Environmental Implications of Artificial Intelligence: Balancing Ecological Costs and Sustainable Growth Pathways

Artificial intelligence (AI) promises to redefine production systems, decision-making processes, and even social relationships, but it raises critical (and ethical) questions about its environmental impact. The paradox is clear: AI can be both part of the solution and part of the problem. On one hand, it enables better forecasting of extreme weather events thanks to data collected from satellites and sensors, supports "smart" electrical grids that integrate renewable sources such as solar and wind power, strengthens "precision agriculture" by optimizing irrigation and fertilization, and makes smart cities more sustainable through intelligent mobility systems and energy optimization. On the other hand, it introduces unavoidable environmental costs linked to high energy and water consumption, the limited availability of raw materials needed to build data-center components, and the challenge of disposing of them sustainably. Experts propose shifting from energy-hungry Red AI to Green AI, based on lighter, less complex models powered by renewable energy. Techniques such as federated learning and pruning, combined with the use of sustainable data centers, recyclable hardware, and distributed architectures, make it possible to drastically reduce consumption without sacrificing performance. The challenge for the future will be to govern AI with a vision that balances innovation and sustainability, environmental justice and technological progress.

In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation. Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.

Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.

Citation (2023), "Index", Tyagi, P., Grima, S., Sood, K., Balamurugan, B., Özen, E. and Eleftherios, T. (Ed.) Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (Contemporary Studies in Economic and Financial Analysis, Vol. 110B), Emerald Publishing Limited, Bingley, pp. 265-276. https://doi.org/10.1108/S1569-37592023000110B018 Publisher: Emerald Publishing Limited Copyright © 2023 Pallavi Tyagi, Simon Grima, Kiran Sood, B. Balamurugan, Ercan Özen, and Thalassinos Eleftherios INDEX Accounting, 146 Actual economic activity, 175 Administrative security controls in SME, current state of, 35–37 Africa, effect of COVID-19 debt accumulation, 165 Agriculture, 133 Alternative finance models, 102 Amazon (technology company), 98 Annual appraisals, 252 Anti-black Money Act (2015), 126 Anti-money laundering (AML), 60, 172 Apple (technology company), 98 Artificial intelligence (AI), 23, 85–86, 172, 217, 244, 252–253 ability of AI and SA to improve PM systems, 257 application of AI and SA in business processes, 255 automating PM Systems, 259–261 in business processes, 255–256 classification models to predict employee performance, 257–259 classification of AI, based on capabilities, 253–254 classification of AI, based on functionality, 254 competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 in HR management, 259 limitations of AI and SA in PM systems, 261–262 for mapping cybersecurity controls for SME, 46–48 in PM systems, 257 retail customers, 218–219 retail digitisation using, 219–220 super, 254 technology and, 218 types, 253 Aspirational Districts Programme, The, 88 Asset management segment, 99 Augmented reality, 23 Authors’ network analysis, citation as per, 68–69 Autonomous robotics, 23 Balance sheet lending (BSL), 100 Bank 4.00, 102 Bank of Baroda, 7, 11–12 Bank of Hindustan, 2 Banking, 2, 98, 206 co-operative banks, 2 commercial banks, 2 industry, 205 payments banks, 2 small finance banks, 2 Banking, financial services, and insurance (BFSI), 29 Banking Regulation Act (1949), 2 Banks, 205 Bibexcel, 135 Bibliometrics, 57, 60 analysis, 141 of entrepreneurial universities, 57 R Package, 135 research, 141 Bibliometrix, 178 Biblioshiny, 178 Big data, 23, 85, 255 analytics, 84 BigTechs, 101 Billion Prices Project, 87 Black box model, 85 Black money, 58–59 Blending data sciences in economic and social issues, 86–87 Blockchain, 132 content analysis, 137–141 data analysis, 135–137 directions for future research, 141–142 implications, 141 methodology, 133–134 technology, 132 Bombay Stock Exchange (BSE), 75–76 Bootstrapping methods, 227 Bring your own device policy (BYOD policy), 40 Business domain-wise mission critical asset security (Business DMCAS), 43–44 Business to-Person lending models (B2P lending models), 101 Business-to-Business lending models (B2B lending models), 101 Businesses, 230 AI in, 255–256 changing priorities of CIA triad based on business domain, 30–31 SA in, 256–257 Buy Now Pay Later (BNPL), 101 C4.5 decision tree, 258 Canara Bank, 7, 9–10, 18 Capability maturity model (CMM), 26 Cashkumar (P2P lending platform), 107 version 2. 0, 108 Cashkumar Peer-to-Peer Lending Platform (Cashkumar P2P Lending Platform), 107–108 digital lending, 100–107 emotional analysis, 116–117 FinTech, 98–100, 108–109 literature review, 108 negative reviews, 119–120 P2P lending, 109–111 positive reviews, 118–119 research methodology, 112 sentiment analysis perspective, 111–112 theoretical background, 107 Castle approach, 28 Casual conversations, 250 Central Bank, 193 Central bank digital currency (CBDC), 190 Central Bank of Nigeria, 190 CIA triad assess priority of CIA triad for DMCA, 43 changing priorities of CIA triad based on business domain, 30–31 CIA trinity, 27 Citation network analysis, 67–68 citation as per authors’ network analysis, 68–69 Cloud computing, 23, 86 Co-authorship analysis, 60 Co-authorship network analysis as authors on money laundering, 62–66 as countries, 64–66 Co-occurrences analysis, 60 of keywords analysis on money laundering, 66–69 network analysis on money laundering, 66 Co-operative banks, 2, 4 Commercial banking, 2 Commercial banks, 2 foreign banks, 3 private sector banks, 3 public sector banks, 3 RRBs, 3 Communications service providers, 101 Companies Act, 105 Competitive pressure, 229 Conceptual model constructs and items measured, 221–223 development of, 221 Conceptual research model, 221 Confidence interval (CI), 227 Consumers, 216 Consumption of goods and services, 22 Content analysis, 137, 179 digitisation of agriculture, 140 supply chain management, 140 sustainable economic development, 140–141 Cooperative savings, 59 Cooperative Societies Act (1912), 4 Core cybersecurity concepts, 26–31 changing priorities of CIA triad based on business domain, 30–31 CIA Triad, 27 Defence in Depth, 28–29 DMCA, 29–30 Coronavirus, 162 Corporation threat agents, 24 Corruption, 56, 124 Cost-benefit analysis, 84 COVID 19, 5, 218 Africa, 165 effect of COVID-19 debt accumulation, 164 debt crisis, 164 debt statistics, 162–164 Europe, 164–165 global debt crisis, 161 heavily indebted countries, 167–168 and Indian Banking, 5 literature review, 161–162 methodology, 161 multilateral organisations, 166–167 pandemic, 160, 234–235 rich countries, 165–166 solution to overcome, 165 Cross industry standard process for data mining (CRISP-DM), 257 Crowdfunding, 99–100 Cryptocurrencies, 98 Customer relationship management (CRM), 253 Customers, 229 demographics of, 219 screening process, 58 Cutting-edge technology, 58 Cyber assets, 27 Cyber risk, 23 Cyberattacks faced by SMEs, experience of, 39–40 Cybercrime, 56 Cybercriminals, 37, 40 Cybersecurity, 23 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 implement prioritised cybersecurity controls for Business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 Cybersecurity framework (CSF), 25 Cyberspace, 23 Data analysis, 224 measurement model, 224–226 structural model, 226–229 test, 224 tool, 88 Data collection, 223–224 Data extraction, 134 Data lags, 91–92 Data localisation problem, 92 Data mining, 85 techniques, 90 Data privacy and security, 91 Data science, 86 techniques, 85 Data-driven policy, India’s stance on, 87–89 Debt crisis, 161, 164 Debt restructuring, 162 Debt Service Suspension Initiative (DSSI), 166 Debt statistics, 162 external debt stocks, 162–163 massive spending by richer countries, 163–164 sovereign default, 164 Decision Tree, 257 Decision-making management, 173 Defence in depth (DiD), 28 Delay payments, 204 Democratisation of opinions, 236 Descriptive analysis, 60 Desktops, 40 Digital circuits, 22 Digital government, 87 Digital India campaigns, 89 Digital lending, 100 alternative finance models, 102 ecosystem, 100–102 functional business models in India, 105–107 funds transfer mechanism as per RBI guidelines, 105 global scenario, 101 governing enactments, 104–105 India, 102 landscape, 102–104 market share, 102 Digital sentiments, 236 Digital technologies, 109 Digital transformation, 216 in agriculture, 141 Digitalisation process of banking industry, 6 Digitisation intelligence competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 retail customers, 218–219 technology and AI, 218 Digitisation of agriculture, 133, 140 Digitisation transforms retailing exchanges, 220 Directorate of Enforcement (ED), 126 Dirty money, 124 Document-term matrix (DTM), 178 Domain-wise Least Cybersecurity Implementation Framework (DLCI Framework), 41 assess priority of CIA triad for DMCA, 43 calculate SME’s DLCI Level, 45–46 calculate SME’s DMCAS Level, 44 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 identify DMCA, 41–43 implement prioritised cybersecurity controls for business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 WCCI for SME, 44 Domain-wise Mission Critical Asset (DMCA), 29–30 Domestic lending by banks, 205 Drug trafficking, 56 e-CNY, 192 E-commerce platforms, 101 sites, 216 Earnings management, 146 conceptual framework, 147 income-targeting approach, 150–151 literature review, 148–150 priority theory of sustainable finance, 147–148 surplus income model, 152–155 surplus income model for sustainability, 151–152 Econometric models, 84 Economic and financial performance in topic modelling framework, relationship between money laundering and, 184–185 Economic growth (EG), 125, 128, 161, 173, 201 Electric vehicles infrastructure, investment target for, 202 Electronic word of mouth (e-WOM), 235 Embed quality assurance, 47 Emotion Lexicon (EmoLEX), 111 Emotional analysis, 116–117 Emotions, 116 Employees, 245, 258 in India, 175 narrow assessment of, 252 performance, 245 threat agents, 24 eNaira, 190, 193–195 CBDC, 190 money, 190 performs, 190 speed wallet, 196 Environmental, social and governance (ESG), 76, 79–81 ESG-based socially responsible investments, 75 Indian investors looking for, 75–76 Environmental accounting, 149 Environmental reporting, 146 Europe, effect of COVID-19 debt accumulation, 164–165 European Central Bank (ECB), 162 External debt stocks, 162–163 External incentives, 154 External stakeholders, 248 Facebook (technology company), 98 Federal Bank, 7, 13–14, 18 Finance models, 102 Financial Action Task Force (FATF), 58, 125 Financial crimes action plan for resolving financial crimes using SRI–ESG model, 81 channel for, 77–78 effects of, 79 terrorism financing devastating part of, 78–79 impact of unethical investments on, 79 Financial crisis (2008), 98 Financial industry, 205 Financial institutions, 205 in achieving sustainable economic goals, 206–207 through priority sector lending, 208–210 Financial markets, 101, 104 Financial performance Bank of Baroda, 11–12 Canara Bank, 9–10 co-operative banks, 4 COVID 19 and Indian Banking, 5 COVID 19, 5 data analysis, 8 data collection, 7 Federal Bank, 13–14 findings, 17–18 foreign banks, 3 HDFC Bank, 14–15 hypothesis testing for net profit, 16–17 hypothesis testing for NPA, 17 ICICI Bank, 12–13 Indian Bank, 10–11 limitation of study, 8 literature review, 5–7 non-performing assets, 4 non-scheduled banks, 4 payments banks, 4 private sector banks, 3 public sector banks, 3 research methodology, 7 research methodology, 8 research objectives, 7 RRB, 3 sample frame, 7 schedule banks, 4 small finance banks, 3–4 in topic modelling framework, relationship between money laundering and economic and, 184–185 Union Bank, 10 Yes Bank, 15–16 Financial services, 98 Financial study, 58 Financial Technology (FinTech), 98 branches of FinTech Industry, 99–100 classified into the following types, 98–99 credit, 102 landscape, 99 lenders, 101 start-ups, 109 Financing segment, 99 Firms, 147 Five-year planning process, 87 Forecasting methods, 84 Foreign Banks, 3 Formal process in PM systems, 249–250 Fugitive Economic Offenders Act (2018), 126 Functional business models in India, 105–107 Funds transfer mechanism as per RBI guidelines, 105 Global economy, 160 Global reporting initiative (GRI), 149 Globalisation of economies, 124 Gold medal, 238 Goods and Service Tax, 126 Google (Private companies), 91, 98 reviews, 114 Governance in India, 87 reporting, 146 Grameen Foundation, The, 87 Green accounting, 149 Green banking assessing funding needed by international agency, 205 investment target for electric vehicles infrastructure, 202 investment target for renewable energy generation, 201–202 investment target to set up smart cities, 203–204 methodology, 206 results, 206 role of financial institutions in achieving sustainable economic goals, 206–207 role of financial institutions through priority sector lending, 208–210 Green development, 210 Green energy, 210 Green growth, 206 Gross domestic product (GDP), 160, 174, 205 HDFC Bank, 7, 14–15, 18 Human bias, 252 Human resources (HR), 172, 255 AI in HR management, 259 Human-based PM systems, 252 Hume’s Aesthetic Theory, 108 Hypothesis testing for net profit, 16–17 for NPA, 17 ICICI Bank, 7, 12–13, 18 ID3 decision tree, 258 Illicit drug trafficking, 59 IMF Fact Sheet, 125 Income smoothing, 151 Income-targeting approach, 150–151 Incremental approach, 84 India current market trend of, 74–75 digital lending, 102 functional business models in, 105–107 measurements to control money laundering terror funding in, 125–126 retail scenario in, 217–218 SRI in, 75 stance on data-driven policy, 87–89 Indian Bank, 7, 10–11 Indian banking industry, 4 sector, 2 Indian Economy future implication of study, 128 impacts of money laundering on, 126–127 impacts of terror financing on, 127–128 measurements to control money laundering terror funding in India, 125–126 nexus of money laundering and terrorism financing, 125 Indian government, 90 Indian investors looking for ESG Investments, 75–76 Industry 1.0, 22 Industry 2.0, 22 Industry 4.0 digitisation, 23 Inferential research approach, 223 Informal economy, 174 Informal process in PM systems, 250 Information and communications technologies (ICTs), 23, 88, 217 assets, 27 Information security, 27 Information security management system (ISMS), 24 Information technology, 124 Institute for Energy Economics and Financial Analysis (IEEFA), 201 Insurance, 98 Intangible assets, 27 Integration of economies, 124 Interest-bearing eNaira, 194–195 Internal stakeholders, 248 International agency, assessing funding needed by, 205 International Energy Agency (IEA), 205 International Monetary Fund (IMF), 163, 175 International Standards Organization, 260 Internet of Things (IoT), 23, 84, 86, 255 Investment, 98 management, 175 target for electric vehicles infrastructure, 202 target for renewable energy generation, 201–202 target to set up smart cities, 203–204 IP Act (2000), 91 Keynesian model, 193 Know Your Customer (KYC), 106 Knowledge management cybersecurity, 40 Laptops, 40 Latent Dirichlet allocation (LDA), 179 Lender’s Club, 110 Lending policies, 205 Lending practices, 206 Link strength, 137 Logical controls, 35 Machine learning (ML), 86, 257 Malware assaults, 39 Management accounting, 148 Managerial discretion, 147 Managers and employees, 245–246 Market share, 102 Marketers, 218 Marketplace lending (MPL), 100 Massive data analysis approach, 59 Measurement model of data analysis, 224–226 Medal, 238 Mendeley database, 133 Mentor–Protege relationship, 250 Micro, small and medium enterprises (MSMEs), 208 Microsoft (Private companies), 91 Minerals, 30 Mission critical assets, 29 MIT Sloan School of Management, 87 Modern banking, 2 Money, 56 Money laundering, 60, 79, 124, 172–173 analysis of word cloud, 181 annual trend of publication, 61 citation network analysis, 67–69 co-authorship network analysis, 62 co-authorship network analysis as authors on, 62 co-authorship network analysis as countries, 64–66 co-occurrences network analysis on, 66–67 countries analysis, 62 data and methodology, 175–179 descriptive analysis, 179–180 historical background of, 58–60 impacts of money laundering on Indian economy, 126–127 investigating TF-IDF, 182–184 link between words, 181 literature review, 173–175 measurements to control money laundering terror funding in India, 125–126 nexus of, 125 relationship between money laundering and economic and financial performance in topic modelling framework, 184–185 research methodology and data, 60 results, 61 sources analysis, 61–62 Money lending, old concept of, 100 Mordor Intelligence, 89 Naïve Bayes classifiers, 258 Narcotic Drugs and Psychotropic Substances Act (NDPS), 59 NASSCOM, 89 National Association of Securities Dealer Automated Quotations, The, 98 National Strategy for Artificial Intelligence by NITI Aayog, 88 Natural catastrophes, 24, 34 Natural disasters, 34 Neo banks, 101 Net profit, hypothesis testing for, 16–17 Netnograhphy, 236 Nigeria CBDC, 190 current features of, 193–194 interest-bearing eNaira, 194–195 literature review, 191–193 no transaction costs, 195 redesigning, 194 security, 195–196 NIRAMAI, 88 NIST, 25 NITI Aayog, 88 Non-banking financial corporations (NBFCs), 100, 206 Non-performing Assets, 4 Non-performing loans (NPL), 162 Non-scheduled banks, 2, 4 Normalisation, 178 NPA, hypothesis testing for, 17 Offline P2P lending, 100 Olympic Games Tokyo (2020), 237 Olympics, 240 destination, 234 players, 239 Online P2P lending platforms, 100, 109–110 Online platforms, 40 Opaque system, 252 Open Government Data (OGD), 87 Opensource Stack software, 87 Oracle (Private companies), 91 Organisation-owned devices, 40 Organisational goals, 246 Organisations, 244 Organised retailers, 230 Organised retailing, 221, 223 Palantir Technologies (American software company), 87 Pandemic, 160 Parallel economy, 124 Partial least square (PLS), 224 Payment, 191 Payments Banks, 2, 4 Performance appraisal (PA), 244 Performance assessments, 249 Performance improvement, notifications of, 249–250 Performance management systems, 244–245 AI and SA, 252–257 AI and SA in PM systems, 257–262 components of, 245–246 formal processes, 249–250 informal processes, 250 inputs to PM systems, 250–251 issues in current PM systems, 251–252 monitoring, 246–247 outputs from PM systems, 251 planning, 246 principles of PM systems, 248–249 processes in PM systems, 249 reviewing, 247 rewarding, 247 stakeholders in PM systems, 247–248 tasks in PM Systems, 246 Person-to-Business lending models (P2B lending models), 101 Person-to-Person lending models (P2P lending models), 101, 109–111 crediting, 109 elements, 100 lending platforms, 100–101, 106 lending services, 104 Physical retailers, 216 Physical security current state of physical security controls in SME, 34 measures, 34 Physical stores, 217 Planning–programming–budgeting system, 84 Platform lending, 100 Policy-making process, 84 in India, 87 Predictive modelling techniques, 85 Prevention of Money Laundering Act (PMLA), 59, 126 Priority sector lending role of financial institutions through, 208–210 Priority theory of sustainable finance, 147–148 PRISMA 2020 Flow Diagram, 133 Private Banks, 8, 18 Private companies, 91 Private giants, 89 Private Sector Banks, 3 Production-linked Incentive Scheme (PLI Scheme), 207 Profitability, 5 of Indian banks, 7 Programmable integrated circuits (PLCs), 22 Public policies, 84 Public Sector Banks, 3, 8, 18 Python application, 112, 114 Qualitative research techniques, 85 Quality assessment, 134 Quasi-legal enterprises, 56 Real-time resource monitoring techniques, 85 Refugee Olympic, 237 Regional Rural Banks (RRB), 2–3 Relative frequency, 183 Renewable energy, 22, 201, 207 investment target for renewable energy generation, 201–202 Research methodology, 85 Reserve Bank of India (RBI), 2, 6, 99, 207 funds transfer mechanism as per RBI guidelines, 105 Reserve Bank of India Act (1934), 2, 4 Resource allocation, 76 Retail business analytics, 219 Retail customers, 218–219 Retail digital promotion, 220–221 Retail industry, 221 Retail scenario in India, 217–218 Retail stores, 216 Retailers, 216–217, 229 Retailing, 220 data analysis, 224–229 data collection, 223–224 development of conceptual model, 221–223 drivers of digitisation and AI in retail, 218–219 implications of study, 229–230 limitation, scope for further research, 230 literature review and hypothesis development, 217 research methodology, 223–223 research objectives, 217 retail digital promotion, 220–221 retail digitisation using AI, 219–220 retail scenario in India, 217–218 Reward system, 247 Rio Olympic (2016), 236 Scalability, 252 Schedule Banks, 4 Scheduled banks, 2 Search strategy, 133 Security awareness training for employees in SMEs, frequency of, 37–38 Security controls in SME, current state of, 34 Security objectives, 27, 29 Selection criterion, 133–134 Sentiment analysis, 238 method, 114 perspective, 111–112 Sentiments, 236 Sin stocks, 80 Small and Medium Enterprises (SME), 22, 31 age of, 32 analysis of research interview results, 41 analysis of research survey results, 32 biggest problems faced by SMEs implementing or deciding/planning to implement cybersecurity controls, 38–39 core cybersecurity concepts, 26–31 current state of administrative security controls in, 35–37 current state of implemented standards or framework in, 32–33 current state of physical security controls in, 34–35 current state of security controls in, 34 current state of technical security controls in, 35 DLCI framework, 41–48 DLCI Level, 45–46 DMCAS Level, 44 experience of cyberattacks faced by, 39–40 frequency of security awareness training for employees in, 37–38 literature review, 24–26 methodology, 31–32 minimum overall cybersecurity controls implementation level, calculate, 44–45 responsible AI for mapping cybersecurity controls for, 46–48 WCCI for, 44 Small and medium-sized businesses (SMBs), 23, 101 Small business owners, 109 Small finance banks, 2–4 Smart Analytics, 252, 254–255 ability of AI and SA to improve PM systems, 257 AI in HR management, 259 application of AI and SA in business processes, 255 automating PM Systems, 259–261 benefits of, 255 classification models to predict employee performance, 257–259 limitations of AI and SA in PM systems, 261–262 in PM systems, 257 SA in business processes, 256–257 Smart cities, investment target to set up, 203–204 Social accounting, 148 Social media, 219, 235 Social reporting, 146 Socially responsible investments (SRI), 74–76, 79–81 action plan for resolving financial crimes using SRI–ESG Model, 81 current market trend of SRI in India, 74–75 effects of financial crimes, 79 ESG and SRI, 79–81 findings of study, 77 heterogeneity, 76 in India, 75 investors, limitations of study, 77 research 77 research methodology, 77 review of socially responsible 75–76 76 terrorism financing devastating part of financial 78–79 impact of unethical investments on financial crimes, 79 unethical investors, 77–78 41 energy generation, 201 234 234 239 239 149 in PM Systems, 247–248 107 Bank of India 2 Data Analysis, 224 theory 235 modelling 224 model of data analysis, 226–229 178 chain management, 140 income model, implication of model, implications for or for sustainability, 151–152 profit, 206 149 Sustainable development, 206 Sustainable development Sustainable economic development, 140–141 Sustainable economic goals, role of financial institutions in 206–207 Sustainable 147 Sustainable growth in India, 206 security 26 18 249 (Private 89 98 controls, 35 security controls in SME, current state of, 35 132 and AI, 221 companies, 98 Technology and and model Technology governance data sciences in economic and social issues, 86–87 India’s stance on data-driven policy, 87–89 research and methodology, findings and 22 frequency 179 investigating TF-IDF, 182–184 financing on Indian economy, impacts of, 127–128 59 financing, 79 devastating part of financial 78–79 nexus of, 125 threat agents, 24 analysis, 178 data methods, 178 agents, 24 Tokyo Olympic (2020), 234 data analysis and findings, literature review, research methodology, 236 Tokyo Olympic 234 179 relationship between money laundering and economic and financial performance in, 184–185 profit, action 236 PM systems, 252 149 253 234 content analysis, 236 data, 236 sentiments, 235 laundering system, 59 investments on financial crimes, impact of, 79 investors, 77–78 payments 6 Union Bank, 7, 10 56 National Security 28 224 analysis, 60 60, 135 23 87 40 of 173 Cybersecurity Implementation 44 22 23 Yes Bank, 7, 18 25 COVID 19 and Financial Performance of Banks in and of Least Cybersecurity for Small and Medium Enterprises for Global Digitalised Economy Money of from to Socially for Financial Technology and of Governance and Policy-making for the the Sentiment Analysis of Cashkumar Peer-to-Peer Lending on Google of Money Laundering and Financing on the Indian Economy the on Implementation of in Earnings Management for Income of Sustainable COVID-19 Global Debt to the Analysis of Money Laundering Research the Central Bank Digital for Payments and Green Banking to Sustainable Economic Digitisation and Artificial Intelligence in Sector on Sentiment Analysis in 2020 Smart and AI for Modern Performance Management

The rapid advancements in deep learning (DL) and artificial intelligence (AI) have led to transformative applications across various domains. Edge devices, including mobile and embedded systems, are crucial for the widespread adoption of machine intelligence. However, training and deploying deep neural networks (DNNs) require substantial computational resources, resulting in high energy consumption. This energy often stems from non-renewable sources, leading to significant carbon dioxide emissions and environmental damage. With the emergence of large-scale models for AI-generated content (AIGC), such as large language models and diffusion models, the energy consumption issue has intensified, creating an urgent demand for sustainable AI solutions. These challenges motivate my research on energy-efficient deep learning toward sustainable AI. To build sustainable AI systems, this thesis works on promoting AI sustainability and using AI for sustainability purposes. To promote AI sustainability, a hardware-software co-design approach is adopted, integrating both bottom-up and top-down strategies for practical, energy-efficient deep learning deployments.The first part of the thesis introduces the bottom-up approach, emphasizing AI algorithm-aware efficient system design. Specifically, edge devices utilize dynamic power management strategies, such as dynamic voltage and frequency scaling (DVFS), to manage energy consumption by adjusting voltage-frequency (VF) levels. System-level configurations, including CPU cores, CPU VF levels, and GPU VF levels, significantly impact both latency and power consumption during DNN inference. Developing effective strategies for these configurations enables system-level optimizations, providing a path to deal with the efficiency challenge. The second part of the thesis presents the top-down approach, which focuses on designing hardware-driven efficient AI algorithms. Given the hardware constraints in AI application environments, this approach explores efficient compression techniques, such as weight pruning and knowledge distillation, to create lightweight models with reduced computational demands, thereby enhancing efficiency. The third part of the thesis examines the efficiency challenges posed by the evolution of large-scale models, which have greatly enhanced the capabilities of AIGC for text and image generation. While these models offer substantial generative potential, they also exacerbate the efficiency dilemma. This section explores strategies to optimize the efficiency of these large-scale models based on their unique characteristics. Beyond efficiency, the deployment of AI in real-world applications, such as autonomous vehicles, underscores the importance of prioritizing safety. The fourth part of the thesis addresses issues of AI fairness, trustworthiness, and security to ensure sustainable AI. This section investigates the potential to reverse-engineer adversarial attacks to infer the adversary's underlying information. In conclusion, this thesis works on building energy-efficient DL for sustainable AI. The objective of the work is not only to enhance the efficiency of AI systems but also to address broader concerns of fairness, trust, and security, paving the way for responsible and sustainable AI deployment in real-world scenarios.--Author's abstract

This chapter investigates the potential for block chain and artificial intelligence (AI) to work together to revolutionise agriculture. Block chain’s decentralised ledger and AI's data-driven decision-making provide disruptive answers to the agriculture sector's multiple concerns, such as climate change and market access. Initially, the chapter presents a complete introduction of block chain and AI technologies, highlighting their distinguishing characteristics and agricultural applications. Block chain enables traceability, supply chain management, and decentralised marketplaces, complimenting artificial intelligence's precision farming, crop monitoring, and decision support systems. In terms of their separate responsibilities, block chain helps with traceability, quality assurance, decentralised marketplaces, and climate resilience, whereas AI helps with precision farming, yield prediction, and animal health monitoring. The intersection of block chain and AI opens significant possibilities for data integrity, safe sharing, smart contracts, and transparent supply chains. This integration enables crop management predictive analytics, autonomous farming machinery, and climate-resilient techniques. However, barriers to seamless integration include data quality, connection, acceptance, scalability, energy usage, and regulatory compliance. To overcome these obstacles, stakeholders must work together to provide fair access and knowledge of new technologies. In conclusion, the chapter depicts a positive future in which block chain and AI drive a more robust, transparent, and productive agriculture industry. It emphasises the importance of social activity and technical understanding in order to fully realise the promise of these advances.

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.

The Artificial Intelligence (AI) industry has developed rapidly in recent years. AI has become an emerging industry in China. This paper first introduces the characteristics and connotations of AI and then compares the status of the AI industry in more developed cities at home and abroad with that in Tianjin. It finds that the AI industry in Tianjin faces some challenges, including insufficient innovation ability, few AI hardware and software products, lack of benchmarking enterprises, and poor integration with the manufacturing industry. To promote the development of the Tianjin AI industry, it is necessary to take some measures to guarantee the AI enterprise growth. Some measures about independent innovation, talents training, policy support, and AI hardware and software products research and development should be adopted.

The Artificial Intelligence (AI) has transformed the world of nutrition by improving the estimation of nutrition habits, maximization of nutritional requirements, quality assessment of food, and creation of population-based health measures. AI applications such as machine learning, natural language processing and computer vision allow the analysis of complex nutritional data to be done precisely and efficiently. In personalised nutrition, AI combines personal eating habits, genome information and health data to provide personalized dietary guidelines which can be used to prevent or control chronic diseases including obesity and diabetes. Nutrient-monitoring devices and image-based dietary monitoring tools provide an even greater support to the continuous and accurate food intake tracking. In the food industry, AI-based algorithms identify quality variations, the predictive quality of the product, and supply chain safety measures. In general terms, AI is involved in terms of the contribution to the nutrition of populations, assessing nutrient shortages, and formulating policy on a large scale by analyzing high levels of dietary data. Although it offers the ability to transform a number of factors, there are still challenges that exist, especially in the areas of data privacy, bias in algorithms, and nutritional information standardization. The combination of AI and the new technologies (the Internet of Things (IoT), block chain, and precision agriculture) can also help to advance sustainable nutrition solutions. This review indicates the latest advances and areas of future advances of AI-based nutrition, with the focus on encouraging health, food security, and personalized diet control in the world. Keywords: Artificial Intelligence (AI), machine learning, personalized nutrition, public health, food quality, precision agriculture, block-chain, sustainable nutrition, data privacy, IoT.

The rapid advancement of artificial intelligence (AI) has profoundly reshaped global educational paradigms. By leveraging its well-established education system, Finland has actively integrated AI into physical education (PE), driving significant advancements in school-based PE. Meanwhile, China’s PE reform is at a critical juncture, facing challenges in resource allocation, pedagogical innovation, and technological integration. This study systematically examines AI applications in Finnish PE, evaluates their pedagogical impact, and explores their applicability in the Chinese context to inform AI-driven innovations in China’s PE reform. A systematic literature review focused on AI applications in PE within Finland and China. Data were retrieved from Web of Science, Scopus, Google Scholar, and CNKI, covering English and Chinese publications from 2014 to 2024. The study adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with thematic content analysis identifying key areas of AI implementation. The findings reveal that Finland has successfully integrated AI into PE, primarily in the following areas: (1) Personalized and Adaptive Learning – AI platforms dynamically adjust training regimens based on students’ physical conditions and learning progress. AI-powered interactive games further enhance student engagement. (2) Performance Analytics and Feedback – The Finnish National Agency for Education collaborates with AI enterprises, utilizing AI-driven data analytics to optimize sports performance and injury prevention. (3) Teacher Training in AI Pedagogy – Finnish educators undergo systematic training in AI-assisted PE instruction, with institutions such as the University of Jyväskylä prioritizing AI competency in teacher preparation. China has initiated pilot projects in AI-powered sports analytics and digital fitness education, reflecting a growing emphasis on AI-driven PE. However, challenges persist, including disparities in technological resources, insufficient AI literacy among educators, and outdated assessment frameworks. AI holds transformative potential for PE, and Finland’s experience offers valuable insights for China’s PE reform. Key recommendations include: (1) Enhancing AI Integration in Teacher Training – Strengthening AI-focused professional development to equip educators with necessary competencies. (2) Advancing AI-Driven Performance Assessment – Utilizing AI technologies to enable objective, data-informed evaluations. (3) Fostering Policy Support for AI Adoption – Promoting AI-integrated pedagogical models to drive innovation in PE instruction. (4) Bridging the Digital Divide – Addressing infrastructural disparities to ensure equitable access to AI-enhanced PE education. Future research should empirically assess the efficacy of AI applications in Chinese PE and their impact on student learning outcomes.

In today's rapidly evolving business landscape, Artificial Intelligence (AI) stands as the proverbial 'elephant in the room,' profoundly shaping diverse sectors and contexts. While debates rage among policymakers, practitioners, and politicians about regulating AI's widespread use, it's undeniable that AI represents a long-awaited digital technology poised to revolutionize organizational performance. Amidst the post-COVID era, the allure of AI has intensified, yet the critical question lingers: Can firms effectively harness AI and other technologies, such as Generative AI and Large Language Models (LLMs), to enhance their existing systems? Through a systematic literature review, a clear correlation between firms’ implementation of AI, including the cutting-edge Generative AI, and their ability to adapt to changing market dynamics, drive operational excellence, and unlock new avenues for growth are explored. Moreover, key drivers of AI and digital adoption, such as the imperative for data-driven decision-making, the quest for customer-centricity, and the drive for sustainable business practices are explored. Our research not only highlights the transformative potential of AI and digital technologies but also provides actionable insights for business leaders navigating the complexities of technology adoption. By understanding the motivations, challenges, and strategic imperatives driving firms' technology choices, including the integration of Generative AI and LLMs, organizations can chart a path towards sustainable growth and competitive advantage in the digital age. This study underscores the revolutionary impact of Generative AI in digital transformation, offering a comprehensive understanding of their role in shaping the future of business.

The use of Artificial Intelligence (AI) in agriculture and energy is not just a technological trend; it is a crucial step towards building a more sustainable and climate-resilient world. This paper explores the exciting ways AI, through tools such as machine learning, deep learning, and the Internet of Things, is transforming the way we enhance agricultural productivity and energy efficiency. By examining recent developments and real-life case studies, it becomes clear how AI-driven precision agriculture is revolutionizing the farming industry. This technology enables farmers to use resources more efficiently, reduces environmental harm, and enhances crop yields. Similarly, intelligent energy management systems can forecast energy consumption patterns and facilitate the more effective use of renewable energy sources. Together, these innovations show that AI has the potential to enhance economic efficiency while also protecting the environment. However, the journey to widespread AI adoption is not without its challenges. Many emerging markets struggle with inadequate infrastructure, limited funding, and weak governance, all of which can hinder progress. There are also important ethical considerations around data privacy, biases in algorithms, and ensuring equal access to technology. To overcome these hurdles, it is crucial to establish robust governance frameworks, implement inclusive policies, and promote international collaboration. By aligning technological advancements with global sustainability goals, AI can become both a catalyst for progress and a protective measure against environmental degradation. Ultimately, unlocking the full potential of AI will require a comprehensive approach that weaves together technology, policy, and ethical considerations, paving the way for an intelligent, equitable, and climate-resilient future.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

Objective: The study aimed to identify the attitudes of music skills teachers in Oman towards the use of artificial intelligence (AI) in teaching and to understand the challenges they face in integrating AI into their teaching methods. Theoretical Framework: The proliferation of modern learning resources and technological advancements, especially the emergence of artificial intelligence, have significantly impacted educational institutions. Oman is striving to align its education system with the digital transformation outlined in Oman Vision 2040, which emphasizes high-quality education, the integration of technology, innovation, entrepreneurship, and skill-building. Method: This study adopted a descriptive analytical method, analyzing a questionnaire completed by music skills teachers in Oman regarding their opinions on AI in teaching. The survey focused on three main axes: knowledge of AI, its use in teaching music skills, and the challenges faced by teachers in integrating AI into their practice. Results and Discussion: The results indicated that music skills teachers in Oman have a positive attitude towards the use of artificial intelligence in teaching. Regarding knowledge of AI, the statement "I strive to develop my knowledge about artificial intelligence and its applications in various fields" received the highest mean score of 4.14 with a standard deviation of 0.808, showing a strong commitment to learning about AI. In terms of using AI in teaching music skills, the statement "I intend to use artificial intelligence technologies in the future" ranked highest with a mean score of 4.24 and a standard deviation of 0.761, reflecting a positive intention to integrate AI into their future teaching practices. However, when it came to challenges, the statement "Lack of specialized training in artificial intelligence for music skills teachers" emerged as the most significant barrier, with the highest mean of 4.26 and a standard deviation of 0.782, pointing to the lack of adequate training as the primary obstacle in AI adoption for teaching music skills. Conclusion: The study concludes that music skills teachers in Oman generally have positive attitudes toward the use of AI in teaching. However, the main challenge identified was the lack of specialized training for teachers, which hinders the effective implementation of AI in music education. Research Implications: This research highlights the need for better training and resources for teachers to effectively incorporate AI in their teaching practices. It also emphasizes the importance of curriculum updates to align with AI advancements. Originality/Value: This study provides valuable insights into the perspectives of music skills teachers in Oman on the integration of AI in teaching, contributing to the growing body of research on AI in education in the Middle East. Contributions: The study contributes to the understanding of the role of AI in music education, specifically in Oman, and identifies key barriers that must be addressed for effective integration.

Artificial Intelligence (AI) has been increasingly used in various fields of medicine. It involves the development of algorithms and computer programs that can learn from and adapt to data, enabling machines to perform tasks without explicit human instructions. The ultimate goal of AI is to create machines that can think and reason like humans. AI has the potential to transform the field of ophthalmology, which leads to improved patient care. This study aims to examine the current state of AI in ophthalmology, the challenges facing its adoption in Indonesia, and the opportunities for growth and development in this field. A literature search was conducted using PubMed, Google Scholar, and Proquest to identify relevant studies and reports related to AI in ophthalmology. AI in ophthalmology has been studied extensively in the field of screening, diagnosis, management, and predicting outcomes. Some studies proved that AI has a relatively high accuracy in diagnosing certain eye diseases, as demonstrated by its high sensitivity and specificity. However, high accuracy does not necessarily mean that AI is ready for clinical practice, especially in Indonesia. Several challenges include the risk of bias, the absence of standard assessment methods for AI, inadequate infrastructure and regulation, ethics, and sociocultural aspects. AI has the potential to revolutionize the ophthalmology field in Indonesia, leading to better patient outcomes and more efficient healthcare systems.