Economic analysis is necessary for environmentalism, as I argued in a recent article. However, the Federal Reserve has undermined this principle through its recent research initiatives, which have major flaws, falling short to fulfill the Fed’s role to protect the environment.
Forecasting inflation has evidently been a challenge, and an inability to detect bank mismanagement does not give the central bank a good track record. The Fed’s weather forecasts have a similar air, where it has been collecting climate data from banks in a “pilot climate scenario analysis exercise.” This onset of climate activism by the Fed is only superficially hopeful. An overseeing entity with a wide influence makes us expect optimal research and reporting from the Fed, giving us a crucial device for climate change information. Though, this is not the case.
David Barker of the Wall Street Journal dissects these Fed studies, critiquing and discrediting the research. For example, we observe critical issues in a paper that claims there is a relationship between increasing temperature and economic growth. But Barker reveals that the paper is full of inconsistencies and ultimately cannot show there is a significant relationship between the two variables. In a similar case, he criticizes a Fed study where the statistical techniques fall short of the research. The flaws in the research, Barker argues, demonstrates the spread of climate propaganda and the observed inability of academic economists to make meaningful contributions to this issue.
Obviously, combating climate change requires rigorous data and analysis to effectively support policies and initiatives that are dependent on research. Without the necessary tools and methods to make meaningful solutions to this contemporary issue, we cannot construct a proper understanding of the climate and its relationship with human activity. As I discussed in my article “Why environmental sustainability needs economics,” economics is one of the most crucial devices in environmental sustainability as it lends to the thorough understanding between human behavior and the environment we live in. Generally, “science” only gets us halfway there, limited to a cause and effect relationship that is lacking in human behavioral modeling within economics.
Thus, the honesty of our data is critical. It is easy to assume that data itself is without flaws, we simply have to observe it and write it down. However, “acquiring, curating, and analyzing data in this domain is uniquely challenging,” says John Mennel from Monitor Deloitte. As we saw the Fed’s poor interpretation of the relationships between temperature and economic activity, effective use of data is difficult. The climate and sustainability data ecosystem is a vast network of relevant information that presents barriers like inconsistencies and errors, data licensing complications, regulations, and the necessary training and experience for meaningful analysis.
The risk of errors accumulates as data is diversified, especially relevant to environmental studies which have a myriad of sources at all levels of climate research. Furthermore, extending climate modeling towards other fields is its own challenge, like social issues where we often try to establish climate impacts on the well-being of the diverse range of individuals. For example, establishing a relationship between climate analysis and social equity may pose analytical biases from quantitative indicators like socioeconomic status and vulnerability. Climate data can only be used under the right circumstances and in many instances requires careful consideration to draw connections between data sets.
Clearly, combating climate change has deeper problems that need to be faced that lie in the information we use. Without properly providing our best interpretations of the information at our disposal, the issue of climate change will never be successfully overcome.