Search

The ability to reduce energy loss at semiconductor surfaces through passivation or surface field engineering has become an essential step in the manufacturing of efficient photovoltaic (PV) and optoelectronic devices. Similarly, surface modification of emerging halide perovskites with quasi-2D heterostructures is now ubiquitous to achieve PV power conversion efficiencies (PCEs) > 22% and has enabled single-junction PV devices to reach 25.7%, yet a fundamental understanding to how these treatments function is still generally lacking. This has established a bottleneck for maximizing beneficial improvements as no concrete selection and design rules currently exist. Here we uncover a new type of tunable passivation strategy and mechanism found in perovskite PV devices that were the first to reach the > 25% PCE milestone, which is enabled by surface treating a bulk perovskite layer with hexylammonium bromide (HABr). We uncover the simultaneous formation of an iodide-rich 2D layer along with a Br halide gradient achieved through partial halide exchange that extends from defective surfaces and grain boundaries into the bulk layer. We demonstrate and directly visualize the tunability of both the 2D layer thickness, halide gradient, and band structure using a unique combination of depth-sensitive nanoscale characterization techniques. We show that the optimization of this interface can extend the charge carrier lifetime to values > 30 μs, which is the longest value reported for a direct bandgap semiconductor (GaAs, InP, CdTe) over the past 50 years. Importantly, this work reveals an entirely new strategy and knob for optimizing and tuning recombination and charge transport at semiconductor interfaces and will likely establish new frontiers in achieving the next set of perovskite device performance records., Main text: 16 pages, 4 figures. Supporting Information: 47 pages, 26 figures, 6 tables

In the usual Bayesian setting, a full probabilistic model is required to link the data and parameters, and the form of this model and the inference and prediction mechanisms are specified via de Finetti's representation. In general, such a formulation is not robust to model mis-specification of its component parts. An alternative approach is to draw inference based on loss functions, where the quantity of interest is defined as a minimizer of some expected loss, and to construct posterior distributions based on the loss-based formulation; this strategy underpins the construction of the Gibbs posterior. We develop a Bayesian non-parametric approach; specifically, we generalize the Bayesian bootstrap, and specify a Dirichlet process model for the distribution of the observables. We implement this using direct prior-to-posterior calculations, but also using predictive sampling. We also study the assessment of posterior validity for non-standard Bayesian calculations, and provide an efficient way to calibrate the scaling parameter in the Gibbs posterior so that it can achieve the desired coverage rate. We show that the developed non-standard Bayesian updating procedures yield valid posterior distributions in terms of consistency and asymptotic normality under model mis-specification. Simulation studies show that the proposed methods can recover the true value of the parameter efficiently and achieve frequentist coverage even when the sample size is small. Finally, we apply our methods to evaluate the causal impact of speed cameras on traffic collisions in England.

This paper develops a multiply robust (MR) dose-response estimator for causal inference problems involving multivalued treatments. We combine a family of generalised propensity score (GPS) models and a family of outcome regression (OR) models to achieve an average potential outcomes estimator that is consistent if just one of the GPS or OR models in each family is correctly specified. We provide proofs and simulations that demonstrate multiple robustness in the context of multivalued causal inference problems.

In this thesis, automated fare collection (AFC) data are used to analyse and quantify transit journey time service quality on the London Underground metro system. The thesis comprises of three main research areas. The first part focuses on characterising passenger journey time variance through the generation of empirical probability distributions of journey times under regular and incident-affected operating conditions. The distributions are parametrically defined, and practical passenger-oriented performance metrics are proposed based on the moments of the distributions. The second area of research involves decomposing total passenger journey times recorded by the AFC data into sub-components that distinguish between the walking and in-vehicle phases of a passenger journey. To achieve this, a Bayesian assignment algorithm is proposed to allocate individual passengers to individual trains. Total journey times are then decomposed into the constituent components of access, on-train, and egress times. In the third area of research, the degree to which different service supply and demand factors influence journey times is analysed. Semiparametric regression methods are applied to quantify the effect of physical station and route characteristics, operational service supply factors, and passenger demand levels for each journey time component. To quantify the effect of individual passenger characteristics on journey times, passenger-level heterogeneity within each journey time component is analysed. As an extension to the access time model, the influence of train headways on passenger wait times at the origin station is also derived. The main outputs of the thesis are the quantification of journey time performance, and the identification of the key service supply and demand factors that impact journey times. The results can be directly applied by operators to guide where potential interventions should be made in order to improve the reliability of journey times for urban rail transit networks. Open Access

The overarching theme of this dissertation is how public transportation impacts the local econ- omy and the wellbeing of society. I concentrate on the wider economic benefits of transportation to provide precise and causal evidence on how large-scale metro projects impact firm location, employment, productivity and women’s feeling of safety. I use spatially disaggregate panel microdata to understand the local impact of metro openings. Developing robust identification strategies and employing panel instrumental variable methodologies ensure the causality of re- sults. There are three empirical investigations in this thesis: the first estimates the impact of the 1999 Jubilee Line Extension on the spatial distribution of firms and employment in Lon- don, and finds empirical evidence for the existence of urban displacement. The second estimates the impact of the same metro opening on firm productivity and develops a project appraisal methodology for the calculation of agglomeration benefits. The estimation finds that within 750 meters to new stations aggregate value added, mean firm productivity and the number of local business units increased. However, areas between 1250 and 2000 meters experienced a decrease in the number of local units but mean firm productivity did not change, leading to decreasing aggregate value added. The third chapter tests the hypothesis that transport con- nectivity improves perceptions of safety for women by investigating whether metro openings in 2018 increased the safety feeling of women around stations in Delhi, India. The analysis finds that the feeling of safety significantly increased within 500 meters to stations. High levels of safety increase women’s freedom of movement, which is a major step in integrating women to the labour force, and these benefits may be separately included in transport project appraisal. Open Access

The overarching theme of this dissertation is the investigation of the relevance of transportation infrastructure for the development and performance of the private sector. I analyse this relationship for both firms located in developing and developed countries to highlight similarities and disparities of the importance of transportation networks for the performance of the firms. For the examination of this relationship, I have chosen two countries that differ substantially on various economic and social factors and their levels of transportation infrastructure: Colombia and Germany. Research on developing countries is often limited by the absence of reliable panel data, this especially applies to data on firms. I propose the use of aggregated Colombian firm data to identify a pseudo-panel of manufacturing firms that can be used to simulate the analysis of firm-level effects. I provide further validity for this method by conducting a Monte Carlo simulation, which provides support for the appropriateness of pseudo-panels in the context of firm-level investigations and further highlights the particular suitability of different panel data estimation techniques for it. The identified pseudo-panel is employed for the subsequent analysis. The elasticity of highway infrastructure on the growth of a firm’s output is significant and positive, however the identified magnitude of this effect of ap- proximately 0.13 to 0.15 is substantially larger than the average effect identified in similar studies for developed countries, hence implying the crucial importance of transportation infrastructure for economic growth in developing countries. The results further show a time lag of this effect of one year, suggesting that firms’ production processes require time to adjust to the transportation infrastructure changes. These results are robust to the inclusion of additional controls and alternative transportation variables used. An additional investigation reveals thattransportation benefits particularly accrue to firms of the heavy industries. A further analysis highlights the role of transportation infrastructure within the context of spatial heterogeneity of this effect across regions. The results indicate the benefits of transportation infrastructure predominantly only accrue to firms located in relatively stronger regions with reported output elasticities of around 0.15. Additional analyses provide no support for the importance of transportation for fostering economic convergence in Colombia. This thesis contains furthermore an examination of the role of the German highway network in influencing firm-level productivity patterns. This work uses German firm-level data and relies on the use of the Levinsohn-Petrin methodology and historical transportation data to conduct a two-step productivity estimation. I further derive two different transportation measurements to investigate the importance of transportation spillover effects. In line with previous studies of the German economic geography, I find spatial differences in productivity levels where larger levels are predominantly found in the districts of the South and West of the country, and lower productivity levels are identified throughout the Eastern districts. The investigation of the whole country identifies positive and significant productivity elasticities of highways where estimates range from 0.03 to 0.07 and 0.061 to 0.23 depending on the choice of method, controls and highway variable. This represents an increase of the firm’s productivity of 0.03 to 0.0.7 per cent following a 1 per cent increase in highways, or alternatively an increase of 0.061 to 0.23 when highway spillover effects are accounted for. This indicates that firms derive benefits from local highways and those located in the surrounding regions and the remainder of the country which points towards large spillover effects. The results further show that while highways are important for a firm’s productivity growth, alternative productivity enhancing factor, predominantly the amount to skilled labour, present relatively more important productivity determinants. The investigation of subsamples designed to capture districts with relatively high and low highway levels points towards diminishing returns of transportation infrastructure and satiation effects. While no consistent differences in the estimated effects could be identified across districts of the Eastern and Western states and for those districts which have recently received new highways under the “Transportation Projects German Unity”, further disaggregate results show that highways particularly benefited firms in rural districts in the West and those located in urban districts in the East. Overall, the robustness of the results of this chapter requires further analyses, however they tentatively point towards productivity enhancing benefits of transportation and additionally reveal a large degree of heterogeneity of this effect. Open Access

Access to the global container liner service network is vital to international trade. Disruptions to this network have major and immediate implications for consumers, industries, markets and national economies. The measurement and understanding of the existing liner service network performance susceptibility to disruptions (transport vulnerability) is therefore of critical importance to public and private stakeholders responsible for ensuring its operability and accessibility. Methodologies that assess transport vulnerability in liner service networks face two key modelling challenges: (i) A lack of historical disruption data on network components (such as ports, canals, and liner services) and (ii) the typical size of realistic liner service networks. The first challenge limits the implementation of methodologies that require prior knowledge of disruption probabilities to quantify the vulnerability of the network. The second challenge creates the need for models capable of capturing key industry practices such as transhipment, empty repositioning, and the ability of vessels to skip disrupted ports while still allowing implementations at realistic global scales. This study addresses the above-mentioned challenges by developing a quantitative framework capable of identifying critical components in large-scale networks with limited or unavailable historical disruption data. The proposed framework consists of a game-theoretic attacker-defender model (ADM) and a cost-based container assignment model (CBCAM) adapted for the analysis of networks under disruptions. The ADM consists of a two-player, zero-sum game between a malevolent agent (attacker) that seeks to maximise disruption costs and an ocean carrier (defender) that aims to minimise routing costs. The CBCAM is used to generate the payoff matrix of the game computing routing costs for the ocean carrier and disruption costs for the attacker on each of their available strategies. Linear program formulations of the joint AD-CBCAM allow for implementations on large-scale realistic liner service networks. This scalability is tested using CPLEX to solve network instances of up to 88 distinct liner services, 230 ports, and 2,648 OD pairs. Results allow establishing performance baselines and test the effectiveness of iterative interventions aimed at increasing the resilience of the system. Open Access

Cities are indispensable for economic development, although urban agglomerations can fail to deliver their potential due to congestion. Metros are key to achieving the benefits of urban densities without the costs of congestion, by means of providing unparalleled fast and high-capacity transportation in densely populated areas. These urban rail systems require large one-off initial investments but also substantial operational costs, in order to provide the regular day-to-day rail service and maintain the metro's fixed and mobile assets in good condition, such as the rail tracks and the trains, respectively. The aim of this thesis is to better understand what determines these operational costs. The analysis is based on a panel of more than 20 large and medium-sized urban rail systems across the world during the period 2005 to 2014. The research is based on the econometric estimation of cost functions for the aggregate operating costs but also for some of its constituents like train service, station service and rolling stock maintenance costs. Likewise, different outputs are considered to evaluate the cost efficiency of the metros, such as car kilometres, passenger journeys and train hours among others. The analysis quantifies the effects of economies of scale and economies of density in metro operations, which can vary significantly between different divisions of the metro operations leading to substantial policy implications. Moreover, the effect of wages, and how this is linked to labour productivity, is also examined. The dissertation also estimates the corresponding cost elasticities for other cost factors such as labour outsourcing, subsidies and network characteristics. Lastly, the research describes the effect of age on urban rail operating costs and also outlines the background time trends on how the metro operating costs could evolve in the future. Open Access

Crowding is a major source of inconvenience for public transport users in densely populated metropolitan areas globally, while eliminating crowding requires costly investments. Crowding can be considered as a cornerstone phenomenon of public transport theory, as the interaction between demand and supply side policies. This PhD thesis aims to improve our understanding of the mechanics behind crowding, using microeconomic modelling techniques. From a demand perspective, the crucial precondition of any objective economic analysis is to reliably quantify the inconvenience caused by crowding. In pursuit of this goal, the thesis develops a statistical model to infer the user cost of crowding from metro passengers' route choice decisions. As an important intermediate research outcome, the thesis delivers a novel passenger-to-train assignment algorithm that recovers the network-level crowding pattern of a metro system. Our method is a unique contribution in the sense that it is based on large-scale automated datasets: we use smart card and automated vehicle location data only. The theoretical part of the thesis provides new insights into crowding pricing and capacity optimisation. One of the key messages of the thesis is that crowding in certain time periods and network segments is an unavoidable feature of optimal public transport provision, when demand fluctuates by time and space, but capacity cannot be differentiated between jointly served markets. We show that pricing can be an efficient tool to tackle the deficiency caused by this technological constraint. The thesis devotes special attention to two policy relevant applications: (i) the external cost of seat occupancy, an externality inversely proportional to the density of crowding, and (ii) the inefficiency of unlimited-use travel passes. Our conclusions may assist researchers and practitioners in better understanding the true cost of public transport usage and the related aspects of optimal policy design, including pricing, subsidisation and capacity provision. Open Access