
Table of contents
DOI: https://doi.org/10.58248/HS58
Overview
A POST consultation of experts highlighted how digital technologies could play a key role in improving our lives, mitigating harm and avoiding catastrophe from the many global challenges.
For example, artificial intelligence (AI) could help to predict and prepare for climate change effects, including extreme weather events, disruption to global supply chains, reduced access to clean water and growing waste (PN 680). Likewise, digital technologies and AI could help remove carbon dioxide from the atmosphere, monitor habitats and reduce biodiversity losses, manage water quality and help to plan for pandemics.[1]
However, AI models emit large quantities of carbon dioxide (PB 57), and digital technologies are contributing to increasing physical electronic waste.[2] Other barriers to implementing these technologies at scale for global societal challenges are a lack of public trust and acceptance in technologies and high infrastructure costs.
Challenges and opportunities
Technology innovations have already begun to help combat climate change:
- Industries and researchers are applying digital technologies to develop tools that remove carbon dioxide from the atmosphere. Carbon dioxide can be permanently stored underground, or used in industrial processes, such as for processing chemicals, minerals, plastics and synthetic fuels (CBP 8841). Researchers are using AI to evaluate new carbon capture, usage and storage strategies.1
- Digital infrastructure, such as smart grids, could help mitigate against the intermittency of some renewable sources of energy, such as wind and solar generation.[3] Advances in renewable energy have put the UK among the world leaders in wind energy capacity, which surpassed 30 gigawatts in 2023.[4]
- Electric and automated vehicle systems could support the transition to net zero emissions (PB 62).[5]
With AI developing rapidly across the public and private sectors, researchers are investigating its potential to help solve environmental challenges:
- AI can be used to help reduce biodiversity loss, which carries risks of ecological and agricultural collapse (CDP 2024-0101). For example, conservation researchers have deployed AI to identify species in their natural environments,[6] and to continuously monitor habitat quality and the effectiveness of interventions.[7],[8]
- Researchers are investigating AI techniques for managing water quality, assessing the health of aquatic organisms, reducing waste and providing insight into fish growth and feeding patterns for the development of smart monitoring systems.[9]
- Researchers have achieved a reduction in food waste through using AI to optimise the production, storage and distribution of food supply chains.[10]
Respondents to the POST horizon scan highlighted that the United Nations (UN) 17 sustainable development goals (SDG) with 169 targets, agreed upon by 195 countries to achieve by 2030, could help countries to identify global sustainability priorities.[11] In a 2024 progress report, the Secretary-General noted that “signs of a determined, sustained global comeback have yet to emerge”, with only 17% out of 135 targets with trend data on track to be achieved by 2030.[12]
The International Telecommunication Union has detailed how digital technologies have the potential to accelerate this progress:[13]
- Access to digital financial services could facilitate better money management and literacy in deprived areas and could aid with SDG 1: No poverty.
- For example, they could facilitate direct contact between patients in remote areas and healthcare professionals through virtual medical consultations (PB 61). This could support SDG 3: Good health and well-being.
- E-government services could help improve efficiency and access for citizens to state resources, particularly in regions with fragile democratic institutions. This could aid with SDG 16: Peace, justice and strong institutions.
- Researchers are investigating how smart water management systems can use sensors to monitor characteristics such as water levels, salt content and pH.[14] This links with SDG 6: Clean water and sanitation and SDG 1: Good health and wellbeing. In 2023, the UN reported that 2.2 billion people still lack safe drinking water and 3.5 million lack sanitation services.[15]
In a 2021 inquiry into lessons learnt from the COVID-19 pandemic, the House of Commons Health and Social Care Committee and Science, Innovation and Technology Committee found that “protocols to share data between public bodies involved in the response were too slow to establish and to become functional”.[16] The committees suggested the use of data and AI to predict and plan for scenarios is well-established in academic literature and could be used for pandemic and natural disaster modelling in real time if the right information and infrastructure is made available.[17], [18]
While digital technologies have shown immense potential to tackle societal challenges, there are many ethical and governance challenges, such as bias, data management, privacy, access and cyber security (PN 708).
Many stakeholders have expressed concerns around the environmental impact of these technologies. One academic study published in 2021 estimated that training ChatGPT-3 led to 1,287 MWh of energy consumption, which is equivalent to the annual energy consumption of around 477 average UK households (PB 57).
The carbon cost of AI models is significantly dependent upon the design of the algorithm and computer hardware, and some can be better optimised for energy-efficiency.[19], [20] Energy efficiency of AI and digital technologies are constantly improving and have done so rapidly in the past few years, due to developments in algorithms, computer hardware and trends such as cloud computing (PN 677). Some researchers have suggested methods for evaluating the carbon footprint of digital information.[21]
In addition, physical electronic waste contains many valuable and toxic rare earth metals and compounds, which require comprehensive recycling programs to ensure environmental and public health safety.2 According to evidence gathered by the Environmental Audit committee in a 2020 inquiry report on electronic waste and the circular economy, a maximum of 12% of electronics are re-used, and 55% are never collected for safe disposal or recycling.[22] In 2022 the UK generated the second most electronic waste per person globally, at 23.9 kilograms.[23]
AI solutions to societal challenges can face obstacles moving from the research phase into implementation at scale. These include:
- a lack of high-quality data for the AI model to learn from and perform tasks correctly[24], [25]
- high infrastructure costs[26]
- a lack of public trust and acceptance in technologies
- a lack of transparency in how large AI models make decisions, which has raised concerns about liability and transparency (PB 57, PN 708)[27]
Key uncertainties/unknowns
The carbon cost of using large AI models is not well understood and researchers have identified a lack of data and tools to make rigorous assessments. To solve climate challenges, experts in the POST consultation highlighted how savings from AI, optimising energy usage and improving efficiency, could offset carbon costs emitted by the models, but may require regulation to result in a net benefit.
Stakeholders such as AI developers, activists and legislators, have raised questions about how the government will regulate AI and what the impact of regulation may be on how it could be applied.[28]
Some technology experts warn against over-hyping the abilities of AI and digital technologies and its potential impacts to transform society.[29],[30],[31] Opportunities from AI and digital technologies will depend on how they are used, geopolitics, access, ownership, safety measures and public attitudes (PB 57).
Digital technologies have the potential to support achieving UN sustainable development goals, but present practical and ethical challenges such as the role of high-income countries in the development of other nations, and whether less economically developed countries will have the skills and infrastructure to implement technical solutions. There are also questions around whether
Key questions for Parliament
- How can the application of AI and related technologies to societal challenges be incentivised and supported by public policy?
- What role should the government, companies and regulators play in leveraging AI-informed solutions with infrastructure-related challenges such as water quality?
- What barriers do organisations face in implementing AI and technical solutions for societal challenges?
- How can AI be regulated to mitigate against ethical and security concerns?
- What policy measures can be implemented to reduce digital and electronic waste?
- How can the carbon cost of algorithms be better tracked, published and improvements incentivised through legislation, for example by requiring auditing of climate impact?
- What is the role of the UK in supporting the 17 SDGs and less economically developed countries in implementing digital technologies for societal challenges?
Related documents
- Climate change and security POSTnote
- Carbon capture usage and storage House of Commons Research Briefing
- Energy consumption of ICT POSTnote
- Artificial intelligence: An explainer
References
[1] Yan, Y., et al. (2021). Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review. Energy and Environmental Science, Volume 14, 1, pages 6122-6157.
[2] Abdelbasir, S.M., et al. (2018). Status of electronic waste recycling techniques: a review. Environmental Science and Pollution Research, Volume 25, pages 16533-16547.
[3] Escobar, J.J.M., et al. (2021) A Comprehensive Review on Smart Grids: Challenges and Opportunities. Sensors, Volume 21.
[4] RenewableUK (online). Wind Energy Statistics, accessed 25 June 2024.
[5] Jayawardana, V., et al. (2022) Learning Eco-Driving Strategies at Signalized Intersections. In proceedings of 2022 European Control Conference, 15 July 2022, pages 383-390.
[6] Choinski, M., et al. (2021). A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest. In Proceedings of International Conference on Computer Information Systems and Industrial Management, 17 September 2021, pages 299-310.
[7] Silvestro, D., et al. (2022). Improving biodiversity protection through artificial intelligence. Nature Sustainability, Volume 5, pages 415-424.
[8] Lahoz-Monfort, J.J., et al. (2021). Comprehensive Overview of Technologies for Species and Habitat Monitoring and Conservation. BioScience, Volume 71, 10, pages 1038-1062.
[9] Mandal, A., et al. (2023). Role of artificial intelligence (AI) in fish growth and health status monitoring: a review on sustainable aquaculture. Aquaculture International, Volume 32, 10, pages 2791-2820.
[10] Ahmadzadeh, S., et al. (2023). A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies. Sustainability, Volume 15, 4.
[11] United Nations Department of Economic and Social Affairs (2015). Transforming our World: The 2030 Agenda for Sustainable Development.
[12] United Nations General Assembly Economic and Social Council (2024). Progress towards the Sustainable Development Goals: Report of the Secretary-General.
[13] International Telecommunication Union (2021). Digital technologies to achieve the UN SDGs.
[14] Singh, M., et al. (2021) IoT based smart water management systems: A systematic review. Materials Today: Proceedings, Volume 26, 14, pages 5211-5218.
[15] United Nations (online). Goal 6: Ensure access to water and sanitation for all.
[16] House of Commons Health and Social Care and Science and Technology Committees (2021). Coronavirus: lessons learned to date, Conclusions and recommendations. Paragraph 62.
[17] Ajagbe, S.A., et al. (2023). Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review. Multimedia Tools and Applications, Volume 83, 2, pages 5893-5927.
[18] Albahri, A.S., et al. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, Volume 118 Part B.
[19] Lannelongue, L., et al. (2021). Green Algorithms: Quantifying the Carbon Footprint of Computation. Advanced Science, Volume 8.
[20] Strubell, E., et al. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645-3650.
[21] Mersy, G., et al. (2023). Toward a Life Cycle Assessment for the Carbon Footprint of Data. In proceedings of the 2nd Workshop on Sustainable Computer Systems, No 14, 2 August 2023, p1-9.
[22] House of Commons Environmental Audit Committee (2020). Introduction: Electronics and E-waste, what are the problems?
[23] Circular (2023). UK generated 2nd largest amount of e-waste as a country in 2022.
[24] Aryai, V., et al. (2020). Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia. Process Safety and Environmental Protection, Volume 148, pages 437-461.
[25] Daniels, R.R., et al. (2023). Single cell genomics as a transformative approach for aquaculture research and innovation. Reviews in Aquaculture, Volume 15, 4, pages 1618-1637.
[26] Mustapha, U.F., et al. (2021). Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA). Reviews in Aquaculture, Volume 13, 4, pages 2076-2091.
[27] Tsolakis, N., et al. (2022). Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation? Annals of Operations Research, Volume 327, 21 pages 157-210.
[28] House of Commons Science, Innovation and Technology Committee (2024).Governance of artificial intelligence (AI).
[29] Deering, I. (2024). The big debate: is generative AI over-hyped? Raconteur.
[30] Giles, M. (2018). Artificial intelligence is often overhyped—and here’s why that’s dangerous. MIT Technology Review.
[31] Gartner (2023). What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle.
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