東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Atmospheric carbon dioxide- Environmental aspects." ]

切換: 標籤 | MARC模式 | ISBD

Towards Net-Zero targets = usage of ...

Sharma, Neha.

FindBook

Google Book

Amazon

博客來

Towards Net-Zero targets = usage of data science for long-term sustainability pathways /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Net-Zero targets/ by Neha Sharma, Prithwis Kumar De.
其他題名:	usage of data science for long-term sustainability pathways /
作者:	Sharma, Neha.
其他作者:	De, Prithwis Kumar.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xxiv, 239 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Carbon dioxide mitigation - Technological innovations. -
電子資源:	https://doi.org/10.1007/978-981-19-5244-9
ISBN:	9789811952449

Towards Net-Zero targets = usage 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

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

3605488
Carbon dioxide mitigation
--Technological innovations.

LC Class. No.: TD885.5.C3

Dewey Class. No.: 363.738746

Towards Net-Zero targets = usage of data science for long-term sustainability pathways /
LDR:03408nmm a2200337 a 4500 001 2314867
003 DE-He213
005 20220916094900.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789811952449 $q (electronic bk.)
020 $a 9789811952432 $q (paper)
024 7 $a 10.1007/978-981-19-5244-9 $2 doi
035 $a 978-981-19-5244-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TD885.5.C3
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 363.738746 $2 23
090 $a TD885.5.C3 $b S531 2023
100 1 $a Sharma, Neha. $3 3209829
245 1 0 $a Towards Net-Zero targets $h [electronic resource] : $b usage of data science for long-term sustainability pathways / $c by Neha Sharma, Prithwis Kumar De.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xxiv, 239 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Advances in sustainability science and technology, $x 2662-6837
505 0 $a 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.
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.
650 0 $a Carbon dioxide mitigation $x Technological innovations. $3 3605488
650 0 $a Carbon dioxide mitigation $x Data processing. $3 3626707
650 0 $a Atmospheric carbon dioxide $x Environmental aspects. $3 548529
650 0 $a Sustainability. $3 1029978
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Machine Learning. $3 3382522
700 1 $a De, Prithwis Kumar. $3 3626706
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in sustainability science and technology. $3 3487627
856 4 0 $u https://doi.org/10.1007/978-981-19-5244-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)