The modern world is in a peculiar place right now. We’ve got the technology and resources to improve our planet, but we often don’t know how to use them despite our best intentions. Or, at the very least, we don’t know where to put our efforts.
Consequently, some investors are looking into Sustainable Development Goals (SDGs). Not only do they want their investments to earn more, but they also want them to do good. If you’re also interested in doing good with your investments, it’s essential to understand the SDGs and their meaning for your portfolio.
In this article, we’ll break down the SDG basics, SDG scores, their relevance to investing, and how SESAMm can help you get and read SDG metrics. But first, a quick review of SDGs.

What SDG means

SDGs, or Sustainable Development Goals, are a set of 17 goals that the United Nations set in 2015 to be achieved by the year 2030, a framework that “provides a shared blueprint for peace and prosperity for people and the planet, now and into the future.” The global goals and the 2030 Agenda for Sustainable Development cover issues such as human rights, poverty, health, education, gender equality, and environmental sustainability, and they were designed to be universal across countries and continents worldwide. Here are the 17 UN Sustainable Development Goals:

• SDG 1: No Poverty: Striving to end poverty in all its forms everywhere. This goal underscores the importance of equitable resource distribution and access to basic needs.

• SDG 2: Zero Hunger: Aiming to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture, thereby ensuring that everyone, everywhere, has enough quality food to lead a healthy life.

• SDG 3: Good Health and Well-being: It emphasizes the need for universal healthcare access, including reproductive, maternal, and child healthcare, and combats health threats by supporting research and development of vaccines and medicines.

• SDG 4: Quality Education: Envisioning inclusive and equitable quality education and lifelong learning opportunities for all, this goal recognizes education as the foundation of empowerment and prosperity.

• SDG 5: Gender Equality: Achieving gender equality and empowering all women and girls to participate fully in societal, economic, and political spheres

• SDG 6: Clean Water and Sanitation: This goal aims to ensure the availability and sustainable management of water and sanitation for all, recognizing the essential role of water resources in sustaining life and ecosystems.

• SDG 7: Affordable and Clean Energy: Promoting access to affordable, reliable, sustainable, and modern energy for all; this goal underscores the critical nature of energy in achieving other SDGs and the transition towards renewable energy sources to combat climate change.

• SDG 8: Decent Work and Economic Growth: It focuses on promoting sustained, inclusive economic growth, full and productive employment, and decent work for all, highlighting the role of the private sector in initiating impactful initiatives.

• SDG 9: Industry, Innovation, and Infrastructure: Aiming to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation, this goal recognizes the importance of a robust infrastructure and an innovative ecosystem as drivers of economic growth and development.

• SDG 10: Reduced Inequalities: This goal seeks to reduce inequality within and among countries, focusing on policies designed to achieve greater equity and involve stakeholders from all sectors of society in decision-making processes.

• SDG 11: Sustainable Cities and Communities: It aims to make cities and human settlements inclusive, safe, resilient, and sustainable, emphasizing the need for green public spaces, improved urban planning, and sustainable construction practices.

• SDG 12: Responsible Consumption and Production: Focusing on promoting resource and energy efficiency, sustainable infrastructure, and providing access to a better quality of life for all, this goal underscores the importance of adopting sustainable practices and reducing waste.

• SDG 13: Climate Action: Taking urgent action to combat climate change and its impacts, this goal underscores the necessity for countries, stakeholders, and the private sector to collaborate in reducing emissions and enhancing renewable energy usage.

• SDG 14: Life Below Water: Aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development, this goal addresses the critical importance of our aquatic ecosystems.

• SDG 15: Life on Land: Protecting, restoring, and promoting sustainable use of terrestrial ecosystems, sustainably managing forests, combating desertification, halting and reversing land degradation, and halting biodiversity loss.

• SDG 16: Peace, Justice, and Strong Institutions: Promoting peaceful and inclusive societies for sustainable development, providing access to justice for all, and building effective, accountable, and inclusive institutions at all levels.

• SDG 17: Partnerships for the Goals: This goal recognizes the importance of revitalizing the global partnership for sustainable development and the role of strong partnerships in achieving the SDGs, involving governments, the private sector, civil society, and others.

What are SDG scores?

Each Sustainable Development Goal has specific targets or indicators that help measure progress toward achieving those targets over time. SDG scores are numerical values given to each entity (country, company, person, etc.) based on their performance in meeting specific targets or indicators for each particular goal. Incorporating these evaluations into the decision-making process is crucial for stakeholders across various sectors, including the private sector, healthcare, financial services, and more. These stakeholders can leverage insights from SDG scores to prioritize initiatives that address critical issues like climate change, emissions reduction, and ecosystem preservation.

How do SDGs relate to ESG?

The environmental, social, and governance (ESG) framework is a tool to achieve and comply with the SDG goals. From a company’s perspective, ESG and SDG frameworks emphasize the importance of measuring and reporting progress. Companies incorporating ESG criteria into their operations often report on their sustainability performance, which can directly show their contribution towards achieving specific SDGs. For investors, ESG metrics provide a tangible way to evaluate companies' potential risks and opportunities related to sustainability, which can also align with the broader objectives of the SDGs.

The SDGs primarily focus on global challenges such as poverty, inequality, climate change, and environmental degradation, which represent the environmental and social pillars of ESG.

Within the same principles, several of these goals directly relate to the governance pillar of ESG. On the one hand, goal 16 aims to reduce corruption and bribery, develop effective and transparent institutions, and ensure inclusive and representative decision-making. On the other hand, goal 17 strives to enhance international cooperation, encourage effective public, public-private, and civil society partnerships, and ensure that policies are coherent and integrated, all of which are governance-related issues.

While the SDGs might not explicitly label these aspects as 'governance' in the way the ESG framework and regulatory landscapes do, the inclusion of these goals demonstrates a clear recognition of the importance of governance in achieving sustainable development.
SDGs and ESG also have different purposes. ESG measures companies’ environmental, social, and governance performance risks and initiatives, while SDGs evaluate any entity’s performance in reaching its goals. Put another way, SDGs represent the goals, while ESG concerns methodology and processes.

Learn more: “Gain Insights From Financial and ESG Data Using AI: A Comprehensive Guide.”

Why are SDGs important to investors?

At the company level, SDGs help align corporate strategy with society’s needs. Because the UN designed SDGs to be measurable, countries, companies, and people can hold themselves accountable for progress toward achieving them. And because the goals are measurable, we can score a company’s efforts, giving you an indicator to invest responsibly by aligning your portfolios with SDGs.

According to a publication by McKinsey & Company, sustainable investing appears to have a positive effect, if any, on returns. In other words, investors care about SDGs not only because they benefit society but also because they measurably support better investment decisions. For example, by incorporating SDGs into company assessments, investors can identify well-run businesses that are better positioned to benefit from the positive effects of improved social and economic conditions. SDGs also allow investors to make better-informed decisions within a defined investment time horizon by focusing on a company’s business exposure toward them. Investors can thus better measure and track a company’s opportunity exposure as a result of its achievement of the SDGs.

How to measure an entity’s SDG score

There are tools available to measure progress toward each goal—and those tools will play an essential role in helping investors decide which entities they want to invest in and which ones they don’t want to support. For example, SESAMm’s platform, TextReveal®, can analyze web data to generate SDG scores for virtually any entity in our data lake.

How SESAMm provides SDG scores

SESAMm provides SDG scores through its platform, TextReveal, a platform that allows investors to gain insights into companies, people, or topics. Specifically, we use artificial intelligence (AI) to track entities’ contributions toward SDGs, including public and private companies.

We track the 17 Sustainable Development Goals and the 169 underlying targets to detect negative news and positive events, using a similar algorithm we use for ESG alerts and gathering alternative data. Each UN SDG item displays a score from 0 to 5 to show the intensity of the company’s positive impact. Then, we translate the information into multiple languages.

We queried the Norwegian carbon capture company, Aker Carbon Capture, using our SDG positive impact dashboard over the past three years. As you might notice, Aker contributes to the goals associated with Partnerships, Climate Action, Clean Energy, and Sustainability. Maybe they could do more regarding Decent Work and Economic Growth, and Responsible Consumption and Production, but overall, the company’s online data shows a positive contribution.

See how SESAMm can help you with your SDG research

SESAMm is the leading provider of AI solutions and analytics for investment firms and corporations.

Our AI and NLP platform, TextReveal:

• Analyzes text in billions of web-based articles and messages
• Generates investment insights, ESG and SDG analysis used in systematic trading, fundamental research, risk management, and sustainability analysis
• Enables a more quantitative approach to leveraging the value of web data that’s less prone to human bias
• Addresses a growing need in public and private investment sectors for robust, timely, and granular sentiment and SDG data

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

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.

Over the past decade, many organizations have improved their carbon footprints, from recyclable and biodegradable packaging and single-use plastic to planting trees and reducing their greenhouse gas emissions. However, some businesses and companies looking to boost their eco-friendly image without committing to serious changes and addressing environmental issues have been associated with false green marketing. We call this "Greenwashing."

Defining Concepts

What is Greenwashing?

Greenwashing is a practice used by businesses to represent themselves as more sustainable than they truly are. Greenpeace and the Environmental Protection Agency define greenwashing as making false and misleading claims about a product's environmental benefits or practices, services, technology, or company practices. Greenwashing typically involves companies spending more money on advertising and marketing than on implementing sustainable business practices that minimize environmental impact. These false green claims can deceive consumers into believing that a product or company is more environmentally friendly than it is, leading to increased sales and profits. As a result, false advertising, misleading initiatives, and groundless claims have increased green investors' exposure to risks emerging from potential lawsuits from activist groups, image deterioration, and heavy losses in assets invested.

