Global Governance of Energy Systems Transition in a Geopolitical Context - Looking at the Emerging Dynamics of Oil and Coal Beyond the High-Income Countries

Despite efforts to decarbonise energy systems gathering momentum in response to intensified calls demanding tangible action and a departure towards cleaner and net-zero energy pathways, oil and coal dominate the global energy systems and are predicted to remain prominent in the foreseeable future in all energy scenarios, threatening the transition to net-zero pathways. Even as the consumption of fossil fuels, particularly coal-fired power declines in the West, India’s continued use of coal, under the current trends, is likely to be one of the dominant contributors towards the global increase in CO2 emissions in 2040. Oil will also continue to remain strategically important to the global energy systems in the near-term scenario and will continue to influence energy markets and global geopolitics. These realities, together, present grave prospects for the world’s environment. Global energy governance processes have a key role to play but a fundamentally different approach will be needed to lead energy systems decarbonisation. Recognizing the importance for holistic insights for successful and effective governance in the context of net-zero transition and the emerging geopolitics of oil and coal, a mixed-methods approach is adopted in this thesis to analyse the potential of global energy governance arrangements for leading the net-zero transition. The use of both quantitative and qualitative methods enables to capture diverse perspectives to explore the key relationships and feedbacks critical for future energy system transition. Document analysis helps to explore the relevant theories, concepts, and trends. This thesis is informed empirically by system-dynamics models exploring scenarios emerging from continued oil price volatility and expansion of coal-fired generation within the context of energy systems transition. The oil model tests the scenarios resulting from supply-side shocks linked to energy security and demand disruptions as a possible consequence of growing pressures of decarbonisation. Damped oscillation in oil prices occur resulting from ongoing readjustments in systems, occurring even long after initial shocks have passed. Without strategic intervention through effective governance arrangements, the world may remain locked-into using oil in the foreseeable future. Global governance institutions can use the low-price window, to help oil producers to diversify their economies and initiate structural reform to gradually steer the world away from oil. Results from India coal SD-model show that even as India succeeds in meeting its Paris commitment of reducing the emission intensity of its GDP by 30-35% below 2005 levels by 2030, the CO2 emissions will continue to rise and can only be stabilised with the use of advanced clean coal technology enabled with capture. The insights from India-coal SD model tested by performing a techno-economic feasibility analysis using both document analysis and Monte Carlo Simulation using @RISK software, reveal that notwithstanding their technological suitability, advanced clean coal technologies enabled with CCS will significantly increase the cost of electricity and struggle in India, as in other emerging economies, without adequate financial support, knowledge transfer and international cooperation. Analysed against the challenges revealed by the scenarios examined by the oil and coal model, this thesis observes that the existing institutional framework lacks the mandate to effectively deal with the complexity of challenges facing energy systems transition towards secure, sustainable and affordable energy pathways. This thesis argues that despite some recent claims that a new era for energy governance is underway, its institutional landscape and capacity has remained largely unchanged over the past few decades and, in its current form, is rendered ineffective, in addressing the emerging challenges of decarbonisation. A multi-actor, multi-regime complex of actors with proper mandate and remit may be effective. The research recommends that closer coordination and cooperation between processes and frameworks engaged in governance of energy systems will be needed to address contemporary energy challenges

Coal in the 21st Century: a climate of change and uncertainty

Coal presents a particular set of challenges when balancing energy policy goals. Despite presenting viable solutions to the problems of energy security and global energy poverty, coal struggles, given its greenhouse-gas drawbacks, in a world of increasingly harmful climate change. Notwithstanding the harm caused to the environment, coal remains an expanding low-price route to meeting local energy needs. It is forecasted to remain a major global resource for the foreseeable future. In the short term it is predicted to have a 26% share of the global energy mix. Recent years have witnessed severe deviations from previously stable trends in coal markets and policy dynamics. According to the predictions by the International Energy Agency (IEA), a variety of factors ranging from the planned phase-out of coal in countries such as Denmark, France and the UK, to changes in policy in China and import-dependency in India, and demand drop in the US have together resulted in the largest decline in coal production in 2015 since 1971 (IEA, Coal Information, 2016). This paper seeks to outline basic coal facts, recent market trends and directions globally and provides an overview of issues shaping the future of coal in the twenty-first century. This paper seeks to outline basic coal facts, recent market trends and directions globally and provide an overview of issues shaping the future of coal in the 21st century

Modern global oil market under stress: system dynamics and scenarios

Modelling techniques developed 40 years ago to gain insight into the issues of energy supply security remain powerful even as the focus of energy technology policy analysis has shifted to consider new problems and research methods more closely linked to pressing environmental challenges. Dynamic modelling continues to be of merit and indeed it has the potential to provide helpful perspectives on contemporary problems such as climate change and the Net Zero transition. This work seeks to help revive system dynamics modelling of the global oil market. The SD-based analysis used in this paper facilitates a rational representation of physical stocks and flows as well as causal linkages that drive decision-making in the global oil system. The use of SD provides insights (intuitive and counter-intuitive) into the macro-level non-linear dynamics of global oil exchanges and permits assessment of the potential impacts of future changes in system behaviour. The model exhibits damped oscillations in oil prices, broadly consistent with real market behaviours. The results further illustrate the resilience of market dynamics in the phase of extreme supply side shocks and reveal the ability of System Dynamics to simulate and model such effects even beyond the point of market failure

Advanced Chalcogen Cathode Materials for Lithium-Ion Batteries

As on today the main power sources of lithium-ion batteries (LIBs) research developments gradually approach their theoretical limits in terms of energy density. Therefore, an alternative next-generation of power sources is required with high-energy densities, low cost, and environmental safety. Alternatively, the chalcogen materials such as sulfur, selenium, and tellurium (SSTs) are used due to their excellent theoretical capacities, low cost, and no toxicity. However, there will be some challenges to overcome such as sluggish reaction of kinetics, inferior cycling stability, poor conductivity of S, and “shuttle effect” of lithium polysulfides in the Li-S batteries. Hence, several strategies have been discussed in this chapter. First, the Al-SSTs systems with more advanced techniques are systematically investigated. An advanced separators or electrolytes are prepared with the nano-metal sulfide materials to reduce the resistance in interfaces. Layered structured cathodes made with chalcogen ligand (sulfur), polysulfide species, selenium- and tellurium-substituted polysulfides, Se1-xSx uniformly dispersed in 3D porous carbon matrix were discussed. The construction of nanoreactors for high-energy density batteries are discussed. Finally, the detailed classification of flexible sulfur, selenium, and tellurium cathodes based on carbonaceous (e.g., carbon nanotubes, graphene, and carbonized polymers) and their composite (polymers and inorganics) materials are explained

Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Abstract Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome