How overconfident are current projections of anthropogenic carbon dioxide emissions?

Analyzing the risks of anthropogenic climate change requires sound probabilistic projections of CO2 emissions. Previous projections have broken important new ground, but many rely on out-of-range projections, are limited to the 21st century, or provide only implicit probabilistic information. Here we take a step towards resolving these problems by assimilating globally aggregated observations of population size, economic output, and CO2 emissions over the last three centuries into a simple economic model. We use this model to derive probabilistic projections of business-as-usual CO2 emissions to the year 2150. We demonstrate how the common practice to limit the calibration timescale to decades can result in biased and overconfident projections. The range of several CO2 emission scenarios (e.g., from the Special Report on Emission Scenarios) misses potentially important tails of our projected probability density function. Studies that have interpreted the range of CO2 emission scenarios as an approximation for the full forcing uncertainty may well be biased towards overconfident climate change projections.economics of climate change, scenarios, data assimilation

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

Concepts, models, and methods in computational heterogeneous catalysis illustrated through CO2 conversion

Theoretical investigations and computational studies have notoriously contributed to the development of our understanding of heterogeneous catalysis during the last decades, when powerful computers have become generally available and efficient codes have been written that can make use of the new highly parallel architectures. The outcomes of these studies have shown not only a predictive character of theory but also provide inputs to experimentalists to rationalize their experimental observations and even to design new and improved catalysts. In this review, we critically describe the advances in computational heterogeneous catalysis from different viewpoints. We firstly focus on modeling because it constitutes the first key step in heterogenous catalysis where the systems involved are tremendously complex. A realistic description of the active sites needs to be accurately achieved to produce trustable results. Secondly, we review the techniques used to explore the potential energy landscape and how the information thus obtained can be used to bridge the gap between atomistic insight and macroscale experimental observations. This leads to the description of methods that can describe the kinetic aspects of catalysis, which essentially encompass microkinetic modeling and kinetic Monte Carlo simulations. The puissance of computer simulations in heterogeneous catalysis is further illustrated by choosing CO2 conversion catalyzed by different materials for most of which a comparison between computational information and experimental data is available. Finally, remaining challenges and a near future outlook of computational heterogeneous catalysis are provided.publishe

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

Green innovations and patents in OECD countries

Green transition is important for the economics of the OECD countries and their transition to cleaner production. This paper estimates a knowledge production function consisting of a system of innovation inputs, innovation outputs, and productivity with feedback effect from productivity on innovation investments. The model accounts for productivity shock, endogeneity of inputs, and their simultaneity and interdependence. Productivity shock is a latent unobserved component that is specified in terms of observable factors. The model is estimated using Bayesian methods organized around Marco Chain Sequential Monte Carlo iteration techniques also known as Particle Filtering. For the empirical part, the paper uses balanced panel data covering 27 OECD countries' green innovation and patents activities observed during the period 1990–2018. Our empirical results show evidence of significant heterogeneity in productivity and its relationship with its identified determinants. The paper also discusses the implications of these results for OECD countries’ green growth strategies

Economic Appraisal of Undeveloped Unconventional Gas: The Bowland United Kingdom Case

