Large-scale modifications to urban underlying surfaces owing to rapid urbanization have led to stronger urban heat island (UHI) effects and more frequent urban heat wave (HW) events. Based on observations of automatic weather stations in Beijing during the summers of 2014–2020, we studied the interaction between HW events and the UHI effect. Results showed that the UHI intensity (UHII) was significantly aggravated (by 0.55 ℃) during HW periods compared to non-heat wave (NHW) periods. Considering the strong impact of unfavorable weather conditions and altered land use on the urban thermal environment, we evaluated the modulation of HW events and the UHI effect by wind speed and local climatic zones (LCZs). Wind speeds in urban areas were weakened due to the obstruction of dense high-rise buildings, which favored the occurrence of HW events. In detail, 35 HW events occurred over the LCZ1 of a dense high-rise building area under low wind speed conditions, which was much higher than that in other LCZ types and under high wind speed conditions (< 30 HW events). The latent heat flux in rural areas has increased more due to the presence of sufficient water availability and more vegetation, while the increase in heat flux in urban areas is mainly in the form of sensible heat flux, resulting in stronger UHI effect during HW periods. Compared to NHW periods, lower boundary layer and wind speed in the HW events weakened the convective mixing of air, further expanding the temperature gap between urban and rural areas. Note that LCZP type with its high-density vegetation and water bodies in the urban park area generally exhibited a mitigating effect on the UHI, whilst at the same time increasing the frequency and duration of HW events during HW periods. Synergies between HWs and the UHI amplify both the spatial and temporal coverage of high-temperature events, which in turn exposes urban residents to additional heat stress and seriously threatens their health. The findings have important implications for HWs and UHII forecasts, as well as for scientific guidance on decision-making to improve the thermal environment and to adjust the energy structure.

/pdf/synergistic-influence-of-local-climate-zones-and-wind-speeds-555zb42j8q.pdf

Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban Heat Island and Heat Waves in the Megacity of Beijing, China

Urban green spaces (UGS) are effective mitigations to excessive urban heat. Landscape metrics (LMs) have been widely used to assess how UGS configuration, i.e., edge and area, shape complexity, and aggregation, may facilitate better cooling. However, application of configurational LMs has produced diverging suggestions for planning and design practice, which cannot provide urban and landscape designers with holistic insights for future sustainable development. Thus, we conducted a systematic review to (1) summarize the contextual and methodological factors in pertinent studies, and (2) synthesize extractable results and implications, and see if the contextual and methodological factors may help to interpret the diversity in planning and design implications. A total of 167 studies were identified, covering 90 cities in 27 countries belonging to 16 Köppen climate zones. Evolving statistical methods have been applied, including spatial, non-spatial, and non-parametric machine-learning analyses. Synthesis of correlation coefficients reveals that patch-level metric SHAPE, and class-level metrics LPI, AI and COHESION yielded generally consistent trends across studies. No consensus was obtained based on patch-level metrics, while class-level analyses suggest aggregated, patchy, larger, and complex-shaped UGS facilitate better cooling. Contextual and methodological factors cannot help interpret the diverging suggestions. Few specific planning and design implications on UGS configuration were given. Future studies are suggested to specify either a land-use or land-cover perspective to align with practical scales in planning and design practice, and to formulate specific implications beyond binary suggestions by echoing the heterogeneity of thermal environment and UGS pattern under precise planning and design contexts with practical illustration. 

Landscape metrics in assessing how the configuration of urban green spaces affects their cooling effect: A systematic review of empirical studies

Linking landscape spatial heterogeneity to urban heat island and outdoor human thermal comfort in Tokyo: Application of the outdoor thermal comfort index

Exploration of urbanization characteristics and their effect on the urban thermal environment in Chengdu, China

Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China’s urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by “Hu line”. The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver.

/pdf/urbanization-level-in-chinese-counties-imbalance-pattern-and-2pgfdxdf.pdf

Urbanization Level in Chinese Counties: Imbalance Pattern and Driving Force

Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations.

/pdf/spatiotemporal-patterns-of-urbanization-in-the-three-most-1mhyod3t49.pdf

Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China Based on Continuous Nighttime Light Data (2000–2018)

In this study, we aim to carry out a coupling analysis of the thermal landscape and environmental carrying capacity of urban expansion in Beijing over the past 35 years to provide scientific grounding for city planning. The paper proposes a conceptual framework and develops an integrated quantitative approach to the coupling analysis between urban expansion, the urban ecological environment, and the urban landscape, including the Urban Eco-Environment Carrying Capacity Index (ECI), Landscape Spatial Structure Index, Landscape Thermal Index (LTI), and Transitional Landscape Index (TLI, Markov Chain Model). Urban expansion has been essentially dominated by policy adjustments and has been further influenced by socioeconomic development, which has contributed to four outbreaks of urban sprawl in Beijing. Urban expansion is an essential factor affecting ecological environment change. The Olympic Games in 2008 was the turning point for the urban landscape. The rate of urban expansion and improvement of the ecological landscape all changed significantly around the year 2008. The urban thermal distribution pattern coincided well with the featured landscape patches, representing an obvious reflection of the difference between urban green spaces and construction, while high-temperature areas were abundant in the urban center. Urban expansion has a positive effect on the ecological environment and landscape pattern when it is fully matured and well planned. It is expected that, by 2025, the ecological environment of Beijing will be significantly improved, and the proportion of high-temperature areas will decrease.

https://www.mdpi.com/2071-1050/13/2/584/pdf

Coupling Analysis of the Thermal Landscape and Environmental Carrying Capacity of Urban Expansion in Beijing (China) over the Past 35 Years

China has become the largest CO2 emission country since 2014. The industrial sector is the largest contributor to CO2 emissions in China. This paper uncovers the spatiotemporal characteristics of the decoupling status of industrial CO2 emissions from economic growth at the provincial level during 1995–2019 in China and analyzed the structural characteristics of the industrial CO2 emissions. The results suggested that 2010 is an important turning point. Since 2010, the decoupling status of industrial CO2 emissions from economic growth has kept a continuously improving trend. During 2016–2019, all provinces achieved decoupling of the industrial CO2 emissions from economic growth. More than 20% achieved absolute strong decoupling. Four subindustries, including raw chemical materials and chemical products, production and supply of electric power and heat power, petroleum processing and coking products, and smelting and pressing of non-ferrous metals, with large CO2 emissions’ contribution and a continuously increasing trend, should be paid more attention in the future CO2 reduction policies formulation.

Decoupling Analysis of CO2 Emissions in the Industrial Sector from Economic Growth in China

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.

Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities

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Papers

Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China Based on Continuous Nighttime Light Data (2000–2018)

Coupling Analysis of the Thermal Landscape and Environmental Carrying Capacity of Urban Expansion in Beijing (China) over the Past 35 Years

Decoupling Analysis of CO2 Emissions in the Industrial Sector from Economic Growth in China

Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities