As climate change tests ecosystem resources, how can the possible impact be modelled and assessed?
In the summer of 2012, a Connecticut nuclear facility suspended operations because ocean temperatures had increased beyond the temperature needed to cool the facility. Seven years earlier, in 2005, Coca-Cola shut down its Kerala, India bottling plant amidst local resident activism around the company’s draw-down on the underground aquifer, among other issues related to water quality and quantity. These two seemingly unrelated cases are clear manifestations of a new form of risk associated with the functioning of ecosystems, as well as human concerns around natural systems. In the face of climate change, this category of ecosystem malfunction risk is likely to expand in the coming years. For financial analysts, the take-away from these examples comes in the form of a question: Do we factor long-term access to natural resource-based inputs into our due diligence processes? This due diligence question is relevant in both an absolute sense (such as in terms of projected ocean water temperatures for cooling nuclear plants) as well as indirectly, in terms of social license to operate as ecosystem resource demands increase, particularly related to water. An added complexity in considering these issues is whether past trends within natural systems will become increasingly inaccurate proxies for the future, and if unexpected ecological shifts in the business context become the norm.
Environmental economists, such as Dave Batker of Earth Economics, assert that this situation – of the past being a poor indicator of future trends – has already come to pass. He believes that today’s financial models are no longer relevant to our current world. Rather, many of today’s models used for understanding the economy and transactions may be analogous to those used just before the Great Depression—which clearly were inadequate for understanding our modern flows of capital. This time, however, the issue is with understanding the flows within nature as they affect business and financial capital transactions.
Coming from environmental analysts, these concerns are not so unusual. However, these issues are increasingly resonant with experts in related fields. For example, The Innovator’s Dilemma author and Harvard professor, Clayton Christensen, questions the tenet “measure what you manage and manage what you measure.” He states: “the way they designed the world, data is only about the past. And when we teach people that they should be data-driven and fact-based and analytical as they look into the future, in many ways we condemn them to take action when the game is over.” We have concluded that future-focused analytics, particularly around long-term capital investments, need to embed today’s ecological data within projections of how natural systems may be changing – given climate change as well as growing demands on natural resources around the world. The interpretation of this data, in turn, is likely to be most robust within a scenarios framework for how the future could play out. So what is the path forward for financial analysts? New, innovative tools are being developed to help analysts think about these emerging ecosystem pressures and assess what they mean for corporate performance and long-term investments. Consider, for example, Microsoft’s first prototype of a Global Ecosystems Model (GEM) and its Madingley Model. The model is being built to assist decision-makers to “rationally make sense of the various pressures to which ecosystems are subject, devise metrics to measure the impacts of these pressures, (and) develop strategies to mitigate these impacts” This model is one of a growing set that was documented in a BSR report entitled “Measuring and Managing Corporate Performance in an Era of Expanded Disclosure.” These models have the potential to be pilot tested and adapted for financial institutions in the coming years to improve understanding risk profiles of projects, which stem from changing ecological systems and the cascading effects on people.
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