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GPT & Sustainability: 4 Use Cases & Best Practices in 2024

GPTs are advanced natural language processing (NLP) models that can understand and generate human language. GPT technologies can improve sustainability efforts and help businesses achieve their environmental, social, and governance (ESG) goals.

Yet, its usage also leads to carbon emissions and since it is a novel technology, ESG and business leaders are not clear about how GPT models can support their sustainability goals.

This article will explore GPT models’ potential to improve carbon accounting and other ESG practices with limited carbon emissions.

GPT sustainability use cases

1- Improving carbon accounting

GPT models can process detailed information in business documents such as invoices and utility bills to create detailed carbon footprint calculations automatically. For instance;

  • Transportation data captured in invoices can give insights into fuel consumption and CO2 emissions
  • Analysis of electricity use on utility bills can underline areas of energy inefficiency.

This level of detailed carbon footprint calculation can lead to more sustainable practices in the fight against climate change.

Real life example from sponsor:

Hypatos’ GPT models can analyze transportation, logistics and utility invoices. With this data, companies can

  • automate their carbon accounting
  • get insights into
    • fuel use and route efficiency in transportation. These insights can guide companies to reduce their carbon footprint by optimizing transportation methods or reconfiguring their supply chains.
    • their electricity usage to improve energy efficiency.

You can try Hypatos’ pretrained models with your documents to have a sense of their accuracy. Production models would have higher levels of accuracy thanks to features like continual learning.

2- Identifying scope 3 risks via public data

Compared to scope 1 and 2, scope 3 emissions embed different challenges. GPT models can help solve them.

Scope 3 emissions refer to all indirect downstream and upstream emissions that occur in a company’s value chain. For some examples, see: Figure 1

Figure 1: Scope 3 emissions. Source: PwC

In its latest environmental report, Microsoft revealed that more than 96 percent of their emissions are in Scope 3.1 Apart from the emissions caused by use of products like XBox, they include emissions from

  • supply chain
  • lifecycle of their hardware and devices
  • travel, and other indirect sources.

Scope 3 is also related to non-financial and societal risks that can impact companies’ reputation and sustainability goals. Environmental and labor related abuses are part of this. For instance, McDonalds is accused of deforrestation of Amazon.2

Another example is Apple’s reputational damage due to its suppliers’ unethical labor practices. 3

Models such as ChatGPT can help solve this issue by analyzing vast amounts of publicly available data, such as news articles, social media posts, industry reports, and more on supplier, transportation, distribution and product use risks. As Microsoft puts forward, scope 3 is the ultimate decarbonization challenge; it necessitates the coevolution of best practices in the business, its customers and suppliers.

3- Faster supply chain data analysis

Supply chains can be complex. The ability of ChatGPT to analyze these supply chains‘ data can identify areas of waste or inefficiency, providing valuable insights for companies to automate their supply-chain operations, minimize waste, and reduce their environmental impact. Through its use, companies can have better grasp on information on WMS activities such as the capacity of inventories, shipments, worker efficiency and more.4

ChatGPT can interpret information about the company’s supply chain. As an example, we asked ChatGPT to convert a supply chain dataset into Python code and then to analyze that code (Figure 2).5 In this example, the model can detect several patterns (Figure 3), which can potentially help reduce waste. Specialized models can achieve significantly better results.

Figure 2: The Python code created by ChatGPT based on supply chain data. Source: OpenAI

Figure 3: ChatGPT interprets the code. Source: OpenAI

4- Informing decision making and shaping policies

Natural language processing (NLP) capabilities of GPT models like ChatGPT can support informed decision making. For example, they can digest and interpret large number of texts from research papers, policy documents, corporate reports and provide summaries and insights. This ability can be important for policymakers, who can use these insights to make well-thought-out policies to fight climate change.

Businesses and organizations could also employ large language models to analyze trends, predict future scenarios, and design effective strategies for achieving global sustainability targets. This can contribute to addressing the worldwide sustainability challenge.6

This technology can help companies and governments address this challenge and make the world a better place for us from an environmental standpoint

Paula Assis, IBM’s General Manager for EMEA 7

ChatGPT’s carbon footprint: a warning note

It is important to acknowledge that the use of language models like ChatGPT and Bloom also contribute to the very problem they’re tasked to solve: increasing carbon emissions.8.Training large language models can be an energy-intensive process. Unless the energy comes from a renewable source, it can result in significant emissions.

A 2019 study from the University of Massachusetts, Amherst, highlighted that training a single AI model can emit as much carbon as five cars over their lifetimes.9

Best practices to mitigate the environmental impact of AI models

Addressing this issue is not straightforward, but there are steps that can be taken:

Transparency

1- It is vital to have transparency about the environmental impact of AI models. Organizations should reveal the energy consumption and carbon footprint of their models.

Reduced environmental impact

2- Reducing the environmental resource consumption (e.g. water use) of AI training processes is key and can be achieved with further research.10. For example, businesses can choose to fine-tune large language models on a focused dataset to accomplish a specific task rather than training new models. This can help reduce energy consumption significantly in case of LLMs.

3- Shifting to renewable energy sources for feeding these processes can reduce the carbon footprint. Google, for instance, has committed to running its entire operation, including its data centers, on carbon-free energy by 2030.11

If you have further questions regarding the topic, reach out to us:

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Cem Dilmegani
Principal Analyst
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Burak Ceylan
Burak is an Industry Analyst in AIMultiple. He received his Masters' degree in Political Science from Middle East Technical University. He has background in researching location-based platforms.

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