Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Evaluation System Based on “DPSIR-EES” Model
- Driving Force Indicator Set: Driving forces are fundamental factors that influence changes in ecological resilience. Existing research identifies economic factors, social development, and ecological construction as key drivers of urban ecological resilience [26,29]. This paper, building on relevant studies, selects GDP per capita and employee wage levels as indicators for economic driving forces, reflecting the impact of regional economic development on ecological resilience. Population size and night-time light intensity are chosen as indicators for social driving forces, demonstrating how population growth and urbanization affect ecological resilience. The proportion of urban green space is used as an indicator for environmental driving forces, highlighting the role of greening activities, such as afforestation, in enhancing ecological resilience. Given that increasing population size results in a corresponding rise in waste and pollutants, population size is considered a negative indicator.
- Pressure Indicator Set: Pressure represents the negative factors that influence ecological resilience as a result of driving forces. This paper selects the GDP growth rate as an indicator of economic pressure. The longstanding competition to increase GDP in China has significantly expanded economic output but has also incurred substantial environmental costs [35], leading to considerable ecological risks in urban development. Therefore, the GDP growth rate is treated as a negative indicator. Additionally, this paper uses the population density and urban unemployment rate as indicators of social pressure, both of which have detrimental effects on ecological resilience. Population density signifies the environmental stress associated with urban population concentration, while the urban unemployment rate illustrates the trade-off between job creation and environmental protection. Increased urban unemployment is likely to result in a compromise of green development goals for economic growth [36]. Finally, industrial emissions of air, water, and solid waste are used as indicators of environmental pressure.
- State Indicator Set: The state refers to the current development status of economic, social, and environmental subsystems under the combined influence of driving forces and pressures. Unlike driving forces, which function as “conditions” for the evolution of ecological resilience, the state primarily represents the “outcome” of this process. Based on existing research [37,38], this paper uses GDP per unit area and fixed asset investment per unit area as indicators of the economic state, reflecting current economic density and investment activity. For the social state, indicators include per capita retail consumption and per capita residential land area, which reflect current consumption levels and living standards. For the environmental state, per capita total water supply and green coverage rate in built-up areas are chosen as indicators, representing the current capacity for resource and environmental support.
- Influence Indicator Set: Influence reflects the direct effects of changes in ecological resilience on economic, social, and environmental subsystems. Unlike state indicators, which represent static conditions, influence indicators capture dynamic changes. Based on existing research [37,39,40], this paper uses local fiscal revenue to represent economic influence, reflecting how driving forces and pressures influence regional economic development. For social influence, the indicators are the elasticity of human resources to economic growth and construction area per unit of GDP, which reflect the interplay between human development, economic growth, and land use. For environmental influence, the proportion of days with good air quality is selected as the indicator.
- Response Indicator Set: Response refers to the management measures and policies implemented to enhance ecological resilience. Firstly, regarding economic response, the tertiary sector is more environmentally friendly than the primary and secondary sectors due to its lower resource consumption and pollution emissions. Therefore, this paper uses the value added by the tertiary sector to gauge the extent of economic structural optimization. Secondly, financial investments in education and technology are used as indicators of social response. Education and technology, fundamental to sustainable development [37], enhance ecological resilience by increasing environmental awareness and advancing high-tech applications. Lastly, this paper employs the rates of harmless treatment of household waste, comprehensive utilization of industrial solid waste, and investment in landscaping as indicators of environmental response.
2.3. Methods
2.3.1. Entropy-TOPSIS Method
2.3.2. Methods for Dynamic Evolution Process
- Kernel density estimation, as a quantization tool with low dependence on the model and great robustness, is widely used in the evaluation of spatial disequilibrium, and the continuous density curve is used to unveil the changes in spatial absolute differences. This paper uses traditional kernel density estimation to examine the overall dynamic distribution of the development level of urban ecological resilience in China. Equations (1) and (2) are the density distribution functions of ERI:In Equation (1), where N denotes the number of cities in each region, Xi represents the independent identically distributed observations, which is ERI of each city, and x and K represent the average value and the kernel density, respectively. h represents the bandwidth; the smaller the bandwidth, the higher the accuracy of the estimation, but the smoothness of the curve is correspondingly lower. Conversely, the larger the bandwidth, the smoother the curve, but the lower the estimation accuracy.
