World of Business with Data and Analytics

211 Pages·2022·8.557 MB·English

by  Neha Sharma, Mandar Bhatavdekar

Studies in Autonomic,
Data-driven and Industrial Computing
Neha Sharma
Mandar Bhatavdekar   Editors
World
of Business
with Data
and Analytics
Studies in Autonomic, Data-driven
and Industrial Computing
Series Editors
Swagatam Das, Indian Statistical Institute, Kolkata, West Bengal, India
Jagdish Chand Bansal, South Asian University, Chanakyapuri, India
The book series Studies in Autonomic, Data-driven and Industrial Computing
(SADIC) aims at bringing together valuable and novel scientific contributions that
address new theories and their real world applications related to autonomic,
data-driven, and industrial computing. The area of research covered in the series
includes theory and applications of parallel computing, cyber trust and security,
grid computing, optical computing, distributed sensor networks, bioinformatics,
fuzzy computing and uncertainty quantification, neurocomputing and deep learning,
smart grids, data-driven power engineering, smart home informatics, machine
learning, mobile computing, internet of things, privacy preserving computation, big
data analytics, cloud computing, blockchain and edge computing, data-driven green
computing, symbolic computing, swarm intelligence and evolutionary computing,
intelligent systems for industry 4.0, as well as other pertinent methods for
autonomic, data-driven, and industrial computing.
The series will publish monographs, edited volumes, textbooks and proceedings
of important conferences, symposia and meetings in the field of autonomic,
data-driven and industrial computing.
·
Neha Sharma Mandar Bhatavdekar
Editors
World of Business with Data
and Analytics
Editors
Neha Sharma Mandar Bhatavdekar
Tata Consultancy Services Ltd. Tata Consultancy Services Ltd.
Pune, Maharashtra, India Pune, Maharashtra, India
ISSN 2730-6437 ISSN 2730-6445 (electronic)
Studies in Autonomic, Data-driven and Industrial Computing
ISBN 978-981-19-5688-1 ISBN 978-981-19-5689-8 (eBook)
https://doi.org/10.1007/978-981-19-5689-8
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Singapore Pte Ltd. 2022
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
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Preface
The recent years have witnessed many unprecedented natural disasters due to signif-
icant changes in the earth’s climate. Large sectors such as manufacturing, transporta-
tion, energy and utilities, consumer products and retail, etc. have a sizeable climate
footprint. Their operations add to Green House Gas (GHG) concentration, which
seems to be one of the reasons behind global warming and climate change. There
have been many discussions around sustainability and climate change and to find out
ways to mitigate the negative impact. However, using technology levers to contain the
adverse effects of climate change is rarely seen as the most significant catalyst. Recent
development in Artificial intelligence (AI) and Machine Learning (ML) coupled with
improved open data can offer an exciting new front in the prevention of climate
change. It can significantly benefit organizations and nations in reducing GHGs,
improving energy efficiency, and reducing waste. Most of the available research
in this area is qualitative in nature, where technology and data have not yet been
leveraged.
In this book, the authors have made an attempt to shift the attention from merely
identifying and analyzing climate risk to actively addressing it by leveraging tech-
nology. AI/ML is an enabling technology that has a critical role in reducing the
carbon footprint. Chapter 1 outlines a brief description of various domains covered
in this book and the carbon emitted by each one of them while performing their
normal activities. The chapter highlights the usage of AI/ML to predict the climate
change consequences arising due to investment in fossil fuel sectors, predicts CO
2
emissions from the transport sector, forecasts average land temperature due to non-
renewal sources of energy, and segments Indian states on the basis of household
carbon emissions. The remaining chapters dive deep into the individual domains and
present insights which can guide us to arrest the negative impacts of climate change.
The financial institutions, energy and transportation sectors, as well as individuals,
can be encouraged to collect data from operations activities and every part of the value
chain, and then use AI-powered data engineering to track emissions throughout their
carbon footprint.
Financial Sector—Chap. 2 emphasizes the crucial role of the bank in the
economic and social sphere as a provider of capital and decision-maker for the
v
vi Preface
sectors to be financed. Nevertheless, they are also exposed to physical and transition
risks due to climate change. The financial institutions may not be the major polluters
themselves, but due to their financial intermediary role, they might have a relation-
ship with companies or investment portfolios that are carbon-intensive in nature.
Financial institutions can contribute towards sustainable development by carrying
out responsible investments into companies that aim at reducing energy consump-
tion and carbon emissions. After an exhaustive literature review and preliminary data
analysis, it is observed that there is a direct relation between the investment by banks
in various fossil fuel sectors and the consequences of climate change.
In Chap. 3, machine learning modelling has been carried out to predict the conse-
quences related to climate change due to the investment by banks in fossil fuel
sectors. The analysis shows that the climate change consequences and the invest-
ments by banks in different fossil fuel sectors are correlated. It has been concluded
that climate change consequences like methane emissions, carbon emissions, etc. are
adversely impacted due to the investment by banks in Tar Sands, Coal Power, Coal
Mining, LNG, Fracked Oil and Gas, Offshore Oil and Gas and Arctic Oil and Gas.
We would recommend that the banks should phase out their investment in fossil fuel
sectors in a strategic manner. There must be more disclosure of financial information
by the banks, investors and companies targeting carbon-intensive sectors.
Non-renewalEnergySector—Chap. 4 highlights how the energy sector has been
the most significant contributor to GHG emissions. It is a difficult and very chal-
lenging goal that several countries need to work on by balancing between advance-
ment, progress and development as well as the climate change crisis. The aim of
the research is to study India’s energy sector, different sources of non-renewable
energy and to clearly distinguish their potential and contributions toward climate
change. The interrelation between energy and climate change has been explored in
this chapter. Increasing emissions have resulted in higher average land temperatures
due to extensive use of non-renewable energy sources for many decades.
Chapter 5 makes an attempt to predict trends in average land temperature due
to CO emissions from all the non-renewable energy resources like ‘coal’, ‘oil’,
2
‘natural gas’, and ‘flaring’. The chapter illustrates the step-by-step process of time
series analysis that is used to forecast temperature values. Predictions with the help
of a machine learning model reveal that if no significant and immediate steps are
implemented, emissions from these sources of energy will continue to rise in the
near future at the current rate of consumption and production. The findings indicate
that the average land temperature in India is predicted to rise by 0.2 °C in the coming
years.
Domestic Sector—GHG emissions from the domestic/household/residential
sector in India are primarily contributed by electricity usage in households and fuel
consumption such as LPG, kerosene, firewood, crop residues and animal dung in
urban and rural homes. Chapter 6 starts with an introduction to climate change,
discusses global initiatives taken to tackle it and highlights the emissions from the
household sector in India. The chapter achieved the objective of comparing residen-
tial and primary energy household emissions to find out the similarity between the
two.
Preface vii
Chapter 7 is focussed to determine the direction of primary energy household
emissions in India from LPG, Kerosene, and Diesel. Besides, it also classifies 35
Indian states and union territories by applying unsupervised machine learning algo-
rithms based on their varying primary energy household emissions from CO ,CH ,
2 4
and N O. This created three clusters, ‘Excessive Emitters’, ‘High Emitters’ and
2
‘Low Emitters’, consisting of 1, 11 and 23 states, respectively. In addition, Kerosene
and LPG have been identified as important factors in the formation of the cluster.
Further, the profiling of the clusters has been carried out by considering population,
per-capita emissions and per-square kilometre emissions. Uttar Pradesh is part of
the Excessive Emission cluster, but it has relatively low per-capita and per-square
kilometre emissions. Most of the union territories are members of the Low Emission
cluster but higher on per square kilometre emissions. Though Delhi belongs to the
Low Emission cluster, it is the highest both in per capita and per square kilometre
emissions.
Transportation Sector—The transport sector is one of the highest GHG emitters
and responsible for the majority of air pollutant precursor emissions. A significant
reason for these high percentages of GHGs emitted by the transport sector is the
extensive consumption of fossil fuel which is used to power combustion engines.
Chapter 8 is a detailed study of the available literature from understanding the impact
of the transport sector as a significant contributor to climate change to the application
of data science for combating the same. The study reveals that countries like the USA,
UK, Canada, and France are the front footers to solve the climate change problem
caused by the transport sector.
Chapter 9 focuses on the analysis of data from various perspectives, presents the
correlation between the attributes and selects the features for building up a model.
This chapter deliberates on building up various sophisticated machine learning
models, evaluating their performances, and suggesting the best model for predicting
CO emissions. After extensive data analysis, it is concluded that as the fuel consump-
2
tion on the highway and in the city increases or the vehicle size increases, the CO
2
emissions also increase. On the other hand, better the economy of a vehicle, lower
the CO emissions.
2
Adverse Effects of AI—Chaps. 1–9 present how AI can be applied to combat
climate risks. Whereas Chap. 10 focuses on elucidating the negative aspects of AI
towards climate change. To power AI systems, energy is required, which comes
from electricity. Most of the electricity comes from non-renewable sources of energy,
which could lead to more carbon dissipation. Inference on a machine learning emis-
sions calculator has been made, and different combinations of hardware, region of
compute and cloud providers for machine learning models, have been analyzed to get
a glimpse of their carbon footprint. Furthermore, this chapter proposes a real-time
carbon emissions calculator for personal computers, which would help to find out
the system’s exploitation in terms of its carbon emissions dynamically.
In this book, the technology, AI/ML, open data, etc., have been extensively
applied to propose cutting edge models to achieve the net-zero carbon emissions
targets set by the Paris Agreement on climate change. We hope this book helps to
viii Preface
improve education, raise awareness, and build knowledge on climate change preven-
tion and mitigation. We think researchers, policymakers, students, teachers, educa-
tional institutions, governments, companies, international organizations, etc., will
benefit immensely by referring to this book. Moreover, we endeavour to provide a
decarbonized environment and a better tomorrow to the next generation through this
book.
Pune, India Neha Sharma
Mandar Bhatavdekar
Acknowledgements
It is exciting to publish the first issue of the A&I Kosh book World of Business with
Data and Analytics! This book that highlights business challenges of organizations
and provides intelligent solutions, which has been a dream for the Analytics and
Insights team. We are highly appreciative of Mr. Aninda Bose, Senior Publishing
Editor from Springer Nature, for his reassurance and steadfast guidance in this unique
endeavour from corporate sector.
We, the editors of the book, take this opportunity to express our heartfelt grat-
itude to the authors for high-quality contributions which deal with state-of-the-
art topics in the areas of data science and artificial intelligence. Without their
support, this book would not have become a reality. We extend our deepest grat-
itude and appreciation to our affiliations—‘Tata Consultancy Services’, ‘Analytics
and Insights Unit’ and ‘Strategy and Marketing Sub-Unit’. We would also like to
thank Mr. Dinanath Kholkar, Vice President and Global Head, Partner Ecosystems
and Alliances, Tata Consultancy Services, for his constant motivation to take on
futuristic and unconventional initiatives.
A special mention of Ms. Dimple Pal and Ms. Smita Taitwale is a must for their
unstinted and focused support towards the making of this book. Our sincere appreci-
ation goes to the entire Leadership and fraternity of TCS for their moral support and
encouragement to work hard toward this initiative of knowledge democratization.
Neha Sharma
Mandar Bhatavdekar
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