3_3_266_2","doi-asserted-by":"publisher","DOI":"10.1002\/wene.56"},{"key":"e_1_3_3_267_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-89656-4_28"},{"key":"e_1_3_3_268_2","doi-asserted-by":"publisher","DOI":"10.3389\/fict.2018.00006"},{"key":"e_1_3_3_269_2","unstructured":"Jim Gao. 2014. Machine learning applications for data center optimization. Retrived from https:\/\/docs.google.com\/a\/google.com\/viewer?url=www.google.com\/about\/datacenters\/efficiency\/internal\/assets\/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf."},{"key":"e_1_3_3_270_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8958"},{"key":"e_1_3_3_271_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2013.10.610"},{"key":"e_1_3_3_272_2","article-title":"Accelerated discovery of sustainable building materials","author":"Ge Xiou","year":"2019","unstructured":"Xiou Ge, Richard T. Goodwin, Jeremy R. Gregory, Randolph E. Kirchain, Joana Maria, and Lav R. Varshney. 2019. Accelerated discovery of sustainable building materials. Preprint arXiv:1905.08222 (2019).","journal-title":"Preprint arXiv:1905.08222"},{"key":"e_1_3_3_273_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-017-9740-2"},{"key":"e_1_3_3_274_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs3071447"},{"key":"e_1_3_3_275_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2014.10.004"},{"key":"e_1_3_3_276_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018GL078202"},{"key":"e_1_3_3_277_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1174082"},{"key":"e_1_3_3_278_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1174082"},{"key":"e_1_3_3_279_2","article-title":"A new predictive model for more accurate electrical grid mapping","author":"Gershenson Dimitry","year":"2019","unstructured":"Dimitry Gershenson, Brandon Rohrer, and Anna Lerner. 2019. A new predictive model for more accurate electrical grid mapping. Retrived from https:\/\/code.fb.com\/connectivity\/electrical-grid-mapping\/.","journal-title":"https:\/\/code.fb.com\/connectivity\/electrical-grid-mapping\/"},{"key":"e_1_3_3_280_2","article-title":"Artificial intelligence in logistics: A collaborative report by DHL and IBM on implications and use cases for the logistics industry","author":"Gesing Ben","year":"2018","unstructured":"Ben Gesing and D. Peterson, and S. Michelsen. 2018. Artificial intelligence in logistics: A collaborative report by DHL and IBM on implications and use cases for the logistics industry. DHL Trend Research, Troisdorf.","journal-title":"DHL Trend Research, Troisdorf"},{"key":"e_1_3_3_281_2","doi-asserted-by":"publisher","DOI":"10.1080\/23249935.2016.1273273"},{"key":"e_1_3_3_282_2","doi-asserted-by":"publisher","DOI":"10.1109\/MTITS.2017.8005582"},{"key":"e_1_3_3_283_2","doi-asserted-by":"publisher","DOI":"10.1672\/08-34.1"},{"key":"e_1_3_3_284_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2019.05.006"},{"key":"e_1_3_3_285_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66387-6_6"},{"key":"e_1_3_3_286_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-100504-0.00003-2"},{"key":"e_1_3_3_287_2","doi-asserted-by":"publisher","DOI":"10.1080\/09654313.2017.1294149"},{"key":"e_1_3_3_288_2","doi-asserted-by":"publisher","DOI":"10.1145\/3192335"},{"key":"e_1_3_3_289_2","doi-asserted-by":"publisher","DOI":"10.1257\/jep.32.4.53"},{"key":"e_1_3_3_290_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)WR.1943-5452.0000570"},{"key":"e_1_3_3_291_2","first-page":"1818","volume-title":"53rd AIAA\/ASME\/ASCE\/AHS\/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA\/ASME\/AHS Adaptive Structures Conference 14th AIAA","author":"Glaessgen Edward","year":"2012","unstructured":"Edward Glaessgen and David Stargel. 2012. The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA\/ASME\/ASCE\/AHS\/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA\/ASME\/AHS Adaptive Structures Conference 14th AIAA. 1818."},{"key":"e_1_3_3_292_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2019.09.008"},{"key":"e_1_3_3_293_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2017.08.1217"},{"key":"e_1_3_3_294_2","article-title":"Global Thermostat","author":"Thermostat Global","year":"2021","unstructured":"Global Thermostat. 2021. Global Thermostat. Retrieved from https:\/\/globalthermostat.com\/.","journal-title":"Retrieved from https:\/\/globalthermostat.com\/"},{"key":"e_1_3_3_295_2","article-title":"Zara clothing company supply chain","author":"Globe SCM","year":"2015","unstructured":"SCM Globe. 2015. Zara clothing company supply chain. SCM Globe.","journal-title":"SCM Globe"},{"key":"e_1_3_3_296_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91800-6_2"},{"key":"e_1_3_3_297_2","doi-asserted-by":"publisher","DOI":"10.1145\/3339399"},{"issue":"4","key":"e_1_3_3_298_2","first-page":"5","article-title":"Computational sustainability: Computational methods for a sustainable environment, economy, and society","volume":"39","author":"Gomes Carla P.","year":"2009","unstructured":"Carla P. Gomes. 2009. Computational sustainability: Computational methods for a sustainable environment, economy, and society. The Bridge 39, 4 (2009), 5\u201313.","journal-title":"The Bridge"},{"key":"e_1_3_3_299_2","first-page":"1","article-title":"CRYSTAL: A multi-agent AI system for automated mapping of materials\u2019 crystal structures","author":"Gomes Carla P.","year":"2019","unstructured":"Carla P. Gomes, Junwen Bai, Yexiang Xue, Johan Bj\u00f6rck, Brendan Rappazzo, Sebastian Ament, Richard Bernstein, Shufeng Kong, Santosh K. Suram, R. Bruce van Dover, and John M. Gregoire. 2019. CRYSTAL: A multi-agent AI system for automated mapping of materials\u2019 crystal structures. MRS Communications 9, 2 (2019), 1\u20139.","journal-title":"MRS Communications"},{"key":"e_1_3_3_300_2","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"e_1_3_3_301_2","volume-title":"Introduction to Climate Dynamics and Climate Modeling","author":"Goosse H.","year":"2008","unstructured":"H. Goosse, P. Barriat, W. Lefebvre, M. Loutre, and V. Zunz. 2008\u20132010. Introduction to Climate Dynamics and Climate Modeling. Cambridge University Press."},{"key":"e_1_3_3_302_2","article-title":"We Can Solve the Climate Crisis by Tracing Pollution Back to Its Sources. A New Coalition Will Make It Possible.","author":"Gore Al","year":"2020","unstructured":"Al Gore and Gavin McCormick. 2020. We Can Solve the Climate Crisis by Tracing Pollution Back to Its Sources. A New Coalition Will Make It Possible. Retrieved from https:\/\/medium.com\/@algore\/we-can-solve-the-climate-crisis-by-tracing-pollution-back-to-its-sources-4f535f91a8dd.","journal-title":"Retrieved from https:\/\/medium.com\/@algore\/we-can-solve-the-climate-crisis-by-tracing-pollution-back-to-its-sources-4f535f91a8dd"},{"key":"e_1_3_3_303_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.07.098"},{"key":"e_1_3_3_304_2","unstructured":"Greenpeace. 2019. Oil in the Cloud: How Tech Companies are Helping Big Oil Profit from Climate Destruction. Retrived from https:\/\/www.greenpeace.org\/usa\/reports\/oil-in-the-cloud\/."},{"key":"e_1_3_3_305_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455665"},{"key":"e_1_3_3_306_2","unstructured":"S. Griffith S. Calisch and L. Fraser. 2020. Rewiring America: A Field Manual for the Climate Fight . Rewiring America."},{"key":"e_1_3_3_307_2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/UT.1998.670074","volume-title":"1998 International Symposium on Underwater Technology","author":"Griffiths G.","year":"1998","unstructured":"G. Griffiths, N. W. Millard, S. D. McPhail, P. Stevenson, J. R. Perrett, M. Peabody, A. T. Webb, and D. T. Meldrum. 1998. Towards environmental monitoring with the Autosub autonomous underwater vehicle. In 1998 International Symposium on Underwater Technology. IEEE, 121\u2013125."},{"key":"e_1_3_3_308_2","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mps028"},{"key":"e_1_3_3_309_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018JD029815"},{"key":"e_1_3_3_310_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8MH00653A"},{"key":"e_1_3_3_311_2","article-title":"The Forrester Wave: Big Data Streaming Analytics, Q1 2016","author":"Gualtieri Mike","year":"2016","unstructured":"Mike Gualtieri, Noel Yuhanna, Holger Kisker, Rowan Curran, Brandon Purcell, Sophia Christakis, Shreyas Warrier, and Matthew Izzi. 2016. The Forrester Wave: Big Data Streaming Analytics, Q1 2016. Forrester.com","journal-title":"Forrester.com"},{"key":"e_1_3_3_312_2","article-title":"Machine Learning for AC Optimal Power Flow","author":"Guha Neel","year":"2019","unstructured":"Neel Guha, Zhecheng Wang, Matt Wytock, and Arun Majumdar. 2019. Machine Learning for AC Optimal Power Flow. Retrieved from http:\/\/www.neelguha.com\/opf.pdf.","journal-title":"Retrieved from http:\/\/www.neelguha.com\/opf.pdf"},{"key":"e_1_3_3_313_2","doi-asserted-by":"crossref","unstructured":"Chathika Gunaratne Ivan Garibay and Nguyen Dang. 2020. Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the ancestral Pueblo. PLoS One 15 12 (2020) e0239922.","DOI":"10.1371\/journal.pone.0239922"},{"key":"e_1_3_3_314_2","article-title":"The Power of Peer Pressure in Combatting Climate Change","author":"Gunther Marc","year":"2010","unstructured":"Marc Gunther. 2010. The Power of Peer Pressure in Combatting Climate Change. Retrieved from https:\/\/www.greenbiz.com\/blog\/2010\/01\/19\/power-peer-pressure-combatting-climate-change.","journal-title":"Retrieved from https:\/\/www.greenbiz.com\/blog\/2010\/01\/19\/power-peer-pressure-combatting-climate-change"},{"key":"e_1_3_3_315_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3209859"},{"key":"e_1_3_3_316_2","first-page":"10","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition Workshops","author":"Gupta Ritwik","year":"2019","unstructured":"Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. 2019. Creating xBD: A dataset for assessing building damage from satellite imagery. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. 10\u201317."},{"key":"e_1_3_3_317_2","unstructured":"Jenny Gustavsson Christel Cederberg Ulf Sonesson Robert Van Otterdijk and Alexandre Meybeck. 2011. Global food losses and food waste. Food and Agriculture Organization of the United Nations Rome."},{"key":"e_1_3_3_318_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10098-012-0556-4"},{"key":"e_1_3_3_319_2","doi-asserted-by":"publisher","DOI":"10.1021\/acsenergylett.7b01022"},{"key":"e_1_3_3_320_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700304"},{"key":"e_1_3_3_321_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aae161"},{"key":"e_1_3_3_322_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11060617"},{"key":"e_1_3_3_323_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.01.057"},{"key":"e_1_3_3_324_2","article-title":"Artificial intelligence for social good","author":"Hager Gregory D.","year":"2019","unstructured":"Gregory D. Hager, Ann Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria, Stephen F. Smith, and Milind Tambe. 2019. Artificial intelligence for social good. Preprint arXiv:1901.05406 (2019).","journal-title":"Preprint arXiv:1901.05406"},{"key":"e_1_3_3_325_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2006.01.002"},{"key":"e_1_3_3_326_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2012.02.032"},{"key":"e_1_3_3_327_2","first-page":"5","article-title":"Global urban typology discovery with a latent class choice model","author":"Han Yafei","year":"2018","unstructured":"Yafei Han. 2018. Global urban typology discovery with a latent class choice model. In Transportation Research Board 97th Annual Meeting.5.","journal-title":"Transportation Research Board 97th Annual Meeting."},{"key":"e_1_3_3_328_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1805770115"},{"key":"e_1_3_3_329_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature12238"},{"key":"e_1_3_3_330_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1244693"},{"key":"e_1_3_3_331_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2004.09.020"},{"key":"e_1_3_3_332_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.earscirev.2006.05.001"},{"key":"e_1_3_3_333_2","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305527"},{"key":"e_1_3_3_334_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPEC48276.2020.9042547"},{"key":"e_1_3_3_335_2","doi-asserted-by":"publisher","DOI":"10.2507\/IJSIMM14(1)9.292"},{"key":"e_1_3_3_336_2","volume-title":"Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming","author":"Hawken Paul","year":"2015","unstructured":"Paul Hawken. 2015. Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books."},{"key":"e_1_3_3_337_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1530-9290.2012.00532.x"},{"key":"e_1_3_3_338_2","article-title":"Who profits from industry 4.0? Theory and evidence from the automotive industry","author":"Helper Susan","year":"2019","unstructured":"Susan Helper, Raphael Martins, and Robert Seamans. 2019. Who profits from industry 4.0? Theory and evidence from the automotive industry. NYU Stern School of Business.","journal-title":"NYU Stern School of Business"},{"key":"e_1_3_3_339_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504427"},{"key":"e_1_3_3_340_2","doi-asserted-by":"publisher","DOI":"10.5555\/2125164.2125194"},{"key":"e_1_3_3_341_2","doi-asserted-by":"publisher","DOI":"10.1080\/09332480.2019.1579578"},{"key":"e_1_3_3_342_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/ab0fe3"},{"key":"e_1_3_3_343_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2018.11.044"},{"key":"e_1_3_3_344_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.04.051"},{"key":"e_1_3_3_345_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0277-x"},{"key":"e_1_3_3_346_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2009.08.010"},{"key":"e_1_3_3_347_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau8825"},{"key":"e_1_3_3_348_2","doi-asserted-by":"publisher","DOI":"10.1021\/es505027p"},{"key":"e_1_3_3_349_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2014.08.012"},{"key":"e_1_3_3_350_2","doi-asserted-by":"publisher","DOI":"10.1191\/0309133304pp403ra"},{"key":"e_1_3_3_351_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2012.0137"},{"key":"e_1_3_3_352_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.11.011"},{"key":"e_1_3_3_353_2","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm.2016.7778742"},{"key":"e_1_3_3_354_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/7\/4\/044009"},{"key":"e_1_3_3_355_2","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-15-00135.1"},{"key":"e_1_3_3_356_2","doi-asserted-by":"publisher","DOI":"10.1071\/WF12157"},{"key":"e_1_3_3_357_2","unstructured":"Isabel Hovdahl. 2019. On the use of machine learning for causal inference in climate economics. Working Papers No. 05\/2019 Centre for Applied Macro- and Petroleum economics (CAMP) BI Norwegian Business School."},{"key":"e_1_3_3_358_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2013.2258181"},{"key":"e_1_3_3_359_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2015.2512237"},{"key":"e_1_3_3_360_2","doi-asserted-by":"crossref","unstructured":"Bohao Huang Jichen Yang Artem Streltsov Kyle Bradbury Leslie M. Collins and Jordan Malof. 2021. GridTracer: Automatic mapping of power grids using deep learning and overhead imagery. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing . DOI:10.1109\/JSTARS.2021.3124519","DOI":"10.1109\/JSTARS.2021.3124519"},{"key":"e_1_3_3_361_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10894-020-00258-1"},{"key":"e_1_3_3_362_2","first-page":"8963","volume-title":"EGU General Assembly Conference Abstracts","volume":"14","author":"Hut R. W.","year":"2012","unstructured":"R. W. Hut, N. C. van de Giesen, and J. S. Selker. 2012. The TAHMO project: Designing an unconventional weather station. In EGU General Assembly Conference Abstracts. Vol. 14. 8963."},{"key":"e_1_3_3_363_2","first-page":"101","volume-title":"Actes de la conf\u00e9rence Traitement Automatique de la Langue Naturelle (TALN\u201918)","author":"Huynh Duy","year":"2018","unstructured":"Duy Huynh and Nathalie Neptune. 2018. Annotation automatique d\u2019images: Le cas de la d\u00e9forestation. In Actes de la conf\u00e9rence Traitement Automatique de la Langue Naturelle (TALN\u201918). 101."},{"key":"e_1_3_3_364_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330674"},{"key":"e_1_3_3_365_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2017.11.009"},{"key":"e_1_3_3_366_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-008-0115-1"},{"key":"e_1_3_3_367_2","article-title":"The Water, Peace and Security Partnership","author":"Education IHE Delft Institute for Water","year":"2019","unstructured":"IHE Delft Institute for Water Education. 2019. The Water, Peace and Security Partnership. Retrieved from https:\/\/ www.un-ihe.org\/water-peace-and-security-partnership.","journal-title":"Retrieved from https:\/\/ www.un-ihe.org\/water-peace-and-security-partnership"},{"key":"e_1_3_3_368_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-018-0153-6"},{"key":"e_1_3_3_369_2","doi-asserted-by":"publisher","DOI":"10.1145\/2771588"},{"key":"e_1_3_3_370_2","article-title":"Portal TerraBrasilis","author":"Espaciais Instituto Nacional de Pesquisas","year":"2020","unstructured":"Instituto Nacional de Pesquisas Espaciais. 2020. Portal TerraBrasilis. Retrieved from http:\/\/terrabrasilis.dpi.inpe.br\/en\/home-page\/.","journal-title":"Retrieved from http:\/\/terrabrasilis.dpi.inpe.br\/en\/home-page\/"},{"key":"e_1_3_3_371_2","volume-title":"Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, J. C. Minx (Eds.)","year":"2014","unstructured":"IPCC. 2014. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, J. C. Minx (Eds.). Intergovernmental Panel on Climate Change."},{"key":"e_1_3_3_372_2","volume-title":"Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R. K. Pachauri and L. A. Meyer (Eds.)","year":"2014","unstructured":"IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R. K. Pachauri and L. A. Meyer (Eds.). Intergovernmental Panel on Climate Change."},{"key":"e_1_3_3_373_2","volume-title":"Global warming of 1.5\u00b1C. An IPCC special report on the impacts of global warming of 1.5\u00b1C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Intergovernmental Panel on Climate Change.","year":"2018","unstructured":"IPCC. 2018. Global warming of 1.5\u00b1C. An IPCC special report on the impacts of global warming of 1.5\u00b1C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Intergovernmental Panel on Climate Change."},{"key":"e_1_3_3_374_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00374-3"},{"key":"e_1_3_3_375_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-019-0398-8"},{"key":"e_1_3_3_376_2","doi-asserted-by":"publisher","DOI":"10.1002\/wcc.423"},{"key":"e_1_3_3_377_2","first-page":"46","article-title":"Climate change induced migrations from a cell phone perspective","author":"Isaacman Sibren","year":"2017","unstructured":"Sibren Isaacman, Vanessa Frias-Martinez, Lingzi Hong, and Enrique Frias-Martinez. 2017. Climate change induced migrations from a cell phone perspective. NetMob (2017), 46.","journal-title":"NetMob"},{"key":"e_1_3_3_378_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3209860"},{"key":"e_1_3_3_379_2","doi-asserted-by":"publisher","DOI":"10.5194\/acp-16-14371-2016"},{"key":"e_1_3_3_380_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2017.09.027"},{"key":"e_1_3_3_381_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323"},{"key":"e_1_3_3_382_2","doi-asserted-by":"publisher","DOI":"10.1145\/2160601.2160616"},{"key":"e_1_3_3_383_2","article-title":"Meta-optimization of optimal power flow","author":"Jamei Mahdi","year":"2019","unstructured":"Mahdi Jamei, Letif Mones, Alex Robson, Lyndon White, James Requeima, and Cozmin Ududec. 2019. Meta-optimization of optimal power flow. In ICML Workshop on Climate Change: How Can AI Help?","journal-title":"ICML Workshop on Climate Change: How Can AI Help?"},{"key":"e_1_3_3_384_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.07.007"},{"key":"e_1_3_3_385_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.11.024"},{"key":"e_1_3_3_386_2","unstructured":"Natasha Jaques Angeliki Lazaridou Edward Hughes \u00c7aglar G\u00fcl\u00e7ehre Pedro A. Ortega D. J. Strouse Joel Z. Leibo and Nando de Freitas. In Freitas Proceedings of the 36th International Conference on Machine Learning ."},{"key":"e_1_3_3_387_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2018.06.011"},{"key":"e_1_3_3_388_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"},{"key":"e_1_3_3_389_2","first-page":"1","volume-title":"2016 IEEE Power and Energy Society General Meeting (PESGM\u201916)","author":"Jiang Huaiguang","year":"2016","unstructured":"Huaiguang Jiang and Yingchen Zhang. 2016. Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine. In 2016 IEEE Power and Energy Society General Meeting (PESGM\u201916). IEEE, 1\u20135."},{"key":"e_1_3_3_390_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2016.0176"},{"key":"e_1_3_3_391_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/ISBB.2015.7344954","volume-title":"2015 International Symposium on Bioelectronics and Bioinformatics (ISBB\u201915)","author":"Jiang Qiling","year":"2015","unstructured":"Qiling Jiang, Liujuan Cao, Ming Cheng, Cheng Wang, and Jonathan Li. 2015. Deep neural networks-based vehicle detection in satellite images. In 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB\u201915). IEEE, 184\u2013187."},{"key":"e_1_3_3_392_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505821.2505828"},{"key":"e_1_3_3_393_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11020412"},{"key":"e_1_3_3_394_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.08.166"},{"key":"e_1_3_3_395_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep33707"},{"key":"e_1_3_3_396_2","doi-asserted-by":"publisher","DOI":"10.3102\/0034654317740335"},{"key":"e_1_3_3_397_2","doi-asserted-by":"publisher","DOI":"10.1029\/2008JD011450"},{"key":"e_1_3_3_398_2","doi-asserted-by":"publisher","DOI":"10.1111\/risa.12601"},{"key":"e_1_3_3_399_2","doi-asserted-by":"publisher","DOI":"10.1021\/es4034364"},{"key":"e_1_3_3_400_2","doi-asserted-by":"publisher","DOI":"10.1021\/es102221h"},{"key":"e_1_3_3_401_2","volume-title":"Peatlands: Guidance for Climate Change Mitigation through Conservation, Rehabilitation and Sustainable Use","author":"Joosten Hans","year":"2012","unstructured":"Hans Joosten, Marja-Liisa Tapio-Bistr\u00f6m, and Susanna Tol. 2012. Peatlands: Guidance for Climate Change Mitigation through Conservation, Rehabilitation and Sustainable Use. Food and Agriculture Organization of the United Nations."},{"key":"e_1_3_3_402_2","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-017-08675-7"},{"key":"e_1_3_3_403_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.12.002"},{"key":"e_1_3_3_404_2","volume-title":"Challenges and Prospects for Data-Driven Climate Change Mitigation","author":"Kaack Lynn Helena","year":"2019","unstructured":"Lynn Helena Kaack. 2019. Challenges and Prospects for Data-Driven Climate Change Mitigation. Ph.D. Dissertation. Carnegie Mellon University, Pittsburgh, PA."},{"key":"e_1_3_3_405_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1619938114"},{"key":"e_1_3_3_406_2","doi-asserted-by":"publisher","DOI":"10.1145\/3314344.3332480"},{"key":"e_1_3_3_407_2","unstructured":"Lynn H. Kaack Priya L. Donti Emma Strubell and David Rolnick. 2020. Artificial intelligence and climate change: Opportunities considerations and policy levers to align AI with climate change goals. Retrived from https:\/\/eu.boell.org\/en\/2020\/12\/03\/artificial-intelligence-and-climate-change."},{"key":"e_1_3_3_408_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aad56c"},{"issue":"1","key":"e_1_3_3_409_2","first-page":"011402\u2013011402\u20131","article-title":"Autonomous electric vehicle sharing system design","volume":"139","author":"Kang Namwoo","year":"2016","unstructured":"Namwoo Kang, Fred M. Feinberg, and Panos Y. Papalambros. 2016. Autonomous electric vehicle sharing system design. Journal of Mechanical Design 139, 1 (2016), 011402\u2013011402\u201310.","journal-title":"Journal of Mechanical Design"},{"key":"e_1_3_3_410_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2017.11.001"},{"key":"e_1_3_3_411_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2905348"},{"key":"e_1_3_3_412_2","doi-asserted-by":"publisher","DOI":"10.1109\/PTC.2019.8810586"},{"key":"e_1_3_3_413_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0093"},{"key":"e_1_3_3_414_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1116-4"},{"key":"e_1_3_3_415_2","doi-asserted-by":"publisher","DOI":"10.3390\/cryst9010054"},{"key":"e_1_3_3_416_2","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-13-00255.1"},{"key":"e_1_3_3_417_2","doi-asserted-by":"publisher","DOI":"10.1145\/3126594.3126662"},{"key":"e_1_3_3_418_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2017.12.019"},{"key":"e_1_3_3_419_2","article-title":"The science and technology of solar geoengineering: A compact summary","author":"Keith David","year":"2018","unstructured":"David Keith and Peter Irvine. 2018. The science and technology of solar geoengineering: A compact summary. In Workshop on Governance of the Deployment of Solar Geoengineering.","journal-title":"Workshop on Governance of the Deployment of Solar Geoengineering"},{"key":"e_1_3_3_420_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.energy.25.1.245"},{"issue":"3","key":"e_1_3_3_421_2","first-page":"71","article-title":"Toward a responsible solar geoengineering research program","volume":"33","author":"Keith David W.","year":"2017","unstructured":"David W. Keith. 2017. Toward a responsible solar geoengineering research program. Issues in Science and Technology 33, 3 (2017), 71\u201377.","journal-title":"Issues in Science and Technology"},{"key":"e_1_3_3_422_2","doi-asserted-by":"publisher","DOI":"10.1145\/3208903.3208923"},{"key":"e_1_3_3_423_2","first-page":"171","article-title":"Integrated assessment models for climate change control","author":"Kelly David L.","year":"1999","unstructured":"David L. Kelly and Charles D. Kolstad. 1999. Integrated assessment models for climate change control. In International Yearbook of Environmental and Resource Economics 1999\/2000. Edward Elgar, 171\u2013197.","journal-title":"International Yearbook of Environmental and Resource Economics 1999\/2000"},{"key":"e_1_3_3_424_2","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821672"},{"key":"e_1_3_3_425_2","doi-asserted-by":"publisher","DOI":"10.1002\/ecs2.1434"},{"key":"e_1_3_3_426_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.forpol.2014.09.005"},{"key":"e_1_3_3_427_2","article-title":"Field-level crop type classification with k nearest neighbors: A baseline for a new Kenya smallholder dataset","author":"Kerner Hannah","year":"2020","unstructured":"Hannah Kerner, Catherine Nakalembe, and Inbal Becker-Reshef. 2020. Field-level crop type classification with k nearest neighbors: A baseline for a new Kenya smallholder dataset. In ICLR Workshop on Tackling Climate Change with Machine Learning.","journal-title":"ICLR Workshop on Tackling Climate Change with Machine Learning"},{"key":"e_1_3_3_428_2","article-title":"Rapid response crop maps in data sparse regions","author":"Kerner Hannah","year":"2020","unstructured":"Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. 2020. Rapid response crop maps in data sparse regions. In KDD Workshop on Humanitarian Mapping.","journal-title":"KDD Workshop on Humanitarian Mapping"},{"key":"e_1_3_3_429_2","volume-title":"Hyperspectral Imaging What is it? How does it work?","author":"Gibbons Kevin P.","year":"2014","unstructured":"Kevin P. Gibbons. 2014. Hyperspectral Imaging What is it? How does it work?Technical Report. Retrived from https:\/\/www.techbriefs.com\/component\/content\/article\/tb\/features\/application-briefs\/19507."},{"key":"e_1_3_3_430_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.10.117"},{"key":"e_1_3_3_431_2","doi-asserted-by":"publisher","DOI":"10.1596\/1813-9450-5057"},{"key":"e_1_3_3_432_2","volume-title":"Welfare Impacts of Rural Electrification: A Case Study from Bangladesh","author":"Khandker Shahidur R.","year":"2009","unstructured":"Shahidur R. Khandker, Douglas F. Barnes, and Hussain A. Samad. 2009. Welfare Impacts of Rural Electrification: A Case Study from Bangladesh. The World Bank."},{"key":"e_1_3_3_433_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.08.237"},{"key":"e_1_3_3_434_2","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-13-0146"},{"key":"e_1_3_3_435_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.126"},{"key":"e_1_3_3_436_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrefrig.2012.06.007"},{"key":"e_1_3_3_437_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gloenvcha.2014.02.008"},{"key":"e_1_3_3_438_2","doi-asserted-by":"publisher","DOI":"10.1002\/0470020598"},{"key":"e_1_3_3_439_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0006392"},{"key":"e_1_3_3_440_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-018-0201-2"},{"key":"e_1_3_3_441_2","doi-asserted-by":"publisher","DOI":"10.1109\/RICE.2018.8509050"},{"key":"e_1_3_3_442_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v34i3.2484"},{"key":"e_1_3_3_443_2","unstructured":"Julian K\u00f6lbel Markus Leippold Jordy Rillaerts and Qian Wang. 2020. Does the CDS market reflect regulatory climate risk disclosures? Working Paper University of Zurich."},{"key":"e_1_3_3_444_2","doi-asserted-by":"publisher","DOI":"10.5555\/2997189.2997318"},{"key":"e_1_3_3_445_2","doi-asserted-by":"publisher","DOI":"10.5555\/2900423.2900637"},{"key":"e_1_3_3_446_2","first-page":"1472","volume-title":"15th International Conference on Artificial Intelligence and Statistics","author":"Kolter J. Zico","year":"2012","unstructured":"J. Zico Kolter and Tommi Jaakkola. 2012. Approximate inference in additive factorial HMMs with application to energy disaggregation. In 15th International Conference on Artificial Intelligence and Statistics. 1472\u20131482."},{"key":"e_1_3_3_447_2","article-title":"Physics-Guided Data Science for Food Security and Climate","author":"Konduri Venkata Shashank","year":"2019","unstructured":"Venkata Shashank Konduri, Jitendra Kumar, Forrest Hoffman, Udit Bhatia, Tarik Gouthier, and Auroop Ganguly. 2019. Physics-Guided Data Science for Food Security and Climate. In KDD Feed Workshop 2019. Retrived from https:\/\/drive.google.com\/file\/d\/1dOGIjbgMGPTpnFIvimpOlPvz28BMw2_Q\/view.","journal-title":"KDD Feed Workshop 2019"},{"key":"e_1_3_3_448_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.12.065"},{"key":"e_1_3_3_449_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.04.005"},{"key":"e_1_3_3_450_2","doi-asserted-by":"publisher","DOI":"10.1002\/2017EF000663"},{"key":"e_1_3_3_451_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13061372"},{"key":"e_1_3_3_452_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.004"},{"key":"e_1_3_3_453_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.2847757"},{"key":"e_1_3_3_454_2","doi-asserted-by":"crossref","unstructured":"Noemi Kreif and Karla DiazOrdaz. 2019. Machine learning in policy evaluation: New tools for causal inference. In Oxford Research Encyclopedia of Economics and Finance . OUP.","DOI":"10.1093\/acrefore\/9780190625979.013.256"},{"key":"e_1_3_3_455_2","volume-title":"Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation","author":"Krile Robert","year":"2016","unstructured":"Robert Krile, Fred Todt, and Jeremy Schroeder. 2016. Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation. Technical Report FHWA-PL-16-012. Federal Highway Administration, Washington, D.C."},{"issue":"1","key":"e_1_3_3_456_2","first-page":"1","article-title":"New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding","volume":"10","author":"Kulp Scott A.","year":"2019","unstructured":"Scott A. Kulp and Benjamin H. Strauss. 2019. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications 10, 1 (2019), 1\u201312.","journal-title":"Nature Communications"},{"key":"e_1_3_3_457_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00054"},{"key":"e_1_3_3_458_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"e_1_3_3_459_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2017.08.009"},{"key":"e_1_3_3_460_2","article-title":"Quantifying the carbon emissions of machine learning","author":"Lacoste Alexandre","year":"2019","unstructured":"Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres. 2019. Quantifying the carbon emissions of machine learning. Preprint arXiv:1910.09700 (2019).","journal-title":"Preprint arXiv:1910.09700"},{"key":"e_1_3_3_461_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.02.069"},{"key":"e_1_3_3_462_2","doi-asserted-by":"publisher","DOI":"10.1243\/09544097JRRT92"},{"key":"e_1_3_3_463_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ufug.2013.11.001"},{"key":"e_1_3_3_464_2","doi-asserted-by":"publisher","DOI":"10.1175\/2009WAF2222330.1"},{"key":"e_1_3_3_465_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295387"},{"key":"e_1_3_3_466_2","article-title":"Zero-shot learning of aerosol optical properties with graph neural networks","author":"Lamb Kara D.","year":"2021","unstructured":"Kara D. Lamb and Pierre Gentine. 2021. Zero-shot learning of aerosol optical properties with graph neural networks. arXiv preprint arXiv:2107.10197 (2021).","journal-title":"arXiv preprint arXiv:2107.10197"},{"key":"e_1_3_3_467_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-019-0440-x"},{"key":"e_1_3_3_468_2","doi-asserted-by":"publisher","DOI":"10.5555\/1889788.1889832"},{"key":"e_1_3_3_469_2","volume-title":"Aerospace Technologies Advancements","author":"Lary D. J.","year":"2010","unstructured":"D. J. Lary. 2010. Artificial intelligence in geoscience and remote sensing. In Aerospace Technologies Advancements. BoD\u2013Books on Demand."},{"key":"e_1_3_3_470_2","first-page":"3","article-title":"Machine learning in geosciences and remote sensing","author":"Lary David J.","year":"2015","unstructured":"David J. Lary, Amir H. Alavi, Amir H. Gandomi, and Annette L. Walker. 2015. Machine learning in geosciences and remote sensing. Geoscience Frontiers 7 (2015), 3\u201310.","journal-title":"Geoscience Frontiers"},{"key":"e_1_3_3_471_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-65633-5_8"},{"key":"e_1_3_3_472_2","doi-asserted-by":"publisher","DOI":"10.5555\/2948890"},{"key":"e_1_3_3_473_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1248506"},{"key":"e_1_3_3_474_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2800535"},{"key":"e_1_3_3_475_2","doi-asserted-by":"publisher","DOI":"10.2514\/6.2015-2272"},{"key":"e_1_3_3_476_2","doi-asserted-by":"publisher","DOI":"10.5555\/3306127.3331808"},{"key":"e_1_3_3_477_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115527"},{"key":"e_1_3_3_478_2","first-page":"1","article-title":"Making Concrete Change, Innovation in Low-carbon Cement and Concrete","author":"Lehne Johanna","year":"2018","unstructured":"Johanna Lehne and Felix Preston. 2018. Making Concrete Change, Innovation in Low-carbon Cement and Concrete. Chatham House Report, Energy Enivronment and Resources Department: London, UK, 1\u201366.","journal-title":"Chatham House Report, Energy Enivronment and Resources Department: London, UK"},{"key":"e_1_3_3_479_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.12.069"},{"key":"e_1_3_3_480_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11111378"},{"key":"e_1_3_3_481_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2015.10.012"},{"key":"e_1_3_3_482_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2896923"},{"key":"e_1_3_3_483_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18082674"},{"key":"e_1_3_3_484_2","doi-asserted-by":"publisher","DOI":"10.2172\/1422303"},{"key":"e_1_3_3_485_2","volume-title":"Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts","author":"Lian Jianming","year":"2018","unstructured":"Jianming Lian, Wei Zhang, Y. Sun, Laurentiu D. Marinovici, Karanjit Kalsi, and Steven E. Widergren. 2018. Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts. Technical Report. Pacific Northwest National Lab, Richland, WA."},{"key":"e_1_3_3_486_2","first-page":"673","article-title":"Does geoengineering present a moral hazard","volume":"40","author":"Lin Albert C.","year":"2013","unstructured":"Albert C. Lin. 2013. Does geoengineering present a moral hazard. Ecology Law Quarterly 40, 3 (2013), 673.","journal-title":"Ecology Law Quarterly"},{"issue":"085103","key":"e_1_3_3_487_2","article-title":"Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty","volume":"27","author":"Ling J.","year":"2015","unstructured":"J. Ling and J. Templeton. 2015. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids 27, 085103 (2015).","journal-title":"Physics of Fluids"},{"key":"e_1_3_3_488_2","doi-asserted-by":"publisher","DOI":"10.14358\/PERS.74.10.1201"},{"key":"e_1_3_3_489_2","article-title":"Application of deep convolutional neural networks for detecting extreme weather in climate datasets","author":"Liu Yunjie","year":"2016","unstructured":"Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, and William Collins. 2016. Application of deep convolutional neural networks for detecting extreme weather in climate datasets. In International Conference on Advances in Big Data Analytics.","journal-title":"International Conference on Advances in Big Data Analytics"},{"key":"e_1_3_3_490_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800254"},{"key":"e_1_3_3_491_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmat.2017.08.002"},{"key":"e_1_3_3_492_2","article-title":"Technical and economic aspects of load following with nuclear power plants","author":"Lokhov Alexey","year":"2011","unstructured":"Alexey Lokhov. 2011. Technical and economic aspects of load following with nuclear power plants. NEA, OECD, Paris, France.","journal-title":"NEA, OECD, Paris, France"},{"key":"e_1_3_3_493_2","article-title":"Intelligent drone swarm for search and rescue operations at sea","author":"Lomonaco Vincenzo","year":"2018","unstructured":"Vincenzo Lomonaco, Angelo Trotta, Marta Ziosi, Juan De Dios Y\u00e1\u00f1ez \u00c1vila, and Natalia D\u00edaz-Rodr\u00edguez. 2018. Intelligent drone swarm for search and rescue operations at sea. Preprint arXiv:1811.05291 (2018).","journal-title":"Preprint arXiv:1811.05291"},{"key":"e_1_3_3_494_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103894"},{"key":"e_1_3_3_495_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2014.0924"},{"key":"e_1_3_3_496_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)UP.1943-5444.0000469"},{"key":"e_1_3_3_497_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2014.07.005"},{"key":"e_1_3_3_498_2","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-6-1157-2013"},{"key":"e_1_3_3_499_2","article-title":"Analyzing sustainability reports using natural language processing","author":"Luccioni Alexandra","year":"2020","unstructured":"Alexandra Luccioni, Emily Baylor, and Nicolas Duchene. 2020. Analyzing sustainability reports using natural language processing. arXiv preprint arXiv:2011.08073 (2020).","journal-title":"arXiv preprint arXiv:2011.08073"},{"key":"e_1_3_3_500_2","volume-title":"Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.). Cambridge University Press, Cambridge, UK.","author":"Lucon O.","year":"2014","unstructured":"O. Lucon, D. \u00dcrge Vorsatz, A. Zain Ahmed, P. Bertoldi, L. F. Cabeza, N. Eyre, A. Gadgil, L. D. D. Harvey, Y. Jiang, S. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C. Pyke, and M. V. Vilari\u00f1o. 2014. Buildings. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.). Cambridge University Press, Cambridge, UK."},{"key":"e_1_3_3_501_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jqsrt.2018.05.002"},{"key":"e_1_3_3_502_2","article-title":"Machine learning-based estimation of forest carbon stocks to increase transparency of forest preservation efforts","author":"L\u00fctjens Bj\u00f6rn","year":"2019","unstructured":"Bj\u00f6rn L\u00fctjens, Lucas Liebenwein, and Katharina Kramer. 2019. Machine learning-based estimation of forest carbon stocks to increase transparency of forest preservation efforts. arXiv preprint arXiv:1912.07850 (2019).","journal-title":"arXiv preprint arXiv:1912.07850"},{"key":"e_1_3_3_503_2","article-title":"Computing robust strategies for managing invasive plants","author":"Lydakis Andreas","year":"2018","unstructured":"Andreas Lydakis, Jenica M. Allen, Marek Petrik, and Tim Szewczyk. 2018. Computing robust strategies for managing invasive plants. Retrieved from https:\/\/marek.petrik.us\/pub\/Lydakis2018.pdf.","journal-title":"Retrieved from https:\/\/marek.petrik.us\/pub\/Lydakis2018.pdf"},{"key":"e_1_3_3_504_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287098.3287100"},{"key":"e_1_3_3_505_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.09.002"},{"key":"e_1_3_3_506_2","article-title":"Monitoring of the Andean Amazon Project","year":"2020","unstructured":"MAAP. 2020. Monitoring of the Andean Amazon Project. Retrieved from https:\/\/maaproject.org\/about-maap\/.","journal-title":"Retrieved from https:\/\/maaproject.org\/about-maap\/"},{"key":"e_1_3_3_507_2","volume-title":"Global Forest Resources Assessment 2015: How Are the World\u2019s Forests Changing?","author":"MacDicken K.","year":"2016","unstructured":"K. MacDicken, \u00d6. Jonsson, L. Pi\u00f1a, S. Maulo, V. Contessa, Y. Adikari, M. Garzuglia, E. Lindquist, G. Reams, and R. D\u2019Annunzio. 2016. Global Forest Resources Assessment 2015: How Are the World\u2019s Forests Changing?FAO."},{"key":"e_1_3_3_508_2","volume-title":"Sustainable Energy-Without the Hot Air","author":"MacKay David","year":"2008","unstructured":"David MacKay. 2008. Sustainable Energy-Without the Hot Air. UIT Cambridge."},{"key":"e_1_3_3_509_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-053018-023725"},{"key":"e_1_3_3_510_2","doi-asserted-by":"publisher","DOI":"10.1002\/2015GL065391"},{"key":"e_1_3_3_511_2","doi-asserted-by":"crossref","unstructured":"Roberto Maestre Juan Ram\u00f3n Duque Alberto Rubio and Juan Ar\u00e9valo. 2018. Reinforcement learning for fair dynamic pricing. In Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing K. Arai S. Kapoor R. Bhatia (Eds). vol. 868 Springer Cham 120\u2013135.","DOI":"10.1007\/978-3-030-01054-6_8"},{"key":"e_1_3_3_512_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aa836d"},{"key":"e_1_3_3_513_2","first-page":"413","article-title":"Wind power forecasting: A systematic literature review","author":"Maldonado-Correa Jorge","year":"2019","unstructured":"Jorge Maldonado-Correa, J. C. Solano, and Marco Rojas-Moncayo. 2019. Wind power forecasting: A systematic literature review. Wind Engineering 45, 2 (2019), 413\u2013426.","journal-title":"Wind Engineering"},{"key":"e_1_3_3_514_2","unstructured":"Kolya Malkin Caleb Robinson Le Hou Rachel Soobitsky Jacob Czawlytko Dimitris Samaras Joel Saltz Lucas Joppa and Nebojsa Jojic. 2018. Label super-resolution networks. In ICLR 2019 Conference ."},{"key":"e_1_3_3_515_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.08.191"},{"key":"e_1_3_3_516_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11116-016-9747-x"},{"key":"e_1_3_3_517_2","doi-asserted-by":"publisher","DOI":"10.1145\/3106426.3115589"},{"key":"e_1_3_3_518_2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3347466"},{"key":"e_1_3_3_519_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1703514114"},{"key":"e_1_3_3_520_2","unstructured":"Antoine Marot Benjamin Donnot Gabriel Dulac-Arnold Adrian Kelly A\u00efdan O\u2019Sullivan Jan Viebahn Mariette Awad Isabelle Guyon Patrick Panciatici and Camilo Romero. 2021. Learning to run a power network challenge: A retrospective analysis. In Proceedings of the Machine Learning Research Competition and Demonstration Track (NeurIPS\u201920) . 133: 112\u2013132."},{"key":"e_1_3_3_521_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2020.106635"},{"key":"e_1_3_3_522_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate3206"},{"key":"e_1_3_3_523_2","doi-asserted-by":"crossref","unstructured":"David Martimort and Wilfried Sand-Zantman. 2016. A mechanism design approach to climate agreements. Journal of the European Economic Association 14 3 (2016) 669\u2013718","DOI":"10.1111\/jeea.12150"},{"key":"e_1_3_3_524_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2016.01.049"},{"key":"e_1_3_3_525_2","article-title":"PVNet: A LRCN architecture for spatio-temporal photovoltaic powerforecasting from numerical weather prediction","author":"Mathe Johan","year":"2019","unstructured":"Johan Mathe, Nina Miolane, Nicolas Sebastien, and Jeremie Lequeux. 2019. PVNet: A LRCN architecture for spatio-temporal photovoltaic powerforecasting from numerical weather prediction. Preprint arXiv:1902.01453 (2019).","journal-title":"Preprint arXiv:1902.01453"},{"key":"e_1_3_3_526_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2013.08.013"},{"key":"e_1_3_3_527_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2010.11.004"},{"key":"e_1_3_3_528_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/7\/3\/034019"},{"key":"e_1_3_3_529_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2014.10.008"},{"key":"e_1_3_3_530_2","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-16-0123.1"},{"key":"e_1_3_3_531_2","doi-asserted-by":"publisher","DOI":"10.5555\/2900728.2900777"},{"key":"e_1_3_3_532_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-8806-4_11"},{"key":"e_1_3_3_533_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2014.985449"},{"key":"e_1_3_3_534_2","unstructured":"Sreejith Menon Tanya Berger-Wolf Emre Kiciman Lucas Joppa Charles V. Stewart Jason Parham Jonathan Crall Jason Holmberg and Jonathan Van Oast. 2016. Animal population estimation using Flickr images. In 2nd International Workshop on the Social Web for Environmental and Ecological Monitoring."},{"key":"e_1_3_3_535_2","article-title":"MethaneSAT","year":"2021","unstructured":"MethaneSAT. 2021. MethaneSAT. Retrieved from https:\/\/www.methanesat.org\/.","journal-title":"Retrieved from https:\/\/www.methanesat.org\/"},{"key":"e_1_3_3_536_2","article-title":"Computer generated building footprints for the United States","year":"2018","unstructured":"Microsoft. 2018. Computer generated building footprints for the United States. Retrieved from https:\/\/github.com\/Microsoft\/USBuildingFootprints.","journal-title":"Retrieved from https:\/\/github.com\/Microsoft\/USBuildingFootprints"},{"key":"e_1_3_3_537_2","first-page":"102526","article-title":"Machine learning for geographically differentiated climate change mitigation in urban areas","author":"Milojevic-Dupont Nikola","year":"2020","unstructured":"Nikola Milojevic-Dupont and Felix Creutzig. 2020. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustainable Cities and Society 64 (2020), 102526.","journal-title":"Sustainable Cities and Society"},{"key":"e_1_3_3_538_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242010"},{"key":"e_1_3_3_539_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2017.10.018"},{"key":"e_1_3_3_540_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wasman.2007.10.003"},{"key":"e_1_3_3_541_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aabf9b"},{"key":"e_1_3_3_542_2","article-title":"Learning for constrained optimization: Identifying optimal active constraint sets","author":"Misra Sidhant","year":"2018","unstructured":"Sidhant Misra, Line Roald, and Yeesian Ng. 2018. Learning for constrained optimization: Identifying optimal active constraint sets. arXiv preprint arXiv:1802.09639 (2018).","journal-title":"arXiv preprint arXiv:1802.09639"},{"key":"e_1_3_3_543_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018WR023528"},{"key":"e_1_3_3_544_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2834219"},{"key":"e_1_3_3_545_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2016.01.030"},{"key":"e_1_3_3_546_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04191-5_23"},{"key":"e_1_3_3_547_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-3-030-22788-3_4","volume-title":"Large Scale Optimization in Supply Chains and Smart Manufacturing","author":"Moehle Nicholas","year":"2019","unstructured":"Nicholas Moehle, Enzo Busseti, Stephen Boyd, and Matt Wytock. 2019. Dynamic energy management. In Large Scale Optimization in Supply Chains and Smart Manufacturing. Springer, 69\u2013126."},{"key":"e_1_3_3_548_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2015.07.009"},{"key":"e_1_3_3_549_2","first-page":"81","volume-title":"Computational Intelligent Data Analysis for Sustainable Development; Data Mining and Knowledge Discovery Series","author":"Monteleoni C.","year":"2013","unstructured":"C. Monteleoni, G. A. Schmidt, F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser, M. Tippett, A. Banerjee, M. B. Blumenthal, A. R. Ganguly, J. E. Smerdon, and M. Tedesco. 2013. Climate jnformatic. In Computational Intelligent Data Analysis for Sustainable Development; Data Mining and Knowledge Discovery Series. T. Yu, N. Chawla, and S. Simoff (Eds.). CRC Press, Taylor & Francis Group, Chapter 4, 81\u2013126."},{"key":"e_1_3_3_550_2","doi-asserted-by":"publisher","DOI":"10.1002\/sam.10126"},{"key":"e_1_3_3_551_2","first-page":"281","article-title":"Fire: An agent and a consequence of land use change","author":"Montgomery Claire A.","year":"2014","unstructured":"Claire A. Montgomery. 2014. Fire: An agent and a consequence of land use change. In The Oxford Handbook of Land Economics. OUP, 281.","journal-title":"The Oxford Handbook of Land Economics"},{"key":"e_1_3_3_552_2","doi-asserted-by":"publisher","DOI":"10.1002\/cssc.201500322"},{"key":"e_1_3_3_553_2","doi-asserted-by":"publisher","DOI":"10.4271\/2017-01-1276"},{"key":"e_1_3_3_554_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.117.004233"},{"key":"e_1_3_3_555_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate3322"},{"key":"e_1_3_3_556_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781316882665"},{"key":"e_1_3_3_557_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11625-017-0521-6"},{"key":"e_1_3_3_558_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2014.11.427"},{"key":"e_1_3_3_559_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2017.02.007"},{"key":"e_1_3_3_560_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12071301"},{"key":"e_1_3_3_561_2","doi-asserted-by":"publisher","DOI":"10.1111\/itor.12337"},{"key":"e_1_3_3_562_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature08823"},{"key":"e_1_3_3_563_2","article-title":"The Supermarket of the Future Knows Exactly What You\u2019re Eating","author":"Mucci Alberto","year":"2016","unstructured":"Alberto Mucci. 2016. The Supermarket of the Future Knows Exactly What You\u2019re Eating. Retrieved from https:\/\/www.vice.com\/en_us\/article\/4xbppn\/the-supermarket-of-the-future-knows-exactly-what-youre-eating.","journal-title":"Retrieved from https:\/\/www.vice.com\/en_us\/article\/4xbppn\/the-supermarket-of-the-future-knows-exactly-what-youre-eating"},{"key":"e_1_3_3_564_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1800371"},{"key":"e_1_3_3_565_2","doi-asserted-by":"publisher","DOI":"10.1080\/24751839.2019.1565653"},{"key":"e_1_3_3_566_2","volume-title":"18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences","author":"Mukkavilli Surya Karthik","year":"2019","unstructured":"Surya Karthik Mukkavilli. 2019. EnviroNet: ImageNet for environment. In 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences. American Meteorological Society."},{"key":"e_1_3_3_567_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_48"},{"key":"e_1_3_3_568_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/48\/3\/035010"},{"key":"e_1_3_3_569_2","volume-title":"2010 AAAI Spring Symposium Series","author":"Mwebaze Ernest","year":"2010","unstructured":"Ernest Mwebaze, Washington Okori, and John Alexander Quinn. 2010. Causal structure learning for famine prediction. In 2010 AAAI Spring Symposium Series."},{"key":"e_1_3_3_570_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.05.005"},{"key":"e_1_3_3_571_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-018-0101-5"},{"key":"e_1_3_3_572_2","first-page":"8","volume-title":"96th Annual Meeting of the Transportation Research Board, Washington, DC","author":"Nam Daisik","year":"2017","unstructured":"Daisik Nam, Hyunmyung Kim, Jaewoo Cho, and R. Jayakrishnan. 2017. A model based on deep learning for predicting travel mode choice. In 96th Annual Meeting of the Transportation Research Board, Washington, DC. 8\u201312."},{"key":"e_1_3_3_573_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2006.888977"},{"key":"e_1_3_3_574_2","article-title":"The study of Earth as an integrated system","author":"Science NASA","year":"2019","unstructured":"NASA Science. 2019. The study of Earth as an integrated system. Retrieved from https:\/\/climate.nasa.gov\/nasa_science\/science\/.","journal-title":"Retrieved from https:\/\/climate.nasa.gov\/nasa_science\/science\/"},{"key":"e_1_3_3_575_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2792680"},{"key":"e_1_3_3_576_2","volume-title":"Negative Emissions Technologies and Reliable Sequestration: A Research Agenda","author":"Medicine National Academies of Sciences, Engineering, and","year":"2019","unstructured":"National Academies of Sciences, Engineering, and Medicine. 