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Renewable energy
Published in Peter M. Schwarz, Energy Economics, 2023
Both wind and solar require land. Entergy (n.d.) provides a comparison of how much land would be required for nuclear, solar, and wind to supply 1,800 megawatts, based on the plants running at 90% of capacity. In actuality, while nuclear plants have a capacity factor of 90% or over, solar has a capacity factor of 30% and wind close to 40%. Therefore, the actual solar requirement would be about 3× the given figure, and about 2.25× for wind. Consider the amount of land needed for a two-reactor 1,800 MW nuclear plant. Such a plant would serve approximately 1.8 million homes. It would encompass about 1,100 acres (a football field is about 1 acre) or 1.7 square miles. If solar energy produces the same amount of power, it would require 7.4 acres of photovoltaic solar panels, and 13,320 acres in all (21 square miles). For wind, it would take 720 2.5 MW turbines, requiring 108,000 acres, or 169 square miles. To the extent that the land has low value, such as desert, or solar is built on land leased from farmers, the opportunity cost would be lower. But the cost of building transmission to get the power to customers might be higher. While the comparison does not include biofuels, they require far more land than wind, and corn-based ethanol in the U.S. uses far more land than Brazilian ethanol produced from sugarcane.
Hazmat Team Spotlight
Published in Robert A. Burke, Hazmat Team Spotlight, 2020
Seattle is the largest city in the Northwest with a population of 783,137 (2020), 1.5 million (daytime population) and 3.7 million in the metropolitan area. The population density is 6,039 people per square mile. Located in the State of Washington between the Puget Sound and Lake Washington, Seattle is located 96 miles South of the border with Canada in King County. Seattle covers an area of approximately 83.6 miles2 with 193 miles of waterfront lying between the Olympic Mountains on the
Problems and Successes of Small Systems in the United States
Published in Joseph A. Cotruvo, Gunther F. Craun, Nancy Hearne, Providing Safe Drinking Water in Small Systems, 2019
Small water systems in South Dakota offer some of the most difficult problems to solve. The state has a large land area and small population (700,000). Population density is 2.5 people per square mile. The state is geographically divided by the Missouri River running north to south through the middle of the state. Aquifers in the east are shallow (less than 30 feet deep), composed mostly of glacial till. Two thirds of the population lives east of the river. Farming is the primary activity in this region. Agricultural runoff is a prime concern.
Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence
Published in International Journal of Sustainable Transportation, 2018
Jaeyoung Lee, Mohamed Abdel-Aty
The modeling results are summarized in Table 2. It is shown that Y1 and Y2 have different significant variable sets. For Y1, overall 9 explanatory variables were significant within 95% BCI. The two exposure variables, ‘Log of population’ and ‘Log of VMT’ were significant and positively associated with Y1, as expected. It was revealed that ‘Proportion of workers whose commute time is 15min or shorter’ has a positive effect on Y1. It implies that people with shorter commute time are more likely to choose a bicycle as their commute mode. ‘Proportion of workers whose commute time is 45min or longer’ was also tried and it was found significant at 90% BCI and has a negative effect. However, when it was used with ‘Proportion of workers whose commute time is 15min or shorter’, both variables became insignificant due to high correlation and they were not simultaneously used. Thus, only ‘Proportion of workers commute time is 15 minor or shorter’ was kept in the final model. ‘Proportion of workers in the tertiary sector’ has a negative effect on Y1. The tertiary sector involves supply of services to consumers and business such as transportation, wholesale, trade, finance, public administration, and so on. It shows that workers in the non-tertiary sector (i.e. agriculture, manufacturing, etc.) are more exposed to bicycle crashes. This is consistent with the study of Rybarczyk and Wu (2014). They found that sales or service (OR = 0.203) and clerical or administrative (OR = 0.113) occupations have significant lower odds of bicycling. In addition, ‘Proportion of households without an available car’ is positively related to Y1. If a household does not have an available vehicle, then the only options are walking, bicycling, or using public transportation. Thus, it increases the propensity of having more bicycle crashes. It was uncovered that the ‘Proportion of high-speed roads (55 mph+)’ and ‘Proportion of trucks in traffic’ were negatively associated with Y1. This result may seem counterintuitive but it only means there are more bicyclists (and related crashes) in an area without high-speed roads and many trucks, such as residential areas or downtown. Two land-use variables were found significant: ‘Number of retail stores per square mile’ and ‘Number of schools per square mile’. Both variables show important facilities accessible by bicycles. If there are many retail stores and schools in an area, more people would choose a bicycle as a transportation mode to get to those destinations.