Quantifying the Sustainability, Livability, and Equity Performance of Urban and Suburban Places in California (original) (raw)
Related papers
Michigan Journal of Sustainability, 2013
The growing importance of nonmarket assets such as the environment, combined with the unprecedented availability of high-resolution data, has renewed broad interest in quantifying sustainability at different spatial levels. Policy-related decision making requires that potential sustainability measures meet three key requirements: (i) sustainability metrics need to be comprehensive so that they reflect the experience of representative households, (ii) sustainability indices need to be comparable across different geographic scales, and (iii) measures of sustainability need to be economically meaningful, ideally tying into the larger system of national accounts. However, conventional sustainability indices do not tend to meet these criteria, which renders them inappropriate for public-policy efforts. The subjectivity and theoretical inconsistency of common sustainability indices presents the biggest obstacle for their adoption as valid public policy targets. This article introduces an urban sustainability index that is based on a theoretically consistent, empirical measure of quality of life. Specifically, this article shows that greenness of cities has a strong positive correlation with urban quality of life, suggesting that the greener the city, the nicer a place to live it is. This is largely because energy efficiency is capitalized into economic activity and ultimately into urban quality of life. This relationship appears to hold across cities of all sizes, clearly emphasizing the direct link between progressive environmental policy and locational desirability. The takeaway message is straightforward: More "greenness" correlates to higher quality of life in urban areas.
Environmental Science & Technology, 2014
Which municipalities and locations within the United States contribute the 9 most to household greenhouse gas emissions, and what is the effect of population density 10 and suburbanization on emissions? Using national household surveys, we developed 11 econometric models of demand for energy, transportation, food, goods, and services that 12 were used to derive average household carbon footprints (HCF) for U.S. zip codes, cities, 13 counties, and metropolitan areas. We find consistently lower HCF in urban core cities 14 (∼40 tCO 2 e) and higher carbon footprints in outlying suburbs (∼50 tCO 2 e), with a range 15 from ∼25 to >80 tCO 2 e in the 50 largest metropolitan areas. Population density exhibits a 16 weak but positive correlation with HCF until a density threshold is met, after which range, mean, and standard deviation of HCF 17 decline. While population density contributes to relatively low HCF in the central cities of large metropolitan areas, the more 18 extensive suburbanization in these regions contributes to an overall net increase in HCF compared to smaller metropolitan areas. 19 Suburbs alone account for ∼50% of total U.S. HCF. Differences in the size, composition, and location of household carbon 20 footprints suggest the need for tailoring of greenhouse gas (GHG) mitigation efforts to different populations. 21 ■ BACKGROUND 22 Demand for energy, transportation, food, goods and services 23 drives global anthropogenic emissions of greenhouse gases 24 (GHGs). Households in the United States alone are directly or 25 indirectly responsible for about 20% of annual global GHG 26 emissions, 1,2 yet represent only 4.3% of total global population. 27 In the absence of comprehensive national climate policy, U.S. 28 states and over 1000 U.S. mayors have committed to GHG 29 reductions. 3 In response, a new protocol exists for managing 30 community-scale GHG emissions that emphasizes contribu-31 tions from households. 4 For compliance and voluntary policies 32 to be effective, information is needed on the size and 33 composition of household carbon footprints for all regions, at 34 metropolitan, county, city, and even neighborhood scales. As 35 global urbanization accelerates, increasing by 2.7 billion people 36 by 2050, 5 the lessons from the data-rich U.S. experience may 37 have increasing importance for planning efforts in urban areas 38 of the world's expanding list of mega-cities. 39 Previous research using a diverse set of methods focused 40 largely on large metropolitan regions or cities has shown that 41 household carbon footprints (HCF) vary considerably, with 42 energy, transportation, or consumption comprising a larger 43 share of the total and with households in some locations 44 contributing far more emissions than others. 6−9 For example, 45 motor vehicles in California comprises 30% of HCF, compared 46 to 6% for household electricity, while electricity is frequently 47 the largest single source of emissions in locations with 48 predominantly coal-fired electricity. 10 Income, household size, 49 and social factors have been shown to affect total HCF, while a 50 large number of factors have been shown to contribute to 51 household energy and transportation-related emissions. 