Charging Station for Electric Bikes Powered by Renewable Energy

This report serves the purpose to answer the research problem of developing a charging station for electric bicycles powered by renewable energy allowing rental companies to create a safer and more sustainable way of commuting. The report was divided into a business and a mechanical part to provide a better overview of each area of operations. The business part focuses on the establishment of the company, SolHavn, the analysis of market environment and customers, as well as the creation of a suitable marketing strategy, and the projection of the expected financial positioning of the company. On the other hand, the mechanical part demonstrates the correct and accurate method to dimension and design a solar charging station that capable of charging 10 electric bike according to appropriate mechanical calculation, assumption and analysis realized. The value of the energy consumption was estimated for the off-grid situation primarily and it mainly focus on the worst case scenario season in Barcelona, Spain. Moreover, the consideration of losses that would happen in electrical cabling and other electronic related component like step-down, charge controller and other was taken into account. Finally, the structural design for the station as demonstrated and would be able to support the load of chosen solar panel and other natural loads after detailed calculation. Besides, other simulation which involved other scenario like hybrid and on-grid was considered and analyzed. The comparison between these other scenarios helped in improving the solar station for further project.<br /

The Implications of Option Pricing Theory on United Kingdom Development Policy

Investment policy makers have consistently sought to promote inward investment through investment incentives of various kinds (e.g. capital grants, depreciation allowances). In applying these instruments, governments seek to influence companies as they apply a traditional investment decision making approach known as Net Present Value (NPV) analysis. Each of the government investment incentives influences some aspect of the NPV calculation. Relatively recent research by McDonald Siegel (1986) has show n that for certain classes of investments, the NPV approach is inaccurate, often by a factor of two or more. This is because the NPV approach neglects the value of the option gained w hen a company chooses not to invest; by waiting a year or more, a company gains insight into macroeconomic and industry factors. If a company chooses to invest today, it must be sure that the return is sufficient to justify giving up the value of this additional information. The value of this option can be quantified, based upon the underlying volatility and trend of the investment, and the company's cost of capital. This research creates an explicit linkage between traditional NPV analysis and the option valuation approach, before considering a whole new set of policy instruments designed to increase a company's likelihood to invest. The research develops several potential new instruments, screens them for the desired behaviour, and selects the most promising instrument. The new instrument is then validated by using an investment case example adapted from the public domain and a large computer model. The research also discusses several related areas. It describes the effect of overlaying Poisson type events on an investment decision (i.e. a sudden shift in the value of the investment), and draws the implications of this thinking on the policy approaches that should be taken by incumbent and opposition regional policy makers. Lastly, the research includes a review of the U.K.'s regional policy objectives and an analysis of different approaches to corporate investment decision making

Estimación del impacto ambiental y social de los nuevos servicios de movilidad

El transporte es fuente de numerosas externalidades negativas, como los accidentes de tráfico, la congestión en las zonas urbanas y la falta de calidad del aire. El transporte también es un sector que contribuye sustancialmente a la crisis climática con más del 16% de las emisiones globales de gases de efecto invernadero como resultado de las actividades de transporte. Muchos creen que la introducción de nuevos servicios de movilidad podría ayudar a reducir esas externalidades. Sin embargo, con cada introducción de un nuevo servicio de movilidad podemos observar factores que podrían contribuir negativamente a la sostenibilidad del sistema de transporte: una cadena de cambios de comportamiento causados por la introducción de posibilidades completamente nuevas. El objetivo de esta tesis es investigar cómo los nuevos servicios de movilidad, habilitados por la electrificación, la conectividad y la automatización, podrían impactar en las externalidades causadas por el transporte. En particular, el objetivo es desarrollar y validar un marco de modelado capaz de capturar la complejidad del sistema de transporte y aplicarlo para evaluar el impacto potencial de los vehículos automatizados.Transport is a source of numerous negative externalities, such as road accidents, congestion in urban areas and lacking air quality. Transport is also a sector substantially contributing to climate crisis with more than 16% of global greenhouse gas emissions being a result of transport activities. Many believe that the introduction of new mobility services could help reduce those externalities. However, with each introduction of a new mobility service we can observe factors that could negatively contribute to the sustainability of the transport system – a chain of behavioural changes caused by introduction of entirely new possibilities. The aim of this thesis is to investigate how the new mobility services, enabled by electrification, connectivity and automation, could impact the externalities caused by transport. In particular the objective is to develop and validate a modelling framework able to capture the complexity of the transport system and to apply it to assess the potential impact of automated vehicles.This work was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement N035297. Moreover, this research has been partially funded by the Spanish Ministry of Science and Innovation through the project: AUTONOMOUS – InnovAtive Urban and Transport planning tOols for the implementation of New mObility systeMs based On aUtonomouS driving”, 2020-2023, ERDF (EU) (PID2019-110355RB-I00)

