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This dissertation proposes a method for automatic assessment of a trucking company's transport efficiency, without manual reporting routines. It focuses primarily on reducing freight transport-related carbon emissions. The goal is to define an instrument which shows the development of key performance indicators such as utilization (weight and volume), route efficiency, fuel use and carbon efficiency. The method could be used to monitor the reactions of trucking companies to external changes in order to find out which legislative measures are most relevant to carbon emissions and which operational efficiency measures are most effective. The monitoring method was developed while surveying the technology sector of computerized routing and scheduling systems and vehicle telematics. The dissertation asks whether the introduction of such systems will enhance transport efficiency in trucking companies with corresponding decreases in carbon emissions, and secondly whether it is possible to obtain further reductions in emissions by improving computerized routing and scheduling systems and vehicle telematics. Quantitive and qualitative methods produced positive answers to both questions.

AbstractCoincidence Analysis (CNA) is a configurational comparative method of causal data analysis that is related to Qualitative Comparative Analysis (QCA) but, contrary to the latter, is custom-built for analyzing causal structures with multiple outcomes. So far, however, CNA has only been capable of processing dichotomous variables, which greatly limited its scope of applicability. This paper generalizes CNA for multi-value variables as well as continuous variables whose values are interpreted as membership scores in fuzzy sets. This generalization comes with a major adaptation of CNA's algorithmic protocol, which, in an extended series of benchmark tests, is shown to give CNA an edge over QCA not only with respect to multi-outcome structures but also with respect to the analysis of non-ideal data stemming from single-outcome structures. The inferential power of multi-value and fuzzy-set CNA is made available to end users in the newest version of the R package cna.

We thank the editors of Comparative Political Studies for having invited us to join this symposium. Rather than addressing separate points made by Munck, Paine, and Schneider, we focus on two related problems that unite their pieces, that are of high relevance beyond this symposium, and that we have addressed only indirectly in our original article. The first problem concerns the over-inflation of the Boolean concept of necessity in Qualitative Comparative Analysis (QCA), the second one ignorance about the formalities of the theory of causation which QCA rests on.

This article applies coincidence analysis ( CNA), a Boolean method of causal analysis presented in Baumgartner (2009a), to configurational data on the Swiss minaret vote of 2009. CNA is related to qualitative comparative analysis (QCA) (Ragin 2008), but contrary to the latter does not minimize sufficient and necessary conditions by means of Quine–McCluskey optimization, but based on its own custom built optimization algorithm. The latter greatly facilitates the analysis of data featuring chain-like causal dependencies among the conditions of an ultimate outcome—as can be found in the data on the Swiss minaret vote. Apart from providing a model of the causal structure behind the Swiss minaret vote, we show that a CNA of that data is preferable over a QCA.

Even after a quarter-century of debate in political science and sociology, representatives of configurational comparative methods (CCMs) and those of regressional analytic methods (RAMs) continue talking at cross purposes. In this article, we clear up three fundamental misunderstandings that have been widespread within and between the two communities, namely that (a) CCMs and RAMs use the same logic of inference, (b) the same hypotheses can be associated with one or the other set of methods, and (c) multiplicative RAM interactions and CCM conjunctions constitute the same concept of causal complexity. In providing the first systematic correction of these persistent misapprehensions, we seek to clarify formal differences between CCMs and RAMs. Our objective is to contribute to a more informed debate than has been the case so far, which should eventually lead to progress in dialogue and more accurate appraisals of the possibilities and limits of each set of methods.