Green logistics: key elements for a well-functioning sustainable logistics system

A growing awareness of the modern society about the direct relationship between a growing global community with increasing total industrial activities on one hand and various environmental problems and a natural limitation of natural resources on the other hand set the base for sustainable or “green” approaches within the supply chain. This paper therefore will look at the issue of “Green Logistics” which seeks to reduce the environmental impact of logistics activities by taking into account functions such as recycling, waste and carbon emission reduction and the use of alternative sources of energy. In order to analyze how these approaches and ideas are being perceived by the system as a whole two models from the area of prospective and scenario planning are being used and described to identify the main drivers and tendencies within the system in order to create feasible hypothesis. Using the URCA/CHIVAS model allows us to identify the driver variables out of a high number of variables that best describe the system “Green Logistics”. Followed by the analysis of the actor’s strategies in the system with the Mactor model it is possible to reduce the complexity of a completely holistic system to a few key drivers that can be analyzed further on. Here the implications of URCA/CHIVAS and Mactor are being used to formulate hypotheses about the perception of Green Logistics and its successful implementation among logistics decision makers by an online survey. This research seeks to demonstrate the usefulness of scenario planning to a highly complex system observing it from all angles and extracting information about the relevant factors of it. The results of this demonstration indicate that there are drivers much beyond the factory walls that need to be considered when implementing successfully a system such as Green Logistics.

Price discovery in dual-class shares across multiple markets

We extend the standard price discovery analysis to estimate the information share of dual-class shares across domestic and foreign markets. By examining both common and preferred shares, we aim to extract information not only about the fundamental value of the rm, but also about the dual-class premium. In particular, our interest lies on the price discovery mechanism regulating the prices of common and preferred shares in the BM&FBovespa as well as the prices of their ADR counterparts in the NYSE and in the Arca platform. However, in the presence of contemporaneous correlation between the innovations, the standard information share measure depends heavily on the ordering we attribute to prices in the system. To remain agnostic about which are the leading share class and market, one could for instance compute some weighted average information share across all possible orderings. This is extremely inconvenient given that we are dealing with 2 share prices in Brazil, 4 share prices in the US, plus the exchange rate (and hence over 5,000 permutations!). We thus develop a novel methodology to carry out price discovery analyses that does not impose any ex-ante assumption about which share class or trading platform conveys more information about shocks in the fundamental price. As such, our procedure yields a single measure of information share, which is invariant to the ordering of the variables in the system. Simulations of a simple market microstructure model show that our information share estimator works pretty well in practice. We then employ transactions data to study price discovery in two dual-class Brazilian stocks and their ADRs. We uncover two interesting ndings. First, the foreign market is at least as informative as the home market. Second, shocks in the dual-class premium entail a permanent e ect in normal times, but transitory in periods of nancial distress. We argue that the latter is consistent with the expropriation of preferred shareholders as a class.