**1. Introduction**

There are currently 17 United Nations Sustainable Development Goals, the socalled SDGs, whose content was updated on the United Nations Website [1]. In its preface, the then current (July 1, 2020) collection of SDGs were presented as

*"the blueprint to achieve a better and more sustainable future for all. They address the global challenges we face, including those related to poverty, inequality, climate change, environmental degradation, peace and justice."*

They are "a call for action by all countries—poor, rich, and middle-income—to promote prosperity while protecting the planet." The SDGs thereby recognize issues that revolve around trying to develop sustainably in a holistic global sense, but that does not mean that they miss the more microscale and pervasive issues where the devil lies in the details. That list is long and growing: ending poverty, building economic growth, and confronting social needs like access to quality education, quality health care, social protection against ordinary and extreme risks, quality opportunities and security in employment, personal security everywhere, food security, promoting equity and justice, and much more. In other words, it is like promoting the public welfare however it is as measured and monitored.

All of this must happen in the context of growing direct and indirect risks from ordinary environmental pollution, extraordinary and sometimes existential risks from climate change, as well as sudden and unrelenting risks from pandemics like COVID-19. While the United Nations asserts accurately that SDGs can "provide a critical framework for COVID-19 recovery," it is also true that pandemics and climate change can expose the extent to which the progress toward achieving any SDGs has not been as significant as one might have hoped or even expected [2]. Both are color-blind, and neither is impressed by social or economic status. In one way or another, both can strike anyone or everyone living anywhere or everywhere.

Coronavirus pandemics and climate change are therefore a cross-cutting theme of enormous concern across the full range of sustainability issues. The very organization of the SDGs shows that this truth has been recognized by the United Nations. "COVID-19 Response" boxes are highlighted close to the tops of the presentations of all 17 of the goals. In addition, climate change has been sustainable development goal for some time; SDG-13, to be specific. Labeled "Take urgent action to combat climate change and its impacts", this goal identifies three critical "targets" for action by decision-makers of all stripes: "strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries, integrate climate change measures into national policies, strategies and planning, and improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning." In addition, it is important to note that the COVID-19 Response Box for SDG-13 calls for: investments that accelerate decarbonization and promote sustainable solutions to energy market distortions, recognition of all climate risks, the creation of green jobs and vibrant employment markets within sustainable and inclusive growth, persistent transitions to more resilient societies that are fair to all, and reliance on international cooperation to most effectively respond to the challenges of climate change, pandemics, and all of the SDGs.

Integrated models are one of the primary tools through which rigorous science can be inserted into deliberations of actions whose goals (social values and costs calibrated in whatever metric is appropriate) extend well into the future—a year or more for viral pandemics up to a century or two for climate change [3–5]. They are, therefore, a means by which decision-makers who are charged with promoting sustainable futures can apply rational and rigorous risk management procedures to their challenges. They are, as well, the means by which decision-makers can organize their thoughts around what emerging data and new science are revealing about the relative likelihoods and fundamental characteristics of possible futures.

This is particularly important because climate change impacts and global pandemics have both shown the tendency of, as put in Flyvbjerg [6], "regressing to the tail" over time. In words, both have shown patterns of never having offered their worst possible outcome; that is, things can always get worse, and there is no reason to believe, ceteris paribus, that that will not always be the case. Technically, this is possible when the tails of distributions are so fat that the mean and variance, among other moments, do not exist; generalized Pareto distributions display this characteristic. **Table 1** in [6] provides a list of the top 10 phenomena (by "tail thickness")

**59**

*On the Value of Conducting and Communicating Counterfactual Exercise…*

**Number of billion-dollar disasters (average per year)**

28 (2.8) \$127.7B

52 (5.2) \$269.6B

59 (5.9) \$510.3B

119 (11.9) \$802.0B

69 (13.8) \$531.7B

44 (14.7) \$456.7B

**258 (6.5) \$1.754.6B**

**Associated costs (average per year)**

(\$12.8B)

(\$27.0B)

(\$51.0B)

(\$80.2B)

(\$106.3B)

(\$152.2B)

**(\$43.9B)**

**Associated fatalities (average per year)**

2808 (281)

2173 (217)

3051 (305)

5212 (521)

3862 (772)

3569 (1190)

**13,249 (331)**

and their calibration metric: earthquakes (Richter Scale max), cybercrime (financial loss), wars (per capita death rate), *pandemics (deaths),* IT procurement (percentage cost overrun), *floods (water volume),* bankruptcies (percentage of firms per industry), *forest fires (area burned),* Olympic Games (percentage cost overrun), and blackouts (number of customers affected). Italics, here, put three of the top 10

More specifically and less technically, the US National Oceanographic and Atmospheric Administration has observed dramatically increasing trends in the number of billion dollar national catastrophes and the fraction of each year's list that can be attributed to anthropogenic climate change [7]. Incredible episodes of enormous and increasing amounts of rain in one place over consecutive days have, for example, begun to occur because climate change has moved steering wind patterns, such as hurricanes like Harvey in 2017 and Florence in 2018, to suddenly not know where to go. Rapid successions of storms that do not diminish in intensity are now more common around the world because subsurface waters are historically hot in the spawning oceans. Damage records are meant to be broken, but Maria broke the bank for the third storm on the same track in less than one month in 2017. Fires from north to south across all of 2019 brought California more burned area and property than any time in history. **Table 1** shows that these and other climate and weather disasters averaged \$80.2 billion with 521 lives lost in the last decade; over the past 2015–2019, the averages were \$106.3 billion with 772 lives lost. In addition, COVID-19 indirectly caused economic damages in the US early in its course that were larger than the Great Depression, at least in terms of the rates of

In the face of these kinds of threats, what are the response options that need modeling support? Mitigation is one—slow the pace of the risk so that the spread of the consequences (symptoms) does not overwhelm social capacities to respond and adapt. That is, "flatten the curve" by social distancing, wearing masks, testing, tracking, and quarantining, sheltering at home, locking down nonessential economic activity (that cannot be done remotely), etc. Or, invest in reducing

*DOI: http://dx.doi.org/10.5772/intechopen.93639*

1980s (1980–1989)

1990s (1990–1999)

2000s (2000–2009)

2010s (2010–2019)

Last 5 years (2015–2019)

Last 3 years (2017–2019)

**Overall (1980–2019)**

*Source: [7].*

**Table 1.**

squarely within the focus of this discussion.

*Billion dollar disasters from climate and weather across the US.*

unemployment and economic loss [8].


*On the Value of Conducting and Communicating Counterfactual Exercise… DOI: http://dx.doi.org/10.5772/intechopen.93639*

#### **Table 1.**

*Environmental Issues and Sustainable Development*

that revolve around trying to develop sustainably in a holistic global sense, but that does not mean that they miss the more microscale and pervasive issues where the devil lies in the details. That list is long and growing: ending poverty, building economic growth, and confronting social needs like access to quality education, quality health care, social protection against ordinary and extreme risks, quality opportunities and security in employment, personal security everywhere, food security, promoting equity and justice, and much more. In other words, it is like

promoting the public welfare however it is as measured and monitored.

All of this must happen in the context of growing direct and indirect risks from ordinary environmental pollution, extraordinary and sometimes existential risks from climate change, as well as sudden and unrelenting risks from pandemics like COVID-19. While the United Nations asserts accurately that SDGs can "provide a critical framework for COVID-19 recovery," it is also true that pandemics and climate change can expose the extent to which the progress toward achieving any SDGs has not been as significant as one might have hoped or even expected [2]. Both are color-blind, and neither is impressed by social or economic status. In one way or another, both can strike anyone or everyone living anywhere or everywhere. Coronavirus pandemics and climate change are therefore a cross-cutting theme of enormous concern across the full range of sustainability issues. The very organization of the SDGs shows that this truth has been recognized by the United Nations. "COVID-19 Response" boxes are highlighted close to the tops of the presentations of all 17 of the goals. In addition, climate change has been sustainable development goal for some time; SDG-13, to be specific. Labeled "Take urgent action to combat climate change and its impacts", this goal identifies three critical "targets" for action by decision-makers of all stripes: "strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries, integrate climate change measures into national policies, strategies and planning, and improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning." In addition, it is important to note that the COVID-19 Response Box for SDG-13 calls for: investments that accelerate decarbonization and promote sustainable solutions to energy market distortions, recognition of all climate risks, the creation of green jobs and vibrant employment markets within sustainable and inclusive growth, persistent transitions to more resilient societies that are fair to all, and reliance on international cooperation to most effectively respond to the challenges of climate change,

Integrated models are one of the primary tools through which rigorous science can be inserted into deliberations of actions whose goals (social values and costs calibrated in whatever metric is appropriate) extend well into the future—a year or more for viral pandemics up to a century or two for climate change [3–5]. They are, therefore, a means by which decision-makers who are charged with promoting sustainable futures can apply rational and rigorous risk management procedures to their challenges. They are, as well, the means by which decision-makers can organize their thoughts around what emerging data and new science are revealing about

the relative likelihoods and fundamental characteristics of possible futures. This is particularly important because climate change impacts and global pandemics have both shown the tendency of, as put in Flyvbjerg [6], "regressing to the tail" over time. In words, both have shown patterns of never having offered their worst possible outcome; that is, things can always get worse, and there is no reason to believe, ceteris paribus, that that will not always be the case. Technically, this is possible when the tails of distributions are so fat that the mean and variance, among other moments, do not exist; generalized Pareto distributions display this characteristic. **Table 1** in [6] provides a list of the top 10 phenomena (by "tail thickness")

**58**

pandemics, and all of the SDGs.

*Billion dollar disasters from climate and weather across the US.*

and their calibration metric: earthquakes (Richter Scale max), cybercrime (financial loss), wars (per capita death rate), *pandemics (deaths),* IT procurement (percentage cost overrun), *floods (water volume),* bankruptcies (percentage of firms per industry), *forest fires (area burned),* Olympic Games (percentage cost overrun), and blackouts (number of customers affected). Italics, here, put three of the top 10 squarely within the focus of this discussion.

More specifically and less technically, the US National Oceanographic and Atmospheric Administration has observed dramatically increasing trends in the number of billion dollar national catastrophes and the fraction of each year's list that can be attributed to anthropogenic climate change [7]. Incredible episodes of enormous and increasing amounts of rain in one place over consecutive days have, for example, begun to occur because climate change has moved steering wind patterns, such as hurricanes like Harvey in 2017 and Florence in 2018, to suddenly not know where to go. Rapid successions of storms that do not diminish in intensity are now more common around the world because subsurface waters are historically hot in the spawning oceans. Damage records are meant to be broken, but Maria broke the bank for the third storm on the same track in less than one month in 2017. Fires from north to south across all of 2019 brought California more burned area and property than any time in history. **Table 1** shows that these and other climate and weather disasters averaged \$80.2 billion with 521 lives lost in the last decade; over the past 2015–2019, the averages were \$106.3 billion with 772 lives lost. In addition, COVID-19 indirectly caused economic damages in the US early in its course that were larger than the Great Depression, at least in terms of the rates of unemployment and economic loss [8].

In the face of these kinds of threats, what are the response options that need modeling support? Mitigation is one—slow the pace of the risk so that the spread of the consequences (symptoms) does not overwhelm social capacities to respond and adapt. That is, "flatten the curve" by social distancing, wearing masks, testing, tracking, and quarantining, sheltering at home, locking down nonessential economic activity (that cannot be done remotely), etc. Or, invest in reducing

the emissions of greenhouse gases and decarbonizing the macroeconomy and thereby reduce the likelihoods of significant harm. Shrinking the tails of the most extreme consequences is another—invest in new adaptations and response actions (therapeutics and vaccines) that can eradicate the explosive nature of potential outbreaks. That is, invest in the development and distribution of new ways to minimize the ravages of the virus or prevent it from invading human beings. Or, invest in forward-looking or responsive adaptations that reduce the consequences of climate change.

These are abstract issues, of course, but confronting them is critical for efforts to manage the controversies that surround action decisions—controversies that can be born of misinterpretations of modeling results and applications, deliberate distortions designed by unscrupulous agents to promulgate false perceptions, exaggerated foci that obscure social, economic, and political complexities, as well as unfounded assertions that attack the integrity of sound scientific practices [9]. These controversies make it clear that modelers need to work continually to improve the models that they employ to answer comparative policy-relevant questions and to communicate their results effectively. They therefore lead to the conclusion that efforts manage climate and health risks need to include exercising novel and traditional methods for improve modeling practices, the understanding of modeling structures, and the communication of modeling results. These efforts are just as important carefully taking account of more widely expressed modeling concerns: assumptions, bias, framing, and immodesty.

Here, similarities and synergies between epidemiologic models of pandemics like COVID-19 and integrated models of longer-term risks from climate change provide a context for productive suggestions about how to structure these efforts strategies like policy-relevant counterfactual exercises, structural model comparison experiments, value of information calculations, out-of-scale reality checks, and model updating are all highlighted, here. The goal is to offer some thoughts about how these research activities can support sound communication for sustainable development. This is especially important because systemic social and economic inadequacies have been laid bare by the COVID-19 pandemic and will be exacerbated by the growing global climate crisis [10].

Section 2 provides some context by reviewing briefly the early history of modeling the COVID-19 coronavirus with reference to the needs and challenges of that enterprise—representing the virus, the consequences of exposure, the implications of responding or not, the need for intervening in the workings of the economy, and so on. Section 3 frames the issue of improving the production and communication of modeling results in a skeptical, frightened, and uncertain world. Tools like methods to identify thresholds of tolerable risk, counterfactual modeling exercises, structured model comparisons, and value of information calculations are introduced and discussed briefly with regard to practicality, context, and experience. Concluding and synthetic remarks occupy the last section.
