Coupled Climate–Environment–Society–Ecosystem Resilience Coordination Analytical Study—A Case Study of Zhejiang Province

Abstract

The aim of this paper is to evaluate the coupled coordination degree of climate, environmental, socio-economic, and ecosystem resilience in Zhejiang Province from 2010 to 2022 and to propose optimization strategies. With the increasing impact of global climate change, the need to explore the construction of resilient cities and sustainable development models has become increasingly pressing. Assessing the coupled coordination among climate, environment, socio-economic, and ecosystem resilience aids in suggesting more precise and effective social and ecological recovery strategies in the context of climate change. Zhejiang Province, serving as a model for China’s urbanization development, demonstrates a balance between the natural environment, economic growth, and social development but still suffers from ecological and environmental pollution problems. In this study, an evaluation system was constructed utilizing the entropy weight method (EWM), and the coupled coordination among climate, environmental, socio-economic, and ecosystem resilience in Zhejiang Province was empirically analyzed over the period from 2010 to 2022. The results show that (1) the climatic-environmental, socio-economic, and ecological subsystems of cities in Zhejiang Province generally show an upward trend, despite fluctuations over different periods. (2) The climatic-environmental-social-ecological system resilience of the cities in Zhejiang Province increased as a whole, and six cities (Hangzhou: 0.805, Quzhou: 0.811, Huzhou: 0.827, Taizhou: 0.829, Wenzhou: 0.856, and Jinhua: 0.857) reached the “well-coordinated” level by 2022; however, the coupling coordination of Jiaxing City and Lishui City decreased from good to intermediate coordination. (3) The coupled coordination degree of climatic-environmental-social-ecological system resilience generally stagnated in each city during 2020–2022. Thus, the climate change adaptation strategy proposed in this study aims to enhance urban adaptive capacity to climate change impacts by controlling pollutant emissions, restoring ecosystems, optimizing industrial structures, and designing urban green spaces.

1. Introduction

Climate change has become a major global concern, increasingly jeopardizing sustainable development [1]. According to the Global Climate Report, combined land and ocean temperatures have increased by an average of 0.07 °C per decade since 1880 [2]. However, the average rate of increase has doubled to 0.17 °C per decade since 1981. The global average temperature is predicted to rise by approximately 0.2 °C per decade over the next two decades [3]. The 2015 Paris Agreement mandates that the increase in the Earth’s temperature be kept below 2 °C, with an ideal limit of 1.5 °C above pre-industrial levels [4]. However, since the end of the 19th century, the Earth’s average surface temperature has already risen by approximately 1.8 °C. The Emissions Gap Report 2023, published by the United Nations Environment Programme, indicates that current national emission reduction commitments under the Paris Agreement are projected to result in global temperatures rising by 2.5–2.9 °C above pre-industrial levels this century, far exceeding the 1.5 °C threshold necessary to avoid the worst impacts of climate change [5]. The two most significant contributors to global warming and climate change are rapid urbanization and significant increases in industrial activity. In recent decades, industrial activities have caused an increase in atmospheric carbon emissions, driven by industrialization and urbanization in many developing countries. The concentration of CO₂ has increased by more than 2 ppm per year over the last decade, as documented in the literature [6]. Furthermore, global CO₂ emissions from human activities have increased by over 400% since 1950 [7]. The problem of excessive emissions continues to intensify as new records for global temperatures, greenhouse gas emissions, and atmospheric CO₂ concentrations were set in 2022 [5]. The excessive increase in CO₂ concentrations depletes the ozone layer, allowing ultraviolet radiation to reach the Earth’s surface, leading to global warming and subsequent changes in global and regional climate patterns. This phenomenon is known as climate change. The indiscriminate emission of greenhouse gases into the atmosphere exacerbates global warming, ocean acidification, and desertification and increases the frequency, duration, and severity of extreme temperature events such as heat waves and record-breaking high temperatures [8]. Furthermore, it contributes to unsustainable urban development patterns, significantly deteriorating air quality since the late twentieth century [9]. This issue has attracted considerable attention from scientific research and major international organizations, including a study by Samoli et al. [10]. The surge in air quality research is closely linked to rapid urbanization and climate change occurring worldwide [11]. During the process of rapid urbanization, a notable increase in industrial activities has become a significant source of air pollution. In particular, the combustion of fossil fuels, such as coal and oil, releases substantial quantities of pollutants, including sulfur dioxide, nitrogen oxides, and particulate matter. The direct emission of these pollutants into the atmosphere results in a substantial decline in air quality and the potential formation of severe air pollution phenomena such as haze. Furthermore, fossil fuel combustion releases greenhouse gases, including carbon dioxide and methane. These greenhouse gases not only exacerbate global warming but also affect the generation and distribution of photochemical ozone, further deteriorating air quality. In turn, there is an impact of warming on air pollution. With global warming, extreme weather events such as heat waves have increased in frequency and intensity. The occurrence of extreme weather events has led to a significant increase in the demand for cooling among urban residents. To meet this demand, urban energy consumption has risen significantly during hot summer months. This increase in energy consumption exacerbates greenhouse gas (GHG) emissions and increases the release of other pollutants, creating a vicious cycle [8]. The accumulation of these pollutants and greenhouse gases represents a significant threat to human health, increasing the risk of respiratory and cardiovascular diseases, and has a detrimental impact on ecosystems over the long term. For instance, acid rain formation damages soil and vegetation, while soil pollution affects crop growth and quality, impacting biodiversity and ecological balance.

Air pollution and climate change exhibit significant impacts on and complex interactions with ecological diversity, socioeconomics, and productivity [12]. The climate–environment–society–ecosystem system involves multiple dimensions and interactions. For instance, climate warming significantly impacts ozone (O₃) concentration in densely vegetated areas, potentially increasing ozone levels in these regions. Prolonged exposure to high ozone concentrations directly threatens plants with toxicity, severely challenging their growth and survival [12]. As climate change intensifies, the frequency and intensity of acidification and extreme weather events increase, exerting significant pressure on plant numbers and variety, potentially leading to a decline in plant communities. This decline in plant communities affects the structure, composition, and function of terrestrial ecosystems and directly impacts the carbon cycle, which, in turn, exerts feedback effects on the climate system, exacerbating global warming trends [13]. An imbalance in the carbon cycle results in a rise in atmospheric carbon dioxide concentration, further exacerbating the greenhouse effect and creating a vicious cycle. The impact of global warming on agricultural productivity is particularly significant, potentially leading to a decline in agricultural productivity. Reduced agricultural productivity not only directly threatens livelihoods but may also compel the agricultural sector to increase industrial activities, such as using more chemical fertilizers and pesticides. While this may boost yields in the short term, it can exacerbate land degradation and ecological imbalances over the long term. Additionally, warming has led to the deterioration of water resources, interacting with the local climate to further increase sensitivity to rising temperatures [14]. To address these challenges, social systems have adopted a range of countermeasures, such as extracting more groundwater and using more fertilizers for agricultural purposes. However, while these measures provide short-term relief, they may create self-perpetuating feedback loops in the long term, adding complexity and challenges to adaptation planning and sustainable development [15]. With the escalating problems of global warming and air pollution, the international community’s vision has expanded from merely mitigating climate change to jointly addressing the combined challenges of mitigating climate change and adapting to human society and the natural environment. This shift has prompted countries to adjust their climate commitments beyond mitigation efforts to include adaptation strategies as a core consideration. Despite active efforts at the global, national, and local levels, it must be recognized that the challenges posed by climate change are likely to intensify over this century due to its complexity and far-reaching impacts [16]. Consequently, integrating climate change adaptation into development planning is increasingly recognized globally in both science and policy as essential for ensuring social and economic development by increasing socio-ecological resilience, maintaining ecosystem services at various scales, and contributing to sustainable development [17]. Therefore, an in-depth study of the coupled effects of climate–environment–society–ecosystem resilience is crucial for developing effective adaptation strategies to climate change.

Significant global economic expansion has occurred in recent decades due to industrialization and urbanization. Particularly as a developing country, China has undergone rapid urbanization and industrialization over the past six decades, leading to a substantial rise in the gross domestic product (GDP) [18]. However, it has also faced severe challenges of ecological degradation and resource overconsumption due to its crude development model and the extensive consumption of resources and energy by its vast industrial system. Consequences such as deforestation, water scarcity, overexploitation of oil, and climate change are direct outcomes of irresponsible natural resource utilization in developing countries. Regrettably, rapid economic expansion in China has resulted in a range of environmental challenges, particularly greenhouse gas emissions [19]. This not only restricts the further development of the urban economy and society but also exacerbates the conflict between urban ecology and development [20]. Therefore, significant attention is given to this issue by the Chinese government, which emphasizes the coordinated development between social systems and ecosystems. Resilience, defined as the system’s capacity to maintain its structure and function amid disturbances, is marked by system integrity, internal hierarchy, complexity, and uncertainty [21]. Maintaining the sustainability of social-ecological systems is crucial in addressing the challenges posed by climate change and air pollution. Thus, studying the adaptive advantages of social-ecological systems to air pollution and climate change [22] can enhance their resilience and advance sustainable development. Zhejiang Province, exemplifying China’s urbanization development, exemplifies a balance between the natural environment, economic growth, and social development [23]. Nevertheless, despite substantial progress, Zhejiang Province continues to confront challenges in ecological environmental protection. Globally, ozone concentrations have generally risen in recent years, particularly in Zhejiang Province, situated in China’s eastern coastal region, where monsoons influence sunny and cloudless summer weather, fostering photochemical reactions and conducive conditions for ozone formation. Concurrently, as one of the most economically advanced regions, Zhejiang Province has experienced a surge in the industrial, transport, and chemical sectors, resulting in a notable rise in nitrogen oxide (NOx) and volatile organic compound (VOC) emissions, primary contributors to ozone pollution. Furthermore, elevated population densities and extensive urbanization have compounded the issue, augmenting pollutants from anthropogenic actions, amid urban sprawl and diminished green spaces and water bodies, hindering the dispersion and mitigation of pollutants. Coupled with interactions with neighboring provinces and sea areas, particularly during adverse meteorological conditions, pollutants tend to accumulate locally, leading to severe ozone pollution.

Building upon this background, the study establishes a coordinated evaluation system for climate–environment–society–ecosystem resilience, employing the entropy weighting method to ascertain the weights of each evaluation system indicator. The concept of entropy originated in thermodynamics within physics. Shannon introduced it in 1948, employing entropy as a measure of the information content carried by a given object. In information theory, greater information reduces uncertainty, resulting in lower entropy, and vice versa [24]. The entropy weighting method (EWM) is an objective assignment technique that yields more accurate weightings compared to subjective methods [25]. Following weight determination, this study analyzed the coupling coordination degree of social-ecological system resilience in Zhejiang Province amid air pollution and climate change from 2010 to 2022 based on factor weights of coupling indicators and a coupling degree formula model. The study aims to achieve the following objectives: (1) Construction of an assessment system: developing a comprehensive evaluation framework to gauge the resilience of social-ecological systems under the influence of climatic-environmental change. (2) Temporal analysis: examining the resilience trend of social-ecological systems in Zhejiang Province from 2010 to 2022. (3) Study of coupling coordination degree: assessing the level of interaction and coordinated evolution among social-ecological system components. (4) Ecological restoration strategies: suggesting climate change adaptation strategies encompassing environmental, ecological, and socio-economic dimensions. The study’s significance lies in comprehensively understanding the response mechanisms of social-ecological systems to climatic and environmental pressures and in proposing recommendations to bolster their resilience against these challenges.

2. Study Area and Methodology

2.1. Overview of the Study Area

Zhejiang Province (118°01′–123°10′ E, 27°02′–31°11′ N) is a provincial-level administrative region situated on the southeast coast of China, adjacent to the southern Yangtze River Delta (Figure 1). It borders the East China Sea to the east, Shanghai and Jiangsu Provinces to the north, Fujian Province to the south, and Anhui and Jiangxi Provinces to the west. Covering an area of 105,500 square kilometers of land and 260,000 square kilometers of sea, the province includes 11 prefecture-level cities: Hangzhou, Ningbo, Wenzhou, Jiaxing, Huzhou, Shaoxing, Jinhua, Quzhou, Zhoushan, Taizhou, and Lishui. Seventy percent of Zhejiang Province consists of mountains and hills, primarily in the western and southern regions. The Yangtze River Delta is recognized as China’s most economically dynamic region with the largest economic output and greatest growth potential. It is also pivotal for air pollution prevention and control efforts [26]. Integral to the Yangtze River Delta urban agglomeration, Zhejiang Province exemplifies balanced development among nature, economy, and society during China’s urbanization process [23]. However, the province faces serious ecological and environmental challenges, particularly atmospheric pollution and ecological degradation, amid its economic boom. According to the “Bulletin of the Second National Pollution Source Census of Zhejiang Province”, Zhejiang Province records the highest emissions of water pollutants: chemical oxygen demand (422,800 tonnes), followed by heavy metals (109,000 tonnes), with ammonia nitrogen (30,900 tonnes) ranking third. Particulate matter emissions (355,500 tonnes), nitrogen oxides (488,600 tonnes), and sulfur dioxide emissions (113,900 tonnes) rank highest among air pollutants. The emission of pollutants such as sulfur dioxide (SO₂), nitrogen oxides (NOx), and particulate matter (PM) severely degrades air quality. Furthermore, economic development and urbanization have led to a rapid increase in the number of motor vehicles, which constitute a significant source of urban air pollution, particularly in densely populated cities and areas with heavy traffic. Nitrogen oxides (NOx) and volatile organic compounds (VOCs) released from motor vehicle exhaust undergo sunlight-induced catalytic reactions to form ozone (O₃), exacerbating photochemical smog pollution and significantly degrading air quality [27]. As one of China’s economically developed regions, Zhejiang Province experiences rapid urbanization, a substantial number of vehicles, and notable vehicle emissions issues. Hence, achieving green sustainable development necessitates studying the coupling and coordination among climate, socio-economic factors, and ecology in Zhejiang Province. This approach will bolster sustainable development, resource allocation optimization, policy formulation, ecological conservation, ecological civilization building, the exploration of green development models, and decision-making processes within the province. Such efforts are crucial for advancing China’s sustainable economic growth and promoting sustainable urban development nationwide.

2.2. Data Sources

The primary data sources for this study include the 2010–2022 Statistical Yearbook of Zhejiang Province, Ecological Environment Bulletin, Agricultural Census Data Bulletin, and Territorial Survey Data Bulletin, as well as statistical and ecological environment bulletins and economic and social development statistics from various cities in Zhejiang Province, along with other publicly available information. The “advanced industrial structure” is calculated using the following formula: value added of tertiary industry/value added of secondary industry. NDVI data is sourced from the China Environmental Science and Data Centre (https://www.resdc.cn, accessed on 28 March 2024), processed using the Google Earth Engine cloud computing platform, and derived from a comprehensive analysis of Landsat5/7/8/9 remote sensing data throughout the year, employing advanced data pre-processing and smoothing techniques. The annual maximum NDVI was obtained by methods such as data pre-processing and data smoothing [28]. Relative humidity index data were obtained by interpolating daily observations from weather stations and aggregating them regionally to obtain annual average relative humidity data for each city in Zhejiang Province. Some indicators such as the “90th percentile of the daily maximum 8 h average concentration of ozone (O₃)”, “area of arable land”, “amount of fertilizer applied”, “sulfur dioxide concentration in the air”, and “average health technicians per 1000 population” have occasional missing values. These missing values were addressed using “Stata 15” software, employing the ipolate linear extrapolation and epolate linear interpolation methods: “ipolate command”: ipolate X year, gen (X1) “epolate command”: ipolate X year, gen (X1) epolate. (See Appendix A for raw data.)

2.3. Research Methodology

The objective of this study is to assess the coupled and coordinated relationship between climate change and regional socio-ecological resilience through the following steps:

(1)

Construction of an evaluation model: Initially, a system integrating various coupling degree indicators is established through a literature review. Using Zhejiang Province as a case study, corresponding indicator data are collected to construct an evaluation model for the coupled coordination of climate, environment, socio-economy, and ecosystem.

(2)

Weight calculation: The entropy method is employed to determine the weights of coupled climate, environmental, socio-economy, and ecological indicators, ensuring scientific rigor and evaluation accuracy.

(3)

Based on the calculated weights and coupling equations, the coupling and coordination of climate, environment, socio-economy, and ecology are analyzed and empirically analyzed to gain a deeper understanding of their interactions.

(4)

Restorative strategies: Drawing from the findings of the coupling and coordination analysis, specific recommendations are formulated to optimize interactions among the climate, environment, socio-economy, and ecosystems, thereby enhancing overall capacity for coordinated development.

The coupled coordination system of regional social and ecological resilience in this study encompasses three primary subsystems: the socio-economy system, ecosystem, and climate–environment system. These subsystems mutually influence, coordinate, and synergize through the exchange of matter, energy, and information, collectively evolving as an integrated entity (Figure 2).

2.3.1. Construction of the Indicator System

The construction of the climatic-environmental-social-ecological resilience evaluation indicator system is characterized by comprehensiveness, complexity, and multidimensionality, and the current research is mostly based on the construction of the indicator system based on social, economic, ecological factors in 3 dimensions (

Table 1. Climate–environment–socio-economy–ecosystem resilience evaluation indicator system table.

System Level	Primary Indicators	Secondary Indicators	Causality	Description of the Indicator
Social economic	Social	Population density x1	−	Reflects population size/(km2·person)
		Per capita housing area x2	+	Reflects the level of housing security for the population/(m2·person)
		Local financial expenditure x3	+	Reflects the level of medical security/(sheets·thousand people)
		Number of primary and secondary school enrollment x4	+	Measurement of system learning capacity/(person)
		Number of beds in medical and health institutions x5	+	Measurement of financial expenditure status/(billion yuan)
		Engel’s coefficient x6	−	Reflects the structure of consumer spending/%
	Economic	Per capita disposable income x7	+	Reflects the income level of residents/(yuan·person)
		Gross local product x8	+	Measures economic strength/(billion yuan)
		Local fiscal revenue x9	+	Measurement of local government financial strength/(billion yuan)
		Animal husbandry and fishery x10	+	Measurement of systematic production capacity/(billion yuan)
		Large-scale industry x11	+	Measures the level of secondary industry development/(billion yuan)
		Advanced industrial structure x12	+	Measures the degree of balance in industrial development
Ecology	Land use	Arable land resources x13	−	Cultivated land area/(hm2)
		Water resources x14	−	Percentage of river water system area/(%)
		Land resources x15	−	Scale of construction land/(hm2)
	Resource use and conservation	Intensity of crop fertilizer application x16	−	Reflecting the degree of ecological pollution by fertilizer use/(t·hm2)
		Greening of urban built-up areas coverage x17	+	Reflecting the green cover of the city/(%)
		Plant cover x18	−	Measure of urban vegetation cover evaluation NDVI
		Forest cover x19	+	Reflects regional forest cover/(%)
Climate–environment	Climate change	Average annual temperature x20	+	Reflects average urban climate change/(°)
		Average annual rainfall x21	+	Reflects average precipitation change in the city/(mm)
		Relative humidity x22	+	Reflects urban humidity change/(mm)
		Air quality excellence index x23	+	Reflects the annual percentage of good air quality in the city/(%)
	Air pollution	Airborne sulfur dioxide concentration x24	−	Emission of sulfur dioxide in the air/(μg/m3)
		Air nitrogen dioxide concentration x25	−	Emission of nitrogen oxides in the air/(μg/m3)
		Ozone concentration indicator x26	−	Ozone concentration in air/(μg/m3)

and Figure 3). Therefore, with reference to relevant studies [29,30,31,32] and on the basis of the characteristics of regional development as well as the focus of this study, a resilience assessment system was established with the climate and environment, socio-economics, and ecology as the first-level indicators. Specifically, the social subsystem within the social dimension primarily assesses society’s stability, risk-resistance, and learning capacity. Demographic characteristics are measured by population density (x1), while per capita housing area (x2) indicates living conditions in Zhejiang. Local financial expenditure (x3) denotes consumption levels and financial support strength. The level of primary and secondary school enrollment (x4) reflects educational popularity and learning ability, and the number of beds in medical and health institutions (x5) measures the capacity for medical and health service provision, while Engel’s coefficient (x6) reflects the structure of residents’ consumption expenditure. Within the economic subsystem under the social dimension, the development and supporting capacity of the economy are assessed. The gross output value of agriculture, forestry, animal husbandry, and fishery (x10) and of large-scale industry (x11) denote production capacity and industrial development scale. The advanced industrial structure (x12) signifies the economy’s trajectory toward sustainable and innovation-driven development, while per capita disposable income (x7), gross local product (x8), and local fiscal revenue (x9) indicate economic strength. The land use subsystem under the ecological dimension primarily assesses ecological background conditions. Arable land (x13), water resources (x14), and land resources (x15) gauge the extent of human land use. Resource utilization and protection within ecological resilience consider ecological coercive factors; crop fertilizer application intensity (x16) indicates ecological pressure. The greening coverage of urban built-up areas (x17) reflects environmental protection efforts, while plant cover (x18) and forest cover (x19) indicate ecological function and balance. The climate change subsystem under the climate–environment dimension assesses Zhejiang Province’s climate conditions, covering average annual temperature (x20), average annual rainfall (x21), relative humidity (x22), and air quality excellence index (x23). The air pollution subsystem under this dimension evaluates specific air pollutant concentrations, such as sulfur dioxide (x24), nitrogen dioxide (x25), and ozone concentration indicators (x26).

2.3.2. Entropy Method

With the entropy method’s help, the indicators’ weights are determined. Then, the comprehensive score of the resilience of the social-ecological system in Zhejiang Province is calculated, as well as the degree of coupling and coordination of the systems. The methodology and specific steps are shown below:

(1) Based on the proposed program data, the original information matrix was created as follows:

$$X={\left(x_{ij}\right)}_{m\times n},$$

(1)

(2) Indicators are normalized to obtain the elements of the normalization matrix $MMN\_Y_{ij}$:

Positive indicators:

$$MMN\_Y_{ij}=\frac{X_{ij}-X_{imin}}{X_{imax}-X_{imin}},$$

(2)

Negative indicators:

$$MMN\_Y_{ij}=\frac{X_{imin}-X_{ij}}{X_{imin}-X_{imax}},$$

(3)

where $X_{ij}$ is the value of the different evaluation indicators $j$ under the first $i$ evaluation dimension; $MMN\_Y_{ij}$ is the standardized value.

(3) The data are dimensionless using the specific gravity method:

$$Q_{ij}=\frac{MMN\_Y_{ij}}{{\displaystyle \sum _{i=1}^{n}MMN\_Y_{ij}}},$$

(4)

$Q_{ij}$ normalization of indicators ($i$ = 1, 2, $n$$j$ = 1, 2, $m$).

(4) The information entropy of the indicator $E_j$ is calculated:

$$E_j=-{[\ln (n)]}^{-1}{\displaystyle \sum _{i=1}^n{Q}_{ij}}\ln Q_{ij},$$

(5)

(5) Objective weights for each indicator $G$ are determined through the information entropy $E_j$:

$$G_j=\frac{1-E_j}{{\displaystyle \sum _{j=1}^m\left(1-E_j\right)}},(j=1,2,\cdots ,m),$$

(6)

2.3.3. Coordinated Modeling of Coupled Systems

The examination of coupled coordination contributes to understanding social-ecological system resilience’s coupling processes and evolutionary patterns amid climate change and air pollution. It also facilitates the identification of shortcomings in urban economic, social, and ecological development. This research holds significant practical implications for establishing a sustainable and efficient urban development model characterized by green, sustainable, and high-quality attributes. As a prevalent analytical approach for assessing the coordinated development of diverse systems, the coupled coordination model finds widespread application in analyzing ecosystem interactions and other systems [29,30,33]. Quantifying the coupling and coordination degrees of each indicator within the system enables the assessment of the system’s coordination level and relative development status. The calculation of the coupled social-ecosystem resilience coordination system is as follows:

(1) Composite index formula:

$$U_i=\sum _{j=1}^{{\mathrm{n}}_j}{G}_j\times Y_i,$$

(7)

(2) Coupling degree equation:

$$C={\left(\frac{{\displaystyle \prod _{i=1}^n}{U}_i}{{\left(\frac{1}{n}{\displaystyle \sum _{i=1}^n}{U}_i\right)}^n}\right)}^{\frac{1}{n}},$$

(8)

where C is the coupling, and $U_i$ is the composite index of the subsystems. In order to further reflect the degree of coupling and coordination of the socio-ecological-climatic-environmental system, the degree of coupling of climatic-environmental-socio-ecological indicators was calculated as shown in Equation (9).

(3) The number of subsystems was $n$:

$n=2$:

$$C=\frac{2\sqrt{U_1{U}_2}}{U_1+U_2}$$

(9)

$n=3$:

$$C=\frac{3\sqrt[3]{U_1{U}_2{U}_3}}{U_1+U_2+U_3}$$

(10)

(4) Coordinated development formula:

$$T=\sum _{i=1}^n{\omega }_i{U}_i$$

(11)

T is an integrated socio-ecological-atmospheric evaluation indicator, calculated as in Equation (11), where the weight is considered. This paper contends that social, ecosystem, and atmospheric systems play equally significant roles in fostering socio-ecological resilience. Thus, the weights ${\omega }_1$, ${\omega }_2$, and ${\omega }_3$ assigned to all three systems in the integrated evaluation indicator T are equal and set to 1/3 each. When computing the integrated evaluation indicator for paired systems, the weight assigned is 0.5.

(5) The coupled coordination is calculated, where D is the degree of related coordination:

$$D=\sqrt{CT}$$

(12)

This study categorizes the degree of coordinated development into ten evaluation levels (see

Table 2. D-value judgment matrix table and its interpretation.

D Coordinated Development Degree	Coordination Level	Degree of Coupled Coordination
(0.0∼0.1)	1	Extreme disorder recession category
[0.1∼0.2)	2	Severe disorder recession category
[0.2∼0.3)	3	Moderate disorder recession category
[0.3∼0.4)	4	Mildly dysfunctional recession category
[0.4∼0.5)	5	Nearly dysfunctional recession category
[0.5∼0.6)	6	Barely coordinated recession category
[0.6∼0.7)	7	Elementary coordination recession category
[0.7∼0.8)	8	Intermediate coordination recession category
[0.8∼0.9)	9	Good coordination recession category
[0.9∼1.0)	10	Quality coordination recession category

) and analytical calculations. This classification aims to assess the interplay among the resilience of climate and environment, socio-economic, and ecosystem subsystems and the level of coordination. Such analysis provides a clearer understanding of coordinated development’s spatial and temporal evolution within the coupled social-ecological system under air pollution and climate change conditions.

3. Comprehensive Assessment and Analysis of Climatic-Environmental-Social-Ecological Resilience in Zhejiang Province

3.1. Spatial and Temporal Trends in the Climate, Environment, Socio-Economics, and Ecology

3.1.1. Spatial and Temporal Trends in the Climate and Environment

The climatic-environmental resilience of cities in Zhejiang Province exhibited varying trends from 2010 to 2022 (Figure 4 and Figure 5). Initially, cities showed significant variability in resilience, with Taizhou (0.213) and Jinhua (0.280) having the lowest values and Quzhou (0.730) the highest. By 2012, resilience declined in most cities, except for Huzhou and Jinhua, which remained stable. From 2012 to 2016, resilience generally improved across the province, with notable increases in Wenzhou (0.225–0.744), Shaoxing (0.115–0.630), and Jinhua (0.247–0.742). However, by 2018, resilience declined in several cities, including Jinhua, Shaoxing, Huzhou, Taizhou, and Quzhou, while Ningbo, Wenzhou, and Jiaxing continued to improve steadily. From 2019 to 2020, resilience generally increased in all cities except for Lishui (0.626–0.585), Taizhou (0.835–0.709), and Zhoushan (0.590–0.523). By 2022, Quzhou City (0.601) saw continued resilience growth compared to that in 2020, whereas most other cities experienced a decline. Overall, environmental resilience improved in most cities from 2010 to 2020; however, this positive trend reversed by 2022, with most cities showing a decline in resilience. This indicates recent challenges to environmental quality and the inability to sustain previous positive growth trends.

3.1.2. Spatial and Temporal Trends in the Climate and Environment

From 2010 to 2022, the socio-economic environmental resilience of cities in Zhejiang Province underwent varied changes, as depicted in Figure 6 and Figure 7. In early 2010, cities generally exhibited low resilience levels, with Hangzhou City (0.068) and Jinhua City (0.074) at the lowest, while other cities ranged from 0.129 to 0.343. Between 2010 and 2013, cities generally experienced an upward resilience trend, notably in Hangzhou, Ningbo, Jiaxing, and Jinhua, with resilience indices ranging from 0.242 to 0.360, while Shaoxing and Lishui remained stable. From 2013 to 2016, resilience continued to grow across almost all cities, with substantial increases in Wenzhou City (0.122–0.510) and negligible change in Shaoxing City. By 2019, resilience growth continued to be significant, with cities like Hangzhou, Wenzhou, Jiaxing, Jinhua, and Quzhou maintaining high growth rates, and notable resilience increases were observed in Taizhou (0.344–0.835). By 2022, most cities continued to exhibit resilience growth, with Ningbo, Wenzhou, Jiaxing, Huzhou, and Jinhua reaching peak resilience indices between 0.942 and 0.802, while Taizhou, Hangzhou, and Zhoushan experienced a brief decline from 2020 to 2022. Overall, from 2010 to 2022, cities in Zhejiang Province exhibited a positive socio-economic development trend, with varying growth rates but overall stability. However, the socio-economic resilience of individual cities exhibited a declining trend from 2020 to 2022.

3.1.3. Spatial and Temporal Trends in Ecological Change

Between 2010 and 2022, Figure 8 and Figure 9 show the evolution of ecological resilience and its spatial and temporal trends across cities in Zhejiang Province. Initially, there were significant differences in resilience between cities, with notable contrasts between those with the lowest and highest resilience. Hangzhou (0.420), Ningbo (0.351), and Huzhou (0.489) had the highest ecological resilience during this period, while Wenzhou (0.126), Lishui (0.126), and Jinhua (0.229) had the lowest. By 2013, most cities showed a gradual increase in resilience, including Hangzhou (0.420–0.487), Jiaxing (0.317–0.415), and Lishui (0.126–0.143), while Taizhou (0.251–0.285) and Quzhou (0.272–0.283) remained stable. In 2016, some cities experienced fluctuations or slight decreases in ecological resilience, with notable decreases in Ningbo (0.086) and Jiaxing (0.150) and a slight decrease in Lishui (0.131), while Quzhou City (0.283) and Shaoxing City (0.096) experienced an increase. However, by 2019, the ecological resilience of most cities in Zhejiang Province showed accelerated growth, with notable increases in Jiaxing City (0.607), Lishui City (0.805), and Zhoushan City (0.775). By 2022, compared to 2020, the resilience of most cities showed a stagnant or slightly increasing trend, although Taizhou City (0.455–0.716) and Jiaxing City (0.573–0.614) showed significant increases. Overall, cities in Zhejiang Province showed an increasing trend in ecological resilience from 2010 to 2020. However, by 2022, most cities showed stagnation or slight improvement, indicating challenges to ecological quality in recent years and an inability to sustain previous positive trends.

3.2. Spatial and Temporal Socio-Economic Trends

The coupled coordination indicators of climate–environment–society–ecology for cities in Zhejiang Province are illustrated in Figure 10, Figure 11 and Figure 12. Figure 10 shows a continuous upward trend in the coupled coordination value D across all cities from 2010 to 2022, indicating improved integration of climatic-environmental systems, socio-economic systems, and ecosystems over time. However, significant fluctuations in the coupled coordination value D are observed in individual cities, such as Ningbo, where the coordination degree increased from 0.390 to 0.636 from 2012 to 2014 before declining to 0.521 in 2016. The trend of coordination index T depicted in Figure 11 closely aligns with the coupled coordination degree D, suggesting sustained synchronization within the climatic-environmental-socioeconomic-ecological system. Figure 12 depicts the coupling coordination C-value, with many cities nearing or reaching a value of 1 in 2022, including Hangzhou (0.982), Wenzhou (0.980), Jinhua (0.995), Zhoushan (0.997), and Taizhou (1.000), indicating a high level of connection between the climate, environment, socio-economy, and ecosystems. Despite this, there are notable fluctuations in the coupling coordination value D of certain cities, such as Shaoxing (0.911–0.772), Ningbo (0.880–0.765), and Jinhua (0.946–0.798), signifying a decline in coupling between these systems from 2013 to 2016. These findings underscore the dynamic nature of system interactions within Zhejiang Province, highlighting both improvements and challenges in achieving sustainable and coordinated development across the climate, environment, society, and ecology.

3.3. Spatial and Temporal Variation in the Degree of Coupling Coordination Characteristics

This study analyzes the dynamics of coupled coordination among climate–environment, social progress, and ecosystem resilience in cities across Zhejiang Province from 2010 to 2022 through a 10-level assessment system (Figure 13). It is observed that Hangzhou exhibits significant synergies between climate and environmental policies, socio-economic development, and ecosystem protection, with its coupling coherence steadily improving from a state of “near dysfunction” in 2010 to “well-coordinated” by 2022. The degree of coupling coordination has steadily increased from an initial state of “endangered disorder” in 2010 to achieving “good coordination” by 2022. Furthermore, Wenzhou, Huzhou, Jinhua, Quzhou, Taizhou, and other cities have made notable progress in climate change adaptation and ecological restoration, with their coupling coordination degree gradually rising to the “good coordination” range. In contrast, Shaoxing, Jiaxing, and Lishui City maintained an “intermediate coordination” level in 2022, consistent with their status in 2020. Ningbo City’s coupling coordination degree advances from “primary coordination” in 2020 to “intermediate coordination” by 2022. Province-wide, cities in central and western Zhejiang Province demonstrate greater dynamism in coupling coherence changes, with a more rapid upward trend, whereas northeastern cities exhibit an overall upward trend but at a slower pace. It is noteworthy, however, that none of the cities achieved the highest level of coupling coordination, known as “high-quality coordination”, indicating that while progress has been made, additional efforts are necessary to sustain and enhance environmental quality.

4. Discussion

4.1. Analysis of the Results of the Coupled Climate–Environment–Society–Ecosystem Resilience Coordination of Cities of Zhejiang Province

Until 2020, the coupling coordination degree of most cities in Zhejiang Province exhibited steady growth, signifying enhanced environmental quality, interaction between socio-economic and ecological systems, and increased resilience. Nevertheless, from 2020 to 2022, the D-value of the coupling coordination degree for cities like Ningbo, Wenzhou, Shaoxing, Jiaxing, Lishui, Zhoushan, etc., exhibited a stagnant or slightly increasing trend. Ecological civilization construction in Zhejiang Province commenced in the 1970s and 1980s, accelerating notably alongside economic development, particularly following the introduction of the “8-8 strategy”. Between 2000 and 2022, Zhejiang Province has experienced several pivotal phases of ecological civilization development. These include initiating the ecological province construction strategy in 2003, endorsing ecological civilization construction in 2010, launching the “Beautiful Zhejiang” initiative in 2013, proposing the goal of achieving Beautiful Zhejiang in 2017, and ultimately achieving the first ecological province status by 2019. Despite policy orientation and sustained efforts contributing to enhanced coupling among the climate, environment, society, and ecosystems in Zhejiang Province, these linkages exhibited a declining trend from 2020 to 2022. The analysis of resilience in the climate–environment subsystem, socio-economic subsystem, and ecosystem indicates that the decline in coupled coordination from 2020 to 2022 primarily stems from reduced resilience in the climate–environment subsystem and delayed enhancement in ecosystem resilience. The decrease in resilience of the climate–environment subsystem may be attributed to the historical focus, before 2014, on atmospheric monitoring and management in Zhejiang Province cities on pollutants such as PM2.5, PM10, sulfur dioxide (SO₂), and nitrogen dioxide (NO₂). Nonetheless, starting from 2014, Zhejiang Province began integrating ozone concentration into air quality monitoring protocols, despite ozone not being classified as a primary pollutant in the atmospheric environment at that time. Nearly a decade of this endeavor has yielded notable achievements in climate–environment prevention and control in Zhejiang Province, notably lowering air pollutant concentrations like sulfur dioxide and nitrogen oxides below China’s secondary air quality standards. Nevertheless, in recent years, the global rise in ozone concentration has emerged as a significant factor impacting atmospheric environmental quality, particularly pronounced in coastal areas like Wenzhou City, Zhoushan City, and Taizhou City, where ozone pollution is increasingly problematic. Consequently, climate–environment resilience in these coastal cities has decreased relative to that in 2020, primarily attributable to worsened atmospheric conditions due to rising ozone levels. Moreover, the challenge of ozone formation complexity has hindered efforts to fully remedy the decline in climate–environment resilience. Similarly, the ecological subsystem trend mirrors that of the climatic-environmental subsystem, with many cities experiencing a delayed upward trajectory during 2020–2022, indicating dual pressures and resulting in diminished or delayed growth in resilience for both subsystems. This situation likely stems from a blend of factors, including heightened human activities, amplified climate change impacts, and inadequate environmental protection and governance measures. Thus, there exists potential for enhancing interactions among climate and environmental governance, green ecological construction, and socio-economic development in Zhejiang Province, necessitating further exploration of their interconnections and deriving strategies for improvement.

4.2. Interactions between Coupling Factors

The study of coupled coordination in climate–environment–society–ecosystem resilience highlights how interactions among these factors reflect the level of coordination among diverse systems. A deeper understanding of these interactions can be attained through analysis of the trends in D (Figure 9) and C (Figure 11). C, being a critical indicator of system coupling, directly reflects the strength of interactions between them. Figure 11 illustrates significant fluctuations in the C-value for cities like Shaoxing, Taizhou, and Ningbo between 2012 and 2016. In Shaoxing, the C-value notably decreased from 0.911 in 2013 to 0.714 in 2015. This decline indicates reduced coherence among the climate, society, and ecosystems of Shaoxing during this period, possibly due to economic development, social changes, and ecological protection imbalances. Further analysis of climate–environment resilience (Figure 3), socio-economic resilience (Figure 5), and ecosystem resilience (Figure 7) in Shaoxing City reveals an upward trend in ecosystem resilience from 2013 to 2015, suggesting improved resilience against external pressures or disturbances. However, during the same period, socio-economic and climatic-environmental resilience in Shaoxing City exhibited static growth, characterized by slow or stagnant rates of improvement. This indicates that while ecosystem resilience has grown in Shaoxing, the growth of socio-economic and climate resilience has not kept pace, leading to decreased overall coherence among the climate, society, and ecosystems. However, looking at a broader scale, the C-value for most cities in 2022 is near or at 1, for instance, in Hangzhou (0.982), Wenzhou (0.980), Jinhua (0.995), Zhoushan (0.997), and Taizhou (1.000). This outcome demonstrates that these cities have achieved high levels of coupling between climate, socio-economics, and ecosystems, establishing more stable interactions. During development, these systems mutually adapt and reinforce each other, creating a virtuous cycle. Changes in the climate and environment, socio-economic conditions, and ecosystem stability significantly influence the other two systems. Concurrently, these systems adjust internally to maintain overall system stability.

In the study of the coupled coordination degree of climate–environment–society–ecosystem resilience, changes in the D-value indicate the extent of synergistic evolution among the climate, environment, society, and ecosystem over time. The D-values across all cities in Zhejiang Province generally exhibited an increasing trend, but from 2020 to 2022, they either decreased or experienced delayed growth, as observed in Wenzhou City (0.892–0.856), Zhoushan City (0.812–0.787), and Lishui City (0.817–0.793). This suggests a decline in the coupling coordination capacity of climate–environment–society–ecosystems within these cities during this period, particularly in the ecosystem subsystems. There was a general decline in ecosystem coupling and coordination capacity. From the perspective of individual subsystems, the climate–environment system and ecosystem subsystems of most cities exhibited a decreasing trend from 2020 to 2022. For example, Wenzhou City saw declines in both the climate–environment system (0.768–0.572) and the ecosystem (0.828–0.730), as did Lishui City (climate–environment system: 0.585–0.491; ecosystem: 0.828–0.730) and Zhoushan City (climate–environment system: 0.523–0.424; ecosystem: 0.808–0.780). The analysis of the decline in climatic-environmental resilience shows that the climatic-environmental resilience of other cities in Zhejiang generally shows an upward trend from 2018 to 2020, with the exception of Shaoxing, which is closely related to the continuous improvement of air quality in Zhejiang. Data indicates that the annual average concentration of negative oxygen ions in forested areas of the province rose from 965/cm³ in 2018 to over 1800/cm³ in 2020, marking an upgrade in air quality from “average” to “fresher”, with ongoing positive trends. The ecological and environmental bulletin of Zhejiang Province reported a significant decrease in PM2.5 concentration in 2018, leading to improved overall air quality compared to the previous year. Specifically, the proportion of good daily air quality (AQI) days averaged 85.3% across the province’s 11 cities, an increase of 2.6 percentage points from the previous year. The annual average PM2.5 concentration dropped to 33 µg/m³, reflecting a year-on-year decrease of 15.4%. Furthermore, the environmental quality of surface water in the province has steadily improved, with an increasing percentage of provincially monitored sections meeting or exceeding the environmental quality standards for all three types of surface water. Overall, lakes and reservoirs exhibit good to excellent water quality. These substantial ecological improvements have positively contributed to enhancing climatic-environmental resilience. Additionally, the Zhejiang Provincial Government implemented rigorous policies and measures during this period, including the Three-Year Action Plan for Blue Sky Defence, and enhanced the management of industrial emissions, vehicle exhaust, and urban dust. These measures have effectively mitigated pollutant emissions and further enhanced air quality. Between 2018 and 2020, Zhejiang Province’s consistent environmental governance and ecological protection efforts led to improved climatic-environmental resilience across most cities, but a reversal occurred in 2020–2022, with a decline observed in all cities. This could be attributed to the prevailing conditions at the time, where effective epidemic control post-2020 coincided with heightened industrial activities aimed at rapid economic recovery. This surge increased pressure on the climate–environment system due to escalated energy consumption and pollutant emissions alongside expanded productivity and production scale. Furthermore, increasing ozone concentrations have been identified as a critical factor contributing to diminished climatic-environmental resilience. Data reveal that between 2013 and 2017, the annual average ozone concentration in 74 Chinese cities rose from 69.6 ppb to 81.5 ppb [34]. Similarly, ozone concentrations in most cities of Zhejiang Province exhibited an increasing trend between 2010 and 2022. This concentration increase adversely affects the climate and environment by exacerbating the greenhouse effect and disrupting ecosystem balance. The rise in ozone concentration correlates closely with substantial emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) during industrial processes [35]. These precursors produce ozone through photochemical reactions in sunlight’s presence. Rapid urbanization in Zhejiang Province has led to increased factory numbers, making industrial emissions a primary source of ozone production. High summer temperatures and intense sunlight act as catalysts for ozone production. In the context of global warming and climate change, increased occurrences of high temperatures and intense radiation further accelerate ozone production, resulting in a rapid rise in ozone concentration [36]. Faced with the escalating issue of ozone pollution, the Zhejiang Provincial Government has initiated active responses. To mitigate the upward trend in ozone concentration, the Zhejiang Provincial Government launched the Three-Year Action Plan for Ozone Pollution Prevention and Control in 2022. Ecosystem resilience mirrored the trend observed in the climate–environment system from 2018 to 2022, showing an increase from 2018 to 2020 followed by a decline in 2020–2022. The increase stemmed from government management policies, while the decline may be linked to accelerated urbanization and the impact of the pandemic. These factors have converted substantial land areas into urban space, replacing natural ecosystems like farmland, forests, and wetlands, thereby compromising ecosystem integrity and stability [37]. Additionally, economic pressures during the epidemic likely contributed to decreased ecological resilience. This was due to reduced environmental investments by some enterprises to cut costs, along with potential impacts on environmental projects from shifts in financial and human resources and changes in policy focus. Conversely, socio-economic system resilience has shown an upward trajectory, underscoring the tension between declining climatic-environmental and ecological trends and rising socio-economic indicators, thus accentuating the challenges of sustainable development.

4.3. Ecological Restoration Strategies

Ensuring environmental and ecosystem protection while transitioning to a green, low-carbon, and circular development model has become a critical issue during our pursuit of economic growth. Sustainable socio-economic development requires an awareness of Jevon’s paradox, which suggests that increasing energy efficiency can paradoxically lead to greater energy and resource consumption [38]. For instance, enhancing landscape heterogeneity can promote environmental conservation to some extent, resulting in decreased human activity intensity as heterogeneity grows [39]. This, in turn, reduces the conversion of ecological resources into productive capital. Excessive landscape fragmentation, however, may diminish biodiversity and impair ecosystem diversity [40,41]. The ecological economics theory emphasizes the interconnectedness of economic growth and environmental protection, highlighting their potential for mutual restriction or reinforcement [42]. The analysis of the coupled and coordinated resilience of climatic-environmental, social, and ecosystem systems in Zhejiang Province revealed a general stagnation in city trends from 2020 to 2022, despite an overall upward trajectory from 2010 to 2022. This observation indicates that significant negative impacts from global warming and climate change persist, despite proactive responses by city governments to external challenges like climate change. Therefore, enhanced research on climate change adaptation and the proposal of precise, effective ecological restoration strategies are imperative.

(1)

Climatic and environmental actions are necessary to manage ozone pollution and address climate change. This involves enhancing the control of ozone precursors like nitrogen oxides (NOx) and volatile organic compounds (VOCs). Strategies include promoting electricity, limiting high-emission industrial activities, adopting vapor recovery nozzles, and ensuring cleaner fuel combustion. Innovative measures are also needed to reduce emissions from the oil and gas industry, particularly during peak ozone formation in the summer. Improving air quality monitoring networks and establishing early warning systems for ozone levels can mitigate health risks associated with ozone exposure. Additionally, adopting “climate-adapted” species sourcing strategies can enhance the long-term survival and resilience of restored ecosystems by leveraging natural genetic variability and selecting species suited to expected climatic conditions [43].

(2)

The ecosystem dimension necessitates enhancing resilience and conserving biodiversity by adhering to international principles and standards for ecological restoration. Adopting an effective and sustainable approach to restoration projects is crucial. This involves understanding and responding to complex ecosystem dynamics, particularly amid climate change, while also considering land use-related rights in management decisions [44]. Initiatives such as afforestation and wetland restoration can enhance ecosystem resilience and self-purification. The restoration of ecosystem structure and function can bolster resilience to environmental change, thereby promoting human well-being and biodiversity [45]. Increased efforts to conserve local biodiversity, particularly in key ecological areas offering essential services, are vital for sustaining natural ecosystems.

(3)

Socio-economic imperatives for industrial restructuring involve optimizing the industrial structure by prioritizing low-carbon, eco-friendly industries over high-pollution, high-energy-consumption sectors. This entails promoting the development and adoption of green technologies. Increasing public awareness and fostering public engagement in environmental conservation can significantly support ecological protection and restoration efforts. Moreover, introducing economic incentives, such as tax incentives for companies utilizing clean energy and rewards for those engaged in ecological preservation, can incentivize the adoption of more sustainable development approaches.

(4)

The consideration of cities’ cooling needs is crucial given their expansion and escalating global warming. Population growth, local warming, urban settlement densification, and reduced evapotranspiration capacity collectively exacerbate the urban heat effect. This necessitates thorough consideration in urban planning for adapting to climate change, particularly addressing the cooling requirements resulting from thermal effects. Urban planning should prioritize the evaporative effect of pavements through increased vegetation cover and the selection of tree species with high transpiration potential. Tree selection should consider not only the tree’s transpiration capacity throughout its life cycle but also the dynamics of the canopy structure as a central aspect of urban greenery design. Furthermore, maximizing the artificial shading effect on streets and alleys can alleviate the discomfort caused by the urban heat island effect [46].

In conclusion, addressing ozone pollution and achieving a sustainable equilibrium between environmental health and socio-economic development necessitates comprehensive measures encompassing stringent pollution control, ecological restoration, socio-economic restructuring, enhanced monitoring, and public engagement.

4.4. Limitations of This Study

While this study offers valuable insights into assessing socio-ecological resilience in Zhejiang Province amid air pollution and climate change, it has certain limitations. Firstly, the methodology and data selection may influence the findings, as the construction of evaluation indices and the use of models introduce subjectivity and uncertainty. Alternative methods and data choices could yield different results, necessitating further analysis and validation. Secondly, due to the complexity of factors affecting socio-ecological resilience and their interrelationships, there is a need for further exploration into indicator selection, theoretical understanding, and mechanistic analysis to construct a more robust evaluation system. Additionally, starting from a regional scale and selecting a single case may limit the generalizability of the findings. Further validation and empirical studies are required to extend the applicability of the results to other regions.

5. Conclusions

This paper empirically analyzes the coordinated coupling of the three systems—social, ecological, and climatic—in Zhejiang Province.

Between 2010 and 2022, the climatic-environmental, socio-economic, and ecological subsystems in Zhejiang Province exhibited an overall upward trend. Initially, regarding the climatic-environmental subsystem, most cities experienced a positive upward trend from 2010 to 2020; however, this trend reversed somewhat during 2020–2022. Secondly, the socio-economic system in all Zhejiang Province cities maintained a consistent upward trajectory from 2010 to 2022, indicating robust socio-economic development. Taizhou, however, exhibited a brief downward trend in the socio-economic system from 2016 to 2018. Lastly, the ecological subsystem exhibited a comparable pattern across Zhejiang Province cities, characterized by fluctuating trends of increase, decrease, resurgence, and decline. This variability likely mirrors the cyclical nature of ecological restoration and the balance between urban development and environmental protection. The overall resilience of the climatic-environmental-social-ecological system in Zhejiang Province exhibited an upward trend from 2010 to 2022. By 2022, six cities—Hangzhou (0.805), Quzhou (0.811), Huzhou (0.827), Taizhou (0.829), Wenzhou (0.856), and Jinhua (0.857)—had achieved “well-coordinated” status, while five cities—Ningbo (0.750), Shaoxing (0.729), Zhoushan (0.787), Jiaxing (0.793), and Lishui (0.793)—attained “intermediate coordination”. While the coupling coordination degree of most cities in Zhejiang Province exhibited stable growth, Jiaxing City (9-8) and Lishui City (9-8) experienced a decline in 2020–2022. The coupled coordination degree of climatic-environmental-social-ecosystemic resilience across all Zhejiang Province cities did not achieve the highest level of “quality coordination”. Despite the overall upward trend observed in cities from 2010 to 2022, a period of general stagnation occurred between 2020 and 2022. This indicates significant ongoing negative impacts from global warming and climate change, despite proactive city government responses to external challenges like climate change. Therefore, the climate change adaptation strategies proposed in this paper for the climatic-environmental, ecological, and socio-economic dimensions are aimed at improving the adaptive capacity of cities to cope with the negative impacts of climate change: (a) Strengthen control of ozone precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs), adopt innovative measures to curb emissions from the oil and gas sector, improve air quality monitoring networks, establish early warning systems for ozone levels, and select seeds or species for planting that are adapted to the expected climatic conditions. (b) Adopt effective and sustainable approaches to ecological restoration projects, bolstering ecosystem resilience and self-purification through initiatives like afforestation and wetland restoration and intensifying efforts to preserve local biodiversity. (c) Optimize industrial structures and increase public awareness. (d) Urban planning should prioritize the evapotranspiration effect of road surfaces, expand vegetation coverage, select tree species with high transpiration potential, consider dynamic changes in canopy morphology in urban green design, and maximize the artificial shading effect of streets and alleys to mitigate urban heat island discomfort.

With the intensification of global climate change impacts, there is an increasing urgency to explore strategies for building resilient cities and sustainable development models. Based on current research findings, future studies on coordinating climate–environment–society–ecosystem resilience coupling could be expanded and further refined. Firstly, a comparative cross-regional study could extend globally, particularly to regions profoundly affected by climate change yet underexplored. This study would elucidate variations and similarities in coupled climate–environment–society–ecosystem resilience across diverse regions, offering a robust backing for shaping global climate change response and sustainable development strategies. Secondly, a detailed analysis of key factors and interaction mechanisms within each subsystem could yield more refined, dynamic models, enhancing study accuracy and foresight. Moreover, the study scope could encompass the implementation effects of climate change adaptation strategies, evaluating policy effectiveness through empirical analyses to underpin policy formulation and adjustments. This research theme holds substantial practical significance. A comprehensive analysis of coupled climate–environment–society–ecosystem coordination could offer city managers a scientific foundation for devising more effective policies addressing climate change, promoting environmental protection, and enhancing urban sustainable development. Simultaneously, this study could heighten public and corporate environmental awareness, foster societal engagement in climate change response and ecological preservation, and ultimately realize the dual goals of economic development and environmental protection.