The need to balance economic growth and its environmental impact continues to be a serious issue in China.As environmental regulation in China increases in importance,it is critical to understand how it impacts econom...The need to balance economic growth and its environmental impact continues to be a serious issue in China.As environmental regulation in China increases in importance,it is critical to understand how it impacts economic growth drivers such as outward foreign direct investment(OFDI)to formulate effective policies.One consideration should be the hidden economy,which can weaken the effects of environmental regulation on OFDI.This study investigates the scale of the hidden economy in 30 provinces and province-level municipalities in China in the period 2004 to 2015.The study uses the multiple indicators and multiple causes(MIMIC)model and the systematic generalized method of moments(GMM)test to analyze the impact of environmental regulation and the hidden economy on China's OFDI.The results show that stronger environmental regulation promotes OFDI.However,the hidden economy inhibits China's OFDI,as the positive effects of environmental regulation that drive OFDI are distorted.From a regional perspective,stronger environmental regulation promotes OFDI as well,while the hidden economy inhibits it.The interaction between environmental regulation and the hidden economy also inhibits it significantly.展开更多
Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid con...Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.展开更多
基金supported by the Humanities and Social Sciences Foundation of the Chinese Ministry of Education[Grant Number.20YJC790031].
文摘The need to balance economic growth and its environmental impact continues to be a serious issue in China.As environmental regulation in China increases in importance,it is critical to understand how it impacts economic growth drivers such as outward foreign direct investment(OFDI)to formulate effective policies.One consideration should be the hidden economy,which can weaken the effects of environmental regulation on OFDI.This study investigates the scale of the hidden economy in 30 provinces and province-level municipalities in China in the period 2004 to 2015.The study uses the multiple indicators and multiple causes(MIMIC)model and the systematic generalized method of moments(GMM)test to analyze the impact of environmental regulation and the hidden economy on China's OFDI.The results show that stronger environmental regulation promotes OFDI.However,the hidden economy inhibits China's OFDI,as the positive effects of environmental regulation that drive OFDI are distorted.From a regional perspective,stronger environmental regulation promotes OFDI as well,while the hidden economy inhibits it.The interaction between environmental regulation and the hidden economy also inhibits it significantly.
基金funded by University of Transport and Commu-nications (UTC) (Grant No.T2019-CT-06TD).
文摘Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.
