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Themes and Overview
Published in Daniel T. Rogers, Environmental Compliance Handbook, 2023
The surface of the Earth is approximately 510,100,000 square kilometers (Coble et al. 1987). Approximately 70%, or 361,800,000 square kilometers, is covered by water, and 30%, or 148,300,000 square kilometers, is land. Of that proportion, approximately 71% is considered habitable, with 10% covered by glaciers and the remaining 19% either being mountainous or extreme desert. Of the proportion of the 71% that is habitable, 50% is used for agriculture, 36% is forest, 11% is shrubland, 2% is urban, and 1% is surface water, composed of lakes and streams (United Nations 2016a).
Themes and Overview
Published in Daniel T. Rogers, Environmental Compliance Handbook, 2023
The surface of the Earth is approximately 510,100,000 square kilometers (Coble et al. 1987). Approximately 70%, or 361,800,000 square kilometers, is covered by water, and 30%, or 148,300,000 square kilometers, is land. The amount of land on Earth that is farmed is estimated at 40%, or 59,320,000 square kilometers (United Nations 2016a). This is a huge number, especially since the amount of urbanized land on Earth is estimated to be only 2.7% and is home to roughly half the current human population (United Nations 2016a).
Yún-lín county, Taiwan
Published in Ine Wouters, Stephanie Van de Voorde, Inge Bertels, Bernard Espion, Krista De Jonge, Denis Zastavni, Building Knowledge, Constructing Histories, 2018
The demographic situation in Taiwan changed dramatically in the last 100 years (Fig. 2). Significantly, between 1945 and now, the population passed from 6 millions to 23 millions. In 2010, the density was about 639 inhabitants per square kilometre (Statistical Bureau 2018). Most of the inhabitants live on the West coast. In the centre of Taiwan, Yún-lín county has a high but among the lowest density of population: 479 inh./km2 (ibid.). During the same period 1945–2017, the society transformed itself from being mostly agricultural to industrial and now services. Today, after this period of rapid growth, the fertility rate dropped drastically (0.9 child per woman) and the population is expected to stabilise. The consequence is ageing (Fig. 3). This is especially noticeable in the countryside: in 2010, the average age of the farm manager was 62 years old and 73% of them had no willing successors (ibid.). The size of the families decreased. The average number of family members living in farmhouses passed from 7.67 in 1964 to 3.81 in 2010 (ibid.). The contribution of the people aged 25–45 living in farmhouses to agriculture work is only 11% (42% for the occupants of 65 and older) (ibid.).
Renewable energy scenario in Telangana
Published in International Journal of Ambient Energy, 2020
D. Madan, P. Mallesham, Suresh Sagadevan, C. Veeramani
Over India, sunlight is accessible almost 10 or11 months in a year. Moreover, the country receives solar energy in the range of approximately 4–7 kWh/m2. Figure 6 shows the potential of solar radiation in India. A square kilometre of the surface is sufficient to harvest electricity up to 20 MW. Telangana has annually average 300 clear sunny days and having the average solar potential of 5.6 kWh/m2. It is set become one of the hot spots for solar energy development in the country given the rapid strides it has made since its formation 2 years ago. It has seen the installation of over 520 MW in the past 12 months and has handed over the generation of 3800 MW through a number of major tenders. This is a part of the state’s target of solar photovoltaic power generation of 5000 MW by 2018–2019 (The Hindu, Business line 2016). Many investors have shown interest and have registered to set up power plants. Therefore; we might hope that the dream of implementing solar strategy 2018 has a chance to be fulfilled. Soon, solar power will occupy a dominant place in the field of power generation and will be contributing significantly in meeting the electricity requirement of the state. Apart from this, through the chief minister’s solar roof top projects and solar pump set projects, the government is trying to increase the production of solar power.
Water level fluctuations of Lake Tana and its implication on local communities livelihood, northwestern Ethiopia
Published in International Journal of River Basin Management, 2020
The area of the lake was classified and calculated by using supervised maximum likelihood classification. The maximum likelihood classification tool considers both the variances and covariance of the class signatures when assigning each cell to one of the classes represented in the signature file. The algorithm used by the maximum likelihood classification tool is based on Bayes’ theorem of decision-making where the cells in each class sample in the multidimensional space are normally distributed (Lillesand et al. 2004; Richards and Jia 2006). The vector files were again converted to the raster grid by using spatial analyst extension of ArcMap 10. To evaluate the performance of the classifiers, the accuracy assessment was carried out by using the validation datasets, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. After classification, calculation of the area in square kilometre comparison of the area within and between years was made and the trend was determined. Percentage change to determine the trend of change is then calculated bywhere ΔA is the per cent change in the area of lake between initial time (t1) and final time (t2); at1 is the area of the lake at t1 time and at2 is the area of the same type in t2 time. The analysis and interpretation of different aspects of changes were done on Microsoft Excel. The results were presented in easily understandable graphs (Figure 2(a,b)).
Urban flooding risk assessment based on FAHP–EWM combination weighting: a case study of Beijing
Published in Geomatics, Natural Hazards and Risk, 2023
Na Sun, Cailin Li, Baoyun Guo, Xiaokai Sun, Yukai Yao, Yue Wang
Once road congestion and other problems occur in places with developed road traffic, the impact on economic activities will be more serious. Road congestion and serious road water accumulation will lead to victims’ inability to escape and rescuers’ inability to rescue. Therefore, the denser the road, the higher the vulnerability of the disaster-affected body. The road data in this study comes from the OpenStreetMap website. The road density is the length of the road per square kilometre. The results are shown in Figure 6(b). Land use/land cover: