Introduction Of Infrastructure Research Articles

Factors Influencing Consumers’ Intentions to Participate in a Formal E-Waste Collection System: A Case Study of Onitsha, Nigeria

Due to the increasing amount of electronic waste (e-waste) generated in Nigeria, challenges such as consumer disposal behaviors have emerged. An understanding of consumers’ intentions to participate in formal e-waste collections is key in increasing the level of participation in an e-waste collection scheme. The theory of planned behavior (TPB) creates an applicable platform for identifying the determinants of recycling intention. Based on the TPB, we develop a theoretical framework to study how influencing factors such as attitude, subjective norm, perceived behavioral control and environmental knowledge influence intentions to participate in formal e-waste collections. Additionally, we extend the research framework to examine whether the factors of infrastructure and economic incentive moderate the relationships between the influencing factors and intention. Using an empirical survey conducted in Onitsha with 384 usable questionnaire responses, we observe that attitude, subjective norm, and environmental knowledge directly influence consumers’ intentions. Statistical results also show that only the factor of infrastructure moderates the relationship between two influencing factors (attitude and subjective norm) and intention. The resulting negative coefficients of regression for the interactions indicate that the introduction of infrastructure will result in a weaker influencing ability of attitude and subjective norm on intention. Thus, the implications of this study in motivating consumers’ intentions suggest it would be beneficial for the government to provide functional and adequately managed infrastructure situated close to the community, such that it can be easily accessed by household consumers.

Estimating a treatment effect under uncertainty with application to a high‐speed railway system

AbstractThis article proposes new treatment effect models and applies them to analyse the effect of high‐speed railways on population density. These models focus on the difference in treatment intensity across treated observations. The difference in treatment intensity occurs as a result of uncertainty over whether the treatment will be properly administered to observations. When evaluating large infrastructure, such as high‐speed railways, the introduction of infrastructure is considered as the treatment for municipalities. The treatment in such cases usually takes time and poses uncertainty whether it will be complete or not. To address such uncertainty, we extend the Roy model—a typical treatment effect model—under a reasonable assumption to enable it to incorporate observations that are treated but their treatment is not complete. Further, we allow the correlation between how these observations are determined and their outcomes and treatment assignments. This article also discusses the identification problem with respect to estimating model parameters. The proposed statistical models are applied to evaluate the effect of the Shinkansen, a high‐speed railway in Japan, on population density. The Canadian Journal of Statistics 42: 337–358; 2014 © 2014 Statistical Society of Canada

Explaining reduction of pedestrian–motor vehicle crashes in Arkhangelsk, Russia, in 2005–2010

Objective. To explain a reduction in pedestrian–motor vehicle crashes in Arkhangelsk, Russia, in 2005–2010.Study design. Retrospective ecological study.Methods. For 2005–2010, police data on pedestrian–motor vehicle crashes, traffic violations, and total motor vehicles (MVs) were combined with data on changes in national road traffic legislation and municipal road infrastructure. Negative binomial regression was used to investigate trends in monthly rates of pedestrian–motor vehicle crashes per total MVs and estimate changes in these rates per unit changes in the safety measures.Results. During the 6 years, the police registered 2,565 pedestrian–motor vehicle crashes: 1,597 (62%) outside crosswalks, 766 (30%) on non-signalized crosswalks, and 202 (8%) on signalized crosswalks. Crash rates outside crosswalks and on signalized crosswalks decreased on average by 1.1% per month, whereas the crash rate on non-signalized crosswalks remained unchanged. Numbers of signalized and non-signalized crosswalks increased by 14 and 19%, respectively. Also, 10% of non-signalized crosswalks were combined with speed humps, and 4% with light-reflecting vertical signs. Pedestrian penalties for traffic violations increased 4-fold. Driver penalties for ignoring prohibiting signal and failure to give way to pedestrian on non-signalized crosswalk increased 7- and 8-fold, respectively. The rate of total registered drivers’ traffic violations per total MVs decreased on average by 0.3% per month. All studied infrastructure and legislative measures had inverse associations with the rate of crashes outside crosswalks. The rate of crashes on signalized crosswalks showed inverse associations with related monetary penalties.Conclusions. The introduction of infrastructure and legislative measures is the most probable explanation of the reduction of pedestrian–motor vehicle crashes in Arkhangelsk. The overall reduction is due to decreases in rates of crashes outside crosswalks and on signalized crosswalks. No change was observed in the rate of crashes on non-signalized crosswalks.