• Energy Research

This study used a cradle-to-cradle Life Cycle Assessment (LCA) approach to evaluate the environmental potentials of urban pavements. For this purpose, the urban road network of the City of Münster (Germany) was selected as the case study, and comprehensive data for several phases were collected. The entire road network is composed of flexible pavements designed according to specific traffic loads and consists of main roads (MRs), main access roads (MARs), and residential roads (RSDTs). Asphalt materials, pavement structures, and maintenance strategies are predefined for each type of road and are referred to as “traditional” herein. Some pavement structures have two possible maintenance strategies, denoted by “A” and “B”, with distinguished periods of intervention. To evaluate the impact of using recycled materials, we considered alternative pavement structures composed of asphalt materials containing a greater amount of reclaimed asphalt pavement (RAP). The study was carried out considering analysis periods of 20, 50, 80, and 100 years and using two indicators: non-renewable cumulative energy demand (nr-CED) and global warming potential (GWP). The results show that the use of higher amounts of RAP can mitigate negative environmental impacts and that certain structures and maintenance strategies potentially enhance the environmental performance of road pavements. This article suggests initiatives that will facilitate the decision-making process of city administrators to achieve more sustainable road pavement constructions and provides an essential dataset inventory to support future environmental assessment studies, particularly for European cities.

This study used a cradle-to-cradle Life Cycle Assessment (LCA) approach to evaluate the environmental potentials of urban pavements. For this purpose, the urban road network of the City of Münster (Germany) was selected as the case study, and comprehensive data for several phases were collected. The entire road network is composed of flexible pavements designed according to specific traffic loads and consists of main roads (MRs), main access roads (MARs), and residential roads (RSDTs). Asphalt materials, pavement structures, and maintenance strategies are predefined for each type of road and are referred to as “traditional” herein. Some pavement structures have two possible maintenance strategies, denoted by “A” and “B”, with distinguished periods of intervention. To evaluate the impact of using recycled materials, we considered alternative pavement structures composed of asphalt materials containing a greater amount of reclaimed asphalt pavement (RAP). The study was carried out considering analysis periods of 20, 50, 80, and 100 years and using two indicators: non-renewable cumulative energy demand (nr-CED) and global warming potential (GWP). The results show that the use of higher amounts of RAP can mitigate negative environmental impacts and that certain structures and maintenance strategies potentially enhance the environmental performance of road pavements. This article suggests initiatives that will facilitate the decision-making process of city administrators to achieve more sustainable road pavement constructions and provides an essential dataset inventory to support future environmental assessment studies, particularly for European cities.

Several studies evaluated the feasibility of using residues to compose asphalt mixtures. However, the demand for treatments are often neglected in determining the environmental impacts. This study ...

Several studies evaluated the feasibility of using residues to compose asphalt mixtures. However, the demand for treatments are often neglected in determining the environmental impacts. This study ...

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.