1,8,11,12 52 A number of studies suggest that geographic differences in 53 emissions are in part explained by population density. 54 Population-dense municipalities tend to be urban centers 55 with employment, housing, and services closely colocated, 56 reducing travel distances, increasing demand for public transit, 57 and with less space for larger homes. Early research by Newman 58 and Kenworthy, 13 using data on 32 global cities, suggested a 59 strong negative log−linear correlation between vehicle fuels and 60 density (Figure S-1 in Supporting Information). More recent 61 work using data from domestic and global cities has also 62 seemed to confirm this relationship, although with more 63 variance than previously thought. 14 One thread of research 64 suggests that urban form (colocation of housing, employment 65
San Francisco's neighborhoods and auto dependency
Cities, 2019
Suburbanization and auto dependency have major problems. An alternative, the walkable neighborhood system, is one of a number of ideas designed to increase walking and other non-auto modes (NAM), sustainability, economic productivity, physical health, and livability. NAM includes walk, bicycle, public transit, and public cars (taxi, ehail ride, car share, car rental). A walkable neighborhood system has a high population density and complementary features that support local business and transit within an attractive walking distance. For a case study, we look at San Francisco, a world class city with high densities comparable to European cities. This article for the first time delineates neighborhoods in terms of walkable areas and correlations with four indicators of sustainability. We delineated 85 walking-area neighborhoods using ArcMAP and analyzed their correlations with NAM, vehicle miles traveled, walk score, and food sources. The hypothesis of a very high correlation of density and NAM is confirmed: densities over 50 persons per neighborhood acre support NAM above 60%. An exponential decrease in auto dependency with density is confirmed, but with a low correlation. The transition is gradual and uneven among neighborhoods. The large variation of performance among neighborhoods with very similar densities needs more research into complementary features. The correlation of density with vehicle miles traveled is very high, −0.807. The correlation of density with Walk Score is moderate, due to Walk Score being concerned with walkability and not with the underlying land uses supporting sustainability. The correlation of density with food sources is very high and the highest of the correlations we found.
The influence of urban form on GHG emissions in the U.S. household sector
Energy Policy, 2014
To better understand the role of sustainable urban development in greenhouse gas (GHG) mitigation, this study examines the paths by which urban form influences an individual household's carbon dioxide emissions in the 125 largest urbanized areas in the U.S. Our multilevel SEM analyses show that doubling population-weighted density is associated with a reduction in CO 2 emissions from household travel and residential energy consumption by 48% and 35%, respectively. Centralized population and polycentric structures have only a moderate impact in our analyses. The results also show that doubling per capita transit subsidies leads to a nearly 46% lower vehicle miles traveled (VMT) and an 18% reduction in transportation CO 2 emissions. Given that household travel and residential energy use account for 42% of total U.S. carbon dioxide emissions, these findings highlight the importance of smart growth policies to build more compact and transit friendly cities as a crucial part of any strategic efforts to mitigate GHG emissions and stabilize climate.
Shen & Guo (2014) have recently developed an array of urban sustainability indicators (USIs) as a tool to measure urban sustainability. Using 2006 data for Saskatoon, Saskatchewan, Canada, they developed a theoretical integrated USI model with a hierarchical index system, to spatially monitor urban sustainability using geo-matic approaches and further statistically detect its spatial patterns. The purpose of this study is to apply Shen and Guo’s general approach to a major American city, Cincinnati, Ohio, utilizing U.S. census data from 2010, to test its utility beyond the original Canadian test case. In doing so, the model and its indicator structure were modified for the American context after a further review of sustainability indicators. Unlike Shen and Guo, however, the model is not subjectively weighted. Nevertheless, the revised model similarly applied both statistical analysis and geo-statistical analysis to explore how urban sustainability was spatially distributed and what spatial patterns (random, dispersed or clustered) for the indices could be found among Cincinnati’s census tracts. This work confirms Shen and Guo’s conclusion that geo-matic tools can be applied to detect spatially urban sustainability patterns, which can be provided visually for urban planners, managers and administrators for use in future policy making and implementation.
Sustainability Science, 2013
The development of a comprehensive sustainability analysis tool for evaluating regional urban systems would present researchers, planners, and policy makers with a powerful tool to study and manage systems, with the goal of encouraging optimum social and economic trends, while maintaining long-term environmental protection that leads to sustainability. This article intends to aid in this effort by presenting a versatile methodology for assessing sustainability as a function of dynamic changes in significant characteristics of urban systems. Using statistical methods, this work presents a strategy for comparatively assessing the impact of social and economic characteristics on system stability at geographic scales which are critical to policy and management. Specifically, it employs the Fisher Information index as a measure of sustainability, in order to distinguish periods of stability. As an application of the approach, six Metropolitan Statistical Areas (MSAs) in Ohio (Cincinnati, Dayton, Cleveland, Akron, Columbus, and Toledo) were evaluated for a regional sustainability assessment. Results from the multiyear analysis suggest two distinct periods in these MSAs: one characterized by 30 years of socioeconomic growth (1970-1999) and another (2000-2009) denoting a change in the trajectory of each system found to be related to economic recession. Columbus was identified as the most stable and sustainable of the MSAs during the study period. In contrast, Toledo exhibited the largest changes in economic trends, as distinguished by excessive increases in the growth rate of vacant housing units, unemployed civilian labor force, and inhabitants below the poverty level (2000-2009). Since such conditions are not desirable for urban systems, they are indicative of movement towards an unsustainable future.
Addressing Place in Climate Change Mitigation: Reducing Emissions in a Suburban Landscape
Applied Geography, 2010
Federal political deadlock has long forced progressive climate change mitigation efforts in the United States to target greenhouse gas emissions and reduction options at regional, state, urban, and local levels. Even as a national mitigation agenda solidifies, researchers and political actors might strategically maintain local places and types of landscape – center cities, older and newer suburbs, and rural areas in different United States regions – as distinct spheres for analysis and action. This local articulation permits ongoing analysis of how place/landscape type-specific conditions structure everyday greenhouse gas emissions and the prospects for reducing them. As such, it promotes mitigation programs that encompass broad, long-term infrastructure and lifestyle transformations for energy efficiency and conservation; not only top-down changes to energy generation technologies. Participatory climate change mitigation research in the Philadelphia suburbs demonstrates how geographically particular metropolitan development patterns shape the prospects for two such policies, residential energy efficiency improvement and the promotion of local food systems. The sprawling suburb investigated here, with the center city its development has helped impoverish, challenges these mitigation options in particular and emissions reduction in general. Deeply problematic elements include both the landscape’s ever-extending physical morphology and the socioeconomic inequalities that this built environment – and the stakeholders who build it – help to create and maintain. Reshaping this and other suburban landscapes can not only promote long-term climate change mitigation but also reduce vulnerability to climate change’s unavoidable impacts.
Environment and Planning B: Urban Analytics and City Science, 2016
The main purpose of this research is to provide new insights for reducing greenhouse gas (GHG) emissions linked to transportation, furthering our knowledge on linkages between urban form and economic constraints, travel behaviour, and ability-to-pay of households based on residential choices and property ownership statuses. With Quebec City (Canada) as a case study, it combines an origin-destination (OD) survey, population census data and land use records for 2006 and rests on a series of structural equations models developed at the grid cell level (3,892 cells), which allows for testing for both direct and indirect effects of urban form, accessibility and socio-economic attributes on GHG emissions, households’ transportation and housing financial burdens and motorization rate. As expected, findings suggest that GHG emissions increase with higher incomes (and education), but mainly for homeowners. Tenants displaying a high expenditure-to-income ratio for housing tend to stay close t...
Suburban solutions: The other side of the story
Town and Country Planning, 2011
How much does urban form affect levels of active travel and transport carbon emissions? It is all too easy for policy-makers to jump to simple conclusions, but the answer is both complex and contested. Here Hugh Barton, Marcus Grant and Michael Horswell report on recent research which casts new light on the impact of neighbourhood planning.