Artificial Intelligence in Water–Energy–Food Model: Comparison
View Latest Version
Please note this is a comparison between Version 3 by Jason Zhu and Version 2 by Jason Zhu.

The role of artificial intelligence (AI) in the Water–Energy–Food (WEF) nexus under the lens of institutional, stakeholder, and innovation theories has been studied recently. Research on AI for WEF nexus management has adopted mostly a technical perspective, neglecting the relevance of management tools and the business model concept. An integrated approach for managing the nexus through AI technologies is proposed to meet sustainable and responsible business models. The gap between research and policy making could be filled by combining scientific data and policy needs with inclusive tools that are technically viable for sustainable resource utilization.

  • artificial intelligence (AI)
  • Water–Energy–Food (WEF) nexus
  • business models

1. Introduction

Over the past two decades, scholars and practitioners have been paying increasing attention to the concept of Water–Energy–Food (WEF). This process has tried to become a nexus for the “right way” to optimize the use of natural resources, promoting environmental sustainability goals [1][2][3]. The WEF nexus concept originates from the international discussion on sustainable development during the World Economic Forum in 2011, used to describe the interconnections and interdependencies among water, food, and energy sectors [4]. Water is needed to generate energy and grow food; energy is required for water supply and produces food; and food can generate energy, reducing waste [5]. This means that any issue in managing one of these resources can affect the others, and each resource can benefit from the synergies coming from an integrated approach to their use.
Conceived as a tool “to promote policy coherence through identifying optimal policy mixes and governance arrangements across the water, energy and food sectors” ([6], p. 165), the WEF nexus has gained prominence after the adoption of the UN 2030 Agenda in 2015 for handling the sustainable development goal (SDG) interconnections, specifically for SDG#2 (zero hunger), SDG#6 (clean water and sanitation), and SDG#7 (affordable and clean energy) [7][8]. According to Le Blanc [9], the successful achievement of the UN 2030 Agenda needs to consider the potential trade-offs and synergies among several SDGs. Indeed, the UN 2030 Agenda footprint recognizes in its conceptualization the existence of interconnections among targets to achieve the SDGs, which require the implementation of coherent policies and solutions across different actors and sectors [10]. Despite the great efforts of academia concerning these issues, there has not been much progress in developing and adopting coherent policies and tools to handle SDGs’ interlinkages [7][11][12]. The literature on the WEF approach has highlighted the existence of several constraints to its implementation to meet SDGs, such as rigid frameworks, entrenched interests, planning and implementation procedures, and a lack of information tools capable of supporting decision-making processes [7][8][12]. Scholars agree on the need to establish coordination and cooperation mechanisms applicable to support institutions and governments in the definition of policy goals and actions for leading to the desired outcomes. However, they struggle to provide insights on the conditions, dynamics, and factors that enable cross-sector coordination and collaboration [6].
Recent studies [12][13][14] found sustainability goals and sustainable performance cannot be achieved without innovations. The overlapping of data and information, the lack of adequate knowledge of human resources, and the unpredictability of climate events could severely affect decision-making processes, leading to sub-optimal solutions and slowing down the sustainability agenda. Artificial intelligence (AI) technologies can process large amounts of data, reveal information that otherwise would remain hidden, and solve complex problems. Yet, the contribution of AI is not limited to data processing. Still, it has the potential to identify science-based solutions for environmental and climate degradation problems that are not biased by specific individual or groups interests [13], supporting multi-stakeholder decision-making processes towards sustainability. AI can help the multiple players involved in the water, energy, and food industries to meet the UN 2030 Agenda.
Some scholars [15][16][17] have highlighted how AI is able to change not only the way to generate and use information for decision making [18], but also the ways of doing business from a sustainable and socially responsible perspective [19][20]. Caprani [21] (p. 103) highlights the significant role of business in achieving global transformational development, but this has been almost entirely ignored by the literature on the WEF that has focused mostly on the other stakeholders (e.g., governments and community). Moreover, while some scholars have highlighted the relevance of digital technologies in the water, energy, and food sectors [15][17][22][23], the potential role of AI for the management of the WEF nexus has been underestimated, as has its contribution to the governance of multiple interactions among the resources, sectors, and institutions involved.

