Artificial Intelligence (AI) is shaping an increasing number of sectors globally. Degradation of the natural environment and the climate crisis are complex issues requiring the most advanced and innovative solutions. AI is expected to impact environmental, financial, and job stability, amongst other areas in the future. 

 But, how much can AI really help contribute to the climate crisis?

Environmental sustainability

Environmentally, Artificial Intelligence can aid management across agriculture, water, energy, and transport. 

In agriculture, AI can better monitor environmental conditions and crop yields. For water resource management, AI can help to reduce or eliminate waste while lowering costs and lessening environmental impact, such as AI-driven localized weather forecasting to help restrict water usage. AI can also manage the supply and demand of renewable energy using deep learning, predictive capabilities, and intelligent grid systems. Finally, AI can help reduce traffic congestion, improve cargo transport, and enable autonomous (or self-driving) cars. 

According to Microsoft and PwC UK, using AI for these environmental applications could contribute $5.2 trillion to the global economy in 2030. Also, AI application could reduce worldwide greenhouse gas (GHG) emissions by 4% in 2030, equivalent to the 2030 annual emissions of Australia, Canada, and Japan combined. 

This positive impact on the environment somewhat explains the broad harnessing of AI to contribute to managing environmental and climate change. 

Financial sustainability 

As a result of the environmental applications, AI could boost global GDP by 3.1 – 4.4% (Microsoft) and can generate a global economic uplift, yielding approximately US$3.6 – 5.2 trillion driven by optimized inputs, higher output productivity, and automation of manual tasks. 

More generally, AI technology can help companies encourage fast consumer decision-making and detect fraud and financial crime through machine learning. For example, automated wealth management services (robot advising) and algorithmic trading are helping financial institutions to optimize financial decisions; and ‘smart ledger’ technology could support the take-up of collective defined contribution (CDC) schemes. 

However, while AI promises to increase financial stability through minimized error margins, it brings new risks such as interconnectedness between financial markets and confusion regarding machine learning decision-making processes when working with AI. Therefore, macro-level standards need to be implemented, and regulators need to tighten governance on the use of AI by companies (Parker Fitzgerald) to mitigate these risks. 

Job sustainability 

There is no denying that smart machines will make today’s jobs more efficient. However, humans are more likely to work with smart machines in the digital enterprises of the future than being replaced by them.

The AI applications to agriculture, water, energy, and transport will also create 18.4 – 38.2 million net jobs globally (broadly equivalent to the number of people currently employed in the whole of the UK), offering many skilled jobs. And this is just the beginning. If these many jobs are being created in these sectors alone, the possibilities are substantial across industries globally. 

Therefore, companies need to train employees to work alongside machines rather than creating a fear culture that jobs will become irrelevant.


In addition to those highlighted in this article, there are so many ways that AI will enable a sustainable future. Companies will be looking to transition into more sustainable and efficient working practices, requiring workforces with the skills to support these changes. Therefore, as a society, we must accept and tackle the future of AI to reap the financial, job, and environmental sustainability and the personal advantages to a more sustainable future on our health, well-being, and lifestyle.

Lead the shift towards artificial intelligence in your organization, explore the latest technologies, such as machine learning and deep learning, with the online Artificial Intelligence MSc at the University of Leeds. 

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