Climate Action and Financial Commitment: Exploring the Legacy and Challenges of COP Conferences
December 1, 2023
•
5 mins read
For nearly three decades, the world has annually witnessed an event of critical importance for the future of our climate: the Conferences of the Parties, better known as the COP. First held in 1995 following the adoption of the United Nations Framework Convention on Climate Change at the Earth Summit in Rio in 1992, these conferences bring together nations that have ratified this treaty in a collective effort to combat climate change, a phenomenon increasingly evident in our daily lives.
Among these conferences, two stand out for their significant impact. The first COP3, held in Kyoto in 1997, marked a turning point with the near-unanimous adoption of the Kyoto Protocol. This agreement, which came into force in 2005 after intense negotiations, mandated signatories to reduce greenhouse gas emissions by at least 5% by 2012. Despite its legally binding nature, some countries attempted to diminish its ambition, and others, like the United States, never ratified it. Canada withdrew from the treaty in 2011, citing the discovery of highly polluting tar sands in Alberta. At the 2012 Doha conference, the Kyoto Protocol was extended until 2020 despite the absence of the US agreement.
COP15, held in Copenhagen in 2009, acknowledged for the first time the necessity of limiting global warming to 2°C above pre-industrial levels and proposed the creation of a Green Climate Fund endowed with 100 billion US dollars annually until 2020. Unfortunately, this initiative lacked legal enforcement and clear rules for fund allocation. By 2014, after the Lima conference, the Green Climate Fund had only amassed 10 billion US dollars.
Then came COP21 in 2015 in Paris, one of the most well-known conferences, which led to the landmark Paris Climate Agreement. This agreement set three primary goals:
Limit Temperature Rise: Keep the global temperature rise well below 2 degrees Celsius above pre-industrial levels while pursuing efforts to limit it to 1.5 degrees Celsius.
Adapt to Climate Impacts: Enhance the ability of countries to adapt to climate change impacts, focusing on resilience and adaptive capacity, especially in vulnerable regions.
Align Financial Flows: Redirect financial flows towards low greenhouse gas emissions and climate-resilient development, ensuring consistent support for mitigation and adaptation.
Once again, the agreement was not, or only minimally, binding: Participant countries were encouraged to define their "Nationally Determined Contributions" to be re-evaluated and submitted to the UN every five years, with each submission expected to be more ambitious than the last. The only legal obligation was the transparency of national contributions and their evaluation by experts.
The Paris Agreement, however, paved the way for landmark climate litigation, including a significant case in the Netherlands where a foundation sued the Dutch government for reducing its climate ambitions. The government lost, with the European Convention on Human Rights forming the legal basis of the decision.
From Words to Deeds: The Struggle for Effective Climate Change Policies at COP28
The climate is a highly complex system with significant inertia; actions taken today might only manifest their effects in a century! As of 2020, global warming is estimated to be around +1.2°C, with an increase of approximately +0.2°C per decade.
Current policies are steering us toward a +3°C increase, underscoring the need for a COP that results in a binding agreement backed by major powers and supported by financial measures. Former UN Secretary-General Ban Ki-Moon had suggested a global tax on financial transactions to fund the Green Climate Fund.
There is also hope for new agreements to ban subsidies for fossil fuels. However, the fact that COP28 is set to take place in the United Arab Emirates, chaired by the CEO of the national oil company, sends a mixed message. It is crucial that Gulf countries play a significant role on the international stage, especially given the recent escalation of the Israeli-Palestinian conflict in late 2023, highlighting their pivotal role in both international peace and climate change issues. On the latter, the trajectory is concerning: 181 million tons of oil were extracted in 2022, an increase of nearly 11% in a year, and gas extractions, though stable over the past year, have risen by 9% since 2012. COP28, therefore, faces legitimate criticism, with the most significant being that the conference could be an exercise in greenwashing. Recent allegations reported by the BBC suggest potential misuse of the COP presidency to secure new oil and gas contracts.
Finally, responsibility contributions remain unresolved: the "Economic North" is primarily responsible for climate change, yet those who will suffer the most are the countries of the "Global South." Some island nations are even at risk of disappearing due to rising sea levels caused by climate change, and certain areas could become uninhabitable by 2050 due to extreme temperatures and humidity, preventing natural cooling processes like sweating. Addressing loss and damage will also be a central point at the conference.
Stay tuned for the second part summarizing the debates and agreements done at COP 28.
In private equity, as in most industries, decision-making counts on accessing accurate and valuable information. However, these firms often encounter significant challenges when sourcing reliable data, especially when dealing with small, private companies. This article dives into the complexities of identifying high-quality information on smaller companies and underscores its value in investment decisions, operational efficiency, and risk management. It also explores how advanced artificial intelligence (AI) technologies are revolutionizing the identification of these risks, leading to higher rewards and more secure investments, thus providing a competitive edge.
The challenge of identifying valuable information for Smaller Firms
Lack of valuable data
Sturgeon's Law, which states that "Ninety percent of everything is crap (or noise)," becomes particularly relevant in the context of data sourcing. For private equity and investment firms focused on small companies, finding the golden nuggets of information amid the overwhelming amount of digital noise can be daunting. The data available on these companies is often sparse, fragmented, and difficult to uncover using conventional methods. This scarcity of reliable information makes it challenging for private equity firms to make informed decisions, heightening the risk of overlooking critical issues that could impact their investment process.
The difficulties extend beyond just locating information. Many small companies operate without a significant online presence or may not be required to disclose as much information as publicly traded firms. This lack of transparency can further blur critical data points. Furthermore, the data that is available is often unstructured, residing in various forms such as social media posts, obscure local news articles, or industry-specific reports. Extracting meaningful insights from these disparate sources requires sophisticated data processing capabilities, which traditional methods often lack. As a result, private equity firms are left with a significant challenge: how to separate valuable data from the noise without missing critical risk indicators, thereby optimizing their deal sourcing and investment strategies.
Diverse language and terminology
Smaller firms frequently face existential risks, and the potential rewards for identifying these risks early on can be significant for the private equity firms that invest in them. However, mainstream methods of risk identification often fall short, as these companies may not use standardized language to describe materiality. Instead, risks are discussed in varied and context-specific ways, complicating the task of recognizing relevant information. Therefore, it is essential to adopt a specialized approach that analyzes and decodes these firms' unique terminologies and business idiosyncrasies, ultimately translating them into a standardized language that can be effectively used in risk assessment.
The diversity in language is not just a barrier to risk identification but also to the communication of these risks within and between private equity firms. When a small firm uses industry-specific jargon or localized expressions to describe potential threats, it can lead to misunderstandings or underestimations of the actual risk. For instance, a manufacturing startup in a developing country might describe supply chain disruptions in terms that do not translate easily to a global investor’s risk framework. Additionally, cultural differences in how risk is perceived and reported can lead to further complications. This linguistic diversity necessitates the use of advanced natural language processing tools that can interpret data through a common lens while considering industry-specific contexts. For an insurance company, understanding financial models, insurance principles, and regulatory frameworks is crucial. Conversely, assessing risks for a beauty company requires a focus on product safety, consumer preferences, and market trends. By appreciating the specific contexts of each industry, private equity firms can better identify and evaluate potential risks, enhancing decision-making processes, risk and portfolio management strategies, and operational efficiency.
The dynamic nature of the industries themselves further complicates the challenge. For example, the tech industry evolves rapidly, with new risks emerging as technologies develop and consumer expectations shift. What might be considered a negligible risk today could become a significant issue tomorrow as regulatory landscapes, market conditions, and technological advancements alter the playing field. In contrast, industries like agriculture or real estate might have more stable risk profiles but are subject to sudden changes due to environmental factors or policy shifts. This variability across industries means that a one-size-fits-all approach to risk assessment is inadequate. Private equity firms must adopt flexible, industry-specific risk models that can adapt to the unique characteristics and evolving landscapes of the sectors they invest in, thus optimizing their AI capabilities.
The Power of AI in Enhancing Risk Management in Small Firms
AI technologies, particularly natural language processing (NLP) and machine learning algorithms, are important tools for private equity firms aiming to monitor and manage risks in small firms. These technologies can sift through vast amounts of data, extracting the valuable 10% and identifying patterns, trends, and subtle nuances in the language used to describe risks. By detecting these patterns, AI can reveal potential risks that might not be immediately apparent through traditional methods. This proactive approach to risk identification allows firms to address issues before they escalate, providing a more comprehensive and nuanced understanding of the risks facing small firms.
