Navigating the Complexity of Climate Change: A Closer Look at the Scientific Method and Its Challenges

  • January 31, 2024
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The physical sciences have greatly advanced knowledge by elucidating the workings of simple phenomena. In a simple phenomenon, we have a limited number of important variables, all of which are identifiable and measurable. This allows us to run a scientific experiment. In such an experiment, we hold all other variables constant and examine the influence of one variable on the phenomenon. We can therefore measure this variable’s direction and how important it is to this phenomenon. We can then do this same experiment to all the other variables to determine their direction of influence and relative importance. We can identify which assumed relationships are correct and which are wrong. We can draw conclusions on hypotheses about simple phenomena.

Complex phenomena, on the other hand, have some or many unmeasurable or unobservable factors or variables, whose influences and interactions may vary. Thus, it is impossible to run a scientific experiment to separate the influences of each factor. This greatly limits the value of empirical or historical evidence on complex phenomena since it is impossible to distinguish between causation and association.

Economists know this problem all too well. Over a hundred years ago, the limits of empiricism in economics were made crystal clear. In the article “The Elasticity of Demand for Wheat,” R. A. Lehfeldt (1914) attempted to determine the elasticity of demand by looking at the historical data of the price of wheat against the consumption of wheat. He attempted to correct for changes in other factors (ceteris paribus) and found the elasticity of the demand for wheat to be a positive factor of +0.6.

Should we conclude from this study that the demand curve for wheat is upward sloping? Hasn’t this empirical study showed that economic theory is wrong? Should we reexamine the theory?

Any sensible economist would explain that what is observed are not points on a stable demand curve but ever-changing intersection points between demand and supply or points moving toward such an equilibrium. A demand curve is like a photograph: it is only valid for that instance since other factors change constantly so that the positions of the curves are different from one instance to the next. It is impossible to empirically measure the slope of a demand curve. This echoes Heisenberg’s uncertainty principle in physics, illustrating the inherent difficulties in simultaneously determining position and velocity of an object.

Yet many other empirical studies since 1914 on different goods and services have demonstrated this inverse relationship between price and quantity demanded. However, this empiricism only supports this complex hypothesis: it can never prove it.

Economists, in general, have had little to say about climate change although they regularly deal with similar complex phenomena. Yet when economists have commented on climate change, they have added insult to injury. William Nordhaus received the Nobel Prize in 2018 for his work on an integrated assessment model that he says measures the impact of man-made climate change on the economy.

So here we are dealing with two complex phenomena: man-made climate change and its impact on the economy, as well as developing a mathematical model to describe their interactions. Never mind that many factors in Nordhaus’s analysis are unobservable, and those that are observable have impacts and interactions that are either unstable or unmeasurable. Also, any measures of these impacts are only statistical estimates. Generally, the larger the model, the larger the variance of the results.

It is normal to have differences of opinions on hypotheses on complex phenomena. These differences of opinions would be irrelevant if it remained at that level, but Nordhaus in his address recommended that governments impose restrictions (e.g., cap and trade, carbon taxes, and regulations) to slow emissions of CO2. The Paris Agreement of 2015, where 195 parties pledged to tackle climate change, aimed to limit global warming to “well below” 2ºC by the end of the century and “pursue efforts” to keep warming within the safer limit of 1.5ºC.

One study showed that burning fossil fuels causes more than 75 percent of anthropogenic greenhouse gas emissions and more than 90 percent of carbon dioxide emissions. Fossil fuels produced from existing oil, gas, and coal fields are more than enough to breach the 1.5ºC limit. Extracting fossil fuels from new oil and gas fields is incompatible with a 1.5ºC limit, according to a report by the International Institute for Sustainable Development and another by the International Energy Agency.

Hence, we have one side of the climate debate imposing on the life, liberty, and property of others on something that will always remain an unproven hypothesis. A recent study found that 99.9 percent of climate studies agree that humans cause climate change. Yet we must wonder how many of these authors inform readers of the limitations of their conclusions? Can we really call them scientists if they do not apply or discuss the scientific method?

Lost in the details of this recent United Nations Intergovernmental Panel on Climate Change report is this important conclusion: “In climate research and modelling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.” This complexity leads to an important conclusion: acknowledging the restricted knowledge about man-made climate change.

In a world often marked by strong opinions, a real discussion on climate change should start with humility, recognizing the limits of human knowledge. Balancing scientific understanding, economic considerations, and policy decisions in this intricate landscape requires a nuanced approach that respects both the complexities of the climate system and the inherent uncertainties in modeling and predictions.

Yet in our opinionated world, such humility is unlikely.