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Towards Net-Zero targetsusage of data science for long-term sustainability pathways /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Net-Zero targetsby Neha Sharma, Prithwis Kumar De.
Reminder of title:	usage of data science for long-term sustainability pathways /
Author:	Sharma, Neha.
other author:	De, Prithwis Kumar.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xxiv, 239 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Carbon dioxide mitigationTechnological innovations.
Online resource:	https://doi.org/10.1007/978-981-19-5244-9
ISBN:	9789811952449$q(electronic bk.)

Towards Net-Zero targetsusage of data science for long-term sustainability pathways /
Sharma, Neha.

Towards Net-Zero targetsusage of data science for long-term sustainability pathways /[electronic resource] :by Neha Sharma, Prithwis Kumar De. - Singapore :Springer Nature Singapore :2023. - xxiv, 239 p. :ill. (some col.), digital ;24 cm. - Advances in sustainability science and technology,2662-6837. - Advances in sustainability science and technology..

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.

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.

ISBN: 9789811952449$q(electronic bk.)

Standard No.: 10.1007/978-981-19-5244-9doiSubjects--Topical Terms:

935438
Carbon dioxide mitigation
--Technological innovations.

LC Class. No.: TD885.5.C3

Dewey Class. No.: 363.738746

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520 $a 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.
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