By Stella Huang, PhD student at Linköping University, Department of Thematic Studies, Sweden.
Graphic Design: Stella Huang
AI, which stands for Artificial Intelligence, is like teaching computers to think and learn like humans. It’s about making machines smart enough to do things that normally need human thinking, such as understanding language, solving problems, and making decisions. AI uses different techniques to help computers become really good at these tasks, like learning from examples and recognizing patterns. The ultimate aim is to make computers do things that would usually need human brain power, making our lives easier and more efficient.
ChatGPT, 2023
Introduction: AI – the good, the bad, and the geopolitics
The rapid development of Artificial Intelligence (AI) makes this technology a major force in addressing real-world challenges and shaping our future. AI is increasingly seen by many as a driver for environmental mitigation, economic development and social change (Cowls et al., 2023). However, the ascent of AI isn’t without its trade-offs. This includes sustainable development and geopolitics. Scholars have unveiled a significant impact of AI technology on sustainable development, both in a positive and a negative sense (Visvizi, 2022, Holzinger et al., 2021, Veniusa, et al., 2020). Moreover, a significant impact on geopolitics has been noted (Wong, 2021; Lee, 2018). Put differently: As AI continues to evolve, so does its impact on sustainable development as well as the dynamics between nations.
This blog covers the geopolitics of renewables. Here it is getting interesting: AI will lead to significant transformations not only through its impact on renewables, but also through the interaction between this new technology, sustainable development, and geopolitics more broadly. And the trade-offs resulting from this interaction are not necessarily only positive in the sense that they would help to bring us closer to achieving the Sustainable Development Goals.
Here we focus on how AI is reshaping the way countries interact with one another through sustainable development. The purpose of this post is to shed light on the intricate ties between AI development, sustainability, and geopolitics. By looking at this relationship, we aim to provide an understanding of how AI requires new ways to discuss sustainable development at the global level.
The text outlines the discussion as follows: First, it describes the broader context of global AI development in relation to sustainable development; specifically, we look into the trade-offs related to the Sustainable Development Goals. Subsequently, it elucidates the complexities arising from geopolitical rivalries in this context. Lastly, brief conclusions and recommendations are provided for how to harness the potential of AI sustainable development internationally.
AI and the Sustainable Development Goals (SDGs)
Over the last few years, AI has become something that is more than just a fancy tool for developers; it has evolved into a significant enabler of sustainable development (Walshe et al., 2021). This concerns many different facets of sustainability. Recent research by Veniusa et al. (2020) reveals that AI is like a power engine that makes all the Sustainable Development Goals (SDGs) come alive, especially in three main areas: the economy, society, and the environment. This isn’t just about tech; it’s about getting closer to a sustainable future. In the related literature covered by this blog post, the potential of AI to help achieve the following SDGs has been noted: 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15.
The economic impact of AI is being highlighted frequently in the related literature as well as policy documents. This technology is able to sift through large data sets to uncover hidden patterns. With this ability to deeply analyze information, AI promises to be a game-changer for industrial production (SDG 9 and 12). Moreover, AI may contribute to the development of new and innovative ideas in different domains related to economic activity (Sony, 2021). In other words, AI has the potential to initiate a massive boost in productivity and efficiency (SDG 8).
AI is also recognized as a game-changer for how society works. With its ability to analyze huge amounts of data in a short amount of time, AI can help to make available food and water to those who need it most (SDG 1; Manning et al., 2022), and to deliver essential public services like healthcare (SDG 3; Agarwal et al., 2021) and education (SDG 4; Zhang & Aslan, 2021). This potential is truly remarkable, and yet, there’s even more! AI is also used to improve urban planning (SDG 11). This includes, for example, assisting the integration of renewable energy through smart grids that align electricity demand with sun and wind availability (Chui et al., 2018). The integration of AI into infrastructure (SDG 9) can also reduce congestion and improve urban mobility (Antonialli et al., 2022), and improve urban resource consumption (SDG 12; Ullah et al., 2020).
