Urban climate change: A comprehensive ecological analysis of the thermo-effects of major Chinese cities

Huang, Jianzhi; Wang, Rusong; Shi, Yao

doi:10.1016/j.ecocom.2009.11.001

Cited by 9 publications

(8 citation statements)

References 28 publications

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“…Their research tried to explore the deep dynamic mechanism of urban space expansion and to provide scientific support for its sustainable development [55]. Huang et al (2010) tried to determine the main factors for a comprehensive analysis of urban thermo-effect. The six main factors of the urban thermo-effect were total size, GDP, the green area, the gross of freight, annual electricity consumption, and the population of the built-up area [56].…”

Section: Resultsmentioning

confidence: 99%

“…Huang et al (2010) tried to determine the main factors for a comprehensive analysis of urban thermo-effect. The six main factors of the urban thermo-effect were total size, GDP, the green area, the gross of freight, annual electricity consumption, and the population of the built-up area [56]. One of the papers selected in this field probed into public safety; Yu and Fang (2017) investigated and attempted to simulate the public safety dynamics of Shanghai using a set of collected indexes that described Shanghai's infrastructure and development, population, livability, crime, and disaster.…”

Section: Resultsmentioning

confidence: 99%

“…The equality of resource allocation in health care under the National Health Insurance System in Taiwan [54] An ideal resource allocation in health care for ensuring most people to access equal health care services Grey incidence analysis Measuring resource allocation of health care services in Taiwan Analysis of socio-economic driving forces on built-up area expansion in Xiamen [55] Exploring the deep dynamic mechanism of urban space expansion and its sustainable development GRA Resource consumption factors and population factors have had a significant driving effect on the expansion of built-up area Urban climate change: A comprehensive ecological analysis of the thermo-effects of major Chinese cities [56] Computing the urban thermo-effect of summer…”

Section: Grey Relative Techniquementioning

confidence: 99%

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Exploring Grey Systems Theory-Based Methods and Applications in Analyzing Socio-Economic Systems

Javanmardi

Liu

2019

Sustainability

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The present study seeks to provide an overview of studies dealing with the grey systems theory from the perspective of socio-economic systems. A scoping review is conducted to explore the studies focusing grey systems theory and its applications in socio-economic systems. The publications selected for analysis were collected through searching the databases of the Web of Science, Scopus, and Science Direct. The publications were selected from 2010 to 2019, and the following keywords were used to detect them: grey systems, grey relational, grey model, grey prediction, grey control, grey incidence, grey cluster, grey decision, and grey input-output plus social, economic, and socioeconomic. Out of the 2375 records found, 147 full papers were screened. Results obtained from the studies were separated into the following categories: social network, healthcare, financial issues, sustainability, tourism, social and cultural, public sectors, urbanization, development, business, economics, demographics, innovation, and entrepreneurship. Findings showed that complexity, uncertainty, and a lack of access to a large collection of data directed researchers to rely on grey systems theory-based socio-economic systems. Furthermore, theory-based grey systems were more effective than other approaches of decision-making and analysis. Drawing on the grey systems theory-based holistic approaches and systems thinking methods helped to obtain better results in analyzing socio-economic systems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Grey Relative Techniquementioning

confidence: 99%

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Exploring Grey Systems Theory-Based Methods and Applications in Analyzing Socio-Economic Systems

Javanmardi

Liu

2019

Sustainability

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“…Note that the variables have different definitions and units. To eliminate differences between the variables, each variable was normalized by dividing the original series by its mean value, using the formula [30]:…”

Section: Explanatory Variables Selectionmentioning

confidence: 99%

“…As each city is a complex, natural, economic and ecological system [28], considering only a few factors may miss important properties and ignore the integrated effect of multiple factors (i.e., their interactions) [29]. [30] treated urban areas as ecological systems and then determined quantitatively the social and ecological factors affecting UHIs using measurements from meteorological stations in major Chinese cities. However, the relative importance of each factor was not reported.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Surface Urban Heat Island Intensity and Associated Determinants across Major Chinese Cities

et al. 2015

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Urban heat islands (UHIs) created through urbanization can have negative impacts on the lives of people living in cities. They may also vary spatially and temporally over a city. There is, thus, a need for greater understanding of these patterns and their causes. While previous UHI studies focused on only a few cities and/or several explanatory variables, this research provides a comprehensive and comparative characterization of the diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese cities. The factors associated with the SUHII were assessed by considering a variety of related social, economic and natural factors using a regression tree model. Obvious seasonal variation was observed for the daytime SUHII, and the diurnal variation in SUHII varied OPEN ACCESSRemote Sens. 2015, 7 3671 seasonally across China. Interestingly, the SUHII varied significantly in character between northern and southern China. Southern China experienced more intense daytime SUHIIs, while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the day time in northern China. In southern China, annual electricity consumption and the number of public buses were found to be important. These results have important theoretical significance and may be of use to mitigate UHI effects.

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Detecting the tipping point between heat source and sink landscapes to mitigate urban heat island effects

et al. 2022

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Urban climate change: A comprehensive ecological analysis of the thermo-effects of major Chinese cities

Cited by 9 publications

References 28 publications

Exploring Grey Systems Theory-Based Methods and Applications in Analyzing Socio-Economic Systems

Exploring Grey Systems Theory-Based Methods and Applications in Analyzing Socio-Economic Systems

Spatiotemporal Variation in Surface Urban Heat Island Intensity and Associated Determinants across Major Chinese Cities

Detecting the tipping point between heat source and sink landscapes to mitigate urban heat island effects

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