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Non-motorized transport (NMT) is the use of a bicycle or walking to travel from one place to another. It is gaining popularity especially in the developed countries due to low transport externalities such as emissions and traffic congestion alongside its benefits to physical and mental health. In this paper, a comprehensive review of the existing literature related to NMT is presented focusing on the factors including built environment, geography, and weather, the health, and environmental benefits of NMT, and the motivational approach for increasing the use of NMT. The built environment, geography and weather, and socioeconomic factors significantly affect the use of NMT as a travel mode. This study reviewed some unique characteristics of NMT especially in developing countries to provide a clear understanding of the dynamics of NMT. Despite existence of vast research on NMT, a comprehensive literature review to evaluate different aspects of NMT seems essential to address the future challenges with significant automobile ownership increase in the developing nations and the associated externalities. The developing nations have to understand the factors of NMT with reference to their socio-economic conditions and perform quantitative analyses to estimate and project benefits of NMT including health benefits. The policy makers in the developing countries should consider the NMT as one element of the solution matrix to address the challenges of road transport.

Mitigating climate change via initiatives such as reducing emissions of greenhouse gases (GHG) and renewable energy (RE) generation helps in reducing environmental pollution, increasing efficiency, and saving costs in the energy industry. Bangladesh recently formulated some policies and initiatives for reducing GHG emissions that have been increasing at an alarming rate lately, which are driven largely by economic growth, energy security, and local environmental concerns. However, little is known about the impacts of the existing policies and initiatives on curbing GHG emissions and promoting environmental sustainability, especially from the agriculture and energy sectors that contribute 44% and 39% to the country’s net emissions, respectively. This study, therefore, reviews the extent of GHG emissions in the country and analyzes the country’s key policies and initiatives for mitigating climate change and promoting environmental sustainability. The key finding is that despite the existence of a national energy policy and initiatives such as RE development, rational and efficient use of energy, Intended Nationally Determined Contributions, clean development mechanism, international and private sector collaboration, GHG emissions are projected to continue to grow at high rates due to population growth, industrialization, energy consumption growth that outpace government initiatives to reduce emission levels. The paper concludes that good governance, behavioral transformation, and public awareness about climate change are vital steps for reducing GHG emissions and promoting environmental sustainability.

There is a growing focus on the role of renewable energy (RE) policies such as feed-in tariffs (FITs), renewable portfolio standards (RPSs), subsidies, incentives, and research and development in the global energy policy mix and in promoting environmental sustainability. Although most developed countries have well-formulated RE policies, in developing countries, such policies face many barriers. This study analyzes the policies, drivers, and barriers to RE deployment for fostering environmental sustainability in the Gulf Cooperation Council (GCC) countries. In the GCC region, the need for economic diversification to reduce dependency on single resource, diminishing hydrocarbon reserve, loss of oil export revenue, climate change mitigation pledges, and abundant solar energy resource are the key drivers for diversifying energy sources to include RE. However, the apparent lack of consolidated policy framework for wide-scale RE utilization calls for a well-articulated policy to advance RE development in each member state. Although FIT and RPS approaches could be effective for initial deployment of small-scale RE projects, a competitive tendering and auctioning mechanisms are more suitable for large-scale projects. Whereas, developing effective energy codes could successfully promote RE deployment, the increased share of RE in energy supply would have synergistic impacts on the region. The GHG emissions avoidance expected to be achieved by the GCC countries will vary between 5 and 247 million tons of CO2 equivalent by 2030. The fulfillment of inspirational RE targets for 2030 would contribute in fulfilling climate change mitigation pledges, environmental sustainability, economic growth, and generating new jobs.

The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

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.

Even though the contribution of the aviation sector to the global economy is very notable, it also has an adverse impact on climate change. Improvements have been made in different areas (i.e., technology, sustainable aviation fuel, and design) to mitigate these adverse effects. However, the rate of improvement is small compared to the increase in the demand for air transportation. Hence, greenhouse gas emissions in the aviation sector are steadily increasing and this trend is expected to continue unless adequately addressed. In this context, this study examined the following: (i) the factors that affect the growth of aviation, (ii) trends in greenhouse gas emissions in the sector, (iii) trends in energy demand, (iv) mitigation pathways of emissions, (v) mitigation challenges for the International Civil Aviation Organization, (vi) achievements in mitigating emissions, (vii) barriers against mitigating emissions, and (viii) approaches of overcoming barriers against emissions mitigation. This study finds that continued research and development efforts targeting aircraft fuel burn efficiency are crucial in reducing greenhouse gas emissions. Although biofuels are promising for the reduction of aviation emissions, techniques to reduce NOx emissions could enhance large-scale deployment. Pragmatic market-based mechanisms, such as the Emissions Trading Scheme (ETS) and/or carbon tax must be enforced on a global scale to capitalize on a collective stakeholder effort to curb CO2 emissions. The findings of this study will help in understanding the emissions and energy consumption scenarios, which will provide a comprehensive package of mitigation pathways to overcome future emissions reduction challenges in the aviation sector.

Drilling waste management for oil and gas industry involves collection, transportation, processing, recycling, disposal, and monitoring of waste materials in order to reduce their effects on health and environment. In the Exploration and Production industry, environmental concerns have made effective management of drilling waste at well sites a great priority. Effective waste management is thus a key element in a successful drilling project. The industry therefore relies on effective expertise and newer technologies to maintain a balanced trade-off between healthy business activities and environmental effects. Over several decades, the industry has recorded remarkable progress in reducing the effects of drilling activities on the environment. The progressive development and adoption of sustainable drilling waste management protocols by operating and servicing companies have been partly responsible for this success. However, new challenges are emerging due to advances in drilling techniques such as extended reach wells, deep water drilling, multi-lateral drilling, and high-pressure wells. Recently, advanced drill waste treatment methods such as nanotechnology based approach, supercritical fluid extraction, and microwave treatment process have been explored and tested in the laboratory and on pilot scale to see if they can help overcome these challenges and increasingly stringent regulations. This paper reviews the shortcomings of the conventional approaches, their treatment technologies, advantages, and applications. This article enhances understanding of waste management and its treatment process based on sustainability concept. Researchers can find this review useful as an update on the state of drill waste management in the oil and gas industry. It can also serve as a useful guide to develop new rigorous sustainable models, protocols, and techniques to manage drilling waste using the noble concept.

This study investigated the challenges of calibration of the PARAMICS microscopic simulation model for the local traffic conditions in the Kingdom of Saudi Arabia. It proposed an adaptive neuro-fuzzy inference system (ANFIS) based calibration protocol for the PARAMICS model. The developed ANFIS model performs adequately in modeling the queue length as a function of two key calibration parameters, namely mean headway time and mean reaction time. The selected values of the calibration parameters obtained through the ANFIS modeling approach were used as the input parameters for the PARAMICS model. The error indices such as mean absolute errors and mean absolute percentage errors of the developed ANFIS model in predicting the queue lengths varied between 1.11 and 1.24, and between 3.44 and 4.06, respectively. The conformance of the PARAMICS output and the measured queue length indicates the validity of the proposed calibration protocol.

Corrosion initiation time of embedded steel is an important service life parameter, which depends on concrete material make-up, exposure environment, and duration of exposure. Early and accurate determination of corrosion initiation time will aid in designing durable reinforced concrete, saves cost and time. This study leveraged on the power of ensemble machine learning by combining the performances of different models in estimating the corrosion initiation time of steel embedded in self compacted concrete using corrosion potential measurement. The concrete specimens were prepared with limestone powder as supplementary addition to Portland cement and was exposed to 5% sodium chloride in accordance with the requirements of ASTM C876 – 15 for 8 months. During the exposure, corrosion potential of the embedded steel was measured, and the recorded datasets were used in training five different machine learning models. With cement, limestone powder, coarse aggregate, fine aggregate, water and exposure period.as input variables, five different models were developed to estimate the corrosion initiation time (determined from the corrosion potential measurements) of the embedded steel. With respect to model predictive performance, the acquired results demonstrated that the random forest (RF) ensemble model amongst other trained models performed best with 85/15 dataset percentage split for the training and testing. RF ensemble performed best with CC and RMSE of 99.01% and 18.2747 mV for training, and 98.67% and 25.0298 mV for testing respectively. Hence, due to its superior and robust performance, this study proposes RF ensemble model in the estimation of corrosion initiation time of embedded steel in reinforced limestone-cement blend concrete.

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

Accurate prediction of non-hydrocarbon (Non-HC) gas components in the gas-oil separators reduces the cost of gas and oil production in petroleum engineering. However, this task is difficult because there is no known relation among the properties of crude oil and the separators. There are studies that attempt to predict hydrocarbons (HCs) components using either Computational Intelligence (CI) techniques or conventional techniques like Equitation-of-State (EOS) and Empirical Correlation (EC). In this paper, we explore the applicability of CI techniques such as Artificial Neural Network, Support Vector Regressions, and Adaptive Neuro-Fuzzy Inference System to predict the Non-HC gas components in gas-oil separator tank. Further, we incorporate Genetic Algorithms (GA) into the Hybrid Computational Intelligence (HCI) models to enhance the accuracy of prediction. GA is used to determine the most favorable values of the tuning parameters in the CI models. The performances of the CI and HCI models are compared with the performance of the conventional techniques like EOS and EC. The experimental results show that accuracy of prediction by CI and HCI models outperform the conventional methods for N2 and H2S gas components. Furthermore, the HCI models perform better than the non-optimized CI models while predicting the Non-HC gas components.

The first remarkable environmental initiatives in Bangladesh were taken because of the Stockholm Conference on Human Environment in 1972. The provision that requires Environmental Impact Assessment (EIA) for any new public and private project was first incorporated in the National Environmental Policy, 1992. Still environmental degradation is one of the major concerns in Bangladesh. Like some other developing countries, it initiates and conducts EIA in order to satisfy international donor agencies. Now, EIA is becoming matured through the positive steps of government, non-governmental organizations (NGOs) and international agencies. However, EIA cannot adequately address all the prevailing environmental issues in Bangladesh because of its inherent and contextual limitations. This article explores the evolution of environmental assessment in Bangladesh, mostly at the strategic level. It investigates some of the policy failures, which resulted due to the absence of environmental assessment at the strategic level. Through a questionnaire survey (e-mail) and extensive literature review, this research concludes that Strategic Environmental Assessment (SEA) as a process can effectively address the limitations of EIA and contribute to policy development in order to ensure sustainable development.