Sitting in on a European Union presentation entitled "Roadmap 2050" I can't help but think that one of the fundamental problems separating public policy and many traditional views of the scientific enterprise is that empirical science primarily looks backwards as a key to understanding what might happen in the future while policy primarily looks forward with the present and the past used as a baseline.
Roadmap 2050 describes a strategy for reducing carbon emissions generated by the EU power grid by 80% by the middle of the century. It is a roadmap that describes different ways in which we can create the future we want. Of course, what we want and how we should best get there are highly debatable points but they provide us with targets to shoot for.
A great deal of science, especially science that most highly values empirical data and observations, has little to say about what should occur in the future. It is reflective, having the capacity to explain what has happened and what the driving factors behind those events are. Another factor is that what should occur is a normative question of value that cannot be easily answered from a perspective that prizes objectivity and dispassionate assessment of facts.
Lessons learned from empirical science can of course be applied to forecasts of the future and policy statements but often there seems to be a distinct split between reflective science and prospective science.
Modeling is one of the ways in which what we know can be applied to a future that is unknown. I myself have a complicated relationship with modeling. I understand that it is an important tool that can be used to forecast possibilities based on the best information we have. But often in practice, they seem overly-simplistic and so rife with assumptions that they are little more than academic exercises.
While climate models are subject to these same issues, I would put them in a different class altogether. The GCM's I have looked at are rigorous, complex, and hold no subjective valuation about what should happen in the future. Rather, they are extensions of existing trends detected in empirical, observed data.
As an undergraduate, I became interested in Geography and environmental problems because I was excited by the prospect of being involved in solving some of the most intractable and persistent problems of our day. But most of the work that I have done is grounded in empirical data, it is reflective and tried to explain what has already happened or what is happening now. I think this is fairly consistent with the experience of many of my peers.
But this leaves us in a position of being unable to effectively answer the questions that motivated me in the beginning. How do we best solve environmental problems? What do we do from here? Are there constructive and rigorous ways that our existing focus on empirical science can be reoriented to be more tangibly relevant to issues of public policy?
Roadmap 2050 describes a strategy for reducing carbon emissions generated by the EU power grid by 80% by the middle of the century. It is a roadmap that describes different ways in which we can create the future we want. Of course, what we want and how we should best get there are highly debatable points but they provide us with targets to shoot for.
A great deal of science, especially science that most highly values empirical data and observations, has little to say about what should occur in the future. It is reflective, having the capacity to explain what has happened and what the driving factors behind those events are. Another factor is that what should occur is a normative question of value that cannot be easily answered from a perspective that prizes objectivity and dispassionate assessment of facts. Lessons learned from empirical science can of course be applied to forecasts of the future and policy statements but often there seems to be a distinct split between reflective science and prospective science.
Modeling is one of the ways in which what we know can be applied to a future that is unknown. I myself have a complicated relationship with modeling. I understand that it is an important tool that can be used to forecast possibilities based on the best information we have. But often in practice, they seem overly-simplistic and so rife with assumptions that they are little more than academic exercises.
While climate models are subject to these same issues, I would put them in a different class altogether. The GCM's I have looked at are rigorous, complex, and hold no subjective valuation about what should happen in the future. Rather, they are extensions of existing trends detected in empirical, observed data.
As an undergraduate, I became interested in Geography and environmental problems because I was excited by the prospect of being involved in solving some of the most intractable and persistent problems of our day. But most of the work that I have done is grounded in empirical data, it is reflective and tried to explain what has already happened or what is happening now. I think this is fairly consistent with the experience of many of my peers.
But this leaves us in a position of being unable to effectively answer the questions that motivated me in the beginning. How do we best solve environmental problems? What do we do from here? Are there constructive and rigorous ways that our existing focus on empirical science can be reoriented to be more tangibly relevant to issues of public policy?
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