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The factors affecting the supply of rubber from Sarawak, East Malaysia, were identified and reviewed in an historical framework. A methodical framework for the general analysis of economic relationships between variables was reviewed and a practical application of the methodology to the supply of rubber from Sarawak was carried out. An econometric analysis of the long run factors affecting the production of rubber was carried out. (1) Two log-differenced autoregressive models of the rubber supply were formulated. (2) The models were tested for parameter constancy to identify structural breaks in the time series and for structural invariance to determine whether they were suitable for policy analysis, forecasting and backcasting. (3) The variables were tested for bivariate Granger Causality to determine the relationships between the factors of production and the output of rubber. (4) Forecast Error Variance Decomposition analysis of multivariate Granger Causality was carried out using a Vector Autoregressive Model. The results confirm the a priori economic theory that long run changes in supply have been affected primarily by changes in area under rubber production and long term price trends. The area planted to rubber has depended upon price incentives and the availability of scarce labour resources. Prices have been affected by the supply of rubber from Sarawak but this is posited to be a reflection of global supply trends affecting prices. While the results generally confirm the economic theory, caution is urged when interpreting the results. The severe inadequacies of the data used highlights the need for more accurate time series and the mainly methodological approach of this study.

<p dir="ltr">Airport parking revenues become essential in maintaining daily aeronautical and non-aeronautical operations and financing capital expenditures. There exist significant variations between different airports in terms of their parking revenues, and such variations will not be eliminated when airport parking revenue is standardized by passenger volume. Given the limited empirical research on airport parking revenues, this study examines the variation of airport parking revenue per locally originating passenger using random-effects regression on a five-year panel dataset. Our regression results reveal a significant positive relationship between airport economy parking price and airport parking revenue per locally originating passenger. Additionally, we find a significant positive relationship between household vehicle ownership and airport parking revenue per locally originating passenger. However, the number of offsite parking service providers can lead to a significant negative effect on airport parking revenue per locally originating passenger. Based on these findings, airport operators can implement strategic management initiatives tailored to local market conditions, with the goal of optimizing airport parking revenues and improving passenger welfare.</p>

This thesis presents models that describe the behaviour of electricity demand and ambient temperature. Important features of both variables are described by mathematical components. These models were developed to calculate the value of electricity demand that is not expected to be exceeded more than once in ten years and to generate synthetic sequences that can be used as input data in simulation software. / PhD Doctorate

<p>This dissertation examines the impact of the expansion of renewable technology at both national and local level, through distinct essays. At the national level, the first paper analyzes the effects of economic and distributional impacts of climate mitigation policy, in the context of a developing country, to understand the interactions between the energy system and the macroeconomic environment. In the case of the local level, the second paper uses synthetic control method, to estimate the effect at the county level of utility scale wind in the development indicators for two counties in the U.S. </p> <p>The first paper assesses the economic and distributional impacts of Nicaragua’s commitments to limit future greenhouse gas emissions in the context of the Paris Agreement, known as the Nationally Determined Contributions (NDCs). The analysis relies on two distinct models. The first is a top-down approach based on a single-country computable general equilibrium (CGE) model, known as the Mitigation, Adaptation and New Technologies Applied General Equilibrium (MANAGE) Model. The second is a bottom-up approach based on the Open-Source energy Modeling System (OSeMOSYS), which is technology rich energy model. The combined model is calibrated to an updated social accounting matrix for Nicaragua, which disaggregates households into 20 representative types: 10 rural and 10 urban households. For the household disaggregation we have used information from the 2014 Living Standards Measurement Study (LSMS) for Nicaragua. Our analysis focuses on the distributional impacts of meeting the NDCs as well as additional scenarios—in a dynamic framework as the MANAGE model is a (recursive) dynamic model. The results show that a carbon tax has greatest potential for reduction in emissions, with modest impact in macro variables. An expansion of the renewable sources in the electricity matrix also leads to significant reduction in emissions. Only a carbon tax achieves a reduction in emissions consistent with keeping global warming below 2°C. Nicaragua’s NDC alone would not achieve the target and mitigation instruments are needed. An expansion of generation from renewable sources, does not lead to a scenario consistent with a 2°C pathway. </p> <p>The second paper measures the impact of wind generation on county level outcomes through the use of the Synthetic Control Method (SCM). SCM avoids the pitfalls of other methods such as input-output models and project level case studies that do not provide county level estimates. We find that the local per capita income effect of utility wind scale is 6 percent (translate into an increase of $1,511 in per capita income for 2019) for Benton County and 8 percent for White county in Indiana (an increase of $2,100 in per capita income for 2019). The per capita income effect measures the average impact, which includes the gains in rents from capital, land, and labor from wind power in these counties. Moreover, we find that most of the rents from wind power accrue to the owners of capital and labor. Even assuming the lowest projections of electricity prices and the highest reasonable cost we still find a 10 percent minimum rate of return to capital for both Benton and White counties’ wind power generators. Furthermore, we find that there are excess rents that could be taxed and redistributed at the county, state, or federal level without disincentivizing investment in wind power.</p>

Game balance has been a challenge for game developers since the time games have become more complex. There have been a handful of proposals for game balancing processes outside the manual labor-intensive play testing methods, which most game developers often are forced to use simply due to the lack of better methods. Simple solutions, like restrictive game play, are limited because of their inability to provide insight on interdependencies among the mechanisms in the game. Complex techniques framed around the potential of AI algorithms are limited by computational budgets or cognition inability to assess human actions. In order to find a middle ground we investigate Game Theory and Mechanism Design concepts. Both have proven to be effective tools to analyse strategic situations among interacting participants, or in this case `players'. We test the feasibility of using these techniques in an Real Time Strategy (RTS) game domain to understand game balance. MicroRTS, a small and simple execution of an RTS game is employed as our model. The results provide promising insight on the effectiveness of the method in detecting imbalances and further inspection to find the cause. An additional benefit out of this technique, besides detecting for game imbalances, the approach can be leveraged to create imbalances. This is useful when the designer or player desires to do so.

