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Vehicles solely powered by electricity are a major technological innovation that combines individual transportation needs and environmental sustainability, yet their market penetration is low. Research has traditionally indicated factors such as the vehicle’s purchasing price, driving range, and charging time as the main barriers to adoption. However, the decision to adopt a technology also depends on what the technology represents to the user; therefore, other factors may be important to explain individuals’ behavior. This study is a quantitative and cross-sectional look at the behavioral intention to adopt battery electric vehicles (BEVs) technology in the context of Macau. The research builds on the unified theory of acceptance and use of technology 2 (UTAUT 2) (Venkatesh et. al., 2012) to explain the characteristics of the local consumers. Besides the addition of image and environmental concern to the theoretical model, the study also put forward and evaluate the construct of technology show-off, an original measure of the visible and experiential characteristics of a technology. A sample of 236 Macau residents was analyzed by structural equation modeling (SEM). The analysis of the data supported the explanatory and predictive power of our model and helped to describe the idiosyncrasies of local residents. The results provide insights related to individual technology acceptance that could be useful in designing more accurate strategies and fostering the uptake of BEVs in Macau or markets that share similarities

A combination of assessment, operational forecast, and future perspective was thoroughly explored to provide an overview of the existing air quality problems in Macao. The levels of air pollution in Macao often exceed those recommended by the World Health Organization (WHO). In order for the population to take precautionary measures and avoid further health risks during high pollution episodes, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MLR) and classification and regression trees (CART) analysis were successfully developed for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. Meteorological variables were selected from an extensive list of possible variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to support an operational air iv quality forecast for Macao. The work involved two phases. On a first phase, the models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. On a second phase, these models were used with 2019 validation data, while a new set of models based on a more extended historical data series, from 2013 to 2018, were also validated with 2019 data. There were no significant differences in the coefficients of determination (R2) and minor improvements in root mean square errors (RMSE), mean absolute errors (MAE) and biases (BIAS) between the 2013 to 2016 and the 2013 to 2018 data models. In addition, for one air quality monitoring station (Taipa Ambient), the 2013 to 2018 model was applied for two days ahead (D2) forecast and the coefficient of determination (R2) was considerably less accurate to the one day ahead (D1) forecast, but still able to provide a reliable air quality forecast for Macao. To understand if the prediction model was robust to extreme variations in v pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and a low pollution episode during 2020. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration for PM2.5 levels exceeding 55 μg/m3 and the maximum hourly concentration for O3 levels exceeding 400 μg/m3. For the low pollution episode, the 2020 period of implementation of the preventive measures for COVID-19 pandemic was selected, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and maximum hourly concentration for O3 levels at 50 μg/m3. The 2013 to 2018 model successfully predicted the high pollution episode with high coefficients of determination (0.92 for PM2.5 and 0.82 for O3). Likewise, the low pollution episode was also correctly predicted with high coefficients of determination (0.86 and 0.84 for PM2.5 and O3, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels. Machine learning methods maybe adopted to provide significant improvements in combination of multiple linear regression (MLR) and classification and regression vi tree (CART) to further improve the accuracy of the statistical forecast. The developed air pollution forecasting model may be combined with other measures to mitigate the impact of air pollution in Macao. These may include the establishment of low emission zones (LEZ), as enforced in some European cities, license plate restrictions and lottery policy, as used in some Asian, tax exemptions on electric vehicles (EVs) and exclusive corridors for public transportations. Keywords: Air pollution; Particulate Matter; Ozone; Macao; Statistical air quality forecast; Pollution episodes; Chinese national holiday; COVID-19

This thesis aims to explore how learning accounting for non-accounting major students could be enhanced through a comprehensive framework connecting individual attributes and learning approaches. While accounting is considered an important and mandatory discipline for business students, it is also considered a difficult subject which deters students’ engagement in the subject. Although the issue has been widely recognized, there are relatively less discussions on possible remedies. Learning accounting is a dynamic process that go beyond the traditional one-to-many learning and teaching settings, all factors revolving the students’ learning environment and process, altogether determine and affect the learning efficiencies. A meta-analysis was first performed to understand the standings of accounting education and to identify gaps within existing literatures in the field of management, accounting, tourism and general education researches. A documentary review on the curricula of all Macau HEI programs involving compulsory accounting courses was also conducted to understand the role and significance of accounting courses for non-accounting college students in Macau. Based on the literature review, this study borrows constructs from theory of planned behavior (TPB) models in its research framework. It attempts to provide a comprehensive framework to understand how students learn accounting efficiently by delving into individual learning styles of the students and their perceived helpfulness and self-efficacy, in respective to different learning techniques. Due to its exploratory nature, a mixed method of qualitative and quantitative methodology is adopted, where both focus group discussions and in-depth interviews are performed with non-accounting students to elicit understandings on student’s difficulties in learning accounting and their preferences for different learning styles. Then, a full-scale quantitative survey is conducted in a Macau higher education institute to assess the learning efficiencies of accounting in different situations. A triangulation with feedback from faculties and administrators are also elicited to provide alternate dimensions on the issues. With no surprise, there is no one best 3 teaching style or method for all students, this thesis contributes to accounting education research by provide a comprehensive diagnosis on the all-round learning stimulus and hindrance in learning accounting. At the end, recommendations are provided for accounting educators to emphasize on the uniqueness of students and to encourage a more dynamic and open environment for learning. Limitations of the studies are also discussed