# Research on the ECC of Chengdu–Chongqing’s Urban Agglomeration in China Based on System Dynamics

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Research Scope and Methodology

#### 2.1. Study Area

^{2}(Figure 1).

#### 2.2. Index System and Data Sources

#### 2.3. AHP-TOPSIS Method for Determining Index Weight

#### 2.3.1. Data Standardization

#### 2.3.2. Analytic Hierarchy Process

- Construction of the judgment matrix. In this study, by consulting a number of experts, all indicators in the ecological environment carrying capacity index system are scored according to importance and the relative importance is expressed by values from 1 to 9; the two-by-two comparison forms the judgment matrix. As a result, the indicator value ${a}_{ij}$ and the judgment matrix A are obtained.
- Calculate the nth product root of the elements in each row of the judgment matrix as follows:$${W}_{\mathrm{i}}=\sqrt[n]{{\displaystyle \prod _{i=1}^{n}}{a}_{ij}}$$
- Regularization and normalization of the vector W
_{i}as follows:$${W}^{\prime}{}_{\mathrm{i}}=\frac{{W}_{\mathrm{i}}}{{\displaystyle \sum _{j=1}^{n}}W}$$ - The maximum eigenvalue is calculated as follows:$${\lambda}_{\mathrm{max}}={\displaystyle \sum _{i=1}^{n}}\frac{{\left(AW\right)}_{i}}{n{W}_{i}}$$
- Consistency check

#### 2.3.3. TOPSIS

- Data standardization

_{ij}= x′

_{ij}× 0.99 + 0.01.

- 2.
- Calculate the entropy value of each indicator as follows:$${\mathrm{f}}_{ij}=\frac{{x}^{\prime \prime}{}_{ij}}{{\displaystyle \sum _{i=1}^{n}}{x}^{\prime \prime}{}_{ij}}$$$${H}_{j}=-\frac{1}{\mathrm{ln}m}{\displaystyle \sum _{j=1}^{m}}{f}_{ij}\mathrm{ln}{f}_{ij}$$
- 3.
- Calculate the entropy weight of each index as follows:$${\omega}_{j}=\frac{1-{H}_{j}}{{\displaystyle \sum _{j=1}^{n}}\left(1-{H}_{j}\right)}$$

#### 2.3.4. Combined Method for Weight Determination

- Calculate the composite weight of the indicator layer as follows:

- 2.
- Normalization of the combined weights of the indicator layers under each criterion layer as follows:

- 3.
- Calculate the weights of the criterion layer corresponding to the indicator layer as follows:

- 4.
- Normalized calculation of the final weights as follows:$${\omega}_{i}=\frac{{\alpha}_{i}}{{\displaystyle \sum _{i=1}^{n}}{\alpha}_{i}}$$

## 3. Model Construction and Parameter Design

#### 3.1. Model Construction

#### 3.1.1. Model Boundary and Causal Analysis

#### 3.1.2. Model Construction and Testing

- 1.
- State equation (L)$$L\left(t\right)=L\left({t}_{0}\right)+{\displaystyle \underset{0}{\overset{t}{\int}}}R\left(t\right)dt$$
_{0}) is the value of the state variable L at time t_{0}. - 2.
- Rate equation (R)$$R\left(t\right)=g\left[L\left(t\right),a\left(t\right),e\left(t\right),c\right]$$
- 3.
- Auxiliary equation (A)$$A\left(t\right)=f\left[L\left(t\right),{A}^{*}\left(t\right),e\left(t\right),c\right]$$
- 4.
- Constant equation (C)$$C\left(t\right)=c$$

#### 3.2. Determination of Decision Variables

#### 3.3. Determination of Program Parameters

_{2}emissions by 2030 and the efforts to achieve carbon neutrality by 2060.

## 4. Results

#### 4.1. Analysis of the Current State of the ECC

#### 4.2. Analysis of the Main Indicators of the Simulation Program

#### 4.3. Analysis of Simulation Results

## 5. Discussion and Suggestions

#### 5.1. Discussion

#### 5.2. Limitations and Recommendations for Future Work

#### 5.3. Suggestions

- The future development pattern of the CCUA should be dominated by the characteristics of the SSP1 path and the development characteristics of the SSP5 path and SSP2 path should be adopted in some aspect or periods, while taking care to avoid the development characteristics of the SSP3 path.
- According to the current actual situation, the CCUA cannot be developed according to the SSP1 path in the short term, so it is suggested that it should develop according to the SSP2 path from 2021 to 2025. Based on the basic continuation of the current development trend, it should gradually change to a sustainable development mode and continuously improve its development level.
- Further, 2026–2035 is a critical period for China to realize socialist modernization. Based on the gradual transformation of the development mode from 2021 to 2025, this period must formally transform the development mode and truly achieve sustainable development, so that the CCUA should develop according to the SSP1 path in this period.
- Then, 2036–2050 is the sprint stage for China to fully build a rich, strong, democratic, civilized, harmonious, and green socialist modern country. In this period, the CCUA continues to develop according to the SSP1 path, while some levels refer to the development characteristics of the SSP5 path.
- The development characteristics of the SSP3 and SSP4 paths should be strongly avoided in the future development of the CCUA.

## 6. Conclusions

- An ECC evaluation system containing 27 indicators is constructed and AHP-TOPSIS is used to determine the index weights and evaluate the ECC of the CCUA from 2000 to 2018. The ECC indices of 16 cities in the CCUA all increased significantly in 18 years, among which Chengdu and Chongqing were in the leading positions, forming the twin core model of the CCUA.
- This study establishes the SD model of the ECC of the CCUA, which objectively reflects the relationship between social, economic, environmental, and economic subsystems. Through the consistency test, it was found that the simulated values are within 10% of the historical data, the model can accurately respond to the evolution of the system, and the results are valid.
- Based on SSPs’ socioeconomic paths, this study obtained the ecological and environmental carrying capacity index of the CCUA in the period 2019–2050 using the SD model simulation. It was found that the ecological and environmental carrying capacity indices based on the five SSP paths all showed obvious growth trends and relatively similar curves, with slight fluctuations in 2025 and 2035. By 2050, the ecological and environmental carrying capacity indices of the paths SSP1, SSP5, SSP2, SSP4, and SSP3 are from high to low in order.
- Through the analysis, this study believes that the CCUA should adopt the most suitable development approach in different periods, among which 2021–2025 should be developed according to the SSP2 path, 2026–2035 should be developed according to the SSP1 path, and 2036–2050 should continue to develop according to the SSP1 path based on the development characteristics of the SSP5 path at some reference levels. In addition, the SSP3 and SSP4 paths should be avoided in future CCUA development due to their poor performance in the future ecological environment carrying capacity simulation.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Subsystem | System Equations |
---|---|

Social subsystem | • Total population = INTEG (population increase, present value of total population) |

• Population growth = total population × population growth rate | |

• Urban population = total population × urbanization rate | |

• Rural population = total population − urban population | |

• Road area per capita = urban road area/total population | |

• Public transportation vehicles per 10,000 people = Public transportation vehicles/total population | |

• Number of beds in health facilities per 10,000 people = Number of beds in health facilities/total population | |

• Per capita, disposable income ratio of urban and rural residents = per capita disposable income of urban residents/per capita disposable income of rural residents | |

Economic subsystem | • Total GDP = INTEG (increase in GDP, the present value of total GDP) |

• Increase in GDP = Total GDP × Rate of GDP increase | |

• GDP per capita = total GDP × 10,000/total population | |

• Share of tertiary sector in total GDP = Tertiary sector GDP/Total GDP | |

• Share of secondary sector in total GDP = Secondary Industry GDP/Total GDP | |

• GDP of Primary Industry = Total GDP − Secondary Industry GDP − Tertiary sector GDP | |

• Total imports and exports = IF THEN ELSE (Time > 2018, Total GDP × (0.002 × (Time - 2018)3 - 0.026 × (Time − 2018)2 + 0.393 × (Time − 2018) + 21.653)/6.5/100, 6.19 × 10^{−}^{5} × (Total GDP ^{1.573})) | |

• Fiscal expenditure on education = Fiscal expenditure × Fiscal expenditure on education as a percentage of fiscal expenditure | |

Resource subsystem | • Arable land area = INTEG (-Arable land reduction, the present value of arable land area) |

• Annual reduction of arable land area = arable land area × reduction rate of arable land area | |

• Arable land per capita = Arable land area × 15/total population/10,000 | |

• Built-up area = INTEG (increase in the built-up area, the present value of built-up area) | |

• Built-up area increase = built-up area × built-up area growth rate | |

• Total energy consumption = INTEG (increase in total energy consumption, the present value of total energy consumption) | |

• Increase in total energy consumption = total energy consumption × growth rate of total energy consumption | |

• Energy consumption per unit of gross regional product = total energy consumption/total GDP | |

Environmental subsystem | • Public green space area = INTEG (increase in the public green space area, the present value of public green space area) |

• Increase in public green space area = public green space area × growth rate of public green space area | |

• Public green space per capita = public green space/total population | |

• Greening coverage rate of built-up area = greening area of built-up area/area of built-up area × 100 | |

• Centralized sewage treatment rate = INTEG (IFTHENELSE (centralized sewage treatment rate ≥ 1, centralized sewage treatment rate = 0.99, centralized sewage treatment rate increase), centralized sewage treatment rate present value) | |

• Centralized sewage treatment rate increase = centralized sewage treatment rate × centralized sewage treatment rate growth rate | |

• Sewage treatment volume = total industrial wastewater discharge × centralized sewage treatment rate | |

• The integrated utilization rate of industrial solid waste = INTEG (IFTHENELSE (integrated utilization rate of industrial solid waste ≥ 1, integrated utilization rate of industrial solid waste = 0.99, integrated utilization rate of industrial solid waste increase), the present value of integrated utilization rate of industrial solid waste) | |

• Increase in the comprehensive utilization rate of industrial solid waste = comprehensive utilization rate of industrial solid waste × comprehensive utilization rate of industrial solid waste growth rate | |

• Industrial solid waste utilization = industrial solid waste generation × comprehensive utilization rate of industrial solid waste |

## References

- Peng, B.; Li, Y.; Elahi, E.; Wei, G. Dynamic evolution of ecological carrying capacity based on the ecological footprint theory: A case study of Jiangsu province. Ecol. Indic.
**2018**, 99, 19–26. [Google Scholar] [CrossRef] - Mealy, P.; Teytelboym, A. Economic complexity and the green economy. Res. Policy
**2020**, 49, 103948. [Google Scholar] [CrossRef] - Delitheou, V.; Meleti, V.; Athanassopoulos, C.G.E. Green economy and smart city. J. Reliab. Intell. Environ.
**2019**, 5, 235–240. [Google Scholar] [CrossRef] - Pitkänen, K.; Antikainen, R.; Droste, N.; Loiseau, E.; Saikku, L.; Aissani, L.; Hansjürgens, B.; Kuikman, P.J.; Leskinen, P.; Thomsen, M.; et al. What can be learned from practical cases of green economy? -studies from five European countries. J. Clean. Prod.
**2016**, 139, 666–676. [Google Scholar] [CrossRef] - Borel-Saladin, J.M.; Turok, I.N. The Green Economy: Incremental Change or Transformation? Environ. Policy Gov.
**2013**, 23, 209–220. [Google Scholar] [CrossRef] - Buseth, J.T. The green economy in Tanzania: From global discourses to institutionalization. Geoforum
**2017**, 86, 42–52. [Google Scholar] [CrossRef] - Wu, X.; Hu, F. Analysis of ecological carrying capacity using a fuzzy comprehensive evaluation method. Ecol. Indic.
**2020**, 113, 106243. [Google Scholar] [CrossRef] - Dai, D.; Sun, M.; Xu, X.; Lei, K. Assessment of the water resource carrying capacity based on the ecological footprint: A case study in Zhangjiakou City, North China. Environ. Sci. Pollut. Res.
**2019**, 26, 11000–11011. [Google Scholar] [CrossRef] - Wu, T.; Sang, S.; Wang, S.; Yang, Y.; Li, M. Remote sensing assessment and spatiotemporal variations analysis of ecological carrying capacity in the Aral Sea Basin. Sci. Total Environ.
**2020**, 735, 139562. [Google Scholar] [CrossRef] - Zomorodian, M.; Lai, S.H.; Homayounfar, M.; Ibrahim, S.; Fatemi, E.; El-Shafie, A. The state-of-the-art system dynamics application in integrated water resources modeling. J. Environ. Manag.
**2018**, 227, 294–304. [Google Scholar] [CrossRef] - Ma, P.; Ye, G.; Peng, X.; Liu, J.; Qi, J.; Jia, S. Development of an index system for evaluation of ecological carrying capacity of marine ecosystems. Ocean Coast. Manag.
**2017**, 144, 23–30. [Google Scholar] [CrossRef] - Wang, D.; Shi, Y.; Wan, K. Integrated evaluation of the carrying capacities of mineral resource-based cities considering synergy between subsystems. Ecol. Indic.
**2020**, 108, 105701. [Google Scholar] [CrossRef] - Liao, S.; Wu, Y.; Wong, S.W.; Shen, L. Provincial perspective analysis on the coordination between urbanization growth and resource environment carrying capacity (RECC) in China. Sci. Total Environ.
**2020**, 730, 138964. [Google Scholar] [CrossRef] - Zhang, M.; Liu, Y.; Wu, J.; Wang, T. Index system of urban resource and environment carrying capacity based on ecological civilization. Environ. Impact Assess. Rev.
**2018**, 68, 90–97. [Google Scholar] [CrossRef] - Bu, J.; Li, C.; Wang, X.; Zhang, Y.; Yang, Z. Assessment and prediction of the water ecological carrying capacity in Changzhou city, China. J. Clean. Prod.
**2020**, 277, 123988. [Google Scholar] [CrossRef] - Wang, G.; Xiao, C.; Qi, Z.; Meng, F.; Liang, X. Development tendency analysis for the water resource carrying capacity based on system dy-namics model and the im-proved fuzzy comprehensive evaluation method in the Changchun city, China. Ecol. Indic.
**2021**, 122, 10732. [Google Scholar] [CrossRef] - Wang, Y.; Zhou, X.; Engel, B. Water environment carrying capacity in Bosten Lake basin. J. Clean. Prod.
**2018**, 199, 574–583. [Google Scholar] [CrossRef] - Xiao, Y. Analysis on the Measurement and Spatial and Temporal Evolution of the Competitiveness of Industrial Green Development in Chengdu-Chongqing City Cluster. Ph.D. Thesis, Chengdu University of Technology, Chengdu, China, 2019. [Google Scholar]
- Liang, Y.; Liu, Y.; Yang, J. Evaluation of green development efficiency and equilibrium characteristics of urban agglomerations in China. Econ. Geogr.
**2019**, 39, 110–111. [Google Scholar] - Huang, L.; Wu, C. Study on the efficiency of green development of urban industries in Yangtze River Economic Zone and its spatial driving mechanism. China Popul. Resour. Environ.
**2019**, 29, 40–49. [Google Scholar] - Wu, C.; Huang, L. Research on the performance assessment of industrial green development in Yangtze River Economic Belt and its synergistic effect. J. China Univ. Geosci. Soc. Sci. Ed.
**2018**, 18, 46–55. [Google Scholar] - Couvet, H.L.C.K.; Weber, J. OECD pressure–state–response indicators for managing biodiversity: A realistic perspective for a French biosphere reserve. Biodivers. Conserv.
**2009**, 18, 1719–1732. [Google Scholar] - Verma, P.; Raghubanshi, A.S. Urban sustainability indicators: Challenges and opportunities. Ecol. Indic.
**2018**, 93, 282–291. [Google Scholar] [CrossRef] - Kc, S.; Lutz, W. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change
**2017**, 42, 181–192. [Google Scholar] [CrossRef] [PubMed] - O’Neill, B.C.; Kriegler, E.; Ebi, K.L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D.S.; van Ruijven, B.J.; van Vuuren, D.P.; Birkmann, J.; Kok, K.; et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change
**2017**, 42, 169–180. [Google Scholar] [CrossRef] [Green Version] - Leimbach, M.; Kriegler, E.; Roming, N.; Schwanitz, J. Future growth patterns of world regions–A GDP scenario approach. Glob. Environ. Change
**2017**, 42, 215–225. [Google Scholar] [CrossRef] - Jiang, L.; O’Neill, B.C. Global urbanization projections for the Shared Socioeconomic Pathways. Glob. Environ. Change
**2017**, 42, 193–199. [Google Scholar] [CrossRef] - Dellink, R.; Chateau, J.; Lanzi, E.; Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change
**2017**, 42, 200–214. [Google Scholar] [CrossRef] - Crespo Cuaresma, J. Income projections for climate change research: A framework based on human capital dynamics. Glob. Environ. Change
**2017**, 42, 226–236. [Google Scholar] [CrossRef] - Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; Van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; et al. The next generation of scenarios for climate change research and assessment. Nature
**2010**, 463, 747–756. [Google Scholar] [CrossRef] - Hallegatte, E.K.J.E.; Riahi, K.L.E.T.; van Vuuren, H.W.D.P. A new scenario framework for climate change researchthe concept of shared climate policy assumptions. Clim. Change
**2014**, 122, 401–414. [Google Scholar] - Hegre, H.; Buhaug, H.; Calvin, K.V.; Nordkvelle, J.; Waldhoff, S.T.; Gilmore, E. Forecasting civil conflict along the shared socioeconomic pathways. Environ. Res. Lett.
**2016**, 11, 54002–54009. [Google Scholar] [CrossRef] - Riahi, K.V.V.D.P. The Shared Socioeconomic Pathways and their energy, land use, andgreenhouse gas emissions implications An overview. Glob. Environ. Change
**2017**, 42, 153–168. [Google Scholar] [CrossRef] - Ying, H.; Chen, S.; Mao, Y. Research on Marine Ecological Carrying Capacity of Ningbo City in China Based on System Dynamics. Sustainability
**2022**, 14, 4568. [Google Scholar] [CrossRef]

Target Layer | Guideline Layer | Weight | Indicator Layer | Current ECC Weight |
---|---|---|---|---|

ECC | Pressure layer B1 | 0.26 | • Total population at the end of the year C1 (10,000 people) | 0.013 |

• GDP per capita C2 (yuan/person) | 0.028 | |||

• Total imports and exports C3 (USD billion) | 0.026 | |||

• Total energy consumption C4 (million tons of standard coal) | 0.009 | |||

• Total annual water supply C5 (million tons) | 0.014 | |||

• Total industrial wastewater discharge C6 (million tons) | 0.169 | |||

State layer B2 | 0.35 | • GDP growth rate C7 (%) | 0.031 | |

• Urbanization rate C8 (%) | 0.022 | |||

• Engel coefficient C9 | 0.023 | |||

• Urban registered unemployment rate C10 (%) | 0.015 | |||

• Area of built-up area C11 (square kilometers) | 0.086 | |||

• Arable land per capita C12 (mu/person) | 0.015 | |||

• Energy consumption per unit of GDP C13 (tons of standard coal/million yuan) | 0.020 | |||

• Wastewater emissions per unit of industrial added value C14 (million tons/billion yuan) | 0.017 | |||

• Public green space per capita C15 (m^{2}/person) | 0.103 | |||

• Forest cover C16 (%) | 0.004 | |||

• The proportion of days with the air quality of grade 2 or higher to the number of days in the year C17 (%) | 0.012 | |||

Response layer B3 | 0.39 | • Share of secondary sector in GDP C18 (%) | 0.026 | |

• Tertiary sector share of GDP C19 (%) | 0.029 | |||

• Share of education spending in fiscal spending C20 (%) | 0.021 | |||

• Number of beds in health institutions per 10,000 people C21 (beds/10,000 people) | 0.056 | |||

• Public transportation vehicles per 10,000 people C22 (vehicles/10,000 people) | 0.039 | |||

• Road area owned per capita C23 (m^{2}/person) | 0.061 | |||

• Urban and rural residents’ per capita disposable income ratio C24 | 0.023 | |||

• The comprehensive utilization rate of industrial solid waste C25 (%) | 0.105 | |||

• Centralized sewage treatment rate C26 (%) | 0.020 | |||

• Greening coverage of built-up areas C27 (%) | 0.010 |

Indicator Type | Current Value | Maximum Value | Minimum Value | Average Value |
---|---|---|---|---|

Population growth rate | 0.006 | 0.018 | 0.001 | 0.003 |

Rate of GDP increase | 0.075 | 0.248 | 0.075 | 0.143 |

The growth rate of total energy consumption | 0.003 | 0.137 | −0.055 | 0.046 |

The reduction rate of arable land area | −0.001 | 0.067 | −0.045 | −0.009 |

The growth rate of public green space | 0.041 | 0.198 | 0.003 | 0.089 |

The growth rate of built-up area | 0.057 | 0.216 | 0.035 | 0.057 |

The growth rate of centralized sewage treatment | 0.006 | 0.021 | −0.105 | 0.003 |

The comprehensive utilization rate of industrial solid waste growth rate | 0.057 | 0.187 | 0.015 | 0.079 |

Indicator Type | Time | SSP1 | SSP2 | SSP3 | SSP4 | SSP5 |

Population growth rate | 2019–2025 2026–2030 2031–2035 2036–2050 | 0.003 0.003 −0.006 −0.006 | 0.006 0.006 −0.005 −0.005 | 0.008 0.008 0.008 −0.003 | 0.005 −0.007 −0.007 −0.007 | 0.003 0.003 −0.0065 −0.0065 |

Rate of GDP increase | 2019–2025 2026–2035 2036–2050 | 0.065 0.045 0.03 | 0.06 0.04 0.03 | 0.05 0.035 0.02 | 0.06 0.04 0.02 | 0.065 0.05 0.04 |

The growth rate of total energy consumption | 2019–2025 2026–2030 2031–2050 | 0.001 −0.030 −0.030 | 0.003 −0.020 −0.020 | 0.045 0.045 −0.05 | 0.003 −0.010 −0.010 | 0.045 0.045 −0.05 |

The reduction rate of arable land area | 2019–2030 2031–2050 | 0.015 0.015 | −0.001 0.010 | −0.010 −0.010 | −0.009 0.005 | −0.015 −0.015 |

The growth rate of public green space | 2019–2050 | 0.100 | 0.041 | 0.003 | 0.041 | 0.003 |

The growth rate of built-up area | 2019–2050 | 0.150 | 0.057 | 0.015 | 0.057 | 0.150 |

The growth rate of centralized sewage treatment | 2019–2050 | 0.030 | 0.016 | 0.006 | 0.006 | 0.016 |

The comprehensive utilization rate of industrial solid waste growth rate | 2019–2050 | 0.010 | 0.006 | 0.002 | 0.002 | 0.008 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ci, X.; Zhang, L.; Wang, T.; Xiao, Y.; Xia, J.
Research on the ECC of Chengdu–Chongqing’s Urban Agglomeration in China Based on System Dynamics. *Sustainability* **2022**, *14*, 10896.
https://doi.org/10.3390/su141710896

**AMA Style**

Ci X, Zhang L, Wang T, Xiao Y, Xia J.
Research on the ECC of Chengdu–Chongqing’s Urban Agglomeration in China Based on System Dynamics. *Sustainability*. 2022; 14(17):10896.
https://doi.org/10.3390/su141710896

**Chicago/Turabian Style**

Ci, Xiaohu, Liping Zhang, Tongxiang Wang, Yi Xiao, and Jun Xia.
2022. "Research on the ECC of Chengdu–Chongqing’s Urban Agglomeration in China Based on System Dynamics" *Sustainability* 14, no. 17: 10896.
https://doi.org/10.3390/su141710896