Green entrepreneurship has a huge role in solving environmental degradation and social problems. As today’s youth are tomorrow’s entrepreneurs, enhancing their green entrepreneurial intention will contribute to the sustainable development of economy in the future. The existing literature has examined the green entrepreneurial intention of college students based on self-efficacy, entrepreneurial creativity, entrepreneurship education, financial support, sustainable development values, and other influencing factors. However, these studies focus on net effect of factors on the results of college students’ green entrepreneurial intention, (...) ignoring the combination of multiple factors, and the relationship between different configurations of factors and the results of college students’ green entrepreneurial intention. Therefore, this study aimed to understand and analyze the influence of the complex relationship between multiple antecedents on college students’ green entrepreneurial intention from a comprehensive perspective. Based on 207 questionnaires collected from colleges in the coastal cities of China, this study used the fuzzy set qualitative comparative analysis method for data analysis. The results showed three dominant paths of great significance to college students’ high green entrepreneurial intention: entrepreneurial creativity—green cognition—dominant path; entrepreneurial creativity—financial support—dominant path; and the future self-continuity—entrepreneurship culture—entrepreneurship education—dominant path. The results of this study can help college educators to plan courses on innovative thinking and green entrepreneurship to improve college students’ entrepreneurial creativity and green cognitive ability. (shrink)

Automatic incident detection plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems to provide timely information to passengers and other stakeholders in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for (...) the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents. (shrink)