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Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

COVID-19 has impacted public and economic health worldwide. To bolster the economy and maintain human life, economic and epidemiological research is vital. Nations have implemented lockdowns intent on slowing the spread of the virus. This research examines how lockdown parameter adjustments can help control a nations fatalities. The study incorporated an SIRD disease model that is simulated over a 200 day period. The goal of the research is to take the SIRD model and use it to create a minimization function that analyzes dynamics that best produce minimal loss of GDP as well as low loss of life in a lockdown. Once the optimization function is solved, the results will be compared to similar studies.

# Sketch Planning Tool for Sustainable and Resilient Urban Goods Distribution [https://doi.org/10.5061/dryad.bk3j9kdjt](https://doi.org/10.5061/dryad.bk3j9kdjt) # Getting Started Before you can use tool, you must enable the Solver add-in in the Excel Options dialog box. Click the File tab, and then click Options below the Excel tab. In the Excel Options dialog box, click Add-Ins. In the Manage drop-down box, select Excel Add-ins, and then click Go. In the Add-Ins dialog box, select Solver Add-in, and then click OK. After you have enabled the Solver add-in, Excel will auto-install the Add-in if it is not already installed, and the Solver command will be added to the Analysis group on the Data tab in the ribbon. Further, you must establish a reference to the Solver add-in. Click the Developer tab, and then in the Code group click Visual Basic command. In the Visual Basic Editor, with a module active, click References on the Tools menu, and then select Solver under Available References. If Solver does not appear under Available References, click Browse, and then open Solver.xlam in the \\Program Files\\Microsoft Office\\Office14\\Library\\SOLVER subfolder. # Navigating the tool This section helps the user navigate the tool through the different worksheets in the Excel file. This tool contains primarily three kinds of worksheets, namely, input-based, output-based, and function-based worksheets. While the input-based worksheets are for the user to view and edit, the output-based worksheets are for view-purpose only, however, the function-based worksheets are protected for the sake of functionality and the user is strongly suggested to not edit them. ## Input-based worksheets ### input The *input* worksheet guides the user to input the necessary data and run the tool. These data inputs include service region parameter values, supply parameter values, and demand parameter values. The authors detail this user input process in the section – Running the tool. ### service region The service region worksheet enlists parameter values of the service region including the characteristics of the service region, the demographics of the population, and the emissions costs in the service region. ### vehicle parameters The vehicle parameters worksheet lists essential parameters of the vehicles deployed in last-mile distribution. ### supply The supply worksheet enlists supply-side parameters including distribution environment as well as those pertaining to the primary distribution channel (e-retailer’s distribution structure) and secondary distribution channel (outsourcing distribution structure). ### demand The demand worksheet enlists pre- and peri-/post- disruption demand parameters. ## Output-based worksheets ### output The output worksheet characterizes and plots the observed demand and service disruption. And with this, the output sheet analyzes e-retailer’s response to disruption evaluating last-mile distribution resilience in the form of Robustness, Redundancy, Resourcefulness, and Rapidity – Resilience Metrics. The tool also evaluates Operational Metrics wherein, Total Delay expresses cumulative delay in terms of number of package-days of delayed service, while the Average Delay evaluates the average number of additional packages delayed on any day, and the average number of days a package is delayed, assuming that packages are delivered on a first-come-first-served basis. Moreover, the output worksheet evaluates Economic Metrics that evaluate the Direct, Indirect, and Total Loss to the e-retailer from the disruption. Note, the Direct Loss evaluates the change in distribution cost relative to pre-disruption distribution cost, and Indirect Loss accounts for the loss from delayed service penalizing late delivery (unmet demand) at $5 per package for every day of delayed service, while the Total Loss is the sum of Direct and Indirect loss, and thereby reflects the explicit and implicit costs to the e-retailer. ## Function-based worksheets ### pre-disruption demand The pre-disruption demand worksheet employs multinomial logit model that depicts consumer choice of shopping channel to estimate total pre-disruption e-commerce demand in the service region. ### main The main worksheet enlists parameters and decision variables relevant to the different optimization models and the simulation framework in the tool. ### optimize The optimize worksheet details e-retailer’s pre- and post- disruption distribution structure optimization model. ### mincost\-w\|o The *mincost\-w\|o* worksheet details e-retailer’s cost minimization model without use of secondary (outsourcing) distribution channel. ### maxcap\-w\|o The *maxcap\-w\|o* worksheet details e-retailer’s capacity maximization model without use of secondary (outsourcing) distribution channel. ### mincost-wo The mincost-wo worksheet details e-retailer’s cost minimization model with use of secondary (outsourcing) distribution channel. ### maxcap-wo The maxcap-wo worksheet details e-retailer’s capacity maximization model with use of secondary (outsourcing) distribution channel. ### miscellaneous The miscellaneous worksheet includes some data for functional purpose. ### results The results worksheet outputs the results of the simulation including daily congestion levels, demand, unserved demand, primary distribution capacity, auxiliary distribution capacity, served demand, cost, cost per package, disruption, and level of service. # Running the tool This section details the essential steps to run the tool. **Step 0.** Start at the input worksheet. Step 0.1. Reset values **Step 1.** Set service region parameter values in the service region worksheet. Step 1.1. Set service region characteristics. Step 1.1.1. Set service region name in cell B2. Step 1.1.2. Select service region location from the dropdown list in cell B3. Step 1.1.3. Scroll through the bar to set service region size in cell B4. Step 1.1.4. Scroll through the bar to set service region population in cell B5. Step 1.1.5. Scroll through the bar to set service region congestion factor in cell B6. Step 1.1.6. Scroll through the bar to set service region facility fixed cost rate parameter in cell B7. Step 1.1.7. Scroll through the bar to set service region facility fixed cost distance parameter in cell B8. Step 1.1.8. Scroll through the bar to set service region discount rate in cell B9. Step 1.2. Set service region demographics. Step 1.2.1. Scroll through the bar to set service region gender ratio in cells B15-16. Step 1.2.2. Scroll through the bar to set service region education levels in cells B19-22. Step 1.2.3. Scroll through the bar to set service region age groups in cells B25-29. Step 1.2.4. Scroll through the bar to set service region income levels in cells B32-38. Step 1.2.5. Scroll through the bar to set service region household size in cell B41. Step 1.2.6. Scroll through the bar to set service region household number of children in cell B41. Step 1.2. Set service region emissions cost. Step 1.3.1. Scroll through the bar to set service region CO2 cost in cell B47. Step 1.3.2. Scroll through the bar to set service region CO cost in cell B48. Step 1.3.3. Scroll through the bar to set service region NOx cost in cell B49. Step 1.3.4. Scroll through the bar to set service region PM cost in cell B50. **Step 2.** Set supply parameter values in the supply worksheet. Step 2.1. Set distribution environment parameter values Step 2.1.1. Scroll through the bar to set e-retailer's planning horizon in cell B2. Step 2.1.2. Scroll through the bar to set e-retailer's working days in cell B3. Step 2.1.3. Scroll through the bar to set e-retailer's working hours in cell B4. Step 2.1.4. Scroll through the bar to set e-retailer's market share in cell B5. Step 2.1.5. Scroll through the bar to set e-retailer's planning horizon in cell B6. Step 2.2. Set primary distribution channel parameter values Step 2.2.1. Select fleet type from the drop-down list in cell B14. Step 2.3. Select outsourcing channel and its parameter values *If no outsourcing channel is deployed* Step 2.3.1. Set outsourcing channel to None from the dropdown list in cell B22. *Else if distribution is outsourced with crowdsourced delivery.* Step 2.3.1. Set outsourcing channel to crowdsourced delivery from the dropdown list in cell B22. Step 2.3.2. Select fleet type from the drop-down list in cell B28. Step 2.3.3. Set fleet size limit in cell B29. Step 2.3.4. Set tour limit in cell B30. *Else if service is outsourced via collection-points for customer pickup.* Step 2.3.2. Set number of collection-points in cell B35. Step 2.3.3. Select fleet type from the drop-down list in cell B37. Step 2.3.4. Set fleet size limit list in cell B38. Step 2.3.5. Set tour limit in cell B39. *Else if distribution is outsourced via Logistics Service Provider’s micro-hubs* Step 2.3.2. Set number of micro-hubs in cell B44. Step 2.3.3. Select fleet type from the dropdown list in cell B46. Step 2.3.4. Set fleet size limit list in cell B47. Step 2.3.5. Set tour limit in cell B48. To view/edit vehicle related parameters refer to *vehicle parameters* worksheet. **Step 3.** Set demand parameter values in the demand worksheet. Step 3.1. Set pre-disruption demand Step 3.1.1. Set pre-disruption demand in cell B2. Step 3.2. Set peri-/post- disruption demand Step 3.2.1. Either select one of the default disruption scenarios or set up a custom disruption scenario. **Step 4.** Solve Step 4.1. Simulate Step 4.2. Analyze **Step 5.** Export results Step 5.1. Export The urban goods distribution system is a critical component of modern society. However, the COVID-19 pandemic exposed significant vulnerabilities in this system, as it struggled to cope with an unforeseen surge in demand. This crisis highlighted the urgent necessity of developing a resilient and sustainable urban goods distribution system capable of efficiently recovering from high-severity disruptions. To address this challenge, our research team previously developed a novel analytical model, the Robustness, Redundancy, Resourcefulness, and Rapidity - Last-Mile Distribution - Resilience Triangle (R4-LMD-RT) framework. In line with the previous work, this work aims to create a sketch-planning tool tailored for local jurisdictions, based on the R4-LMD-RT model. This tool assists in the strategic planning of urban goods distribution systems, identifying land use requirements and proposing sustainable and resilient strategies, such as urban consolidation, micro-hubs, alternative delivery points, and zero-emission vehicles. As part of a case study, the authors validate the effectiveness of this planning tool by applying it to the city of Los Angeles for a COVID-19-like disruption. The outcome of this research paves the way for more sustainable and resilient urban goods distribution systems in the post-pandemic world.

{"references": ["Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B., Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang, Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T., Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y., Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Br\u00e9on, F.-M., Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K. & Schellnhuber, H. J. (2020). Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications 11, 5172 (2020). https://doi.org/10.1038/s41467-020-18922-7", "Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., & van Vuuren, D. P. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1\u20132), 213\u2013241. https://doi.org/10.1007/s10584-011-0156-z", "Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747\u2013756. https://doi.org/10.1038/nature08823", "Myhre, G., Highwood, E. J., Shine, K. P., & Stordal, F. (1998). New estimates of radiative forcing due to well mixed greenhouse gases. Geophysical Research Letters, 25(14), 2715\u20132718. https://doi.org/10.1029/98gl01908", "Strassmann, K. M. and Joos, F. (2018). The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle\u2013climate simulations, Geosci. Model Dev., 11, 1887\u20131908, https://doi.org/10.5194/gmd-11-1887-2018", "Thomas, M. A., and Lin, T. (2018). A dual model for emulation of thermosteric and dynamic sea-level change. Climatic Change, 148(1\u20132), 311\u2013324. https://doi.org/10.1007/s10584-018-2198-y"]} Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections. Earth's Future. In Press. Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO2 emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community. We acknowledge the Carbon Monitor initiative (Liu et al., 2020) for providing the COVID-19 CO2 sectoral emission data used to construct the proposed TEPs. In addition, we acknowledge the developers of the BernSCM (Strassmann and Joos, 2018) that was utilized in this study to relate TEP CO2 emissions to their respective CO2 atmospheric concentrations. Furthermore, we thank the Texas Tech University McNair Scholars Program and the Multi-Hazard Sustainability (HazSus) research group for guidance and support throughout the course of this study. Analyses presented herein were performed using the RedRaider computing cluster at Texas Tech University. We thank the team at the High Performance Computing Center (HPCC) for their generous support. In addition, the equipment support from the Vice President for Research & Innovation for T.L.'s HazSus Research Group is gratefully acknowledged.

Walkability has become a research topic of great concern for preserving public health, especially in the era of the COVID-19 outbreak. Today more than ever, urban and transport policies, constrained by social distancing measures and travel restrictions, must be conceptualized and implemented with a particular emphasis on sustainable walkability. Most of the walkability models apply observation and subjective methods to measure walkability, whereas few studies address walkability based on sense perception. To fill this gap, we aim at investigating the perceived neighbourhood walkability (PNW) based on sense perception in a neighbourhood of Brussels. We designed a survey that integrates 22 items grouped into 5 dimensions (cleanness, visual aesthetics, landscape and nature, feeling of pressure, feeling of safety), as well as the socio-demographic attributes of the participants. Using various statistical methods, we show that socio-demographics have almost no effects on perceived neighbourhood walkability. Nonetheless, we found significant differences between groups of different educational backgrounds. Furthermore, using a binomial regression model, we found strong associations between PNW and at least one item from each grouping dimension. Finally, we show that based on a deep neural network for classification, the items have good predictive capabilities (78% of classification accuracy). These findings can help integrate sense perception into objective measurement methods of walkable environments. Additionally, policy recommendations should be targeted based on differences of perception across socio-demographic groups.

Abstract: COVID-19, caused by the SARS-CoV-2 virus, has been expanding. SARS-CoV caused an outbreak in early 2000, while MERS-CoV had a similar expansion of illness in early 2010. Nanotechnology has been employed for nasal delivery of drugs to conquer a variety of challenges that emerge during mucosal administration. The role of nanotechnology is highly relevant to counter this “virus” nano enemy. This technique directs the safe and effective distribution of accessible therapeutic choices using tailored nanocarriers, as well as the interruption of virion assembly, by preventing the early contact of viral spike glycoprotein with host cell surface receptors. This study summarises what we know about earlier SARS-CoV and MERS-CoV illnesses, with the goal of better understanding the recently discovered SARS-CoV-2 virus. It also explains the progress made so far in creating COVID-19 vaccines/ treatments using existing methods. Furthermore, we studied nanotechnology- based vaccinations and therapeutic medications that are now undergoing clinical trials and other alternatives.

Abstract Coal energy landscapes have changed dramatically over the last decades, including geographic shifts in production and consumption, technological changes that have reduced labour demand and led to relatively new mining practices (e.g. invasive mountain-top approaches), changed economic footprints, a shutdown of capacities or a complete end of mining in many regions with massive impacts on regional and local economies, community well-being, social capital, et cetera. Then the Covid-19 pandemic and Russia´s invasion of Ukraine have fundamentally affected the global economy, disrupted energy markets, and shattered existing estimates about development trends, challenging the progress and speed of the low-carbon energy transition and coal phase-out. This article provides a brief reflection on the changing landscapes of coal and their possible futures, and serves as an introduction to the Special Issue of Moravian Geographical Reports on “The death of coal in the energy transition? Regional perspectives”.

This article examines outstanding “sustainable” skyscrapers that received international recognition, including LEED certification. It identifies vital green features in each building and summarizes the prominent elements for informing future projects. Overall, this research is significant because, given the mega-scale of skyscrapers, any improvement in their design, engineering, and construction will have mega impacts and major savings (e.g., structural materials, potable water, energy, etc.). Therefore, the extracted design elements, principles, and recommendations from the reviewed case studies are substantial. Further, the article debates controversial design elements such as wind turbines, photovoltaic panels, glass skin, green roofs, aerodynamic forms, and mixed-use schemes. Finally, it discusses greenwashing and the impact of COVID-19 on sustainable design.