2019. Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. The National Academies Press, Washington, DC."},{"key":"e_1_3_3_577_2","article-title":"Carbon Intensity API","author":"ESO National Grid","year":"2019","unstructured":"National Grid ESO. 2019. Carbon Intensity API. Retrieved from https:\/\/carbonintensity.org.uk\/.","journal-title":"Retrieved from https:\/\/carbonintensity.org.uk\/"},{"key":"e_1_3_3_578_2","article-title":"Insight: Nuclear Fusion","author":"Physics Nature","year":"2016","unstructured":"Nature Physics. 2016. Insight: Nuclear Fusion. Retrieved from https:\/\/www.nature.com\/collections\/bccqhmkbyw.","journal-title":"Retrieved from https:\/\/www.nature.com\/collections\/bccqhmkbyw"},{"key":"e_1_3_3_579_2","article-title":"NCX","year":"2021","unstructured":"NCX. 2021. NCX. Retrieved from https:\/\/www.ncx.com.","journal-title":"Retrieved from https:\/\/www.ncx.com"},{"key":"e_1_3_3_580_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2013.12.010"},{"key":"e_1_3_3_581_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aabff4"},{"key":"e_1_3_3_582_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2017.12.016"},{"key":"e_1_3_3_583_2","doi-asserted-by":"publisher","DOI":"10.5555\/1942947"},{"key":"e_1_3_3_584_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.07.020"},{"key":"e_1_3_3_585_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.12.018"},{"key":"e_1_3_3_586_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1719367115"},{"key":"e_1_3_3_587_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.10.023"},{"key":"e_1_3_3_588_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aae2be"},{"key":"e_1_3_3_589_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0109583"},{"key":"e_1_3_3_590_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.05.023"},{"key":"e_1_3_3_591_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-014-0028-6"},{"key":"e_1_3_3_592_2","article-title":"Andrew Ng: Artificial Intelligence is the New Electricity","author":"Business Stanford Graduate School of","year":"2017","unstructured":"Stanford Graduate School of Business. 2017. Andrew Ng: Artificial Intelligence is the New Electricity. Retrieved from https:\/\/www.youtube.com\/watch?v=21EiKfQYZXc.","journal-title":"Retrieved from https:\/\/www.youtube.com\/watch?v=21EiKfQYZXc"},{"key":"e_1_3_3_593_2","article-title":"UCS Position on Solar Geoengineering","author":"Scientists Union of Concerned","year":"2019","unstructured":"Union of Concerned Scientists. 2019. UCS Position on Solar Geoengineering. Retrieved from https:\/\/www.ucsusa.org\/sites\/default\/files\/attach\/2019\/gw-position-Solar-Geoengineering-022019.pdf.","journal-title":"Retrieved from https:\/\/www.ucsusa.org\/sites\/default\/files\/attach\/2019\/gw-position-Solar-Geoengineering-022019.pdf"},{"key":"e_1_3_3_594_2","article-title":"Energy Department Awards $5.5 Million to Apply Machine Learning to Geothermal Exploration","author":"Energy U.S. Office of Energy Efficiency & Renewable","year":"2019","unstructured":"U.S. Office of Energy Efficiency & Renewable Energy. 2019. Energy Department Awards $5.5 Million to Apply Machine Learning to Geothermal Exploration. Retrieved from https:\/\/www.energy.gov\/eere\/articles\/energy- department-awards-55-million-apply-machine-learning-geothermal-exploration.","journal-title":"Retrieved from https:\/\/www.energy.gov\/eere\/articles\/energy- department-awards-55-million-apply-machine-learning-geothermal-exploration"},{"key":"e_1_3_3_595_2","doi-asserted-by":"crossref","unstructured":"Timothy Oladunni and Sharad Sharma. 2016. Hedonic housing theory\u2013a machine learning investigation. In 2016 15th IEEE International Conference on Machine Learning and Applications.","DOI":"10.1109\/ICMLA.2016.0092"},{"key":"e_1_3_3_596_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2019.03.043"},{"key":"e_1_3_3_597_2","volume-title":"8th International AAAI Conference on Weblogs and Social Media","author":"Olteanu Alexandra","year":"2014","unstructured":"Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. 2014. CrisisLex: A lexicon for collecting and filtering microblogged communications in crises. In 8th International AAAI Conference on Weblogs and Social Media."},{"key":"e_1_3_3_598_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2015.09.037"},{"key":"e_1_3_3_599_2","article-title":"Ubenwa: Cry-based diagnosis of birth asphyxia","author":"Onu Charles C.","year":"2017","unstructured":"Charles C. Onu, Innocent Udeogu, Eyenimi Ndiomu, Urbain Kengni, Doina Precup, Guilherme M. Sant\u2019Anna, Edward Alikor, and Peace Opara. 2017. Ubenwa: Cry-based diagnosis of birth asphyxia. Preprint arXiv:1711.06405 (2017).","journal-title":"Preprint arXiv:1711.06405"},{"key":"e_1_3_3_600_2","article-title":"Developing the World\u2019s First Indicator of Forest Carbon Stocks & Emissions","author":"O\u2019Shea Tara","year":"2019","unstructured":"Tara O\u2019Shea. 2019. Developing the World\u2019s First Indicator of Forest Carbon Stocks & Emissions. Retrieved from https:\/\/www.planet.com\/pulse\/developing-the-worlds-first-indicator-of-forest-carbon-stocks-emissions\/.","journal-title":"Retrieved from https:\/\/www.planet.com\/pulse\/developing-the-worlds-first-indicator-of-forest-carbon-stocks-emissions\/"},{"key":"e_1_3_3_601_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219924"},{"key":"e_1_3_3_602_2","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/PowerAfrica.2018.8521063","volume-title":"2018 IEEE PES\/IAS PowerAfrica","author":"Otieno Fred","year":"2018","unstructured":"Fred Otieno, Nathan Williams, and Patrick McSharry. 2018. Forecasting energy demand for microgrids over multiple horizons. In 2018 IEEE PES\/IAS PowerAfrica. IEEE, 457\u2013462."},{"key":"e_1_3_3_603_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.01.024"},{"key":"e_1_3_3_604_2","article-title":"Pachama","year":"2021","unstructured":"Pachama. 2021. Pachama. Retrieved from https:\/\/pachama.com\/.","journal-title":"Retrieved from https:\/\/pachama.com\/"},{"key":"e_1_3_3_605_2","volume-title":"Climate Change 2014 Synthesis Report","author":"Pachauri Rajendra K.","year":"2014","unstructured":"Rajendra K. Pachauri. 2014. Climate Change 2014 Synthesis Report. IPCC."},{"key":"e_1_3_3_606_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.042003"},{"key":"e_1_3_3_607_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature01131"},{"key":"e_1_3_3_608_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-005-2631-2"},{"issue":"5","key":"e_1_3_3_609_2","article-title":"A survey of optimal power flow methods.","volume":"4","author":"Pandya K. S.","year":"2008","unstructured":"K. S. Pandya and S. K. Joshi. 2008. A survey of optimal power flow methods. Journal of Theoretical & Applied Information Technology 4, 5 (2008), 450\u2013458.","journal-title":"Journal of Theoretical & Applied Information Technology"},{"key":"e_1_3_3_610_2","doi-asserted-by":"publisher","DOI":"10.1109\/MPE.2015.2397334"},{"key":"e_1_3_3_611_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.03.079"},{"key":"e_1_3_3_612_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.10.053"},{"key":"e_1_3_3_613_2","unstructured":"Faizal Parish A. A. Sirin D. Charman Hans Joosten T. Yu Minaeva and Marcel Silvius. 2008. Assessment on peatlands biodiversity and climate change: Main report. Global Environment Centre Kuala Lumpur and Wetlands International Wageningen."},{"key":"e_1_3_3_614_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.11.040"},{"key":"e_1_3_3_615_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2018.10.028"},{"key":"e_1_3_3_616_2","doi-asserted-by":"publisher","DOI":"10.1002\/2017EF000735"},{"key":"e_1_3_3_617_2","doi-asserted-by":"publisher","DOI":"10.1109\/GHTC.2014.6970293"},{"key":"e_1_3_3_618_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISGTEurope.2017.8260289"},{"key":"e_1_3_3_619_2","volume-title":"Basic Methods of Policy Analysis and Planning","author":"Patton C. V.","year":"2015","unstructured":"C. V. Patton, D. S. Sawicki, and J. Clark. 2015. Basic Methods of Policy Analysis and Planning. Taylor & Francis"},{"key":"e_1_3_3_620_2","doi-asserted-by":"publisher","DOI":"10.1145\/3241036"},{"key":"e_1_3_3_621_2","article-title":"Decarbonization of industrial sectors: The next frontier","author":"Pee A.","year":"2018","unstructured":"A. Pee, D. Pinner, O. Roelofsen, K. Somers, E. Speelman, and M. Witteveen. 2018. Decarbonization of industrial sectors: The next frontier. Retrieved from https:\/\/www.mckinsey.com\/industries\/oil-and-gas\/our-insights\/decarbonization-of-industrial-sectors-the-next-frontier.","journal-title":"Retrieved from https:\/\/www.mckinsey.com\/industries\/oil-and-gas\/our-insights\/decarbonization-of-industrial-sectors-the-next-frontier"},{"key":"e_1_3_3_622_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203889046"},{"key":"e_1_3_3_623_2","first-page":"93","article-title":"Using reality mining to improve public health and medicine","volume":"149","author":"Pentland Alex","year":"2009","unstructured":"Alex Pentland, David Lazer, Devon Brewer, and Tracy Heibeck. 2009. Using reality mining to improve public health and medicine. Studies in Health Technology and Informatics 149 (2009), 93\u2013102.","journal-title":"Studies in Health Technology and Informatics"},{"key":"e_1_3_3_624_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13290-7_7"},{"key":"e_1_3_3_625_2","unstructured":"Gregorij V. Pereverzev and P. N. Yushmanov. 2002. ASTRA. Automated System for TRansport Analysis in a tokamak. Aspen Technology Inc. San Diego CA."},{"key":"e_1_3_3_626_2","volume-title":"AGU Fall Meeting Abstracts","author":"Perignon M. C.","year":"2018","unstructured":"M. C. Perignon, P. Passalacqua, T. M. Jarriel, J. M. Adams, and I. Overeem. 2018. Patterns of geomorphic processes across deltas using image analysis and machine learning. In AGU Fall Meeting Abstracts."},{"key":"e_1_3_3_627_2","first-page":"1","volume-title":"2016 IEEE Power and Energy Society General Meeting (PESGM\u201916)","author":"Pertl Michael","year":"2016","unstructured":"Michael Pertl, Kai Heussen, Oliver Gehrke, and Michel Rezkalla. 2016. Voltage estimation in active distribution grids using neural networks. In 2016 IEEE Power and Energy Society General Meeting (PESGM\u201916). IEEE, 1\u20135."},{"key":"e_1_3_3_628_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2102"},{"key":"e_1_3_3_629_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00066"},{"key":"e_1_3_3_630_2","article-title":"Hedonic residential property price estimation using geospatial data: A machine-learning approach","author":"Picchetti Paulo","year":"2017","unstructured":"Paulo Picchetti. 2017. Hedonic residential property price estimation using geospatial data: A machine-learning approach. Instituto Brasileiro de Economia.","journal-title":"Instituto Brasileiro de Economia"},{"key":"e_1_3_3_631_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-016-0099-7"},{"key":"e_1_3_3_632_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120725"},{"key":"e_1_3_3_633_2","first-page":"225","volume-title":"The RFF Reader in Environmental and Resource Policy","author":"Pizer William A.","year":"2006","unstructured":"William A. Pizer. 2006. Choosing price or quantity controls for greenhouse gases. In The RFF Reader in Environmental and Resource Policy. Wallace E. Oates (Ed.). Resources for the Future, 225\u2013234."},{"key":"e_1_3_3_634_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2012.08.031"},{"key":"e_1_3_3_635_2","article-title":"PlantSnap","year":"2021","unstructured":"PlantSnap. 2021. PlantSnap. Retrieved from https:\/\/www.plantsnap.com\/.","journal-title":"Retrieved from https:\/\/www.plantsnap.com\/"},{"key":"e_1_3_3_636_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2003.12.007"},{"key":"e_1_3_3_637_2","first-page":"485","article-title":"Food security and food production systems","author":"Porter J. R.","year":"2014","unstructured":"J. R. Porter, L. Xie, A. J. Challinor, K. Cochrane, M. M. Howden, D. B. Lobell, and M. I. Travasso. 2014. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, Vulnerability. IPCC, 485\u2013533.","journal-title":"Climate Change 2014: Impacts, Adaptation, Vulnerability."},{"key":"e_1_3_3_638_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-007-0339-5"},{"key":"e_1_3_3_639_2","article-title":"PowerTAC","year":"2019","unstructured":"PowerTAC. 2019. PowerTAC. Retrieved from https:\/\/powertac.org\/.","journal-title":"Retrieved from https:\/\/powertac.org\/"},{"key":"e_1_3_3_640_2","article-title":"Agriculture commodity arrival prediction using remote sensing data: insights and beyond","author":"Prasad Gautam","year":"2019","unstructured":"Gautam Prasad, Upendra Reddy Vuyyuru, and Mithun Das Gupta. 2019. Agriculture commodity arrival prediction using remote sensing data: insights and beyond. In KDD Feed Workshop 2019.","journal-title":"KDD Feed Workshop 2019"},{"key":"e_1_3_3_641_2","doi-asserted-by":"publisher","DOI":"10.1002\/wcc.198"},{"key":"e_1_3_3_642_2","doi-asserted-by":"publisher","DOI":"10.1145\/2483852.2483870"},{"key":"e_1_3_3_643_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0417-3"},{"key":"e_1_3_3_644_2","article-title":"Project Zamba Computer Vision for Wildlife Research & Conservation","author":"Zamba Project","year":"2019","unstructured":"Project Zamba. 