State growth management, smart growth and urban containment: A review of the US and a study of the heartland

Boyle, Robin; Mohamed, Rayman

doi:10.1080/09640560701475337

Cited by 39 publications

(18 citation statements)

References 49 publications

(58 reference statements)

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“…Few countries, mainly in China and Europe, have already introduced policies that promote compact cities or reduce urban sprawl in order to steer urban development trajectories towards reducing their environmental impact 41,42 . At the same time, examples from USA and Australia show that not all planning initiatives towards compact development have been effective 43,44 . Urban planning outcomes in the Global South are further challenged by conflicting realities, i.e.…”

Section: Discussionmentioning

confidence: 99%

Direct and indirect loss of natural area from urban expansion

2019

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Global losses of natural area are primarily attributed to cropland expansion, while the role of urban expansion is considered minor. However, urban expansion can induce cropland displacement, potentially leading to a loss of forests elsewhere. The extent of this effect is unknown. This study shows that indirect forest losses, through cropland displacement, far exceed direct losses from urban expansion. On a global scale, urban land increased from 33.2 to 71.3 Mha between 1992 and 2015, leading to a direct loss of 3.3 Mha of forests, and an indirect loss of 17.8-32.4 Mha. In addition, this urban expansion led to a direct loss of 4.6 Mha of shrublands, and an indirect loss of 7.0-17.4 Mha. Guiding urban development towards more sustainable trajectories can thus help preserve forests and other natural areas at a global scale.

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Section: Discussionmentioning

confidence: 99%

Direct and indirect loss of natural area from urban expansion

2019

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“…With the aim of determining the universal standard for evaluating the success degree of smart growth, the sustainable degree (SD) is defined to evaluate the urban smart growth level. Unfortunately, constructing an SD model seems to be quite complex work, which covers the fields of society, economy, and ecology [31][32][33].…”

Section: Selection Of the Secondary Indicatorsmentioning

confidence: 99%

A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

Ren

2019

Sustainability

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Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

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“…With the development of the economy, urban expansion is occurring throughout the world [1][2][3]. By 2025, two-thirds of the world's population is expected to live in cities [4].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen

Liu

Zhao

et al. 2020

Sustainability

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The spatiotemporal characteristics of near-surface wind in Shenzhen were investigated in this study by using hourly observations at 92 automatic weather stations (AWSs) from 2009 to 2018. The results show that during the past 10 years, most of the stations showed a decreasing trend in the annual mean of the 10 min average wind speed (avg-wind) and the mean of the 3 s average wind speed (gust wind). Over half of the decreasing trends at the stations were statistically significant (p < 0.05). Seasonally, the decrease in wind speed was the most severe in spring, followed by autumn, winter, and summer. The distribution of wind speed tends to be greater in the east and coastal areas for both avg-wind and gust wind. From September to March of the following year, the prevailing wind direction in Shenzhen was northerly, and from April to August, the prevailing wind direction was southerly. The seasonal wind speed distribution exhibited two different types, spring–summer type and autumn–winter type, which may be induced by their different prevailing wind directions. The analysis by the empirical orthogonal function (EOF) method confirmed the previous findings that the mean wind speed was decreasing in Shenzhen and that two different seasonal wind speed spatial distribution patterns existed. Such a study could provide references for wind forecasting and risk assessment in the study area.

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State growth management, smart growth and urban containment: A review of the US and a study of the heartland

Cited by 39 publications

References 49 publications

Direct and indirect loss of natural area from urban expansion

Direct and indirect loss of natural area from urban expansion

A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen

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