A Systematic Mapping of Artificial Intelligence Solutions for Sustainability Challenges in Latin America and the Caribbean
Keywords:Artificial Intelligence, AI, Machine Learning, ML, Climate Change, Sustainability, Biodiversity, Data Science
The environmental health of Latin America and the Caribbean (LAC) is crucial to the survival of the planet. LAC countries occupy 13% of the Earth’s landmass yet contain 60% of the terrestrial life. The region is particularly brittle as climate change looms on the horizon, and its economic reliance on exploiting natural resources is accelerating its biodiversity loss. Central to these problems is that LAC is the most economically unequal region globally. Thus, it faces unique challenges in promoting sustainable development. This paper explores whether and how Artificial Intelligence (AI) may provide methods to accelerate the changes needed to increase resilience and facilitate adaptation. Starting with a systematic mapping of the research on AI for sustainability in LAC, we present a diagnosis of the current situation structured along the proposed axes of climate change, human vulnerability, and biodiversity. Then, we give some illustrative examples of potential directions for further work with applicability to the region. Due to its often overlooked resources, capabilities, and particularly fragile geolocation, LAC is called to play an oversize role in the planet’s sustainability in the coming decades.
Mitchell Aide, Matthew Clark, Ricardo Grau, David López-Carr, Marc Levy, Daniel Redo, Martha Bonilla-Moheno, George Riner, María J Andrade-Núñez, and María Muñiz. Deforestation and reforestation of Latin America and the Caribbean (2001–2010). Biotropica, 45(2):262–271, 2013.
United Nations. World population prospects, 2019.
Instituto Nacional de Lenguas Indígenas. Catálogo de Lenguas Indigenas Nacionales: Variantes Lingüisticas de México con sus autodenominaciones y referencias geoestadísticas . Diario Oficial,
México, pages 31–111, 2008.
Martin Walter. Extractives in Latin America and the Caribbean. The
Basics. Inter-American Development Bank, pages 1–20, 2016.
WCMC UNEP. The State of Biodiversity in Latin America and The
Caribbean. UNEP-WCMC, 2016.
David Eckstein, Vera Künzel, and Laura Schäfer. Global climate
risk index. Who Suffers Most from Extreme Weather Events, pages
Ariadna Reyes. Revealing the Contribution of Informal Settlements
to Climate Change Mitigation in Latin America: A Case Study of
Isidro Fabela, Mexico City. Sustainability, 13(21):12108, 2021.
Aimee van Wynsberghe. Sustainable AI: AI for sustainability and
the sustainability of AI. AI and Ethics, 1(3):213–218, 2021.
Mark Coeckelbergh. AI for climate: freedom, justice, and other
ethical and political challenges. AI and Ethics, 1(1):67–72, 2021.
Yoshua Bengio, Yann Lecun, and Geoffrey Hinton. Deep learning
for AI. Communications of the ACM, 64(7):58–65, 2021.
Vincent Pedemonte. AI for Sustainability: An overview of AI and the SDGs to contribute to the European policymaking. Futurium, 2020.
Muhammad Tanveer, Shafiqul Hassan, and Amiya Bhaumik. Aca- demic policy regarding sustainability and artificial intelligence (AI). Sustainability, 12(22):9435, 2020.
Junyi Wu and Shari Shang. Managing uncertainty in AI-enabled decision making and achieving sustainability. Sustainability, 12(21):8758, 2020.
Stéphanie Camaréna. Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia. Sustainability, 13(16):9314, 2021.
D Krishnaveni, V Harish, and A Mansurali. AI and Business Sustainability: Reinventing Business Processes. In Reinventing Man- ufacturing and Business Processes Through Artificial Intelligence, pages 153–172. CRC Press, 2021.
Trina Som. Sustainability in Energy Economy and Environment: Role of AI Based Techniques. In Computational Management, pages 647–682. Springer, 2021.
Naoum Tsolakis, Dimitris Zissis, Spiros Papaefthimiou, and Niko- laos Korfiatis. Towards AI driven environmental sustainability: an application of automated logistics in container port terminals. International Journal of Production Research, pages 1–21, 2021.
Isaac Sakyi Damoah, Anthony Ayakwah, and Ishmael Tingbani. Artificial intelligence (AI)-enhanced medical drones in the healthcare supply chain (HSC) for sustainability development: A case study. Journal of Cleaner Production, 328:129598, 2021.
Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and software technology, 64:1–18, 2015.
K James, N Randall, and N Haddaway. A methodology for systematic mapping in environmental sciences, 2016.
Nayat Sanchez-Pi, Luis Martí, Ana Bicharra Garcia, Ricardo Yates, Marley Vellasco, and Carlos Coello. A Roadmap for AI in Latin America. AI in Latin America of the Global Partnership for AI (GPAI) Paris Summit, 2022.
Justice Mensah. Sustainable development: Meaning, history, princi- ples, pillars, and implications for human action: Literature review. Cogent Social Sciences, 5(1):1653531, 2019.
StaffsKeele.Guidelinesforperformingsystematicliteraturereviews in software engineering. Technical report, EBSE, 2007.
E.Pereira,G.andEstevez,R.Krimmer,M.Janssen,andT.Janowski. Towards a smart sustainable city roadmap. In ACM International Conference Proceeding Series, pages 527–528, 2019.
T. Paz, V. Rocha, P. Campos, I. Paz, R. Caiado, A. Rocha, and G. Lima. Hybrid method to guide sustainable initiatives in higher education: a critical analysis of Brazilian municipalities. Interna- tional Journal of Sustainability in Higher Education, 2022.
S. Rosales, A. Clark, L. Huebner, R. Ruzicka, and E. Muller. Rhodobacterales and Rhizobiales Are Associated With Stony Coral Tissue Loss Disease and Its Suspected Sources of Transmission. Frontiers in Microbiology, 11, 2020.
A. Rashid and A. Chennu. A trillion coral reef colors: Deeply anno- tated underwater hyperspectral images for automated classification and habitat mapping. Data, 5(1), 2020.
A. Mayfield. Machine-Learning-Based Proteomic Predictive Mod- eling with Thermally-Challenged Caribbean Reef Corals. Diversity, 14(1), 2022.
J. Hendee, L. Gramer, J. Kleypas, D. Manzello, M. Jankulak, and C. Langdon. The integrated coral observing network: Sensor solu- tions for sensitive sites. In International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pages 669– 673, 2007.
N. Raja, A. Lauchstedt, J. Pandolfi, S. Kim, A. Budd, and W. Kiessling. Morphological traits of reef corals predict extinction risk but not conservation status. Global Ecology and Biogeography, 30(8):1597–1608, 2021.
S. Pittman and K. Brown. Multi-scale approach for predicting fish species distributions across coral reef seascapes. PLoS ONE, 6(5), 2011.
J. Turner, X. Cheng, N. Saferin, J.. Yeo, T. Yang, and B. Joe. Gut microbiota of wild fish as reporters of compromised aquatic environments sleuthed through machine learning. Physiological genomics, 54(5):177–185, 2022.
A. Cotrina, A. Salazar, C. Oviedo, S. Bandopadhyay, P. Mondaca, R. Valentini, N. Rojas, C. Torres, M. Oliva, B. Guzman, and G. Meza. Integrated cloud computing and cost effective modelling to delineate the ecological corridors for Spectacled bears (Tremarctos ornatus) in the rural territories of the Peruvian Amazon. Global Ecology and Conservation, 36, 2022.
A. Lorena, L. Jacintho, M. Siqueira, R. Giovanni, L. Lohmann, A. De Carvalho, and M. Yamamoto. Comparing machine learning classifiers in potential distribution modelling. Expert Systems with Applications, 38(5):5268–5275, 2011.
T. Püschel, J. Marcé-Nogué, J. Gladman, B. Patel, S. Almécija, and W. Sellers. Getting Its Feet on the Ground: Elucidating Paralouatta’s Semi-Terrestriality Using the Virtual Morpho-Functional Toolbox. Frontiers in Earth Science, 8, 2020.
C. Loeffler, L. Tartaglione, M. Friedemann, A. Spielmeyer, O. Kap- penstein, and D. Bodi. Ciguatera Mini Review: 21st Century Environmental Challenges and the Interdisciplinary Research Efforts Rising to Meet Them. International Journal of Environmental Research and Public Health, 18(6):1–27, 2021.
J.López-Moreno,J.Ceballos,F.Rojas-Heredia,J.Zabalza-Martinez, I. Vidaller, J. Revuelto, E. Alonso-González, E. Morán-Tejeda, and J. García-Ruiz. Topographic Control of Glacier Changes Since the End of the Little Ice Age in the Sierra Nevada de Santa Marta Mountains, Colombia. Journal of South American Earth Sciences, 104, 2020.
A. Finn, P. Kumar, S. Peters, and J. O’Hehir. Unsupervised spectral-spatial processing of drone imagery for identification of pine seedlings. ISPRS Journal of Photogrammetry and Remote Sensing, 183:363–388, 2022.
J. Calil, B. Reguero, A. Zamora, I. Losada, and F. Méndez. Com- parative Coastal Risk Index (CCRI): A Multidisciplinary Risk Index for Latin America and the Caribbean. PLoS ONE, 12(11), 2017.
V. Velasco, R. Martell-Dubois, W. Soon, G. Velasco, S. Cerdeira- Estrada, E. Zúñiga, and L. Rosique. Predicting Atlantic Hurricanes Using Machine Learning. Atmosphere, 13(5), 2022.
D. Brambilla, L. Longoni, and M. Papini. Regional Methods for Shallow Landslide Hazard Evaluation: A Comparison between Italy and Central America. WIT Transactions on Engineering Sciences, 67:185–196, 2010.
N. Molina-Gómez, D. Calderón-Rivera, R. Sierra-Parada, J. Díaz- Arévalo, and P. López-Jiménez. Analysis of Incidence of Air Quality on Human Health: A Case Study on the Relationship between Pol- lutant concentrations and Respiratory Diseases in Kennedy, Bogotá. International Journal of Biometeorology, 65(1):119–132, 2021.
A. Devincentis, H. Guillon, R. Díaz Gómez, N. Patterson, F. Van Den Brandeler, A. Koehl, J. Ortiz-Partida, L. Garza-Díaz, J. Gamez- Rodríguez, E. Goharian, and S. Sandoval Solis. Bright and Blind Spots of Water Research in Latin America and the Caribbean. Hydrology and Earth System Sciences, 25(8):4631–4650, 2021.
E.Glize,M.Huguet,M.Lucas,M.Sutton,andG.Trédan.Clustering Sargassum Mats from Earth Observation Data. In CEUR Workshop Proceedings, volume 2766, 2020.
M. Varona, R. Ortiz, M. Arana, and P. Ortiz. La incidencia de la opinión social en el grado de vulnerabilidad de los edificios patrimoniales. El caso del centro histórico de Popayán (Colombia). Ge-Conservacion, 17:267–279, 2020.