At the same time, AI can improve our relationship with the environment (Nti et al., 2022). In that regard, the new technologies are increasingly used to enable or improve low-carbon energy systems (SDG 7; Shi et al., 2022; Liang et al., 2021). At the same time, AI is employed to address challenges related to climate action (SDG 13), life below water (SDG 14), and life on land (SDG 15). For instance, AI advancements help to comprehend climate change and to model its impacts on biodiversity. Concerning SDG 14, AI assists in identifying marine pollution through detection algorithms (Gambín et al., 2021). Similarly, for SDG 15, the integration of AI techniques into thermal imaging aids the conservation of endangered species (Santangeli et al., 2020).
Negative trade-offs
Meanwhile, AI is not without its downsides. The literature covered here identifies negative impacts on the following SDGs: 1, 2, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 (see also next section).
Economically, AI tends to favor those who have relevant knowledge and skills and leave behind those who lack them. In turn, this might shift jobs from humans to machines and exacerbate economic disparities, (Rampersad, 2020). Moreover, AI may change money flows and thereby weaken the position of workers (Bloomberg, 2016), which may come with negative effects in terms of work relations (SDG 8) and equality (SDG 10) (Lane & Saint-Martin, 2021). Through new discrepancies in developing infrastructures, AI can also have a negative impact on the inclusiveness of industry (SDG 9) (Brynjolfsson & McAfee, 2014).
Socially, AI is unevenly distributed. For example, AI-enhanced agricultural devices may not be accessible for small and independent farmers in developing countries, and thus increase the gap between larger producers (SDG 8); in turn, this may affect (or even worse: compromise) progress in the fight to end poverty (SDG 1) and to eradicate hunger (SDG2) (Lioutas et al., 2021).
Environmentally, AI applied to achieve SDG 13 on climate action can be set off by the high energy needs that come with the application of AI solutions (van Wynsberghe et al., 2022). This is particularly severe where energy sources are not renewables (SDG 7). In the short run, the prioritization of energy for AI solutions might compromise different goals that aim at improvements in terms of living conditions. Given the potential to contribute positively to various SDGs, the integration of AI seems inevitable. However, it also looms as a potential catalyst to deepen social divides and displace a significant portion of the workforce. In the subsequent sections, the interplay between AI advancements and the subsequent emergence of geopolitical tensions is presented.
Geopolitical rivalry makes AI development complicated
The double-sided nature of AI extends all levels, from cities and other settlements up to international politics. When it comes to geopolitics, the potential of AI is currently fuelling new rivalries between nations. In particular, this struggle involves major, technology-controlling powers — namely the United States, China, and (perhaps) the European Union (EU).
As these nations invest in research, development, and deployment of AI technologies, a competitive drive emerges to establish supremacy in this rapidly evolving field (Ulnicane et al., 2021). This includes AI technologies meant to advance sustainability. Notably, AI’s contribution to the shift toward a low-carbon economy (see SDGs 9 and 12), especially in automation, bears the potential to induce substantial workforce transformations. For example, it is estimated that at least one million Chinese workers will be affected by 2030 (McKinsey, 2017). Hence, AI has the potential to spearhead societal change, but inadequate planning could lead to negative outcomes such as unemployment, thereby exacerbating socioeconomic inequality (SDG 10). In the worst-case scenario, elevated unemployment rates could pose a threat to social stability (SDG16), which is an essential foundation for sustainable development.
The potential inequalities stemming from AI are not confined solely to domestic labor markets; they extend to the global economy. AI has been characterized as a “the winner takes all” issue (Bremmer & Kupchan, 2018; Lee, 2018), that is an issue where only a few entities equipped with substantial data, computational prowess, and expertise, are in a position to pioneer and implement advanced AI technologies. Just a handful of companies in select countries enjoy a considerable edge. In turn, this enables these companies to provide tailored and efficient services, leading to substantial market dominance and potentially vast profits. In contrast, smaller businesses, startups, and less technologically developed countries face limited opportunities and thus struggle to compete. This may compromise progress towards achieving technology, science, and capacity building (SDG 17).
This lopsided distribution of wealth and power amplifies global economic disparities (SDG 10) and accentuates the digital divide (SDG 9), with some regions or entities amassing distinct advantages while others lag behind (SDG2). In turn, nations with restricted access to AI progress may become increasingly reliant on those possessing the technology, weakening their position in diplomatic and economic negotiations. Politically, this situation contributes to a growing inclination among leaders to engage in zero-sum thinking and neo-mercantilist behaviour – instead of developing new possibilities for cooperation and exchange (SDG 17). Reminiscent of the Thucydides trap, the strong imbalance that may come with AI can foster resentment, and ignite fears and geopolitical rivalries between nations and regions.
Renewable energy: an issue of data and sovereignty
The energy transition is another field where the introduction of AI has the potential for new power shifts (SDG 7). The geopolitics of renewables literature offers important starting points for understanding how the use of AI to achieve environmental sustainability can play into geopolitical tensions. Struggles for control of technology and infrastructure such as cables and wind farms are central elements in this body of text (Scholten, 2023). The use of AI to achieve SDGs goes, however, beyond shifts in hardware, industry, and talents; the novelty here is control of information and disputes over data ownership, and access. Sovereignty is a key issue in that regard. Think of the impact of renewables on biodiversity (SDG 14, 15). Through satellites and sensors, AI is increasingly used for earth monitoring purposes. This adds a new dimension to the geopolitics of renewables.
Let’s consider a scenario where Eastern European nations like Hungary seek to enhance their renewable energy capacities by investing in solar power generation (SDG 7). To optimize their solar energy systems, these countries adopt AI-driven algorithms that align solar panels according to real-time weather and sunlight data. This effort to install an AI-optimized solar energy system may curtail existing dependencies on electricity imports, and thus strengthen the country’s position in the region. What is more, if a technology supplier from outside of the region is being chosen, new dependencies would be the result. What is more, such a move would evoke questions about the possibility of sensitive information being compromised. Given the overall geopolitical situation, this hypothetical energy transition could therefore have significant geopolitical reverberations that go far beyond the relationship within the region. Solar power would in essence become a question of political influence and control.
Consider another scenario involving offshore wind energy and AI in the South China Sea—a region characterized by territorial disputes. These struggles primarily center on claims to islands, maritime borders, natural resources, and control of sea lanes. The utilization of AI to optimize energy production could amplify this competition. Let’s say the Philippines, one of the nations involved in these struggles, opts to make substantial investments in offshore wind energy near this sea territory. To preempt potential equipment failures and maintenance requirements, the country has decided to fine-tune these installations with advanced AI-driven predictive maintenance algorithms. To control the operation of the wind farm, this algorithm is capable of piecing together in real-time various types of data from the sea area around the wind farm (SDG 14). In view of the regional setting, it appears likely that the competition for supplying this technology would go beyond mere economics.
Another example of the potential of AI instruments for sustainable development affecting geopolitics is the monitoring of deforestation (SDG 13 and 15). This type of data may be seen as suited for applying pressure on individual countries, which could lead to efforts to minimize its use. Further, nations that develop and control advanced AI technologies for environmental management could gain significant geopolitical influence. Countries that rely heavily on these technologies may become dependent, potentially leading to an imbalance of power and a loss of autonomy. For example, imagine a technologically advanced nation developing sophisticated AI models to predict natural disasters. Other countries may become reliant on these predictions, leading to a power shift where the advanced nation gains influence over those relying on its technology.
Conclusions and Intelligent (?) suggestions
As the role of AI in shaping our world expands, harnessing the transformative power of AI to achieve sustainable goals requires a joint effort that transcends geopolitical rivalries. In an increasingly interconnected world, international cooperation to mitigate geopolitical rivalry is needed. By focusing on shared interests and leveraging AI as a tool for positive change, nations can navigate the complexities of international relations and avoid geopolitical pitfalls. This requires collaborative efforts to establish common norms, ethical frameworks, and international regulations to mitigate tensions. Thereby, the international community can lay the groundwork for a future where AI contributes to a more sustainable, equitable, and peaceful world.
To develop further suggestions on what international AI governance could look like, we used ChatGPT to come up with propositions. In the following paragraphs, we document the result of our search:
Multilateral Collaborative Platforms: establish and strengthen multilateral platforms that facilitate dialogue, knowledge sharing, and joint initiatives among nations. These platforms could bring together governments, international organizations, academia, and private sector stakeholders to collectively address sustainable challenges using AI technologies.
Norms and Standards Development: as the world collaboratively uses AI to address common challenges, it is natural to craft norms, guidelines, and standards, brushing ethical AI frameworks, and data-sharing protocols together. The transparency practices of international cooperation will promote a harmonized approach while reducing potential conflicts.
