We humans are pleased when things go smoothly. Many statistical methods are modeled after this linear thinking. But the world is not a straight line. If we want to understand reality, we need to learn to deal with structural breaks, outliers, and changes. The COVID-19 crisis is one such structural break and is reflected in practically every data series; the same goes for wars and economic crises.
In the data we collect on natural phenomena, such as temperatures and extreme weather events, we can observe not only long-term trends but also massive, unexpected, rapid changes. The global climate system, much like the economic system, is based on highly complex, non-linear dynamics that are in a constant state of flux.
As an econometrician, I try to get a handle on such structural breaks and outliers and investigate what triggers them. Econometrics, the economic science based on data, has developed many robust tools over the past 50 or 60 years to analyze trends and structural breaks in economic development. These can be applied to physical climate dynamics.
I use econometric methods to better understand how our economy and the global weather and climate system are interconnected. To do this, I combine weather data, economic data, emissions data, and population data. On the one hand, I analyze the potential impacts of climate change on our economic development and the costs we might face. On the other hand, I calculate which measures can bring about a sustainable reduction in CO2 emissions.
In interdisciplinary research groups, for example, we scrutinize the effects of transport policy measures. We examine emissions data for a time period, looking for abrupt increases or decreases in CO2 emissions that cannot be explained by population or economic developments. In many cases, we are able to determine that the abrupt changes in CO2 emissions are attributable to a climate or transport policy measure.
For instance, we have shown that in the European transport sector, only a few measures had a lasting effect on emissions: only ten effective measures in 15 countries over almost 25 years. In all of them, CO2 pricing was a central instrument; in eight of the ten cases, however, complementary measures were also implemented, such as subsidies or new standards for fuel-efficient engines.
At Technische Universität Berlin, in the research group led by Linus Mattauch, I am developing an open-source model for Berlin and other federal states as well as for other European countries, which allows decision-makers to estimate the impacts of climate and transport policy measures in advance based on data: What will happen to traffic emissions if Berlin introduces a congestion charge? What impact will this have on economic growth? And what are the likely associated costs?
The model can also look at social impacts. With climate policy, it is particularly crucial to consider these from the outset and to be transparent about a measure’s effects on different population groups. Our model can help minimize social disruption resulting from climate policy.
I enjoy working in a field that will change completely in the next 20 or 30 years. Where there is change, there is dynamism, and where there is dynamism, I, as a researcher, can make a contribution. Many people are looking for answers and ideas, especially in the climate and energy sector. I hope that we can persuade policymakers to use robust socioeconomic and economic tools to develop and implement more professional measures for climate protection.