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Mining for Change

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ISBN: 9780198851172 Year: Pages: 512 DOI: 10.1093/oso/9780198851172.001.0001 Language: English
Publisher: Oxford University Press Grant: UNU WIDER
Subject: Economics --- Electrical and Nuclear Engineering --- Environmental Sciences
Added to DOAB on : 2020-06-25 23:58:38
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Abstract

For a growing number of countries in Africa the discovery and exploitation of natural resources is a great opportunity, but one accompanied by considerable risks. In Africa, countries dependent on oil, gas, and mining have tended to have weaker long-run growth, higher rates of poverty, and greater income inequality than less resource-abundant economies. In resource-producing economies, relative prices make it more difficult to diversify into activities outside of the resource sector, limiting structural change. Economic structure matters for at least two reasons. First, countries whose exports are highly concentrated are vulnerable to declining prices and volatility. Second, economic diversification matters for long-term growth. This book presents research undertaken to understand how better management of the revenues and opportunities associated with natural resources can accelerate diversification and structural change in Africa. It begins with chapters on managing the boom, the construction sector, and linking industry to the resource—three major issues that frame the question of how to use natural resources for structural change. It then reports the main research results for five countries—Ghana, Mozambique, Uganda, Tanzania, and Zambia. Each country study covers the same three themes—managing the boom, the construction sector, and linking industry to the resource. One message that clearly emerges is that good policy can make a difference. A concluding chapter sets out some ideas for policy change in each of the areas that guided the research, and then goes on to propose some ideas for widening the options for structural change.

Risk, Ruin and Survival: Decision Making in Insurance and Finance

Authors: --- ---
ISBN: 9783039285167 / 9783039285174 Year: Pages: 210 DOI: 10.3390/books978-3-03928-517-4 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Business and Management
Added to DOAB on : 2020-06-09 16:38:56
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Developing techniques for assessing various risks and calculating probabilities of ruin and survival are exciting topics for mathematically-inclined academics. For practicing actuaries and financial engineers, the resulting insights have provided enormous opportunities but also created serious challenges to overcome, thus facilitating closer cooperation between industries and academic institutions. In this book, several renown researchers with extensive interdisciplinary research experiences share their thoughts that, in one way or another, contribute to the betterment of practice and theory of decision making under uncertainty. Behavioral, cultural, mathematical, and statistical aspects of risk assessment and modelling have been explored, and have been often illustrated using real and simulated data. Topics range from financial and insurance risks to security-type risks, from one-dimensional to multi- and even infinite-dimensional risks.

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

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