Asset-level data is information about physical and non-physical assets tied to company ownership information. Asset-level data can be aggregated at the company, regional, or global level, suiting needs ranging from asset managers through to policy makers and NGOs.
Analysis using asset-level data is characterised by the following:
- Bottom-Up: Asset-level exposure is aggregated up to the company level rather than inferred from company-level reporting.
- Fundamental: Fundamental asset attributes (for example, location, technology, and age) inform analysis rather than disclosed metrics (for example, carbon intensity) enabling more sophisticated and flexible analysis.
- Comparable: Standardization can ensure accurate company comparisons and avoids embedded methodological assumptions.
- Forward-looking: Asset attributes (such as age) can enhance analysis of company future performance and enable validation of company projections.
- Efficient: It can significantly reduce reporting burdens and reduce time and money spent on assuring voluntary disclosures.
- Timely: Asset-level data can be updated in real time as events occur (like mergers or asset commissioning) rather according to annual reporting cycles.
- Transparent: Asset attributes are transparent and are based on real observational data, giving stakeholders access to the same data as company executives.
- Scalable: The marginal costs of data acquisition and analysis decrease with scale of the dataset.
- Science-driven: Unlocks scientific approaches to analysis which are repeatable and testable.
- Unbiased: Assessment of environmental factors informed by asset-level data do not rely on the (non-expert) opinions of corporate boards.
- Self-improving: Science and technology-driven risk analysis and data acquisition improve continuously with new generations of technology and research. Costs also reduce over time.
Asset-level data resides in a wide range of different locations. It exists in existing company disclosures to financial markets, regulators, and government agencies (in multiple jurisdictions and in different languages); in voluntary disclosures; in existing proprietary and non-proprietary databases; in public and private research institutions; and in academic research. The challenge is finding the relevant sources, integrating the data, cleaning the data, and then of course making the data available for analysis. While physical assets are the primary focus in highly polluting sectors, such as power generation and upstream fossil fuel production, non-physical asset-level data such as human capital, intellectual property, or reputational capital could be equally, if not more, important for other sectors.
Asset-Level Data Example: Power Station
|Asset||Per Power Station||Example|
|Nameplate||NameLocationOwnershipAgeNominal Capacity||Anytown Power Station(54.39,-35.71)60% Comp. A, 40% Comp B.Commissioned 19851200 MW|
|Activity||Power Generation||6.24 TWh/yr|
|Technology||Conversion TechnologyCooling TechnologyPollution Treatment||Coal-fired subcritical boilerHybrid cooling towerFGD, Electrostatic Precipitator|