By Tshilidzi Marwala
The Genesis
Sustainability encompasses strategies and practices aimed at safeguarding and conserving our world. This is envisioned as an approach for ourselves and generations to come.
The often-quoted definition from the UN World Commission on Environment and Development reads, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”
This idea is drawn out by the sustainable development goals (SDG). As we envision a more sustainable world and reframe our practices accordingly, artificial intelligence (AI) becomes an invaluable tool in our arsenal.
Sustainable practices balance the economic, social, and environmental considerations of development with future generations in mind. According to UCLA, sustainable practices are methods used to promote the well-being of people, the environment, health, and the economy. This statement assumes that our resources are finite and suggests methods for allocating them towards long-term objectives.
In contemplating Artificial Intelligence solutions in this endeavor, the words of the Kenyan activist Wangari Maathai stand out starkly, “We often preoccupy ourselves with the symptoms, whereas if we went to the root cause of the problems, we would be able to overcome the problems once and for all.”
Maathai outlined that prioritizing identifying and resolving the root causes of our challenges aligns with the holistic and long-term approach to sustainable development.
AI presents the possibility of doing just this. When we consider agriculture within the sustainability framework, the concept extends beyond food production to encompass the protection and preservation of our planet.
Sustainable agriculture minimizes detrimental environmental impacts while maximising long-term productivity, resilience, and ecosystem health. Embracing sustainable agricultural practices ensures that our food systems meet present and future needs.
This represents one consideration in our understanding of the convergence of Artificial Intelligence and sustainable practices. While I will provide a broad overview of the possibilities related to the various SDGs, I will briefly reference some interventions that apply to this industry. Of course, the SDGs are inextricably linked, and progress on any metric has a potential ripple effect.
In the aftermath of the pandemic, there have been some overarching questions we are called to respond to. We are still grappling with the lingering effects of the pandemic, growing geopolitical tensions, inflationary pressures, unsustainable levels of debt, and troughs in growth. Addressing these challenges is crucial for achieving sustainability in the long term.
Sustainable development requires not only environmental sustainability but also economic and social resilience. Seven years ago, the United Nations (UN) established the SDGs. This was a comprehensive reframing and reworking of the previous millennium development goals (MDGs).
The Dynamics In AI and Agriculture
We are now just past the midpoint towards the envisioned just, equitable, and more sustainable future, and progress must be made faster. In fact, across many metrics, we have completely stalled. Although this is partly a legacy of the COVID-19 pandemic, I am mindful that we were off track before this.
The 2023 update warned, “The promises enshrined in the SDGs are in peril.” Although the slowdown in progress towards the SDGs affects all nations, it disproportionately impacts poorer countries because of their limited representation on the global platform.
Once more, the growing economic disparity between developed and developing countries, coupled with the unequal effects of the climate crisis, is emphasized as a significant worry. This indicates that not only do we find ourselves in peril, but we are also jeopardising for generations to come.
Although the technology cannot be viewed as a panacea, it certainly provides some answers and allows us to approach sustainability with Maathai’s words in mind. In 2020, Vinuesa et al. found that across the SDGs, 134 goals could be enabled by Artificial Intelligence although 59 could be inhibited.
As the authors assert, “The current choices to develop a sustainable-development-friendly Artificial Intelligence by 2030 have the potential to unlock benefits that could go far beyond the SDGs within our century.”
Through our own research, we have outlined various uses of Artificial Intelligence with regard to SDG targets. This provides some insight into the convergence of Artificial Intelligence and sustainable practices.
For example, in terms of SDG 6, which emphasises water and sanitation, we argue that machine learning provides myriad solutions in this instance, from predictive analysis to manage our supply networks to data analysis to track water consumption and water end users, for instance to management of sewage treatment plants or desalination plants.
With regards to SDG 13, on climate change, and SDG 7, on affordable and clean energy, we have looked at the possibilities of wind speed nowcasting to better predict weather phenomena through machine learning models.
Along with Evan Hurwitz, we have looked into the intersection between Artificial Intelligence and economic theory and introduced new economic concepts with the goal of creating more efficient markets, which is applicable to SDG 8, focused on decent work and economic growth.
In terms of SDG 9, which looks at industry, innovation and infrastructure, I propose using Artificial Intelligence for condition monitoring in mechanical and electrical systems.
This approach aims to demonstrate the effectiveness of condition monitoring in preventing equipment failures, extending their lifespan, minimizing downtime, and reducing maintenance expenses, which is important in automated agriculture.
We have researched technology around SDG 16, or peace, justice and strong institutions, extensively including Artificial Intelligence models to detect fraud, machine learning to model militarised conflict and the inclusion of low-resource languages in natural language processing to widen inclusion.
With regards to SDG 15, which looks at life on land, we have researched the input of missing streamflow data in the detection of floods and the prediction of platinum prices using neural networks for a clean energy transition.
We have also looked extensively at Artificial Intelligence and international relations, with applications for predicting and avoiding interstate conflict, challenging climate change, spurring a technology revolution, facilitating trade, safeguarding human rights and outlining and improving the international financial architecture alongside Bhaso Ndzendze.
These are but a few examples of Artificial Intelligence based solutions from my own research. Of course, from an agricultural point of view, there are various biotechnology interventions that can be considered.
