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India hosts the world's second largest population and offers the world's largest potential for urbanization. India's urbanization trajectory will have crucial implications on its future GHG emission levels. Using household microdata from India's 60 largest cities, this study maps GHG emissions patterns and its determinants. It also ranks the cities with respect to their household actual and "counter-factual" GHG emissions from direct energy use. We find that household GHG emissions from direct energy use correlate strongly with income and household size; population density, basic urban services (municipal water, electricity, and modern cooking-fuels access) and cultural, religious, and social factors explain more detailed emission patterns. We find that the "greenest" cities (on the basis of household GHG emissions) are Bareilly and Allahabad, while the "dirtiest" cities are Chennai and Delhi; however, when we control for socioeconomic variables, the ranking changes drastically. In the control case, we find that smaller lower-income cities emit more than expected, and larger high-income cities emit less than expected in terms of counter-factual emissions. Emissions from India's cities are similar in magnitude to China's cities but typically much lower than those of comparable U.S. cities. Our results indicate that reducing urban heat-island effects and the associated cooling degree days by greening, switching to modern nonsolid cooking fuels, and anticipatory transport infrastructure investments are key policies for the low-carbon and inclusive development of Indian cities.

This paper presents a hexa-band frequency reconfigurable planar antenna, printed on a 1.6 mm thicker FR 4 substrate and backed by a truncated ground plane. The given antenna operates in four different frequency modes, depending on the state of the two lumped element switches. The proposed antenna works at six frequencies, 2.10, 2.40, 3.35, 3.50, 5.28, and 5.97 GHz. These frequency bands are dedicated to useful wireless applications, including 3G Advanced (2.10 GHz), Wireless Fidelity (Wi-Fi) (2.40 GHz), WiMAX (3.35 GHz), WiMAX (3.5 GHz), WLAN (5.28 GHz) and fixed-satellite and mobile satellite services (5.97 GHz). Satisfactory gain of 1.96, 2.20, 2.671, 2.81, 3.80, and 3.88 dBi, efficiency of 92.5, 94.5, 94.56, 95.0, 93.8, and 97.0% and bandwidth of 332, 485, 1020, 1080, 512, and 465 MHz has been obtained at 2.10, 2.40, 3.35, 3.50, 5.28, and 5.97 GHz, respectively. The modeling and simulations are conducted in CST MWS (2014). The simulated reflection coefficient and radiation pattern are validated in antenna measurement facility. In addition, the specific absorption rate of the antenna on a flat section of human body is also studied. The antenna is compact, low profile, and vastly suitable for multi-band wireless devices.

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Urban consumption of processed and fast foods is a challenge to nutrition security. Observed differences in urban versus rural consumption are commonly attributed to higher income levels in urban areas. Yet, there is still no clear understanding why and how urban dwellers consume differently. Using India as case study, we analyze expenditure on processed foods and consumption of food away from home (FAFH) of urban, metropolitan, and rural populations using OLS regression models. We show that urban households spend more on processed foods and consume more FAFH than rural households. Most of this difference can be attributed to differing socio-economic and demographic factors, such as higher income, or smaller urban household size. However, even after controlling for these factors, we find differences not only between rural and urban areas but also between different urban areas: households in large metropolitan areas consume more than households in smaller non-metropolitan urban areas. These inter-urban variations suggest that the dichotomy of urban versus rural consumption does not adequately capture the full spectrum of food consumption complexity. Our findings indicate that the multidimensional process of urbanization is affecting how people consume food beyond shaping their socio-economic and demographic status and highlight the need to account for the role of urbanizationbeyond an urban-rural dichotomy-when addressing the challenges associated with changing food consumption patterns.

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