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In an era of environmental crisis, the world is at a perilous moment. The fight between sustainability and economical choice has been a contentious matter and the world is facing environmental uncertainties today. The main purpose of the present study is to prepare an integrated exploration of the development of consciousness in all business categories. This study also focuses on the indispensable roles and impact of top management in merging the commitment and abilities to environmental leadership and thus, adds a contemporary insight into how businesses, economic stakeholders, government authorities, and the general human sphere can mitigate the runaway menace of environmental issues. Finally, the current authors conclude that the environment is the supernatural force that is making life a possibility on the planet. Therefore, every human operation should be regulated by the approaches mentioned in this study and efforts made through the strategic principles and theories of leadership.

In recent years, sustainability concerns have gained increasing attention among countries and stakeholders worldwide. Towards the transition to sustainable rural development, the rural web framework (RWF) has become a consistent tool. Indicators from the RWF have been used to explore sustainable rural development for decision-making tasks, which improves the social, economic, and environmental performance of rural regions. However, the application of the RWF in studies is on the decline. Furthermore, there is a lack of literature reviews on the importance of the RWF and its relationship with different facets of sustainable development. We conducted a systematic literature review (SLR) (a) to explore how studies have used the RWF in the context of sustainable development and (b) to identify areas for further research. This study found that the RWF has mostly been used in developed countries, with fewer applications in developing countries. We suggest that there should be increased application of the RWF, particularly in developing countries, to broaden the rural web–sustainable development discourse and its relevance. This paper presents several areas where the indicators of the RWF can be applied to illustrate their relevance for policy decisions towards the achievement of the sustainable development goals (SDGs).

By using the 2017 National Household Travel Survey (NHTS) data, this study explores the status quo of ownership and usage of conventional vehicles (CVs) and alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate the significance of the 2017 NHTS data. The results show that after two decades of development, AFVs only occupy about 5% of annual vehicle sales, and their share does not show big increases in recent years. Meanwhile, although HEVs still dominate the AFV market, the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership, income still seems to be a major factor influencing AFV adoption, with the median annual household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000 and $200,000, respectively. Besides, AFV households are more likely to live in urban areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of old drivers are much smaller than CVs, indicating this age group might still have concerns regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by BEVs. BEVs might still be able to replace CVs for meeting most travel demands after a single charge, considering most observed daily trip distances are fewer than 93.5 km for CVs. However, the observed max daily trip distances of AFVs are still much smaller than CVs, implying increasing the endurance to meet extremely long-distance travel demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the future.

Consumer interest in farmers’ markets (FMs) has dramatically increased during the past decade. The number of FMs in the United States has grown from 1755 in 1994 to 8140 in 2019 (USDA, 2019). To evaluate the economic impacts (EIs) of FMs in the Washington, DC metropolitan area, we collected FMs’ consumer data and used IMPLAN-based social accounting matrices to evaluate the direct, indirect, and induced economic impacts of FMs. The empirical results from IMPLAN provide the direct gross sales, income figures, and an estimate of the number of jobs in the study region. The results show the average total output of USD 36,181,059, total employment of 663 people, total value-added creation of USD 19,019,226, and total labor income created of USD 8,653,350 in the region. The FM average income multiplier is 1.51, which indicates that a USD 1 increase in personal income (PI) for an FM translates into USD 1.51 in PI across the economy of the region. We also highlight the impact of FMs as an important component of the circular economy (CE). To this end, we present a qualitative approach examining the potential of a CE as applied to the farmers’ markets in the Washington, DC metropolitan area using qualitative data from focus groups. The goal of the circular economy is to provide more sustainability in the local economy.

Engineers must take a leading role in addressing the challenges of mitigating climate change and adapting to the inevitable changes that our world is facing. To improve climate literacy, technical education must include problem formulation and solutions that consider complex interactions between engineered, Earth, and societal systems, including trade-offs among benefits, costs, and risks. Improving engineering students’ climate literacy must also inspire students’ motivation to work toward climate solutions. This paper highlights the content and pedagogical approach used in a class for engineering students that helped contribute to significant gains in engineering students’ climate literacy and critical thinking competencies. A total of 89 students fully participated in a pre/post climate literacy questionnaire over four years of study. As a whole, students demonstrated significant gains in climate-related content knowledge, affect, and behavior. Substantial differences were observed between students in different engineering disciplines and male vs. female students. Assessment of critical thinking showed that students did an excellent job formulating problem statements and solutions in a manner that incorporated a multidimensional systems perspective. These skills are critical for students to address climate change effectively in their eventual professions.

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

In the United States, today’s ranches are engaging in small-scale nature-based endeavors to diversify their income base. But the geographic boundary of the land they own creates a relatively small area within which to operate, and fragmented ownership diminishes the ability of any single landowner to produce nature-based income. Collective action among nearby landowners can produce a set of resources from which all members of the group can profit. Such action can enhance the economic, social, and environmental sustainability of grasslands and the populations that use them. This article shows that common-interest communities can be used to provide and allocate wildlife and other resources on ranchlands, enabling individual landowners to generate more income from selling nature-based experiences to customers. Common-interest communities are familiar in urban settings but they have not yet been used in this setting. Thus, the article proposes a new approach to ranchland management based upon a familiar set of largely private legal arrangements. More broadly, the article illustrates the relevance of private law and private property to sustainable development by explaining how property owners can use private law to engage in environmentally beneficial and economically profitable enterprises on the vast privately owned landscape of the U.S. Great Plains.

Community sociologists have examined community attachment through an almost exclusive focus on people’s social relations. Recent research efforts have noted the neglect of the physical place in traditional community sociological studies. Doing this has brought the physical environment into their discussions of community attachment. Despite this progress, we remain limited in our understanding of the physical environment’s contribution to peoples’ attachment to their communities and whether its effect on community attachment is applicable in the context of urban settings. In an effort to expand our knowledge of this topic, this study explored the contributions of the urban physical environment on community attachment. By selecting the Discovery Green Park as a typical form of physical environment in Houston, Texas, this study sought to investigate how people’s levels of community attachment could be predicted by: (1) peoples’ interactions with an urban park; (2) people’s emotional connections with such a park; and (3) peoples’ social interactions with others within the park. After conducting a series of block model regression analyses, we found that community attachment was not completely defined by social factors, but also depended upon peoples’ emotional connections with the local physical environment and the social interactions happening in those settings.