At a first glance, science-driven policy consulting sounds good. Scientists discover problems and propose solutions, we implement them accordingly. Is it really that easy? No, it isn’t.
Scientists are not data collecting and modeling robots, and politicians are not welfare committed altruists. Both are bound by their own weaknesses. Scientists may know much more than everyone else, but not nearly enough to overcome the boundaries of their own expertise. A lack of data and an insufficient understanding of the earth system, as well as the driving forces of economic growth and development are a limiting factor of scientific policy analysis. Hence the results of their models are as informative in some respects as delusive in others. Not to mention our common weakness for money, influence, self-affirmation and certain religious and ideological beliefs. Politicians are driven by similar motives in pursuing their agendas and are only partially constrained or guided by political institutions.
No wonder then that climate models and reality diverge regularly and that results are often less than useful for policy analysis. Two new papers exemplify the thin ice of climate modeling and its adequacy for policy analysis. German climate scientist Hans von Storch asked in a recent paper “Can climate models explain the recent stagnation in global warming?”. He answers this question with reasonable doubt:
In recent years, the increase in near-surface global annual mean temperatures has emerged as considerably smaller than many had expected. We investigate whether this can be explained by contemporary climate change scenarios. In contrast to earlier analyses for a ten-year period that indicated consistency between models and observations at the 5 % confidence level, we find that the continued warming stagnation over fifteen years, from 1998 -2012, is no longer consistent with model projections even at the 2 % confidence level.
Add the results of the recent NBER-Paper Climate Change Policy: What do the models tell us? by Robert S. Pyndick:
Very little. A plethora of integrated assessment models (IAMs) have been constructed and used to estimate the social cost of carbon (SCC) and evaluate alternative abatement policies. These models have crucial flaws that make them close to useless as tools for policy analysis: certain inputs (e.g. the discount rate) are arbitrary, but have huge effects on the SCC estimates the models produce; the models’ descriptions of the impact of climate change are completely ad hoc, with no theoretical or empirical foundation; and the models can tell us nothing about the most important driver of the SCC, the possibility of a catastrophic climate outcome. IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.
The global climate is still a black box and we only have a dim view of its interior. Yet, while models don’t fully capture reality, economists don’t hesitate to use them for another round of sophisticated storytelling. So much for science-driven policy! But honestly, does anybody believe science drives policy? In fact it is often the other way round. When the British economist Nicolas Stern issued his famous Stern-Report in 2009, politicians were all too happy to refer to the results of the study in order to justify their long advocated policy. Despite the fact that Stern´s analysis wasn’t dealing with specific regions and disaggregated policy solutions, German environmental minister Sigmar Gabriel (Social Democrats) stated the Stern-Report confirmed Germany’s climate policy goals and instruments. Like other scientists who surrender their independence, Stern became a useful idiot for a political agenda. There is no doubt that science has its uses in informing rational policy making. But be careful if anybody says that science is telling you to bite the bullet and quietly accept any policy.