Progress, knowledge gap and future directions of urban heat mitigation and adaptation research through a bibliometric review of history and evolution

He, Bao‐Jie; Wang, Wei; Sharifi, Ayyoob; Liu, Xiao

doi:10.1016/j.enbuild.2023.112976

Cited by 66 publications

(19 citation statements)

References 195 publications

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“…A large number of literatures discussed the impacts of some static driving factors on SUHI effect, such as underlying surface, climatic conditions, urban form and air pollution [ 29 , 37 , 78 ]. Regrettably, the impacts of socio-economic dynamic process on SUHI effect were rarely mentioned [ 54 ]. Although the SUHI effect is a physical geographical phenomenon, but in essence, it is a response of local thermal environment to several social-economic processes in the context of urbanization development [ 79 ], such as the evolution processes of urban population, industrial structure and resident consumption structure.…”

Section: Discussionmentioning

confidence: 99%

Contrary to expectation: The surface urban heat island intensity is increasing in population shrinking region while decreasing in population growing region-A comparative analysis from China

Liu,

Zhang

2024

PLoS ONE

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Exploring the complex relationship between population change and surface urban heat island (SUHI) effect has important practical significance for the ecological transformation development of shrinking cities in the context of the prevalence of urban shrinkage and the global climate change. This paper compares the population change and SUHI effect between population shrinking region (Northeast Region, NR) and population growing region (Yangtze River Delta, YRD) in China, and explores their differences in driving mechanisms, using GIS spatial analysis and Geodetector model. Our results indicated that there are significant differences in population changes and SUHI intensity between these two regions. About 72.22% of the cities in the NR were shrinking, while their SUHI intensities increased by an average of 1.69°C. On the contrary, the urban population in the YRD shows a linear growth trend, while their SUHI intensities decreased by 0.11°C on average. The results of bivariate Moran’s I index also indicated that the spatial correlation between the urban population changes and the SUHI intensity changes are not significant in the above regions. Furthermore, there are significant differences in the primary drivers of SUHI variations between these two regions. In the NR, underlying surface changes, including the changes of green coverage and built-up areas, are the most important driving factors. However, atmospheric environment changes, such as carbon dioxide emission and sulfur dioxide emission, are the key drivers in the YRD. Northam’s theory of three-stage urbanization and environmental Kuznets curve hypothesis are powerful to explain these differences.

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Section: Discussionmentioning

confidence: 99%

Contrary to expectation: The surface urban heat island intensity is increasing in population shrinking region while decreasing in population growing region-A comparative analysis from China

Liu,

Zhang

2024

PLoS ONE

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“…Table 1 presents the indicator system of the UEES by restructuring the indicators reported in the statistical yearbooks into five dimensions. For instance, the construction (D1) dimension consists of six indicators: the development intensity of built-up areas (C1), population density of built-up areas (C2), density of the road network (C3), retrofitted areas of old residential communities per 10,000 people (C4), the ratio of the increasing areas of new residential buildings to the increasing Total area of cultural buildings in the district m 2 Total resident population in the district (10,000 people) Cultural building areas per 10,000 people 5 Note: 1 The primary industry refers to agriculture, forestry, animal husbandry, and fishery. Secondary industries include mining, manufacturing, electricity, heat, gas, and construction.…”

Section: Identification Of Assessment System Dimensions and Indicatorsmentioning

confidence: 99%

“…Cities face severe challenges, such as environmental pollution, heat islands, urban flooding, and greenhouse gas (GHG) emissions, due to urbanization, population increase, and climate change [1,2]. These challenges threaten economic growth, environmental quality, and human wellbeing.…”

Section: Introductionmentioning

confidence: 99%

Developing an Urban Environment Examination System by Incorporating Construction, Economic, Environmental, Cultural and Development Dimensions

Ren,

Li,

Peng

et al. 2024

Sustainability

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The sustainable development goals (SDGs) suggest sustainable cities and communities (Goal 11) as inclusive, safe, resilient, and sustainable human settlements. However, existing efforts in urban sustainability have mainly focused on ecological and environmental sustainability, with little attention paid to development, economic, and cultural sustainability. Moreover, a lack of adaptability is a barrier to cross-nation or cross-region implementation of many urban sustainability frameworks due to diverse urban contexts. Furthermore, most studies have developed sustainability frameworks and guides only for one-time assessments and the progress of urban sustainability has been neglected. Improving urban sustainability by considering multiple dimensions and local adaptability through dynamic assessment remains a question. Therefore, this study aims to develop an urban environment examination system (UEES) framework that includes construction, economy and industry, environment and resource, municipal and cultural facilities, and development potential dimensions. The UEES framework consists of 31 indicators associated with critical information (i.e., reported by the statistical yearbooks) from different levels of government in China. An annual update of statistical data ensures the availability of dynamic data, allowing urban managers and decision makers to regularly track urban sustainability. Furthermore, the UEES framework was applied to Qingdao, an urbanizing city in China, to examine the feasibility of the UEES framework. The results indicate that well-urbanized districts could perform better in terms of overall sustainability (e.g., Shinan, Shibei, Laoshan, Chengyang), while districts in suburban and outer suburban areas had lower urban sustainability levels. However, well-urbanized districts could not perform better in all dimensions than suburban and outer suburban districts. Moreover, the transition from less-urbanized to well-urbanized districts could lead to environmental deterioration and economy deceleration (if industries are not well upgraded), but culture and development sustainability can be improved. Overall, this study provides a methodological framework that prioritizes comprehensiveness and local adaptability to perform regular urban environment examinations for urban sustainability in China and provides a reference for urban sustainability improvement by identifying aspects with low scores in Qingdao, China.

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“…In this perspective, land cover characteristics like the distribution patterns of built‐up and vegetation cover are often used (Du et al ., 2014; Gazi & Mondal, 2018; Rasul & Ibrahim, 2017; Saha et al ., 2020; Sultana & Satyanarayana, 2020). UHI studies can be carried out with the goal of smart urban landscaping, assessing micro climate changes, improving sustainable energy use etc (He et al ., 2023). Green landscaping has significant potential in reducing local heat hazards.…”

Section: Introductionmentioning

confidence: 99%

Ambient temperature modelling from surface characteristics and associating urban morphology with thermal discomfort

Dutta,

Basu,

Agrawal

2024

Singap J Trop Geogr

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Urban heat island assessment is of paramount importance when monitoring microclimate changes, increased heat stress, mortality and energy consumption. Simply analysing land surface temperature patterns for human comfort and health assessment is often inadequate. In this study, we attempt to resolve this inadequacy with ambient temperature modelling from multiple surface characteristics for a tropical megacity. A 336 datapoint‐based multilinear regression model was formulated to predict the maximum summer air temperature with a mean absolute error of 0.694°C. Specific locations of critical thermal environments were demarcated with Getis‐Ord statistic. Based on our results, extreme hotspots covered 10.53 per cent of the city on a daily basis at 99 per cent confidence level. The application of an outdoor thermal comfort index highlighted the existence of very strong heat stress zones over 14.67 per cent of the study area. The air temperature hotspots were verified with intra‐urban variation in carbon sequestration. This environmental parameter was used to strengthen the observed results since, carbon sequestration directly links urbanization with degradation of thermal environments. Further, five urban morphological parameters were analysed to conclude that building density and height were the most significant urban design factors leading to increased air temperature. Simultaneous local climate zone mapping depicted that heat islands were dominated by mid and high‐rise built‐up areas, of which 68.44 km2 of that area is suitable for vertical and roof gardening. Another 33.29 km of road stretch was delineated within low‐rise built‐up areas with scope for green landscaping to improve urban sustainability.

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Progress, knowledge gap and future directions of urban heat mitigation and adaptation research through a bibliometric review of history and evolution

Cited by 66 publications

References 195 publications

Contrary to expectation: The surface urban heat island intensity is increasing in population shrinking region while decreasing in population growing region-A comparative analysis from China

Contrary to expectation: The surface urban heat island intensity is increasing in population shrinking region while decreasing in population growing region-A comparative analysis from China

Developing an Urban Environment Examination System by Incorporating Construction, Economic, Environmental, Cultural and Development Dimensions

Ambient temperature modelling from surface characteristics and associating urban morphology with thermal discomfort

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