Sustainability Analytics and Modeling最新文献

Transportation networks are prone to various types of disturbances, ranging from regular rush hour congestion to occasional closures due to construction zones, accidents, etc. It is impossible to avoid all disruptions, but detecting the critical points of networks (i.e., nodes that noticeably affect the connectedness of the network when closed) would help urban transportation managers prioritize preventive actions. Centrality measures are used to quantify the importance of network nodes. In this study, we calculated various centrality measures for six existing urban road networks and evaluated their importance to the connectivity and functionality of the networks via a percolation method. Along with well-established centrality measures such as betweenness, communicability, and the clustering coefficient, we evaluated the collective influence (CI) and the enhanced collective influence (ECI) indices in a transportation context. We found that nodes with high values of CI or ECI are not the ones with high values of betweenness, communicability and the clustering coefficient. Nevertheless, failures of nodes with high values of CI, ECI or betweenness centrality most significantly affect the connectivity and functionality of urban road networks. We identified three distinct sets of vital nodes in the networks we analyzed. Hence, we conclude that a set of centrality measures should be used to detect vital topological nodes of urban networks rather than just one centrality measure. Moreover, we investigated employing various aspects of CI and ECI to reveal the critical nodes of urban road networks.

Communities continue to experience significant and damaging disasters, which has prompted governments to devise solutions to protect lives and reduce overall impacts. One emerging strategy is the development of resilience hubs, which can serve the community during disasters and everyday conditions. However, most research and guidance for resilience hubs remain largely theoretical and do not account for community needs. Moreover, research and practice have not fully integrated transportation into resilience hub design, such as how travel to and from resilience hubs is facilitated.

Consequently, we conducted an empirical study leveraging statistical tools and models using data from a survey of Edmonton, Canada, residents (n = 950) conducted between November 2022 and February 2023. Through descriptive statistics and discrete choice models, we uncover important results related to resilience hub usage, transportation design, and mode choice in both normal and disaster conditions. Modelling results found a strong influence of household characteristics on the normal usage of resilience hubs, while individual characteristics were more influential on hub usage as a temporary shelter. No clear patterns of variables influenced mode choice (travel to/from hubs), except the insignificance of resilience hub usage (i.e., trip purpose) for normal conditions. For mode, the results showed a strong preference for private vehicles, yet still a relatively high multi-modal split (e.g., walking, transit, shared mobility). Residents also preferred highly localized resilience hubs with a variety of transportation options, services, and amenities. Using these results, we provide a series of practice-oriented recommendations for communities in the design and operations of resilience hubs.

As the effects of climate change on the environment become more apparent, numerous initiatives have been launched to achieve carbon neutrality and environmental sustainability. Travelers may be adding to global warming by engaging in climate–altering practices like flying or staying in hotels. Thus, this study examined the connection between tourism and carbon emissions, considering a few additional unique aspects.  The long–run and short–run coefficients were calculated by applying the autoregressive distributed lag (ARDL) method using data spanning 1990–2019. According to projections, tourism in Malaysia makes it more difficult to reach carbon neutrality. Traveler numbers along with fossil fuel energy use, economic growth, financial development, gross fixed capital formation, and population have a positive association with long–term carbon emissions. However, the findings revealed a negative link between renewable energy consumption and carbon emissions. Several methods, including Canonical Cointegration Regression (CCR), Dynamic Ordinary Least Squares (DOLS), and Fully Modified Ordinary Least Squares (FMOLS), were used to independently verify the results. The results of this research have important policy implications for achieving carbon neutrality and promoting sustainable tourism development in Malaysia by boosting renewable energy use.

The present study investigates the relationship between economic growth, globalization, population density, energy consumption, trade openness, and carbon dioxide (CO₂) emissions under the Environmental Kuznets Curve (EKC) framework for selected highly cross-correlated countries (Middle East and North Africa-MENA) from 1994 to 2014. The control variables are used based on previous empirical research. Cross-sectional dependence tests, cointegration analysis, panel data estimators (models with Driskoll-Kraay standard errors, FGLS, FMOLS, and DOLS), and Granger causality are performed. Empirical findings validate the EKC hypothesis and suggest that energy use significantly and negatively contributes to environmental quality. Moreover, globalization increases environmental degradation, whereas trade openness and population density may reduce carbon dioxide emissions within MENA economies. Policy implications towards economic growth decoupled from fossil fuels could benefit environmental upgrades and sustainability.

Many industries, including consumer electronics and telecommunications equipment, are characterized with short product life-cycles, constant technological innovations, rapid product introductions, and fast obsolescence. Firms in such industries need to make frequent design changes to incorporate innovations, and the effort to keep up with the rate of technological change often leaves little room for the consideration of product reuse. In this paper, we study the design for reusability and product reuse decisions in the presence of both a known rate of incremental innovations and a stochastic rate of radical innovations over time. We formulate this problem as a Markov Decision Process. We provide both analytical and numerical results, the latter based on over 425,000 problem instances generated over the entire range of model parameters. Our steady-state results confirm the conventional wisdom that a higher probability of radical innovations would lead to reductions in the firm’s investments in reusability as well as the amount of reuse the firm ends up doing. Interestingly, the design for reusability decreases much more slowly than the actual reuse. We identify some specific scenarios, however, where there is no tradeoff between the possibility of radical innovations and the firms reusability and reuse decisions. Through computational experiments, we also provide insights into the negative impact of radical innovations on firm profits, but show that the environmental impact of increased radical innovation is not necessarily negative. Our results also have several implications for policy makers seeking to encourage reuse. In particular, increasing end-of-life costs through take-back legislation is a safe and effective policy lever.

The use of packaging materials for food products and the associated environmental impact has received increasing attention in public discourse over recent years. At the same time, packaging solutions provide functional properties that help to preserve fresh produce and protect food from external damage. Given these trade-offs, it can be very challenging for retailers to make the right decisions, lowering their environmental impact. This is further complicated by the complex nature of inventory dynamics in the case of perishable products, as multiple factors can impact food waste and the environmental impact at the retailer. This research proposes a simulation model to investigate the effect of packaging as well as a variety of other factors on profit, waste and the environmental impact of products. For this purpose, the environmental impacts of three different products and their corresponding packaging solutions were quantified using a life cycle approach, and a number of experiments considering different demand patterns, service level requirements, batch sizes and order policies were carried out. The findings of this research are manifold, highlighting the importance of considering inventory and packaging decisions within an integrated framework. Moreover, while the shelf life extension obtained from the use of packaging has clear economic benefits due to the resulting lower waste levels, the environmental impact varies depending on the considered products, packaging material and environmental indicator.

The technology of biodegradable mulch films (BDMs) is an environmentally-friendly substitute for polyethylene (PE) mulches in agricultural production. Given the technology is new to the market, it is not easy for farmers to adopt it. Additionally, farmers’ decision on BDMs adoption is a multiple-stage process. To understand the decision process, this paper employs a double hurdle model to explore the factors affecting farmers’ adoption and willingness to pay for BDMs using the sample data from China. The results show farmers follow a two-stage decision-making process, farmers have to overcome the first hurdle, i.e., adoption or non-adoption, and then decide how much they are willing to pay for BDMs. The role of technology-specific characteristics is more important than adopter-specific characteristics in the adoption of BDMs. Understanding the impact of factors on farmers’ decision-making process can assist policymakers in designing programs, specifically tackling difficulties confronting farmers at different stages of decision-making.

The increasing use of single-use plastic bags has led to a global concern. Cities in developing countries are enforcing a (single-use) plastic bags ban to reduce the use of plastic in the retail supply chain. In Nepal, some retailers such as fashion outlets and pharmacies support the ban and others like dairy shops protest. This analysis aims to investigate how retailer heterogeneity in terms of shop characteristics and social association influences bag use behavior and compliance with the plastic bag ban in Nepal. This study based on the 1300 retailers’ plastic bag use data in three different plastic bag ban scenarios from 13 municipalities of Nepal shows that retailers’ response to the ban depends on their shop type, the nature of the goods they sell, and their social association. In addition, retailers' perception also affect their response to the plastic ban decision. Their perception of the performance of regulatory authority is likely to affect their compliance. Similarly, retailers who consider that the municipality has strong enforcement in place, tend to cooperate with the ban. This article highlights the need for retailer-specific interventions to reduce bag use.

Climate change causes range shifts of species and habitats, thus making existing reserve networks less suitable in the future. Existing optimisation procedures hence need to be adapted in order to account for changes in the spatial distribution of habitat types as well as their relative occurrence. We develop a multi-objective optimisation procedure that considers these dynamic changes. We demonstrate the functioning of the model by applying it to a conceptual case study. In this case study, we aim to gain an understanding of the consequences of not adapting the reserve network despite climate change and the optimal adaptation pathways for different funding levels. Finally, we consider whether larger flexibility in terms of when to adapt the reserve network by providing a one-off discounted payment instead of regular payment improves the outcome achieved. We find that the optimisation procedure is suitable to identify adaptation pathways as the outcome is improved, especially for habitat types that become increasingly threatened. Initially providing a one-off discounted payment instead of regular payments leads to higher habitat protection. In order to facilitate future research, we provide the code of the optimisation on request.

Considering that cities are responsible for a considerable share of energy consumption and GHG emissions, a complete environmental life cycle assessment (LCA) on an urban scale is gaining importance. This has led to numerous studies in this field of research, ranging from environmental impact investigation of building components (walls, insulation layers) to the carbon emission analysis of several buildings and networks, open spaces, and transportation facilities on a broader scale. This study aims to contribute to the field of research by conducting a comprehensive analysis of existing literature studies on varying scales. The primary objective is to determine whether the current studies have adequately addressed the gap in knowledge by effectively implementing Life Cycle Assessment (LCA) methodologies throughout the entire lifecycle of buildings while ensuring a sufficient level of detail and reliability in their findings. Additionally, this investigation seeks to evaluate the extent to which urban-scale LCA analyses have been undertaken in previous literature and assess the accuracy of the corresponding results. Furthermore, the study intends to identify the necessary measures that can be implemented to enhance the value and validity of these research outcomes. Ultimately, the findings of this research endeavor will identify the shortcomings and gaps in the current body of knowledge, thereby facilitating the development of a roadmap for future studies in this field of research. The gap analysis of this research field reveals important findings. Firstly, only 9% of previous studies have examined the entire urban scale, highlighting a significant research gap that should be addressed in future studies. Secondly, the bottom-up approach used in many studies introduces substantial uncertainty, necessitating improved modeling and uncertainty analysis. Thirdly, the building-by-building approach, while comprehensive, is time-consuming and costly, calling for research on enhancing its efficiency. Additionally, future studies should consider stock evolution beyond buildings, incorporate multiple LCA indicators, and integrate LCA with life cycle costing (LCC) in urban scale assessments. These identified gaps present promising avenues for future research in built environment LCA.