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Introduction
Published in Peter M. Schwarz, Energy Economics, 2023
Energy economics uses the tools of economics to analyze the supply and demand of energy.1 There are many basic principles involved such as the law of demand, consumer preferences, elasticity of demand, substitutes in consumption, economies of scale, elasticity of supply, market structures (perfect competition, monopoly, and oligopoly), inefficient outcomes due to market as well as government failures, substitutes in production, the impact of technology changes on costs, and the markets for inputs including energy, labor, and capital. In addition, there are some ideas that are unique to the energy markets such as the problem of allocating a nonrenewable resource between present and future periods. External costs and benefits play a big role in energy markets and in energy policy. Comparisons of marginal costs and marginal benefits are important for understanding policy options and for choosing among possible policies to meet a given objective.
Technology
Published in Dain Bolwell, Governing Technology in the Quest for Sustainability on Earth, 2019
This tendency is apparent because greater efficiency makes the resource effectively cheaper. However, its degree does depend on the elasticity of demand for the resource, so there is not a rigid link between efficiency and resource use. Demand elasticity is partly a function of the availability of substitutes, and this also must be taken into account. According to energy economist Harry Saunders (1992, p. 131), there is a further factor linking efficiency with increased energy consumption: at the macroeconomic level, more efficient technology also leads to faster economic growth, which in turn increases energy use throughout the economy. Thus technological progress that improves energy efficiency will tend to increase overall energy use.
Entropic boundary conditions towards safe artificial superintelligence
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Santiago Núñez-Corrales, Eric Jakobsson
The third area of risk revolves around the human brain. At the same time, let us consider the energy economics of the brain. Brains constitute an archetype of complexity. The evolutionary and energy cost of brains can only be explained by a series of structural principles capable of bringing balance between the effectiveness of its anatomical function and the physiological and metabolic constraints of the organism (Bullmore & Sporns, 2012). This explains, for instance, the hierarchically modular organisation observed in brains as complex information processing systems (Bassett et al., 2010). The energy consumed by the brains appears to delineate a metabolic budget where approximately 30% of it is dedicated to basal functions (e.g., sustaining resting and action potentials), and around 70% to spontaneous brain activity (e.g., cortical computation, active signal processing) as given by the cerebral metabolic rate of glucose (Tomasi et al., 2013). It is safe to suppose that localised implants such as those in the cochlea do not radically alter the metabolic budget of the brain, thereby exerting little changes to the distribution of brain activity. However, since there is no prior evolutionary history of brains intensively interconnecting with high-speed devices driven by ASI agents, it is hard to determine the limits of both brain plasticity and energetics. Since the cortical region appears to be information and energy dense, it is a likely target for the development of brain-machine interfaces of the sort speculated to be able to tame ASI risks.
Impact of climate change on sectoral electricity demand in Turkey
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Denizhan Guven, M. Ozgur Kayalica, Gulgun Kayakutlu, Erkan Isikli
Various methodologies and models with different specifications can be found in the energy economics literature. One is the Structural Time Series Model (STSM) introduced by Harvey et al. (1986). Prior to STSM, unit-root tests associated with cointegration techniques had been widespread in energy demand forecast models for years (Hendry and Juselius 2000). Despite their frequent use, the cointegration method and unit-root test have been criticized for their weak statistical characteristics and unnecessary and/or misleading procedure (Harvey 1997; Maddala and Kim 1998). Seeking to replace the cointegration method, Harvey (1997) combined the flexibility of time series models and the interpretation of the conventional regression model. STSM is well-suited to unobservable trends varying stochastically over time; thus, the combination of STSM and Autoregressive Distribution Lag (ARDL) can produce energy forecast functions. This structure allows for both stochastic trends and stochastic seasonality to determine price/income elasticities. Furthermore, the generalized model discussed in Section 3 contains the key factors of income, price, urbanization and climate change, as well as a stochastic UEDT. The UEDT can be described as a factor for exogenous effects such as energy efficiency developments, changes in behaviors, tastes and legislation. As a result, STSM/UEDT models have been used to forecast energy demand by various studies, as shown in Table 2.
Experimental investigation of parallel type -evacuated tube solar collector using nanofluids
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Subramaniam Babu Sasikumar, Harikrishnan Santhanam, Muhamad Mat Noor, Madhesh Devasenan, Hafiz Muhammad Ali
George and Kalaivanan (2017) studied the thermal performance and optimization parameters using the Response Surface Methodology (RSM). The values of energy gain, efficiency and solar fraction. Also, Genetic algorithm toolbox in RSM have used to optimize the efficiency and water temperature in this system. Raghurajsinh, Parmar, and Bhojak (2016) reviewed the application of solar energy in the evacuated tube collector with the support of heat pipe technology. The Evacuated Tube Collector helps to improve the energy economics, consumption, and efficiency. The comparison of design parameters and theoretical model have reviewed in this work. Kadyan (2018) reported that domestic and industrial sectors are mostly dependence of evacuated water heaters for a continuous supply of hot water. Also, performed the experimentation in the ETC and obtained the system maximum efficiency of 51% and the system overall heat loss coefficient as 1.81 W/m2K. Siddharth Arora et al. (2011) investigated the working fluid of ETC under different operating conditions. It have grasped that the heat transfer rate of the circulating fluid and the air gap heat transfer coefficient are cause effect in heat transfer. Besides, the report have used in the designing of an absorption unit where the generator section using the heat available from the ETC.