Green Artificial Intelligence and Digitalization Facilitate: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Tao Li
,
Jianqiang Luo
,
Kaitong Liang
,
Chaonan Yi
,
Lei Ma

Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” devices like TinyML, which supports devices from cameras to wearables, offering low-power IoT computing. 

  • climate governance
  • environmental sustainability
  • green AI
  • TinyML

1. Introduction

Major economies are regaining growth while facing challenges of carbon reduction and environmental protection [1]. Digital wave offers opportunities to achieve the goal of “Dual Carbon” [2], and green AI (Artificial Intelligence) is a tool designed to achieve quality development in a low-carbon economy [3]. As basic research on AI continues to progress [4][5][6][7], AI is accelerating from the “cloud” to the “edge” in the fields of smart manufacturing and smart cities [8], into smaller IoT devices for low-carbon energy-efficient computing and public information monitoring [9].
Negative impacts of AI on climate policy include increased electricity consumption and carbon emissions [10], i.e., threats such as cryptocurrencies. A shift to sustainable AI is imperative [3][11][12]. Inclusive, credible, explainable, ethical, and responsible technological approaches are required to drive smart city transformation [13] to mitigate planetary issues in a sustainable manner [13][14].
TinyML (Tiny Machine Learning) supports smart cameras, remote monitoring devices, wearable devices, audio capture hardware, and various sensors [15]. The power consumption and carbon footprint of TinyML devices are much lower than those of cloud computing and ordinary mobile devices, i.e., TinyML devices operate at a MHz level and consume power at a mW level, which is 1000 orders of magnitude lower than cloud computing and mobile devices; CO2 emission levels are at a kg level, which is one order of magnitude lower.
Undoubtedly, an ecosystem of at least tens of billions of IoT devices will gain machine learning capabilities [16]. Low-carbon and green TinyML can create a healthier and more sustainable environment [17].

2. Green Artificial Intelligence

Due to the rapid development of the digital era, smart technologies are seen as an effective tool for solving challenging issues facing the world today and mitigating environmental, social, and economic crises on a global scale [18]. The EU’s “Green New Deal” sets out the strategic goal of decoupling economic growth from resource use [19][20]. Industry 4.0 has given rise to the new concept of Cities 4.0 [21], which aims to improve quality of life, productivity, and sustainability of cities with AI [22]. Sustainability profoundly influences the direction of energy, transportation, housing, and agriculture [23]. Green AI advocates a “circular economy” that aims to reduce, reuse, and recycle across sectors and geographies [24]. Thus, the research value of green AI is highlighted. Human capital, financing power, technological innovation, and government policies play critical roles in the green transformation of AI [25]. Patent and open source provide the technical knowledge required to integrate intelligence and greenness [26].
The essence of AI innovation lies in fulfilling efficient and accurate intelligent algorithms [27] to promote humanistic and responsible technological development [28] and social progress [29][30]. Through continuous exploration and improvement of green AI technologies to reduce dependence on natural resources, and multi-disciplinary cooperation and application, people are forging a sustainable [14] intelligent path for the future of human beings and the planet [31]. TinyML [32] implements efficient machine learning models on edge devices with low power consumption and low resource consumption [33], reduces energy dependence, lowers the burden on the environment, which drives green AI technologies, and redefines smart cities [13].
The application and promotion of TinyML is increasing [34]. In recent years, TinyML as a typical technology for green AI is continuously progressing in climate governance, environmental protection, precision agriculture, and smart cities, as demonstrated below.

2.1. In Environmental and Climate Governance

Reducing carbon footprint is crucial. TinyML as green AI is an important tool for realizing climate and environmental policies [35], e.g., overcoming limitations of traditional sensors and monitoring systems [36], superior efficiency and energy saving advantages over traditional machine learning algorithms when running on small devices [37], low power consumption, high efficiency and energy saving capabilities, and low storage costs in environmental radiation-monitoring systems [38][39]. It also offers adaptive unsupervised anomaly detection for extreme environments [40], thus facilitating provision of accurate data for weather forecasting and disaster warnings [41]. The combination of TinyML and CloudML (cloud machine learning) even enhances environmental monitoring and climate prediction [42].