M. Gallardo. Measuring vulnerability to multidimensional poverty with bayesian network classifiers. Economic Analysis and Policy, 73:492–512, 2022.
R.Batista,O.Villar,H.González,andV.Milián.Culturalchallenges of the malicious use of artificial intelligence in Latin American regional balance. In European Conference on the Impact of Artificial Intelligence and Robotics, pages 7–13, 2020.
D. Perino, X. Yang, J. Serra, A. Lutu, and I. Leontiadis. Ex- perience: Advanced network operations in (Un)-connected remote communities. In International Conference on Mobile Computing and Networking, pages 204–213, 2020.
J. Montoya-Rincon, S. Azad, R. Pokhrel, M. Ghandehari, M. Jensen, and J. Gonzalez. On the Use of Satellite Nightlights for Power Outages Prediction. IEEE Access, 10:16729–16739, 2022.
D. Barredo-Ibañez, D. De-La-Garza-Montemayor, Á. Torres- Toukoumidis, and P. López-López. Artificial intelligence, commu- nication, and democracy in Latin America: a review of the cases of Colombia, Ecuador, and Mexico. Profesional de la Informacion, 30(6), 2021.
J. Seawright. Roots in society: Attachment between citizens and party systems in Latin America. Cambridge University Press, 2018.
E. MacArayan, D. Balabanova, and G. Gotsadze. Assessing the
field of health policy and systems research using symposium abstract submissions and machine learning techniques. Health Policy and Planning, 34(10):721–731, 2019.
R. Garcia, U. Thoene, and D. Davilas. Digital Health and Artificial Intelligence: Advancing Healthcare Provision in Latin America. IT Professional, 24(2):62–68, 2022.
I. Shibi, L. Aswathya, R. Jishaa, V. Masandb, and J. Gajbhiyec. Virtual screening techniques to probe the antimalarial activity of some traditionally used phytochemicals. Combinatorial Chemistry and High Throughput Screening, 19(7):572–591, 2016.
A. Rubio-Solis, T. Massoni, A. Musah, G. Birjovanu, W.P. Dos San- tos, and P. Kostkova. Zika virus: Prediction of Aedes Mosquito Larvae Occurrence in Recife (Brazil) using online extreme learning machine and neural networks. In International Conference on Digital Public Health, pages 101–110. Association for Computing Machinery, 2019.
E. Pelaez. A fuzzy cognitive map (FCM) as a learning model for early prognosis of seasonal related virus diseases in tropical regions. In International Conference on eDemocracy & eGovernment, pages 150–156, 2019.
M. Moreno-Fergusson, W. Guerrero Rueda, G. Ortiz Basto, I. Arevalo, and B. Sanchez–Herrera. Analytics and Lean Health Care to Address Nurse Care Management Challenges for Inpatients in Emerging Economies. Journal of Nursing Scholarship, 53(6):803– 814, 2021.
D.Aarons.Issuesinbioethics:Ethicsinhealthpolicyandguidelines in healthcare. West Indian Medical Journal, 55(2):113–119, 2006.
R. Bojorque and F. Pesántez-Avilés. Academic quality management system audit using artificial intelligence techniques. Advances in Intelligent Systems and Computing, 965:275–283, 2020.
M. Ruiz-Cantisani, J. Vargas-Florez, and C. Castro-Zuluaga. Active learning using inter-university networks in Latin America with supply chain resilience projects in micro and small enterprises. In IISE Annual Conference and Expo, pages 405–410, 2021.
E. Walker, A. Ogan, R. Baker, A. De Carvalho, T. Laurentino, G. Rebolledo-Mendez, and M. Castro. Observations of collaboration in cognitive tutor use in Latin America. Lecture Notes in Computer Science, 6738:575–577, 2011.
J. Serna Gómez, F. Díaz-Piraquive, Y. Muriel-Perea, and A. Díaz. Advances, Opportunities, and Challenges in the Digital Transfor- mation of HEIs in Latin America. Lecture Notes in Educational Technology, pages 55–75, 2021.
Z. Yi, N. Zurutuza, D. Bollinger, M. Garcia-Herranz, and D. Kim. Towards equitable access to information and opportunity for all: Mapping schools with high-resolution satellite imagery and machine learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, volume 1, pages 60–66, 2019.
S. Gutierrez, S. Perez, and M. Munguia. Artificial Intelligence in e-Learning Plausible Scenarios in Latin America and New Grad- uation Competencies. Revista Iberoamericana de Tecnologias del Aprendizaje, 17(1):31–40, 2022.
A. Asadikia, A. Rajabifard, and M. Kalantari. Region-income-based prioritisation of sustainable development goals by gradient boosting machine. Sustainability Science, 2022.
J. Domínguez, I. Pinedo, and J. Gonzalez. Research activities in renewable energy sources integration with GIS at CIEMAT. In Int. Congress on Environmental Modelling and Software, volume 2, pages 1247–1254, 2008.
Z. Tao, H. Kazmi, and F. Mehmood. Optimal data-driven control of embedded micro-grids in developing countries. In International Conference on Industrial Artificial Intelligence, 2019.
P. Vinueza-Naranjo, H. Nascimento-Silva, R. Rumipamba- Zambrano, I. Ruiz-Gomes, D. Rivas-Lalaleo, and N. Patil. Iot-based smart agriculture and poultry farms for environmental sustainability and development. Innovations in Communication and Computing, pages 379–406, 2022.
P. Imbach, E. Fung, L. Hannah, C. Navarro-Racines, D. Roubik, T. Ricketts, C. Harvey, C. Donatti, P. Läderach, B. Locatelli, and P. Roehrdanz. Coupling of pollination services and coffee suitability under climate change. Proceedings of the National Academy of Sciences of the USA, 114(39):10438–10442, 2017.
M. Billib, K. Bardowicks, and E. Holzapfel. Decision support system for sustainable irrigation in Latin America. Symposium HS3006, 1(315):18–24, 2007.
E. Abraham, J. Reis, A. Colossetti, A. Souza, and R. Toloi. Neural network system to forecast the soybean exportation on Brazilian port of Santos. IFIP Advances in Information and Communication Technology, 514:83–90, 2017.
V. Masson-Delmotte, P. Zhai, A. Pirani, S. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. Matthews, T. Maycock, T. Waterfield, O. Yelekçi, R. Yu, , and B. Zhou. The Physical Science Basis. Tech- nical report, Intergovernmental Panel on Climate Change (IPCC), 2021.
Priya Donti and Zico Kolter. Machine Learning for Sustainable Energy Systems. Annual Review of Environment and Resources, 46:719–747, 2021.
David Rolnick, Priya Donti, Lynn Kaack, Kelly Kochanski, Alexan- dre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic- Dupont, Natasha Jaques, and Anna Waldman-Brown. Tackling Climate Change with Machine Learning. ACM Computing Surveys, 55(2):1–96, 2022.
P. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belka- cemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley. Mitigation of Climate Change. Technical report, Intergovernmental Panel on Climate Change (IPCC), 2022.
H. Pörtner, D. Roberts, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S.Löschke, V. Möller, A. Okem, and B. Rama. Impacts, Adaptation, and Vulnerability. Technical report, Intergovernmental Panel on Climate Change (IPCC), 2022.
Jose Samaniego. The economics of climate change in Latin America and the Caribbean: Paradoxes and challenges. CEPAL. División de Desarrollo Sostenible y Asentamientos Humanos, 2014.
Silvia Santos, Mohamad Hejazi, Gokul Iyer, Thomas Wild, Matthew Binsted, Fernando Miralles-Wilhelm, Pralit Patel, Abigail Snyder, and Chris Vernon. Power sector investment implications of climate impacts on renewable resources in Latin America and the Caribbean. Nature communications, 12(1):1–12, 2021.
IPCC. Climate Change 2014 synthesis report. Technical report, IPCC, 2014.
Gregg Garfin, Angela Jardine, Robert Merideth, Mary Black, and Sarah LeRoy. Assessment of climate change in the southwest United States: a report prepared for the National Climate Assessment. Island Press, 2013.
Stephen Pacala and Robert Socolow. Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science, 305(5686):968–972, 2004.
Ilissa B Ocko, Tianyi Sun, Drew Shindell, Michael Oppenheimer, Alexander N Hristov, Stephen W Pacala, Denise L Mauzerall, Yangyang Xu, and Steven P Hamburg. Acting rapidly to deploy readily available methane mitigation measures by sector can im- mediately slow global warming. Environmental Research Letters, 16(5):054042, 2021.
Almeida Prado and Fernando Amaral. How much is possible? An integrative study of intermittent and renewables sources deployment. A case study in Brazil. In Renewable-Energy-Driven Future, pages 511–538. Elsevier, 2021.
Jim Lazar. Teaching the “Duck” to Fly. Regulatory Assistance Project Montpelier, 2016.
Daniel Schrag. Is shale gas good for climate change? Daedalus, 141(2):72–80, 2012.
Leonardo Ramos and Manuel Montenegro. Una Nueva Forma de Reducir la Intermitencia Eléctrica de Manera Sustentable, Caso de Estudio: un Sistema Híbrido Tipo Rebombeo Solar en México. Ingeniería, 24(3):209–223, 2019.
David MacKay. Sustainable Energy-without the hot air. UIT Cambridge, 2008.
Priya Donti, David Rolnick, and J Zico Kolter. Dc3: A learning method for optimization with hard constraints. arXiv:2104.12225, 2021.
Stavros Karagiannopoulos, Petros Aristidou, and Gabriela Hug. Data-driven local control design for active distribution grids using off-line optimal power flow and machine learning techniques. IEEE Transactions on Smart Grid, 10(6):6461–6471, 2019.
Bernt Viggo Matheussen, Ole-Christoffer Granmo, and Jivitesh Sharma. Hydropower optimization using deep learning. In Interna- tional Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 110–122. Springer, 2019.
Stéfano Frizzo Stefenon, Matheus Henrique Dal Molin Ribeiro, Ademir Nied, Kin-Choong Yow, Viviana Cocco Mariani, Leandro dos Santos Coelho, and Laio Oriel Seman. Time series forecasting using ensemble learning methods for emergency prevention in hy- droelectric power plants with dam. Electric Power Systems Research, 202:107584, 2022.
Adrian Stetco, Fateme Dinmohammadi, Xingyu Zhao, Valentin Robu, David Flynn, Mike Barnes, John Keane, and Goran Nenadic. Machine learning methods for wind turbine condition monitoring: A review. Renewable energy, 133:620–635, 2019.
B Yeter, Y Garbatov, and C Guedes Soares. Life-extension classifi- cation of offshore wind assets using unsupervised machine learning. Reliability Engineering & System Safety, 219:108229, 2022.
Bruna Oliveira, Leticia Oliveira, Paulo Carvalho, and Clodoaldo Oliveira. Experimental Assessment and Modeling of a Floating Photovoltaic Module with Heat Bridges. IEEE Latin America Transactions, 19(12):2079–2086, 2021.
Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, and Morgane Riviere. An introduction to electrocat- alyst design using machine learning for renewable energy storage. arXiv:2010.09435, 2020.
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras- Domingo, Caleb Ho, and Weihua Hu. Open catalyst 2020 (OC20) dataset and community challenges. ACS Catalysis, 11(10):6059– 6072, 2021.
Cindy Viviescas, Lucas Lima, Fabio Diuana, Eveline Vasquez, Camila Ludovique, Gabriela Silva, Vanessa Huback, Leticia Magalar, Alexandre Szklo, and Andre Lucena. Contribution of Variable Renewable Energy to increase energy security in Latin America: Complementarity and climate change impacts on wind and solar resources. Renewable and sustainable energy reviews, 113:109232, 2019.
Rebecca Scafutto and Carlos Souza. Detection of methane plumes using airborne midwave infrared (3–5 μm) hyperspectral data. Re- mote Sensing, 10(8):1237, 2018.
Aaron Davitt, Gavin McCormick, Colin McCormick, Christy Lewis, Gabriela Volpato, Lekha Sridhar, Nick Amuchastegui, Heather Cou- ture, Thomas Kassel, and Matthew Gray. Climate TRACE-Tracking Real time Atmospheric Carbon Emissions: Making greenhouse gases emissions visible through remote sensing and artificial intelligence. In AGU Fall Meeting. AGU, 2021.
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, and Friedrich Fraundorfer. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4):8–36, 2017.
RosianeFreitas,JoãoCavalcanti,SergioCleger,NiroHiguchi,Carlos Celes, and Adriano Lima. Estimating Amazon Carbon Stock Using AI-Based Remote Sensing. Commun. ACM, 63(11):46–48, oct 2020.
Maria Antonia Brovelli, Yaru Sun, and Vasil Yordanov. Monitoring forest change in the amazon using multi-temporal remote sensing data and machine learning classification on google earth engine. ISPRS International Journal of Geo-Information, 9(10):580, 2020.
Juan Argañaraz, Marcos Landi, Sandra Bravo, Gregorio Gavier- Pizarro, Carlos Scavuzzo, and Laura Bellis. Estimation of Live Fuel Moisture Content From MODIS Images for Fire Danger Assessment in Southern Gran Chaco. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12):5339–5349, 2016.
Ahmed Mehdawi and Baharin Ahmad. Classification of forest change by integration of remote sensing data with Neural Network techniques. In International Conference on System Engineering and Technology, pages 1–5, 2012.
Raphael Rossel and Johan Bouma. Soil sensing: A new paradigm for agriculture. Agricultural Systems, 148:71–74, 2016.
Victor Galaz, Miguel A Centeno, Peter W Callahan, Amar Causevic, Thayer Patterson, Irina Brass, Seth Baum, Darryl Farber, Joern Fischer, David Garcia, et al. Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67:101741, 2021.
Weiliang Zeng, Tomio Miwa, and Takayuki Morikawa. Application of the support vector machine and heuristic k-shortest path algorithm
to determine the most eco-friendly path with a travel time con- straint. Transportation Research Part D: Transport and Environment, 57:458–473, 2017.
Emmanouil Rigas, Sarvapali Ramchurn, and Nick Bassiliades. Man- aging Electric Vehicles in the Smart Grid Using Artificial Intelli- gence: A Survey. IEEE Transactions on Intelligent Transportation Systems, 16:1619–1635, 2015.
Ján Drgonˇa, Javier Arroyo, Iago Figueroa, David Blum, Krzysztof Arendt, Donghun Kim, Enric Ollé, Juraj Oravec, Michael Wetter, and Draguna Vrabie. All you need to know about model predictive control for buildings. Annual Reviews in Control, 50:190–232, 2020. Ján Drgonˇa and Nstuezo Fobi. Climate Change AI Dataset Wishlist. Technical Report arXiv:000.000, Climate Change AI, 2022. Mohammad Rahimi, Seyed Mohamad Moosavi, Berend Smit, and Alan Hatton. Toward smart carbon capture with machine learning. Cell Reports Physical Science, 2(4):100396, 2021.
Sanggyu Chong, Sangwon Lee, Baekjun Kim, and Jihan Kim. Applications of machine learning in metal-organic frameworks. Coordination Chemistry Reviews, 423:213487, 2020.
Zhu Zhongming, Lu Linong, Yao Xiaona, Zhang Wangqiang, and Liu Wei. AR6 Synthesis Report: Climate Change 2022. Technical report, IPCC, 2022.
D Reidmiller, C Avery, D Easterling, K Kunkel, K Lewis, T May- cock, and B Stewart. Fourth national climate assessment, Volume II: Impacts, risks, and adaptation in the United States. US Global Change Research Program, 2018.
Ed Hawkins, Thomas Osborne, Chun Kit Ho, and Andrew Challinor. Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agricultural and forest meteorology, 170:19–31, 2013.
J Sheffield, Eric Wood, M Pan, H Beck, G Coccia, A Serrat- Capdevila, and K Verbist. Satellite remote sensing for water resources management: Potential for supporting sustainable develop- ment in data-poor regions. Water Resources Research, 54(12):9724– 9758, 2018.
Xuan-Hien Le, Duc-Hai Nguyen, Sungho Jung, Minho Yeon, and Giha Lee. Comparison of deep learning techniques for river streamflow forecasting. IEEE Access, 9:71805–71820, 2021. Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frédéric Frappart, Melanie Becker, Mohammad Tourian, and Catherine Prigent. A combined use of in situ and satellite-derived observations to char- acterize surface hydrology and its variability in the Congo River Basin. Hydrology and Earth System Sciences Discussions, pages 1–47, 2021.
Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, and Nuno Carvalhais. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743):195–204, 2019.
George Karniadakis, Ioannis Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422–440, 2021.
Mark Risser and Michael Wehner. Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during hurricane harvey. Geophysical Research Letters, 44(24):12–457, 2017.
Karen McKinnon and Andrew Poppick. Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines. Journal of Agricultural, Biological and Environmental Statistics, 25(3):292–314, 2020. Karen McKinnon, Andrew Poppick, and Isla Simpson. Hot extremes have become drier in the United States Southwest. Nature Climate Change, 11(7):598–604, 2021.
Edoardo Nemni, Joseph Bullock, Samir Belabbes, and Lars Bromley. Fully convolutional neural network for rapid flood segmentation in synthetic aperture radar imagery. Remote Sensing, 12(16), 2020. Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, and Sam Madge. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878):672–677, 2021.
Andrew Crane-Droesch, Ben Kravitz, and John Abatzoglou. Using deep learning to model potential impacts of geoengineering via solar radiation management on US agriculture. In AGU Fall Meeting, volume 2018, pages GC11I–1011, 2018.
Michée Lachaud, Boris Bravo-Ureta, and Carlos Ludena. Economic effects of climate change on agricultural production and productivity in Latin America and the Caribbean (LAC). Agricultural Economics, 2021.
Ali Dinler. Reducing balancing cost of a wind power plant by deep learning in market data: A case study for turkey. Applied Energy, 289:116728, 2021.
Aldo Caliari, Daniela Berdeja, Patricia Miranda, Stefanie Ostfeld. Atlas de Vulnerabilidad: La Pandemia en America Latina y el Caribe. Technical report, Jubilee USA Network and Latindad, 2021.
Javier Bronfman. Challenges for optimizing social protection programmes and reducing vulnerability in Latin America and the Caribbean. CEPAL Review, 2021.
World Bank. Taking on Inequality: Poverty and Shared Prosperity. Washington: World Bank Group, 2016.
CEPAL, UN. Social Panorama of Latin America. ECLAC, 2022. Marco Stampini, Marcos Robles, Mayra Sáenz, Pablo Ibarrarán, and Nadin Medellín. Poverty, vulnerability, and the middle class in Latin America. Latin American Economic Review, 25(1):1–44, 2016. Jorgelina Hardoy and Gustavo Pandiella. Urban poverty and vul- nerability to climate change in Latin America. Environment and Urbanization, 21(1):203–224, 2009.
Ivonne Acevedo, Francesca Castellani, Giulia Lotti, and Miguel Székely. Informality in the time of COVID-19 in Latin America: Implications and policy options. PloS One, 16(12):e0261277, 2021. C. Elgin, M. Kose, F. Ohnsorge, and S. Yu. Understanding Infor- mality. Technical report, SSRN, 2021.
Melina Altamirano. Economic vulnerability and partisanship in Latin America. Latin American Politics and Society, 61(3):80–103, 2019. Jorge Rojas. Society, environment, vulnerability, and climate change in Latin America: Challenges of the twenty-first century. Latin American Perspectives, 43(4):29–42, 2016.
Mark Sullivan and Peter Meyer. Latin America and the Caribbean: Impact of COVID-19. Congressional Research Service, 2022.
R Angulo. Social vulnerability and psychological vulnerability: the great challenge of mental health in Latin America in the face of COVID-19. Panamerican Journal of Neuropsychology, pages 10– 15, 2020.
James Pick, Avijit Sarkar, and Elizabeth Parrish. The Latin American and Caribbean digital divide: a geospatial and multivariate analysis. Information Technology for Development, 27(2):235–262, 2021.
S Ziegler, J Segura, M Bosio, and K Camacho. Rural Connectivity in Latin America and the Caribbean: A Bridge for Sustainable Development in a Time of Pandemic, 2020.
Franz Drees-Gross and Pepe Zhang. Less than 50% of Latin America has fixed broadband, 2021.
Gabriela Azócar, Marco Billi, Rubén Calvo, Nicolas Huneeus, Marta Lagos, Rodolfo Sapiains, and Anahí Urquiza. Climate change perception, vulnerability, and readiness: inter-country variability and emerging patterns in Latin America. Journal of Environmental Studies and Sciences, 11(1):23–36, 2021.
Lykke Andersen, Dorte Verner, and Manfred Wiebelt. Gender and climate change in Latin America: an analysis of vulnerability, adaptation and resilience based on household surveys. Journal of International Development, 29(7):857–876, 2017.
Laura Comerón. Vulnerabilidad de las mujeres frente a la violencia de género en contexto de desastres naturales en Latinoamérica y Caribe. Trabajo Social Hoy, 2015(76):7–34, 2015.
Ana Fernández, Johannes Waldmüller, and Cristina Vega. Comu- nidad, vulnerabilidad y reproducción en condiciones de desastre. Abordajes desde América Latina y el Caribe. Íconos, 24(66):7–29, 2020.
Rodney Martínez, Eduardo Zambrano, Juan Nieto, Julián Hernández, and Felipe Costa. Evolución, vulnerabilidad e impactos económicos y sociales de El Niño 2015-2016 en América Latina. Investigaciones Geográficas (España), 2017(68):65–78, 2017.
Alfonso Rodríguez-Morales. Climate change, rainfall, society and disasters in Latin America: relations and needs. Revista Peruana de Medicina Experimental y Salud Pública, 28(1):165–166, 2011. Ivan Ramírez and Jieun Lee. COVID-19 and Ecosyndemic vulnera- bility: Implications for El Niño-sensitive countries in Latin America. International Journal of Disaster Risk Science, 12(1):147–156, 2021. Gustavo Nagy, Ulisses Azeiteiro, Johanna Heimfarth, José Verocai, and Chunlan Li. An assessment of the relationships between extreme weather events, vulnerability, and the impacts on human wellbeing
in Latin America. International journal of Environmental Research and Public Health, 15(9):1802, 2018.
Lynnette Wood, Alex Apotsos, Patricia Caffrey, and Kenneth Gibbs. Fostering uptake: lessons from climate change vulnerability assess- ments in Africa and Latin America. Development in Practice, 27(4):444–457, 2017.
Rogelio Flores-Ramírez, Alejandra Berumen-Rodríguez, Marco Martínez-Castillo, Luz Alcántara-Quintana, Fernando Díaz-Barriga, and Lorena Diaz. A review of Environmental risks and vulnerability factors of indigenous populations from Latin America and the Caribbean in the face of the COVID-19. Global Public Health, 16(7):975–999, 2021.
Steven Prager, Jesús José Rodríguez De Luque, and Carlos Eduardo Gonzalez. Climate change vulnerability in the agricultural sector in Latin America and the Caribbean. Technical report, International Center for Tropical Agriculture, 2016.
Arielle Rosenthal. Victual Vulnerability: Determinants, Targets and Mitigations of Food Insecurity in Latin America. PhD thesis, McGill University, 2021.
Erlick, June. Natural Disasters in Latin America and the Caribbean: Coping with Calamity. Routledge, 2021.
Camilo Mora, Derek P. Tittensor, Sina Adl, Alastair G. Simpson, and Boris Worm. How many species are there on earth and in the ocean? PLOS Biology, 9(8):1–8, 08 2011.
Juliana Wojciechowski, Fernanda Ceschin, Suelen Pereto, Luiz Ribas, Luis Bezerra, Jaqueline Dittrich, Tadeu Siqueira, and Andre Padial. Latin american scientific contribution to ecology. In Anais da Academia Brasileira de Ciências, volume 89 of 4, pages 2663–2674, oct 2017.
Francisco Alpízar, Róger Madrigal, Irene Alvarado, Esteban Brenes, Ashley Camhi, Jorge Maldonado, Jorge Marco, Alejandra Martínez, Eduardo Pacay, and Gregory Watson. Mainstreaming of Natural Capital and Biodiversity into Planning and Decision-Making: Cases from Latin America and the Caribbean, 2020. JulianAlstonandPhilipPardey.AgricultureintheGlobalEconomy. Journal of Economic Perspectives, 28(1):121–46, 2014. OECD/FAO. Latin American Agriculture: Prospects and Challenges. Jesús Soria-Ruiz, Yolanda Fernández-Ordoñez, Guillermo Medina- García, Juan Quijano-Carranza, Martha Ramírez-Guzmán, Liliana Aguilar-Marcelino, and Leila Vazquez-Siller. Agriculture in Latin America: Recent Advances and Food Demands by 2050. Information and Communication Technologies for Agriculture—Theme IV, pages 139–154, 2021.
Ricardo Cavieses-Núñez, Miguel Ojeda-Ruiz, Alfredo Flores- Irigollen, Elvia Marín-Monroy, and Carlos Sánchez-Ortíz. Focused small-scale fisheries as complex systems using deep learning models. Latin American Journal of Aquatic Research, 49(2):342–353, 2021. Tina Christmann and Imma Menor. A synthesis and future research directions for tropical mountain ecosystem restoration. Scientific Reports, 11(1):23948, December 2021.
Célia Ralha, Carolina Abreu, Cássio Coelho, Alexandre Zaghetto, Bruno Macchiavello, and Ricardo Machado. A multi-agent model system for land-use change simulation. Environmental Modelling & Software, 42:30–46, 2013.
George Sakr, Imad Elhajj, George Mitri, and Uchechukwu Wejinya. Artificial Intelligence for Forest Fire Prediction. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pages 1311–1316, 2010.
Rodrigo Sierra, Felipe Campos, and Jordan Chamberlin. Assessing biodiversity conservation priorities: ecosystem risk and representa- tiveness in continental Ecuador. Landscape and Urban Planning, 59(2):95–110, 2002.
Richard Gibbs. The Human Genome Project changed everything. Nature Reviews Genetics, 21(10):575–576, October 2020.
André Garraffoni, Thiago Araújo, Anete Lourenço, Loretta Guidi, and Maria Balsamo. A new genus and new species of freshwater Chaetonotidae (Gastrotricha: Chaetonotida) from Brazil with phy- logenetic position inferred from nuclear and mitochondrial DNA sequences. Systematics and Biodiversity, 15(1):49–62, 2017. Desmond Higgins and Paul Sharp. CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 73(1):237–244, 1988.
Ehsan Maleki, Hossein Babashah, Somayyeh Koohi, and Zahra Kavehvash. High-speed all-optical dna local sequence alignment
  
based on a three-dimensional artificial neural network. J. Opt. Soc. Am. A, 34(7):1173–1186, Jul 2017.
Pauline Kim. AI and Inequality. The Cambridge Handbook on Artificial Intelligence & the Law, 2021.
Peeyush Kumar, Ranveer Chandra, Chetan Bansal, Shivkumar Kalya- naraman, Tanuja Ganu, and Michael Grant. Micro-climate Prediction - Multi Scale Encoder-decoder based Deep Learning Framework. In Knowledge Discovery and Data Mining, August 2021.
Khizir Mahmud, Jahangir Hossain, and Graham Town. Peak- Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles. IEEE Access, 6:29353–29365, 2018. Arghya Bhowmik and Tejs Vegge. AI Fast Track to Battery Fast Charge. Joule, 4(4):717–719, 2020.
Lynn Kaack, Priya Donti, Emma Strubell, George Kamiya, Felix Creutzig, and David Rolnick. Aligning artificial intelligence with climate change mitigation. Nature Climate Change, pages 1–10, 2022.
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Hung Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeffrey Dean. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. TechRxiv, 2022.
Max Roser and Esteban Ortiz-Ospina. Global extreme poverty. Our World in Data, 2013.
World Bank. Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) - China, 2022.
Martin Ravallion. The economics of poverty: History, measurement, and policy. Oxford University Press, 2015. AijunLiu,MauriceOsewe,YangyanShi,XiaofeiZhen,andYanping Wu. Cross-Border E-Commerce Development and Challenges in China: A Systematic Literature Review. Journal of Theoretical and Applied Electronic Commerce Research, 17(1):69–88, 2022. PengChao,MBiao,andChenZhang.Povertyalleviationthroughe- commerce: Village involvement and demonstration policies in rural China. Journal of Integrative Agriculture, 20(4):998–1011, 2021. Dongshi Chen, Hongdong Guo, Qianqian Zhang, and Songqing Jin. E-commerce Adoption and Technical Efficiency of Wheat Production in China. Sustainability, 14(3):1197, 2022.
Hannah Leslie, Donna Spiegelman, Xin Zhou, and Margaret Kruk. Service readiness of health facilities in Bangladesh, Haiti, Kenya, Malawi, Namibia, Nepal, Rwanda, Senegal, Uganda and the United Republic of Tanzania. Bulletin of the World Health Organization, 95(11):738, 2017.
Kenneth Fleming, Susan Horton, Michael Wilson, Rifat Atun, Kristen DeStigter, John Flanigan, Shahin Sayed, Pierrick Adam, Bertha Aguilar, and Savvas Andronikou. The Lancet Commission on diagnostics: Transforming access to diagnostics. The Lancet, 398(10315):1997–2050, 2021.
Emily Senay, Todd Cort, William Perkison, Jasminka Goldoni Laes- tadius, and Jodi Sherman. What Can Hospitals Learn from The Coca-Cola Company? Health Care Sustainability Reporting. NEJM Catalyst Innovations in Care Delivery, 3(1):CAT–21, 2022.
Rory Leisegang. Outcomes and cost-effectiveness of different models of delivery of antiretroviral therapy. PhD thesis, University of Cape Town, 2018.
Fei Tao and Qinglin Qi. Make more digital twins. Nature, 573(7775):490–491, September 2019.
André Abade, Paulo Ferreira, and Flavio de Barros Vidal. Plant diseases recognition on images using convolutional neural networks: A systematic review. Computers and Electronics in Agriculture, 185:106125, 2021.
Andre Abade, Ana de Almeida, and Flavio Vidal. Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks. In International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pages 450–458. SciTePress, 2019.
H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. AL- Rahamneh. Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications, 17(1):31– 38, March 2011.
Victor Galaz, Miguel Centeno, Peter Callahan, Amar Causevic, Thayer Patterson, Irina Brass, Seth Baum, Darryl Farber, Joern Fischer, David Garcia, Timon McPhearson, Daniel Jimenez, Brian King, Paul Larcey, and Karen Levy. Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67:101741, 2021.
Marzieh Mehrnejad, Alexandra Branzan Albu, David Capson, and Maia Hoeberechts. Detection of stationary animals in deep-sea video. In Oceans, pages 1–5, 2013.
Pengfei Zhu, Tao Peng, Dawei Du, Hongtao Yu, Libo Zhang, and Qinghua Hu. Graph regularized flow attention network for video animal counting from drones. IEEE Transactions on Image Processing, 30:5339–5351, 2021.
Shaveta Malik, Tapas Kumar, and A. Sahoo. Image processing techniques for identification of fish disease. In IEEE International Conference on Signal and Image Processing, pages 55–59, 2017. Yuhao Wang, Ivan Wang-Hei Ho, Yang Chen, Yuhong Wang, and Yinghong Lin. Real-time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation. IEEE Internet of Things Journal, pages 1–1, 2021.
Odette Lawler, Hannah Allan, Peter Baxter, Romi Castagnino, Ma- rina Corella Tor, Leah Dann, Joshua Hungerford, Dibesh Karma- charya, Thomas Lloyd, María López-Jara, Gloeta Massie, Junior Novera, Andrew Rogers, and Salit Kark. The COVID-19 pandemic is intricately linked to biodiversity loss and ecosystem health. The Lancet Planetary Health, 5(11):e840–e850, 2021. SusumuAnnaka.Politicalregime,datatransparency,andCOVID-19 death cases. Population Health, 15:100832, 2021.
Martin Lnenicka and Anastasija Nikiforova. Transparency-by- design: What is the role of open data portals? Telematics and Informatics, 61:101605, 2021.
Cristiano Aguiar. "O uso das Tecnologias da Informação e Comu- nicações nas políticas de acesso à informação pública na América Latina. In IPEA, editor, Anais do I Circuito de Debates Acadêmicos, volume 1 of 1, pages 1–15. Consultoria Legislativa – Câmara dos Deputados, 2011.
Transparency International. "Índice de percepção da corrupção", 2022.