The Great Lakes & St. Lawrence Blue Growth Fund is a diversified investment strategy to attract mainstream ..... Offered by Corymbus Asset Management (www.corymbus.co), a FinTech ... advanced systems such as GPS, sensors and software.
The Great Lakes-St. Lawrence Blue Growth Fund “A diversified investment mechanism to build and capture wealth for the long-term sustainable management of the Great Lakes and St. Lawrence River” Product Development Report Fund Design – Asset Allocation Strategy – Investment Proposition Peter Adriaens, PhD PE BCEEM NAE Professor of Engineering, Entrepreneurship and Finance The University of Michigan at Ann Arbor Fern Jones, MBA CFA FRM Plain View Advisors, Inc., New York, NY March 14th, 2017
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Executive Summary
The Great Lakes-St. Lawrence region is a major commercial hub that leverages its store of natural capital in the World’s third largest economy ($5.8 trillion in 2015) across multiple industry sectors. These include manufacturing, mining, health care, agriculture and commodities, and recreation industries. It is a net exporter of wealth derived from natural resources, and hence, the embedded value of water in growth and wealth generation is a key differentiator for the region. Post-2007 economic shifts resulting from industry macro-drivers – such as the adoption of the industrial internet-of-things (IoT) and platform business models - have reshaped traditional industry value chains. Yet, the investment models that capture and drive sustainable market value from water in these evolving industry ecosystems have not changed. The Great Lakes & St. Lawrence Blue Growth Fund is a diversified investment strategy to attract mainstream capital in a multi-asset portfolio with local impact and global exposure to build and grow long-term support for the Basin’s wealth generating capacity. The Fund seeks to generate excess returns by selecting investments for risk-adjusted performance from the underlying real economy and capital market components of the benchmark. Structured as an innovation on the ‘core-and-satellite’ investment model, the Fund combines illiquid and cash-flow generating real assets with globally diversified securities relevant to emerging industry clusters that drive economic activity. Risks in the core assets will be hedged using managed futures contracts and insurance investments. The result of the design process, based on a reasonably complete deal flow of venture capital and private equity investments, bonds, stocks and infrastructure deals with relevance to the region is a bifurcated fund structure that focuses on infrastructure investments with local exposure, and public securities with global provenance. The infrastructure component may be structured as bonds, project finance, or structured finance to capture both the real assets (e.g. farm land, forests, water utilities, harbors, drainage systems) and the green innovative supply chain components that support these assets. Target initial high value investments include industrial IoT (internet-of-things) relevant to water, smart agriculture, and forestry. Deal flow sourcing for the fund is based on a two-stage process: (i) financial network mapping of public and private equities to define the company universe of thematic industry clusters, and (ii) top down investment filters based on unstructured and financial metrics to define lagging and forward looking indicators. The diversification benefit of this approach combines low correlation components with disparate return volatilities. The asset allocation strategy will be driven by the diversification benefit of industry cluster investments: low correlation between and within asset segments combined with superior returns. Relevant benchmark analyses indicate risk-adjusted returns of up to 7.3% across all assets, with a Sharpe ratio of 1.3. The next steps for the Fund are to (i) model cash flows of target infrastructure investment opportunities, (ii) select time series of potential stocks and bonds, and (iii) vet potential short-list investments across asset classes (to avoid the “black box” approach perception of the Fund) to develop ‘efficient frontier’ of portfolio allocations. The modeling of actual investments enables the assignment of impact metrics for investments and will form the basis for creation of a term-sheet from which a legal prospectus can be drafted.
