How to integrate real-world user behavior into models of the market diffusion of alternative fuels in passenger cars - An in-depth comparison of three models for Germany

The future market diffusion of alternative fuels in the passenger car sector is of great interest to both carmakers and policymakers in order to decrease CO$_{2}$ emissions. The decision to buy a car is not totally objective and only partly based on cost. For this reason, those modeling the future market evolution of cars powered by alternative fuels try to include behavioral and non-cost related aspects. This paper analyzes the integration of user behavior into market diffusion models and compares three models that include this aspect. The comparison comprises three parts: first, it compares the modeling approaches, then uses a harmonized data set to model the future market diffusion of alternative fuel vehicles, with and without behavioral aspects. The most important aspects of user behavior included in the models are the use of charging infrastructure, the limited model availability, the consideration of range anxiety as a hampering factor or the willingness-to-pay-more for alternative drivetrains as a supporting factor, as well as a distinction of users\u27 driving distances. User behavior is considered in various ways, but always has a limiting effect on electric vehicle market diffusion. While a model that distinguishes individual users and driving distances stresses the high relevance of this aspect, it is considered less important in models with a more aggregated inclusion of user behavior based on logit functions

Measurement invariance in the social sciences:Historical development, methodological challenges, state of the art, and future perspectives

This review summarizes the current state of the art of statistical and (survey) methodological research on measurement (non)invariance, which is considered a core challenge for the comparative social sciences. After outlining the historical roots, conceptual details, and standard procedures for measurement invariance testing, the paper focuses in particular on the statistical developments that have been achieved in the last 10 years. These include Bayesian approximate measurement invariance, the alignment method, measurement invariance testing within the multilevel modeling framework, mixture multigroup factor analysis, the measurement invariance explorer, and the response shift-true change decomposition approach. Furthermore, the contribution of survey methodological research to the construction of invariant measurement instruments is explicitly addressed and highlighted, including the issues of design decisions, pretesting, scale adoption, and translation. The paper ends with an outlook on future research perspectives.</p

Crime-Inhibiting, Interactional and Co-Developmental Patterns of School Bonds and the Acceptance of Legal Norms

The usually negative relationship between school bonds and juvenile delinquency implies that adolescents with strong ties to school are less likely to engage in delinquency. Still, it is not always clear whether the impact of school bonds on delinquency is direct or mediated by other, more proximate causes. This article examines the dynamic interrelations between social bonds and the acceptance of legal norms over the course of adolescence. Results reveal that school bonds directly affect the acceptance of legal norms, but not delinquency. However, norms are directly and reciprocally related to delinquency. Therefore, school bonds can be considered an indirect preventive factor for delinquent behavior. Moreover, the closely related school bond and norm dimensions share a common developmental pattern during adolescence

Can Metabolite- and Transcript-Based Selection for Drought Tolerance in Solanum tuberosum Replace Selection on Yield in Arid Environments?

Climate models predict an increased likelihood of drought, demanding efficient selection for drought tolerance to maintain yield stability. Classic tolerance breeding relies on selection for yield in arid environments, which depends on yield trials and takes decades. Breeding could be accelerated by marker-assisted selection (MAS). As an alternative to genomic markers, transcript and metabolite markers have been suggested for important crops but also for orphan corps. For potato, we suggested a random-forest-based model that predicts tolerance from leaf metabolite and transcript levels with a precision of more than 90 independent of the agro-environment. To find out how the model based selection compares to yield-based selection in arid environments, we applied this approach to a population of 200 tetraploid Solanum tuberosum ssp. tuberosum lines segregating for drought tolerance. Twenty-four lines were selected into a phenotypic subpopulation (PPt) for superior tolerance based on relative tuber starch yield data from three drought stress trials. Two subpopulations with superior (MPt) and inferior (MPs) tolerance were selected based on drought tolerance predictions based on leaf metabolite and transcript levels from two sites. The 60 selected lines were phenotyped for yield and drought tolerance in 10 multi-environment drought stress trials representing typical Central European drought scenarios. Neither selection affected development or yield potential. Lines with superior drought tolerance and high yields under stress were over-represented in both populations selected for superior tolerance, with a higher number in PPt compared to MPt. However, selection based on leaf metabolites may still be an alternative to yield-based selection in arid environments as it works on leaves sampled in breeder’s fields independent of drought trials. As the selection against low tolerance was ineffective, the method is best used in combination with tools that select against sensitive genotypes. Thus, metabolic and transcript marker-based selection for drought tolerance is a viable alternative to the selection on yield in arid environments

Assessment of drought tolerance and its potential yield penalty in potato

Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding.</jats:p