Climate disclosure and benchmarking are increasingly important requirements for companies of all sizes. To do so, we will use large volumes of structured and unstructured data to build contextual landscapes, assess exposures, identify opportunities, build capacity, and devise and execute climate strategies. must be collected and analyzed.
But for all but the largest organizations, much of the collation and analysis work is still manual, and getting the necessary data and climate talent can be a major challenge. . Artificial intelligence (AI) and machine learning (ML) are increasingly being used to collate and process data, uncover blind spots and data gaps, and uncover decision-useful information. Such automation will greatly accelerate access to climate change learning by organizations, providing a competitive advantage.
Manifest Climate is committed to harnessing this potential. Manifest Climate’s AI-enabled assessment model works with climate experts and uses advanced ML to provide a broad range of assessments of how well an organization’s climate-related disclosures meet various disclosure frameworks and standards. It can be evaluated from a point of view and to assess the usefulness of decision making. Based on these assessments, manually verified by experts, Manifest Climate can recommend concrete actions that clients can take to improve their overall climate response.
The company’s data science manager, Ahmad Ghazi, has been working in the climate space for less than a year. While he is impressed with the extent of his use of AI in the climate field, he manifests that he is generally not underpinned by the true human climate expertise that Climate employs. I’m here.
The relationship between climate and financial risk
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“To get the modeling and results you want, you need the flavor of the climate that the experts bring,” Garge says. “Part of my desire to participate was that I wanted to learn from and access that expertise. Expertise is what makes the difference.”
Use AI to manage complex climate data
Manifest Climate’s vision is to combine the best of both worlds to expand access to corporate climate resilience and capabilities. Climate risk planning still relies heavily on human intervention to collate and analyze unstructured data. Manifest Climate uses Natural Language Processing (NLP) to pull information from various sources such as corporate reports, conference calls, PDFs, and websites. The company has developed a methodology that encompasses approximately 200 data points and manually labels the data to create machine learning algorithms that can be certified, structured and presented from a variety of sources.
Instead of climate expertise driving the process and AI capabilities supporting it, the opposite is true. “We have years of roots in the TCFD and other standards, so we have expertise in justifying the decisions we make and how we structure this data,” says Manifest. said Louie Woodall, Climate Product Content Director.
AI models get better the more data they have to process and the more experts they train on what “good stuff” is. For those 200 or so points, it may be easier to get better quality data than others. “We need a human hand to get more examples and evaluate disclosure of data points,” Garge says. “For example, there are more structured basic emissions targets and net-zero targets, but other targets are less widely disclosed and unclear,” Gurge said.
“Currently, there is no standardized definition for specific climate disclosures,” Woodall added. “There is ambiguity regarding certain concepts, such as what constitutes a ‘well-defined process’ for the integration of climate risk management. , ML, and AI to assess whether a disclosure includes this information. “
Transform data to drive strategy
Climate expertise will play a key role here, and manual labeling of climate disclosures will lead to more automated assessment and certification over time. It should also be remembered that the field is still relatively in its infancy, so organizations that are still in the early stages of their efforts may want guidance with human capabilities rather than digital interfaces. .
“We have extensive data sets and expect to further automate data generation, but we still need consultants and climate expertise to transform that data into meaningful stories for our clients.” says Woodall. “Clients want a human touch to explain the meaning behind it, and they want to know how to improve climate management and strategy.”
Manifest Climate’s comprehensive climate assessment methodology enables companies to comply with the Task Force on Climate-related Financial Disclosures (TCFD), the Securities and Exchange Commission’s (SEC) upcoming climate change disclosure rules, international sustainability standards, and more. Assess alignment across multiple global frameworks and standards. Board of Trustees (ISSB) Imminent Climate Reporting Standards. Use gap analysis to show where there are opportunities for improvement or risks. It also benchmarks a company’s climate change progress against peers and climate change leaders.
“Planning the disclosures of internationally active companies to multiple standards and identifying relevant data for all standards is very helpful,” says Woodall. “You can structure your data in a way that works across any climate disclosure framework.”
An example of the importance of measuring consistency across frameworks comes amid the imminent indication that non-EU companies may be subject to EU sustainability rules under the Corporate Sustainability Reporting Directive. .
Data demand
Such developments demonstrate the fact that climate reporting is constantly evolving, and Manifest Climate has something built into its model as well. “New insights and best practices have emerged from the dataset to feed our methodology,” he says. “It gives us more data points to leverage when analyzing companies. It can be challenging, but it can also be exciting.”
Human experts are always needed. But only by leveraging AI and ML to expand knowledge and insights will all organizations have access to the climate expertise they need to successfully navigate this journey.
“I don’t think anyone is assessing companies at a level of detail that allows us to derive decision points, gap analyses, and recommendations. because there is no one,” says Ghazi. “For me, that’s what makes the difference.”