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1. Embedded Value of Water in the Great Lakes Basin The Great Lakes-St. Lawrence River states and provinces are globally important sources of renewable natural capital which, through proper stewardship, can ensure continued economic growth and wealth generation in perpetuity. In 2015, a study by the Harvard Business School’s Institute for Strategy and Competitiveness reported on the structure of industry clusters and their impact on the Great Lakes-St. Lawrence regional economy1. Using network models, findings indicated the presence of multiple interconnected supply chain clusters with variable regional distributions including automotive, mining, plastics, commodities, information technology and analytical instruments. Other industry clusters based on Bloomberg financial value chain data include agriculture, chemical, industrials machining, energy, and water. Industry clusters have increasingly been embraced as drivers for economic growth and long term value creation, including by the U.S. Economic Development Administration. However, viable investment strategies based on industry cluster and ecosystem mapping have only recently been designed for pension funds and asset managers in Finland. The wealth derived by these industries from Great Lakes-St. Lawrence renewable resources such as water can be quantified using Embedded Values Assessment (EVA).2 By analogy to the insurance industry, the availability – and guaranteed quality – of water has long term “insurance value” for companies that depend on it (“the stakeholder”), and should be quantified as “virtual water value”. EVA methods comprise water trade, water footEmbedded Value is commonly used in the insurance printing, economic input/output analysis, life cycle (health or life) industry, and is defined by the assessment (LCA), and network models. American Academy of Actuaries as “the consolidated value of the shareholders’ interests” in the company. Hence, the “embedded value of virtual water” can be used to construct water alpha, i.e. new Alternatively, it can be defined as the present value investment assets with above-market performance of all future shareholder cash flows from the covered characteristics or lower risk profiles. in-force business (value of the insurance portfolio). Investment theses based on water alpha have garnered significant interest from the financial services community in the last few years – primarily led by NGOs and index providers in the context of ESG (Environmental, Social, and Governance) risk ratings, but also so-called “CleanWeb” companies that have proliferated in the IoT revolution. Data- and service-driven business models in water infrastructure and virtual water are starting to transform how companies capture value in the water space beyond traditional infrastructure investment and lending. According to MSCI ESG Research and Trucost (an S&P Dow Jones Indices Company), water in the capital markets is viewed through an enterprise risk management (ERM) lens for business operations. In other words, water affects the cost and revenue growth of business, and thus is expected to impact future performance or credit risk rating of the securities structured from these companies (stocks, bonds, green bonds). The Investor Water Hub, a recent financial industry effort headed up by Ceres, an NGO, is focused on integrating water risk and financial asset risk pricing in investment allocations, with 58 fixed income investors, private and public equity managers, and faith-based investors.
1 Ketels, 2011. Clusters and Regional Economies: Implications for the Great Lakes-St. Lawrence Region. Harvard Business School. Report to the Conference for Great Lakes and St Lawrence Governors and Premiers. 2
Gasparatos, A. 2010 Embedded value systems in sustainability assessment tools and their implications. Journal of Environmental Management Volume 91, Issue 8, Pages 1613–1622.
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2. Product Development Approach The value chain of our approach to design a new investment fund to capturing the virtual value of water is shown in Figure 1. Starting with articulating the intentionality3 of the Fund, the design follows the formulation of conceptual units, defines the asset class segments that best fit these units, and articulates exemplar constituents (underlying assets) that fit the risk and return profile. The benchmarks for each asset class segment were then used to simulate portfolio allocations and return profiles. The overall investment proposition is based on target combinations real and financial asset allocations.
Figure 1. Development of Blue Growth Fund investment proposition
a. Fund Intentionality The Great Lakes-St. Lawrence Blue Growth Fund seeks to attract mainstream investors to participate in a multi-asset portfolio of projects and other real assets, as well as a securities portfolio that generate above-market financial returns and contribute to the vitality of the region’s waters. Investments are strategically designed to improve the underlying value of natural capital at competitive financial returns that help build long-term sustainable growth of the Region. Impact measures. The fund will capture the virtual value of water in various economic sectors of the Great Lakes Basin, and investment outcomes will be tracked using economic and environmental metrics. Designed for mainstream investors and global relevance, it will achieve local impact through regionally sourced exposures derived to be consistent with the broad investment themes. Intentionality: additionality& differentiation. Maintain and preserve the renewable natural capital to ensure continued economic growth and wealth generation in perpetuity, differentiated by an industry cluster-driven investment filter and asset selection process. Additionality is achieved in two ways: (i) the industry cluster model emphasizes mutual reinforcement of supply chain value between the publicly traded corporate securities and the private financial assets; (ii) A cross-sectoral asset selection strategy results in diversification between and within asset class segments. Benchmark performance. The multi-asset fund structure is a liquid alternative investment strategy with a risk/return profile of stocks-bonds-real asset cash flow combinations of benchmark indices and absolute returns. The Fund will seek to exceed Impact Investing benchmarks (6.3% IRR across all vintage years), to attract mainstream investors (Comparable Universe, 8.6% IRR). Fiduciary compliance. The investment exposure is “economically equivalent” - with respect to the expected returns, risk profile and targeted time horizon of beneficiaries - to comparable investment opportunities without the collateral benefits. Its risk/reward profile and holding period are aligned with investment mandates of pension funds, endowments, family offices, and superannuities. Effective deployment of capital. Strategic investments in the real economy are balanced with financial assets to achieve ESG impact while delivering appropriate mainstream risk and return outcomes.
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Example: Norges Bank Investment Management Investor Expectations: Water Management. ISBN 978-82-7553520-5 NBIM Investor Expectations: Water Management (online)
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Sustainable management and conservation have long been an aspiration for natural resources, typically financed though conservation-type funds, a subset of impact investing4. The challenge is that typical conservation and restoration projects, their cash flow patterns, associated risks, and, more generally, their operational characteristics do not easily conform to the risk and return expectations of mainstream capital markets. How can conservation finance be ‘mainstreamed’? Despite the opportunity in the current low interest environment where investors are looking for yield with appropriate levels of risk, challenges for mainstreaming conservation finance are substantial. They include the cost to source projects or other investment opportunities, the inexperience of matching project funding needs with experienced investment The Great Lakes-St. Lawrence Blue Growth Fund capital, the lack of scale which drives up transaction seeks to mainstream conservation finance costs, and the unpredictability of cash flow sources in investments in real assets that support a diverse the face of generally small-sized projects and variable growth economy in the region. lock-up periods. Transparency, liquidity, and consistency (TLC) are key. Risk disclosures from regulation, project pipeline development, and technology maturity are the main drivers for transparency requirements. Liquidity is important from the perspective of variable cash flows, long-term offtake contracts, and time-to-bankable projects, when - for example - equity can be converted to senior debt positions. Consistency is important for scaling and replicating investments. Too many conservation finance projects are bespoke and cannot be easily standardized or aggregated. b. Fund Design and Investment Units With these requirements in focus, the Fund relies on a transparent and robust policy substrate that governs water use in the region: The Great Lakes-St. Lawrence River Basin Water Resources Compact. The Compact is a legally binding interstate agreement among the US states of Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania and Wisconsin, and the Canadian provinces of Ontario and Quebec. The compact details how the states and its industries manage the use of the Great Lakes Basin's water supply, and represents the means by which states implement the bilateral commitments under the Great Lakes-St. Lawrence River Basin Sustainable Water Resources Agreement. The Fund will be structured to leverage an industry cluster-driven deal sourcing and (capital allocation strategy. Growth funds - as proposed here - need to leverage on-going economic momentum, while identifying value-added investment components (‘alpha’) Industry clusters are geographic that will diversify the fund, and catalyze new economic concentrations of interconnected businesses opportunity. and supportive organizations that make A 2015 study on the structure of industry clusters and their regions uniquely productive and competitive. impact on the Great Lakes-St. Lawrence regional economy indicated the presence of multiple interconnected supply chain clusters with variable regional distributions including automotive, mining, plastics, commodities, information technology and analytical instruments. Other industry clusters not highlighted in the report, but inferred from Bloomberg financial data, include agriculture and forestry, energy and water, and shipping.
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Credit Suisse, WWF, and McKinsey & Company (2014). Conservation Finance – Moving beyond donor funding toward an investor-driven approach.
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Figure 2. Exemplar industry clusters and their link to the design of thematic investment opportunities in the Great Lakes Region. [Legend: 1. PlasticsAuto-Mining (manuf. cluster); 2. Agriculture-Forestry-Chemical Manufacturing (Ag. cluster); 3. Building-energy efficiency-industrial IoT (Smart building cluster); 4. Water infra-IoT-IT (smart water cluster); 5. Energy-water-construction (green infrastructure cluster)
Industry clusters consist of financially-connected industry segments (NAICS codes), that based on Bloomberg or FactSet financial data. These represent supply chain relationships that can be specified regionally (e.g. Great Lakes) or by sector (e.g. agriculture, manufacturing). Economic development strategies based on industry cluster analysis have been embraced recently, including by the U.S. Economic Development Administration, to identify white spaces and supply chain gaps.