Joint Research and Development: across borders, resources, expertise, and data gather like tributaries flowing into a river of AI innovation, fueling the promise of sustainable development and addressing the call of common challenges.
Technology Transfer and Capacity Building: sharing AI knowledge and expertise can empower countries to harness AI’s potential for their own sustainable development. Inclusive Diplomacy: it is here that common goals are identified, align interests, and build trust among nations. Within these strategies, the potential of AI may be unleashed and become not only a tool but a catalyst for unity, progress, and a shared journey toward a world of sustainable possibilities.
References
Agarwal, Y., Jain, M., Sinha, S., & Dhir, S. (2020). Delivering high‐tech, AI‐based health care at Apollo Hospitals. Global Business and Organizational Excellence, 39(2), 20-30. https://doi.org/10.1002/joe.21981
Antonialli, F., Martinesco, A., & Mira-Bonnardel, S. (2022). Artificial intelligence as a determinant for reshaping the automotive industry and urban mobility services. International Journal of Automotive Technology and Management, 22(3), 324-351. https://doi.org/10.1504/IJATM.2022.124828
Bremmer, I. A. N., & Kupchan, C. (2018). Top risks 2018. https://www.eurasiagroup.net/files/upload /Top_Risks_2018_Report.pdf
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Bloomberg. (2016). The Income Inequality Crisis. https://www.bloomberg.com/news/videos/2016-08-01 /poorer-than-their-parents-the-income-inequality-crisis
Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869. https://doi.org/10.3390/en11112869
China’s State Council. (2017). Notice of the State Council Issuing the New Generation of Artificial Intelligence Development Plan. State Council Document No. 35. https://flia.org/wp-content/uploads /2017/07/A-New-Generation-of-Artificial-Intelligence-Development-Plan-1.pdf
Cowls J, Tsamados A, Taddeo M, Floridi L. (2023). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Soc 38, 283–307 (2023). https://doi.org/10.1007/s00146-021-01294-x
European Commission. (2019). The European Green Deal. https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1576150542719&uri=COM%3A2019%3A640%3AFIN [Accessed 10 August 2023]
European Commission. (2020). On artificial intelligence – A European approach to excellence and trust. White Paper. COM (2020) 65 final, 19 February. https://ec.europa.eu/info/sites/default/files/ commission-white-paper-artificial-intelligence-feb2020_en.pdf. [Accessed 12 August 2023].
European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. COM (2021) 206 final, 21 April. https://eur-lex.europa.eu/resource.html?uri=cellar:e0649735-a372-11eb-9585- 01aa75ed71a1.0001.02/DOC_1&format=PDF. [Accessed 12 August 2023].
Gambín, Á. F., Angelats, E., González, J. S., Miozzo, M., & Dini, P. (2021). Sustainable marine ecosystems: deep learning for water quality assessment and forecasting. IEEE access, 9, 121344-121365. http:// 10.1109/ACCESS.2021.3109216
Johansson, A. (2022). CHINA’S AI ECOSYSTEM. Stockholm School of Economics. https://www.hhs.se/contentassets/bc962221471a415ba8ac01fbbf160277/chinas-ai-ecosystem-nov-2022.pdf
Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin.
Lane, M. and A. Saint-Martin (2021). The impact of Artificial Intelligence on the labour market: What do we know so far?”. OECD Social, Employment and Migration Working Papers, No. 256, OECD Publishing, Paris, https://doi.org/10.1787/7c895724-en.
Liang, S., Yang, J., & Ding, T. (2022). Performance evaluation of AI driven low carbon manufacturing industry in China: An interactive network DEA approach. Computers & Industrial Engineering, 170, 108248.https://doi.org/10.1016/j.cie.2022.108248
Lioutas, E. D., Charatsari, C., & De Rosa, M. (2021). Digitalization of agriculture: A way to solve the food problem or a trolley dilemma?. Technology in Society, 67, 101744. https://doi.org/10.1016/j.techsoc.2021.101744
Manning, L., Brewer, S., Craigon, P. J., Frey, J., Gutierrez, A., Jacobs, N., … & Pearson, S. (2022). Artificial intelligence and ethics within the food sector: Developing a common language for technology adoption across the supply chain. Trends in Food Science & Technology, 125, 33-42. https://doi.org/10.1016/j.tifs.2022.04.025
McKinsey. (2017). Job lost, jobs gained: workforce transitions in a time of automation. Chicago, New York and Londond, McKinsey global Institution.
National Artificial Intelligence Research Resource Task Force (NAIRR), (2023). Strengthening and Democratizing the U.S. Artificial Intelligence Innovation Ecosystem: An Implementation Plan for a National Artificial Intelligence Research Resource. https://www.ai.gov/wp-content/uploads
/2023/01/NAIRR-TF-Final-Report-2023.pdf
Nti, E. K., Cobbina, S. J., Attafuah, E. E., Opoku, E., & Gyan, M. A. (2022). Environmental sustainability technologies in biodiversity, energy, transportation and water management using artificial intelligence: A systematic review. Sustainable Futures, 4, 100068. https://doi.org/10.1016/j.sftr.2022.100068
Santangeli, A., Chen, Y., Kluen, E., Chirumamilla, R., Tiainen, J., & Loehr, J. (2020). Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land. Scientific reports, 10(1), 10993. https://doi.org/10.1038/s41598-020-67898-3
Scholten, D. (2023) Handbook on the Geopolitics of the Energy Transition. Edward Elgar Publishing.
Shi, C., Feng, X., & Jin, Z. (2022). Sustainable development of China’s smart energy industry based on artificial intelligence and low‐carbon economy. Energy Science & Engineering, 10(1), 243-252. https://doi.org/10.1002/ese3.856
Sony, M., Antony, J., Mc Dermott, O., & Garza-Reyes, J. A. (2021). An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector. Technology in Society, 67, 101754. https://doi.org/10.1016/j.techsoc.2021.101754
The Economist. (2023). Big tech and the pursuit of AI dominance (26 March). https://www.economist.com/business/2023/03/26/big-tech-and-the-pursuit-of-ai-dominance
Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116, 68-74. https://doi.org/10.1016/j.jbusres.2020.05.019
Reuters. (2023). From Baidu to Alibaba, Chinese firms working on ChatGPT-style technology. The Economic Times, Tech. https://economictimes.indiatimes.com/tech/technology/from-baidu-to-alibaba- chinese-firms-working-on-chatgpt-style-technology/articleshow/99378876.cms [Accessed 13 August 2023]
Ulnicane, I., Eke, D. O., Knight, W., Ogoh, G., & Stahl, B. C. (2021). Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies. Interdisciplinary Science Reviews, 46(1-2), 71-93. https://doi.org/10.1080/03080188.2020.1840220
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313-323. https://doi.org/10.1016/j.comcom.2020.02.069
Walshe, R., Koene, A., Baumann, S., Panella, M., Maglaras, L., & Medeiros, F. (2021). Artificial intelligence as enabler for sustainable development. In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-7). IEEE. http://doi.org/10.1109/ice/itmc52061.2021.9570215
White House. (2021). Executive Order on promoting competition in the American Economy. White House Briefing Room, 9 July. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/07/09 /executive-order-on-promoting-competition-in-the-american-economy/. [Accessed 10 August 2023].
Wong, P. N. (2021). Techno-Geopolitics: US-China Tech War and the Practice of Digital Statecraft. Taylor & Francis.
Wu, F., Lu, C., Zhu, M. et al. (2020). Towards a new generation of artificial intelligence in China. Nat Mach Intell 2, 312–316. https://doi.org/10.1038/s42256-020-0183-4
van Wynsberghe A, Vandemeulebroucke T, Bolte L, Nachid J. (2022). Special Issue “Towards the Sustainability of AI; Multi-Disciplinary Approaches to Investigate the Hidden Costs of AI”. Sustainability, 14(24):16352. https://doi.org/10.3390/su142416352
Visvizi A. Artificial Intelligence (AI) and Sustainable Development Goals (SDGs): Exploring the Impact of AI on Politics and Society. Sustainability. 2022; 14(3):1730. https://doi.org/10.3390/su14031730
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., … & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 1-10. https://doi.org/10.1038/s41467-019-14108-y
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
The Geopolitics of Renewables 