1. Introduction
Since the beginning of the 21st century, global urbanisation has accelerated, reaching a rate of 55% by 2018, and it is expected to reach 68% by 2050 [
1]. These trends, in conjunction with the associated increases in population density, exacerbate global climate change. The Intergovernmental Panel on Climate Change (IPCC) reported that the current global temperature has significantly risen compared with that of the last century and predicted an increase of 1–3.7 °C by the end of the 21st century. However, the increase in impervious surfaces within cities that reduce heat dissipation has contributed to the urban heat island (UHI) effect. Urban temperatures are typically higher than suburban temperatures [
2], increasing the risk of extremely high temperatures [
3,
4]. Recent natural disasters caused by persistently high temperatures, such as volcanic eruptions, forest fires, and glacier melting, have resulted in irreversible damage in the context of the environment [
5,
6].
With an increased research focus on thermal environmental challenges, scholars have studied the causes and discussed the impact mechanisms of urbanisation-induced land-use changes and landscape functional and morphological effects [
7,
8]. Recent studies have assessed the influence of natural geographic conditions on climate change vulnerability [
9,
10]. In terms of optimising thermal environmental measures, constructing ecological cities has emerged as a means of maintaining ecosystem functions. However, while urban ecosystems are complex natural–social–economic composite systems [
11], most research has focused exclusively on one aspect of heat environment risk assessment, neglecting the overall urban ecosystem [
12]. This causes an imbalance in ecological and economic development, impeding sustainable societal development [
13]. Therefore, in 2014, the IPCC proposed an environmental risk framework driven by climatic systems and socioeconomic development, primarily including hazards, exposure, and vulnerability (IPCC 2014). These three aspects better reflect the complex climate change risks, focusing on the interactions between risk drivers and multiple risks [
14]. However, existing research often considers the urban thermal environment formed by the aggregation of high-surface-temperature patches as hazards of urban thermal environment risk [
15] and ignores the impact of disasters on people. Heat exposure poses a significant threat to human health with the increasing intensity and frequency of extremely high temperatures [
16]. The human body senses extreme temperature changes, which cause comfort or discomfort, referred to as the human discomfort index (DI) [
17]. However, human perceptions of thermal comfort are influenced by temperature as well as humidity, wind speed, and solar radiation [
18].
To mitigate urban thermal environmental issues and control UHI, slowing population growth and urban development has been suggested [
19]. This approach may exacerbate societal challenges without aptly addressing the root problem. Increasing blue–green spaces is a better solution to UHI [
20]. Indeed, urban blue–green spaces can create a ‘cool island effect’ through evapotranspiration, shading, and heat absorption, mitigating thermal environmental effects [
21]. However, certain blue–green spaces fail to provide effective cooling conditions due to various factors, such as climate conditions and terrain, among others [
22], an issue often overlooked in research. Moreover, owing to limited land resources, cities are unable to solve the urban heat island issue by markedly increasing blue–green spaces [
23]. Nevertheless, by changing the urban development model, restructuring urban land-use patterns, and adopting a socio-ecological strategy, cities can achieve long-term sustainable development [
24].
Urban thermal environment risk assessment requires the analysis of interactions and cold–heat relationships between thermal risk patches to alleviate the urban heat island effect. Lin et al. revealed the influence of patch morphology on UHI, revealing that blue–green spaces with better connectivity have a stronger cooling capacity [
25]. Ecological networks, a scientific spatial planning method, can achieve connectivity between urban spatial structure and function [
26]. To solve the urban heat island challenge, a spatial structure model has been developed, comprising the source, corridor, and node. These networks are categorised as either heat island networks constructed out of areas with higher thermal environmental risks as source sites or cold island networks constructed by assessing ecosystem services, ecological sensitivity, and landscape connectivity [
27]. Qian and Li showed that constructing networks by connecting fragmented cool islands can effectively cool the entire area [
28], while poorly connected cool islands can reduce overall network functionality [
29]. However, the strategy of constructing cold island networks based on thermal environmental risk assessment can better address the negative impacts attributed to high urban development [
30]. Meanwhile, few studies have constructed cooling networks with the assessment of integrated socio-ecological factors, potentially causing one-sided functioning of cooling networks.
Additionally, the construction of a cold island network to optimise the ecological spatial pattern has been widely discussed [
31]. Currently, the minimum cumulative resistance model (MCR) is employed to primarily construct cold conduction networks, while circuit theory is rarely applied in this area. Compared with MCR, circuit theory can more accurately determine corridors’ width and range while offering a certain advantage in identifying key nodes [
32]. The constructed cold island network is more stable, which will be more conducive to alleviating urban thermal environmental risks and gradually achieving comprehensive social and ecological development [
33].
The developmental benefits of socio-ecology were coupled using DI instead of traditional surface temperature indices, thereby incorporating human perceptions into thermal environmental risk assessments. Ultimately, spatial optimisation of thermal comfort is achieved by constructing thermal environmental risk ecological networks. Additionally, the aim was to balance the contradictions between human activities and ecological risk in urban development, considering humans as the carriers of risk effects to optimise the constructed ecological network, achieve sustainable development of the urban thermal environment, and improve the well-being of residents. To this end, a case study in Fuzhou, the capital city of Fujian Province in China, which has a pronounced heat island effect and rapid urban development, was conducted (
Figure 1).
5. Discussion
This study refined several commonly used methods to assess the thermal environment, integrating socio-ecological benefits to conduct risk assessments and relying on the results to construct a thermal risk ecological network. However, with the progression of urbanisation, the thermal environmental risk in Fuzhou continues to escalate, necessitating further optimisation of the ecological network’s functionality and structure. Therefore, in the context of existing research, the optimisation strategies for thermal comfort in Fuzhou in two respects were explored further.
5.1. Thermal Environmental Risk Optimisation under Socio-Ecological Trade-Offs
5.1.1. Spatial Correlation Analysis of Thermal Environmental Risk
Urbanisation leads to the formation of UHI, increasing thermal environmental risks [
54], and natural geographical conditions are the fundamental causes of climatic differences [
55]. Therefore, the MGWR method was used to explore the impact of urban socio-ecological factors on environmental thermal comfort [
56]. Research methods are shown in
Supplementary Note S2. The spatial distribution characteristics of the regression coefficients of each explanatory variable calculated using the MGWR model are illustrated in
Figure 9.
Social development and thermal comfort were positively correlated overall. The regression coefficient for 2005 and 2020 ranged between −3.855 and 3.538 (
Figure 9a) and −2.525 and 2.699 (
Figure 9b), respectively. Moreover, the R
2 in 2020 had increased by 0.07 compared to 2005 (
Table 6). This indicates that with rapid urbanisation, the risk of the urban thermal environment increases. Zhang et al. [
57] have shown an increasing trend in the correlation between population and UHI. The authors posited that population density is an indirect factor for UHI, while the direct factor might be the comprehensive development of society, as confirmed in this study. Based on the spatial distribution characteristics, the impact of socioeconomic factors on thermal comfort in 2005 was unstable, with a clear two-tiered spatial pattern. By 2020, areas with a positive correlation between socioeconomic factors and thermal comfort increased, while the correlation tended to stabilise. This is primarily because in 2005, Fuzhou was in its early stages of development, with significant urban–rural differences, and the thermal comfort changes in remote areas were largely influenced by the development of the central city [
58]. However, with societal development, some of the forested areas were also developed, directly impacting environmental thermal comfort [
59]. In addition, unlike the study by Zhao et al., this study suggests a stronger correlation between the comfort of the thermal environment in suburban areas and the intensity of social development. The thermal environment in suburban areas is more susceptible to damage from urban development. This may be due to the particularity of mountainous coastal areas, where the development of suburban areas can cause damage to the surrounding ecological space, leading to a decrease in thermal comfort [
60].
Unlike in the case of social explosion, the correlation between natural geography and thermal comfort is strong. This is consistent with most current research findings, e.g., Hou ranked the determining factors of urban thermal environment and found that the NDVI has a greater impact on thermal environment than urban morphology and human activity levels [
61]. The regression coefficient for 2005 ranged from −1.120 to 6.819 (
Figure 9c). The regression coefficients in the south-east and certain eastern coastal areas were higher than in the western mountainous regions due to the more variable climates in coastal areas [
62], resulting in weaker correlations with natural geographical factors. The R2 for 2020 increased to 0.806, with an overall strong correlation. Although the coastal areas retained a relatively weak correlation, the two-tiered phenomenon observed in 2005 was mitigated. The regression coefficients of 2020 were higher than those of 2005 (
Figure 9d), indicating that natural geographical conditions have a more pronounced impact on comfort. This phenomenon will further exacerbate the instability of environmental thermal comfort, affecting the health of urban residents [
63].
