global-corporate-ghg-emissions-20222023

alitaqishah/global-corporate-ghg-emissions-20222023 is a ESG & Climate data product published on Kaggle and indexed by The Alternative Data Encyclopedia.

Alitaqishah publishes global-corporate-ghg-emissions-20222023 as a Kaggle offering in the ESG & Climate signal family. The listing has drawn 379 downloads and 48 likes on Kaggle.

The data

Alitaqishah maintains it as part of its ESG & Climate portfolio and lists it through Kaggle.

The source tags it with global, weather and climate and exploratory data analysis.

Structure, access and licensing

License: CC0: Public Domain. Size: 20379. Last updated: 2026-04-13.

Access is through a dataset that is downloaded from Kaggle for use in a notebook. Its most recent recorded snapshot is from 2026-04-13.

The signal

Environmental performance data spanning corporate disclosures of emissions and resource use plus independent measurement of physical emissions from assets worldwide. The category combines self-reported baselines with sensor- and satellite-derived ground truth to quantify climate exposure.

Physical measurements frequently contradict reported inventories, creating relative-value signals when asset-level data reveals underreported emissions at specific operators. Analysts track decarbonization progress — steel mill conversions, grid intensity, flaring activity — as leading indicators of cost structure, regulatory exposure and capital expenditure. Climate events mapped against supplier locations anticipate earnings disruptions that consensus models miss. Disclosure systems collect scope 1-3 greenhouse gas inventories, energy mix, water withdrawal and deforestation exposure from tens of thousands of companies annually, scored on standardized frameworks. Independent trackers estimate facility-level emissions from satellites, remote sensing and machine learning across hundreds of millions of assets, with monthly updates. Derived products include portfolio carbon footprints, transition-risk screens and methane or plume alerts.

Non-profit disclosure platforms run annual questionnaire cycles aligned with major reporting frameworks and license responses with scores to investors. Measurement coalitions fuse satellite spectra, night lights, thermal anomalies and sector models to attribute emissions to individual assets without self-reporting bias. Quants join both layers to corporate hierarchies and supply-chain graphs, calibrating reported figures against measured ones.

Caveats and compliance

Self-reported data suffers from selective participation, inconsistent boundaries and greenwashing incentives, so cross-sectional comparisons demand care. Modeled emissions inherit assumptions about utilization and fuel mix that can lag reality by months. Regulatory frameworks differ by jurisdiction, complicating global screening.

Disclosure regimes such as CSRD in Europe and climate-reporting rules elsewhere are converting voluntary data into audited obligations, raising quality but also litigation sensitivity around claims. Buyers should verify that licensed datasets respect confidentiality choices made by disclosing companies.

Who uses this signal

Sustainable-investment teams use disclosures and scores for portfolio construction and engagement; credit and insurance analysts assess physical and transition risk; commodity traders monitor industrial activity through measured emissions. Corporate buyers apply the same data to supply-chain decarbonization programs.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading