Exploring how product descriptors and packaging colors impact consumers' perceptions of plant-based meat alternative products.

While consumers have been increasingly trying to reduce their meat consumption due to rising concerns about its impact on their health and the environment, many still find animal-based foods more attractive than plant-based foods, thus hindering their adoption. Could marketing cues such as product descriptors and packaging colors help make these products more attractive to consumers? Across two studies, we tested the effects of product descriptors and packaging colors on meat eating consumers' perceptions of, and behavioral intentions toward, plant-based meat alternative products. Study 1 revealed that a "plant-based" (vs. "meat alternative") descriptor positively impacted perceptions of healthiness and eco-friendliness, as well as trial likelihood, and negatively impacted predicted quantity consumed. Study 2 provided some evidence for the moderating role of packaging color, and more specifically for the (mis)matching effects of product descriptor and packaging color on product perceptions and behavioral intentions. Results revealed that, when using a "meat alternative" descriptor, mismatching (vs. matching) it with a green (vs. red) packaging color negatively impacted perceptions of eco-friendliness and trial likelihood. Conversely, when using a "plant-based" descriptor, matching (vs. mismatching) it with a green (vs. red) packaging color negatively impacted predicted satiety. Overall, our research suggests that marketing cues can, to some extent, impact consumers' perceptions of plant-based meat alternative products and related behavioral intentions, and offers many avenues for future research.

Impact of urban pollution on freshwater biofilms: Oxidative stress, photosynthesis and lipid responses.

Urban ecosystems are subjected to multiple anthropogenic stresses, which impact aquatic communities. Artificial light at night (ALAN) for instance can significantly alter the composition of algal communities as well as the photosynthetic cycles of autotrophic organisms, possibly leading to cellular oxidative stress. The combined effects of ALAN and chemical contamination could increase oxidative impacts in aquatic primary producers, although such combined effects remain insufficiently explored. To address this knowledge gap, a one-month experimental approach was implemented under controlled conditions to elucidate effects of ALAN and dodecylbenzyldimethylammonium chloride (DDBAC) on aquatic biofilms. DDBAC is a biocide commonly used in virucidal products, and is found in urban aquatic ecosystems. The bioaccumulation of DDBAC in biofilms exposed or not to ALAN was analyzed. The responses of taxonomic composition, photosynthetic activity, and fatty acid composition of biofilms were examined. The results indicate that ALAN negatively affects photosynthetic yield and chlorophyll production of biofilms. Additionally, exposure to DDBAC at environmental concentrations induces lipid peroxidation, with an increase of oxylipins. This experimental study provides first insights on the consequences of ALAN and DDBAC for aquatic ecosystems. It also opens avenues for the identification of new biomarkers that could be used to monitor urban pollution impacts in natural environments.

De-stigmatizing the "win-win:" making sustainable consumption sustainable.

In this article, we review research on the discrepancy between consumers' high self-reported interest in sustainable products and these products' poor performance in the marketplace. We offer theoretically derived reasons for why framing sustainable products as "win-wins" (i.e., offering benefits to the self and to the greater good) might present a solution to this intention-behavior gap. Although prior research has typically found negative effects of win-win framing on sustainable consumption, we propose framing sustainable consumption as a win-win provides greater environmental, behavioral, and psychological benefits than framing it only in terms of benefits to the self or to the greater good. We discuss how these outcomes may operate due to differences in moral hypocrisy, licensing, and goal representation, and offer avenues for future research to test these moderators in an effort to improve the efficacy of win-win framing.

Understanding the relationship between resource scarcity and object attachment.

People generally respond to resource scarcity through one of two pathways: scarcity-reduction or control-restoration. We draw from recent work on the solidity (versus liquidity) of consumption opportunities to offer a new lens through which to view how the two pathways that follow from resource scarcity relate to object attachment. In this review, we discuss when each pathway predicts stronger (versus weaker) object attachment. We also offer several open questions for when resource scarcity might prompt consumers to forgo the security afforded by stronger attachments for the flexibility afforded by weaker attachments.

Dataset of chemical and near-infrared spectroscopy measurements of fresh and dried poultry and cattle manure.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.