<p dir="ltr">In this thesis, we delve into the exploration of several nonparametric and semiparametric econometric models within the Bayesian framework, highlighting their applicability across a broad spectrum of microeconomic and macroeconomic issues. Positioned in the big data era, where data collection and storage expand at an unprecedented rate, the complexity of economic questions we aim to address is similarly escalating. This dual challenge ne- cessitates leveraging increasingly large datasets, thereby underscoring the critical need for designing flexible Bayesian priors and developing scalable, efficient algorithms tailored for high-dimensional datasets.</p><p dir="ltr">The initial two chapters, Chapter 2 and 3, are dedicated to crafting Bayesian priors suited for environments laden with a vast array of variables. These priors, alongside their corresponding algorithms, are optimized for computational efficiency, scalability to extensive datasets, and, ideally, distributability. We aim for these priors to accommodate varying levels of dataset sparsity. Chapter 2 assesses nonparametric additive models, employing a smoothing prior alongside a band matrix for each additive component. Utilizing the Bayesian backfitting algorithm significantly alleviates the computational load. In Chapter 3, we address multiple linear regression settings by adopting a flexible scale mixture of normal priors for coefficient parameters, thus allowing data-driven determination of the necessary amount of shrinkage. The use of a conjugate prior enables a closed-form solution for the posterior, markedly enhancing computational speed.</p><p dir="ltr">The subsequent chapters, Chapter 4 and 5, pivot towards time series dataset model- ing and Bayesian algorithms. A semiparametric modeling approach dissects the stochastic volatility in macro time series into persistent and transitory components, the latter addi- tional component addressing outliers. Utilizing a Dirichlet process mixture prior for the transitory part and a collapsed Gibbs sampling algorithm, we devise a method capable of efficiently processing over 10,000 observations and 200 variables. Chapter 4 introduces a simple univariate model, while Chapter 5 presents comprehensive Bayesian VARs. Our al- gorithms, more efficient and effective in managing outliers than existing ones, are adept at handling extensive macro datasets with hundreds of variables.</p>

<p dir="ltr">This thesis investigated the international trade dynamics of wood pellets within the context of renewable energy transitions amid climate change concerns. In the first chapter, by employing gravity models with different estimators and specifications, we analyzed the determinants of trade flows of wood pellets. Additionally, we forecasted the future trade values of wood pellets under five shared socioeconomic pathways (SSP) scenarios. Our results showed the effects of some factors such as GDP of exporters, contiguity, and the distance between the two trading countries, were consistent with the economic theory. On the other hand, some other factors exhibited unexpected effects or conflicting results across the models. Regarding projections under five SSP scenarios, our results indicated substantial growth in trade flows, although potential overestimations are acknowledged due to the imposed assumptions. SSP3, which reflects a nationalistic scenario, is projected to have the smallest trade flows, while SSP5 anticipates the highest trade flows due to diminishing inequality and high GDP growth. Also, regional shifts in trade patterns were forecasted, with East Asia and Southeast Asia gaining prominence in imports and exports, respectively. Conversely, Europe’s imports and exports as well as North America’s exports are expected to decrease their shares in the global trade. Overall, our findings emphasize the complexity of trade determinants and underscore the need for nuanced forecasting methodologies to anticipate future trade dynamics accurately amidst evolving global scenarios of wood pellet trade.</p><p dir="ltr">The second chapter evaluated the effects of the Paris Agreement on the international trade of wood pellets. The growing concern about climate change has encouraged the global communities to take actions toward climate-change mitigation. As a form of such efforts, the Paris Agreement was signed in 2015 by 196 parties around the world and went into force in 2016. As a means to mitigate climate change, wood pellets have been used as fuels alternative to fossil fuels. Traditionally, Europe was the primary importer of wood pellets, mostly sourced from the United States and Canada. In the last decade, there has also been a significant uptake in East Asia, indicating shifting trade patterns and market dynamics in the wood pellet industry. This study employed an event-study framework to analyze the impact of the Paris Agreement on the global trade of wood pellets from 2014 to 2019, using import and export data at the regional level. Our results revealed distinct patterns in responses to the Paris Agreement in terms of adjustment speed and magnitude. Europe exhibited a rapid increase in both imports and exports immediately after the Paris Agreement. East Asia demonstrated a delayed yet substantial rise in imports, particularly after 2018. North America also swiftly expanded exports, following the agreement, while Southeast Asia emerged as an important exporter, particularly in supporting the East Asian market from 2017 onwards. We also found an increase in exports of non-pellet wood fuels from Africa. This finding indicates that international climate agreements not only contribute to the overall expansion of the global market of wood pellets but also reshape the market by involving more countries in international efforts to mitigate climate change.</p>