Find Attribute
Attribute: Determine whether executive remuneration explicitly incorporates any aspect of nature. 'Nature' here includes topics such as biodiversity, ecosystems, land use, conservation, deforestation, reforestation, habitat protection, water use, soil health, or marine resources, etc. Synonymous or closely related terms (e.g., land rehabilitation, freshwater management, habitat restoration, etc) are also considered evidence. Exclude metrics strictly related to climate change, greenhouse gas emissions, or carbon reduction. Answer clearly: Is nature incorporated into executive remuneration? (Yes/Unknown). Answer 'Yes' if there is direct evidence explicitly linking nature to executive remuneration. If evidence is insufficient or unclear, answer 'Unknown.'
Introduction:

All Street Sevva provides suite of ESG focused AI Metrics in both a user interface and by API. This allows Financial, ESG and Risk Analysts to get answers to their questions about a company, fund, sector or even competitors.

What is a Data Metric?

A data metric (also sometimes called data point, attribute, field or variable) are information about entities that is available in both ESG Metrics User interface and in the Sevva ESG Metrics API.

Metric card:
Metric name:Determine whether executive remuneration explicitly incorporates any aspect of nature. 'Nature' here includes topics such as biodiversity, ecosystems, land use, conservation, deforestation, reforestation, habitat protection, water use, soil health, or marine resources, etc. Synonymous or closely related terms (e.g., land rehabilitation, freshwater management, habitat restoration, etc) are also considered evidence. Exclude metrics strictly related to climate change, greenhouse gas emissions, or carbon reduction. Answer clearly: Is nature incorporated into executive remuneration? (Yes/Unknown). Answer 'Yes' if there is direct evidence explicitly linking nature to executive remuneration. If evidence is insufficient or unclear, answer 'Unknown.'
Metric key:filings-nature-executive-renumeration
Type:Questions & Answers
Type description:AI Question & Answer Metrics use public disclosures to give a boolean (1/0 or yes/no) answer to the question.
Type update frequency:AI Question & Answer Metrics are updated as new filings are disclosed. The lag varies by company, but is typically between 1 day and 1 month.
Type accuracy:AI Questions are trained to be over 90% accurate. We're constantly improving and refining our data to make it even better.
Type coverage:70,000 companies
Type history:3 years of history
Example values:

Trial UI options: You can access this metric for free by signing in Sevva Platform
Trial API options: You can access this metric for free with the Sevva API

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