Why do biofuel LCAs produce no conclusive results? OE Emergence: Complexity and Organization
A review of the Lifecycle Assessment literature concerning biofuels found no conclusive answers on the important and policyrelevant questions of whether biofuels can help reduce emissions of greenhouse gases, and whether they are an efficient source of energy. This inconclusiveness is attributed to the problematic specification of these papers, which cannot give actionable and policy relevant answers. The main problems in the specification of the papers are: the reliance on aggregatebased modeling rather than investigating the impact of specific policies, the absence of integration within a dynamic economic model that includes the price effects, the focus on emissions quantities rather than the environmental impact of the emissions, and missing. This paper draws on insights from economics and philosophy of science to explain the underlying reasons why LCA studies fail to reach conclusive answers. Introduction A previous paper16 overviewed the Lifecycle Assessment literature concerning biofuels and found that no conclusive answers emerge from this literature on the important and policy relevant questions of whether biofuels can help reduce emissions of greenhouse gases, and whether they are an efficient source of energy. This inconclusiveness is attributed to the problematic specification of these papers, which cannot give actionable and policy relevant answers. The main problems in the specification of the papers are: the reliance on aggregate based modeling rather than investigating the impact of specific policies, the absence of integration within a dynamic economic model that includes the price effects, the focus on emissions quantities rather than the environmental impact of the emissions, and missing. This paper draws on insights from economics and philosophy of science to explain the underlying reasons why LCA studies fail to reach conclusive answers. Complexity and predictability The first problem LCA's encounter is that of irreducible complexity, which is hard to systemize and reduce for straightforward analysis. Under this broad heading we can list Quirin's points about the differing energy quantities that go into making fertilizers, the difference in crop yields, and the allocation of co products as well as Larson's points about the inclusion of climate active species, other emissions, co products and soil sequestration. This also includes Deluchchi's points about the consumption and production of energy and materials, land use change and other emissions. All these critiques have the same essence: LCA's analyze complex phenomena but and do not account for all the factors that matter in them. Warren Weaver1 in his discussion of the evolution of scientific understanding of complex phenomena begins by defining complex phenomena and how science treats them. Weaver 1 argues that before the Twentieth Century, physical science's greatest advances and most momentous contributions to human welfare came from applying the scientific method to studying questions that involved only two (or only a few) variables. Relatively straightforward theories and experiments were sufficient to establish scientific rules which then became very important for human knowledge and society. Enormous gains from science and technology ensued from applying the scientific method to these laws and rules. Weaver then explains that the twentieth century presented an attempt to apply the methods of science studying a few variables to studying many more variables studying complexity. He draws the distinction here between two types of complexity: disorganized complexity and organized complexity. Weaver1 defines disorganized complexity as: "a problem in which the number of variables is very large, and one in which each of the many variables has a behavior which is individually erratic, and may be totally unknown. But in spite of this helter skelter or unknown behavior of all the individual variables, the system as a whole possesses certain orderly and analyzable average properties." 1 Emergence: Complexity and Organization As examples of this type of complexity he cites a telephone exchange predicting the average frequency of calls, or an insurance company attempting to assess death rates. The key feature of disorganized complexity can be seen to be the lack of complex interrelations between the multiplicity of variables. Weaver argues that organized complexity is amenable to investigation by statistical and mathematical techniques. Because there are no complex interrelations between the variables, the totality of the variables can be assessed using statistical and mathematical techniques. Organized complexity, on the other hand, is not amenable to easy analysis with mathematical and analytical techniques. The distinction, Weaver insists, is not in the number of factors or variables, but rather in the existence of complex interrelations between the multiplicity of factors. "They are all problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole"1. These complex interrelations make trying to study the complex systems difficult because one cannot reduce the complexity away. This distinction between "organized" and "disorganized" complexity is similar to the distinction between the concepts of Extremistan and Mediocristan, presented by Taleb in The Black Swan. Taleb defines Mediocristan problems as being scalable problems, where a large sample cannot be altered significantly by the introduction of a single observation, no matter how large or small it is relative to the others. These scalable problems are ones where the range of variation of the variables is not wide enough for one observation to skew the total results. Examples of distributions that are from Mediocristan include height, weight, calorie consumption, car accidents, mortality rates2. Extremistan, on the other hand refers to situations where one extreme observation can disproportionately impact the aggregate or mean. In these distributions, the value of one observation can be so high or low compared to the rest that it could completely alter the final result. Taleb provides the example of the wealth of a group that includes Bill Gates. The mere introduction of Gates, even to a very large group of a thousand people, would completely change the metrics for the group, since Gates would account for 99.9% of the wealth of the entire group. Further examples include: book sales, number of references on Google, populations of cities, financial markets, and inflation rates2. FAO Hayek3 illustrates this point by demonstrating the difference between physics and other fields of inquiry. "More particularly, what we regard as the field of physics may well be the totality of phenomena where the number of significantly connected variables of different kinds is sufficiently small to enable us to study them as if they formed a closed system for which we can observe and control all the determining factors; and we may have been led to treat certain phenomena as lying outside physics precisely because this is not the case. If this were true it would certainly be paradoxical to try to force methods made possible by these special conditions on disciplines regarded as distinct because in their field these conditions do not prevail." For Hayek, it is the simplicity of the questions that physics tackles that makes these questions suitable for the methods of physics. Questions which do not exhibit this simplicity are, according to Hayek, unsuitable to be examined using the tools of physics. In agreement with, and elaboration on, Weaver, Hayek defines the complexity of systems to be dependent on "the minimum number of elements of which an instance of the pattern must consist in order to exhibit all the characteristic attributes of the class of pattern in question"3. As we move from simple physical inanimate systems that are amenable to investigation by physics' methods, we progressively witness increasing degrees of organized complexity, and increasing numbers of irreducible relationships that cannot be abstracted away in any attempt to study or manage the system. Here, it is useful to turn to the more recent literature on Complexity Studies, which provides a useful insight into the issue of reductionism. Tamas Vicsek4 argues: "Although it might sometimes not matter that details such as the motions of the billions of atoms dancing inside the sphere's material are ignored, in other cases reductionism may lead to incorrect conclusions. In complex systems, we accept that processes that occur simultaneously on different scales or levels are important, and the intricate behaviour of the whole system depends on its units in a nontrivial way. Here, the description of the entire system's behaviour requires a qualitatively new theory, because the laws that describe its behaviour are qualitatively different from those that govern its individual units." 2 Emergence: Complexity and Organization As these interrelations increase, the investigation of the systems then must be able to account for all of them in order to accurately study the system. One will need all the data that is relevant to the question to be included in the analysis. As we move towards investigating complex social and economic systems, we are faced with two main problems that make such studies difficult. The first problem is the lack of data. A lot of the important relations in complex systems do not have adequate data measuring them though this could in some instances be remedied with better data collection, the real problem remains when one remembers that a lot of the data needed is simply unquantifiable and immeasurable. The second problem is the proliferation and unknowability of the real relations governing such complex phenomena. With many interrelated factors and variables, it can be impossible to determine what the actual relations between different variables are, and how they inf