2019. Project Zamba Computer Vision for Wildlife Research & Conservation. Retrieved from https:\/\/zamba.drivendata.org\/.","journal-title":"Retrieved from https:\/\/zamba.drivendata.org\/"},{"key":"e_1_3_3_645_2","unstructured":"UN Global Pulse. 2013. Landscaping Study: Digital Signals & Access to Finance in Kenya. Retrived from https:\/\/www.unglobalpulse.org\/projects\/Kenyan-access-finance."},{"key":"e_1_3_3_646_2","article-title":"Using mobile phone data and airtime credit purchases to estimate food security","author":"Pulse UN Global","year":"2015","unstructured":"UN Global Pulse. 2015. Using mobile phone data and airtime credit purchases to estimate food security. New York: UN World Food Programme (WFP), Universit\u00e9 Catholique de Louvain, Real Impact Analytics, Pulse Lab New York.","journal-title":"New York: UN World Food Programme (WFP), Universit\u00e9 Catholique de Louvain, Real Impact Analytics, Pulse Lab New York"},{"key":"e_1_3_3_647_2","unstructured":"UN Global Pulse. 2017. Improving Professional Training in Indonesia with Gaming Data. http:\/\/unglobalpulse.org\/sites\/default\/files\/ProjectBrief-ImprovingProfressionalTraininginIndonesiawithGamingData.pdf."},{"key":"e_1_3_3_648_2","unstructured":"UN Global Pulse. 2017. Social Media and Forced Displacement: Big Data Analytics & Machine Learning. Retrived from https:\/\/www.unhcr.org\/innovation\/wp-content\/uploads\/2017\/09\/FINAL-White-Paper.pdf."},{"key":"e_1_3_3_649_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v35i3.2529"},{"key":"e_1_3_3_650_2","first-page":"115","article-title":"Automated blood smear analysis for mobile malaria diagnosis","volume":"31","author":"Quinn John A.","year":"2014","unstructured":"John A. Quinn, Alfred Andama, Ian Munabi, and Fred N. Kiwanuka. 2014. Automated blood smear analysis for mobile malaria diagnosis. Mobile Point-of-Care Monitors and Diagnostic Device Design 31 (2014), 115.","journal-title":"Mobile Point-of-Care Monitors and Diagnostic Device Design"},{"key":"e_1_3_3_651_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2017.0363"},{"key":"e_1_3_3_652_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2017.02.017"},{"key":"e_1_3_3_653_2","first-page":"3402","volume-title":"Advances in Neural Information Processing Systems","author":"Racah Evan","year":"2017","unstructured":"Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, and Chris Pal. 2017. ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In Advances in Neural Information Processing Systems. 3402\u20133413."},{"key":"e_1_3_3_654_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature17439"},{"key":"e_1_3_3_655_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2004.825910"},{"key":"e_1_3_3_656_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.11.080"},{"key":"e_1_3_3_657_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2015.04.014"},{"key":"e_1_3_3_658_2","article-title":"Rainforest Connection","author":"Connection Rainforest","year":"2021","unstructured":"Rainforest Connection. 2021. Rainforest Connection. Retrieved from https:\/\/rfcx.org.","journal-title":"Retrieved from https:\/\/rfcx.org"},{"key":"e_1_3_3_659_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2017.11.039"},{"key":"e_1_3_3_660_2","article-title":"Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations","author":"Raissi Maziar","year":"2017","unstructured":"Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. 2017. Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561 (2017).","journal-title":"arXiv preprint arXiv:1711.10561"},{"key":"e_1_3_3_661_2","unstructured":"Eric Ralls. 2018. Systems and methods for electronically identifying plant species. US Patent App. 15\/973660."},{"key":"e_1_3_3_662_2","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133825"},{"key":"e_1_3_3_663_2","doi-asserted-by":"publisher","DOI":"10.5555\/2030470.2030472"},{"key":"e_1_3_3_664_2","doi-asserted-by":"publisher","DOI":"10.1145\/1989734.1989739"},{"key":"e_1_3_3_665_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aafa8f"},{"key":"e_1_3_3_666_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2014.10.018"},{"key":"e_1_3_3_667_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gloenvcha.2016.09.006"},{"key":"e_1_3_3_668_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1817480116"},{"key":"e_1_3_3_669_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2008.0131"},{"key":"e_1_3_3_670_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2009.2025137"},{"key":"e_1_3_3_671_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1810286115"},{"key":"e_1_3_3_672_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-92252-2"},{"key":"e_1_3_3_673_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2633287"},{"key":"e_1_3_3_674_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.04.065"},{"key":"e_1_3_3_675_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2003.11.005"},{"key":"e_1_3_3_676_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2014.10.005"},{"key":"e_1_3_3_677_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"e_1_3_3_678_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2015.12.001"},{"key":"e_1_3_3_679_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.11.052"},{"key":"e_1_3_3_680_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2018.2855689"},{"key":"e_1_3_3_681_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v39i1.2776"},{"key":"e_1_3_3_682_2","article-title":"Restor","year":"2021","unstructured":"Restor. 2021. Restor. Retrieved from https:\/\/restor.eco\/.","journal-title":"Retrieved from https:\/\/restor.eco\/"},{"key":"e_1_3_3_683_2","article-title":"Drought forecasting based on machine learning of remote sensing and long-range forecast data","author":"Rhee J.","year":"2016","unstructured":"J. Rhee, J. Im, and S. Park. 2016. Drought forecasting based on machine learning of remote sensing and long-range forecast data. APEC Climate Center, Republic of Korea.","journal-title":"APEC Climate Center, Republic of Korea"},{"key":"e_1_3_3_684_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2018.2886164"},{"key":"e_1_3_3_685_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2014.2376873"},{"key":"e_1_3_3_686_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2010.04.027"},{"key":"e_1_3_3_687_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2018.11.016"},{"key":"e_1_3_3_688_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.environ.032108.105046"},{"key":"e_1_3_3_689_2","volume-title":"2010 AAAI Spring Symposium Series","author":"Robertson Joel","year":"2010","unstructured":"Joel Robertson and Del J. DeHart. 2010. An agile and accessible adaptation of Bayesian inference to medical diagnostics for rural health extension workers. In 2010 AAAI Spring Symposium Series."},{"key":"e_1_3_3_690_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.09.060"},{"key":"e_1_3_3_691_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcbb.12338"},{"key":"e_1_3_3_692_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10584-013-0777-5"},{"key":"e_1_3_3_693_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apgeog.2011.10.005"},{"key":"e_1_3_3_694_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40725-017-0052-5"},{"key":"e_1_3_3_695_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-018-0170-7"},{"key":"e_1_3_3_696_2","volume-title":"1st International Conference on Educational Data Mining","author":"Romero Crist\u00f3bal","year":"2008","unstructured":"Crist\u00f3bal Romero, Sebasti\u00e1n Ventura, Pedro G. Espejo, and C\u00e9sar Herv\u00e1s. 2008. Data mining algorithms to classify students. In 1st International Conference on Educational Data Mining."},{"key":"e_1_3_3_697_2","doi-asserted-by":"publisher","DOI":"10.1093\/wentk\/9780190866112.001.0001"},{"key":"e_1_3_3_698_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kws241"},{"key":"e_1_3_3_699_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1222463110"},{"key":"e_1_3_3_700_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2016.07.001"},{"key":"e_1_3_3_701_2","doi-asserted-by":"publisher","DOI":"10.1016\/0022-0531(74)90066-0"},{"key":"e_1_3_3_702_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijggc.2015.05.018"},{"key":"e_1_3_3_703_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.108"},{"key":"e_1_3_3_704_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.346.6213.1063"},{"key":"e_1_3_3_705_2","doi-asserted-by":"publisher","DOI":"10.5555\/3215359.3215367"},{"key":"e_1_3_3_706_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002616"},{"key":"e_1_3_3_707_2","doi-asserted-by":"publisher","DOI":"10.1080\/03081060.2011.600092"},{"key":"e_1_3_3_708_2","article-title":"Direct air capture of carbon dioxide: ICEF roadmap 2018","author":"Sandalow David","year":"2018","unstructured":"David Sandalow, Julio Friedmann, and Colin McCormick. 2018. Direct air capture of carbon dioxide: ICEF roadmap 2018. Retrieved from https:\/\/www.icef-forum.org\/pdf2018\/roadmap\/ICEF2018_Roadmap_Draft_for_Comment_20181012.pdf. (2018).","journal-title":"Retrieved from https:\/\/www.icef-forum.org\/pdf2018\/roadmap\/ICEF2018_Roadmap_Draft_for_Comment_20181012.pdf"},{"key":"e_1_3_3_709_2","unstructured":"Tuomas Sandholm. 1980. Very-Large-Scale Generalized Combinatorial Multi-Attribute Auctions: Lessons from Conducting $60 Billion of Sourcing. Carnegie Mellon University."},{"key":"e_1_3_3_710_2","doi-asserted-by":"publisher","DOI":"10.1561\/9781680838015"},{"key":"e_1_3_3_711_2","doi-asserted-by":"crossref","unstructured":"M. C. Sarofim Shubhayu Saha M. D. Hawkins D. M. Mills Jeremy J. Hess Radley M. Horton Patrick L. Kinney Joel D. Schwartz and Alexis St Juliana. 2016. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment . U.S. Global Change Research Program Washington DC.","DOI":"10.7930\/J0MG7MDX"},{"key":"e_1_3_3_712_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs10081237"},{"key":"e_1_3_3_713_2","volume-title":"Transport, in IPCC, Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2014: Mitigation of Climate Change, Chapter 8. Geneva. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.)","author":"Schaeffer R.","year":"2014","unstructured":"R. Schaeffer, R. Sims, J. Corfee-Morlot, F. Creutzig, X. Cruz-Nunez, D. Dimitriu, and M. D\u2019Agosto. 2014. Transport, in IPCC, Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2014: Mitigation of Climate Change, Chapter 8. Geneva. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J. C. Minx (Eds.). Cambridge University Press, Cambridge."},{"key":"e_1_3_3_714_2","article-title":"Costs of mitigating CO2 emissions from passenger aircraft","volume":"6","author":"Sch\u00e4fer Andreas W.","year":"2015","unstructured":"Andreas W. Sch\u00e4fer, Antony D. Evans, Tom G. Reynolds, and Lynnette Dray. 2015. Costs of mitigating CO2 emissions from passenger aircraft. Nature Climate Change 6 (2015), 412\u2013417.","journal-title":"Nature Climate Change"},{"key":"e_1_3_3_715_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2019.03.004"},{"key":"e_1_3_3_716_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2015.2431"},{"key":"e_1_3_3_717_2","article-title":"Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks","author":"Schmidt Victor","year":"2019","unstructured":"Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, and Yoshua Bengio. 2019. Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks. In ICLR AI for Social Good Workshop.","journal-title":"ICLR AI for Social Good Workshop"},{"key":"e_1_3_3_718_2","doi-asserted-by":"publisher","DOI":"10.1002\/2017GL076101"},{"key":"e_1_3_3_719_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10584-005-3485-y"},{"key":"e_1_3_3_720_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000134875.15919.0f"},{"key":"e_1_3_3_721_2","doi-asserted-by":"publisher","DOI":"10.1145\/3381831"},{"key":"e_1_3_3_722_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.193823"},{"key":"e_1_3_3_723_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2017.11.009"},{"key":"e_1_3_3_724_2","article-title":"Sense","year":"2021","unstructured":"Sense. 2021. Sense. Retrieved from https:\/\/sense.com.","journal-title":"Retrieved from https:\/\/sense.com"},{"key":"e_1_3_3_725_2","article-title":"Interactive online machine learning approach for activity-travel survey","author":"Seo Toru","year":"2017","unstructured":"Toru Seo, Takahiko Kusakabe, Hiroto Gotoh, and Yasuo Asakura. 2017. Interactive online machine learning approach for activity-travel survey. Transportation Research Part B: Methodological 123, (2017), 362\u2013373.","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_3_726_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-019-0356-8"},{"key":"e_1_3_3_727_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_3_728_2","doi-asserted-by":"crossref","unstructured":"Chaopeng Shen. 2018. A trans-disciplinary review of deep learning research for water resources scientists. Water Resources Research 54 11 (2018) 8558\u20138593.","DOI":"10.1029\/2018WR022643"},{"key":"e_1_3_3_729_2","volume-title":"Geoengineering the Climate: Science, Governance and Uncertainty","author":"Shepherd John G.","year":"2009","unstructured":"John G. Shepherd. 2009. Geoengineering the Climate: Science, Governance and Uncertainty. Royal Society."},{"key":"e_1_3_3_730_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature12829"},{"key":"e_1_3_3_731_2","first-page":"383","article-title":"Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks","volume":"4","author":"Shevchik S. A.","year":"2018","unstructured":"S. A. Shevchik, C. Kenel, C. Leinenbach, and K. Wasmer. 2018. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing 4 (2018), 383\u2013391.","journal-title":"Additive Manufacturing"},{"key":"e_1_3_3_732_2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate2841"},{"key":"e_1_3_3_733_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3209878"},{"key":"e_1_3_3_734_2","volume-title":"Workshop on Machine Learning for Social Robotics","year":"2015","unstructured":"Kyriacos Shiarlis, Joao Messias, Maarten van Someren, Shimon Whiteson, Jaebok Kim, Jered Hendrik Vroon, Gwenn Englebienne, Khiet Phuong Truong, No\u00e9 P\u00e9rez-Higueras, Ignacio P\u00e9rez-Hurtado, Rafael Ramon-Vigo, Fernando Caballero, Luis Merino, Jie Shen, Stavros Petridis, Maja Pantic, Lasse Hedman, Marten Scherlund, Rapha\u00ebl Koster, and Herv\u00e9 Michel. 2015. TERESA: A socially intelligent semi-autonomous telepresence system. In Workshop on Machine Learning for Social Robotics."},{"key":"e_1_3_3_735_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.282.5389.728"},{"key":"e_1_3_3_736_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"e_1_3_3_737_2","volume-title":"Characterization of the Life Cycle Environmental Impacts and Benefits of Smart Electric Meters and Consequences of their Deployment in California","author":"Sias Glenn Gregory","year":"2017","unstructured":"Glenn Gregory Sias. 2017. Characterization of the Life Cycle Environmental Impacts and Benefits of Smart Electric Meters and Consequences of their Deployment in California. Ph.D. Dissertation. UCLA."},{"key":"e_1_3_3_738_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2018.01.028"},{"key":"e_1_3_3_739_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"e_1_3_3_740_2","doi-asserted-by":"publisher","DOI":"10.5555\/2887007.2887108"},{"key":"e_1_3_3_741_2","article-title":"Decentralized flood forecasting using deep neural networks","author":"Sit Muhammed","year":"2019","unstructured":"Muhammed Sit and Ibrahim Demir. 2019. Decentralized flood forecasting using deep neural networks. Preprint arXiv:1902.02308 (2019).","journal-title":"Preprint arXiv:1902.02308"},{"key":"e_1_3_3_742_2","article-title":"The race to code the curb","author":"Small Andrew","year":"2019","unstructured":"Andrew Small and Laura Bliss. 2019. The race to code the curb. Citylab. Retrieved from https:\/\/www.citylab.com\/transportation\/2019\/04\/smart-cities-maps-curb-data-coord-sidewalk-tech-street-design\/586177\/.","journal-title":"Citylab"},{"key":"e_1_3_3_743_2","article-title":"Small Robot Company","author":"Company Small Robot","year":"2021","unstructured":"Small Robot Company. 2021. Small Robot Company. Retrieved from https:\/\/www.smallrobotcompany.com\/.","journal-title":"Retrieved from https:\/\/www.smallrobotcompany.com\/"},{"key":"e_1_3_3_744_2","doi-asserted-by":"publisher","DOI":"10.1002\/2018EA000370"},{"key":"e_1_3_3_745_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2016.01.038"},{"key":"e_1_3_3_746_2","doi-asserted-by":"publisher","DOI":"10.5555\/2999325.2999464"},{"key":"e_1_3_3_747_2","doi-asserted-by":"publisher","DOI":"10.1177\/0954409716657849"},{"key":"e_1_3_3_748_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.41"},{"key":"e_1_3_3_749_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2008.12.003"},{"key":"e_1_3_3_750_2","article-title":"SwRI Developing Methane Leak Detection System for DOE","author":"Institute Southwest Research","year":"2016","unstructured":"Southwest Research Institute. 2016. SwRI Developing Methane Leak Detection System for DOE. Retrieved from https:\/\/www.swri.org\/press-release\/swri-developing-methane-leak-detection-system-doe.","journal-title":"Retrieved from https:\/\/www.swri.org\/press-release\/swri-developing-methane-leak-detection-system-doe"},{"key":"e_1_3_3_751_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2005.852370"},{"key":"e_1_3_3_752_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13201-018-0821-8"},{"key":"e_1_3_3_753_2","doi-asserted-by":"publisher","DOI":"10.1002\/csr.175"},{"key":"e_1_3_3_754_2","first-page":"6","article-title":"Hydropower greenhouse gas emissions","volume":"24","author":"Steinhurst William","year":"2012","unstructured":"William Steinhurst, Patrick Knight, and Melissa Schultz. 2012. Hydropower greenhouse gas emissions. Conservation Law Foundation 24 (2012), 6.","journal-title":"Conservation Law Foundation"},{"key":"e_1_3_3_755_2","unstructured":"William Steinhurst Patrick Knight and Melissa Schultz. 2012. Hydropower Greenhouse Gas Emissions: State of the Research . Synapse Energy Economics Inc. https:\/\/www.nrc.gov\/docs\/ML1209\/ML12090A850.pdf."},{"key":"e_1_3_3_756_2","doi-asserted-by":"publisher","DOI":"10.1257\/aer.98.2.1"},{"key":"e_1_3_3_757_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-018-0194-x"},{"key":"e_1_3_3_758_2","unstructured":"High-Level Commission on Carbon Prices. 2017. Report of the high-level commission on carbon prices. World Bank Publications."},{"key":"e_1_3_3_759_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-02411-5"},{"key":"e_1_3_3_760_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2014.0116"},{"key":"e_1_3_3_761_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2008.09.030"},{"key":"e_1_3_3_762_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.09.040"},{"key":"e_1_3_3_763_2","doi-asserted-by":"publisher","DOI":"10.1080\/17450101.2014.902655"},{"key":"e_1_3_3_764_2","doi-asserted-by":"publisher","DOI":"10.5194\/acp-15-8631-2015"},{"key":"e_1_3_3_765_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1355"},{"key":"e_1_3_3_766_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cstp.2019.04.005"},{"key":"e_1_3_3_767_2","volume-title":"Mobile on-farm digital technology for smallholder farmers","author":"Sukkarieh Salah","year":"2017","unstructured":"Salah Sukkarieh. 2017. Mobile on-farm digital technology for smallholder farmers. 218\u2013229. Technical Report."},{"key":"e_1_3_3_768_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biocon.2009.05.006"},{"key":"e_1_3_3_769_2","first-page":"474","volume-title":"22nd International Conference on Extending Database Technology (EDBT\u201920)","author":"Sun Chong","year":"2020","unstructured":"Chong Sun, Nader Azari, and Chintan Turakhia. 2020. Gallery: A Machine Learning Model Management System at Uber. In 22nd International Conference on Extending Database Technology (EDBT\u201920). 474\u2013485."},{"key":"e_1_3_3_770_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.09.118"},{"key":"e_1_3_3_771_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.10.015"},{"key":"e_1_3_3_772_2","doi-asserted-by":"publisher","DOI":"10.1039\/C7EE03420B"},{"key":"e_1_3_3_773_2","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2010.73"},{"key":"e_1_3_3_774_2","doi-asserted-by":"publisher","DOI":"10.1021\/acscombsci.6b00153"},{"key":"e_1_3_3_775_2","doi-asserted-by":"publisher","DOI":"10.1080\/09500693.2011.597453"},{"key":"e_1_3_3_776_2","doi-asserted-by":"publisher","DOI":"10.1177\/0361198119838982"},{"key":"e_1_3_3_777_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000752"},{"key":"e_1_3_3_778_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-017-0233-1"},{"key":"e_1_3_3_779_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9310.2010.00603.x"},{"key":"e_1_3_3_780_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.03.018"},{"key":"e_1_3_3_781_2","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1109\/ECCE.2017.8096493","volume-title":"2017 IEEE Energy Conversion Congress and Exposition (ECCE\u201917)","author":"Tavakoli R.","year":"2017","unstructured":"R. Tavakoli and Z. Pantic. 2017. ANN-based algorithm for estimation and compensation of lateral misalignment in dynamic wireless power transfer systems for EV charging. In 2017 IEEE Energy Conversion Congress and Exposition (ECCE\u201917). 2602\u20132609."},{"key":"e_1_3_3_782_2","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-11-00094.1"},{"key":"e_1_3_3_783_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2007.2076"},{"key":"e_1_3_3_784_2","doi-asserted-by":"publisher","DOI":"10.1029\/2008GL033423"},{"key":"e_1_3_3_785_2","volume-title":"The Future of Trucks","author":"Teter Jacob","year":"2017","unstructured":"Jacob Teter, Pierpaolo Cazzola, and Timur G\u00fcl. 2017. The Future of Trucks. International Energy Agency."},{"key":"e_1_3_3_786_2","article-title":"Agriculture, forestry, and fishing, value added","author":"Bank The World","year":"2017","unstructured":"The World Bank. 2017. Agriculture, forestry, and fishing, value added. Retrieved from https:\/\/data.worldbank.org\/indicator\/NV.AGR.TOTL.CD.","journal-title":"Retrieved from https:\/\/data.worldbank.org\/indicator\/NV.AGR.TOTL.CD"},{"key":"e_1_3_3_787_2","doi-asserted-by":"publisher","DOI":"10.1145\/3205289.3205321"},{"key":"e_1_3_3_788_2","doi-asserted-by":"publisher","DOI":"10.5751\/ES-10200-230241"},{"key":"e_1_3_3_789_2","article-title":"Thorvald","year":"2021","unstructured":"Thorvald. 2021. Thorvald. Retrieved from https:\/\/sagarobotics.com\/.","journal-title":"Retrieved from https:\/\/sagarobotics.com\/"},{"key":"e_1_3_3_790_2","article-title":"electricityMap","year":"2019","unstructured":"Tomorrow. 2019. electricityMap. Retrived from https:\/\/www.electricitymap.org.","journal-title":"https:\/\/www.electricitymap.org"},{"key":"e_1_3_3_791_2","article-title":"Tomorrow","year":"2019","unstructured":"Tomorrow. 2019. Tomorrow. Retrieved from https:\/\/www.tmrow.com\/.","journal-title":"Retrieved from https:\/\/www.tmrow.com\/"},{"key":"e_1_3_3_792_2","doi-asserted-by":"publisher","DOI":"10.1029\/2019MS002002"},{"key":"e_1_3_3_793_2","doi-asserted-by":"publisher","DOI":"10.1021\/es5052759"},{"key":"e_1_3_3_794_2","volume-title":"Is your company ready for a zero-carbon future?","author":"Topping N.","year":"2019","unstructured":"N. Topping. 2019. Is your company ready for a zero-carbon future? Retrived from https:\/\/hbr.org\/2019\/06\/is-your-company-ready-for-a-zero-carbon-future."},{"key":"e_1_3_3_795_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENG.2017.04.016"},{"key":"e_1_3_3_796_2","doi-asserted-by":"publisher","DOI":"10.1177\/0265813516659286"},{"key":"e_1_3_3_797_2","doi-asserted-by":"publisher","DOI":"10.3141\/2527-02"},{"key":"e_1_3_3_798_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9637-z"},{"key":"e_1_3_3_799_2","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2017.1356464"},{"key":"e_1_3_3_800_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pursup.2015.09.002"},{"key":"e_1_3_3_801_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-019-0172-5"},{"key":"e_1_3_3_802_2","volume-title":"Not Just Hot Air: Putting Climate Change Education into Practice","year":"2015","unstructured":"UNESCO. 2015. Not Just Hot Air: Putting Climate Change Education into Practice. United Nations Educational, Scientific and Cultural Organization."},{"key":"e_1_3_3_803_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosust.2013.05.004"},{"key":"e_1_3_3_804_2","article-title":"Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan","author":"Energy U.S. Department of","year":"2012","unstructured":"U.S. Department of Energy. 2012. Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan. Retrieved from https:\/\/www.energy.gov\/eere\/fuelcells\/downloads\/fuel-cell-technologies-office-multi-year-research-development-and-22.","journal-title":"Retrieved from https:\/\/www.energy.gov\/eere\/fuelcells\/downloads\/fuel-cell-technologies-office-multi-year-research-development-and-22"},{"key":"e_1_3_3_805_2","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP.2016.7501696"},{"key":"e_1_3_3_806_2","first-page":"255","volume-title":"European Conference on Computer Vision","author":"van Gemert Jan C.","year":"2014","unstructured":"Jan C. van Gemert, Camiel R. Verschoor, Pascal Mettes, Kitso Epema, Lian Pin Koh, and Serge Wich. 2014. Nature conservation drones for automatic localization and counting of animals. In European Conference on Computer Vision. Springer, 255\u2013270."},{"key":"e_1_3_3_807_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"e_1_3_3_808_2","article-title":"The devil is in the tails: Fine-grained classification in the wild","author":"Van Horn Grant","year":"2017","unstructured":"Grant Van Horn and Pietro Perona. 2017. The devil is in the tails: Fine-grained classification in the wild. Preprint arXiv:1709.01450 (2017).","journal-title":"Preprint arXiv:1709.01450"},{"key":"e_1_3_3_809_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.03.034"},{"key":"e_1_3_3_810_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-1127(99)00271-6"},{"key":"e_1_3_3_811_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.11.002"},{"key":"e_1_3_3_812_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fusengdes.2013.03.003"},{"key":"e_1_3_3_813_2","doi-asserted-by":"publisher","DOI":"10.1177\/0963662515613702"},{"key":"e_1_3_3_814_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2014.10.006"},{"key":"e_1_3_3_815_2","article-title":"How artificial intelligence will affect the future of energy and climate","author":"Victor David G.","year":"2019","unstructured":"David G. Victor. 2019. How artificial intelligence will affect the future of energy and climate. Retrieved from https:\/\/www.brookings.edu\/research\/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate\/.","journal-title":"Retrieved from https:\/\/www.brookings.edu\/research\/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate\/"},{"key":"e_1_3_3_816_2","doi-asserted-by":"publisher","DOI":"10.5888\/pcd13.160099"},{"key":"e_1_3_3_817_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.895830"},{"key":"e_1_3_3_818_2","doi-asserted-by":"publisher","DOI":"10.1002\/0470036427"},{"key":"e_1_3_3_819_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2016.12.095"},{"key":"e_1_3_3_820_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2014.02.064"},{"key":"e_1_3_3_821_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2015.12.001"},{"key":"e_1_3_3_822_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep22482"},{"key":"e_1_3_3_823_2","doi-asserted-by":"publisher","DOI":"10.17775\/CSEEJPES.2015.00046"},{"key":"e_1_3_3_824_2","doi-asserted-by":"publisher","DOI":"10.3390\/s120100189"},{"key":"e_1_3_3_825_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3212707"},{"key":"e_1_3_3_826_2","first-page":"1","volume-title":"2018 IEEE Power & Energy Society General Meeting (PESGM\u201918)","author":"Wang Hao","year":"2018","unstructured":"Hao Wang and Baosen Zhang. 2018. Energy storage arbitrage in real-time markets via reinforcement learning. In 2018 IEEE Power & Energy Society General Meeting (PESGM\u201918). IEEE, 1\u20135."},{"key":"e_1_3_3_827_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113998"},{"key":"e_1_3_3_828_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2019.02.011"},{"key":"e_1_3_3_829_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2016.04.044"},{"key":"e_1_3_3_830_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.11.130"},{"key":"e_1_3_3_831_2","doi-asserted-by":"publisher","DOI":"10.1038\/npjcompumats.2016.28"},{"key":"e_1_3_3_832_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32124-9"},{"key":"e_1_3_3_833_2","article-title":"WattTime","year":"2021","unstructured":"WattTime. 2021. WattTime. Retrieved from https:\/\/www.watttime.org\/.","journal-title":"Retrieved from https:\/\/www.watttime.org\/"},{"key":"e_1_3_3_834_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2420792"},{"key":"e_1_3_3_835_2","doi-asserted-by":"publisher","DOI":"10.1080\/17583004.2018.1522095"},{"key":"e_1_3_3_836_2","volume-title":"IMS-ISBA Meeting on Data Science in the Next 50 Years","author":"Welling Max","year":"2015","unstructured":"Max Welling. 2015. Are ML and statistics complementary? In IMS-ISBA Meeting on Data Science in the Next 50 Years."},{"key":"e_1_3_3_837_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijggc.2020.103223"},{"key":"e_1_3_3_838_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317908"},{"key":"e_1_3_3_839_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2014.08.008"},{"key":"e_1_3_3_840_2","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2015.0178"},{"key":"e_1_3_3_841_2","doi-asserted-by":"publisher","DOI":"10.1093\/reep\/rew018"},{"key":"e_1_3_3_842_2","unstructured":"Ami Wiesel Avinatan Hassidim Gal Elidan Guy Shalev Mor Schlesinger Oleg Zlydenko Ran El-Yaniv Sella Nevo Yossi Matias Yotam Gigi et\u00a0al. 2018. Ml for flood forecasting at scale. (2018)."},{"key":"e_1_3_3_843_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011658"},{"key":"e_1_3_3_844_2","article-title":"Integrating physics-based modeling with machine learning: A survey","author":"Willard Jared","year":"2020","unstructured":"Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2020. Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919 (2020).","journal-title":"arXiv preprint arXiv:2003.04919"},{"key":"e_1_3_3_845_2","unstructured":"Sella Nevo Vova Anisimov Gal Elidan Ran El-Yaniv Pete Giencke Yotam Gigi Avinatan Hassidim Zach Moshe Mor Schlesinger Guy Shalev Ajai Tirumali Ami Wiesel Oleg Zlydenko and Yossi Matias. 2019. ML for flood forecasting at scale. Preprint arXiv:1901.09583 ."},{"key":"e_1_3_3_846_2","volume-title":"Climate Change Needs Behavior Change: Making the Case for Behavioral Solutions to Reduce Global Warming","author":"Williamson K.","year":"2018","unstructured":"K. Williamson, A. Satre-Meloy, K. Velasco, and K. Green. 2018. Climate Change Needs Behavior Change: Making the Case for Behavioral Solutions to Reduce Global Warming. Technical Report. Center for Behavior and the Environment. Retrived from https:\/\/rare.org\/wp-content\/uploads\/2019\/02\/2018-CCNBC-Report.pdf."},{"key":"e_1_3_3_847_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/45\/5\/004"},{"key":"e_1_3_3_848_2","article-title":"Excess inventory wastes carbon and energy, not just money","author":"Winston Andrew","year":"2011","unstructured":"Andrew Winston. 2011. Excess inventory wastes carbon and energy, not just money. Harvard Business Review.","journal-title":"Harvard Business Review"},{"key":"e_1_3_3_849_2","volume-title":"Power Generation, Operation, and Control","author":"Wood Allen J.","year":"2013","unstructured":"Allen J. Wood, Bruce F. Wollenberg, and Gerald B. Shebl\u00e9. 2013. Power Generation, Operation, and Control. John Wiley & Sons."},{"key":"e_1_3_3_850_2","unstructured":"S. W. Wood and Annette Cowie. 2004. A review of greenhouse gas emission factors for fertiliser production. Climate Technology Centre and Network."},{"key":"e_1_3_3_851_2","doi-asserted-by":"publisher","DOI":"10.1088\/0029-5515\/37\/6\/I02"},{"key":"e_1_3_3_852_2","article-title":"Flow: Architecture and benchmarking for reinforcement learning in traffic control","author":"Wu Cathy","year":"2017","unstructured":"Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, and Alexandre M. Bayen. 2017. Flow: Architecture and benchmarking for reinforcement learning in traffic control. Preprint arXiv:1710.05465 (2017).","journal-title":"Preprint arXiv:1710.05465"},{"key":"e_1_3_3_853_2","volume-title":"1st Annual Conference on Robot Learning","author":"Wu Cathy","year":"2017","unstructured":"Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, and Alexandre M. Bayen. 2017. Emergent behaviors in mixed-autonomy traffic. In 1st Annual Conference on Robot Learning."},{"key":"e_1_3_3_854_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2016.2550530"},{"key":"e_1_3_3_855_2","doi-asserted-by":"publisher","DOI":"10.3390\/app6060166"},{"key":"e_1_3_3_856_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12112118"},{"key":"e_1_3_3_857_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504139"},{"key":"e_1_3_3_858_2","doi-asserted-by":"publisher","DOI":"10.5555\/3042817.3043078"},{"key":"e_1_3_3_859_2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.2020.0976"},{"key":"e_1_3_3_860_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.145301"},{"key":"e_1_3_3_861_2","doi-asserted-by":"publisher","DOI":"10.5555\/2936924.2937038"},{"key":"e_1_3_3_862_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2013.03.023"},{"key":"e_1_3_3_863_2","doi-asserted-by":"publisher","DOI":"10.1097\/00124784-200006060-00010"},{"key":"e_1_3_3_864_2","first-page":"363","volume-title":"4th International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology","volume":"1","author":"Ygge Fredrik","year":"1999","unstructured":"Fredrik Ygge, J. M. Akkermans, Arne Andersson, Marko Krejic, and Erik Boertjes. 1999. The HOMEBOTS system and field test: A multi-commodity market for predictive power load management. In 4th International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, Vol. 1. 363\u2013382."},{"key":"e_1_3_3_865_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2695438"},{"key":"e_1_3_3_866_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2017.11.003"},{"key":"e_1_3_3_867_2","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298229"},{"key":"e_1_3_3_868_2","article-title":"Convolutional neural networks predict fish abundance from underlying coral reef texture","volume":"31","author":"Young Grace","year":"2018","unstructured":"Grace Young, Vassileios Balntas, and Victor Prisacariu. 2018. Convolutional neural networks predict fish abundance from underlying coral reef texture. MarXiv. August 31 (2018).","journal-title":"MarXiv. August"},{"key":"e_1_3_3_869_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2018.11.021"},{"key":"e_1_3_3_870_2","doi-asserted-by":"publisher","DOI":"10.1111\/padr.12102"},{"key":"e_1_3_3_871_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2014.0257"},{"key":"e_1_3_3_872_2","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm47815.2020.9303008"},{"key":"e_1_3_3_873_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2008.07.025"},{"key":"e_1_3_3_874_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2017.10.001"},{"key":"e_1_3_3_875_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9577-z"},{"key":"e_1_3_3_876_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-016-9326-8"},{"key":"e_1_3_3_877_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000427"},{"key":"e_1_3_3_878_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2017.08.010"},{"key":"e_1_3_3_879_2","doi-asserted-by":"publisher","DOI":"10.4018\/jats.2012010102"},{"key":"e_1_3_3_880_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.04.161"},{"key":"e_1_3_3_881_2","first-page":"1","volume-title":"2016 IEEE Power and Energy Society General Meeting (PESGM\u201916)","author":"Zhang Xiao","year":"2016","unstructured":"Xiao Zhang, Gabriela Hug, J. Zico Kolter, and Iiro Harjunkoski. 2016. Model predictive control of industrial loads and energy storage for demand response. In 2016 IEEE Power and Energy Society General Meeting (PESGM\u201916). IEEE, 1\u20135."},{"issue":"1","key":"e_1_3_3_882_2","first-page":"213","article-title":"Deep reinforcement learning for power system applications: An overview","volume":"6","author":"Zhang Zidong","year":"2019","unstructured":"Zidong Zhang, Dongxia Zhang, and Robert C. Qiu. 2019. Deep reinforcement learning for power system applications: An overview. CSEE Journal of Power and Energy Systems 6, 1 (2019), 213\u2013225.","journal-title":"CSEE Journal of Power and Energy Systems"},{"key":"e_1_3_3_883_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.07.033"},{"key":"e_1_3_3_884_2","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/978-3-319-71273-4_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Zhao Jianing","year":"2017","unstructured":"Jianing Zhao, Daniel M. Runfola, and Peter Kemper. 2017. Quantifying heterogeneous causal treatment effects in world bank development finance projects. In Machine Learning and Knowledge Discovery in Databases. Yasemin Altun, Kamalika Das, Taneli Mielik\u00e4inen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka \u017ditnik, Michelangelo Ceci, and Sa\u0161o D\u017eeroski (Eds.). Springer International Publishing, Cham, 204\u2013215."},{"key":"e_1_3_3_885_2","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.11.1.94"},{"key":"e_1_3_3_886_2","doi-asserted-by":"crossref","unstructured":"Yu Zheng. 2015. Methodologies for cross-domain data fusion: An overview. IEEE Transactions on Big Data 1 1 (2015) 16\u201334.","DOI":"10.1109\/TBDATA.2015.2465959"},{"key":"e_1_3_3_887_2","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"},{"key":"e_1_3_3_888_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11071907"},{"key":"e_1_3_3_889_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.5033702"},{"key":"e_1_3_3_890_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57618-3_6"},{"key":"e_1_3_3_891_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.01.076"},{"key":"e_1_3_3_892_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-013-9417-4"},{"key":"e_1_3_3_893_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2017.12.030"},{"key":"e_1_3_3_894_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"e_1_3_3_895_2","unstructured":"arXiv preprint arXiv:2010.09435 2020 An introduction to electrocatalyst design using machine learning for renewable energy storage"},{"key":"e_1_3_3_896_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1202473109"},{"key":"e_1_3_3_897_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.08.010"},{"key":"e_1_3_3_898_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2868648"},{"key":"e_1_3_3_899_2","volume-title":"The Utilization of Supervised Machine Learning in Predicting Corrosion to Support Preventing Pipelines Leakage in Oil and Gas Industry","author":"Zukhrufany Stiffi","year":"2018","unstructured":"Stiffi Zukhrufany. 2018. The Utilization of Supervised Machine Learning in Predicting Corrosion to Support Preventing Pipelines Leakage in Oil and Gas Industry. Master\u2019s thesis. University of Stavanger, Norway."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485128","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485128","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485128","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:35Z","timestamp":1750191515000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":898,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2,28]]}},"alternative-id":["10.1145\/3485128"],"URL":"https:\/\/doi.org\/10.1145\/3485128","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,7]]},"assertion":[{"value":"2020-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}