Role of Low Carbon Energy Technologies in Near Term Energy Policy

In the first part of this thesis, we use a multi-model framework to examine a set of possible future energy scenarios resulting from R&D portfolios of Solar, Nuclear, Carbon Capture and Storage (CCS), Bio-Fuels, Bio-Electricity and Batteries for electric transportation. We show that CCS significantly complements Bio-Electricity, while most of the other energy technology pairs are substitutes. From the probabilistic analysis of future energy scenarios we observe that portfolios with CCS tend to stochastically dominate those without CCS; portfolios with only renewables tend to be stochastically dominated by others; and that there are clear decreasing marginal returns to scale. We also find that, with higher damage risk, there is more incentive for technical advancement in CCS and less incentive for development of Solar energy technology. In the second part of this thesis, we examine the optimal R&D portfolio changes at the different R&D budget levels and how risk in climate damages affects the optimal R&D portfolio. We find that the optimal portfolio is generally not robust to risk, and the optimal investments in the energy technologies vary with risk in climate damages; however R&D investments in certain energy technologies, such as Nuclear, are robust under the different risk cases. We note that while CCS plays a significant role in the optimal portfolio when there is no risk in climate damages, it plays an even more significant role in the higher climate damage risk cases. We also find that R&D investment in the Biofuels energy technology increases significantly with increase in climate damage risk, while Solar, Batteries for Electric Transportation and Bio-Electricity technologies go out of favor with increases in climate damage risk. We also propose a methodology for obtaining solutions to subset portfolio problems, based on the characteristics of the individual technologies. We prove that the subset portfolio problem is optimal if the individual technology does not interact with any of the other technologies, we confirm this in our empirical portfolio problem. In the third part of this thesis, we conduct an illustrative global sensitivity analysis on a large scale integrated assessment model with a view to determining the primary drivers of uncertainty in the model and examining the effect of structural uncertainty on the model. We compare our results to a previous paper which conducted a one factor at a time sensitivity analysis and find that both sensitivity methods provide the same result which is different from findings from the previous paper. We find that model interactions are present even in our very limited illustrative analysis. We also conduct most of the steps needed for a full global sensitivity analysis of the model and highlight the challenges in conducting this analysis on the GCAM model. We show that there exist a need for global sensitivity analysis for accurate determination of the principal drivers of uncertainty in integrated models

Nonparallel Emotional Speech Conversion

We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real applications. We achieve nonparallel training based on an unsupervised style transfer technique, which learns a translation model between two distributions instead of a deterministic one-to-one mapping between paired examples. The conversion model consists of an encoder and a decoder for each emotion domain. We assume that the speech signal can be decomposed into an emotion-invariant content code and an emotion-related style code in latent space. Emotion conversion is performed by extracting and recombining the content code of the source speech and the style code of the target emotion. We tested our method on a nonparallel corpora with four emotions. Both subjective and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation available at http://www.jian-gao.org/emoga

APOE E4 is associated with impaired self-declared cognition but not disease risk or age of onset in Nigerians with Parkinson's disease

The relationship between APOE polymorphisms and Parkinson's disease (PD) in black Africans has not been previously investigated. We evaluated the association between APOE polymorphic variability and self-declared cognition in 1100 Nigerians with PD and 1097 age-matched healthy controls. Cognition in PD was assessed using the single item cognition question (item 1.1) of the MDS-UPDRS. APOE genotype and allele frequencies did not differ between PD and controls (p > 0.05). No allelic or genotypic association was observed between APOE and age at onset of PD. In PD, APOE ε4/ε4 conferred a two-fold risk of cognitive impairment compared to one or no ε4 (HR: 2.09 (95% CI: 1.13-3.89; p = 0.02)), while APOE ε2 was associated with modest protection against cognitive impairment (HR: 0.41 (95% CI 0.19-0.99, p = 0.02)). Of 773 PD with motor phenotype and APOE characterized, tremor-dominant (TD) phenotype predominated significantly in ε2 carriers (87/135, 64.4%) compared to 22.2% in persons with postural instability/gait difficulty (PIGD) (30/135) and 13.3% in indeterminate (ID) (18/135, 13.3%) (p = 0.037). Although the frequency of the TD phenotype was highest in homozygous ε2 carriers (85.7%), the distribution of motor phenotypes across the six genotypes did not differ significantly (p = 0.18). Altogether, our findings support previous studies in other ethnicities, implying a role for APOE ε4 and ε2 as risk and protective factors, respectively, for cognitive impairment in PD