The estimation of production potential provides the foundation for commercial viability appraisal of natural resources. Due to uncertainty around production assessment approaches in the unconventional petroleum production field, an appropriate production estimation methodology which addresses the requisite uncertainty at the planning stage is required to guide energy policy and planning. This study proposes applying the numerical unconventional production estimation method which relies on geological parameters, (pressure, porosity, permeability, compressibility, viscosity and the formation volume factor) as well as the rock extractive index (a measure of technical efficiency) and develops a model that estimates the appropriate values for four of the parameters required based on a depth correlation matrix while a stochastic process guides the other parameters based on known data range. The developed model is integrated with a numerical model to estimate gas production potential and developed framework is eventually applied to undeveloped shale gas wells located in the Bowland shale, central Britain. The results account for below ground uncertainty and heterogeneity of wells. A sensitivity analysis is applied to consider the relative impacts of individual parameters on production potential. The estimated daily initial gas production rate ranges from 15,000scf to 319,000scf while estimated recovery over 12 years is approximately 1.1bscf in the reference case for wells examined. In relation to cost, A cost analysis is executed, which guides the identification of cost parameters. This study identifies key cost parameters and then develop a non-static model by examining the trends over the years as well as proposes a work break down cost estimation equation. In addition, a methodology in estimating the costs of developing unconventional gas resources based on the production technique is proposed. In addition, the sources of uncertainty in shale gas development cost estimation are examined and identified. It is found that there is an insignificant correlation of cost parameters with oil prices suggest that additional factors need to be analysed. These empirical model and results suggest that the market oil price impact on shale gas production cost although important but restrained by other factors which may include financial revenue hedging programs aimed at securing higher revenues or endogenous efficiency gains which direct production strategy in low oil prices situations. The results from the learning curve and innovation study shows that drilling technology has driven cost reduction and increased lateral lengths while the hydraulic fracturing technology has relied on more material use volumes. The additional demand in stimulation sand and other production materials as well as their disposal can lead to exogenous cost implications. Other expected exogenous cost implications are environmental, regulation and fiscal regimes which can aid or deter technology adoption in different regions. The overarching economic appraisal methodology is based on integration of the depth dependent correlation matrix, bottom up cost estimation and the undeveloped unconventional gas development decision models. Additionally, other input and output parameter scenarios are modelled as well as the impact of carbon emission regulation and mitigation

The Value of Learning about Critical Energy System Uncertainties

In this thesis, a sensitivity analysis is used to systematically classify and rank parametric uncertainties in an energy system optimisation model of the United Kingdom, ETI-ESME. A subset of the most influential uncertainties are then evaluated in a model which investigates the process of resolving uncertainty over time — learning. The learning model identifies strategies and optimal pathways for staged investment in these critical uncertainties. By soft-linking the learning model to an energy system optimisation model, the strategies also take into account the system-wide trade-offs for investment across individual or portfolios of technologies. A global sensitivity analysis method, the Method of Morris, was used to efficiently analyse the model over the full range and combination of input parameter values covering technology costs and efficiencies, resource costs, and technology/infrastructure build-rate and resource-constraints. The results of the global sensitivity analysis show that very few parameters are responsible for the majority of variation in the outputs from the model. These critical uncertainties can be separated into two groups according to their suitability for learning. Some of the important uncertainties identified, such as the price of fossil fuel resources available to the UK, are not amenable to learning and must be managed through risk-based approaches. The parameters which are amenable to learning, the availability of domestic biomass, and the rate at which carbon capture and storage technologies can be deployed, are then investigated using the learning model. The learning model is formulated as a stochastic mixed-integer programme, and gives insights into the dynamic trade-offs between competing learning options within the context of the whole energy system. A UK case study shows that, if the resources are known to be available, total discounted net benefit of the availability of 150TWh/year of domestic biomass is £30bn, while the ability to build CCS plant at a rate of 2GW/year is worth up to £34bn. Together, the value increases non-linearly to a maximum of £59bn. This represents up to 17% of UK’s discounted total energy system cost over the next four decades as quantified by the ETI-ESME model. The learning model quantifies the cost threshold below which investment in an uncertain learning project is optimal. The threshold is a proxy for maximum no-regret investment over the aggregate total of research, commercialisation and deployment and could be of use to research funding agencies. The results show that when the likelihood of success of the project is 20%, one-stage learning projects of £10bn or below are always undertaken. For the same likelihood of suc- cess, dividing a project into two-stages more than doubles the investment threshold to £22bn as it allows strategies in which investment switches away from a project if it fails. Dividing a project into multiple stages is particularly beneficial if most of the uncertainty is front-loaded, enabling switching to an alternative. The precise strategy to follow is a complex function of the cost, duration, net benefit and probability of success of each learning project, as well as the interac- tions between the project outcomes