A Capital Markets-Based Water Risk Assessment of Key Industrial Water Users in the Great Lakes Region: Indicators for Portfolio Managers

Abstract

This project explores capital markets risk exposure from water use in key industrial sectors in the Great Lakes region, represented by a subset of the region's largest companies and water users. The largest industrial water users in the Great Lakes region include (in decreasing order): thermoelectric, industrial, domestic/public supply, and commercial sectors. It is salient to make the distinction between water withdrawal and consumptive use, whereby the former is largely returned to the source reservoir after use in business operations, and the latter is removed from available supplies.

Industry-specific water risks can be viewed through several lenses: watershed stewardship, impact of water as a natural resource constraint on corporate operations, and risk pricing of water in the capital markets as a result of curtailed operations and growth. The approach taken here builds on portfolio theory by integrating share price trends, with corporate accounting and voluntary disclosure data to extract a share price volatility risk metric - waterBeta - reflective of water and weather risk. The approach leverages signal processing waterBeta algorithms developed by Equarius Risk Analytics, a fintech firm, which prices water/weather risk directly into share price volatility, as a risk premium. The signal is derived from value-at-risk (VaR) models, which captures the short term ‘tail’ of extreme market volatility risks in share price behavior relative to industry and sector-specific benchmarks. Simply put, a higher waterBeta means a company is more prone to capital market volatility as a result of climate risks.

Our results indicate that, by comparing nine companies across four industry sectors, the waterBeta signal is lowest for utilities, followed by health care, consumer discretionary, and industrials. Companies with high waterBeta tend to exhibit a higher degree of tail risk volatility in their short term share price, have a high percentage of facilities operating in water stressed regions, and exhibit low water intensities (WI). Interestingly, these same high waterBeta companies also tend to have high fixed asset turnover ratios, indicating high waterBeta companies are more dependent on fixed assets. Conversely, low waterBeta companies exhibit low VaR, high water intensities and a high percent of facilities in water stressed locations. However, these companies have low fixed asset turnover ratios, and are thus inefficient at generating revenue from fixed assets. Even though our subset of companies was too small for sector-wide generalizations, it appears that when an entity has higher fixed asset turnover ratios, even small changes in water intensity or exposure to high water risk areas can have a significant impact on waterBeta. This is the case with Archer Daniels Midland (ADM). However, the opposite trend can be observed, and is exemplified by the thermoelectric companies, which are the most inefficient at generating revenue from fixed assets and have the highest WI but exhibit the lowest waterBeta values. This is largely due to the fact that thermoelectric plants/companies rely almost exclusively on surface water sources, such as the Great Lakes, and tend to have corporate/industry wide water risk management strategies in place, given their high dependency on water.

It should be noted that this capital markets risk at this time provides limited feedback to the companies on how to address this volatility, given that the model is multiparametric. Addressing water intensity (how much water a company uses to generate revenue) only has impact if its efficiency to generate revenue from its physical assets can be addressed. We are currently identifying factors that enable more targeted corporate risk management actions. As noted, the sample in this study was small and regionally focused. Broader universes of companies across multiple sectors such as represented in the ‘500’ index will serve to develop imputation and learning models to scale capital markets-based water risk observations.

Keywords: water, finance, risk, environmental engineering, fintech, machine learning, artificial intelligence, capital markets, Great Lakes

JEL Classification: G11, G12, G13, G14, G18, G32, O16, P28, Q25, Q41, Q51, Q54