5.1.2. Strategies for Optimising Thermal Environmental Risks
Overall, environmental thermal comfort is influenced by social and ecological factors, which showed an increasing trend over time, consistent with previous findings [
55]. While Zheng et al. [
64] reported that large-scale urbanisation exacerbates thermal environmental risks, we did not observe significant changes in the thermal environmental risks in the central urban area from 2005 to 2020; however, the surrounding development areas and some rural areas had higher thermal environmental risks. Hence, when urban development reaches a certain stage, the impact of urbanisation on thermal environmental risks is reduced. Therefore, when facing a thermal environment risk, it is necessary to integrate the dual factors of social development and environmental protection. This approach can prevent urban development from falling into ecological protection or extreme economic development situations [
24], achieving sustainable development of the urban climate environment. However, social development does not only directly impact thermal comfort but also the natural environment, which indirectly exacerbates thermal risks [
65]. Accordingly, we propose thermal environmental risk optimisation strategies for three areas: ‘central city’, ‘suburban areas’, and ‘forest under-space’.
The central city area has the highest thermal environmental risk, with high natural vulnerability and social exposure. However, the current development of this area is relatively stable. Hence, subsequent development in the central city area should focus on ecological restoration and environmental micro-renovation, e.g., increasing greenery through community gardens, rooftop gardens, and urban green walls, which can create cooling ‘green islands’ within the main city area [
66]. Urban green spaces of moderate size and relatively complex shapes have better cooling effects [
67]. In addition, for high-density urban areas in the centre, it is also necessary to focus on the comfort of the internal thermal environment of buildings. In this process, attention should be paid to energy consumption and its impact on the external environment [
68].
Suburban areas around the main city are high value in terms of economic development, and the ecological conditions of this region have not been excessively damaged. These areas are therefore optimal for scientific management and development. However, urban development boundaries must be tightly regulated. Otherwise, land overuse and disorderly expansion will further intensify thermal environmental risks. Additionally, in areas with good habitat quality, ecological corridors can be established to strengthen the connection between external source areas and the central city, maintaining energy transmission inside and out to achieve cooling effects [
69].
Wong et al. [
70] purported that expanding green infrastructure is the most effective measure to address urban heat risk and that most of the forest under-space in Fuzhou constitutes an important cooling source on the periphery of its main urban area. The development cost of this region is relatively high and its benefits are low, making it more suitable for maximising ecological values.
5.2. Optimisation Strategies for Thermal Environmental Ecological Networks
5.2.1. Network Pattern Optimisation Based on Urban Green Infrastructure
Based on the results of the heat risk network construction in Fuzhou in 2020, we found that owing to urban development, the central urban area and the south-eastern coastal region lack network connectivity, potentially hindering the ecological network from fully functioning, particularly in the central urban area with severe heat risk. Compared with the existing cold conduction network, the network constructed in this study lacks associations with the internal sources of the central urban area [
33]. In a study by Lin et al. [
71], a green infrastructure network for the main urban area of Fuzhou was constructed, with source areas primarily comprising urban parks within the main city, meeting residents’ living, leisure, and recreational needs. Hence, our research references their methods and uses MSPA to supplement ecological source areas in Fuzhou. Zhao et al. [
72] demonstrated the important relationship between core areas and urban thermal environments. Therefore, based on comprehensive social and ecological analysis, we extracted the core area as a source area supplement, which will benefit the stability of the overall cooling network structure. This network construction method that combines social, ecological, and spatial forms will further enrich cooling networks. After screening for area and connectivity, we added five ecological source areas to the central urban region and four to the south-eastern coastal region, increasing the area by 49.67 km
2 (
Figure 10a). Using the optimised source areas, we identified 51 Level 3 corridors, 50 Level 2 corridors, and 9 Level 1 corridors (
Figure 10b–d). Meanwhile, through ecological node identification, we found 85 ecological pinch points, 101 barriers, and 24 stepping stones (
Figure 10e).
After optimisation, the distribution of the ecological network became more uniform, with improved integrity and connectivity. This optimisation can better alleviate heat environmental risks in rapidly developing areas, such as the central urban area and south-eastern coastal region [
73]. Additionally, the proportion of secondary corridors in the optimised network has significantly increased and is primarily distributed in the central region, indicating that the added source areas in the central city can enhance the stability of the overall network. However, the land in the central urban area has high economic value, and excessive ecological construction might intensify social issues [
13]. The increased number of barriers in the network directly reflects this phenomenon. Therefore, corridor construction in this region requires more comprehensive assessment and management, utilising compound structures or fragmented land spaces to establish links between the main urban area and external ecological source areas. Corridors passing through urban areas have a more pronounced improvement effect on the overall thermal environment than those on the urban periphery [
74]. Compared to the network constructed by Liu et al., the network in this study does not include the Minjiang River as a source area, primarily due to the influence of social development [
33]. The Minjiang River has certain thermal environmental risks that prevent it from serving as a source area. Compared with previous studies, the network in this study combines ecological and social benefits, balances the contradiction between their development, and improves the reliability and ease of implementation of the network. Finally, although nine ecological source areas were added to the network, their functionality was relatively lacking. The ecological source areas added within the main city have a high heat risk owing to surrounding construction [
75], and their ecological functions can be improved in future developments through optimising plant diversity, increasing green coverage and ecological engineering design [
8]. The source areas added in the coastal region are constrained primarily by connectivity, which can be optimised by increasing their scale and setting stepping stones [
73].
5.2.2. Network Structure Optimisation Based on Node Characteristics
For ecological pinch points, the strategy should prioritise ecological conservation and use human intervention as a supplementary method. If the current land-use type of the pinch point is for ecological purposes, stricter controls should be in place, establishing ecological ‘red lines’ to avoid artificial exploitation. If the main coverage type is farmland, ecological protection forest belts can be established around the farmland to protect the agricultural ecosystem chain, enhancing the ecological function of the pinch point. For ecological obstacle points, the strategy should prioritise human intervention, with natural recovery as a supplementary approach. The restoration of these obstacle points requires a balance between ecological and economic values. Planting economically valuable crops, such as lychee and longan, can reduce ecological resistance while maximising social and ecological benefits [
76]. In addition, small-scale green spaces offer the same cooling effect [
77]. Initiating afforestation in areas with high resistance in the main urban area, which has high heat risks and low green coverage, is suggested. Lastly, stepping stones play an essential role as transit points in the network. By attracting ecological flows and material exchanges, a potential stepping-stone network can be formed [
78]. Stepping stones can also provide additional pathways for heat flow, resulting in improved cooling effects [
73]. During the construction of stepping stones, the node regions can be expanded, prioritising the preservation of key stepping-stone areas’ original ecology. Given the rich natural resources in Fuzhou, some stepping-stone areas can be transformed into multifunctional regions such as forest farms or forest parks.
5.3. Limitations
The study model was designed to provide a comprehensive assessment of heat environmental risks based on socio-ecological benefits. However, there are certain limitations. First, in our model, hazards, exposure, and vulnerability were treated as parallel relationships, possibly overlooking trade-offs and synergies between ecology and society. Second, this study only considered the supply effects of the heat environment. As different regions have varying population needs, spatial differences in supply and demand could emerge. Therefore, future studies should incorporate human needs. Third, the corridors constructed in this study used areas with low heat environmental risks as sources, resulting in a network integrating regional cool island spaces. Future studies should combine high-risk and low-risk areas to construct different spatial network patterns using the source–sink theory. Finally, the ecological network constructed by this research institute can only address the thermal environmental risks in Fuzhou. In the future, strategies for addressing the thermal environmental risks in Fuzhou can be proposed on a larger scale, taking into account the surrounding areas.
6. Conclusions
This study evaluated the environmental risk caused by heat in Fuzhou on three dimensions: hazards, exposure, and vulnerability. Temperature and humidity were combined to calculate environmental thermal comfort and the human footprint index was applied for exposure and natural geographical conditions for vulnerability. Following risk categorisation, ecological source areas were extracted, and an ecological network was established using a linkage map. The analysis included ecological pinch points, barriers, and stepping stones within the network. Finally, optimisation strategies for thermal comfort in Fuzhou were proposed from two angles. The primary outcomes of this study were threefold. (1) The central urban area of Fuzhou and other study areas have prominent heat environmental risks, with annual increases. The area with a high risk level in 2020 had exhibited a substantial increase from that in 2005. Thus, to optimise thermal environmental risks, the central urban area should focus on ecological restoration and urban micro-renewal. Moreover, suburban areas must control excessive urban expansion, while understory areas should prioritise ecological protection. (2) Influenced by the increased thermal environment risk, the source area in 2020 was reduced compared with that in 2005, and source areas were unevenly distributed, while the south-eastern coastal area and central city lacked vital cooling sources. Following optimisation, nine ecological source areas that were more evenly distributed were added. This improvement in integrity and connectivity is more conducive to mitigating the thermal environmental risks in Fuzhou. (3) The stability and functionality of the constructed cooling network for Fuzhou City in 2020 was reduced compared with that in 2005, primarily reflected by the shortened average corridor length, reduced ecological pinch points, and increased choke points. Hence, intervention in pinch points must be reduced, the repair of choke points strengthened, and the protection of the stepping-stone area expanded. This study evaluates urban thermal environmental risks from a social ecological perspective and identifies corresponding cooling networks. This method has certain universality and can provide reference for the formulation of urban climate development policies.