Case study by MioTech

In the spirit of showcasing leadership and raising standards of responsible investment among all our signatories, we are pleased to publish case studies of all the winning and shortlisted entries for the PRI Awards 2021.

Provide a short overview of the research innovation being proposed for the award

MioTech has incorporated a set of remote sensing data to its ESG data and risk monitoring platform AMI, which provides geospatial estimations of key environmental phenomena. Remote sensing has enabled MioTech to identify and compare observational environment-related risks as well as actions on climate change adaptation at both national and sub-national levels for more than 50 countries. This allows for standardised, reliable and intuitive environmental data to be made available for ethical or sustainability-minded investors.

Investors can use data on regional distributions of greenhouse gases (GHGs), air pollutants and landcover types, retrieved by remote sensing, to re-evaluate investments, especially those in energy-intensive sectors, in accordance with location and environmental contamination standards.

MioTech initiated this project in July 2020 and officially launched it the following December. Since then, the company has focused on improving source continuity of remote sensing data, refining image quality and expanding its scope of observations. Satellite imagery and statistics for air pollutants and vegetation (the normalised difference vegetation index, or NDVI) became available in January 2021, while night-time light imagery data is under development. As a result, enormous progress has been achieved in three main areas: GHGs, atmospheric pollutants including nitrogen dioxide and carbon monoxide, and particulate matter.

Provide a description of the research innovation or report your organisation has introduced or published, and why you decided to undertake this approach

MioTech’s Remote Sensing tool is designed to enhance the application of technology in the ESG sector to compensate for some longstanding drawbacks of existing data. These include:

  • Continuity: Traditional environmental data is often desultory and disjointed. In contrast, remote sensing data is continuous across both time and space, which can make up for the lack of readings where in-situ data is inaccessible.
  • Comparability: Current ESG benchmarks vary by agencies’ preferences. In contrast, remote sensing metrics are based on common physiochemical properties, which can reduce potential biases and boost comparability across the globe.
  • Frequency: In recent years, the rapid expansion of commercial satellite penetration has greatly increased satellite temporal resolution, thus supporting frequent updates of satellite imagery. ESG data from corporate annual reports or media reports can appear outdated compared with real-time remote sensing data. MioTech has defined meaningful environmental and climate metrics based on raw Earth observation data for nowcasting and ESG evaluation.

The Remote Sensing tool offers the following innovative features:

  • Reliability: Raw data sources include internationally recognised platforms such as ESA Sentinel-5P, Sentinel-2, NASA Landsat, etc. These satellites not only have stable service periods but also wide coverage. The statistics and satellite imagery are also processed with MioTech’s optimised remote sensing algorithms before being displayed.
  • Promptness: MioTech’s remote sensing data are updated monthly for time-variant metrics like air pollutants or annually for less sensitive parameter such as NDVI.
  • Scalability: For data from Greater China, Europe and the US going back two years, a two-tier remote sensing data taxonomy has been applied within this timeframe: Tier-1 for national level data and Tier-2 for sub-national level data. The satellite imagery and extracted statistics can be freely customised by users to either track countries’ commitments to tackling climate change, or to measure regional environmental risks.
  • Intuitiveness: The tool is user-friendly and functional. Unlike the usual satellite imagery products offered for geographic information system (GIS) analysts, MioTech’s remote sensing data are displayed on an intuitive interface; the tool offers useful functions such as time-series analysis, horizontal comparison, quantitative ranking and image auto-play to offer users valuable analysis of geospatial data.

MioTech’s remote sensing tool offers new insights into three main topics:

  • Global warming: Early identification of GHG emissions prior to disclosures is now possible with the GHG estimates (for methane and ozone) this tool provides.
  • Environmental pollution and protection: A new source of air pollution data is provided in addition to self-reported corporate data, allowing users to supplement or verify existing pollution information.
  • Vegetation conservation and biodiversity: Evidence shows a correlation between vegetation conservation and biodiversity. Landcover type reflected in the NDVI index can be adopted as an ESG metric when evaluating macro- or corporate-level sustainable investments.

Outcomes, benefits, challenges and next steps: provide an outline as to:

  • why you believe the report, process or approach is different and the aspects you believe are innovative;
  • the value this approach has provided or a summary of the key conclusions;
  • what you have you learned from this approach or report that can be applied more broadly.

MioTech’s integration of remote sensing data is cutting-edge because it adds macro insights above the corporate level, inspired by the concept of “Spatial Finance”, which involves the application of geospatial data to investment processes. As more remote sensing metrics become available, they will enable growing analysis of corporate behaviour in the context of the macro ESG information from the locations in which they operate.

The tool facilitates easy access for data-tracking. For example, it allows users to trace environmental footprints in regions where in-situ or self-reported data are unavailable.

Remote sensing data can be more reliable and consistent than self-reported data. With the implementation of new regulations such as the EU Taxonomy, remote sensing data can offer a means of detecting greenwashing activities hidden behind corporate sustainability and ESG reporting.

At the same time, MioTech’s analysis of remote sensing data has led to three specific insights:

  • Remote sensing data indicates correlations between regional economic development and environmental quality. Areas with stronger economies often exhibit higher pollution levels, whereas underdeveloped regions are associated with lower levels of pollution.
  • Remote sensing metrics can reveal the environmental and social impacts of different events. During the COVID-19 lockdown, a plummet in the atmospheric density of NO2 (a major air pollutant commonly associated with transport and industrial activities) was observed in most metropolitan areas, while NO2 concentrations rebounded quickly when the lockdown ended.
  • Remote sensing data can support investment decision-making. Detecting areas with higher pollution levels can allow investors to consider possible economic consequences and act before companies are subjected to environmental penalties or tightening regulations.

The further development of remote sensing data for ESG purposes is expected to lead to a number of outcomes. For example, easy access to remote sensing data could galvanise the media to more widely cover ESG issues. It could be applied to tracking progress towards carbon neutrality, whether at the corporate or national level. And night-time light radiance will be the next key remote sensing metric that MioTech will elaborate on, which can identify the patterns of the activities of manufacturing plants, potentially alerting investors to underlying issues, while it could also be applied to estimating carbon emissions.