2. Theoretical Background

Over the past two decades, scholars have used the WEF approach as a multidimensional tool to describe the complexity of human–environment interactions, finding several interpretations and applications [10]. Scholars agree in the belief that the WEF nexus approach requires the establishment of strong cross-sectoral boundary coordination of policies, governance, and managerial tools, whose complexity justifies, at least partially, their slow implementation [6][7][8][12][23][24]. Indeed, the complexity of the nexus is due to the interaction of environmental resources, the intersection of multiple interests (e.g., government, private sector, and society), and the distribution of environmental and institutional risks, reflected in the incidence, frequency, and duration of natural hazards and in the resilient capacities of institutions to predict and cope with the risks [8]. The governance of the WEF nexus requires the establishment of coordination mechanisms among stakeholders (e.g., state, private sector, and civil society), sectors (e.g., water, energy, and food), and scales (e.g., political/administrative, ecological, and technical). Water, energy, and food follow different regulations and are administered by different government levels, and these make the establishment of coordinated policies and governance particularly complex.
However, studies focus almost exclusively on identifying environmental resource interactions, proposing alternative technical solutions to optimize their use, such as reducing the water and energy consumption from producing food. These solutions must consider the context in which they are implemented, considering the strong context-dependence of water, energy, and food security. This means that any solution cannot be detached from the analysis of the context [25]. Institutional theory [26] sustains the need for institutions to adapt to the external environment to survive. As several scholars have evidenced [27][28][29][30], WEF nexus implementation is grounded not only in the institutional capacity to coordinate and establish the right policies, strategies, and solutions, but also in the firms’ willingness to address the sustainability challenge and household/society engagement by adopting a multi-stakeholder approach.
The role played by the business for translating WEF principles on the ground was already highlighted during the World Economic Forum [31] (p. 3): “Accelerating the involvement of the private sector through making the business case for sustainability and the WEF nexus is essential for driving change and getting to scale”. Nevertheless, studies on how multi-stakeholder interests involved in the WEF nexus can converge have found little space in the literature on the WEF nexus. The management of interconnections between water, energy, and food resources requires a holistic understanding of the multiple interests involved. In this regard, according to stakeholder theory [32], value creation in businesses, as well as in all types of organizations, depends on their ability to satisfy the interests of all groups that have a stake in the activities that make up the business, managing the conflicts and potential trade-offs that come from the relationships among the actors [33]. The engagement of all stakeholders involved in the WEF nexus towards achieving environmental sustainability and, specifically, the UN 2030 goals is far from a simple issue.
Studies about the barriers to adopting the WEF approach have not been followed by the development of managerial tools to capture the interconnections and interdependencies among SDGs, especially to support the decision-making processes of the business. Albrecht et al. [12] (p. 4) state that “Methods have largely been borrowed or adapted from conventional disciplinary approaches, e.g., efficiency analysis based on engineering process studies, economical supply chain and commodity-chain analyses, and agronomic soil-plan-water assessment”. Stakeholder engagement may be carried out to different levels: information sharing, consultation, consensus building, decision making, and partnership. A lack of data and low levels of communication reduce the willingness to collaborate and the propensity to invest in new projects and assume the related risks [34][35].
The overlapping of data and information, the lack of adequate knowledge of human resources, and the unpredictability of climate events could severely affect decision-making processes, leading to sub-optimal solutions and slowing down the sustainability agenda. These have brought about the development of digital technologies in the last decade, such as IoT, big data, and AI.
The term AI is used to define machines’ abilities to display human skills such as reasoning, learning, and planning to solve problems. This is possible thanks to machine learning (ML) models and algorithms, capable of analyzing and learning from large amounts of data that computers receive from external sensors, such as video cameras, satellites, and so forth. AI can correctly interpret external data and use the information to reach specific goals and activities by a flexible configuration [36]. “The interaction between AI and human intelligence is based on algorithms that could help managers to make the right decisions, generating a cultural drift in which a large number of data, connection and interaction become part of the standard management and organization” [16] (pp. 19, 284). Kahnemna et al. [37] highlighted how these algorithms are able to assume more efficient decisions than humans can take. For this reason, it appears particularly suitable to be employed as a tool to support complex decision processes [18] such as those required by the WEF nexus.
In more detail, according to the innovation theory, digital technologies favor the collaborative acquisition of information and the sharing of knowledge [38]. AI can allow a holistic understanding of the potential consequences of policies, technologies, and environmental practices [8]. The new possibilities unlocked by the I4.0 technologies can help businesses to address several SDGs [39]. AI represents a tool to manage information in sustainable business models, allowing the business to address the UN 2030 Agenda goals. Its contribution to the sustainability challenge led to a vast application of AI technologies in many industries to reduce natural resource uses.
Several studies [14][16][17][40][41] have fostered the application of AI to address sustainability challenges. ML models have been increasingly used in the water sector for predicting and optimizing water resource use, in the energy sector to support forecasting and decision making on energy planning, production, distribution, maintenance, and agriculture for weather prediction, fertilizers, irrigation optimizations, and so on. More sophisticated software and digital technologies are needed as systems become more complicated. One of these consists of the virtual reproduction of physical products that provide a real-time snapshot of their status, called digital twins, that, thanks to AI algorithms, can predict the future performance of physical assets without intervening in the product. Digital twins have found application in the water, energy, and agriculture sectors, enabling users to assume decisions without being present, saving time and costs and improving sustainability.
The main advantages recognized by research on AI for sustainability are the higher data accuracy and information transparency, the cost and time efficiency, the availability of science-based solutions not biased by self-group interests, and the increased predictability of future scenarios that enhance system resilience to climate changes. In particular, [15] highlighted how AI has changed the way to make decisions and “revolutionized the ways of doing business”, influencing management practices and business models to meet SDGs. The business model consists of “a representation of how a related set of decision variables in the area of business strategy, architecture and economics are addressed to create a sustainable competitive advantage in defined markets” [42]. Technological innovation can influence business management and practices, creating the condition for reducing the cost and increasing the value, combining profit interest with sustainability goals [43]. Other scholars [14][16][17][40][44][45] have highlighted how the adoption of AI by firms, as well as through partnerships with other companies, can give a decisive boost to the UN 2030 Agenda. Nevertheless, AI can also negatively affect SDGs, considering that some aspects can increase social inequalities if not correctly addressed. Moreover, the electronic equipment needed to elaborate all data required needs to be disposed of at the end of its life cycle, raising questions about their impact on the environment. All these doubts are still waiting for an answer from international organizations called upon to ensure progress without increasing disparities among populations [46].

3. Discussion and Theoretical Implications

The management of interlinkages existing among the several SDGs emerged as one of the challenging issues for achieving the sustainability targets. Scholars have widely investigated the WEF nexus, especially from a biophysical and technical perspective, and many models and tools have been developed to estimate the interconnections existing among the three resources [11]. However, they lack evidence of the socio-technical dimension that analyzes human and AI interactions. Models such as deep learning and neural networks are able to analyze the interactions among thousands of variables. Still, these often operate in isolated systems, whilst the external environment involves the interaction among human, ecological, political, economic, and social systems. The understanding of the economic, environmental, and social effects produced by the human implementation of AI solutions is critical; however, it remains unclear. Despite the substantial efforts made to identify and quantify the interlinkages between water, energy, and food sectors, there continues to be a wide gap between science and policy making in effectively incorporating the solutions in policy agendas. The distance between “theory” and “practice” seems to be due to several obstacles that have been highlighted by scholars, among which researchers found: rigid sectoral frameworks, entrenched interests, planning and implementation procedures, lack of information tools, and the ability to support decision-making processes [7][8][11]. These barriers call for contextualized multilevel analyses and multi-stakeholder dialogues that consider all variables and interests involved. Many WEF nexus studies developed and applied a specific method, adapting its characteristics to the case studies analyzed. AI has been identified as the most promising solution to break down the barriers and accelerate the path towards sustainability. AI is considered a technological solution that supports decision-making processes, solves overlapping information issues, and contributes to reaching SDGs [16][17]. The collection, elaboration, and analysis of all necessary data are beyond human skills that could consequently undertake sub-optimal decisions or decide not to assume the risk. Thanks to the development of suitable technology, such as AI, it is possible to predict future scenarios, increase organizations’ resilience, and evaluate the possible outcomes of alternative solutions [16][17][40]. However, AI also highlights adverse effects that have been rarely considered in the articles analyzed. Previous studies [14][47] evidenced that ML models increase data processing speed, but they also increase the risks associated with data hacking. Cybersecurity would require the establishment of effective protocols and policies and specific training and educational programs for the organizations and stakeholders involved. Another adverse effect is that related to machine and technological device waste disposal and to energy consumption related to their functioning [48]. Evaluating only the positive aspects could be detrimental for the achievement of sustainability goals that technology claims to foster. This would require the performance assessment of any intervention, considering the economic, social, and environmental impact [49] produced on all stakeholders, including the impact of AI technologies, as well as other digital technologies. The analysis of the articles in current data collection highlights that the policy and decision makers lack access to comprehensive tools that include all stakeholders and consider the multi-scale nature and context-dependence of the nexus. Digital platforms have been developed to define and quantify the interconnectivity between water, energy, and food resources and include integrative and holistic management strategies to plan the future allocation of these resources. Moreover, current results evidence that in the literature, the attention to the role of AI for rethinking and redesigning business models for sustainability, including the WEF nexus challenge, is still under-researched. The combination of WEF natural resource information with a business model, thanks to the use of AI, may allow considering both technical and financial issues within companies while respecting the WEF approach and contributing to the UN 2030 Agenda [41]. AI applications, with the support of other digital technologies, capable of gathering and elaborating biophysical and technical data with financial data, may support business managers’ and external investors’ decision-making processes by proposing alternative solutions of investments. A digitalized platform or database can offer alternative solutions that include data on water, energy, and land consumption, and the cost analysis for each solution could support consistently addressing the WEF nexus. Mosalam and El-Barad [41] propose a comprehensive, integrated platform between a business model and the WEF nexus. This platform uses a web-based knowledge-sharing system to facilitate the cooperation between investors and researchers, supporting the decision-making process. This contribution opens the space to the effective involvement of the private sector in the path of the sustainability challenge. As previous studies [16][17] have highlighted, these technologies would require a complete rethinking of the managerial practices and business models from a sustainability perspective and may increase disparities between developed and developing countries and between well-off people and the poor. AI requires skilled knowledge and financial resources that could benefit some segments of the population over others. As opposed to other analysis methodologies, the WEF nexus places the water–energy–food dimensions on the same level, recognizing the interdependence between resources. The complexity of the nexus is such that it does not allow a single solution. Indeed, an integrated approach is needed to find solutions that ensure the supply of food, water, and energy to a constantly growing population without upsetting the planet’s environmental balance. There are no model solutions but rather opportunities to improve food, water, and energy security in a context of sustainability. The growing pressure on natural resources (also induced by climate change) could contribute to overcoming the current stalemate, providing a cue for new opportunities and paving the way for the integrated planning of all resources and sustainable development goals. While the Millennium Development Goals (MDGs) are aimed at equitable access to natural resources, the SDGs are global challenges that require global solutions. In order to arrive at global solutions, natural resources should be managed collaboratively, taking into due consideration the WEF nexus and not competitively following non-integrated approaches (i.e., Integrated Water Resources Management). Comprehensive and integrated resource planning would be useful for managing trade-offs and could maximize the benefits shared by multiple sectors, thus helping to lower