And, of course, Silicon Valley information technology will bring important, even dramatic, efficiency gains in the production and management of energy and physical goods a prospect also taken up below. The first two decades of commercialization, after the s, saw a fold reduction in costs. But the path for improvements now follows what mathematicians call an asymptote; or, put in economic terms, improvements are subject to a law of diminishing returns where every incremental gain yields less progress than in the past Figure 4.
This is a normal phenomenon in all physical systems. Bragging rights for gains in efficiency—or speed, or other equivalent metrics such as energy density power per unit of weight or volume then shrink from double-digit percentages to fractional percentage changes.
Such progress is economically meaningful but is not revolutionary. The physics-constrained limits of energy systems are unequivocal. Batteries are bound by the physical chemistry of the molecules chosen. Similarly, no matter how much better jet engines become, an A will never fly to the moon. The limits are long established and well understood.
There needs to be wind for the turbine to turn. While researchers keep unearthing new non-silicon options that offer tantalizing performance improvements, all have similar physics boundaries, and none is remotely close to manufacturability at all—never mind at low costs. Future advances in wind turbine and solar economics are now centered on incremental engineering improvements: economies of scale in making turbines enormous, taller than the Washington Monument, and similarly massive, square-mile utility-scale solar arrays.
For both technologies, all the underlying key components—concrete, steel, and fiberglass for wind; and silicon, copper, and glass for solar—are all already in mass production and well down asymptotic cost curves in their own domains. In fact, all manufacturing processes experience continual improvements in production efficiency as volumes rise. As for modern batteries, there are still promising options for significant improvements in their underlying physical chemistry.
There are no subsidies and no engineering from Silicon Valley or elsewhere that can close the physics-centric gap in energy densities between batteries and oil Figure 5. The energy stored per pound is the critical metric for vehicles and, especially, aircraft. Finally, when it comes to limits, it is relevant to note that the technologies that unlocked shale oil and gas are still in the early days of engineering development, unlike the older technologies of wind, solar, and batteries.
Tenfold gains are still possible in terms of how much energy can be extracted by a rig from shale rock before approaching physics limits. Silicon logic has improved, for example, the control and thus the fuel efficiency of combustion engines, and it is doing the same for wind turbines. Similarly, software epitomized by Uber has shown that optimizing the efficiency of using expensive transportation assets lowers costs. Uberizing all manner of capital assets is inevitable.
In the energy world, one of the most vexing problems is in optimally matching electricity supply and demand Figure 6. Here the data show that society and the electricity-consuming services that people like are generating a growing gap between peaks and valleys of demand.
The net effect for a hydrocarbon-free grid will be to increase the need for batteries to meet those peaks. All this has relevance for encouraging EVs. In terms of managing the inconvenient cyclical nature of demand, shifting transportation fuel use from oil to the grid will make peak management far more challenging. EV refueling will exacerbate the already-episodic nature of grid demand.
To ameliorate this problem, one proposal is to encourage or even require off-peak EV fueling. Green enthusiasts make extravagant claims about the effect of Uber-like options and self-driving cars.
However, the data show that the economic efficiencies from Uberizing have so far increased the use of cars and peak urban congestion.
The former can be associated with reducing energy use; but it is also, and more often, associated with increased energy demand. Cars use more energy per mile than a horse, but the former offers enormous gains in economic efficiency. Computers, similarly, use far more energy than pencil-and-paper. Every energy conversion in our universe entails built-in inefficiencies—converting heat to propulsion, carbohydrates to motion, photons to electrons, electrons to data, and so forth.
All entail a certain energy cost, or waste, that can be reduced but never eliminated. But, in no small irony, history shows—as economists have often noted—that improvements in efficiency lead to increased, not decreased, energy consumption. If at the dawn of the modern era, affordable steam engines had remained as inefficient as those first invented, they would never have proliferated, nor would the attendant economic gains and the associated rise in coal demand have happened.
We see the same thing with modern combustion engines. Global computing and communications, all told, now consumes the energy equivalent of 3 billion barrels of oil per year, more energy than global aviation. Student Loan Reviews. GMAT Courses. Admissions Consulting. Free Stuff. Practice Tests. GMAT Tutoring. Mobile Apps. Student Loans. Which Course is right for you? Sign In Join now. My Profile Logout.
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If you take the GMAT, t he most important score you receive will be your composite score, which can range from to The composite score takes your only your scores from the Verbal and Quantitative sections into account. Your scores on Analytical Writing and Integrated Reasoning are not included in this score. The Verbal and Quantitative sections both have score ranges of , in one-point increments. The score range for Analytical Writing is , in half-point increments, and the score range for the Integrated Reasoning section is , in one-point increments.
The GMAT is taken on the computer, and it is an adaptive test. This means that, when you begin the Quantitative and Verbal sections, the first question you see in each section will be medium difficulty.
If you answer that question correctly, the next question will be slightly harder, and if you answered incorrectly, the next question will be slightly easier. This process continues throughout the entire section for both Quantitative and Verbal. Adaptive testing is used to get more accurate scores by selecting specific questions with varying difficulty levels from a larger pool. Both Verbal Reasoning and Quantitative Reasoning are scored the same way.
Their score range is , in one-point increments. The three section scores are generally reported separately and not combined into a single composite score. The GRE is also typically taken on the computer, and it is section-level adaptive. This means that your score on the first section of both Verbal Reasoning and Quantitative Reasoning will affect the difficulty of the questions tested on the second section for each subject.
Unlike the GMAT, where each individual question determines the difficulty of the next question, on the GRE, your score on the entire section determines the difficulty of the next section on that subject.
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