The chemicals industry, often perceived as the backbone of modern economies, is undergoing a notable shift. With the world's focus now fixed on environmental, social, and governance (ESG) initiatives, this sector finds itself at the crossroads of risk and opportunity. In this “ESG Data Trends,” we dive deeper into the chemicals’ market ESG performance, studying the example of Ineos.

The chemicals industry: riding the ESG wave

Post-2020, the chemical market has seen an increase in web mentions. Several factors—from gas shortages rattling this energy-intensive market to escalating environmental concerns—have ushered in a new era of sustainability discussions. But which chemicals are stealing the limelight?
Chlorine, Ammonia, and Base Chemicals like Ethylene and Propylene account for over half of the chemical web mentions. And it's not just about their volume. The narrative is changing too. The industry is leaning towards eco-conscious production, championing innovations like recycled propylene, Renewable-Benzene, and Green ammonia.

What's interesting about this is the emphasis on ESG initiatives over ESG risks. It's a clear signal that the industry is taking action toward sustainability and is making tangible strides. When looking at the industry’s ESG risks mentions, we found that Arkema has the highest percentage of ESG Risks driven mainly by environmental incidents and impact on biodiversity due to a chemical plant explosion in 2017, followed by UOP LLC, which displays the highest proportion of Social related risks as a consequence of layoffs.

Conversely, across the industry, the volume of ESG initiatives indicates a significant commitment to sustainable related practices. Environmental-related practices are the most mentioned initiatives in the chemicals industry; precisely, two pillars stand out in ESG initiatives: climate change reduction and circular economy strategies. LyondellBasell displays the highest percentage of ESG initiatives mentions due to its climate change reduction and circular economy strategies, where the company is working towards greenhouse gas reductions and advancing plastic waste recycling. Despite having the highest environmental risk mentions, Arkema has the highest social-related initiatives with corporate social responsibility.

Case study: Ineos

The TextReveal Dashboard detected another chemicals company with an increasing number of mentions, the British multinational Ineos. After the announcement of Ineos Grenadier's off-roader in 2020, the number of mentions more than doubled, increasing Ineos' overall volume. Later on, the company’s mentions have been relatively increasing after cooling down from the announcement, with a significant increase in 2022 following M&A and collaboration announcements, sustainability actions, and controversies around its CEO, Jim Ratcliffe.

We also detected a geographical shift in mentions. Once dominant in the US, Ineos mentions dropped from 65% in 2015 to roughly 30% in 2022. Europe, on the other hand, has seen a spike from 25% to over 65%. Sentiment analysis offers another layer of insight.

While the sentiment has largely remained steady, there have been dips, especially during periods associated with fracking controversies and environmental incidents, including a toxic chemical spill. Digging deeper into Ineos’ ESG risks, there has been a decrease over the recent years; nonetheless, before 2019, we captured a relatively higher number of risks, mainly environmental–related controversies, coming from mentions about overexploitation of resources, namely fracking. Social-related risks display a significant proportion of data driven by social dialogue controversies as we capture multiple mentions of protests, particularly in 2017.

While Ineos ESG risks mentions represent 2.46% of its overall data share, its ESG initiatives mentions represent 5.91% of its web presence, signaling a more positive outlook for the firm, at least from a perception point of view. Furthermore, we detected that environmental–related initiatives are the main focus for Ineos, particularly climate change, while social initiatives arise, particularly in 2018, due to product safety mentions.

Data sources

To produce this analysis, we combined natural language processing with billions of textual web data related to the chemicals market. Using NLP-powered models gives us an edge as we can extract ESG, SDG, and financial insights that aren’t necessarily obvious or easy to detect. These insights help investors make better investment decisions. SESAMm leverages artificial intelligence and machine learning technologies to help you decipher and understand timely sentiment, trends, and ESG metrics on a wide range of public and private companies.

Reach out to SESAMm

TextReveal's web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or request a demo, contact one of our representatives.

We are proud to share that SESAMm has been highlighted in Elaia's Annual Sustainability Report for 2022. Elaia has been historically committed to fostering sustainability and ESG excellence, and SESAMm is honored to be identified as a top ESG-focused company within its portfolio.

The report underscores SESAMm’s pioneering AI technologies, capable of analyzing billions of textual data, from articles to blogs. This technology equips private equity firms, asset managers, and leading financial institutions with accurate and timely insights into ESG controversies, trends, and positive impact indicators.

This acknowledgment supports SESAMm’s dedication to reshaping the ESG landscape. Our keen focus on aligning market operations with the UN Sustainable Development Goals (SDGs) positions us as a leader in the sustainable finance ecosystem.

To learn more about SESAMm’s role in advancing sustainable finance, we invite you to download the full report.

Reach out to SESAMm

TextReveal's web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or request a demo, contact one of our representatives.

The AI field is growing, and whether good or bad, people are doing more than talking about it; they’re using it more than ever. However, despite this increased use, I’ve noticed that, for some, their perception tends to alternate between false and too-high expectations of AI.

One case, in particular, was in 2021, Gartner placed natural language processing (NLP) at the top of its list of loaded expectations in terms of the Gartner hype cycle. As a result, many expected a potential “winter of AI,” so to speak. Yet, in 2022, we discovered the potential that we haven’t even touched on the true value AI could deliver.

Will there be a “winter of AI,” and are expectations bloated?

No, I don’t think so. As the past year has shown us, AI still has more to offer, a pocket of value that we have yet to see. I believe that while many people now accept that AI will be a transformative force—thanks to the fast democratization of large language models—our society hasn’t yet fully considered the actual changes it will make by lowering the barrier to access intelligence globally.

Progress in image generation, analysis, and computer vision—think autonomous driving—has leaped and bounded in the past year, and so has the progress in NLP, particularly in the natural language understanding (NLU) and natural language generation (NLG) aspects. We’re at a tipping point that will likely transform our world in the same way that the internet has.

Tipping point for AI

Today, we’re seeing the development of natural language processing through large language models, such as with the emergence of ChatGPT based on OpenAI’s large language model version GPT-3.

Astounding fact: ChatGPT’s growth in user adoption skyrocketed past one million users within a week of launching. In comparison, no other tech company has reached this feat in this short of a time frame. But the adoption rate is only part of it.

This advance has profoundly affected creative jobs because this might be the first time an AI generative system can create high-quality content. In public mode, users have tapped ChatGPT to do everything, from generating basic reports and ideas to writing lectures and producing code.

With a high adoption rate comes great opportunity. Any startup seeing this level of success could become the most funded project ever. And more, there’s revenue. OpenAI, as the example, could make one billion dollars by 2024, according to a report via Reuters.

On the other side of the same coin, however, there are greater risks due to AI generative system advancement. For example, with AI assistance, human hackers can develop more sophisticated phishing campaigns—hacking mechanisms based on social engineering.

Competition, specificity, and focus for AI advancement

Despite the risks, we still haven’t seen what’s yet to come with generative AI. GPT-4, for instance, is rumored to launch in 2023. I believe it will be a massive improvement over GPT-3, which is already mind-blowing.

And on the point of NLG and these large language models, there’s a lot that’s feasible in process automation. For context, creative content gets the most attention; it’s the area that makes more headlines. But I would also watch advancements in technical content and automated code generation, for example.

Process automation

Because of today’s AI advancements, it’s now possible for tools like ChatGPT to generate near-ready-to-use source code. That means instead of only being fun to play around with, these are becoming enterprise tools, making it possible for developers to automate technical tasks at scale.

NLP—specifically natural language understanding, which SESAMm works on—is not untouched by these applications. Many of these large language models can perform zero-short learning, which means NLU can be performed without pre-training, a huge advance in this industry. However, zero-short learning is insufficient for many advanced sentiment and ESG analysis tasks. We still need additional data sets to fine-tune the data for a specific purpose.

What does this mean for the natural language generation sector? Many startups—especially anything around chatbots—have folded, some just in Q4 of 2022. ChatGPT’s success means it’s solved and replaced the need for many of them, and basically, anything content creation on the B2C side has and will struggle.

Defensive edge

Otherwise, things are looking good in our sector. For example, at SESAMm, we’re focused on what I call “last-mile AI.” In our specific business application, you can’t bypass the need for a data set because we’re trying to attain a precise result for specific, often risk-related applications. Open-source large language models like GPT-3 and BERT can get you mostly there, and that’s fine for general purposes. But for “last-mile AI” applications, there’s a lot you can’t do without additional work.

And here lies what I think is one of SESAMm’s defensive edges: the “last-mile AI.”

Instead of finding ways to protect its algorithms, the AI business community would do better to defend its use cases because the algorithm’s value will decrease progressively. In contrast, the value of a use case’s purpose and the data set used to achieve the use case will grow.

Competitive edge

Computing power and the resources it takes to train large language models remain challenging to applications like OpenAI. It takes electricity, heat, and money to train these models, and AI has an environmental impact. So far, we’ve justified this cost in the name of optimization—meaning that we put in this extra cost upfront so that the likely efficiency will offset or reduce that cost later—but it’s still a cost to incur.

AI companies, especially those in the NLG space, will do well to find their competitive edges, areas optimized for a specific purpose like “last-mile AI.” Companies like OpenAI will likely continue to optimize their models for quicker responses but don’t necessarily have the problem of solving for a specific use case.

At SESAMm, for instance, a big challenge and expertise we developed in-house is inference time—or how quickly we can apply the model to an article or an individual sentence. Because we’re processing so much live content, the more time it takes to process—milliseconds multiplied by a billion—the more costly it is.

Our data lake currently holds over 20 billion articles, messages, etc., from over 14 years, and we add 10 million more daily. That’s a lot of content to analyze. But we make it so our clients can access the data within seconds.

The need to optimize models for fast inference and adapt to deep industry-specific use cases will remain one of the key reasons companies will have to continue re-training their own models. That doesn’t mean large language models don’t add value here. Their open-source versions simply become an impressive building block for any NLP application and accelerate the rate of innovation and productivity in the whole field.

My summary thoughts on AI for 2023

When Google launched BERT in November 2018, we quipped that Google had open-sourced this system as a joke because no one could put it into production because BERT was so big. Many companies didn’t have the computing capabilities to do anything with it at the time. Now we do.

This year, Google did it again; they released a model that’s even bigger than GPT-3. Of course, almost no one besides Google can put that model into production now. But my point is that there will always be computing, resources, and other challenges to making AI advancements. That’s why I think AI companies must focus on defensive and competitive edges.

Regardless of the challenges, I see good things happening in the NLU space being massively improved by large language models. I see improvements as we incorporate these models today compared to deep-learning models trained from scratch a few years ago. I also see a significant decrease in the amount of data we need to fine-tune results, reaching and focusing on the final client use case more quickly.

From a natural language generation perspective, I believe large language models will transform the world. And I’m really excited about this era because this transformation supports my deepest purpose, leveraging AI to accelerate innovative decision-making. We do this by giving decision-makers access to technology that analyzes research content, news, and discussions. And if we increase the rate of innovation or the quality of decision-making by 10% globally, the impact could be huge for all industries: healthcare, finance, fashion, you name it. Industry leaders can make better ESG and SDG choices that will affect our world on a grander scale.

2023 will be an exciting time for AI, specifically for NLG and NLU. Of course, we’ll continue to see AI innovations. But more importantly, leaders will have better insights to make better decisions, creators will create more—and more complex—content, and overall, the applications will become more specific to solving the needs of particular use cases.

Here’s to the new era of AI in 2023. Cheers!

About SESAMm

SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. SESAMm provides datasets and NLP capabilities through TextReveal^® to generate alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporate studies, and more. With access to SESAMm’s massive data lake, comprised of 20 billion articles and messages and growing, its clients can make better investment decisions.