Essays on Demand Estimation, Financial Economics and Machine Learning

In this era of big data, we often rely on techniques ranging from simple linear regression, structural estimation, and state-of-the-art machine learning algorithms to make operational and financial decisions based on data. This calls for a deep understanding of practical and theoretical aspects of methods and models from statistics, econometrics, and computer science, combined with relevant domain knowledge. In this thesis, we study several practical, data-related problems in the particular domains of sharing economy and financial economics/financial engineering, using appropriate approaches from an arsenal of data-analysis tools. On the methodological front, we propose a new estimator for classic demand estimation problem in economics, which is important for pricing and revenue management. In the first part of this thesis, we study customer preference for the bike share system in London, in order to provide policy recommendations on bike share system design and expansion. We estimate a structural demand model on the station network to learn the preference parameters, and use the estimated model to provide insights on the design and expansion of the system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage, and that currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings, in which the available set of products or services depends on demand. In the second part of this thesis, we study demand estimation methodology when data has a long-tail pattern, that is, when a significant portion of products have zero or very few sales. Long-tail distributions in sales or market share data have long been an issue in empirical studies in areas such as economics, operations, and marketing, and it is increasingly common nowadays with more detailed levels of data available and many more products being offered in places like online retailers and platforms. The classic demand estimation framework cannot deal with zero sales, which yields inconsistent estimates. More importantly, biased demand estimates, if used as an input to subsequent tasks such as pricing, lead to managerial decisions that are far from optimal. We introduce two new two-stage estimators to solve the problem: our solutions apply machine learning algorithms to estimate market shares in the first stage, and in the second stage, we utilize the first-stage results to correct for the selection bias in demand estimates. We find that our approach works better than traditional methods using simulations. In the third part of this thesis, we study how to extract a signal from option pricing models to form a profitable stock trading strategy. Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of their implied and realized roughness. A strategy that goes long the roughest-volatility stocks and short the smoothest-volatility stocks earns statistically significant excess annual returns of 6% or more, depending on the time period and strategy details. Standard factors do not explain the profitability of the strategy. We compare alternative measures of roughness in volatility and find that the profitability of the strategy is greater when we sort stocks based on implied rather than realized roughness. We interpret the profitability of the strategy as compensation for near-term idiosyncratic event risk. Lastly, we apply a heterogeneous treatment effect (HTE) estimator from statistics and machine learning to financial asset pricing. Recent progress in the interdisciplinary area of causal inference and machine learning has proposed various promising estimators for HTE. We take the R-learner algorithm by [73] and adapt it to empirical asset pricing. We study characteristics associated with standard factors, size, value and momentum through the lens of HTE. Our goal is to identify sub-universes of stocks, ``characteristic responders", in which size, value or momentum trading strategies perform best, compared with the performance had they been applied to the entire universe. On the other hand, we identify subsets of ``characteristic traps" in which the strategies perform the worst. In our test period, the differences in average monthly returns between long-short strategies restricted to ``characteristic responders" and ``characteristic traps" range from 0.77% to 1.54% depending on treatment characteristics. The differences are statistically significant and cannot be explained by standard factors: a long-short of long-short strategy generates alpha of significant magnitude from 0.98% to 1.80% monthly, with respect to standard Fama-French plus momentum factors. Simple interaction terms between standard factors and ex-post important features do not explain the alphas either. We also characterize and interpret the characteristic traps and responders identified by our algorithm. Our study can be viewed as a systematic, data-driven way to investigate interaction effects between features and treatment characteristic, and to identify characteristic traps and responders

Value Based Management in Young Innovative Growth Companies

Young innovative growth companies (in the following abbreviated by YIGC) are stunning investors and promise exceptional development: New media businesses like Apple, Google, Facebook, Netflix or Amazon have been showing enormous stock price performances which by far exceed the S&P 500. After an initiation period of negative cashflows, business innovations create trends and finally promise huge abnormal returns. Previous academic research suggests a divide between value based and growth directed entrepreneurial orientation. Authors taking the perspective of neoclassical economic theory explain that value based orientation is inseparable from the sustainable generation of shareholder value and implies the comparatively slow long-term growth of a business. A balanced strategy weighing risk and growth potential is essential. Value based companies are traditionally associated to established business sectors here technologic progresses are clearly predictable. Their markets are stable and settled. Classical value based companies rely on traditional financing strategies. Growth orientation seems to be at odds with this traditional value based conception at first sight since in the start up phase usually little shareholder value is created. The potential of YIGC lies in future development and extraordinary technological advancements. Growth policy is exposed to risk to a larger extent than value based strategies since YIGC operate in a highly uncertain, rapidly changing technological environment and usually are exposed to high market dynamics. For this reason young innovative growth companies frequently have to rely on alternative and more risky financing strategies than value based corporations. Previous research in sum implies that classical value based theories are hardly applicable to young innovative growth companies. Intuition however suggests that extraordinary growth stories like Google‘s or Apple‘s would hardly be possible with sustainable value orientation. The key issues and research questions of this empirical study emerge from this supposed contradiction. The study provides a synthesis of so far theoretical insights on VBM and YIGC, I conduct a systematic review of available empirical research on existing VBM applications in YIGC and - for lack of specific publications – on related businesses i.e. large growth corporations and innovative SME . Referring to previous insights on VBM applications in these businesses I conduct an own empirical research in German YIGC, concerning their value-based orientation in entrepreneurial practice in the form of a qualitative interview-based study. The study has made several important contributions which are valuable to both academic research and management practice. The contributions comprise novel methodologies of research an analysis in YIGC on the on hand and completely new insights on the value based management practice in German YIGC, which emerge from the case study research. The study has provided a comprehensive overview on the key facets of value-based management. While previous studies are either purely shareholder oriented or emphasize the stakeholder perspective mainly, this analysis has brought both aspects together into a comprehensive model of value based management. The study comprises a comprehensive review of YIGC related empirical studies in value based management and has shown that value based management in YIGC cannot focus on shareholder value only but has to include the stakeholder perspective. In the empirical part, the study has developed a novel stage model which drafts a prototypical YIGC growth and development cycle and includes a set of value based indicators. The model has been verified by case study analysis. A YIGC scorecard model is the second major methodological outcome. It originates in the analysis of 5 German YIGC and helps YIGC to analyse their value based principles and improve them, to encourage growth and development. Then the extensive case study has enlarged empirical knowledge on German YIGC: The following important points have been made: YIGC correspond in value orientation, essentially all participating companies refer to the categories developed in the course of the review: value based human resource orientation, customer orientation, social responsibility orientation, supply-chain orientation and shareholder value orientation. While conventional growth is measured quantitatively, YIGC growth and shareholder value potential is based on a range of qualitative factors, which are born by all stakeholder groups. This makes an extension of the classical shareholder value concept for instance in the form of the suggested scorecard model necessary. YIGC pass a typical growth and development cycle, which here has been subdivided into three stages. At the emerging growth stage YIGC rise debt and external equity capital to finance an initial business idea which in the beginning does not yet create substantial benefit. At the accelerating growth stage, the business concept is profitable, cash flows grow strongly and the equity base increases. Debts can slowly be reduced. At the consolidating growth stage, cashflows consolidate and a young and innovative business concept allows no further exponential growth. Cashflows and the equity base now grow more slowly, unless the company decides to change its business structure and culture to become a large conventional growth organization.Administración y Dirección de Empresa

Arctic Valley Trails Feasibility Assessment and Master Planning Support

A Project Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Project ManagementThis project conducted a feasibility assessment to determine whether it is feasible to implement new trails at Arctic Valley. This assessment was executed by reviewing literature, collecting primary and secondary data, organizing data into tables, maps, interview logs, and documents. The data was analyzed and presented in summary reports elaborating on data collected, analysis performed, and findings. Findings were organized into six product packages for knowledge transfer to Anchorage Ski Club (ASC). These included a Stakeholder Feedback Report, Historical Infrastructure Analysis Report, Literature Review, Chugach State Park Regulation Change Process Model, Trail Mapping and Models Analysis, and Feasibility Assessment Report. Upon conclusion of the research and reports, the feasibility of trails implementation was affirmed. It is tenable to build trails within ASC’s Concession Contract area in accordance with regulatory guidelines, modern trail construction best practices, and ASC’s financial capabilities. The data collected, analysis performed, and products created were transferred to the project sponsor and advisory committee. ASC was recommended to utilize the results conveyed and perform alternatives analysis of their proposed projects and investment opportunities

Regional Transportation Hot Spot Forum Marin/Sonoma 101 Corridor, MTI Report F-01-02

The entire Hwy. 101 corridor in Marin County and 10 miles in Sonoma County were at F service level as early as 1995. Tourists and recreational destinations make this a seven-day-a-week traffic hot spot. Three failed elections in the past revealed an electorate divided and unable to reach consensus on solutions. Recent work by public and private leaders in both counties was leading toward an election in November 2004. On April 11, 2002, the Mineta Transportation Institute cosponsored a regional transportation forum with The Commonwealth Club of California in Marin County, California. Several representatives from key Marin County and Sonoma County transportation-related agencies and community organizations joined to discuss the corridor and the many possible actions that could provide alternatives and relieve congestion. The forum concluded with a set of recommendations for next steps. This publication is an edited version of the April 11 Forum