Crafting Next Generation Eco-Label Policy

Eco-labels present a promising policy tool in the effort to achieve sustainable consumption. Many questions remain, however, about the extent to which eco-labels can contribute to sustainability efforts and how to maximize their effectiveness. This Article deploys research from evolutionary psychology, behavioral law and economics, and norm theory to offer specific insights for the design and implementation of eco-labels to enhance their influence on sustainable consumer choice. Notably, this research suggests possibilities for eco-labels to shape or expand consumer preferences for green goods, and thereby enhance eco-label influence on consumer behavior by extending it beyond eco-minded consumers. We suggest that public exposure of the label (so that people see it) and the exposure of the purchasing behavior (so that other people can see that you have bought the product) are key elements to the success of eco-labels--the social context around product purchasing may be as important as the eco-label itself. We recommend that behavioral insights be used to improve eco-labeling as traditionally understood by incorporating knowledge about behavioral tendencies into label design so as to allow for more accurate matching of consumers\u27 preexisting environmental preferences to eco-labeled goods, and develop next-generation eco-labeling policy with the potential to significantly expand the market for eco-labeled goods. Specifically, 1) Eco-labels could be purposefully designed and implemented to attract consumers motivated by social norms; 2) Eco-labels could appeal to a wider range of abstract norm alternate more broadly or locally accepted and strong abstract that are stronger and/or more broadly accepted or locally-salient; and 3) Eco-labels could highlight private, near and near-term benefits

Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits