This paper introduces a new algorithm (BUNTUS-Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions.In social science, the areas with high population densities are regarded as urban or city areas [8]. Economics defines the city by its political, cultural, and economic characteristics [9].This work forms the first part of a study of the global distribution of trends in urban fossil fuel emissions and related quantities. That study requires a definition of urban extent so we can attribute emissions as urban or not. The needs of the overall study provide some important requirements for the algorithm we use for the urban extent. We will use these so often throughout the paper that we label them here: R1The study is global, so we may only choose globally consistent and available datasets. R2 We wish to study enough cities to establish patterns, so the algorithm must be computationally efficient enough for large-scale use. R3 Changes in urban extent can be small, so the algorithm must work at high resolution, no more than 1 km. R4 The study must be long enough to establish trends. We estimate this requires two decades of data.In this paper, we propose a novel and multiple-step approach which satisfies these requirements. We define an urban area as a contiguous (i.e., simply connected) and compact region including a pre-defined urban center and which satisfies several criteria relating to density and surface properties. The urban boundary is the bounding polygon for this region. The compactness requirement means that gaps such as green belts (but not water) should be included in the urban area. The criteria which define the urban area must be deducible from datasets satisfying R1, R3, and R4.The outline of the paper is as follows. In Section 2, we review existing methods for determining urban extent, focusing on their utility for our task. In Section 3, we describe our methodology and its underlying data sets. In Section 3, we also present available validation for the method and three case studies of different cities. In Section 4, we validate and discuss the results with a particular focus on the uncertainties of the method and summary of the main results. Section 5 comprises of discussion and Section 6 comprises of conclusion.

Pakistan has a severe electricity load shading problem. Government is trying to find out all ways for electricity generation. Alternative energy board is working to find out the energy potential using all alternative resources. Board has an objective to produce 9700 MW by 2030 to overcome load shading problem. A research was designed to find out the solar energy potential as an alternative source of energy from rooftops of residential areas in district Lahore. Punjab Governments servants housing society Lahore is selected. The society has minimum slope, aspect and shadows effects on the roofs. Also houses in PGSHS have same house structures and good town plan. A few portions of roofs are digitized to measure the available rooftop area for Photovoltaic panel's installation. GIS models are used to find out solar energy potential monthly as well as yearly for the year of 2014. The potential estimated is 39,613,072 kWh/year. The monthly total energy consumption of the society is 347,140 kWh which is only 11% of energy production from PV solar panels. As the estimated energy is 9 times than the energy demand of the society, extra energy can be used in local/national electricity transmission grid. Solar PV energy would be supplement to compensate energy shortfall in local area.

We use a globally consistent, time-resolved data set of CO2 emission proxies to quantify urban CO2 emissions in 91 cities. We decompose emission trends into contributions from changes in urban extent, population density and per capita emission. We find that urban CO2 emissions are increasing everywhere but that the dominant contributors differ according to development level. A cluster analysis of factors shows that developing countries were dominated by cities with the rapid area and per capita CO2 emissions increases. Cities in the developed world, by contrast, show slow area and per capita CO2 emissions growth. China is an important intermediate case with rapid urban area growth combined with slower per capita CO2 emissions growth. Urban per capita emissions are often lower than their national average for many developed countries, suggesting that urbanisation may reduce overall emissions. However, trends in per capita urban emissions are higher than their national equivalent almost everywhere, suggesting that urbanisation will become a more serious problem in the future. An important exception is China, whose per capita urban emissions are growing more slowly than the national value. We also see a negative correlation between trends in population density and per capita CO2 emissions, highlighting a strong role for densification as a tool to reduce CO2 emissions.

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