From the perspective of environmental economics, how can AI help achieve the SDGs? In this brief essay, multi-awarded author, university lecturer on international law & economics, and AI research consultant C.P.T.  Osorio shares his thoughts on AI & the Economics of Sustainable Development. Can AI and other deep tech sufficiently address the global challenges of sustainable, equitable development?


SwissCognitive Guest Blogger: Chad Patrick Osorio, University of the Philippines Los Banos, Sociov – “All Hands on Deck: AI and the Economics of Sustainable Development”


The focus of the United Nations on Sustainable Development is unquestionable. It seeks to permeate the concept into every aspect of its projects and programmes all over the world.

One of the most popular, yet simplest, definitions of Sustainable Development is “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This means thinking not just of ourselves and our consumption, but of the generations to come as well.

Sustainable development also means equitable development. It is important to always keep in mind that in our quest for sustainability, we must not forget the plight of developing countries, vulnerable groups and underserved populations.

Sustainable Development and Economics

When we talk about sustainable and equitable development, an understanding of economics is necessary.

Economics is a social science focused on the efficient allocation of resources in order to maximize human happiness. It is the study of scarcity, and how it could be overcome.

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While many people associate economics with money, it’s not necessarily just about that: money is simply the easiest fungible means to correlate to human happiness. After all, money can provide access to essential and non-essential resources alike which can comprise satisfaction. However, we all know that money isn’t necessarily the end-all and be-all of human contentment.

This is why, aside from money-based measures for economic growth like the Gross Domestic Product and Gross National Product, we have other measures which could also indicate economic growth, such as the Human Development Index and the World Happiness Report.

At the same time, this is why the study of economics can be applied, directly or subsidiarily, to other concepts not necessarily involving money, including political economy and the economics of power, the economics of information, and the economic analysis of law and policy, among many others.

Two key aspects of the economics of sustainable development are natural resource economics and environmental economics. The first field focuses on Earth’s natural resources, including extraction to meet supply, demand for provisions, utilization, and allocation. On the other hand, the latter looks at how socio-economic activities impact the natural environment, and vice versa. It concentrates on non-provisioning ecosystem resources, including life system and habitat support, amenity services, waste sink, and biodiversity.

This is why, if advocating for sustainable development and the proper allocation of both environmental and natural resources, it is important to consider not just the wants and needs of the present generation, but plan for sufficient contingencies for future generations as well.

Understanding the SDGs

The ultimate aim of the UN is to advance the 17 Sustainable Development Goals (SDGs), using them as guiding light towards a better 2030. The SDGs evolved from the Millennium Development Goals.

From the headers alone, seven of the SDGs are directly environmentally-related, but it’s important to note that they do not exist in vacuums. These goals are interconnected. This means, for instance, that both SDG 14: Life Below Water and SDG 15: Life On Land affect SDG 2: Zero Hunger. SDG 13: Climate Action is a necessary component of SDG 3: Good Health and Well-Being. The number of examples on how the SDGs are interrelated are factorial. Thus, they must be viewed not as isolated areas requiring only skilled expertise in their respective fields, but rather also from the macro, multidisciplinary perspective.

There are so many new technologies available which could potentially prove ground-breaking in meeting these goals. In fact, applying economics, one of the key considerations is the environment-economy trade-off, illustrated by the Environmental Kuznets Curve. This concept states that as economies improve, environmental deterioration is bound to increase; however, there comes a turning point at which the income can compensate for the environmental deterioration, and can thus be used to invest in programs, projects and technologies which could contribute to environmental improvement.

This is true to a certain extent. However, regardless of the different technologies being able to replicate the production of natural resources to meet human needs and wants, it will be immensely difficult, if not impossible, for them to replicate non-provisioning environmental resources.

Take, for example, biodiversity as a non-provisioning human resource. Biodiversity cannot be artificially manufactured: studies show that introduction of genetically-manipulated species into the wild “diminish rather than enhance biodiversity.” Therefore, the argument that technology can make up for environmental degradation fails this test, considering that any loss in biodiversity should be deemed irreversible, and exponentially so. This in turn supports the idea that permanent harms such as this cannot in any measurable way be mitigated by all the money in the world.

A Global Neural Network to Meet the SDGs

One of the most touted new technologies in recent time is AI. There are many different kinds of AI use-cases, and its application in forwarding sustainable development is myriad.

When we talk about AI, sustainable development and applied economics, one of the first use-cases which comes to mind is usually about sustainable finance: how can AI help find ESG-conscious investors to boost ROI? After all, sustainable development comes at a price.

Searching through thousands of SDG-focused projects and applying predictive analytics to identify which ones could have the statistically-significant chances of success could prevent opportunity losses stemming from inefficient project financing.

Another use case is intelligent image analysis from satellites, and how it can inform policymakers and key actors areas of priority for investments in their protection and development.

Power grids integrating AI for efficiency purposes can serve the goals of SDG 7: Affordable and Clean Energy, SDG 11: Sustainable Cities and Communities, and SDG 12: Responsible Consumption and Production.

Innovation is also a key concept in economics. Neural networks can identify innovative practices and techniques and connect them to various SDGs to come up with novel applications of existing technologies to address industry-specific challenges.

There are just some of the many more examples of how AI and sustainable development can intersect, and these comprise just a very small sample. Indeed, estimates show that by 2030, AI could potentially contribute up to US$5.2 trillion to the global economy, if sufficiently integrated into environmental applications. It can significantly increase green production, lower carbon emissions and add millions of skilled jobs to the labor market worldwide.


As an environmental economist and lawyer, I am always for the fair application of sustainable, equitable development through domestic and international legal mechanisms implementing the SDGs.

As someone working in the field of high tech, I deeply believe that AI, combined with a humanist perspective, can help meet these goals in so many different ways, as outlined above.

However, as I always tell my students, this optimism comes with great caution. AI and other deep tech only serve as a safety net; they’re not necessarily the complete answer to all the challenges faced by sustainable development.

Let’s go back to the Kuznets Curve. Originally, it was conceptualized to apply to equality. Its thesis was that poor economies have increasing inequalities up until a certain point, where afterwards economies will continue to improve but the inequalities would decrease.

If this were true, however, rich countries would have low rates of inequality. However, we know that this is not the case.

I remain hopeful about the Environmental Kuznets Curve, especially with the enormous potential utility that AI and other similar technologies can provide towards meeting the SDGs. However, we should not rely on these technologies alone.

Understanding and implementing sustainable development is a multi-perspective, multi-disciplinary challenge, and as such, both deep tech and economics are just two of the many fields which could be explored to forward its goal. In order to achieve the SDGs, we will need more than just these two fields: rather, we need all hands on deck, working together, for a better, more sustainable future.

About the Author:

Chad Patrick Osorio is Senior Lecturer for Economics at the University of the Philippines Los Banos. He is External Consultant and former Head of Research for ALPHA10X, and current Chief Legal Officer of Sociov, a data-driven coaching and mentoring platform. Send comments and queries to