Evaluation of Transportation Demand Management (TDM) Strategies and Its Prospect in Saudi Arabia

Nowadays it is reiterated in the literature to put more emphasis on Transportation Demand Management (TDM) strategies rather than the traditional transportation strategies which are based on “supply-side” tactic specifically in rapidly growing developing countries. The experiences of many cities reveal that as capacity is increased, demand increases at a similar rate, and subsequently in the long-term, drivers experience no net travel time advantages and the society suffers from the impacts of costly road bills and environmental degradation. This paper suggests emphasizing on TDM strategies to ensure sustainable transportation. The increasing trend of passenger cars in Saudi Arabia makes it more important to concentrate on TDM strategies as a tool for curbing vehicular pollution and congestion. This paper investigated the concept of TDM strategies focusing on the effect of TDM strategies on vehicular emissions and congestion. The analysis of limited scale interviews with experts revealed that tele-working, E-government, electronic shopping, congestion and parking pricing, increased fuel pricing, preferential treatment of HOV, Light Rail Transit (LRT) might be the potential TDM measures. The concerned authorities should think of an innovative mode of public transit services and continuously improve the services to encourage people to switch to public transit. Finally, this paper recommends adopting a public participatory approach in developing TDM strategies which will significantly contribute in reducing vehicular emissions and congestion and ultimately ensure sustainable transportation

Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models

Greenhouse Gas Emissions from Solid Waste Management in Saudi Arabia—Analysis of Growth Dynamics and Mitigation Opportunities

The continuous growth in population, urbanization, and industrial development has been increasing the generation of solid waste (SW) in the Kingdom of Saudi Arabia. Consequently, the associated greenhouse gas (GHG) emission is also following an increasing trend. The collection and use of greenhouse gases emitted from solid waste management practices are still limited. A causality analysis examined the driving factors of the emissions from solid waste management. The methane (CH4) emissions from municipal solid waste (MSW) increased with an increase in gross domestic product (GDP) per capita and urban population, and an increase in foreign direct investment (FDI) inflows and literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The CH4 emission generated from industrial solid wastes was found to be positively related to GDP per capita, urban population, and FDI inflows. However, a decrease in the unemployment rate was likely to increase CH4 emissions from industrial solid wastes. The future greenhouse gas emissions were projected under different possible socio-economic conditions. The scenario analysis based on different variations of population and GDP growth revealed that methane emission from total waste would increase at an average annual rate of 5.13% between 2020 and 2050, and is projected to reach about 4000 Gg by the end of the year 2050. Although the Kingdom has been taking some initiatives towards climate change mitigation, it has significant opportunities to adopt some of the best practices in solid waste management including reduction, recycling, composting and waste-to-energy, and carbon capture and utilization. This study also put emphasis on developing appropriate policy approaches for climate change mitigation based on the circular economy which is gaining momentum in the Kingdom

Review of Smart City Energy Modeling in Southeast Asia

The Southeast Asian region has been eagerly exploring the concepts of smart city initiatives in recent years due to the enormous opportunities and potential. The initiatives are in line with their plan to promote energy efficiency, phase down/out fossil fuel-based generation, and reduce greenhouse gas emission intensity and electrification of various sectors in addition to renewable energy targets and policies to achieve net zero emissions by 2050 or 2060. However, the major challenges for these countries are related to leadership, governance, citizen support, investment, human capacity, smart device heterogeneity, and efficient modeling and management of resources, especially the energy systems. An intelligent energy system is one of the most significant components for any functional smart city, where artificial intelligence (AI), the internet of things (IoT), and big data are expected to tackle various existing and evolving challenges. This article starts with a brief discussion of smart city concepts and implementation challenges. Then, it identifies different types of smart city initiatives in Southeast Asian countries focusing on energy systems. In addition, the article investigates the status of smart systems in energy generation and storage, infrastructure, and model development. It identifies the unique challenges of these countries in implementing smart energy systems. It critically reviews many available energy modeling approaches and addresses their limitations and strengths, focusing on the region. Moreover, it also provides a preliminary framework for a successful energy system that exploits AI, IoT, and big data. Finally, the roadmap for a successful energy system requires appropriate policy development, innovative technological solutions, human capacity building, and enhancement of the effectiveness of current energy systems

Greenhouse Gas Emission Dynamics of Saudi Arabia: Potential of Hydrogen Fuel for Emission Footprint Reduction

The growth of population, gross domestic product (GDP), and urbanization have led to an increase in greenhouse gas (GHG) emissions in the Kingdom of Saudi Arabia (KSA). The leading GHG-emitting sectors are electricity generation, road transportation, cement, chemicals, refinery, iron, and steel. However, the KSA is working to lead the global energy sustainability campaign to reach net zero GHG emissions by 2060. In addition, the country is working to establish a framework for the circular carbon economy (CCE), in which hydrogen acts as a transversal facilitator. To cut down on greenhouse gas emissions, the Kingdom is also building several facilities, such as the NEOM green hydrogen project. The main objective of the article is to critically review the current GHG emission dynamics of the KSA, including major GHG emission driving forces and prominent emission sectors. Then, the role of hydrogen in GHG emission reduction will be explored. Finally, the researchers and decision makers will find the helpful discussions and recommendations in deciding on appropriate mitigation measures and technologies

Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model

Abstract The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5–2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data

A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization

Planning and protection of DC microgrid: A critical review on recent developments

Nowadays, direct current (DC) microgrid is gaining importance due to the wide utilization of DC loads, integration of solar photovoltaic (PV) and energy storage devices, and no frequency and reactive power control issues. However, planning and protection of such microgrid are complicated due to the connection of several distributed generators (DGs), loads, utility grids, and energy storage systems (ESSs) to the DC bus with power electronic converters. Hitherto, the protection of DC microgrid has not drawn sufficient attention due to the high cost and immature protective devices for DC faults. Furthermore, the reliability of DC microgrid highly depends on proper planning, protection, and power-sharing among the DGs, ESSs, and utility grids. Thus, this article provides a critical summary of two main aspects of DC microgrids, such as protection and planning. Since the mentioned aspects are mutually dependent, a discussion of an aggregated point of view is included. The advantages and disadvantages of different protection and planning approaches are well documented. The possible improvements of existing protection and planning methods are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress