AI for SDGs - How Can AI Help Solve Environmental Challenges?　

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Chair: Dr. Yuko HARAYAMA, Former Executive Member of the Council for Science and echnology Policy, Cabinet Office, Government of Japan; Professor Emeritus, Tohoku University

Dr. Kotaro KAWAMATA, Director, Environmental Policy Bureau, Ministry of the Environment, Government of Japan
“Localizing SDGs – Circulating and Ecological Economy –” [PDF, 4192 KB]
Japanese government has been promoting the “Circulating and Ecological Economy”, or CEE, to reflect the philosophy of the 2030 Agenda and achieve the SDGs. Japan has started a new programme and provided support for local governments to vitalize local  resources and economies. By connecting municipalities with experts to find the most effective way to cope with social, environmental, and economic issues, this programme will translate the principles of CEE into reality. “Society 5.0”, including AI, is expected to contribute to this.

Dr. Lothar MENNICKEN, Counselor, Science and Technology, Embassy of the Federal Republic of Germany in Japan
“German AI Initiatives – Using Digital Technologies for Sustainable Solutions” [PDF; 1002 KB]
Germany wants to become a pioneer in achieving the Agenda 2030 SDGs by implementing the National Sustainable Development Strategy. R&D funded by the Federal Ministry of Education and Research will contribute to sustainable innovations in energy supply, circular economy, mobility and agriculture by increasing resource efficiency through new digital technologies, processes and services. Applying knowledge of sustainability research to make the digital transformation itself more sustainable is a major goal.

Renaud FRANCOU, Foresighter, Author, Former Project Manager at Fondation Internet Nouvelle Génération (FING)
“Making the Digital and Environmental Transitions Converge” [PDF; 1192 KB]
The environmental transition sets an inevitable time frame for our societies, while the digital transition is the driving force of our times. The environmental transition has a clear goal but is straining to find a way, while the digital transition is affecting everyday life and impelling change but without any clear collective goal.
The one has a goal to reach; the other, a way to go. They need each other! How to make these two major transitions converge? How can artificial intelligence help?

Chair: Prof. Dr. Wolfgang KETTER, Director of the Institute of Energy Economics and Chaired Professor of Information Systems, University of Cologne

Prof. Frédérick GARCIA, INRA research director, MIA (Applied Mathematics and  Informatics), INRA, Toulouse
“AI in Agriculture for Tackling Environmental Challenges” [PDF;  2885 KB]
Artificial Intelligence (knowledge representation, machine learning, automated decision making…) is at the heart of the digital transformation of agriculture. After introducing the new methods and technologies which characterise digital agriculture, I will focus on the potential of AI in agriculture for tackling environmental  challenges. I will conclude by having a look at the limitations and the environmental impacts of AI and digital agriculture.

Dr. Eisaku SHIRATANI, Vice President, National Agriculture and Food Research Organization (NARO), Japan
“Smart Agri-Food Industry for Climate Change Mitigation/Adaptation in Japan”  
This talk will provide an overview of NARO’s research. ICT technologies for smart agriculture, such as selfdriving agrimachines, have been developed. Technologies of impact assessment, mitigation, and adaptation for climate change, such as a system that provides 1 km grid-square agro-meteorological data, and a disaster prevention support system for irrigation ponds have also been  developed. Through the development of these technologies, NARO intends to strengthen the competitiveness of the agri-food industry in Japan.

Prof. Dr.-Ing. Cornelia WELTZIEN, Director, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) ; Professor, Technical University Berlin (TUB)
“AI Applications – Leading the Way to an Environment Controlled Agriculture!?” [PDF; 1993 KB]
Future farms will engage in sustainable and environmentally friendly production, but in a manner as efficient as mass-production. They will carry out many small-scale and precise interventions as  efficiently as one largescale intervention. These intelligent farms will employ IoT, cloud computing, and AI tools, exploiting collective data and knowledge to improve agricultural decision making, as well as contributing to collective data and knowledge bases. AI will lead the way to an Environment Controlled Agriculture.

Chair: Prof. Dr. Arisa EMA, Assistant Professor, Institute for Future Initiatives, The University of Tokyo

Prof. Dr. Wolfgang KETTER, Director of the Institute of Energy Economics and Chaired Professor of Information Systems, University of Cologne
“(R)evolutions in Mobility: A CASE for Using AI and Smart Markets to Create Sustainable Cities” 
Urban populations lead the way in adopting smart tech. Urban areas can also facilitate positive change with municipal policy. The transition to sustainable energy and smart cities is wickedly complex, tempting business and policymakers with growth, but offering a minefield of unintended consequences. As artificial intelligence and machine learning enable Connected, Autonomous, Shared and Electric (CASE) mobility, how can we use the power of smart markets to create sustainable, fair and liveable cities?

Prof. Christophe CÉRIN, Professor in Computer Science and Engineering, LIPN, CNRS UMR 7030, Université Paris 13
“AI for Smart Buildings” [PDF; 1434 KB]
What is the difference between an Intelligent Building and a Smart Building? What is the difference between a Building Management System and a Cyber Physical System? In the jungle of terminology, the computerscientist-in-chief will try to convince you that  (computer) systems that leverage purely theoretical views are the source of better energy management, thanks to AI. A difficulty must be confronted in the ubiquitous world of cloud computing: more than a panacea, cloud is a reality.

Prof. Dr. Masaru YARIME, Associate Professor, Division of Public Policy, Hong Kong University of Science and Technology; Visiting Associate Professor, Graduate School of Public Policy, The University of Tokyo
“AI-Based Innovation to Tackle Climate Change: Technological Opportunities and Institutional Challenges in Smart Cities in Japan” [PDF; 5229 KB]
Smart cities play an important role in tackling climate change by promoting renewable energy sources and improving energy efficiency while strengthening resilience against natural disasters. At the same time, it is crucial to address societal concerns concerning privacy, safety, and public security in managing a vast amount of sensitive data. We will discuss policy and institutional challenges in governing AI-based innovation through open data and stakeholder collaboration in Japan and beyond.

Chair: Prof. Christophe CÉRIN, Professor in Computer Science and Engineering, LIPN, CNRS UMR 7030, Université Paris 13

Jochen SCHWILL, CEO, NEXT Kraftwerke, Germany
Hirobumi WADA, General Manager, Office of Digital Innovation Promotion, Tohoku Electric Power Co., Inc.
“Virtual Power Plant (VPP) Demonstration Project Activities of Tohoku Electric Power and NEXT Kraftwerke” [PDF; 1900 KB]
“VPP demonstration project of Tohoku EPCO with Next Kraftwerke” [PDF; 1688 KB]
The presentation will introduce the VPP project that Tohoku Electric Power has been working on with Next Kraftwerke, offering remote control of generators belonging to corporate customers and the batteries of local governments and households. The use of AI technology necessitates the prediction of electricity load and PV output, and optimization based on such data. In addition, use cases of AI for the optimal energy management system and power plant facilities inspection will be introduced.

Dr. Peter HOFFMANN, Senior Scientist, Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht
“How Can Machine Learning Algorithms Be Used to Develop Innovative Climate Service Products?” [PDF; 2430 KB]
With the increasing amount of climate change data and information, climate service developers are faced with the task of efficiently integrating and analysing large datasets coming from a multitude of sources to develop innovative climate service products. Machine Learning algorithms are helpful tools that are increasingly used to achieve this goal, but their full potential and guidelines for quality assurance need to be explored and extended. Consequently, expert networks should be established.

Prof. Dr. Bahareh KAMRANZAD, Assistant Professor, The Hakubi Center for Advanced Research and Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University
“Towards the SDGs with Ocean Renewable Energies; Application of AI in Reducing the Uncertainties for a Sustainable Development”
Renewable energies are suitable alternatives to mitigate the negative impacts of global warming. Ocean renewable energies are promising resources to provide part of the energy demand in coastal areas. However, they are affected by climate change, as well. As climate change projections with various scenarios involve a large dataset, AI can be an efficient approach to investigate the climate change impact on ocean resources by reducing the uncertainties and providing a high accuracy estimation.

Prof. Dr. Jean-Pierre VILOTTE, Scientific Deputy for Intensive Computing and Data at the CNRS-INSU ; Director of the Parallel Computing and Data Analysis Centre at Institute de Physique du Globe de Paris
“New Multi-Source Data and Machine Learning Challenges in Earth Sciences and Environmental Change Hazard Monitoring” [PDF; 14786 KB]
Understanding and monitoring environmental change hazard, together with quantifying the probability and the impact of extreme events has important societal-economical implications. Research is being accelerated by the growing wealth and diversity of multi-source data from Earth observation; data analytics, together with expanding computational capabilities. In this context, machine learning techniques are gaining increasing attention even though they are facing new challenges associated to this wealth of multi-source, multi-scale and multi-modal data. We will provide a brief overview, in the context of the CNRS-INSU, of some of these data and ML challenges, and scientific domain illustrations in the context of environmental hazard monitoring.