- The traditional Markov chains are used to study the problem of stochastic transfer of time and state in the absence of an aftereffect [43]. By constructing a Markov transition probability matrix, this method addresses the limitations of kernel density estimation in revealing internal transition information and predicting long-term trends. Therefore, this paper uses the traditional Markov chain to characterize the internal dynamic evolution law of ERI in China.In Equation (3), it shows that the probability of ERI X in the current state j period t depends on its ERI in the previous period, t − 1. Equation (4) shows the probability that ERI will transfer from state i in the current period to state j in the next period. By arranging all types of transfer probabilities in the form of a matrix, the transfer probability matrix pij of China’s urban ecological resilience is formed.
2.3.3. Dagum Gini Coefficient Decomposition Method
2.3.4. Focused Stepwise Quantitative Case Analysis
3. Results
3.1. The National Level Analysis of ERI
3.2. The Regional Level Analysis of ERI
3.3. The City-Level Analysis of ERI
4. The Dynamic Evolution of ERI
4.1. ERI’s Kernel Density Estimation
4.2. The ERI’s Probability of Transition
5. Regional Difference Decomposition
5.1. Total Regional Differences and Intra-Regional Differences
5.2. Inter-Regional Differences
5.3. Sources of Regional Differences and Contribution Rate
6. Analysis on Factors Affecting Ecological Resilience
6.1. Research Design and Variable Selection
6.2. Data Calibration
6.3. Empirical Analysis
6.3.1. Necessity Test
6.3.2. Configuration Analysis
- Driven by technology and environment type;
- 2.
- Driven by technology, organization, and environment type;
- 3.
- Driven by informatization level type;
6.3.3. Robustness Test
7. Discussion
7.1. Conclusions
7.2. Theoretical Implications
7.3. Practical Implications
7.4. Limitations and Future Research
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Giddings, B.; Hopwood, B.; O’Brien, G. Environment, economy and society: Fitting them together into sustainable development. Sustain. Dev. 2002, 10, 187–196. [Google Scholar] [CrossRef]
- Wang, Q.; Yuan, X.; Cheng, X.; Mu, R.; Zuo, J. Coordinated development of energy, economy and environment subsystems-A case study. Ecol. Indic. 2014, 46, 514–523. [Google Scholar] [CrossRef]
- Li, W.; Yi, P. Assessment of city sustainability-Coupling coordinated development among economy, society and environment. J. Clean. Prod. 2020, 256, 120453. [Google Scholar] [CrossRef]
- Yu, B. Ecological effects of new-type urbanization in China. Renew. Sustain. Energy Rev. 2021, 135, 110239. [Google Scholar] [CrossRef]
- Wang, J.; Wang, J.; Zhang, J. Spatial distribution characteristics of natural ecological resilience in China. J. Environ. Manag. 2023, 342, 118133. [Google Scholar] [CrossRef] [PubMed]
- Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
- Kondyli, J. Measurement and evaluation of sustainable development A composite indicator for the islands of the North Aegean region, Greece. Environ. Impact Assess. Rev. 2010, 30, 347–356. [Google Scholar] [CrossRef]
- Martin, R.; Sunley, P.; Gardiner, B.; Tyler, P. How Regions React to Recessions: Resilience and the Role of Economic Structure. Reg. Stud. 2016, 50, 561–585. [Google Scholar] [CrossRef]
- Li, D.; Yang, W.; Huang, R. The multidimensional differences and driving forces of ecological environment resilience in China. Environ. Impact Assess. Rev. 2023, 98, 106954. [Google Scholar] [CrossRef]