The Great Lakes - St. Lawrence Blue Growth fund leverages the rich and diverse industrial clusters of the region into a mutually-reinforcing and scalable asset curation and allocation structure (Figure 3). Details on investment components and constituents follow in subsequent sections. •
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Local exposure through real asset investments (‘water alpha’). Comprised of infrastructure financing vehicles (structured finance, green performance bonds) to prove out new and emerging technologies in large infrastructure context, the ‘water alpha’ is focused on real assets in conservation, reclamation, industrial renewal, and sustainable infrastructure. The risk-return profile is benchmarked to private equity, real estate and infrastructure indices Globally diversified exposure to thematic stocks and bonds. The financial asset universe reflects the thematic industry clusters that drive Great Lakes economic activity. The risk return profile is benchmarked to S&P and US Aggregate Bond indices Managed futures-type contracts. Increasingly, insurance and derivatives instruments are included in conservation finance, related to the diverse water alpha assets. This allows investors to participate in (‘hedge’) future growth opportunities while minimizing their risk exposure. The risk-return profile is Figure 3. Conceptual units of the Fund benchmarked to hedge indices
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Investment strategies based on an industry cluster-based universe of companies have only recently been designed for pension funds and wealth management in Finland. Deal sourcing of underlying assets, and investment filters for allocation strategies based on industry clusters are expanded hereafter, and were detailed in prior reports.5 c. Investment Process and Constituents The security selection process and top-down selection filters are based on a rational multi-steps strategy to identify investment components that leverage the assets and growth opportunities of the Region. Step 1. Financial network mapping – identification of industry clusters Financial network maps rely on corporate disclosed and estimated data from Bloomberg and FactSet that capture value chains in target industries. Using the supply chain function on the terminal, crosssectoral financial relationships are identified that are directly related to the direct (COGS, cost of goods sold) and indirect (SG&A, sales, general and administrative) costs in relevant supply chains. Thus, industry segments (NAICS sectors) are identified, as exemplified below for the manufacturing, plastics, and metals cluster6. Public and private databases Financial network maps - industry clusters Step 2. Sourcing of public and private companies The identified NAICS sectors are the primary filter for sourcing securities and private deals. Public companies are identified through Bloomberg, while private companies are sourced using private databases (e.g. CleanTech group, Pitchbook). Further down in the selection process, corporates are chosen using regional activities, ESG metrics, and information on their strategic investment portfolios in private companies. For example, in four industry thematics (water & wastewater, agriculture & food, biofuels & biochemical, waste & recycling): • •
Dow Chemical and Dupont have co-invested in 13 private companies. Up to 34 corporates in Great Lakes region (out of 209 corporates) have investments in 1-9 deals
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Adriaens, P. and A. Tahvanainen. (2016). Financial Technology for Industrial Renewal. https://www.etla.fi/en/publications/financial-technology-for-industrial-renewal/. ISBN 978-951-628-670-2 (pdf) 6
Ketels, 2011. Clusters and Regional Economies: Implications for the Great Lakes-St. Lawrence Region. Harvard Business School. Report to the Conference for Great Lakes and St Lawrence Governors and Premiers.
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The linkage between corporates and startups (and projects) in an ‘incubation-like business environment’ is an important component of mainstreaming and scaling conservation finance7. Corporate coinvestment and the opportunity for offtake contracts significantly de-risk market development for the new technology firm and the investor. This ‘incubator’ model is core to the deployment hubs (D-Hubs) of the Global CleanTech Cluster Association, a global economic development and trade group focused on building out green supply chains in cooperation with economic development clusters and investors (www.globalcleantech.org). Step 3. Application of Financial and Unstructured Data Filters for Portfolio Allocation All companies are analyzed using the KeyStone Compact®, a risk rating tool based on big data derived from a wide range of social media and other public sources, to understand industry risk profiles and investment grade rating of startups, SMEs and lines of business (LOB) for global corporations in emerging industries8. Offered by Corymbus Asset Management (www.corymbus.co), a FinTech company, the tool transforms non-financial data into actionable allocation signals (Figure 4). Per the chart, data are collected from publicly available sources such as social networks, financial sites, and the company’s own website, using APIs and scaping tools. In the analytical process, the tools collect information on risk factors that are relevant to the company’s value capture position in the industry and its investment grade. These company-specific factors reflect propensities of strategic, operational or financial risk. In the case of value capture assessment, dependencies and leveragability in the value chain are identified, as well as competitive differentiation and industry connectivity. In the From a big data perspective, we can now cluster case of investment grade, risk drivers such as companies with similar risk profiles that can be market diversification, profitability, scalability easily understood and interpreted: management, and capital efficiency are extracted. market and industry risk signaling, similar to insurance or ratings agencies. Following risk mapping, portfolio allocation of public securities is based on Capital Asset Pricing Models (CAPM), and selection or size of private investments is driven by co-investments with (private or corporate) venture and private equity funds. Private deal flow distribution, growth equity placements, and exit multiples in four investment thematics (insert, next page), are based on 135 identified deals in the Great Lakes region alone (20% of total).
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Credit Suisse Group AG and McKinsey Center for Business and Environment. (2016) Conservation Finance- From Niche to Mainstream: The Building of an Institutional Asset Class. 8
Corymbus Asset Management – Transforming Non-Financial Data into Actionable Signals. http://www.youblisher.com/p/1562495-KeyStone-Compact-Emerging-Business/
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Figure 4. KeyStone Compact® – Unlocking value from business models using big data algorithmics
Examples of corporates with strategic or venture investments in the same thematics include: Private Investment - Industry Cluster Themes ----------------------- 1. Water & Wastewater Investment size (growth equity): $15-30MM. Exit multiple (acquisition): 2.5 - 10x 2. Agriculture & Food Investment size (growth equity): $20-70MM. Exit multiple (acquisition): 4 - 6x 3. Biofuels & Biochemicals Investment size (growth equity): $30-70M Exit multiple (acquisition): 1.2 -1.8x 4. Recycling & Waste Investment size (growth equity): $5-15M Exit multiple (acquisition): 3 - 13x
Smart Water: Exelon, Hydrogenics, IBM, Synergis, SPX Corporation Agriculture-Food-Smart Farming: Bunge, ADM, Ceres, Pfizer, Comcast Metals-Automotive-Plastics: Mitsui, Dow, GM, Ford, Dupont, Hitachi Private investment deal flow is stable and growing, as is evident from the agriculture and food thematic chart of investments (Figure 5). The highest growth in this space is in the precision agriculture subdomain, characterized by (i) technologies that incorporate advanced systems such as GPS, sensors and software to improve efficiency over traditional agriculture practices, and (ii) software used by farmers to
Figure 5. Deal flow of private investments in the food and agriculture sector (Source: CleanTech Group)
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monitor, analyze and optimize sustainable production of their farmlands. Exemplar Private Investments BrightFarms: “Builds and finances hydroponic rooftop greenhouses at grocery retailers with the purpose of eliminating time, distance and cost from the food supply chain. The company generates revenues through guaranteed-purchase contracts with retailers.” (NY) FarmLogs: “Provides software-as-a-service to help farmers plan plantings and operations to increase crop yield. The company's product tracks expenses, creates and monitors crop plans, manages inventory and sales, and monitors weather and crop prices.” (Michigan) InPipe Technology: “Developer of technology to treat municipal wastewater by adding microbes to sewer pipes en route to treatment facilities.” (Illinois) Andalyze (structured debt): “Developer of sensors for heavy metals contamination in water using catalytic DNA technologies. Revenue generation through service contracts.” (Illinois). Infrastructure and project investment Recognizing that the Great Lakes and St. Lawrence Blue Growth Fund exposures to water alpha need to be scalable, reliable, and cash flow driven, commitments to infrastructure investment are central to the value proposition of the fund9. We will consider the following mechanisms of infrastructure investment vehicles, aimed at the application and de-risking of new technologies to capture the value of water: 1. Project finance. This is the most common approach to financing infrastructure deals based upon a non-recourse or limited recourse financial structure, in which project debt and equity used to finance the project are paid back from the cash flow generated by the project. A number of examples relevant to the investment thematics of the Fund are highlighted in the insert. Large scale investment in green infrastructure provides economies of scale, and savings of 40 - 96 % of total project cost (10% returns; Project Finance Examples 15% risk off IRR). Public-private partnerships (PPP) for port modernization 2. Environmental Impact bonds ($1.7 bn.), vessels with high fuel efficiency and reduced (EIB). Long-term financing of emissions ($4 bn.), and resilient infrastructure systems infrastructure renewal, or new (stormwater, floodwater reduction). construction of green infrastructure. A recent example Waste-to-bioenergy facility ($300M.) financing using is the EIB issued by the debt/equity, debentures or common bonds paid from longWashington DC Water Authority to term offtake contracts of electricity or fuel commodities. fund a green infrastructure project in its DC Clean Rivers Project, a $2.6 billion program to control stormwater runoff and improve the District’s water quality, creating a healthier future for District residents.10 The $25 million,
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Assessing the Market Size for Large-scale Adoption of Green Infrastructure in the Great Lakes Basin, S. K. Sinha, R. Pettit, J. Ridgway, A. Eidson, J. Silfen, G. Peralta, and G. Cannito, Environmental Consulting & Technology, Inc., Report, 46 pp, January 2017. 10
DC Water, Goldman Sachs and Calvert Foundation Pioneer Environmental Impact Bond. http://www.goldmansachs.com/media-relations/press-releases/current/dc-water-environmental-impactbond.html.
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tax-exempt EIB was sold in a private placement to the Goldman Sachs Urban Investment Group and the Calvert Foundation, offers a 3.75% coupon, which can double (7.5%) depending on long term performance. 3. Structured Finance Master Trust of Water Infrastructure Related Projects. This form of infrastructure finance is a subset of project finance that allows a revolving pool of (waterdependent) asset-backed securities to be included in one portfolio. By creating a Master Trust, multiple series (e.g. projects under a cluster theme) of notes, bonds or other debt securities can be issued, and the proceeds from the issue are used to acquire a revolving pool of cash-flow generating infrastructure assets (e.g. farm land, drainage systems, small utilities). Each series of securities has an interest in the entire asset pool of the trust, and thus distributes the risk across multiple assets. Providing Liquidity from Real Asset Investments: Managed Futures Contracts A managed futures account (MFA) or managed futures fund is a type of alternative investment in which trading in the futures markets is managed by another entity than the Fund. Managed futures accounts include, but are not limited to, commodity pools. In the case of the Great Lakes - St Lawrence Blue Growth Fund, we aim to structure investment opportunities with exposure to commodities, agriculture, energy and currency as appropriate, but won’t be limited to MFA sensu strictu. Rather, since the purpose of the ‘futures contracts’ is to track cash flow and future value of the real assets (virtual value of water in Great Lakes basin), a number of instruments will be considered, including insurance and derivatives. An example of a Great Lakes shipping derivative is described in the insert11. Insurance contracts are increasingly considered in conservation and impact finance to address risk mitigation concerns. For example, reinsurers invest in Funds to actively manage claim payout frequency from flooding and other climate risks. Great Lakes Shipping Derivative Similarly, futures or hedging contracts Low water levels in the Great Lakes have recently had have been applied in the transitioning of significant financial impacts on the region’s commercial traditional agricultural practices to shipping. Cargo capacity is a function of a ship’s draft, the organic models, to reduce fertilizer distance between water level and the ship’s bottom, and runoff and manage algae growth in lower water levels force ships to reduce cargo loads to receiving waters. Hedge contracts lock prevent running aground in shallow harbors and locks. in crop prices at a premium over nonFinancial risk transfer instruments, such as index-based organic commodities, guaranteeing insurance contracts, can be adapted for managing these future offtake cash flows. risks. Binary financial contracts can be indexed to water Case Smart Water levels and priced according to predefined thresholds and combined to form hedging portfolios with different It is not inconceivable that insurance or objectives for the shippers. hedging contracts will be structured around the proposed smart water-based asset-backed securities or EIB. The (near)real time monitoring afforded by so called ‘climate smart’ IoT infrastructure deployment in water treatment, drainage, or water use systems allows for tracking and measurement of billing, demand-side management, and system failure (Figure 6). In theory, smart
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Meyer, E. S., G. W. Characklis, C. Brown, and P. Moody (2016), Hedging the financial risk from water scarcity for Great Lakes shipping, Water Resour. Res., 52, 227–245, doi:10.1002/2015WR017855.
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water IoT facilitates more efficient resource distribution, optimizes utility labor, limits water losses, and assists end users in understanding water usage patterns to promote conservation. An example how emerging industry clusters in the smart water space are developing around existing water infrastructure is shown in Figure 6. Automated plant & facilities management services are layered on mesh networks of sensors and advanced metering systems integrated in pumps pipes and valves. These, in turn, inform premise-based technologies driving demand-side management in residential, commercial and irrigation uses. In the last decade, innovative companies with smart water capabilities have moved to the Great Lakes states, after having been incubated in Israel, and Silicon Valley (e.g. ImagineH2O). Large-scale integration of these innovations in water infrastructure systems has been lagging, in part due to costs of system-wide retrofitting, uncertain future benefits and costs, and economies of scale.