costs and ensure sustainable use of natural resources [50]. Technological innovation, environmental technologies, and research to identify appropriate and adaptive technologies represent an essential component of the nexus approach, which must include structured accompaniment processes, training, technology transfer, and technical assistance in managing technological solutions. If the equivalent of a Kyoto Protocol for the use of food, water, and energy resources globally is conceivable, and if it is necessary to combat land grabbing, water grabbing, and the hoarding of renewable and non-renewable energy sources, then the nexus approach lends itself to establishing itself as a global model for informing development cooperation actions at both the global and local level [51]. In fact, technological innovation, especially that linked to the development of AI, is necessary to increase the productivity of resources, while investments that force development into unsustainable paths should be carefully avoided. It is believed that AI can provide answers to most of the Global Goals, such as food, water, and energy security: that is, AI has come to be an influential aspect of a country’s economy. Hence, for the good of one’s country and people, a national AI strategy must be developed. If the investments that increase the productivity of water and land were planned in view of the nexus, they would have limited impacts on energy productivity and the environment and, indeed, could potentially increase the overall efficiency of resource management. Furthermore, the adequate integration of climate change in the planning of investments in infrastructures could considerably reduce the risk indicated by future climate projections and linked to the physical and economic performances of technologies sensitive to pedoclimatic conditions. A coherent climate change mitigation policy based on local natural heritage and an adaptation strategy that balances the risk of inaction with the risk of inadequate adaptation techniques, accompanied by careful consideration of all the interconnections of the WEF nexus, is essential for setting food, climate, and energy policies suitable for sustainable development. Current studies provide several contributions to academic research on these issues. First, it evidences the existing gap in the literature on the WEF nexus, which has almost exclusively focused on biophysical and technical aspects, excluding the examination of financial, economic, and social issues. Second, current studies highlight the scarce knowledge about the WEF nexus application to the UN 2030 Agenda and its potential contributions. Thirdly, current studies underline the need to consider AI’s support of WEF nexus management and to generally solve trade-offs and increase the synergies among SDGs, essential for achieving the UN 2030 Agenda. Fourth, current studies are unique, evidencing the necessity to consider the combination of AI, business models, the WEF nexus, and SDGs, extending knowledge to economic and social impact assessment of AI-based solutions. This may incentivize the adoption of sustainable solutions to optimize the WEF nexus and SDG interlinkages.

References

  1. Di Vaio, A.; Trujillo, L.; D’Amore, G.; Palladino, R. Water governance models for meeting sustainable development Goals: A structured literature review. Util. Policy 2021, 72, 101255.
  2. Laspidou, C.S.; Mellios, N.K.; Spyropoulou, A.E.; Kofinas, D.T.; Papadopoulou, M.P. Systems thinking on the resource nexus: Modeling and visualization tools to identify critical interlinkages for resilient and sustainable societies and institutions. Sci. Total Environ. 2020, 717, 137264.
  3. Olawuyi, D. Sustainable development and the water-energy-food nexus: Legal challenges and emerging solutions. Environ. Sci. Policy 2020, 103, 1–9.
  4. Hoff, H. Understanding the nexus. In Background Paper for the Bonn 2011 Nexus Conference: The Water, Energy and Food Security Nexus; Stockholm Environment Institute: Stockholm, Sweden, 2011.
  5. Stringer, L.C.; Quinn, C.H.; Berman, R.J.; Le, H.T.V.; Msuya, F.E.; Orchard, S.E.; Pezzuti, J.C.B. Combining Nexus and Resilience Thinking in a Novel Framework to Enable More Equitable and Just Outcomes; Sustainability Research Institute Paper No. 193; Sustainability Research Institute: Leeds, UK, 2014; Volume 73.
  6. Weitz, N.; Strambo, C.; Kemp-Benedict, E.; Nilsson, M. Closing the governance gaps in the water-energy-food nexus: Insights from integrative governance. Glob. Environ. Chang. 2017, 45, 165–173.
  7. Pahl-Wostl, C. Governance of the water-energy-food security nexus: A multilevel coordination challenge. Environ. Sci. Policy 2019, 92, 356–367.
  8. Kurian, M. The water–energy–food nexus: Trade-offs, thresholds and transdisciplinary approaches to sustainable development. Environ. Sci. Policy 2017, 68, 97–106.
  9. Le Blanc, D. Towards integration at last? The sustainable development goals as a network of targets. Sustain. Dev. 2015, 23, 176–187.
  10. Srigiri, S.R.; Dombrowsky, I. Governance of the water-energy-food nexus for an integrated implementation of the 2030 Agenda: Conceptual and methodological framework for analysis. Discuss. Pap. 2021, 2, 1–28.
  11. Simpson, G.B.; Jewitt, G.P. The water-energy-food nexus in the Anthropocene: Moving from ’nexus thinking ’to ’nexus action. Curr. Opin. Environ. Sustain. 2019, 40, 117–123.
  12. Albrecht, T.R.; Crootof, A.; Scott, C.A. The water–energy–food nexus: A systematic review of methods for nexus assessment. Environ. Res. Lett. 2018, 13, 043002.
  13. Silvestre, B.S.; Ţîrcă, D.M. Innovations for sustainable development: Moving toward a sustainable future. J. Clean. Prod. 2019, 208, 325–332.
  14. Nishant, R.; Kennedy, M.; Corbett, J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Infor. Manag. 2020, 53, 102104.
  15. Di Vaio, A.; Hassan, R.; Alavoine, C. Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technol. Forecast. Soc. Chang. 2022, 174, 121201.
  16. Di Vaio, A.; Palladino, R.; Hassan, R.; Escobar, O. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. J. Bus. Resear. 2020, 121, 283–314.
  17. Di Vaio, A.; Boccia, F.; Landriani, L.; Palladino, R. Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability 2020, 12, 4851.
  18. Mikalef, P.; Framnes, V.A.; Danielsen, F.; Krogstie, J.; Olsen, D. Big Data Analytics Capability: Antecedents and Business Value. In Proceedings of the Pacific Asia Conference on Information Systems, Langkawi Island, Malaysia, 16–20 July 2017; p. 136.
  19. Schneider, S.; Leyer, M. Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions. Manag. Dec. Econ. 2019, 40, 223–231.
  20. Bebbington, J.; Unerman, J. Achieving the United Nations Sustainable Development Goals. Account. Audit. Account. J. 2018, 31, 2–24.
  21. Caprani, L. Five Ways the Sustainable Development Goals are Better than the Millennium Development Goals and Why Every Educationalist Should Care. Manag. Educ. 2016, 30, 102–104.
  22. Duan, W.L.; Chen, Y.N.; Zou, S.; Nover, D. Managing the water-climate- food nexus for sustainable development in Turkmenistan. J. Clean. Prod. 2019, 20, 220.
  23. Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Infor. Manag. 2021, 57, 101994.
  24. Benson, D.; Gain, A.K.; Rouillard, J.J. Water governance in a comparative perspective: From IWRM to a’nexus’ approach? Water Altern. 2015, 8, 756–773.
  25. Endo, A.; Tsurita, I.; Burnett, K.; Orencio, P.M. A review of the current state of research on the water, energy, and food nexus. J. Hydrol. Reg. Stud. 2017, 11, 20–30.
  26. DiMaggio, P.J.; Powell, W.W. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Am. Sociol. Rev. 1983, 48, 147–160.
  27. Urbinatti, A.M.; Benites-Lazaro, L.L.; Carvalho, C.M.D.; Giatti, L.L. The conceptual basis of water-energy-food nexus governance: Systematic literature review using network and discourse analysis. J. Integr. Environ. Sci. 2020, 17, 21–43.
  28. Ghodsvali, M.; Krishnamurthy, S.; de Vries, B. Review of transdisciplinary approaches to food–water–energy nexus: A guide towards sustainable development. Environ. Sci. Policy 2019, 101, 266–278.
  29. Liu, J.; Yang, H.; Cudennec, C.; Gain, A.K.; Hoff, H.; Lawford, R.; Qi, J.; de Strasser, L.; Yillia, P.T.; Zheng, C. Challenges in operationalizing the water–energy–food nexus. Hydrol. Sci. J. 2017, 62, 1714–1720.
  30. Mohtar, R.H.; Daher, B. Water–energy–food nexus framework for facilitating multi-stakeholder dialogue. Water Int. 2016, 41, 655–661.
  31. Bonn Conference. Messages from the Bonn2011 Conference: The Water, Energy and Food Security Nexus—Solutions for a Green Economy. In The Water, Energy and Food Security Nexus—Solutions for a Green Economy, Bonn; Stockholm Environment Institute: Stockholm Sweden, 2011.
  32. Freeman, R.E. Stakeholder Management: Framework and Philosophy; Pitman: Mansfield, MA, USA, 1984.
  33. Parmar, B.L.; Freeman, R.E.; Harrison, J.S.; Wicks, A.C.; Purnell, L.; De Colle, S. Stakeholder theory: The state of the art. Acad. Manag. Ann. 2010, 4, 403–445.
  34. Bergendahl, J.A.; Sarkis, J.; Timko, M.T. Transdisciplinarity and the food energy and water nexus: Ecological modernization and supply chain sustainability perspectives. Res. Conserv. Recycl. 2018, 133, 309–319.
  35. Villamor, G.B.; Griffith, D.L.; Kliskey, A.; Alessa, L. Integrating public/local and scientific knowledge in model development for food-energy-water systems. In Proceedings of the 9th International Congress on Environmental Modelling and Software, Ft. Collins, CO, USA, 24–28 June 2018.
  36. Kaplan, A.; Haenlein, M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 2019, 62, 15–25.
  37. Kahneman, D.; Rosenfield, A.M.; Gandhi, L.; Blaser, T. Noise. Harv. Bus. Rev. 2016, 38–46.
  38. Tsui, E. Exploring the KM toolbox. Knowl. Manag. 2000, 4, 11–14.
  39. Schroeder, P.; Anggraeni, K.; Weber, U. The relevance of circular economy practices to the sustainable development goals. J. Ind. Ecol. 2019, 23, 77–95.
  40. Mondejar, M.E.; Avtar, R.; Diaz, H.L.B.; Dubey, R.K.; Esteban, J.; Gómez-Morales, A.; Hallam, B.; Mbungu, N.T.; Okolo, C.C.; Prasad, K.A.; et al. Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Sci. Total Environ. 2021, 794, 148539.
  41. Mosalam, H.A.; El-Barad, M. Design of an integration platform between the water-energy nexus and a business model applied for sustainable development. Water Sci. Technol. 2020, 81, 1398–1405.
  42. Morris, M.; Schindehutte, M.; Allen, J. The entrepreneur’s business model: Toward a unified perspective. J. Bus. Res. 2005, 58, 726–735.
  43. Dirican, C. The Effects of Technological Development and Artificial Intelligence Studies on Marketing. J. Manag. Market. Logist. 2015, 2.
  44. Palomares, I.; Martínez-Cámara, E.; Montes, R.; García-Moral, P.; Chiachio, M.; Chiachio, J.; Alonso, S.; Melero, F.J.; Molina, D.; Fernández, B.; et al. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Appl. Intell. 2021, 51, 6497–6527.
  45. Vinuesa, R.; Azizpour, H.; Leite, I.; Balaam, M.; Dignum, V.; Domisch, S.; Felländer, A.; Langhans, S.D.; Tegmark, M.; Nerini, F.F. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020, 11, 233.
  46. Sachs, J.D.; Schmidt-Traub, G.; Mazzucato, M.; Messner, D.; Nakicenovic, N.; Rockström, J. Six Transformations to achieve the Sustainable Development Goals. Nat. Sustain. 2019, 2, 805–814.
  47. Denning, D.E. Is Quantum Computing a Cybersecurity Threat? Although quantum computers currently don’t have enough processing power to break encryption keys, future versions might. Am. Sci. 2019, 107, 83–86.
  48. Hao, K. Training a Single AI Model Can Emit as much Carbon as Five Cars in Their Lifetimes. Available online: https://www.technologyreview.com/s/613630/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/ (accessed on 8 November 2021).
  49. Sarnacchiaro, P.; Boccia, F. Some remarks on measurement models in the structural equation model: An application for socially responsible food consumption. J. Appl. Stat. 2018, 45, 1193–1208.
  50. Uen, T.S.; Chang, F.J.; Zhou, Y.L.; Tsai, W.P. Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes. Sci. Total Environ. 2018, 633, 341–351.
  51. Malagó, A.; Comero, S.; Bouraoui, F.; Kazezyılmaz-Alhan, C.M.; Gawlik, B.M.; Easton, P.; Laspidou, C. An analytical framework to assess SDG targets within the context of WEFE nexus in the Mediterranean region. Resour. Conserv. Recycl. 2021, 164, 105205.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback