International Technology Spillovers in Climate-Economy Models: Two Possible Approaches

This paper analyzes two possible methodologies of modeling international technology spillovers in a climate-economy CGE model. Technological change, by affecting productivity, energy and carbon intensity, eventually influences the amount of CO2 emissions, the costs and the timing of the policies targeted at their reduction. Technological change is here defined so as to include also the diffusion and adoption phase. In an increasingly integrated world, new products and technologies developed in one region will eventually diffuse internationally. The two approaches described in this paper are based on two mechanisms used to model technological change in climate models: learning curves, total factor productivity and the autonomous energy efficient improvement parameter. This paper considers spillovers mediated by international trade in capital goods. In particular, it looks at how imports machinery and equipments from the OECD countries can affect the technology variables related to CO2 emissions: learning rates in the first approach, productivity, energy and carbon intensity in the second one.Climate Policy, International Trade, Learning Curves, International Technology Spillovers, Biased Technical Change

Long-Distance Recreational Travel Behavior and Implications of Autonomous Vehicles

Have you ever wondered how people travel long distances and how it could be affected by the emergence of autonomous vehicles (AVs)? This dissertation aims to answer those questions by studying the current behavior of long-distance recreational travelers and their preference in the age of AVs. This dissertation has four main goals. First, it seeks to develop a reliable way to measure people’s satisfaction with long-distance recreational trips and understand the similarities and differences between long- and short-distance travel satisfaction. Second, it looks at the connection between how people travel, how satisfied they are with their travel experiences, and how this relates to their overall satisfaction with their destination. Third, it explores how people feel about using AVs for long-distance travel and tries to understand what influences their decisions. Lastly, it looks at the impact of vehicle automation, the interior of AVs, and how people use their time during travel on their choices and preferences. The necessary data is gathered through a survey of 696 people who visited national parks in the US. The survey responses are analyzed to understand the research objectives, and some interesting insights are obtained. First, a survey instrument (i.e., a list of questions) is developed to accurately measure long-distance travelers’ satisfaction. The analysis discovers that the factors that affect satisfaction with long-distance travel differ from those that affect short-distance travel. Second, a strong link is established between people’s satisfaction with their travel experiences (on the way) and their overall tourism experience (at destination). Third, the study suggests people might travel more frequently and for longer distances with the introduction of AVs. This result means that we should not only focus on managing tourism destinations but also consider the impact on traffic and infrastructure leading to these destinations. Finally, the study finds that people are interested in using their travel time more productively in AVs, but we should be mindful of the negative consequences, such as increased energy consumption and space requirements. In conclusion, this dissertation sheds light on long-distance travel behavior and the potential changes that could come with using AVs. It emphasizes the importance of enjoying the journey, the impact on tourism, and the need for sustainable transportation. So, next time you plan a road trip, remember there’s more to consider than just getting to your destination

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)

In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose

How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?

The incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors— plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on ‘riding down the learning curve’ and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study.Learning-by-doing, Experience Curves, Learning Curves, Climate Policy

Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Many data sets, from different and seemingly unrelated marketing domains, all involve paths—records of consumers\u27 movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers\u27 motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena. We close with a brief look into the future of path-based models, and a call for researchers to address some of these emerging issues

Sharing Economy Last Mile Delivery: Three Essays Addressing Operational Challenges, Customer Expectations, and Supply Uncertainty

Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice