Fintech for ESG and the Circular Economy (eBook)

Ernesto D. R. Santibanez Gonzalez, Vinay Kandpal, Peterson K. Ozili, Prasenjit Chatterjee (Herausgeber)

eBook Download: EPUB

2024
444 Seiten
Wiley-Scrivener (Verlag)
978-1-394-23398-4 (ISBN)

Infos

This book showcases research, theoretical, and validated work associated with digital finance to enhance the quality of a more sustainable environment.

The primary objective of Fintech for ESG and the Circular Economy is to evaluate how fintech advancements and sustainable practices effectively drive transformative and sustainable change. It aims to motivate individuals with ideas and discussions to promote financial technology and foster creativity for a more sustainable and equitable future.

The book delves into the intersection of technology and sustainability, offering insights into how big data, machine learning, and blockchain technology are transforming ESG practices and the circular economy. It highlights the potential of FinTech to drive sustainable finance, explores the current role of cyber ecosystems and digital currencies in sustainable finance, and examines the intricate legal landscape surrounding green finance in India. The book also discusses the influence of ESG factors on financial decision-making and the integration of sustainability metrics into financial analysis, supported by examples of how companies and investors are adopting these practices.

Additionally, the book explores the role of financial institutions in enabling crowdfunding platforms, particularly in African markets, and provides case studies that demonstrate their impact on financial inclusion and entrepreneurship. It also analyzes the legal implications of blockchain and smart contracts within FinTech, the convergence of digital business models with ESG principles, and the role of digital currencies in promoting financial inclusion and sustainable economic growth, particularly in India. The book concludes with an exploration of tokenomics and its potential to incentivize sustainable behaviors, and examines how digital finance innovations in Tanzania are improving financial inclusion by overcoming barriers to financial services for the unbanked population.

Audience

This book will be of interest to scholars, researchers, academicians, and students of commerce, banking, finance, sustainability, and financial technology. This book is helpful for business professionals, researchers, and technical industry workers in finance and artificial intelligence.

Ernesto D.R. Santibanez Gonzalez, PhD, is a professor at the University of Talca, Talca, Chile. His research focuses on developing long-term strategies for climate change and sustainability to develop the planet. He has written more than 100 publications in high-impact scientific journals and managed numerous international research projects. He has also served as an editor for several journals.

Vinay Kandpal, PhD, is a professor in the Department of Management Studies, Graphic Era University, Dehradun, India. He obtained his doctorate in finance from Kumaun University, India. He has published more than 60 research papers and six books on diverse subjects, such as sustainability, fintech, smart cities, etc.

Peterson K. Ozili, PhD, is a senior economist at the Central Bank of Nigeria, Abuja, Nigeria. He holds a doctorate in accounting and finance from Northampton University in the United Kingdom and a doctorate in economics from Nile University of Nigeria, Abuja, Nigeria. He has extensive research interests in economics, finance, business, and accounting. He has made several contributions to financial inclusion research.

Prasenjit Chatterjee, PhD, is a professor of mechanical engineering and dean at MCKV Institute of Engineering, West Bengal, India. He has over 6,500 citations and many research papers in various international journals and peer-reviewed conferences. He has authored several books on intelligent decision-making, fuzzy computing, supply chain management, etc. He developed a multi-criteria decision-making method called Measurement of Alternatives and Ranking through functional mapping of criterion sub-intervals.

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Data-Driven Sustainability: Unlocking the Potential of Machine Learning and Big Data for ESG Integration & the Circular Economy

Priya Soni

School of Business & Commerce, Department of Business Administration, Manipal University, Dehmi Kalan, Off Jaipur-Ajmer Expressway, Jaipur, Rajasthan, India

Abstract

The paradigm of sustainable development has assumed a central position in international agendas as the globe faces rising difficulties brought on by climate change, resource depletion, and environmental degradation. Environmental, social, and governance (ESG) principles have become crucial indicators in this environment for evaluating the sustainability and moral implications of corporate practices. Achieving long-term ecological balance also requires the adoption of circular economy techniques, which put a premium on reducing waste and improving resource efficiency.

The revolutionary potential of big data and machine learning (ML) technologies is thoroughly explored in this chapter with a view to promoting ESG objectives and accelerating the shift to a circular economy. These innovative technologies’ confluence presents unheard-of prospects for the analysis of huge, complicated information, the extraction of insightful conclusions, and the facilitation of datadriven decision-making for sustainable practices.

Organizations may acquire a thorough insight into their sustainability performance by using extensive data sources that include environmental impacts, social responsibility indexes, and corporate governance frameworks. By recognizing patterns, forecasting future trends, and evaluating risk factors, ML algorithms further improve predictive skills, enabling stakeholders to execute proactive sustainability policies.

Circular economy activities may be optimized to reduce waste creation, enhance recycling procedures, and support circular product design through the intelligent processing of various datasets linked to material lifecycles, supply chain management, and product consumption patterns. ML models allow for dynamic demand forecasting, adaptive resource allocation, and the development of closed-loop systems that promote resource conservation and sustainable growth.

In summary, the application of big data and ML to the circular economy and ESG provides a paradigm-shifting step toward a sustainable future. Businesses, decision-makers, and stakeholders may make wise choices, track development, and collaborate to meet global sustainability targets by leveraging the power of data-driven insights. To guarantee that these breakthroughs function as agents of change in the effort to create a regenerative and socially responsible society, it is necessary to strike a careful balance between technical developments and ethical considerations.

Keywords: Big data, machine learning, ESG (environmental, social, and governance), circular economy, sustainability, ethical business practices

1.1 Introduction to Big Data and Machine Learning

The amount of data produced from many sources has increased tremen-dously in today’s linked society. Big data, a massive collection of data, offers enormous potential for businesses to learn important lessons and make wise decisions. However, the complexity and size of big data cannot be handled by the conventional methods of data analysis and processing. Herein lies the application of machine learning (ML), a branch of artificial intelligence.

The phrase “Big data” refers to extremely large and intricate data sets that are too complex for standard data processing software to handle, store, and analyze efficiently. These datasets are defined by the three Vs: variety (both organized and unstructured), velocity (fast data creation), and volume (huge amounts of data). Big data is information gathered from a wide range of sources, such as sensors, social media, transactional records, and more (Wang, 2020) [1].

Contrarily, the artificial intelligence discipline of ML enables computer systems to learn from data without explicit programming. ML algorithms employ patterns in the data to enhance their performance over time rather than depending on predetermined rules. These algorithms can find hidden patterns, anticipate the future, and change their behavior on the fly in response to input [2].

Numerous industries, including banking, healthcare, marketing, and transportation, have undergone radical change because of the union of big data and ML. Their combined strength enables organizations to analyze enormous volumes of data, resulting quickly and effectively in more accurate decision-making, improved customer experiences, and the identification of profitable possibilities.

As companies and organizations try to strike a balance between economic development, environmental preservation, and social responsibility, sustainability has become a crucial worldwide concern. Big data and ML are crucial to the advancement of sustainability initiatives because they offer strong tools for gathering, analyzing, and drawing conclusions from vast volumes of data (Wang, 2020) [3]. The following are some significant ways that these technologies promote sustainability:

Data-Driven Decision Making: Big data enables businesses to collect and analyze significant amounts of data from a variety of sources, including supply chain records, social media, satellite imaging, and environmental sensors. These data may be processed by ML algorithms, which can then spot trends and produce insightful findings. These revelations make it possible to make decisions based on facts in fields like resource management, energy efficiency, waste minimization, and sustainable product design (Mishra, 2019) [4].

Environmental Monitoring and Management: Real-time monitoring of environmental factors including air quality, water quality, and biodiversity is made possible by big data and IoT (Internet of Things) devices. These data may be processed by ML algorithms to find abnormalities, forecast environmental changes, and maximize resource use. This aids in effective pollution management, early warning systems for natural catastrophes, and ecosystem preservation (Mishra, 2019) [5].

Supply Chain Transparency: Big data may provide businesses with complete insight into their supply chains, allowing them to follow the flow of products and raw materials from their point of origin to their destination. This data may be analyzed by ML to detect possible dangers, evaluate the social and environmental effects of supply chain operations, and assist sustainable sourcing techniques.

Energy efficiency and smart grid management are made possible by big data analytics and ML, which may be used to optimize energy usage patterns, forecast demand changes, and operate smart grids. As a result, there is less energy wasted, more dependence on renewable energy sources, and an energy infrastructure that is more durable (Grattieri, 2020) [6].

Integration of the Circular Economy: Figure 1.1 provides evidence of the integration of techniques such as big data, deep learning, and machine learning, which can help facilitate the shift to a model based on product lifecycles, consumer behavior, and waste streams. These technologies can spot chances for material recovery, recycling, and remanufacturing, reducing waste and fostering environmentally friendly manufacturing methods.

Figure 1.1 The role of big data and machine learning in sustainability.

Big data and ML are useful tools for measuring the social effect of corporate operations and activities. Organizations may better understand the mood and perception of their stakeholders and use these insights in their social responsibility practices by analyzing data from social media, consumer reviews, and staff surveys.

Predictive Analytics for Climate Change: The phenomena of climate change are dynamic and complicated. Big data and ML algorithms may examine past weather patterns, climate data, and other pertinent information to anticipate potential future climate scenarios. The creation of adaptable solutions and laws to lessen the effects of climate change benefits greatly from these ideas.

Environmental, Social, and Governance (ESG) and Circular Economy: Circular economy and environmental, social, and governance (ESG) are two interrelated frameworks that direct companies and organizations toward ethical behavior. These frameworks have become increasingly important in recent years as businesses have realized how important it is to take into account not just their financial success but also their effects on society, the environment, and corporate governance.

1.2 Environmental, Social, and Governance (ESG)

Environmental: The “E” in ESG stands for environmental impact and management of a company’s interaction with the environment. This includes initiatives to control waste and pollution, lower carbon emissions, conserve resources, encourage the use of renewable energy sources, and more. Companies are rated according to how committed they are to sustainability and how eco-friendly they are.

Social: The “S” in ESG stands for a company’s social effect and its interactions with different stakeholders, such as its workers, clients, suppliers, and neighbors. Fair labor practices, employee well-being, diversity and inclusion, community involvement, and contributions to society’s welfare are all examples of social elements.

Corporate governance and the framework that monitors a company’s activities are...

Produkt-Details

• Erscheint lt. Verlag: 1.10.2024
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Recht / Steuern ► Wirtschaftsrecht
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management
• Schlagworte: Big Data Analytics in Sustainable Finance • Central Bank Digital Currencies • Challenges and Consequences of Blockchain Technology in Sustainable Finance • Climate Finance Cross-Border Digital Financial Flows • Distributed Ledger Technology • Economic/Financial Data Fusion • Economic Integration • Electricity Prices and Power Derivatives • socio-economic policy
• ISBN-10: 1-394-23398-1 / 1394233981
• ISBN-13: 978-1-394-23398-4 / 9781394233984