Carbon dioxide emission and bio-capacity indexing for transportation activities: A methodological development in determining the sustainability of vehicular transportation systems

Exploring the nexus of industrial production and energy consumption on CO2 emissions in Bangladesh through ARDL bounds testing insights

Elevated CO2 emissions are a primary cause of the sustainability challenges, including rising sea levels and extreme weather patterns, faced by Bangladesh and the world. This study examines the intricate relationship between CO2 emissions and various economic and industrial factors in Bangladesh, using the autoregressive distributed lag (ARDL) bound test. By analyzing data from 1971 to 2020, the research identifies both short-run and long-run dynamics influencing CO2 emissions. The findings reveal that industrial production and non-renewable energy consumption have a significant positive impact on CO2 emissions, while agricultural activities and fertilizer consumption exhibit a negative effect.The study underscores the need for Bangladesh to transition towards renewable energy sources and improve agricultural practices to mitigate CO2 emissions. Advanced econometric techniques, including the ARDL Bound Test, CUSUM, and CUSUMSQ, are employed to ensure the robustness of the results. The ARDL framework yields key metrics: RMSE = 0.034, MSE = 0.001, AIC = -160.002, BIC = -139.651, R-squared = 0.801, and adjusted R-squared = 0.753, to explore the CO2 emissions nexus in Bangladesh.The study concludes that, while industrial and energy factors significantly contribute to CO2 emissions, enhancing renewable energy use and adopting climate-smart agricultural practices are essential for sustainable environmental management. Policy recommendations include promoting renewable energy adoption, implementing carbon capture technologies, and revising carbon tax policies to achieve long-term sustainability and environmental conservation.

Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM)

The indicators of economic and sustainable development ultimately significantly depend on carbon dioxide (CO2) emissions in every country. In Bangladesh, there is an increasing trend in population, industrialization, as well as electricity demand generated from different sources, ultimately increasing CO2 emissions. This study explores the relationship between CO2 emissions and other significant relevant indicators. Moreover, the authors aimed to identify which model is effective at predicting CO2 emissions and assess the accuracy of the prediction of different models. The secondary data from 1971 to 2020, was collected from the World Bank and the Bangladesh Road Transport Authority's publicly accessible website. The generalized additive model (GAM), the polynomial regression (PR), and multiple linear regression (MLR) were used for modeling CO2 emissions. The model performance is evaluated using the Bayesian information criterion (BIC), Akaike information criterion (AIC), Root mean square error (RMSE), R-square, and mean square error (MSE). Results revealed that there are few multicollinearity problems in the datasets and exhibit a nonlinear relationship among CO2 emissions. Among the models considered in this study, the GAM model has the lowest value of RMSE = 0.008, MSE = 0.000063, AIC = -303.21, BIC = -266.64 and the highest value of R-squared = 0.996 compared to the MLR and PR models, suggesting the most appropriate model in predicting CO2 emissions in Bangladesh. Findings revealed that the total CO2 emissions and other relevant risk factors is non-linear. The study suggests that the Generalized additive model regression technique can be used as an effective tool for predicting CO2 emissions in Bangladesh. The authors believed that the findings would be helpful to policymakers in designing effective strategies in the areas of a low-carbon economy, encouraging the use of renewable energy sources, and focusing on technological advancement that reduces CO2 emissions and ensures a sustainable environment in Bangladesh.

Estimated public health benefits of a low-emission zone in Malmö, Sweden

Air pollution is one of the leading causes of morbidity and mortality worldwide. Low-emission zones (LEZ) have been increasingly implemented in cities throughout Europe as a measure to reduce the adverse health effects and premature deaths associated with traffic-related air pollution. In the present study, a health impact analysis was conducted to estimate the effect of a hypothetical LEZ on mortality and morbidity in Malmö, Sweden. Baseline health statistics were gathered from health registers and applied to each resident according to individual-level data on age and/or sex. Concentration-response parameters were derived from current epidemiological literature, specifically meta-analyses. A Gaussian dispersion model (AERMOD) combined with a detailed emission database was used to calculate NO₂ emissions from traffic, which could be applied on an individual-level using data on each person's residential coordinates. The adjusted exposure scenario replaced all vehicles on municipal roads having Euro 5 or lower emission standards with Euro 6 equivalents. This LEZ would, on average, decrease NO₂ concentrations by 13.4%, preventing an estimated 9-26 deaths in Malmö each year. Additionally, 12 respiratory disease hospitalizations, 8 childhood asthma cases, and 9 cases of hypertensive disorders of pregnancy were estimated to be avoided annually. These results suggest that LEZs can effectively improve air quality, reduce greenhouse gas emissions, and safeguard public health.

Customer demand-driven low-carbon vehicles combined strategy and route optimisation integrated decision

With the optimal operating cost and optimal carbon emission target of the chemical logistics companies, a low-carbon routing optimisation with a multi-energy type vehicle combined problem is proposed by considering the concept of the logistics companies' low-carbon behaviour. An integrated decision-making of multi-energy type vehicles combined strategy and route optimisation based on customer demand is presented, and an improved genetic algorithm is designed. A case study is then applied based on the data collected from the case research. The effectiveness of the improved genetic algorithm is tested. The two joint objectives of operating cost and carbon emission are examined through the cost analysis of environmental energy vehicles and traditional energy vehicles in different combination scenarios. The case analysis shows that a rational multi-energy type vehicle combination with route optimisation has a significant correlation with the operating cost and carbon emissions, while the environmental vehicle purchasing cost reduction and subsidy policy affect the operating cost.

Spatiotemporal analysis of traffic congestion, air pollution, and exposure vulnerability in Tanzania

Using new satellite data from the European Space Agency's Sentinel-5P system, this article investigates the spatial and temporal dynamics of vehicular traffic congestion, air pollution, and the distributional impacts on vulnerable populations in Dar es Salaam, Tanzania. The metro region's rapid growth in vehicle traffic exceeds road network capacity, generating congestion, transport delays, and air pollution from excess fuel use. Dangerously high pollution levels from tailpipe emissions put the health of vulnerable residents at risk, calling for the need to adopt continuous air-quality monitoring and effective pollution control. Our results highlight significant impacts of seasonal weather and wind-speed factors on the spatial distribution and intensity of air pollution from vehicle emissions, which vary widely by area. In seasons when weather factors maximize pollution, the worst exposure occurs along the wind path of high-traffic roadways. The study identifies priority areas for reducing congestion to yield the greatest exposure reduction for young children and the elderly in poor households. This new research direction, based only on the use of free global information sources with the same coverage for all cities, offers metropolitan areas in developing regions the opportunity to benefit from the rigorous analyses traditionally limited to well-endowed cites in developing countries.

Evaluation of the selection of proper metro and tram vehicle for urban transportation by using a novel integrated MCDM approach

This paper presents a novel integrated multi-criteria decision-making model consists of the CRITIC (CRiteria Importance Through Intercriteria Correlation) technique and the EDAS (The Evaluation based on Distance from Average Solution) method to evaluate the selection of the urban rail vehicles operated in the public transport systems. In order to determine the selection criteria in a more realistic perspective, a board of experts consists of seven members, who perform as senior executives in the public transport institutions of the country, was constructed and many round tables meetings were organized with together the members of the board for determining the procedure to follow to reach meaningful and applicable results. At the beginning of the research, approximately fifty selection criteria were determined by the research team and some of them were eliminated during the preparation process of this study. Finally, 22 factors were determined as the selection criteria, which will be used in this research. These factors are the selection criteria directly or indirectly affecting the selection process and results. Also, rail tram types currently operated in the various cities of Turkey were determined as decision alternatives. These options were evaluated by using the proposed integrated MCDM approach. When the obtained results are evaluated, it can be seen that the proposed model has the potential to give very successful results for evaluating the selection process of the urban rail systems. Moreover, it can be implemented as a decision support system by decision-makers, who perform in the public transport authorities. Finally, it can also be applied for decision-making problems faced in the various fields.

A new carbon emission reduction mechanism: Carbon Generalized System of Preferences (CGSP)

Countries throughout the whole world, including China, are working together to curb the greenhouse effect, but the effects seem very limited in spite of the fact that various low-carbon development strategies have been adopted, particularly in industrial enterprises. Therefore, carbon emissions caused by the public should be taken seriously, and the public should be encouraged to engage in behavior that limits carbon emissions. Therefore, this article introduces a new incentive mechanism called the Carbon Generalized System of Preferences (CGSP), which was first introduced in Guangdong Province, China. It is believed that this new mechanism matches the role of leadership in Guangdong in the urbanization and economic development of China by addressing the small sources of greenhouse gases (GHGs) and by issuing carbon coins. Compared with Chinese Certified Emission Reduction (CCER), the development scope, management level, and novel criteria of CGSP are very different but relatively easy for the public to accept. The CGSP shows that the network platform, reduced carbon emissions, and urban pilots are all compatible with the goals of the nation and city, and they promote the CGSP in different ways. Because of its consistency with ecological civilization in China, the prospect of the CGSP is bright; however, there are some challenges, such as policy and economic factors, combined with pollution control.

E. M. Browning M, Rigolon A. Noncommunicable Diseases, Park Prescriptions, and Urban Green Space Use Patterns in a Global South Context: The Case of Dhaka, Bangladesh

Urban green space use is often associated with improved physical and mental health and lower noncommunicable disease (NCDs) burdens. Factors that influence green space visits have been documented in cities of the Global North, but evidence of urban green space use patterns for cities in the Global South is scarce. The aim of this study is to investigate factors influencing urban green space use patterns in Dhaka, Bangladesh, a megacity of the Global South, with a particular focus on how poor health condition and healthcare professionals' prescriptions to exercise outdoors (park prescriptions-ParkRx) impact the green space use of middle-aged adults. We collected green space characteristics and use factors (i.e., availability, accessibility, attractiveness, and attachment), health condition, ParkRx, and urban green space use intensity (i.e., frequency and duration) via a self-reported questionnaire from 169 middle-aged residents of Dhaka. We used multivariate modeling to estimate the association of green space characteristics, health condition, and ParkRx with use intensity. We further applied a mediation analysis to determine the influence of ParkRx on the relationship between residents' poor health conditions and use intensity. We found that green space availability and accessibility did not significantly influence use intensity, but attractiveness was negatively associated with use intensity. Green space use intensity was significantly and positively associated with attachment to the green space, poor health condition (i.e., having noncommunicable diseases), and ParkRx. ParkRx significantly mediated the relationship between health condition and use intensity. We observed limited supply, poor access, and low attractiveness when studying the urban green spaces in Dhaka, but these qualities did not affect use intensity, as found in many case studies in the Global North. In contrast, urban green space use intensity in our case study is mostly dependent on poor health condition and park prescriptions.

Sustainability efficiency and carbon inequality of the Chinese transportation system: A Robust Bayesian Stochastic Frontier Analysis

This study focuses on the sustainability efficiency of the Chinese transportation system by investigating the relationship between CO₂ emission levels and the respective freight and passenger turnovers for each transportation mode from January 1999 to December 2017. A novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed by taking carbon inequality into account. In this model, the aggregated variance/covariance matrix for the three classical distributional assumptions of the inefficiency term-Gamma, Exponential, and Half-Normal-is minimized, yielding lower Deviance Information Criteria when compared to each classical assumption separately. Results are controlled for the impact of major macro-economic variables related to fiscal policy, monetary policy, inflationary pressure, and economic activity. Results indicate that the Chinese transportation system shows high sustainability efficiency with relatively small random fluctuations explained by macro-economic policies. Waterway, railway, and roadway transportation modes improved sustainability efficiency of freight traffic while only the railway transportation mode improved sustainability efficiency of passenger traffic. However, the air transportation mode decreased sustainability efficiency of both freight and passenger traffic. The present research helps in reaching governmental policies based not only on the internal dynamics of carbon inequality among different transportation modes, but also in terms of macro-economic impacts on the Chinese transportation sector.

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

Energy conservation, cost, and emission reduction are the research topics of most concern today. The aim of this paper is to reduce the cost and carbon emissions and improve the sustainable development of sheep transportation. Under the typical case of the “farmers–middlemen–slaughterhouses” (FMS) supply model, this paper comprehensively analyzed the factors, sources, and types of cost and carbon emissions in the process of sheep transportation, and a quantitative evaluation model was established. The genetic algorithm (GA) was proposed to search for the optimal path of sheep transportation, and then the model solving algorithm was designed based on the basic GA. The results of path optimization indicated that the optimal solution can be obtained effectively when the range of basic parameters of GA was set reasonably. The optimal solution is the optimal path and the shortest distance under the supply mode of FMS, and the route distance of the optimal path is 245.6 km less than that of random path. From the cost distribution, the fuel power cost of the vehicle, labor cost in transportation, and consumables cost account for a large proportion, while the operation and management cost of the vehicle and depreciation cost of the tires account for a small proportion. The total cost of the optimal path is 26.5% lower than that of the random path, and the total carbon emissions are 36.3% lower than that of random path. Path optimization can thus significantly reduce the cost of different types and significantly reduce the proportion of vehicle fuel power cost and consumables cost, but the degree of cost reduction of different types is different. The result of the optimal path is the key to be explored in this study, and it can be used as the best reference for sheep transportation. The quantitative evaluation model established in this paper can systematically measure the cost and carbon emissions generated in the sheep transportation, which can provide theoretical support for practical application.

Investigation on the carbon sequestration capacity of vegetation along a heavy traffic load expressway

Carbon sequestration by vegetation plays an important role in the global carbon cycle. More emphasis on the carbon sequestration of roadside vegetation will help to reduce the total carbon emissions from the transportation sector. In the current study, the Shanghai-Nanjing G42 expressway in east China was selected to investigate and calculate the carbon sequestration of roadside vegetation including trees, shrubs and herbs. Findings of the current study revealed that the total carbon sequestration of all the vegetation was about 97,000 tons per year. Results also indicated that trees have a higher carbon sequestration capacity (γ) in unit land area compared to shrubs and herbs. The γ value of most of the shrubs was lower than that of tree; however, species such as Nerium indicum, Jasminum mesnyi and Forsythia suspense have better carbon sequestration capacity than some other tree species. The γ value of herbs was too low, compared with trees and shrubs. The findings of the current study will be of great benefit to make the vegetation planting strategy for express highways in the areas with similar geographic characteristics and climate.

A soft traffic management approach for achieving environmentally sustainable and economically viable outcomes: An Australian case study

Transport infrastructural strain is a pressuring issue for the urbanised world due to the increasing demand for public transport. Transport operators, planners and policymakers are constantly searching for low-cost solutions to such transport issues. Therefore, it is critical to developing an environmentally sustainable and economically viable efficient traffic network to relieve traffic pressure (i.e., traffic congestion, transport infrastructure investment needs). The objective of this research is to propose a "Soft Traffic Management (STM)" concept to proactively analyse the traffic impact of transport planning strategy before implementation. This study investigates the effectiveness of a STM for easing the traffic pressure by carrying out a pilot research project on the proposed South East Busway extension in Logan City, Australia, by employing a stepwise process consisting of a multi-stage analysis and stakeholder-based modelling approach. The results indicate that the extended busway can significantly relieve traffic congestion. In addition, the proposed strategy has significantly positive impacts on the environment since it aims to reduce air pollution and fuel consumption as well as to improve the safety and efficiency of the whole transport system. This study confirms the effects of STM on improving the use of existing infrastructure more efficiently and deferring future transport infrastructure investments.

Design of a heuristic environment-friendly road pricing scheme for traffic emission control under uncertainty

Road transportation is one of the main sources of atmospheric emissions in many countries and areas. Road pricing, is not only effective for urban transportation management, but also helpful in reducing the negative externalities caused by transportation. In this study, an inexact two-phase minimal emission programming (TMEP) model is proposed for design of the environment-friendly toll scheme with an acceptable road network performance. Through introduction of fuzzy stochastic programming, multiple uncertainties involved in vehicle emission evaluation are dealt with; the Traffic Performance Index (TPI) based constraints are incorporated to reflect the decision-maker's requirements for network congestion management. The solution method is proposed for generating the range of fuzzy stochastic objectives. An optimal toll scheme associated with the minimal emission based flow pattern is obtained through searching for a set of the best and the worst optimal solutions. A numerical experiment and a real-world road network in Beijing of China are used to illustrate the application of the developed method. In the case study, the toll scheme is obtained at the desired congestion level. The effects of emission and congestion abatement are analyzed under different policy scenarios. The proposed TMEP method can generate the toll scheme with obvious improvements in total emission reduction and congestion mitigation.

Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.