- Han, S.; Wang, B.; Ao, Y.; Bahmani, H.; Chai, B. The coupling and coordination degree of urban resilience system: A case study of the Chengdu-Chongqing urban agglomeration. Environ. Impact Assess. Rev. 2023, 101, 107145. [Google Scholar] [CrossRef]
- Li, F.-j.; Yang, H.-w.; Ayyamperumal, R.; Liu, Y. Pollution, sources, and human health risk assessment of heavy metals in urban areas around industrialization and urbanization-Northwest China. Chemosphere 2022, 308, 136396. [Google Scholar] [CrossRef] [PubMed]
- Tang, Y.; Wang, Y. Impact of digital economy on ecological resilience of resource-based cities: Spatial spillover and mechanism. Environ. Sci. Pollut. Res. 2023, 30, 41299–41318. [Google Scholar] [CrossRef] [PubMed]
- Van Meerbeek, K.; Jucker, T.; Svenning, J.-C. Unifying the concepts of stability and resilience in ecology. J. Ecol. 2021, 109, 3114–3132. [Google Scholar] [CrossRef]
- Abdi, R.; Rogers, J.B.; Rust, A.; Wolfand, J.M.; Philippus, D.; Taniguchi-Quan, K.; Irving, K.; Stein, E.D.; Hogue, T.S. Simulating the thermal impact of substrate temperature on ecological restoration in shallow urban rivers. J. Environ. Manag. 2021, 289, 112560. [Google Scholar] [CrossRef] [PubMed]
- Shi, C.; Zhu, X.; Wu, H.; Li, Z. Assessment of Urban Ecological Resilience and Its Influencing Factors: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration of China. Land 2022, 11, 921. [Google Scholar] [CrossRef]
- Huang, J.; Zhong, P.; Zhang, J.; Zhang, L. Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China. Ecol. Indic. 2023, 154, 110763. [Google Scholar] [CrossRef]
- Botequilha-Leitao, A.; Diaz-Varela, E.R. Performance Based Planning of complex urban social-ecological systems: The quest for sustainability through the promotion of resilience. Sustain. Cities Soc. 2020, 56, 102089. [Google Scholar] [CrossRef]
- Chen, T.; Li, Y. Urban design strategies of urban water environment orientation based on perspective of ecological resilience. Sci. Technol. Rev. 2019, 37, 26–39. [Google Scholar] [CrossRef]
- Li, J.; Jiang, Y.; Zhai, M.; Gao, J.; Yao, Y.; Li, Y. Construction and application of sponge city resilience evaluation system: A case study in Xi’an, China. Environ. Sci. Pollut. Res. 2023, 30, 62051–62066. [Google Scholar] [CrossRef]
- Yuan, Y.; Zheng, Y.; Huang, X.; Zhai, J. Climate resilience of urban water systems: A case study of sponge cities in China. J. Clean. Prod. 2024, 451, 141781. [Google Scholar] [CrossRef]
- Hsiao, H. Spatial distribution of urban gardens on vacant land and rooftops: A case study of ‘The Garden City Initiative’ in Taipei City, Taiwan. Urban Geogr. 2022, 43, 1150–1175. [Google Scholar] [CrossRef]
- Wang, S.; Cui, Z.; Lin, J.; Xie, J.; Su, K. The coupling relationship between urbanization and ecological resilience in the Pearl River Delta. J. Geogr. Sci. 2022, 32, 44–64. [Google Scholar] [CrossRef]
- Xiong, Y.; Li, C.; Zou, M.; Xu, Q. Investigating into the Coupling and Coordination Relationship between Urban Resilience and Urbanization: A Case Study of Hunan Province, China. Sustainability 2022, 14, 5889. [Google Scholar] [CrossRef]
- Hu, H.; Yan, K.; Fan, H.; Lv, T.; Zhang, X. Detecting regional unevenness and influencing factors of ecological resilience in China. Energy Environ. 2024, 0958305X241230619. [Google Scholar] [CrossRef]
- Zhang, Q.; Huang, T.; Xu, S. Assessment of Urban Ecological Resilience Based on PSR Framework in the Pearl River Delta Urban Agglomeration, China. Land 2023, 12, 1089. [Google Scholar] [CrossRef]
- Wang, F.; Wong, W.-K.; Wang, Z.; Albasher, G.; Alsultan, N.; Fatemah, A. Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions. Resour. Policy 2023, 85, 103747. [Google Scholar] [CrossRef]
- Yuan, K.; Hu, B.; Li, X.; Niu, T.; Zhang, L. Exploration of Coupling Effects in the Digital Economy and Eco-Economic System Resilience in Urban Areas: Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration. Sustainability 2023, 15, 7258. [Google Scholar] [CrossRef]
- Zhang, M.; Ren, Y. Impact of Environmental Regulation on Ecological Resilience A Perspective of “Local-neighborhood” Effect. J. Beijing Inst. Technol. (Soc. Sci. Ed.) 2022, 24, 16–29. [Google Scholar]
- Yang, D.; Gao, X.; Xu, L.; Guo, Q. Constraint-adaptation challenges and resilience transitions of the industry environmental system in a resource-dependent city. Resour. Conserv. Recycl. 2018, 134, 196–205. [Google Scholar] [CrossRef]
- Simonson, W.D.; Miller, E.; Jones, A.; Garcia-Rangel, S.; Thornton, H.; McOwen, C. Enhancing climate change resilience of ecological restoration-A framework for action. Perspect. Ecol. Conserv. 2021, 19, 300–310. [Google Scholar] [CrossRef]
- Nie, C.; Lee, C.-C. Synergy of pollution control and carbon reduction in China: Spatial–temporal characteristics, regional differences, and convergence. Environ. Impact Assess. Rev. 2023, 101, 107110. [Google Scholar] [CrossRef]
- He, X.; Cai, C.; Shi, J. Evaluation of tourism ecological security and its driving mechanism in the Yellow River Basin, China: Based on open systems theory and DPSIR model. Systems 2023, 11, 336. [Google Scholar] [CrossRef]
- Sarkki, S.; Komu, T.; Heikkinen, H.I.; Garcia, N.A.; Lepy, E.; Herva, V.-P. Applying a synthetic approach to the resilience of Finnish reindeer herding as a changing livelihood. Ecol. Soc. 2016, 21, 14. [Google Scholar] [CrossRef]
- Zhu, S.; Feng, H.; Shao, Q. Evaluating Urban Flood Resilience within the Social-Economic-Natural Complex Ecosystem: A Case Study of Cities in the Yangtze River Delta. Land 2023, 12, 1200. [Google Scholar] [CrossRef]
- Yan, C.; Zhao, F.; Niu, H. Environmental Target Responsibility System, Environmental Governance and Endogenous Economic Growth. Econ. Res. J. 2024, 59, 133–152. [Google Scholar]
- Tan, Y.; Xu, H.; Zhang, X. Sustainable urbanization in China: A comprehensive literature review. Cities 2016, 55, 82–93. [Google Scholar] [CrossRef]
- Zhang, J.; Zhang, L.; Wang, S.; Fan, F. Study on regional sustainable development efficiency measurement and influencing factors: Based on DPSIR-DEA Model. China Popul. Resour. Environ. 2017, 27, 1–9. [Google Scholar]
- Shi, D.; Guan, J.; Liu, J. Ecological security evaluation of tourism towns based on DPSIR-EES-matter element. Acta Ecol. Sin. 2021, 41, 4330–4341. [Google Scholar]
- Zhao, R.; Fang, C.; Liu, H.; Liu, X. Evaluating urban ecosystem resilience using the DPSIR framework and the ENA model: A case study of 35 cities in China. Sustain. Cities Soc. 2021, 72, 102997. [Google Scholar] [CrossRef]
- Li, Q.; An, Z.; Wei, J. Evaluation and Spatial Correlation Analysis of Spatial Ecological Security in Beijing-Tianjin-Hebei Based on DPSIR-EES Model. Ecol. Econ. 2023, 39, 156–161+187. [Google Scholar]
- Tong, Z.; Chen, Y.; Malkawi, A.; Liu, Z.; Freeman, R.B. Energy saving potential of natural ventilation in China: The impact of ambient air pollution. Appl. Energy 2016, 179, 660–668. [Google Scholar] [CrossRef]
