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Understanding the drivers of energy and material flows of cities is important for addressing global environmental challenges. Accessing, sharing, and managing energy and material resources is particularly critical for megacities, which face enormous social stresses because of their sheer size and complexity. Here we quantify the energy and material flows through the world's 27 megacities with populations greater than 10 million people as of 2010. Collectively the resource flows through megacities are largely consistent with scaling laws established in the emerging science of cities. Correlations are established for electricity consumption, heating and industrial fuel use, ground transportation energy use, water consumption, waste generation, and steel production in terms of heating-degree-days, urban form, economic activity, and population growth. The results help identify megacities exhibiting high and low levels of consumption and those making efficient use of resources. The correlation between per capita electricity use and urbanized area per capita is shown to be a consequence of gross building floor area per capita, which is found to increase for lower-density cities. Many of the megacities are growing rapidly in population but are growing even faster in terms of gross domestic product (GDP) and energy use. In the decade from 2001-2011, electricity use and ground transportation fuel use in megacities grew at approximately half the rate of GDP growth.

Increasing photovoltaics industry in the United Kingdom and environmental factors associated with this are discussed.

The implementation of integrated energy consumption regulations for domestic energy sector in the United Kingdom is discussed.

This study provides a whole-systems simulation on how to halve global CO2 emissions by 2050, compared to 2010, with an emphasis on technologies and costs, in order to avoid a dangerous increase in the global mean surface temperature by end the of this century. There still remains uncertainty as to how much a low-carbon energy system costs compared to a high-carbon system. Integrated assessment models (IAMs) show a large range of costs of mitigation towards the 2°C target, with up to an order of magnitude difference between the highest and lowest cost, depending on a number of factors including model structure, technology availability and costs, and the degree of feedback with the wider macro-economy. A simpler analysis potentially serves to highlight where costs fall and to what degree. Here we show that the additional cost of a lowcarbon energy system is less than 1% of global GDP more than a system resulting from low mitigation effort. The proposed approach aligns with some previous IAMs and other projections discussed in the paper, whilst also providing a clearer and more detailed view of the world. Achieving this system by 2050, with CO2 emissions of about 15GtCO2, depends heavily on decarbonisation of the electricity sector to around 100gCO2/kWh, as well as on maximising energy efficiency potential across all sectors. This scenario would require a major mitigation effort in all the assessed world regions. However, in order to keep the global mean surface temperature increase below 1.5°C, it would be necessary to achieve netzero emission by 2050, requiring a much further mitigation effort. [ABSTRACT FROM AUTHOR]

In view of urbanization trends coupled with climate-change challenges, it is increasingly important to establish less-harmful means of urban living. To date, urban metabolism (UM) studies have quantified the aggregate material and energy flows into and out of cities and, further, have identified how consumer activity causes these flows. However, little attention has been paid to the networks of conversion processes that link consumer end-use demands to aggregate metabolic flows. Here, we conduct a systematic literature search to assemble a database of 202 urban energy, water, and waste management processes. We show how the database can help planners and policy makers choose the preferred process to meet a specific resource management need; identify synergies between energy, water, and waste management processes; and compute optimal networks of processes to meet an area's consumer demand at minimum environmental cost. We make our database publicly available under an open-source license and discuss the possibilities for how it might be used alongside other industrial ecology data sets to enhance research opportunities. This will encourage more holistic UM analyses, which appreciate how both consumer activity and the engineered urban system work together to influence aggregate metabolic flows and thus support efforts to make cities more sustainable. [ABSTRACT FROM AUTHOR]

Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste. [ABSTRACT FROM AUTHOR]