Transition to Electric Vehicles In the California Automobile Industry

This dissertation presents a comprehensive study on the market adoption of electric vehicle and policy impact of the Zero Emission Vehicle (ZEV) mandate in the California automotive market. This research is primarily consisting of three parts. The author first built a technology innovation pricing model based on multi-nomial logit modelling method. This studies the dynamics among customer preferences, market acceptance and policy impact on vehicle pricing in the California automotive market. Results show that the ZEV mandate could profoundly enhance the market adoption of electric vehicles. There is a threshold on the magnitude of policy intervention. If the number of credits per vehicle is less than the threshold, increasing intervention promotes the EV market penetration; however, beyond the threshold, policy primarily benefits automakers. In the second step, the author presents a decision model for electric vehicle attributes. This research first characterizes the market adoption rate of electric vehicle models under government subsidy and derives optimal vehicle attributes with respect to consumers\u27 preferences and product-based subsidy. The proposed model was then applied to the California\u27s automotive market. Our results also suggest that industry leaders and followers may choose different product strategy and market segments due to different battery manufacturing costs. In the last part, the author constructed a series of scenarios for the transition to battery electric cars and used the Market Acceptance of Advanced Automotive Technologies model to analyze the price competition in the California electric vehicle market. Considering the ZEV mandate already in place, this part investigates the role of this regulation in influencing the pricing decisions of different electric vehicle models and enhancing the overall market adoption rate. It was found that the 200-mile range electric vehicle had remarkable unilateral influence on the pricing of the 100-mile range electric vehicle. It suggests that the 200-mile range electric vehicle will become the core driving force in electric vehicle diffusion in the California electric vehicle market. The ZEV mandate remarkably reduced the prices of both models and increased corresponding annual demand. However, this policy showed considerable influence of changing the structure of the California electric vehicle market

Effect Modifications of Overhead-View and Eye-Level Urban Greenery on Heat-Mortality Associations: Small-Area Analyses Using Case Time Series Design and Different Greenery Measurements.

BackgroundThe protective effect of urban greenery from adverse heat impacts remains inconclusive. Existing inconsistent findings could be attributed to the different estimation techniques used.ObjectivesWe investigated how effect modifications of urban greenery on heat-mortality associations vary when using different greenery measurements reflecting overhead-view and eye-level urban greenery.MethodsWe collected meteorological and daily mortality data for 286 territory planning units between 2005 and 2018 in Hong Kong. Three greenery measurements were extracted for each unit: a) the normalized difference vegetation index (NDVI) from Landsat remote sensing images, b) the percentage of greenspace based on land use data, and c) eye-level street greenery from street view images via a deep learning technique. Time-series analyses were performed using the case time series design with a linear interaction between the temperature term and each of the three greenery measurements. Effect modifications were also estimated for different age groups, sex categories, and cause-specific diseases.ResultsHigher mortality risks were associated with both moderate and extreme heat, with relative risks (RRs) of 1.022 (95% CI: 1.000, 1.044) and 1.045 (95% CI: 1.013, 1.079) at the 90th and 99th percentiles of temperatures relative to the minimum mortality temperature (MMT). Lower RRs were observed in greener areas whichever of the three greenery measurements was used, but the disparity of RRs between areas with low and high levels of urban greenery was more apparent when using eye-level street greenery as the index at high temperatures (99th percentile relative to MMT), with RRs for low and high levels of greenery, respectively, of 1.096 (95% CI: 1.035, 1.161) and 0.985 (95% CI: 0.920, 1.055) for NDVI (p=0.0193), 1.068 (95% CI: 1.021, 1.117) and 0.990 (95% CI: 0.906, 1.081) for the percentage of greenspace (p=0.1338), and 1.103 (95% CI: 1.034, 1.177) and 0.943 (95% CI: 0.841, 1.057) for eye-level street greenery (p=0.0186). Health discrepancies remained for nonaccidental mortality and cardiorespiratory diseases and were more apparent for older adults (≥65 years of age) and females.DiscussionThis study provides new evidence that eye-level street greenery shows stronger associations with reduced heat-mortality risks compared with overhead-view greenery based on NDVI and percentage of greenspace. The effect modification of urban greenery tends to be amplified as temperatures rise and are more apparent in older adults and females. Heat mitigation strategies and health interventions, in particular with regard to accessible and visible greenery, are needed for helping heat-sensitive subpopulation groups in coping with extreme heat. https://doi.org/10.1289/EHP12589

Measuring Recovery to Build up Metrics of Flood Resilience Based on Pollutant Discharge Data: A Case Study in East China

Building “disaster-resilient” rather than “disaster-resistant” cities/communities requires the development of response capabilities to natural disasters and subsequent recovery. This study devises a new method to measure resilience via recovery capability to validate indicators from social, economic, infrastructural, and environmental domains. The pollutant discharge data (waste-water and waste-gas discharge/emission data) of local power plants, sewage treatment plants and main factories were used to monitor recovery process of both people’s living and local industrial production as the waste water/gas is released irregularly during the short disaster-hit period. A time series analysis of such data was employed to detect the disturbance on these infrastructures from disasters and to assess community recovery capability. A recent record-breaking flash flood in Changzhou, a city in eastern-central China, was selected as a case study. We used ordinal logistic regression to identify leading proxies of flood resilience. A combination of six variables related to socioeconomic factors, infrastructure development and the environment, stood out and explained 61.4% of the variance in measured recovery capability. These findings substantiate the possibility of using recovery measurement based on pollutant discharge to validate resilience metrics, and contribute more solid evidences for policy-makers and urban planners to make corresponding measures for resilience enhancement

An Updated Functional Annotation of Protein-Coding Genes in the Cucumber Genome

Background: Although the cucumber reference genome and its annotation were published several years ago, the functional annotation of predicted genes, particularly protein-coding genes, still requires further improvement. In general, accurately determining orthologous relationships between genes allows for better and more robust functional assignments of predicted genes. As one of the most reliable strategies, the determination of collinearity information may facilitate reliable orthology inferences among genes from multiple related genomes. Currently, the identification of collinear segments has mainly been based on conservation of gene order and orientation. Over the course of plant genome evolution, various evolutionary events have disrupted or distorted the order of genes along chromosomes, making it difficult to use those genes as genome-wide markers for plant genome comparisons.Results: Using the localized LASTZ/MULTIZ analysis pipeline, we aligned 15 genomes, including cucumber and other related angiosperm plants, and identified a set of genomic segments that are short in length, stable in structure, uniform in distribution and highly conserved across all 15 plants. Compared with protein-coding genes, these conserved segments were more suitable for use as genomic markers for detecting collinear segments among distantly divergent plants. Guided by this set of identified collinear genomic segments, we inferred 94,486 orthologous protein-coding gene pairs (OPPs) between cucumber and 14 other angiosperm species, which were used as proxies for transferring functional terms to cucumber genes from the annotations of the other 14 genomes. In total, 10,885 protein-coding genes were assigned Gene Ontology (GO) terms which was nearly 1,300 more than results collected in Uniprot-proteomic database. Our results showed that annotation accuracy would been improved compared with other existing approaches.Conclusions: In this study, we provided an alternative resource for the functional annotation of predicted cucumber protein-coding genes, which we expect will be beneficial for the cucumber's biological study, accessible from http://cmb.bnu.edu.cn/functional_annotation. Meanwhile, using the cucumber reference genome as a case study, we presented an efficient strategy for transferring gene functional information from previously well-characterized protein-coding genes in model species to newly sequenced or “non-model” plant species