Geospatial ESG – The emerging application of geospatial data for gaining ‘environmental’ insights on the asset, corporate and sovereign level
The WWF-SIGHT team recently published a white paper with the World Bank and Global Canopy and other collaborators which illustrates their work on developing quantitative geospatial ESG methodologies, focusing on the ‘E’ pillar. These methods are designed to gain greater insight into the environmental impact of assets, companies, and sovereign states. The aim is to improve the ability of financial actors to differentiate on environmental performance to aid realignment towards truly sustainable development.
Since 2015, the WWF-SIGHT team at WWF has been developing geospatial methods to better understand, the ‘where, what and who’ of harm to the natural world – today tracking impacts to over 300,000 sites, considering millions of commercial assets globally. These methods have proven of interest to the financial sector, as they enable additional insight into the environmental performance of assets, companies, and states, at high frequency and independent of any actor.
To give a sense of the relevancy, the team assesses nearly three million commercial assets globally every quarter. This work highlights specific issues of projects and investment programs such as the Belt and Road Initiative and the Stiegler’s Gorge Hydroelectric Power Station Project in the Selous Game Reserve World Heritage site. Or highlights high-level potential red flags across entire sectors (limited by asset data availability).
For example, as of Dec 2021, WWF is aware of some 733 commercial assets within or bordering 101 natural and mixed World Heritage sites (out of 257), involving 200 primary holders. Within or bordering IUCN Category I Protected Areas, WWF defined 1,592 assets, held by 267 different holders across some 257 unique protected areas. Within or bordering IUCN Category II, some 2,757 assets, 751 holders within 556 unique protected areas. Companies identified include major multinationals, but frequently junior and private companies are also involved.
The geospatial approach based around satellite imagery has a high potential cadence (daily, weekly), is fully independent, able to be tailored to monitor sector-specific variables, and is often globally scalable. These traits mean that it has potential, either via direct measurements or combined with other data approaches to give grounded real insight to unravel the true impact of companies and importantly their supply chains in near real-time. However, complications arise and to really begin to understand which assets globally have created and have ongoing serious environmental impacts it’s necessary to assess assets in far greater detail and consider and account for the various complexities – to correctly assign both initial and ongoing environmental impact and correctly interpret the various legal exceptions to operate in specific areas.
The white paper ‘Geospatial ESG’ released today, documents these methods in detail.
The reports key findings are:
Asset level to corporate level screening has been achieved for sectors such as oil and gas, mining, fishing, shipping, cement, steel and the power sector. Indeed, commercial factors such as Asset Resolution, Verisk Maplecroft, Reprisk, Bloomberg and others already offer geospatial ESG-derived data products. Some, such as the Trase tool, even manage to generate insights from incomplete asset data, providing estimates of a company’s direct deforestation risk.
It is possible to determine impact on habitats, conservation areas (considering the intactness and importance variance of each individual conservation area), freshwater exposure, etc, and ongoing monitoring of land degradation, emissions, tailing dam growth and volumetric expansion of the mines.
However, observational data is improving year on year, with major intergovernmental initiatives pushing to significantly improve the ‘environmentally relevant’ data portfolio for initiatives such as the UNʼs Sustainable Development Goals (SDGs). And as the mainstreaming of geospatial ESG continues we can expect to see improvement in commercial asset and supply chain datasets.
Significantly our understanding of the health of ecosystems at scale are likely to dramatically improve with new ground sampling methods, such as eDNA, and landscape audio, with complex machine learning models amalgamating these new species of ground data insights with other geospatial data. Creating more robust insights within a geospatial ESG assessment.