- Hwang, C.-L.; Yoon, K.; Hwang, C.-L.; Yoon, K. Methods for multiple attribute decision making. In Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey; Springer: Berlin/Heidelberg, Germany, 1981; pp. 58–191. [Google Scholar]
- Quah, D. Galton’s fallacy and tests of the convergence hypothesis. Scand. J. Econ. 1993, 95, 427–443. [Google Scholar] [CrossRef]
- Dagum, C. A New Approach to the Decomposition of the Gini Income Inequality Ratio; Springer: Berlin/Heidelberg, Germany, 1998. [Google Scholar]
- Lerman, R.I.; Yitzhaki, S. Improving the accuracy of estimates of Gini-coefficients. J. Econom. 1989, 42, 43–47. [Google Scholar] [CrossRef]
- Pappas, I.O.; Woodside, A.G. Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. Int. J. Inf. Manag. 2021, 58, 102310. [Google Scholar] [CrossRef]
- Drazin, R. The processes of technological innovation: David A. Tansik book review editor Louis G. Tornatzky and Mitchell Fleischer. Lexington, MA: D.C. Heath & Company, 1990. 298 pages. £44.95. J. Technol. Transf. 1991, 16, 45–46. [Google Scholar]
- Hwang, B.-N.; Huang, C.-Y.; Wu, C.-H. A TOE Approach to Establish a Green Supply Chain Adoption Decision Model in the Semiconductor Industry. Sustainability 2016, 8, 168. [Google Scholar] [CrossRef]
- Fu, S.; Liu, J.; Wang, J.; Tian, J.; Li, X. Enhancing urban ecological resilience through integrated green technology progress: Evidence from Chinese cities. Environ. Sci. Pollut. Res. 2023, 31, 36349–36366. [Google Scholar] [CrossRef] [PubMed]
- Suki, N.M.; Suki, N.M.; Sharif, A.; Afshan, S.; Jermsittiparsert, K. The role of technology innovation and renewable energy in reducing environmental degradation in Malaysia: A step towards sustainable environment. Renew. Energy 2022, 182, 245–253. [Google Scholar] [CrossRef]
- Dai, B.; Cao, J.; Chen, G.; Ma, C. Study on the relationship between informatization and marine eco-efficiency in China–taking 11 coastal provinces as examples. Front. Mar. Sci. 2024, 11, 1362554. [Google Scholar] [CrossRef]
- Yang, X.; He, L.; Xia, Y.; Chen, Y. Effect of government subsidies on renewable energy investments: The threshold effect. Energy Policy 2019, 132, 156–166. [Google Scholar] [CrossRef]
- Matinaro, V.; Liu, Y.; Poesche, J. Extracting key factors for sustainable development of enterprises: Case study of SMEs in Taiwan. J. Clean. Prod. 2019, 209, 1152–1169. [Google Scholar] [CrossRef]
- Nesta, L.; Vona, F.; Nicolli, F. Environmental policies, competition and innovation in renewable energy. J. Environ. Econ. Manag. 2014, 67, 396–411. [Google Scholar] [CrossRef]
- Vis, B.; Dul, J. Analyzing relationships of necessity not just in kind but also in degree: Complementing fsQCA with NCA. Sociol. Methods Res. 2018, 47, 872–899. [Google Scholar] [CrossRef] [PubMed]
- Witt, M.A.; Fainshmidt, S.; Aguilera, R.V. Our Board, Our Rules: Nonconformity to Global Corporate Governance Norms. Adm. Sci. Q. 2022, 67, 131–166. [Google Scholar] [CrossRef]
- Greckhamer, T.; Furnari, S.; Fiss, P.C.; Aguilera, R.V. Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strateg. Organ. 2018, 16, 482–495. [Google Scholar] [CrossRef]
- Ding, H. What kinds of countries have better innovation performance?–A country-level fsQCA and NCA study. J. Innov. Knowl. 2022, 7, 100215. [Google Scholar] [CrossRef]
- Huang, X.; An, X.; Lv, W. Does ESG rating divergence affect corporate credit ratings? Financ. Econ. Res. 2024, 1–17. Available online: http://kns.cnki.net/kcms/detail/44.1696.f.20240517.1015.002.html (accessed on 17 March 2024).
- Thomann, E.; Maggetti, M. Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools. Sociol. Methods Res. 2020, 49, 356–386. [Google Scholar] [CrossRef]
- Ma, T.; Liu, Y.; Jia, R. Multiple Driving Paths of High-Tech SME Resilience from a “Resource-Capability-Environment” Perspective: An fsQCA Approach. Sustainability 2023, 15, 8215. [Google Scholar] [CrossRef]
Component | Dimension | Indicators | Calculation | Type |
---|---|---|---|---|
Driving forces | Economy | Urban economic output | GDP per capita (10,000 CNY) | + * |
Urban per capita income | Average wages of employees (CNY) | + | ||
Society | Population growth trends | Natural population growth rate (‰) | ||
Urban vitality | Night-time light intensity | + | ||
Environment | Ecosystem state | Area of green space/total administrative area (%) | + | |
Pressures | Economy | Pressure for economic growth | GDP growth rate (%) | |
Society | Pressure for population agglomeration | Population density (persons/km2) | ||
Pressure for social employment | Urban unemployment rate (%) | |||
Environment | Pollution emission intensity | Wastewater, waste gas, and waste solid emission/GDP (t/CNY) | ||
State | Economy | Urban economic density | GDP per unit area (10,000/CNY) | + |
Urban investment | Per capita investment in fixed assets (10,000 CNY/km2) | + | ||
Society | Urban consumer dynamism | The total retail sales of consumer goods per capita (10,000 CNY) | + | |
Urban residential carrying capacity | Residential land area per capita (m2/10,000 persons) | + | ||
Environment | Urban water resources | Total water supply per capita (10,000 m3) | + | |
Urban livability | Greening coverage of urban built-up areas (%) | + | ||
Influences | Economy | Urban economic development | Local fiscal revenues (10,000 CNY) | + |
Society | Population and economic growth elasticity | Natural population growth rate/GDP growth rate | ||
Urban land intensification capacity | Area of construction land per unit of GDP (m2/GDP) | |||
Environment | Urban ecological quality | Proportion of days with good air quality (%) | + | |
Responses | Economy | Degree of optimization of economic structure | Proportion of added value of the tertiary industry (%) | + |
Society | Expenditure on urban science and technology | Science and technology expenditure/total fiscal expenditure (%) | + | |
Expenditure on urban education | Education expenditure/total fiscal expenditure (%) | + | ||
Environment | Urban life quality | Harmless treatment rate of domestic waste (%) | + | |
Urban environmental governance | Comprehensive utilization rate of general industrial solid waste (%) | + | ||
Expenditure on urban environmental protection | Landscaping investment expenditure/total fiscal expenditure (%) | + |
t + 1 | Type | Low | Medium–Low | Medium–High | High |
---|---|---|---|---|---|
Overall | Low | 0.6214 | 0.3100 | 0.0629 | 0.0057 |
Medium–low | 0.1314 | 0.4714 | 0.3714 | 0.0257 | |
Medium–high | 0.0171 | 0.1286 | 0.5914 | 0.2629 | |
High | 0.0014 | 0.0057 | 0.0600 | 0.9329 | |
Eastern | Low | 0.7067 | 0.2667 | 0.0267 | 0.0000 |
Medium–low | 0.1333 | 0.5800 | 0.2867 | 0.0000 | |
Medium–high | 0.0200 | 0.0833 | 0.7167 | 0.1800 | |
High | 0.0000 | 0.0000 | 0.0414 | 0.9586 | |
Center | Low | 0.6300 | 0.2450 | 0.1150 | 0.0100 |
Medium–low | 0.1100 | 0.3750 | 0.4500 | 0.0650 | |
Medium–high | 0.0350 | 0.0900 | 0.5050 | 0.3700 | |
High | 0.0000 | 0.0053 | 0.0947 | 0.9000 | |
Western | Low | 0.5550 | 0.2950 | 0.1000 | 0.0500 |
Medium–low | 0.0650 | 0.4500 | 0.3750 | 0.1100 | |
Medium–high | 0.0350 | 0.1300 | 0.3650 | 0.4700 | |
High | 0.0091 | 0.0364 | 0.1000 | 0.8545 | |
Northern | Low | 0.6242 | 0.2818 | 0.0788 | 0.0152 |
Medium–low | 0.1152 | 0.4758 | 0.3485 | 0.0606 | |
Medium–high | 0.0273 | 0.1364 | 0.5061 | 0.3303 | |
High | 0.0061 | 0.0242 | 0.0970 | 0.8727 | |
Southern | Low | 0.5892 | 0.3568 | 0.0541 | 0.0000 |
Medium–low | 0.1162 | 0.4892 | 0.3919 | 0.0027 | |
Medium–high | 0.0108 | 0.0865 | 0.6973 | 0.2054 | |
High | 0.0000 | 0.0000 | 0.0378 | 0.9622 |
Type | Variables | Descriptive Statistics | Calibration | |||||
---|---|---|---|---|---|---|---|---|
Mean | SD | Min | Max | 0.75 | 0.5 | 0.25 | ||
outcome variable | Ecological resilience | 0.516 | 0.025 | 0.456 | 0.607 | 0.526 | 0.512 | 0.499 |
condition variables | Green innovation | 583.954 | 1564.599 | 3 | 16,215 | 392.750 | 123 | 54 |
Informatization level | 303.108 | 12.220 | 271.765 | 334.501 | 312.569 | 303.149 | 293.924 | |
Government support | 0.003 | 0.001 | 0.001 | 0.010 | 0.004 | 0.003 | 0.0025 | |
Market competition | 1521.918 | 2036.471 | 54 | 13,027 | 1723.5 | 847 | 418 | |
Industrial structure | 1.345 | 0.648 | 0.386 | 5.072 | 1.511 | 1.194 | 0.988 | |
Social concerns | 55.185 | 62.507 | 1.450 | 511.337 | 61.531 | 33.804 | 17.690 |
Causal Conditions | High Resilience | Non-High Resilience | ||
---|---|---|---|---|
Consistency | Coverage | Consistency | Coverage | |
Green innovation | 0.647 | 0.691 | 0.410 | 0.424 |
~Green innovation | 0.460 | 0.446 | 0.701 | 0.658 |
Informatization level | 0.684 | 0.707 | 0.390 | 0.390 |
~Informatization level | 0.410 | 0.409 | 0.707 | 0.684 |
Government support | 0.500 | 0.512 | 0.584 | 0.579 |
~Government support | 0.589 | 0.594 | 0.508 | 0.496 |
Market competition | 0.685 | 0.716 | 0.383 | 0.388 |
~Market competition | 0.414 | 0.410 | 0.720 | 0.689 |
Industrial structure | 0.521 | 0.541 | 0.551 | 0.554 |
~Industrial structure | 0.570 | 0.567 | 0.543 | 0.524 |
Social concerns | 0.654 | 0.674 | 0.426 | 0.425 |
~Social concerns | 0.443 | 0.443 | 0.674 | 0.654 |
Causal Conditions | High Ecological Resilience | |||
---|---|---|---|---|
H1 | H2a | H2b | H3 | |
Green innovation | ● * | • | ⊗ | |
Informatization level | ● | ● | ● | ● |
Government support | ● | ● | ⊗ | |
Market competition | ● | ● | ● | ⊗ |
Industrial structure | ⊗ | ⊗ | ⊗ | |
Social concerns | ● | • | ⊗ | |
Raw coverage rate | 0.476 | 0.205 | 0.206 | 0.081 |
Unique coverage rate | 0.281 | 0.015 | 0.020 | 0.031 |
Consistency | 0.810 | 0.828 | 0.818 | 0.826 |
Total coverage | 0.547 | |||
Total consistency | 0.813 |
Causal Conditions | High Ecological Resilience | |||
---|---|---|---|---|
H1 | H2a | H2b | H3 | |
Green innovation | ● * | • | ⊗ | |
Informatization level | ● | ● | ● | ● |
Government support | ● | ● | ⊗ | |
Market competition | ● | ● | ● | ⊗ |
Industrial structure | ⊗ | ⊗ | ⊗ | |
Social concerns | ● | • | ⊗ | |
Raw coverage rate | 0.476 | 0.205 | 0.206 | 0.081 |
Unique coverage rate | 0.281 | 0.015 | 0.020 | 0.031 |
Consistency | 0.810 | 0.828 | 0.819 | 0.826 |
Total coverage | 0.547 0.813 | |||
Total consistency |
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Yuan, X.; Liu, R.; Huang, T. Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China. Systems 2024, 12, 311. https://doi.org/10.3390/systems12080311
Yuan X, Liu R, Huang T. Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China. Systems. 2024; 12(8):311. https://doi.org/10.3390/systems12080311
Chicago/Turabian StyleYuan, Xiaoling, Rang Liu, and Tao Huang. 2024. "Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China" Systems 12, no. 8: 311. https://doi.org/10.3390/systems12080311