A blueprint for “a better and more sustainable future": that’s the aim of the United Nations General Assembly’s Sustainable Development Goals (UN SDGs), which have a target year of 2030.1 Those goals include a future without poverty or hunger, with better education, reduced inequality, and built and natural environments that are protected from the worst impacts of climate change.

While the overarching goals may seem ambitious, the targets underpinning these goals are matched with specific and actionable indicators. So, while the theme of Goal 3, “Ensure healthy lives and promote well-being for all at all ages”, may seem daunting, an underlying target indicator - to halve the number of deaths due to road collisions2 – is a desirable outcome with progress that can be measured.

Within the U.S. municipal market, the underlying indicators, or metrics, included in the UN SDGs are a measure of the problems issuers are faced with. Changes in metrics can indicate whether a community has progressed towards achieving the goals which can represent a desirable outcome for its members.

To help investors better identify and measure their contribution to and alignment with these goals, ICE has introduced a MuniSDG service with over 80 metrics that span the 17 sustainable development goals. Each metric, or target indicator, provides quantifiable information related to how well each goal is being met. Metrics are constructed using publicly available data sources, including data from a variety of departments with the U.S. government (e.g., Census Bureau, Department of Education, Environmental Protection Agency, Center for Disease Control, etc.). Whenever possible, historical data is processed in addition to the most recently available year.

Since the original data comes at a variety of resolutions (state, county, and census tract), we first downscale all data onto a 100 meter grid. The downscaling process makes use of auxiliary spatial data (e.g., residential and industrial building locations) to allocate the data onto the finer grid. Once the data is on the grid, it is re-aggregated back up to any boundary of interest. That boundary could be, for example, a geographic boundary associated with a municipality or school district, or the area within the 20-min drive time to a hospital. This approach enables us to quantify the environmental conditions and social challenges of debt issuing communities.

To allow for an easy comparison and interpretation of the metrics, each issuer-level metric, such as road_deaths_per_100000 – the number of deaths due to road collisions per 100,000 residents - is percent ranked against all other issuers within a data year. This will give users the ability to assess how well an individual issuer is progressing in reaching a goal compared to its peers. The historical time series for each metric also allows users to determine whether changes are being made in a direction that aligns with a UN goal.

UN SDGs may seem mysterious and lofty. They can be, however, tangible and achievable. With the right tools, data-driven investment decisions have the potential to drive changes that help realize the SDGs and the sustainable future that the UN envisions.

1 https://www.un.org/sustainabledevelopment/sustainable-development-goals/

2 https://sdgs.un.org/goals/goal3