2.2. In Precision Agriculture

TinyML-based green AI addresses key challenges in precision agriculture by providing better tuning of environmental parameters [43], reducing resource consumption [44], improving crop yield and quality [45], and promoting sustainable development. As an example, the TinyML intelligent control system outperforms traditional models in maintaining temperature and humidity balance [46], reducing system response time and resource consumption [47], achieving smarter and more efficient food production [48], reducing energy waste and environmental pollution [49], and thus protecting the environment and mitigating climate change [17].

2.3. In Smart Cities

Industry 4.0 has spawned the new concept of Cities 4.0 [21]. TinyML better realizes the collection and management of urban data. The Intelligent Transportation System (ITS), based on TinyML IoT, can reduce traffic congestion and pollution [44], and promote the green and low-carbon development of smart cities [18][22][50].

2.4. Technological Innovation

TinyML promotes technological innovation through algorithmic optimization. The TinyML algorithm improves energy output efficiency by implementing a Square Cross-section Two-phase Closed heat pump (SCTC) in a photovoltaic (PV) system [51]. TinyReptile, a decentralized edge machine learning model, combines TinyML and coalitional meta-learning to improve computational efficiency and performance [52]. Generalized TinyML benchmarking framework based on different operating system platforms lays the foundation for evaluation [53]. The TinyML compression algorithm reduces memory usage and computational complexity [54], enabling energy-efficient reasoning on Unmanned Aerial Vehicles (UAVs) [55]. The efficiency of the C4.5 decision tree algorithm is improved by determining economic granularity interval in TinyML algorithm optimization [56].

2.5. Competition and Synergy between Proprietary and Open Source

Differences between open source and proprietary have different impacts on the software industry [57] and can be strategically complementary [58] and balanced [59]. Open source and patents have strong synergies [60]. Patents often have a technological lead over open source, but open source can also compete effectively [61]. Patents use a lock-in strategy, while open source offers greater flexibility and freedom [62][63]. Open source is more reliable and secure due to open management and auditing of source code [64][65]. RIVICE (Open Source River Ice Model) demonstrates the benefits of open source in environmental research collaboration and problem solving [66]. Migration timing framework from patents to open source provides strategic guidance [24], and the governance model and platform ecosystem will change [67].
The literature referred to above shows that TinyML’s patent and open-source technologies provide additional opportunities for climate governance and environmental protection in multiple areas. Synergy needs to be further investigated.

This entry is adapted from the peer-reviewed paper 10.3390/su151813779

References

  1. Chong, C.T.; Fan, Y.V.; Lee, C.T.; Klemeš, J.J. Post COVID-19 ENERGY sustainability and carbon emissions neutrality. Energy 2022, 241, 122801.
  2. Ren, S.; Hao, Y.; Wu, H. Digitalization and environment governance: Does internet development reduce environmental pollution? J. Environ. Plan. Man. 2023, 66, 1533–1562.
  3. Schwartz, R.; Dodge, J.; Smith, N.A.; Etzioni, O. Green AI. Commun. ACM 2020, 63, 54–63.
  4. Li, P.; Peng, X.; Xu, C.; Han, L.; Shi, S. Novel extended mixed controller design for bifurcation control of fractional-order Myc/E2F/miR-17-92 network model concerning delay. Math. Methods Appl. Sci. 2023, 1–21.
  5. Huang, C.; Wang, J.; Chen, X.; Cao, J. Bifurcations in a fractional-order BAM neural network with four different delays. Neural Netw. 2021, 141, 344–354.
  6. Xu, C.; Cui, Q.; Liu, Z.; Pan, Y.; Cui, X.; Ou, W.; Rahman, M.; Farman, M.; Ahmad, S.; Zeb, A. Extended Hybrid Controller Design of Bifurcation in a Delayed Chemostat Model. Match Commun. Math. Comput. Chem. 2023, 90, 609–648.
  7. Ali, Z.; Rabiei, F.; Hosseini, K. A fractal–fractional-order modified Predator–Prey mathematical model with immigrations. Math. Comput. Simul. 2023, 207, 466–481.
  8. Satyanarayanan, M. How we created edge computing. Nat. Electron. 2019, 2, 42.
  9. Arowolo, M.O.; Ogundokun, R.O.; Misra, S.; Agboola, B.D.; Gupta, B. Machine learning-based IoT system for COVID-19 epidemics. Computing 2023, 105, 831–847.
  10. Jin, L.; Duan, K.; Tang, X. What Is The Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data From China. Sustainability 2018, 10, 145.
  11. Dhar, P. The carbon impact of artificial intelligence. Nat. Mach. Intell. 2020, 2, 423–425.
  12. Song, M.; Cao, S.; Wang, S. The impact of knowledge trade on sustainable development and environment-biased technical progress. Technol. Forecast. Soc. 2019, 144, 512–523.
  13. Yigitcanlar, T.; Foth, M.; Kamruzzaman, M. Towards Post-Anthropocentric Cities: Reconceptualizing Smart Cities to Evade Urban Ecocide. J. Urban. Technol. 2019, 26, 147–152.
  14. Yigitcanlar, T.; Cugurullo, F. The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities. Sustainability 2020, 12, 8548.
  15. Rajapakse, V.; Karunanayake, I.; Ahmed, N. Intelligence at the Extreme Edge: A Survey on Reformable TinyML. ACM Comput. Surv. 2023, 55, 1–30.
  16. Manavalan, M. Intersection of Artificial Intelligence, Machine Learning, and Internet of Things—An Economic Overview. Glob. Discl. Econ. Bus. 2020, 9, 119–128.
  17. Vuppalapati, C.; Ilapakurti, A.; Kedari, S.; Vuppalapati, J.; Kedari, S.; Vuppalapati, R. Democratization of AI, Albeit Constrained IoT Devices Tiny ML, for Creating a Sustainable Food Future. In Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA, 9–12 March 2020; pp. 525–530.
  18. Yigitcanlar, T. Technology and the City: Systems, Applications and Implications; Routledge: London, UK, 2016.
  19. Rivas, S.; Urraca, R.; Bertoldi, P.; Thiel, C. Towards the EU Green Deal: Local key factors to achieve ambitious 2030 climate targets. J. Clean Prod. 2021, 320, 128878.
  20. Skjrseth, J.B. Towards a European Green Deal: The evolution of EU climate and energy policy mixes. Int. Environ. Agreem. 2021, 21, 25–41.
  21. Chauhan, A.; Jakhar, S.K.; Chauhan, C. The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal. J. Clean. Prod. 2020, 279, 123854.
  22. D’Amico, G.; L’Abbate, P.; Liao, W.; Yigitcanlar, T.; Ioppolo, G. Understanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices. Sensors 2020, 20, 4391.
  23. Geels, F.W. Socio-technical transitions to sustainability: A review of criticisms and elaborations of the Multi-Level Perspective. Curr. Opin. Environ. Sustain. 2019, 39, 187–201.
  24. Bellandi, M.; De Propris, L. Local productive systems’ transitions to industry 4.0+. Sustainability 2021, 13, 13052.
  25. Zhai, X.; An, Y. Analyzing influencing factors of green transformation in China’s manufacturing industry under environmental regulation: A structural equation model. J. Clean. Prod. 2020, 251, 119760.
  26. Acs, Z.; Sanders, M. Endogenous Growth Theory and Regional Extensions. In Handbook of Regional Science; Fischer, M.M., Nijkamp, P., Eds.; Springer: Berlin/Heidelberg, Germany, 2021; pp. 615–634.
  27. Faisal, A.; Yigitcanlar, T.; Kamruzzaman, M.; Currie, G. Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy. J. Transp. Land Use 2019, 12, 45–72.
  28. Yigitcanlar, T.; Corchado, J.M.; Mehmood, R.; Li, R.Y.M.; Mossberger, K.; Desouza, K. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. J. Open Innov. Technol. Mark. Complex. 2021, 7, 71.
  29. Yigitcanlar, T.; Kankanamge, N.; Regona, M.; Maldonado, A.R.; Rowan, B.; Ryu, A.; Desouza, K.C.; Corchado, J.M.; Mehmood, R.; Li, R.Y.M. Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia? J. Open Innov. Technol. Mark. Complex. 2020, 6, 187.
  30. Dennis, S.; Paz, A.; Yigitcanlar, T. Perceptions and Attitudes Towards the Deployment of Autonomous and Connected Vehicles: Insights from Las Vegas, Nevada. J. Urban. Technol. 2021, 28, 75–95.
  31. Yigitcanlar, T.; Han, H.; Kamruzzaman, M.; Ioppolo, G.; Sabatini-Marques, J. The making of smart cities: Are Songdo, Masdar, Amsterdam, San Francisco and Brisbane the best we could build? Land Use Policy 2019, 88, 104187.
  32. Alajlan, N.N.; Ibrahim, D.M. TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications. Micromachines 2022, 13, 851.
  33. Garcia-Retuerta, D.; Chamoso, P.; Hernández, G.; Guzmán, A.S.R.; Yigitcanlar, T.; Corchado, J.M. An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection. Electronics 2021, 10, 765.
  34. Ndiaye, M.; Oyewobi, S.S.; Abu-Mahfouz, A.M.; Hancke, G.P.; Kurien, A.M.; Djouani, K. IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution. IEEE Access 2020, 8, 186821–186839.
  35. Dutta, D.L.; Bharali, S. TinyML Meets IoT: A Comprehensive Survey. Internet Things 2021, 16, 100461.
  36. Bamoumen, H.; Temouden, A.; Benamar, N.; Chtouki, Y. How TinyML Can be Leveraged to Solve Environmental Problems: A Survey. In Proceedings of the 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Virtual, 20–21 November 2022; pp. 338–343.
  37. Prakash, S.; Stewart, M.; Banbury, C.; Mazumder, M.; Warden, P.; Plancher, B.; Reddi, V.J. Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers. arXiv 2023, arXiv:2301.11899.
  38. Das, K.P.; Chandra, J. A survey on artificial intelligence for reducing the climate footprint in healthcare. Energy Nexus 2023, 9, 100167.
  39. Istofa; Prajitno, P.; Susila, I.P.A. Systematic Literature Review of TinyML for Environmental Radiation Monitoring System. In Proceedings of the 6th Mechanical Engineering, Science and Technology International Conference (MEST 2022), Okinawa, Japan, 20–21 December 2022; Atlantis Press: Amsterdam, The Netherlands, 2023; pp. 461–473.
  40. Antonini, M.; Pincheira, M.; Vecchio, M.; Antonelli, F. An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments. Sensors 2023, 23, 2344.
  41. Vuppalapati, C.; Ilapakurti, A.; Chillara, K.; Kedari, S.; Mamidi, V. Automating Tiny ML Intelligent Sensors DevOPS Using Microsoft Azure. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 10–13 December; pp. 2375–2384.
  42. Zhang, J.; Chen, C.; Peng, J.; Liang, J.; Nikhath, K. Early warning method and system of building environmental security based on TinyML and CloudML technology. In Proceedings of the 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), Kunming, China, 15–17 July 2022.
  43. Rathnappriya, R.H.K.; Sakai, K.; Okamoto, K.; Kimura, S.; Haraguchi, T.; Nakandakari, T.; Setouchi, H.; Bandara, W.B.M.A.C. Global Sensitivity Analysis of Key Parameters in the APSIMX-Sugarcane Model to Evaluate Nitrate Balance via Treed Gaussian Process. Agronomy 2022, 12, 1979.
  44. Helman, D.; Yungstein, Y.; Mulero, G.; Michael, Y. High-Throughput Remote Sensing of Vertical Green Living Walls (VGWs) in Workplaces. Remote Sens. 2022, 14, 3485.
  45. Balaska, V.; Adamidou, Z.; Vryzas, Z.; Gasteratos, A. Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions. Machines 2023, 11, 774.
  46. Sishodia, R.P.; Ray, R.L.; Singh, S.K. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens. 2020, 12, 3136.
  47. Ihoume, I.; Tadili, R.; Arbaoui, N.; Benchrifa, M.; Idrissi, A.; Daoudi, M. Developing a TinyML-Oriented Deep Learning Model for an Intelligent Greenhouse Microclimate Control from Multivariate Sensed Data. In Intelligent Sustainable Systems, Singapore; Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A., Eds.; Springer Nature: Singapore, 2023; pp. 283–291.
  48. Macedo, F.L.; Nóbrega, H.; de Freitas, J.G.; Ragonezi, C.; Pinto, L.; Rosa, J.; Pinheiro de Carvalho, M.A. Estimation of Productivity and Above-Ground Biomass for Corn (Zea mays) via Vegetation Indices in Madeira Island. Agriculture 2023, 13, 1115.
  49. Neethirajan, S. SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals. Agriculture 2023, 13, 436.
  50. Muthuramalingam, S.; Bharathi, A.; Rakesh Kumar, S.; Gayathri, N.; Sathiyaraj, R.; Balamurugan, B. IoT Based Intelligent Transportation System (IoT-ITS) for Global Perspective: A Case Study; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 279–300.
  51. Eshghi, H.; Zamen, M.; Kahani, M. Energy and environmental investigation on photovoltaic system performance by application of square cross-sectional two-phase closed thermosyphon. Environ. Sci. Pollut. Res. 2023.
  52. Ren, H.; Anicic, D.; Runkler, T.A. TinyReptile: TinyML with Federated Meta-Learning. In Proceedings of the The International Joint Conference on Neural Network (IJCNN) 2023, Gold Coast, Australia, 18–23 June 2023.
  53. Osman, A.; Abid, U.; Gemma, L.; Perotto, M.; Brunelli, D. TinyML Platforms Benchmarking. In Proceedings of the International Conference on Applications in Electronics Pervading Industry, Environment and Society, Genova, Italy, 26–27 September 2022; Saponara, S., De Gloria, A., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2022; pp. 139–148.
  54. Signoretti, G.; Silva, M.; Andrade, P.; Silva, I.; Sisinni, E.; Ferrari, P. An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors 2021, 21, 4153.
  55. Raza, W.; Osman, A.; Ferrini, F.; Natale, F.D. Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs. Drones 2021, 5, 127.
  56. Li, T.; Ma, L.; Liu, Z.; Liang, K. Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching. J. Open Innov. Technol. Mark. Complex. 2020, 6, 149.
  57. Economides, N.; Katsamakas, E. Two-Sided Competition of Proprietary vs. Open Source Technology Platforms and the Implications for the Software Industry. Manag. Sci. 2006, 52, 1057–1071.
  58. West, J. How open is open enough?: Melding proprietary and open source platform strategies. Res. Policy 2003, 32, 1259–1285.
  59. Maier, A.; Yang, S.H.; Maleki, F.; Muthukrishnan, N.; Forghani, R. Offer Proprietary Algorithms Still Protection of Intellectual Property in the Age of Machine Learning? In Proceedings of the Bildverarbeitung für die Medizin 2022, Wiesbaden, Germany, 26–28 June 2022; Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T., Eds.; Springer Fachmedien Wiesbaden: Wiesbaden, Germany, 2022; pp. 345–350.
  60. Kamath, V.; Morgan, J.; Ali, M.I. Industrial IoT and Digital Twins for a Smart Factory: An open source toolkit for application design and benchmarking. In Proceedings of the 2020 Global Internet of Things Summit (GIoTS), Dublin, Ireland, 3 June 2020; pp. 1–6.
  61. Llanes, G.; de Elejalde, R. Industry equilibrium with open-source and proprietary firms. Int. J. Ind. Organ. 2013, 31, 36–49.
  62. Zhu, K.X.; Zhou, Z.Z. Research Note-Lock-In Strategy in Software Competition: Open-Source Software vs. Proprietary Software. Inform. Syst. Res. 2012, 23, 536–545.
  63. August, T.; Chen, W.; Zhu, K. Competition among Proprietary and Open-Source Software Firms: The Role of Licensing in Strategic Contribution. Manag. Sci. 2020, 67, 3041–3066.
  64. Kilamo, T.; Hammouda, I.; Mikkonen, T.; Aaltonen, T. From proprietary to open source—Growing an open source ecosystem. J. Syst. Softw. 2012, 85, 1467–1478.
  65. Boulanger, A. Open-source versus proprietary software: Is one more reliable and secure than the other? IBM Syst. J. 2005, 44, 239–248.
  66. Lindenschmidt, K.-E. RIVICE—A Non-Proprietary, Open-Source, One-Dimensional River-Ice Model. Water 2017, 9, 314.
  67. O’Mahony, S.; Karp, R. From proprietary to collective governance: How do platform participation strategies evolve? Strateg. Manag. J. 2022, 43, 530–562.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback