ABSTRACT
Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.
ABSTRACT
The outbreak of COVID-19 has had tremendous impacts on human health and the world economy. Studies have focused on the impact of COVID-19 on potential tourists and tourism destinations from the perspectives of individuals, industries and organizations, and they have provided some measures for tourism recovery. However, under the situation of individual restriction, research has not systematically explained residents' desire for trips of different distances and factors or the similarities and differences in the factors affecting tourism willingness for trips of different distances. In this context, a measurement of eco-environmental values is used to investigate these issues to help the tourism economy recover. Using questionnaires covering all provinces in mainland China, this paper investigates residents' travel willingness to make trips of different distances, and it utilizes binary logistic regression analysis to examine the factors that help predict tourists' travel intentions. In addition, the patterns of willingness to travel different distances are displayed in maps generated by ArcGIS software. The results suggest that the objective COVID-19 confirmed case distribution follows distance decay theory;however, the distribution patterns of travel willingness are not in accordance with distance decay. The factors that have a significant impact on predicting travel willingness regarding the three kinds of trip distances are educational background, cognition of COVID-19, and geographical division factors. Income and the severity of the pandemic situation play different roles in predicting travel willingness in this study. Overall, the findings of this study extend the application of distance decay theory, which contributes to tourism studies in the COVID-19 context. The findings are also beneficial for tourism recovery and crisis management against the backdrop of pandemic normalization.
ABSTRACT
The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students' academic performances using e-learning data. Consequently, we collected students' datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student's academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students' performances during the semester.
ABSTRACT
Purpose>The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.Design/methodology/approach>The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.Findings>The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.Originality/value>The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.
ABSTRACT
Background: After coronavirus disease 2019 (COVID-19) was declared a pandemic, movement restrictions were implemented across sub-Saharan Africa. There has been much speculation on what the long-term impacts on urban transport might be. Objectives: The aim of this paper is to identify the revealed and future travel impacts of the pandemic. Method: To pursue this aim, evidence was compiled from two sources: secondary big data;and a ( n = 15) two-wave Delphi panel survey of experts in the region. Results: It is predicted that longer-term impacts will take the form of: reduced travel by, and accessibility for, low-income households residing in peripheral locations because of decreased welfare;reduced transport service availability;operator reduction (particularly amongst unsubsidised formal operators);increased remote activity participation for a minority of better resourced households with white-collar workers;and disrupted trip distributions as the mix of city-centre land use changes in response to business attrition in economic recession rather than to disrupted bid rents. Conclusion: The major impact of the pandemic is likely to be on welfare, rather than on trip substitution. There is a need, therefore, to focus policy on the mitigation of these impacts and, more particularly, on ways of measuring changes in transport disadvantage and exclusion so that reliable data are available to inform mitigation strategies. The mitigation strategies considered should include investment in affordable ‘digital connectivity’ as a means of complementing accessibility from physical proximity and mobility. The pandemic also highlights the need to develop more robust transport planning practices to deal with uncertainty.