AI's ability to process unstructured data is particularly valuable in this context. Many of the risks that small firms face are discussed informally in places like social media, niche blogs, or local news outlets. Traditional risk management tools might overlook these sources, but AI-powered tools can analyze them in real-time, detecting emerging threats as they develop. Moreover, AI can cross-reference these insights with structured data from financial reports, regulatory filings, and other formal documents to create a holistic risk profile. This multidimensional analysis helps private equity firms not only identify risks but also understand their potential impact, enabling more informed, data-driven decision-making that enhances operational efficiency and competitive edge.
Beyond risk identification, AI also enhances risk mitigation strategies. By continuously monitoring data and learning from new information, AI systems can adapt to changing conditions, offering updated risk assessments that reflect the latest developments. This dynamic approach allows private equity firms to stay ahead of potential issues, making it possible to implement preventative measures rather than reacting to crises after they occur. In this way, AI capabilities contribute significantly to the optimization of risk management processes.
How SESAMm’s Advanced Technology Enhances Risk Assessment
SESAMm’s TextReveal® is at the forefront of this technological revolution, enabling private equity firms to efficiently navigate the vast digital landscape and extract the crucial information needed for informed decision-making. Through our proprietary data lake amounting to over 25 billion online articles with 15 years of historical data and our AI algorithms, TextReveal® can quickly identify and retrieve valuable insights, even when the information is deeply buried or highly specific. The tool's ability to analyze and understand the diverse language and terminology used in discussions about risks on the web empowers private equity firms to objectively assess the materiality of certain risks or identify emerging threats that have yet to be formally recognized.
TextReveal® goes beyond merely identifying risks—it categorizes them, providing context that helps private equity firms understand the severity and relevance of each risk. For example, if a small biotech firm is mentioned in discussions about regulatory hurdles, TextReveal® can determine whether these mentions are isolated incidents or part of a broader trend. It can also assess whether the language used suggests an imminent threat or a longer-term concern, enabling firms to prioritize their responses accordingly. Additionally, TextReveal® integrates sentiment analysis, which can gauge the overall tone of discussions surrounding a company, offering further actionable insights into potential reputational risks.
SESAMm has developed a proprietary metric – the Intensity Score, which calculates an event's relevance based on its news coverage and sentiment. It uses negative sentiment, article dispersion, and empirical ESG risk measures to determine how likely an article is to represent a high-risk controversy. The Intensity Score gives TextReveal users a clear understanding of which events require their attention.
Users can also opt to receive email alerts for the more severe controversies, ensuring they’re always aware of significant risks. In addition to the severity, controversies are also categorized by risk and sub–risk type, making it easy to analyze specific areas of concern.
Moreover, SESAMm's platform is designed to be intuitive and user-friendly, making it accessible to investment professionals who may not have a technical background. This ease of use ensures private equity firms can quickly incorporate AI-driven insights into their risk management processes without a steep learning curve. By streamlining the data analysis process, TextReveal® allows firms to focus on strategic decision-making, confident they have a comprehensive understanding of the risks and opportunities associated with their investments and portfolio companies. This level of operational efficiency and optimization is key to maintaining a competitive edge in the fast-paced world of private equity.
TextReveal’s Risk Assessment module enables deep company and thematic research in multiple languages through on-the-fly keyword searches. Users have full access to articles, sentiment analysis, and trending topics to get a complete understanding of the risks. We’ve even developed an AI Text Summary feature that provides a quick summary of a selected article, saving time and enabling a faster analysis.
In summary, the integration of AI tools and natural language processing technologies is transforming risk management in private equity, particularly for firms dealing with small, private companies. By leveraging these advanced tools, private equity firms can enhance their due diligence processes, better monitor risks and controversies, and ultimately make more informed investment decisions that lead to higher rewards and operational efficiency.
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.
One of the biggest challenges in risk monitoring is sifting through mountains of irrelevant data. Whether you're using search engines, financial news platforms, or even specialized in-house analytics, you end up with too much noise. Scrolling through to page 12 of Google is not only time-consuming, but leaves you with the nagging feeling that you could still be missing something.
Artificial Intelligence (AI) is a hot topic, with new breakthroughs and possible applications popping up every day. The question is no longer simply “can AI help me with that?” but rather “how can I use AI to help with that?” For Environmental, Social, and Governance (ESG) controversy and risk monitoring, AI is used to sift through enormous data sets at unparalleled speeds, bringing critical insights to the forefront faster and more efficiently than humanly possible.
When there are hundreds of companies to monitor, for example in a large investment portfolio or a group of suppliers, the advantages of AI are obvious. But what about smaller portfolios? How do you know it’s time to start using AI? Based on our experience working with private equity firms, asset managers and commercial banks, we’ve pulled together five signs that it’s time to consider AI.
1. Overwhelmed by Data: There's Too Much Noise
An AI-powered tool filters out the noise, even in situations where seemingly only humans would be able to do it, giving you the peace of mind that there’s no controversy lurking in the dark corners of the web. All of the key information is gathered in one place, ready for you to evaluate and decide the best course of action.
2. Difficulty Finding Critical Information: The Black Hole of Private Companies
On the flip side - sometimes instead of finding too much data, you can’t find any data at all. For private companies, information can be scarce, especially for smaller companies based overseas, where the only news coverage is local and in the local language. In this case, ESG ratings agencies often aren’t able to fill the gap either. There are millions of firms worldwide and less than 50,000 of them are covered by rating agencies (source).
AI, on the other hand, enables systematic coverage and statistically relevant results without human intervention, analyzing millions of websites and providing coverage on millions of public & private companies. If you are struggling to find information on a company, AI might be the answer.
3. Can't Accurately Analyze an Event: The Context is Missing
Beyond the actual controversy or event itself, understanding the context and history around it is essential for risk assessment. Is this a one-off concern or part of a recurring pattern? To get the full picture, you need to take a closer look not only at the company in question, but the key players, i.e. key executives, and the industry as a whole to understand if this is within the norms.
AI has an important role to play here also. By simply expanding the search, AI can provide you with a full picture of the controversy, including a quick summary and a benchmark against competitors in just a matter of minutes.
4. Missed Critical Window for Action: The Cost of Inefficiency
Markets can change quickly - and it’s only getting worse as information is spreading faster and more widely. The more time it takes you to gather and analyze information, the less time you have to react. This can be a challenge whether you are monitoring 30 companies or 100’s. If you find yourself trapped in a cycle of reacting to news rather than acting proactively, AI can help. Because AI scans and analyzes information in seconds, the alerts to potential controversies are in near real-time, allowing you as much time as possible to take action.
5. Missing ESG Expertise: The Knowledge Gap
To top it all off, ESG is complex and constantly evolving. Understanding what data is relevant and how to evaluate it requires real expertise. ESG rating agencies provide some guidance, but they typically leverage self-reported data - which is naturally biased. Take greenwashing for example where a company misleads its stakeholders, investors, and consumers about its environmental practices by communicating positive environmental performance contrary to its actual, less positive execution. It’s difficult to identify greenwashing using self-reported data.
Because AI relies on external stakeholders, such as online forums or news sources, it offers an unbiased take on a company’s ESG performance. Additionally, by choosing an AI with ESG expertise built-in, you benefit from an expert analysis without increasing the burden on your team.
As the speed and amount of information available continues to grow, AI offers a scalable way to monitor your partners, suppliers and portfolio. To learn more and find out if AI is a good fit for your company, contact our experts at SESAMm.
Acentra Health is a healthcare technology and services company working with public and commercial partners to modernize healthcare delivery. Its solutions span claims and encounter management, provider services, care management, and quality oversight.
As Acentra Health expands its platform and capabilities, mergers and acquisitions play an important role in strengthening the solutions it provides to healthcare partners and the populations they serve.
The Challenge
As part of its growth strategy, Acentra Health evaluates companies that may complement its technology, services, or market reach.
During early-stage evaluation, access to timely external information can help provide additional context about a potential target, including potential risks, market considerations, and areas that may warrant further diligence.
The team sought an efficient way to organize and assess relevant external information to supplement its broader evaluation and diligence processes
The Solution
To support early-stage deal evaluation, Acentra Health uses SESAMm’s AI Deal Screening Reports as one input in its screening process.
Generated quickly, the reports provide a structured overview of publicly available information related to a company, including potential risk considerations, competitive landscape insights, and market context.
By consolidating external information into a concise report, the tool helps the team develop an initial view of the broader environment surrounding a potential target and identify areas that may warrant additional investigation.
The Impact
By incorporating AI-powered screening into its workflow, Acentra Health supplements its broader evaluation and diligence processes with additional external intelligence.
SESAMm’s reports provide an efficient way to organize and review relevant external information during early-stage evaluation, helping the team identify topics for further consideration and supporting informed decision-making as Acentra Health evaluates strategic growth opportunities.
Stay ahead with the latest in ESG and AI intelligence
Join our mailing list to receive new reports, event invites, and updates from SESAMm directly to your inbox.
.png)