There is uncertainty in the world, more now than ever. The VUCA concept, which was used to characterize the Volatility, Uncertainty, Complexity, and Ambiguity felt in the United States following the Cold War, is now applicable on a global scale today. In fact, the COVID-19 pandemic is one clear example of the VUCA concept’s manifestation in our current environment. As one article by Edward Ghabour published on Trading Industry points out, it is evident that “all four characteristics of VUCA are true of the challenges we face due to the coronavirus.”
- Volatility: Changes due to COVID-19 are taking place every day and are unpredictable, dramatic, and fast.
- Uncertainty: No one can predict with confidence when the pandemic will end.
- Complexity: The pandemic is affecting all aspects of life — including health care, business, the economy and social life — in complex ways.
- Ambiguity: There is no “best practice” that organizations can follow to manage the challenges caused by the pandemic.”
In addition to these, COVID-19 is highlighting two more factors that make effective and careful decision making even more urgent: (i) how uncertain longer-term trends are, and (ii) how much short-term events and actions can impact long term trajectories. Within this VUCA world, smart and efficient approaches to decision making and leadership need to be taken. Leaders must adapt to this changing environment and be proactive.
On the one hand, it is clear that short-term actions have consequences. At this specific juncture, it is crucial that decisions and investments create positive outcomes. With climate change and fiscal sustainability concerns (exacerbated by the aging of the population and growing costs to run the economy), second chances are becoming scarce. Simulation and modeling can help policymakers come to informed decisions, reducing uncertainty and risk.
Development of Simulations
Specifically, simulation models can help us assess the impact of events, under varying assumptions. Models have been used to address issues for quite some time, but the sophistication of modeling has evolved greatly. An example of historical modeling is the use of “microworlds” or “management flight simulators” in the late 1980s. Models today are more advanced and better reflect reality, without necessarily working in silos. It has been demonstrated through various studies that simulation has the potential to “mimic the dynamic behavior of a system.” Furthermore, simulation allows for testing future paths before they become real, to be better prepared. This testing can be done in seconds.
Examples of these more advanced simulators include Sustainable Asset Valuation (SAVi) applications, which consider various potential future risks, such as those related to climate change, both concerning policy (e.g., the introduction of a carbon price) and infrastructure performance (e.g., damage to roads). SAVi shows that, with the use of simulation models, decisions can be science-based and data-driven. For instance, projects may be designed to take risks into account. As a result, unexpected costs that may emerge and render assets stranded and not economically viable in the future can be avoided.
One specific example of a SAVi project assessment that accounts for climate change impacts is the assessment of Pocem and Kalivac hydropower plants in Albania. The results of the simulation suggest that, when accounting for the projected precipitation reductions in specific locations due to climate change, the expected Net Present Value (NPV) of the project is significantly lower than a conventional project assessment would suggest. Solar and wind power become preferred options for power generation.
These findings support the case for more sophisticated simulations, which can help decision makers anticipate the economic effects of climate change. In the case of the Vjosa river, these assessments were used to support the decision to abandon the hydropower project, establish a natural park instead and focus on solar and wind power.
The evolving use of simulations
In addition to more sophisticated models and modelling techniques, how these models are used has changed. In recent years, we have transitioned from “models that can help us prove our point” to “models that are used in exploratory ways to reduce uncertainty and risk”. As an example, traditional cost-benefit analyses (CBAs) are typically used to prove that an infrastructure project is expected to generate benefits. This type of modeling only examines the costs and direct outcomes of a project, independent of the systems in which it exists.
However, it happens that, as stated in a Global Policy article by Susan Rose-Ackerman, “if a ‘looming’ disaster, such as global warming, builds up slowly over a long-time horizon and may precipitate sudden discontinuous and irreversible costs, then the standard cost/benefit test […] cannot deal with some fundamental aspects of the problem.”
To remain on the theme of hydropower, for example, climate change-induced flooding in India in 2013 caused 10 major hydropower plants to fail. Unfortunately, events like these are becoming more and more common as climate risks become greater. These hydropower plants were constructed without accounting for increased rain intensity and volume over time due to climate change, which modeling could have predicted. Thus, typical cost-benefit modeling generally fails to account for trade-offs and side effects.
Modeling has, on the other hand, evolved from this systematic and sectoral process to a systemic and participatory process. Modeling has the potential to capture the effects of a project on multiple economic actors, sectors, and dimensions of development. To capture these effects, knowledge integration is required. This is especially true for sustainable recovery planning, where lock-in effects can lead to the creation of side effects.
The SAVi modeling approach, for example, integrates knowledge by linking system dynamics and project financing models, and will soon include a spatial dimension. This will be achieved by using InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) which takes us a step closer to modelling how ecosystem changes can affect project outcomes. This type of integrated modeling, with the inclusion of spatial analysis, allows “decision makers to assess quantified trade-offs associated with alternative management choices and to identify areas where investment in natural capital can enhance human development and conservation.”
Several examples exist that show the effective use of simulation in policymaking. The New Climate Economy project of the World Resources Institute (WRI) has been working with various governments, including Indonesia, Ethiopia, Brazil, and Saint Lucia. The project aim is to connect existing climate mitigation planning efforts to new and broader low-carbon development planning and then again to existing national development planning. This work is integrating seemingly different policy efforts into a unified process, supported by integrated models like the Green Economy Model (GEM).
Similarly, the Global Green Growth Institute (GGGI) has been using modeling to support sustainable industrial planning in Cambodia. The United Nations Environment Programme (UNEP) has been using integrated modeling for Green Economy Planning since 2009, with the creation of a global green economy model included in the flagship green Economy Report published in 2009. Several more examples are available, across sectors and domains of planning, as explained here. Various countries are taking the initiative to develop more systemic and customized models, such as in the case of Kyrgyzstan and Kazakhstan, to create low-carbon and climate-resilient national and sectoral development plans.
Simulation for Green Recovery
The green recovery planning process requires this more systemic and participatory approach. There is now urgency for embracing uncertainty and using systems thinking. Silo approaches are bound to backfire in an environment characterized by rapid change and VUCA.
Social, economic, and environmental challenges are deeply interconnected; thus, taking a silo approach to solving them will create unintended tradeoffs. The same can be said for assessments that are carried out at the sectoral level, without considering incoming pressures from other sectors, or outgoing impacts on other sectors. Implementing projects without considering all interdependent systems that may be affected will lead to unintended outcomes. These outcomes can put livelihoods of those who rely on these systems at risk.
A report by the Climate Investment Funds (CIF) and the Green Climate Fund (GCF) suggests that, based on several case studies, deliberate planning and “investments supporting energy efficiency, renewable energy and resilience building led to improved rates of pilot programme replication, project continuity, scale, and knowledge sharing.” The authors further noted that in cases where “the funds’ respective investments built on one another, supported thematically or geographically complementary objectives, or were aligned with knowledge sharing efforts, enhanced outcomes were most likely.”
From this we see that a collaborative approach needs to be taken when planning for recovery. Exploratory simulation can help identify the best options for all actors involved, and hence for development. Green recovery strategies must involve multiple actors, consider all interdependent systems, and assess all outcomes. In summary, what can modelling and simulation offer? They provide a deeper understanding of baseline trajectories, supporting the identification of issues and opportunities. They further support the estimation of scenario and investment outcomes across (i) sectors, (ii) economic actors, (iii) dimensions of development, (iv) over time, and (v) in space (on a map). This information can support the creation of recovery packages that maximize benefits for all, taking a systems perspective that reduces the emergence of potential new risks and side effects and gives rise to new opportunities. This is why the creation of integrated assessments, with models that are systemic and co-created, is crucial for long term sustainability.