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Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

Abstract The residential sector accounts for most of energy-consumption in developing countries in the form of traditional energy. The use of commercial energy is nominal and confined mostly to urban areas where fuelwood is already monetized. A model, based on an end-use/process analysis approach, is developed on a spreadsheet, which is capable of simulating scenarios to address issues of increasing traditional energy-demand caused by population growth, sustainable supply capacity of the existing energy resources, potential for development of new and renewable energy resources, technology. This paper is divided into two parts: general energy issues and the modelling approach, and the application of this approach to Nepal in the context of fuelwood-supply sustainability.

A key determinant and outcome of successful environmental education is ‘pro-environmental behavior’, i.e., behavior that involves conscious action to mitigate adverse environmental impacts at personal or community level, e.g., reducing resource consumption and waste generation, avoiding toxic substances, and organizing community awareness initiatives. However, some theorists have sought to move away from rationalist models of behavioral modification, towards holistic pedagogical initiatives that seek to develop action competence. In light of the global push towards achievement of the Sustainable Development Goals (SDGs), emerging evidence suggests that education initiatives should foster action competence so students may be equipped to contribute to sustainable development as part of their education. The UNESCO Education for Sustainable Development (ESD) Roadmap 2030 has also identified key priority areas to strengthen ESD in formal curricula. This article reports two informal environmental education initiatives for promoting action competence and pro-environmental behaviors in school-aged children. The authors recommend that formal education settings (e.g., schools) should incorporate self-directed, free-choice project-based learning to augment environmental education programs and promote students’ action competence for contribution to attainment of SDGs. To this end, we propose a Free-Choice Project-based Learning for Action Competence in Sustainable Development (ACiSD) Curriculum, comprising six implementation dimensions, namely: (1) project duration and teaming arrangements, (2) topic selection, (3) student support, (4) teacher support, (5) learning environments, and (6) digital access and equity. For each implementation dimension, we recommend action steps to help educators implement this curriculum in their own educational settings, with the aid of an illustrative worked example.

Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.

Abstract Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentrations. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOx) increased with higher CAVs. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

Abstract Conversion of carbon contained in the solid residues (tars + biochar) derived from urban biomass gasification named herein TC would allow enhancing the yield of carbon species (CO/CO2) in synthetic gas. For this purpose, three low cost materials have been tested as possible catalysts: iron species (reduced Fe), bone meal (BM), and ashes (ash) recovered from biochar complete oxidation. The parametric study used the following as variables: air GHSV, onset of reaction temperature, reaction time to optimize CO/CO2 molar ratio and tar content in the produced gas. Results showed an autocatalytic effect of biochar leading to the catalytic conversion of approximately 78% of tars by the native metals contained in TC. The catalytic effect was further enhanced by adding Fe, BM, and extra ash. Addition of Fe catalyst resulted in significant heat generation (temperature increase of ca. 500 °C) and a twofold decrease in reaction time to consume all the carbon. Use of ash and BM as catalysts exhibit heat generation comparable to Fe, along with an improved reaction time, complete tars conversion and a CO/CO2 molar ratio to above 1.3 in the produced gas.

The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC), real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs) is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV) in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.