In recent years, new concepts have emerged alongside greenwashing:

• Greenhushing refers to a company’s refusal to publicize ESG information. The company may fear pushback from stakeholders who would find its sustainability efforts lacking or from investors who believe ESG undermines returns.
• Greenwishing, or unintentional greenwashing, describes a practice where a company hopes to meet certain sustainability commitments but simply does not have the means to do so.

High-Profile Greenwashing Case Studies

When talking about greenwashing, the usual suspects are the oil and gas industry, the food and beverage sector, and other environmentally impactful industries. However, the financial industry has also been embroiled in its own greenwashing controversies.

DWS Greenwashing Analysis

DWS Group has been at the center of repeated greenwashing allegations. In April 2025, the firm was fined €25 million by German prosecutors for misleading ESG claims, building on years of scrutiny. It all started in 2021 with whistleblower claims that DWS overstated its ESG credentials, triggering investigations by both U.S. and German authorities. A police raid in June 2022 led to the CEO’s resignation, and in 2023, the SEC fined DWS $25 million for ESG misstatements. Lawsuits in Germany also allege false advertising around ESG. Greenpeace condemned DWS’s ESG bonus scheme as cosmetic rather than meaningful. Despite public commitments to sustainability, these controversies underscore a pattern of overstated ESG practices designed to attract investors.

BNY Mellon Greenwashing Analysis

BNY Mellon faced similar regulatory action. In May 2022, the SEC fined BNY Mellon Investment Adviser, Inc. $1.5 million for misleading statements about ESG integration in mutual funds. Although the funds were marketed as ESG-focused, the SEC found that BNY Mellon failed to apply ESG quality review as consistently as claimed, raising concerns about greenwashing in the financial sector.

The Challenges of Detecting Greenwashing

It’s challenging to produce an accurate assessment of environmental, social, and governance (ESG) factors, which creates opportunities for companies to hide ineffective and fake green initiatives. According to Regtank, the main challenges to detecting greenwashing include:

• Lack of reporting standards – There’s no universal set of standards for ESG compliance.
• Lack of transparency – Companies often don’t disclose the specifics of their “green campaigns,” making it hard for investors and consumers to verify their claims.
• Limited consumer awareness – Misleading marketing can exploit consumers’ eco-consciousness and brand loyalty, reducing scrutiny of false green claims.
  

These gaps lead to inaccurate ESG data and scores, allowing greenwashers to avoid accountability. Ultimately, detecting greenwashing requires careful scrutiny of company claims and a deep understanding of their supply chains and operations.

How Artificial Intelligence Detects Greenwashing

As greenwashing practices become more common, activist investors, journalists, and the general public are using social media, news outlets, and blogs to highlight false claims. Artificial intelligence (AI) has become an invaluable tool in the early detection of greenwashing by analyzing vast amounts of public data.

At SESAMm, we use generative AI and LLMs to identify greenwashing risks across billions of web-based articles. Our data lake covers over 25 billion articles in more than 100 languages from four million news sources, blogs, social media platforms, and forums, analyzing data on five million public and private companies. Through our AI platform, we generate reliable, timely, and comprehensive insights to detect greenwashing, monitor ESG controversies, and identify related risks.

The Regulatory Landscape

The rise of greenwashing is not going unnoticed by regulators, as frameworks like the Corporate Sustainability Reporting Directive (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD) directly target misleading sustainability claims and hollow marketing.

1. The CSRD significantly strengthens the requirements for companies to substantiate their sustainability commitments. Mandating standardized and detailed ESG disclosures directly addresses the practice of greenwashing, where companies exaggerate their environmental credentials in marketing without meaningful follow-through. Under the CSRD, companies can no longer rely on vague or selectively presented data—any gaps or inconsistencies in their sustainability claims will be exposed in public filings, making greenwashing much riskier. This means an end to cherry-picked data and a shift toward more comprehensive, comparable, and verifiable ESG performance for investors and stakeholders.
2. The CSDDD (if it stands) further reinforces these efforts by obligating companies to go beyond marketing statements and prove they’re actively managing environmental and human rights impacts throughout their supply chains. This directive closes loopholes that greenwashing often exploits, such as highlighting only direct operations while ignoring supplier practices. By requiring due diligence on environmental impacts across the value chain, the CSDDD aims to turn sustainability from a branding exercise into a legal and operational priority. If real supply chain actions don’t support a company’s green claims, it could face legal action and reputational damage.

Looking Ahead

Looking ahead, greenwashing will continue to face intense scrutiny from regulators, investors, and the public. With evolving regulatory frameworks like CSRD and CSDDD, the pressure is on for companies to ensure genuine environmental responsibility—not just green advertising. At SESAMm, we believe that the combination of regulatory rigor and advanced AI technologies will play a critical role in uncovering false green claims and supporting investors in navigating ESG risks with greater transparency and accountability.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.