Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China.

Exploring key impact factors and their effects on urban residents' transport carbon dioxide (CO2) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi'an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters' strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.

Temporal attention affects contrast response function by response gain.

Orienting attention to a specific point in time has been shown to improve the contrast sensitivity at the attended time point and impair it earlier or later. This phenomenon could be explained by temporal attention increasing the effective contrast of the target presented at the attended time point which leads to changes in contrast psychometric function by contrast gain. Another explanation is that temporal attention multiplicatively amplifies the amplitude of behavioral or neural response to contrast, resulting in alterations in contrast psychometric function by response gain. To explore the underlying mechanism, we adopted a temporal cueing orientation discrimination task using audio pre-cues composed of different frequency components to induce different attentional allocations in the time domain and targets of various contrast intensities to measure contrast psychometric functions. Obtained psychometric functions for contrast sensitivity were fitted for different conditions with discrepant attentional states in time. We found that temporal attention manipulated by cue affected contrast psychometric function by response gain, indicating that multiplying the contrast response of the visual target occurring at the selected point in time by a fixed factor is a crucial way for temporal attention to modulate perceptual processing.