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The social confinement resulting from the COVID-19 crisis temporarily reduced greenhouse gas emissions. Although experts contend that the decrease in pollution rates was not drastic, some surveys detect growth in social concern about the climate. In this new climate-conscious environment, municipalities and local governments are promoting a new way of living and caring for cities, even before they can regain national and international freedom of movement. This work analyzes the connections between new climate awareness arising from the COVID-19 crisis, proposals of sustainable citizenship around the world, and its communication on Twitter to educate the new eco-conscious audience. The methodology mixes quantitative and qualitative analysis, using the Twitonomy Premium tool and the Twitter research tool with data extracted at the end of December 2020. Among the top ten most influential and active accounts, the results show educational institutions, local institutions, companies, neighborhoods, associations, and influencers. The impossibility of living in the city has not prevented citizen education and commitment to make real change for when that city and its citizens return to normality. However, this new normality must be different: more ecological, more responsible, more sustainable, and practiced from early childhood.

In this study, the Transit Network Design Problem (TNDP) is studied to determine the set of routes and frequency on each route for public transportation systems. To ensure the important concerns of planners like route length, route configuration, demand satisfaction, and attractiveness of the transit routes, the TNDP is solved to generate a set of routes by proposing an initial route set generation (IRSG) procedure embedded into the NSGA-II algorithm. The proposed IRSG algorithm aims to produce high-quality initial route set solutions to reach better optimization procedures. Moreover, the Multi-Objective Mixed-Integer Non-Linear Programming (MOMINLP) model is proposed to formulate the frequency setting problem on each route by minimizing the total travel time of passengers (user costs) and operator costs simultaneously, while maximizing the service coverage area near all the bus stops. The MOMINLP model is solved by applying the NSGA-II algorithm to produce a Pareto front between the first and the second objective functions. The model was applied to the Fargo-Moorhead Area (FMA), a small urban area. Results were compared with the existing transit network to measure the efficiency of the NSGA-II solution methodology. The proposed algorithm was found to considerably decrease the total travel time of passengers.

Due to their unique nature, construction projects are considered one of the world’s most hazardous and incident-prone industrial sectors. The present study aimed to analyze health, safety and environmental (HSE) risks relating to construction projects based on the project management body of knowledge (PMBOK) and sustainability approach. This study was conducted with the participation of 30 experts, using the semi-quantitative risk assessment technique, in nine areas of the project management’s body of knowledge, based on the fuzzy analytic hierarchy process. Risk, in this study, was estimated using a two-dimensional matrix of incident probability and severity, each of which has four sub-parameters. The HSE risks pertaining to each of the nine areas of PMBOK were identified. After that, the two dimensions of risk, including incident probability and severity, were measured. Thirty-seven risk sources associated with nine areas of the PMBOK were identified. Risk analysis revealed that 20 sources were at an unacceptable risk level, and 17 risks were at a tolerable risk level. Identifying HSE-related risk sources in accordance with the nine areas of PMBOK, and using FAHP to assess the risk of these hazards in construction projects, can lead to a more realistic estimate of risk in construction projects. The presented method in the current study can create a novel perspective in terms of the construction industry’s risk management and assessment.

This study explores the impact of oil depletion on the energetic efficiency of oil extraction and refining in California. These changes are measured using energy return ratios (such as the energy return on investment, or EROI). I construct a time-varying first-order process model of energy inputs and outputs of oil extraction. The model includes factors such as oil quality, reservoir depth, enhanced recovery techniques, and water cut. This model is populated with historical data for 306 California oil fields over a 50 year period. The model focuses on the effects of resource quality decline, while technical efficiencies are modeled simply. Results indicate that the energy intensity of oil extraction in California increased significantly from 1955 to 2005. This resulted in a decline in the life-cycle EROI from 6.5 to 3.5 (measured as megajoules (MJ) delivered to final consumers per MJ primary energy invested in energy extraction, transport, and refining). Most of this decline in energy returns is due to increasing need for steam-based thermal enhanced oil recovery, with secondary effects due to conventional resource depletion (e.g., increased water cut).

The aim of this paper is to evaluate some villages in the mountain area of Sibiu County, Romania, as “smart” tourist villages. The research was carried out in two stages: Stage 1 was collection of information on (a) food products registered in different national and international quality schemes and (b) the number of tourist structures by type, comfort category, and accommodation capacity. Stage 2 was a case study conducted among 32 rural tourism entrepreneurs from 3 villages in Mărginimea Sibiului, using a questionnaire with 22 items on the criteria for evaluation of “smart” villages. The answers were systematized, statistically processed, and interpreted. The main results are as follows: (a) Concerning products, producers, and gastronomic points (PGL) registered, there are 9 products in the quality scheme “mountain product”, 10 producers on the platform “Gusturisibiene”, 1 producer of “telemea de Sibiu”, a registered product with protected geographical indication, and 2 PGL. (b) The number of officially registered tourist structures is 93, of which 72% are agritourism guesthouses. (c) The results for the evaluation of villages as “smart” are that 78.2% of locals use the internet, 74% own at least one smart device, 60.16% of energy used is renewable. There are low values of water and air pollution; there are local job and business opportunities (93.75%); future local development activities will target agriculture with all its sectors and agritourism; public administrations have implemented 7 types of activities to increase the attractiveness of localities; and quality of life, services, and social life aspects are perceived to be at high standards.

Achieving economic development is one of the most important economic goals of every country. Identifying the determinants of economic growth, is a useful tool for adopting appropriate economic policies. This study, therefore, empirically examines the impact of trade openness, foreign direct investment, and financial development on economic growth, across 62 countries over the period 1995–2016. These countries are divided into two groups: low-income and high-income countries. We employ the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) estimation techniques on the cross-country panel data. The findings show a positive long run association between trade openness, foreign direct investment (FDI), financial development, labor, government expenditure, and economic growth in low-income countries, with a positive and negative short run effect from capital and government expenditures, respectively. For high-income countries, a positive long run association between trade openness, FDI, capital, and economic growth exist. The short run estimates indicate a positive effect on trade openness and capital as well as a negative effect on government expenditure. Our study shows that the adoption of policies that improves access to skilled labor and international trade, affect the attainment of a sustainable economic development.

Enhancing academic engagement in university students can help enrich students’ educational experience. Drawing on the Conservation of Resources Model and the Job Demand-Resources Model, this study aims to explore the links between undergraduates’ achievement goal orientation and academic engagement (AE), by examining the mediating functions of perceived school climate (PSC) and academic self-efficacy (ASE). Using whole-group sampling, 571 Chinese undergraduates were selected using a self-reporting method to explore the impacts of mastery-approach goals (MAGs) and performance-avoidance goals (PAGs) on AE, as well as the chain mediating effects of PSC and ASE. The findings show that both MAGs and PAGs have a positive, direct, predictive effect on university students’ AE. Additionally, both goal orientations indirectly predict AE through PSC and ASE, separately. The results showed there was also a significant chain mediating effect of PSC and ASE, where for both goal orientations, AE was positively predicted. This study highlights the role of environmental as well as personal factors in facilitating self-regulated learning among university students, and it discusses implications for future research.

3D city models are a useful tool to analyze the solar potential of neighborhoods and cities. These models are built from buildings footprints and elevation measurements. Footprints are widely available, but elevation datasets remain expensive and time-consuming to acquire. Our hypothesis is that the GIS cadastral data can be used to build a 3D model automatically, so that generating complete cities 3D models can be done in a short time with already available data. We propose a method for the automatic construction of 3D models of cities and neighborhoods from 2D cadastral data and study their usefulness for solar analysis by comparing the results with those from a hand-built model. The results show that the accuracy in evaluating solar access on pedestrian areas and solar potential on rooftops with the automatic method is close to that from the hand-built model with slight differences of 3.4% and 2.2%, respectively. On the other hand, time saving with the automatic models is significant. A neighborhood of 400,000 m2 can be built up in 30 min, 50 times faster than by hand, and an entire city of 967 km2 can be built in 8.5 h.