Transportation energy is a significant portion of the energy consumption of the US economy. While various policies such as changing the fuel mix and alternative fuels are proposed to make the system more efficient, the efficacy of land use policies such as changing the urban form and densification have been subject to considerable debate. In this paper, I use a rich dataset compiled from different sources to test the effectiveness of urban form on energy consumption in the transportation sector. I proxy the consumption with retail sales from gas stations for most of the conterminous United States at a county level. Using both demographic, economic and landscape characteristics of urban form I tease out the effect of different dimensions on energy consumption. I find that compact and contiguous urban form is modestly associated with lower energy consumption and is more important than demographic concentration in explaining the variance.
The measurement and characterization of urbanization crucially depends upon defining what counts as urban. According to The Indian Planning Commission, less than a third of the Indian population lives in urban areas, and while Indian cities are increasingly important to the economy, India is perceived fundamentally as a rural country. In this paper, we show that this received wisdom is an artefact of the definition of urbanity and the official statistics vastly undercount the level of urbanization and its importance for development policies in India. We begin by creating temporally-consistent, high-resolution population maps from sub district level population data available from the Indian Census for 2001 and 2011. The modeling framework is a two-step process that applies a Random Forest-based model to generate a prediction weighting layer subsequently used to inform a gridded dasymetric redistribution of original census counts at 100 m resolution (Stevens et al. 2015). We then apply density thresholds, contiguity conditions, distance based clustering and minimum population sizes to construct urban agglomerations for the entire country. Compared to the official estimates, we find that this approach counts 8%-30% (depending on thresholds) more urban population in 2011. We find large urban agglomerations that span large portions of Kerala and the Gangetic plain. Thus, while official estimates count more cities in the country, we delineate fewer cities but large urban regions that span jurisdictional boundaries. This has implication for urban policies.
Why are many plans not implemented? Common explanations for this question are planners have little power, or they failed to account for political or environmental uncertainty, or they failed to include to enough voices during the planning process. Weaving different strands of implementation and strategic planning literature, I provide an alternative account by challenging the premise that plans realise their potential only when they are implemented. I argue that theoretical frameworks that we base our understanding of plans and their purposes do not allow us to explain the ways in which plans are used. Monitoring implementation of plans, presupposes that we know what plans are there to monitor. It privileges published plans and ignores all the other plans that guide urban development. By jettisoning implementation as a key criterion by which to evaluate the effectiveness of plans, we can begin to focus on myriad of ways in which plans are used by plan makers as well as others. A better question to ask is, ``How are these plans used and when are they useful?” In asking those questions, we can create different evaluative frameworks for different types of plans. Some unimplementable plans are worth making.
Abstract While urban form affects building energy consumption, the pathways, direction and magnitude of the effect are disputed in the literature. This paper uses a unique dataset to examine the effect of urban form on residential electricity consumption in Ningbo, China. Using survey and utility bill data of 534 households in 46 neighborhoods in the city, we model the electricity use of households using a multi-level regression model. We find that neighborhood street configuration and tree shade are important in controlling residential electricity consumption and, consequently, greenhouse gas emissions. Our results suggest that seasonality and dwelling type condition the effect of neighborhood densities on electricity consumption. Neighborhood density is associated with household electricity consumption in summer months, while there is no such association in the winter months. As neighborhood density increases, households in slab and tower apartments in dense urban neighborhoods consume more electricity in summer months, which can be partly explained by exacerbated heat island effect. Interestingly, the neighborhood density is negatively associated with electricity consumption for single-family houses, suggesting that the effect of neighborhood density is different for different types of dwelling units.