Net-zero aviation: Transition barriers and radical climate policy design implications

While air transport decarbonization is theoretically feasible, less attention has been paid to the complexity incurred in various ‘transition barriers’ that act as roadblocks to net-zero goals. A total of 40 barriers related to mitigation, management, technology and fuel transition, finance, and governance are identified. As these make decarbonization uncertain, the paper analyzes air transport system's growth, revenue, and profitability. Over the period 1978–2022, global aviation has generated marginal profits of US$20200.94 per passenger, or US$202082 billion in total. Low profitability makes it unlikely that the sector can finance the fuel transition cost, at US$0.5–2.1 trillion (Dray et al. 2022). Four radical policy scenarios for air transport futures are developed. All are characterized by “limitations”, such as CO2 taxes, a carbon budget, alternative fuel obligations, or available capacity. Scenario runs suggest that all policy scenarios will more reliably lead to net-zero than the continued volume growth model pursued by airlines

Car careers: A socio-psychological evaluation of aspirational automobile ownership

There is a general consensus that private car ownership is a significant barrier to transport system change, specifically in regard to injuries, space, air pollutants, or greenhouse gas emissions. Observed changes in automobile characteristics also suggest that the system is becoming less sustainable, given trends towards larger cars with greater mass and horsepower. It is thus relevant to understand how the automobile system progresses. National statistics provide data on the technical side of car ownership, such as changes in vehicle specifics or national fleet size. This paper complements this view with a socio-psychological perspective on aspirational car ownership, i.e. the type of car people preferred to drive if given a free choice. Data is derived from an online panel (n = 1,211) representative of the German population, and also contains information on current car ownership, use, driving style, traffic behavior, attitudes towards traffic risks and safety measures, as well as political orientation. This allows for a discussion of driver segments in relation to the characteristics of cars, and hence to better understand the socio-psychological drivers of the development of the automobile system

Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction

This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ‘black-box’ models have grown. Theoretical assertions often propose a trade-off between model complexity (predictive performance) and transparency (interpretability); however, empirical evidence supporting this claim is limited and inconsistent. To address this gap, we introduce a novel interpretability scoring system - a Machine Learning Interpretability Rank-based scale - that combines objective measures such as the number of model parameters with subjective interpretability rankings across different model types. This comprehensive methodology includes stratified sampling, model tuning, and a two-step ranking system to operationalize this trade-off. Results reveal a significant negative correlation between interpretability and predictive performance for intrinsically interpretable models, reinforcing the notion of a trade-off. However, this relationship does not hold for black-box models, suggesting that for these algorithms, predictive performance can be prioritized over interpretability. This study contributes to the ongoing discourse on explainable AI, providing practical insights and tools to help researchers and practitioners achieve a balance between model complexity and transparency. We recommend to prioritise more interpretable models when predictive performance is comparable. Our scale provides a transparent and efficient framework for implementing this heuristic and improving parameter optimization. Further research should extend this analysis to unstructured data, explore different interpretability methods, and develop new metrics for evaluating the trade-off across diverse contexts

A global review of marine air pollution policies, their scope and effectiveness

Shipping is associated with various environmental impacts, such as pollutants discharged to air and sea. Much of this pollution appears to be unregulated, and global emissions from shipping are expected to more than triple between 2020 and 2050. This paper reviews global, national, regional and port-level legislative approaches that have been implemented to reduce emissions of carbon dioxide (CO2), nitrous oxides (NOx), sulphur oxides (SOx) and particulate matter (PM). Policies are identified on the basis of a systematic review of the literature in combination with a detailed analysis of the respective global, national and local policy initiatives. Findings suggest that many policies are voluntary or, in ports, incentive-based; regulatory approaches are largely limited to Emission Control Areas. Policies also focus on efficiencies, they are not concerned with absolute pollutant and greenhouse gas levels. No policies incentivizing or forcing the transition to zero-carbon fuels were identified. As ports can define limits to pollution, for instance by demanding shore power use, they can significantly affect the clean development of the sector. Further legislation will be needed nationally to counterbalance the lack of supranational ambition on pollutants and climate change mitigation

Are emissions from global air transport significantly underestimated?

Air transport is energy-intense, and considerable attention has been paid to the sector's use of fuel and emissions of greenhouse gases. Commercial aviation is believed to currently emit about 1 Gt CO2 per year, if considering global bunker fuel use (scope 1 in the Greenhouse Gas Protocol). A growing database is becoming available on scope 1–3 emissions; this is, including up- and downstream emissions, and it is now possible to assess the aviation system's carbon intensity more comprehensively. This paper investigates the annual reports of 26 of the largest airlines in the world by market capitalisation, finding that reporting on emissions for scopes 1–3 is still inconsistent and characterised by reporting gaps. Yet, available data suggests that scope 3 emissions are significant (about 30% of scope 1 emissions). These findings have repercussions for the sector's net-zero ambitions, climate governance, consumer choices and air transport finance, as the overall contribution from air travel to climate change remains underestimated. Results suggest that it is in the sector's interest to present robust, transparent, consistent and accurate emission inventories – and to engage with the implications

Decision tree ensembles for automatic spectroscopic classification of tidal disruption events

The principal objective of this study was to develop a reliable model for the automatic classification of tidal disruption events (TDEs) using spectroscopic data. A total of 147 TDE spectra and 3626 spectra of various supernova types and AGNs were included in the data, sourced from PESSTO-SSDR1-4. An ensemble learning approach was employed using bagging with decision trees as base learners, optimized through cost-sensitive analysis and Bayesian hyperparameter tuning. A high test accuracy of 97.67 per cent, with balanced precision and recall, was achieved by the optimized model. To enhance TDE detection, a dynamic threshold adjustment was applied, prioritizing recall, which increased from 47.22 per cent to 83.33 per cent. Most TDEs were correctly identified due to this adjustment, with a reduction in precision from 85.00 per cent to 22.22 per cent and a decrease in overall accuracy from 97.67 per cent to 88.23 per cent, reflecting the prioritization of recall over precision. Relative to their occurrence in our data set, SN IIn, SN IIP, SN II, and AGNs are the most likely objects to be misclassified as TDEs. The effectiveness of the proposed methodology in accurately classifying TDEs while managing the rate of false positives is demonstrated by these results. This approach is particularly valuable in TDE detection, where minimizing false negatives is crucial to ensuring these rare events are not missed. The potential of ensemble learning, combined with cost-sensitive analysis and threshold optimization, in handling data sets in astrophysical research is highlighted by the study, offering a robust tool for future TDE classifications. The proposed method could be particularly beneficial for upcoming large-scale surveys

COVID-19 and pathways to low-carbon air transport until 2050

The COVID-19 pandemic has led to an unprecedented decline in global air transport and associated reduction in CO2 emissions. The International Civil Aviation Organization (ICAO) reacted by weakening its own CO2-offsetting rules. Here we investigate whether the pandemic can be an opportunity to bring the sector on a reliable low-carbon trajectory, with a starting point in the observed reduction in air transport demand. We model a COVID-19 recovery based on a feed-in quota for non-biogenic synthetic fuels that will decarbonize fuels by 2050, as well as a carbon price to account for negative externalities and as an incentive to increase fuel efficiency. Results suggest that until 2050, air transport demand will continue to grow, albeit slower than in ICAO's recovery scenarios, exceeding 2018 demand by 3.7–10.3 trillion RPK. Results show that synthetic fuels, produced by 14–20 EJ of photovoltaic energy, would make it possible to completely phase out fossil fuels and to avoid emissions of up to 26.5 Gt CO2 over the period 2022–2050

Physical activity through place attachment: Understanding perceptions of children and adolescents on urban places by using photovoice and walking interviews

Public urban places and their environmental characteristics impact youth's physical activity (PA) through perceptions. The objective of this study was to use a qualitative participatory approach with children and adolescents to understand how their attachment to urban places perceived as PA-friendly or unfriendly is related to their PA behaviour. Ninety-three participants aged six to 17 from six neighbourhoods with varying objective walkability engaged in photovoice and walking interviews. Data were analysed by using the tripartite framework of place attachment (PPP model), which was adapted for application to PA behaviour and supplemented by photographs. Themes were identified for each (sub-)dimension of the PPP model with person, place and process factors influencing attachment. Further subdimensions (PA and other behaviours) and categories (travel mode, trip length and frequency of visits) were added to the PPP model. Urban design recommendations were derived by age and gender to promote PA through place attachment

Dual checkpoint blockade of CD47 and LILRB1 enhances CD20 antibody-dependent phagocytosis of lymphoma cells by macrophages

Antibody-dependent cellular phagocytosis (ADCP) by macrophages, an important effector function of tumor targeting antibodies, is hampered by ‘Don´t Eat Me!’ signals such as CD47 expressed by cancer cells. Yet, human leukocyte antigen (HLA) class I expression may also impair ADCP by engaging leukocyte immunoglobulin-like receptor subfamily B (LILRB) member 1 (LILRB1) or LILRB2. Analysis of different lymphoma cell lines revealed that the ratio of CD20 to HLA class I cell surface molecules determined the sensitivity to ADCP by the combination of rituximab and an Fc-silent variant of the CD47 antibody magrolimab (CD47-IgGσ). To boost ADCP, Fc-silent antibodies against LILRB1 and LILRB2 were generated (LILRB1-IgGσ and LILRB2-IgGσ, respectively). While LILRB2-IgGσ was not effective, LILRB1-IgGσ significantly enhanced ADCP of lymphoma cell lines when combined with both rituximab and CD47-IgGσ. LILRB1-IgGσ promoted serial engulfment of lymphoma cells and potentiated ADCP by non-polarized M0 as well as polarized M1 and M2 macrophages, but required CD47 co-blockade and the presence of the CD20 antibody. Importantly, complementing rituximab and CD47-IgGσ, LILRB1-IgGσ increased ADCP of chronic lymphocytic leukemia (CLL) or lymphoma cells isolated from patients. Thus, dual checkpoint blockade of CD47 and LILRB1 may be promising to improve antibody therapy of CLL and lymphomas through enhancing ADCP by macrophages

Engineering an inducible leukemia-associated fusion protein enables large-scale ex vivo production of functional human phagocytes

Ex vivo expansion of human CD34+ hematopoietic stem and progenitor cells remains a challenge due to rapid differentiation after detachment from the bone marrow niche. In this study, we assessed the capacity of an inducible fusion protein to enable sustained ex vivo proliferation of hematopoietic precursors and their capacity to differentiate into functional phagocytes. We fused the coding sequences of an FK506-Binding Protein 12 (FKBP12)-derived destabilization domain (DD) to the myeloid/lymphoid lineage leukemia/eleven nineteen leukemia (MLL-ENL) fusion gene to generate the fusion protein DD-MLL-ENL and retrovirally expressed the protein switch in human CD34+ progenitors. Using Shield1, a chemical inhibitor of DD fusion protein degradation, we established large-scale and long-term expansion of late monocytic precursors. Upon Shield1 removal, the cells lost self-renewal capacity and spontaneously differentiated, even after 2.5 y of continuous ex vivo expansion. In the absence of Shield1, stimulation with IFN-γ, LPS, and GM-CSF triggered terminal differentiation. Gene expression analysis of the obtained phagocytes revealed marked similarity with naïve monocytes. In functional assays, the novel phagocytes migrated toward CCL2, attached to VCAM-1 under shear stress, produced reactive oxygen species, and engulfed bacterial particles, cellular particles, and apoptotic cells. Finally, we demonstrated Fcγ receptor recognition and phagocytosis of opsonized lymphoma cells in an antibody-dependent manner. Overall, we have established an engineered protein that, as a single factor, is useful for large-scale ex vivo production of human phagocytes. Such adjustable proteins have the potential to be applied as molecular tools to produce functional immune cells for experimental cell-based approaches