Figure 6. Smart water infrastructure system (top) and industry cluster analysis of candidate companies (bottom).
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As has become the case in the smart electric grid, it is anticipated that the various monitoring points in the system can be entered in a distributed ledger, and data can be monetized using blockchain technology smart contracts12. Near real-time information drives contracts in demand side data, services, maintenance, and ancillary industries. Better predictions on adaptive management and underlying risks can afford the development of insurance and hedging mechanisms on cash flows and costs in the system. d. Benchmarking Investment Assets and Portfolio Allocation Strategies A recent Cambridge Associates report argued that ‘the opportunity to enhance performance with private investments is more widely available than is often assumed’i. It addresses the argument that for conservation finance to become a durable and feasible mainstream asset, liquid and illiquid investments need to be smartly integrated.13 This is the rationale behind setting up a combined mainstream securities – water alpha (real assets) model, while matching industry clusters with sustainable growth opportunities in the Great Lakes-St. Lawrence region. Reports from fiscal year 2015 on the impact of private allocations to endowments and foundations demonstrated a return premium of 3.6% for a minimum of 15% allocation. Given the mix of real assets with local exposure, the private investments will be selected primarily to generate risk-adjusted financial returns (new technologies, cash-flow generating assets) as described earlier, while achieving social and environmental outcomes (e.g. reducing algal blooms or shoreline restoration). It is envisioned that the Fund will evolve from a simple bifurcated structure comprised of Great Lakes-St. Lawrence region-focused transactions as the core, with a portfolio of listed broad market securities as the starting satellite portfolio. The goal is to build an innovative portfolio to capture the investment opportunities served by Great Lakes-St. Lawrence water-related ecosystem services, financial securities and economic transactions. Through organizing principles such as a) industry cluster analysis, b) smart monitoring of ongoing water resource provisioning and maintenance, as well as, c) robust modeling of Great Lakes/St. Lawrence-specific risk factors within the global market portfolio, the Fund can offer institutional and mainstream investors a Target universe for the Blue Growth Fund compelling Great Lakes-St. Lawrence-centric Comprises a category of assets commonly referred to value proposition. as “real assets.” With no consensus definition, real No matter how the category of real assets will estate and commodities are traditionally included be defined, the institutional investment among real assets – goods that are themselves a store community has embraced the benefits of real of wealth, or with which we associate a “real” cost of asset investment as the environment of production and “real” demand for use. historically low returns has persisted across world markets. Exposures in this category are sought out for their inflation sensitivity, low or negative correlation to traditional stocks and bonds, as well as, low correlation among the various segments within the category. The benchmark indices used for the fund are provided in Figure 7.
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Tapscott, Don; Tapscott, Alex (May 2016). The Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business, and the World. pp. 72, 83, 101, 127. ISBN 978-0670069972. 13
Cambridge Associates LLC (2016). The 15% Frontier.
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Figure 7. Benchmarking indices for the liquid alt Blue Growth Fund Using these benchmark indices, and considering the portfolio assets in the Great Lakes region that are part and parcel of the industry cluster thematics, a few portfolio exemplars were structured (Figure 8). The allocations range from 15% commitment to real assets in the Fund’s core (water alpha; CV/PE, debt, infrastructure) to 40% of total.
Figure 8. Exemplar portfolio allocations to core and satellite components of the fund
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In these simulations, the diversity of assets reflects the structure of the thematic industry ecosystems considered for the Fund (see, for example, Figure 6). Infrastructure assets need to be central to the core for this Fund to be of interest to asset managers and institutional investors, and allocations are increased as the fund simulation transitions from 15% to 70% real assets (Figure 8). Since at this point, it is not clear whether infrastructure will be financed using green performance bonds, project finance (private equity and debt), or structured finance (securitization of cash flows), relevant 10-year benchmarking indices are used to compute the risk-adjusted returns and risks to the Fund. In the satellite unit of the Fund, the simulations consider stocks and bonds (both muni and corporate) relevant to the Great Lakes Region, as well as REITs. The correlation between asset class segments is minimized as per Figure 7 data. Using these allocations, risk-adjusted returns and portfolio Sharpe ratios were calculated to compare these portfolio allocations. Given the increasing commitments to riskier and potentially more volatile asset class segments (VC/PE, REIT and stocks), it is no surprise that the risk-adjusted returns increase with increasing allocations to the core. The maximum return at 70% core asset allocation was calculated to be 7.3%, based on the benchmarking indices. The Sharpe ratios increase as well, as core assets in the real economy are less volatile because they are not priced (traded) daily, and thus volatility is averaged over longer time periods.
Figure 9. Risk and return profiles of selected portfolios of the Great Lakes-St. Lawrence Blue Growth Fund. 3. Conclusions and Next Steps
The design of the Great Lakes-St. Lawrence Blue Growth Fund proceeds from the Fund’s intentional motivation that encompasses economic and environmental outcomes to seek an investment mechanism that can function ‘like a sovereign wealth fund’. However, the Fund’s structure ‘allows it to capture value from real assets while maintaining and growing a sustainable economy’ in the region. The Fund design is governed by the following principles:
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1. Relatively complex selection criteria and overall risk/return profile are attractive to mainstream investors 2. Core investments exhibit a significant, high growth exposure to real assets in the region, while maintaining global exposure to best in class broad market securities 3. Overall portfolio investments achieve environmental impact on the watersheds of the Great Lakes basin to sustain future generations of economic growth and prosperity The result of the design process —based on a comprehensive analysis of deal flow in venture capital and private equity investments, as well as, bonds, stocks and infrastructure deals with relevance to the region— is a bifurcated fund structure, focused on infrastructure investments with local exposure, and public securities with global provenance. The infrastructure component may be comprised of bonds, project finance, or structured finance to capture both the real assets (e.g. farm land, forests, water utilities, harbors, drainage systems) and the green innovative supply chain components that support these assets. The fund has an ‘incubation component’ that connects the global corporations with the regional startups that make-up corporate investment portfolios. Recognition of this connection between established and emerging enterprises, helps to aggregate and streamline investments in the fund. Environmental impact metrics are provided through the ‘smart water’ (integration of IoT with all projects and investment) programs in the fund, i.e. sensors and (water resource) software models will track impacts and provide themselves opportunities for future data monetization from the investments. The next steps for the Fund are to (i) model cash flows of target infrastructure investment opportunities, (ii) select time series of potential stocks and bonds, and (iii) vet potential short-list investments across asset classes (to avoid the “black box” approach perception of the Fund) to develop ‘efficient frontier’ of portfolio allocations. The modeling of actual investments enables the assignment of impact metrics for investments and will form the basis for creation of a term-sheet from which a legal prospectus can be drafted. Since most institutional investors focus on a mix of benchmarking and absolute returns (e.g. Caisse de Depot infrastructure group uses 80% absolute returns and 20% benchmark indices), we will model Fund performance, returns, and structure based on actual assets, real time pricing, project cash flows, stock and bond total returns, and equity acquisition multiples. Portfolio allocations will be based on waterrisk adjusted returns (e.g. by using waterBeta financial asset risk adjustments; Equarius Risk Analytics, www.equariusrisk.com) as determined using CAPM and related broad market conceptions. The environmental impact from real projects and investments will be quantified using Blue Accounting metrics14, and Great Lakes water resource and water quality models (e.g. through our partner LimnoTech, www.limno.com). Blue Accounting was launched with a $4 million grant from the Charles Stewart Mott Foundation, as an ambitious attempt to improve management of the Great Lakes by establishing regional goals and using metrics to measure progress. These metrics will inform leaders as they prioritize funding and drive actions in their states and cities.
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https://www.mott.org/news/articles/blue-accounting/
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Appendices A. Venture Capital and Private Equity Deals in the Great Lakes Region a. b. c. d.
Water & Wastewater Recycling & Waste Biofuels & Biochemicals Agriculture & Food
B. Acquisitions of Venture Capital and Private Equity Deals in the Great Lakes Region a. b. c. d.
Water & Wastewater Recycling & Waste Biofuels & Biochemicals Agriculture & Food
C. Corporate Strategic and Venture Investments D. Government Municipal Bonds in the Great Lakes Region E. Corporate Green Bonds for Companies in the Great Lakes Region F. Fund Component Benchmarks G. Portfolio Composition, Returns and Sharpe ratios
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