With the environmental carrying capacity reaching its limits and the decreasing margin benefits of traditional production factors, the green transformation and green development through technological innovations has been a major direction for the future development of Chinese industries. However, the characteristics and heterogeneities of various types of industries call for different approaches regarding technological innovations. How to choose the most effective mode of technological innovation according to the characteristics of a certain industry has been a key issue. This paper measures the green total factor productivity of 32 industrial trades using the Slacks Based Measure(SBM)-DDF method. The effects of three innovation modes in the green transformation of industrial industry, including the independent innovation(Ⅱ), the technology introduction(TI), and the government support(GS), are empirically analyzed based on industry heterogeneity. Results indicate that the green total factor productivities of different industries show significant differences if taking into account the energy input and the undesirable output of pollutant emissions. The green total factor productivities of traditional high input,high pollution, and high energy consumption industrial trades were significantly lower than those with obvious green features. The year of 2009 is a leap year for the industrial green transformation in China. For resource-intensive industries, the II and the GS are the important ways to achieve green transformation. For labor-intensive industries, the TI is the best path to achieve green transformation, while for technology-intensive industries, the II is the primary driving force for the promotion of green developments. In addition, the innovation-compensating effect of the current Chinese environmental regulations to the resource-intensive industries has been revealed. Improving the overall scale and the industrial concentration of the industries is also beneficial for the green transformation of the industries. 相似文献
Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.
Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.
Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.
Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance. 相似文献
AbstractObjective: The current study investigated whether older drivers’ driving patterns during a customized on-road driving task were representative of their real-world driving patterns.Methods: Two hundred and eight participants (male: 68.80%; mean age?=?81.52 years, SD?=?3.37 years, range?=?76.00–96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants’ real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant’s vehicle.Results: During the 4-month period prior to completing the on-road driving task, participants’ median real-world driving trip distance was 2.66?km (interquartile range [IQR]?=?1.14–5.79?km) and their median on-road driving task trip distance was 4.41?km (IQR?=?2.83–6.35?km). Most participants’ on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n?=?198).Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants’ real-world driving patterns. 相似文献