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This book discusses the use of technology, data science and open data to achieve the net-zero carbon emissions target set up by the Paris Agreement on climate change. There have been many discussions around sustainability and climate change solutions to mitigate the negative impact. However, using technology levers to tackle climate challenges is rarely seen as the most significant catalyst. The available research in this field is generally qualitative in nature, where technology and data have not yet been leveraged. By using AI/ML, the book predicts the climate change consequences arising due…mehr
This book discusses the use of technology, data science and open data to achieve the net-zero carbon emissions target set up by the Paris Agreement on climate change. There have been many discussions around sustainability and climate change solutions to mitigate the negative impact. However, using technology levers to tackle climate challenges is rarely seen as the most significant catalyst. The available research in this field is generally qualitative in nature, where technology and data have not yet been leveraged. By using AI/ML, the book predicts the climate change consequences arising due to investment in fossil fuel sectors, estimates CO2 emissions from the transport sector, forecasts average land temperature due to non-renewable sources of energy, and segments Indian states on the basis of household carbon emissions. The researchers, policymakers, students, teachers, educational institutions, governments, regulators, companies, international organizations, etc., will benefit immensely by referring to this book. Moreover, the endeavour of this book is to provide a decarbonized environment and a better tomorrow to the next generation.
Neha Sharma is a data science crusader who advocates its application for achieving sustainable goals, solving societal, governmental and business problems as well as promotes the use of open data. She has more than 22 years of experience and presently working with Tata Consultancy Services and is a Founder Secretary, Society for Data Science. Prior to this, she has worked as Director of premier Institute of Pune that run post-graduation courses like MCA and MBA. She is an alumnus of a premier College of Engineering and Technology, Bhubaneshwar and completed her PhD from prestigious Indian Institute of Technology, Dhanbad. She is Senior IEEE member, Secretary – IEEE Pune Section and ACM Distinguished Speaker. She is an astute academician and has organized several national and international conferences and published several research papers. She is the recipient of “Best PhD Thesis Award” and “Best Paper Presenter at International Conference Award” at National Level. She is a well-known figure among the IT circle and well sought over for her sound knowledge and professional skills. Neha Sharma has been instrumental in integrating teaching with the current needs of the Industry and steering students towards their bright future.
Prithwis Kumar De is a Data, Analytics and Artificial Intelligence (AI) expert with over 24 years of experience and a Sustainability Evangelist. He is currently working with Tata Consultancy Services. Prior to this, he has worked with Accenture, CRISIL, Ernst and Young and the Indian Institute of Foreign Trade. He has managed and supported clients across industries such as BFSI, retail, CPG, life sciences and healthcare, telecom, among others, to drive accelerated business outcomes through data and analytics. He holds a PhD in Econometrics (Applied Statistics). Dr De is a Fellow of the Royal Statistical Society, UK. He has been granted Chartered Statistician (CStat) status, the highest professional honour in Statistics by the Royal Statistical Society. Dr Prithwis is a member of the American Statistical Association (PStat®), USA. He is a distinguished speaker and has published several papers in professional and refereed journals, books and newspapers. With a passion for sustainability, Prithwis enjoys working on a wide range of topics related to ESG, climate change, business, technology and AI to address the rapidly emerging needs in this space. He opines that data, analytics and technology play a critical role in creating data-driven environment-friendly solutions towards achieving net-zero emissions target. Through this book, he would like to provide the future generations a decarbonized environment and a better tomorrow.
Inhaltsangabe
Chapter 1. Climate Change and AI in the Financial, Energy, Domestic and Transport Sectors.- Chapter 2. Role of Banking Sector in Climate Change – Literature Review and Data Preparation.- Chapter 3. Application of Machine Learning to Predict Climate Change Consequences due to Investments by Banks in Fossil Fuel Sectors.- Chapter 4. Effect of Non-Renewable Energy Sources on Climate Change in India- Literature Review and Data Preparation.- Chapter 5. Using Machine Learning to Predict the Effect of Non-Renewable Energy Sources on Climate Change in India.- Chapter 6. Impact of Household Emissions on Climate Change in India – Literature Review and Data Preparation.- Chapter 7. Use of Unsupervised Learning Algorithms to Segment Indian States based on Primary Energy Household Emissions.- Chapter 8. Application of Machine Learning in Climate Change for Transport Sector – Literature Review and Data Preparation.- Chapter 9. Application of Machine Learning to Predict CO2 Emission from Transport Sector to Mitigate Climate Change.- Chapter 10. Carbon Emission Calculator: Impact of AI on Climate Change.
Chapter 1. Climate Change and AI in the Financial, Energy, Domestic and Transport Sectors.- Chapter 2. Role of Banking Sector in Climate Change - Literature Review and Data Preparation.- Chapter 3. Application of Machine Learning to Predict Climate Change Consequences due to Investments by Banks in Fossil Fuel Sectors.- Chapter 4. Effect of Non-Renewable Energy Sources on Climate Change in India- Literature Review and Data Preparation.- Chapter 5. Using Machine Learning to Predict the Effect of Non-Renewable Energy Sources on Climate Change in India.- Chapter 6. Impact of Household Emissions on Climate Change in India - Literature Review and Data Preparation.- Chapter 7. Use of Unsupervised Learning Algorithms to Segment Indian States based on Primary Energy Household Emissions.- Chapter 8. Application of Machine Learning in Climate Change for Transport Sector - Literature Review and Data Preparation.- Chapter 9. Application of Machine Learning to Predict CO2 Emission from Transport Sector to Mitigate Climate Change.- Chapter 10. Carbon Emission Calculator: Impact of AI on Climate Change.
Chapter 1. Climate Change and AI in the Financial, Energy, Domestic and Transport Sectors.- Chapter 2. Role of Banking Sector in Climate Change – Literature Review and Data Preparation.- Chapter 3. Application of Machine Learning to Predict Climate Change Consequences due to Investments by Banks in Fossil Fuel Sectors.- Chapter 4. Effect of Non-Renewable Energy Sources on Climate Change in India- Literature Review and Data Preparation.- Chapter 5. Using Machine Learning to Predict the Effect of Non-Renewable Energy Sources on Climate Change in India.- Chapter 6. Impact of Household Emissions on Climate Change in India – Literature Review and Data Preparation.- Chapter 7. Use of Unsupervised Learning Algorithms to Segment Indian States based on Primary Energy Household Emissions.- Chapter 8. Application of Machine Learning in Climate Change for Transport Sector – Literature Review and Data Preparation.- Chapter 9. Application of Machine Learning to Predict CO2 Emission from Transport Sector to Mitigate Climate Change.- Chapter 10. Carbon Emission Calculator: Impact of AI on Climate Change.
Chapter 1. Climate Change and AI in the Financial, Energy, Domestic and Transport Sectors.- Chapter 2. Role of Banking Sector in Climate Change - Literature Review and Data Preparation.- Chapter 3. Application of Machine Learning to Predict Climate Change Consequences due to Investments by Banks in Fossil Fuel Sectors.- Chapter 4. Effect of Non-Renewable Energy Sources on Climate Change in India- Literature Review and Data Preparation.- Chapter 5. Using Machine Learning to Predict the Effect of Non-Renewable Energy Sources on Climate Change in India.- Chapter 6. Impact of Household Emissions on Climate Change in India - Literature Review and Data Preparation.- Chapter 7. Use of Unsupervised Learning Algorithms to Segment Indian States based on Primary Energy Household Emissions.- Chapter 8. Application of Machine Learning in Climate Change for Transport Sector - Literature Review and Data Preparation.- Chapter 9. Application of Machine Learning to Predict CO2 Emission from Transport Sector to Mitigate Climate Change.- Chapter 10. Carbon Emission Calculator: Impact of AI on Climate Change.
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