Effects of Different Methods of Aggregation of Probabilities on the R&d Investment Portfolio for Optimal Emissions Abatement: An Empirical Evaluation

This thesis examines two possible orders of combining multiple experts in elicitations with multiple de-composed events: Should experts be combined early or later in the decision process? This thesis is in conjunction with the paper (Baker & Olaleye, 2012) where we show that it is best to combine experts early as later combination leads to a systematic error. We conduct a simulation to more fully flesh out the theoretical model. We also conduct a theoretical analysis aimed at determining how significantly these two methods differ. We find that all results are in accordance with the theory but combining experts later might lead to less error in some cases due to randomness. We then conduct an empirical evaluation of the two methods using data from a previous study. We show that the experts exhibit some form of correlation. The impact of using the two methods of combining experts is then evaluated using an optimal R&D investment portfolio model. We find that the elicitation inputs have a significant effect on the outcome of the optimal portfolio and that there is an advantage from combining experts early

Decision Frameworks and the Investment in R&D

In this paper we provide an overview of decision frameworks aimed at crafting an energy technology Research & Development portfolio, based on the results of three large expert elicitation studies and a large scale energy-economic model. We introduce importance sampling as a technique for integrating elicitation data and large IAMs into decision making under uncertainty models. We show that it is important to include both parts of this equation – the prospects for technological advancement and the interactions of the technologies in and with the economy. We find that investment in energy technology R&D is important even in the absence of climate policy. We illustrate the value of considering dynamic two-stage sequential decision models under uncertainty for identifying alternatives with option value. Finally, we consider two frameworks that incorporate ambiguity aversion. We suggest that these results may be best used to guide future research aimed at improving the set of elicitation data. This is a pre-print version of the article “Decision frameworks and the investment in R&D” by Erin Baker, Olaitan Olaleye, Lara Aleluia Reis, published in Energy Policy, Volume 80, May 2015, Pages 275-285, ISSN 0301-4215, http://dx.doi.org/10.1016/j.enpol.2015.01.027

Obstacles and Policy Measures Toward COVID-19 Vaccination: Creating a Sustainable Road Map for Malawi

The Coronavirus Disease 2019 (COVID-19) pandemic is a public health threat for Malawi which is facing several challenges concurrently including disease burden; inadequate finances; illiteracy; and public mistrust in government. In this pandemic, vaccines are the most reliable and cost-effective public health intervention, and the Malawian government has instituted an action plan which includes prioritizing the vaccination of traditional and religious leaders, increased vaccination sites to include workplaces and shopping malls, and health promotion. However, there is still considerable hesitancy around the use of the available vaccines in Malawi. In this paper, we explore the multiple interrelated factors driving COVID-19 vaccine hesitancy in Malawi. It is therefore recommended that the Malawian government embrace multicomponent and wide-ranging strategies to address COVID-19 vaccine hesitancy in the country. This includes reviving trust in national health authorities by offering population-specific, target-driven, and effective, transparent, and timely communication to its citizens and relevant stakeholders about the safety and efficacy of the COVID-19 vaccine

MAPT allele and haplotype frequencies in Nigerian Africans: Population distribution and association with Parkinson's disease risk and age at onset

INTRODUCTION: The association between MAPT and PD risk may be subject to ethnic variability even within populations of similar geographical origin. Data on MAPT haplotype frequencies, and its association with PD risk in black Africans are lacking. We aimed to determine the frequencies of MAPT haplotypes and their role as risk factors for PD and age at onset in Nigerians. METHODS: The haplotype and genotype frequencies of MAPT rs1052553 were analysed in 907 individuals with PD and 1022 age-matched healthy controls from the Nigeria Parkinson's Disease Research network cohort. Clinical data related to PD included age at study, age at onset (AAO), and disease duration. RESULTS: The frequency of the H1 haplotype was 98.7% in PD, and 99.1% in controls (p = 0.19). The H2 haplotype was present in - 1.3% of PD and 0.9% of controls (p = 0.24). The most frequent MAPT genotype was H1H1 (PD - 97.5%, controls - 98.2%). The H1 haplotype was not associated with PD risk after accounting for gender and AAO (Odds ratio for H1/H1 vs H1/H2 and H2/H2: 0.68 (95% CI:0.39-1.28); p = 0.23). CONCLUSIONS: Our findings support previous studies that report a low frequency of the MAPT H2 haplotype in black ancestry Africans but document its occurrence in Nigerians. The MAPT H1 haplotype was not associated with an increased risk or age at onset of PD in this cohort