In a 2023 report, the Food and Agriculture Organization of the UN ( FAO ) revealed that while progress on many indicators has stagnated or reversed, achieving a world with zero hunger and sustainable agriculture remains feasible.
The report highlighted urgent actions needed to address inequalities, transform agri-food systems, invest in sustainable practices, and improve resilience against shocks.
Some positive trends include the conservation of plant genetic resources and water use efficiency, while challenges persist in areas such as chronic hunger, food insecurity, malnutrition, and agricultural losses due to natural disasters.
Importantly, the report highlights the significance of coordinated efforts to achieve SDGs by 2030 and provides insights to guide transformative change.
As the report states, “To achieve the food and agriculture-related SDG targets, urgent, coordinated actions and policy solutions are imperative to address entrenched inequalities, transform agri-food systems, invest in sustainable agricultural practices, and bolster resilience against shocks. Improving data capabilities plays a key role in ensuring progress.”
The International Council of Biotechnology Solutions outlines some of the possible UN solutions.
For instance, with regards to SDG 2, or the objective of zero hunger, biotechnology can enhance crop yields, improve crop resistance to pests and diseases, and increase nutritional value through the introduction of interventions. Biotechnology has the innate potential to suppress insects like the Fall Armyworm.
This was first detected in 2016 in West Africa and can cause extensive maize yield losses in 39 African countries, affecting over 300 million African farm families. Biotechnology has proven effective in protecting against various pests. Through advances in biotech, maize that is resistant to the Fall Armyworm can be grown.
Genetically modified (GM) crops, for example, have the potential to address food security challenges, particularly in regions with resource constraints. However, the ethics of GMOs must be scientifically and ethically considered.
Given that, according to WHO, approximately 735 million people across the globe face hunger, it is astounding that our energies have yet to be focused on harnessing the power of biotechnology to eradicate hunger, for example.
For SDG 6, or the aim of clean water and sanitation, agriculture biotechnology can contribute to global efforts to maintain clean waterways with the implementation of future nitrogen use efficient (NUE) genetically modified crops as an example.
Biotechnology can also contribute to SDG 7, aimed at affordable and clean energy, through the production of biofuels and renewable energy sources, such as bioethanol and biodiesel, reducing the reliance on fossil fuels.
Tied to this is SDG 13, or climate action, where biotechnology can assist in climate change mitigation by developing carbon capture technologies, bioenergy solutions, and genetically engineered plants that sequester more carbon dioxide.
To preserve life on land, biotechnology can preserve water and topsoil through sustainable farming, enable more precise pest control, better-preserving biodiversity and use less land for farming crops resulting in less deforestation and the preservation of biodiversity.
In 2022, we wrote about the importance of harnessing technology to combat food insecurity. We outlined how leveraging advanced technologies such as Artificial Intelligence and drones in agriculture offers promising avenues to enhance food security.
Moreover, we argued that embracing such technological innovations alongside sustainable practices is crucial to realigning progress with the SDGs and addressing the urgent global crisis of food insecurity. However, there are some significant hurdles to consider when using AI for sustainable practices.
To return to the work of Vinuesa et al., “We are at a critical turning point for the future of Artificial Intelligence. A global and science-driven debate to develop shared principles and legislation among nations and cultures is necessary to shape a future in which Artificial Intelligence positively contributes to the achievement of all the SDGs.”
Harnessing Artificial Intelligence for the attainment of the SDGs requires consideration of human rights, ethical frameworks, governance considerations, its impact on inequalities within and between countries, potential discrimination, and its effects on democracy and political stability. As Artificial Intelligence capabilities grow, new safeguards and technologies will be necessary to address emerging ethical concerns.
Striking the balance between these forces, both apparent and emerging, requires us to understand the challenges. We have to interrogate how we will respond to these broad shifts and why it is crucial for us to have a common blueprint in place.
On the one hand, there are technical hurdles, including bridging the digital divide, ensuring interoperability or the exchange between different devices across regions, and safeguarding data security and privacy.
This includes challenges around transparency, bias and even infrastructure deficits. Tied to this are concerns about privacy and cybersecurity, which will become increasingly challenging as Artificial Intelligence capabilities grow. New safeguards and technologies will be necessary to address these concerns.
On the other hand, there are regulatory and policy challenges, an area where there has been a distinct lag. Policymakers need to define clear data governance frameworks, address intellectual property rights and take into account ethical considerations, including issues of bias and discrimination that inform inequitable Artificial Intelligence systems.
We have to ensure that these systems are inclusive and based on equitable structures and systems. Public engagement and education will be vital in shaping the future of AI to align with human values and aspirations. The automation of tasks through AI and robotics also has the potential to displace certain jobs, leading to unemployment and economic inequality. We have to prepare the workforce to adapt and respond to these changes.
Training large Artificial Intelligence models can also be energy-intensive, contributing to environmental concerns. Finding ways to make Artificial Intelligence development and usage more environmentally sustainable is important.
As we consider sustainable practices through an Artificial Intelligence lens, we must also consider the challenges. Moving forward with Artificial Intelligence requires us to do this simultaneously.
The environmentalist Peter Hawken once said, “Sustainability, ensuring the future of life on Earth, is an infinite game, the endless expression of generosity on behalf of all.
” As I have sought to demonstrate today, it can be argued that Artificial Intelligence is the very key to playing this game — and reframing how we approach sustainable practices.