A New Approach to Evaluate the Sustainability of Ecological and Economic Systems in Megacity Clusters: A Case Study of the Guangdong–Hong Kong–Macau Bay Area
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Ideas
2.2. Overview of the GBA
2.3. Data Sources
2.4. Accounting for the Emergy of the GBA
2.5. Entropy Method
2.5.1. Data Standardization
2.5.2. Indicator Information Entropy Value
2.5.3. System Evaluation
2.6. Evaluation of the Degree of System Coordination Development
3. Results and Analysis
3.1. System Emergy Flow Analysis
3.2. Social Subsystem Emergy Analysis
3.3. Economic Subsystem Emergy Analysis
3.4. Natural Subsystem Emergy Analysis
3.5. Sustainable Development Analysis
4. Discussion
5. Conclusions
5.1. Common Problems of the GBA City Cluster
5.2. Classification of City Clusters from an Emergy Perspective
- (1)
- Category 1 cities have coordinated development degrees between 0.0 and 0.135, mainly including four cities, Hong Kong, Shenzhen, Dongguan, and Zhuhai, whose main emergy characteristics include having the highest ED in the GBA city cluster, indicating that the economic development of this category of cities is faster, but the contradiction between people and land is prominent, while the land area is a serious constraint to sustainable development; the EUC values are higher, with the EUC value of Hong Kong being the highest, with Dongguan, Zhuhai, and Shenzhen being just below Macau, which indicates that the residents of these cities have higher living standards and quality. The lower EYR indicates that their eco-economic efficiency is lower in the GBA; the lowest ESR and the highest environmental load rate indicate that the dependence on external emergy is extremely high, and the system is under great environmental pressure.
- (2)
- Category 2 cities have coordinated development degrees ranging between 0.143 and 0.179, mainly including four cities: Guangzhou, Macau, Foshan, and Zhongshan. Their main emergy characteristics are the emergy flow, social subsystem, economic subsystem, and natural subsystem, which are all between category 1 and category 3 cities and are in the middle of the whole GBA.
- (3)
- Category 3 cities have coordinated development degrees ranging between 0.192 and 0.369, mainly including Zhaoqing, Jiangmen, and Huizhou, which are cities with low ED and EUC levels in the GBA city cluster. This indicates that the land area still has some potential, but the residents’ living standards and quality of life are relatively low. The ESR is high, with Zhaoqing and Jiangmen even exceeding 50%, indicating a high natural environmental support capacity; however, the ELR (102–103 orders of magnitude) is still much higher than the national average (10° orders of magnitude, 2016) [42], indicating that the system’s environmental pressure remains high.
5.3. Empirical Evidence Shows That Emergy Analysis Can Effectively Evaluate the Sustainability of Urban Cluster Eco-Economic Systems
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Li, G.M.; Kuang, Y.Q.; Huang, N.S.; Chang, X.Y.; Yao, S.J. Evaluation of Urban Quality Basaed on Coupling Coordination Degree: A Case Study of Pearl River Delta City Clusters. Mod. Urban Res. 2015, 6, 93–100. [Google Scholar]
- Gottmann, J. Megalopolis or the Urbanization of the Northeastern Seaboard. Econ. Geogr. 1957, 33, 189–200. [Google Scholar] [CrossRef]
- Scott, A.J. Global City-Regions: Trends, Theory, Policy; Oxford University Press: Oxford, UK, 2001. [Google Scholar]
- Florida, R.; Gulden, T.; Mellander, C. The Rise of the Mega-Region. Camb. J. Reg. Econ. Soc. 2008, 1, 459–476. [Google Scholar] [CrossRef]
- Peter, H.; Kathy, P. The Polycentric Metropolis: Learning from Mega-City Regions in Europe; Taylor and Francis: New York, NY, USA, 2006; p. 2012. [Google Scholar]
- Yang, T.Z. The Study of Sustainable Development in the Coastal City Agglomeration of Shandong Province Based on the Emergy-Ecological Footprint. Master’s Thesis, Ocean University of China, Qingdao, China, 2010. [Google Scholar]
- Fang, C.; Zhou, C.; Gu, C.; Chen, L.; Li, S. A proposal for the theoretical analysis of the interactive coupled effects between urbanization and the eco-environment in mega-urban agglomerations. J. Geogr. Sci. 2017, 27, 1431–1449. [Google Scholar] [CrossRef]
- Wang, P.; Liu, X.P.; Yao, X.Y.; Chen, X.; Kong, F.X. Evaluation of Sustainable Development of Ningxia Ecological Economic System Based on Emergy Analysis. Ecol. Econ. 2018, 34, 70–73. [Google Scholar]
- Li, Q.; Wu, J.; Su, Y.; Zhang, C.; Wu, X.; Wen, X.; Huang, G.; Deng, Y.; Lafortezza, R.; Chen, X. Estimating ecological sustainability in the Guangdong-Hong Kong-Macao Greater Bay Area, China: Retrospective analysis and prospective trajectories. J. Environ. Manag. 2022, 303, 114167. [Google Scholar] [CrossRef] [PubMed]
- Pan, H.; Zhuang, M.; Geng, Y.; Wu, F.; Dong, H. Emergy-based ecological footprint analysis for a mega-city: The dynamic changes of Shanghai. J. Clean. Prod. 2019, 210, 552–562. [Google Scholar] [CrossRef]
- Pan, B.H. The global climate will continue to deteriorate in 2021. Ecol. Econ. 2022, 38, 1–4. [Google Scholar]
- Ma, S.J.; Wang, R.S. The Social-Economic-Natural Complex Ecosystem. Acta Ecol. Sin. 1984, 4, 1–9. [Google Scholar]
- Odum, H.T.; Brown, M.T.; Brandt-Williams, S.L. Handbook of Emergy Evaluation; University of Florida: Gainesville, FL, USA, 2000. [Google Scholar]
- Odum, H.T. Environmental Accounting: Emergy and Environmental Decision Making; John Wiley & Sons: New York, NY, USA, 1996; pp. 88–156. [Google Scholar]
- Odum, H.T. Self-Organization, Transformity, and Information. Science 1988, 242, 1132–1139. [Google Scholar] [CrossRef] [Green Version]
- Liu, G.Y.; Yang, Z.F. Emergy Theory and Practice: Ecological Environmental Accounting and City Green Management; Science Press: Beijing, China, 2018. [Google Scholar]
- Lan, S.F.; Qin, P.; Lu, H.F. Emergy Analysis of Eco-Economic System; Chemical Industry Press: Beijing, China, 2002; pp. 167–193. [Google Scholar]
- Xianhang, X.; Anuar, A.M.; Arshad, M. Analysis on International Competitiveness of Service Trade in the Guangdong–Hong Kong–Macao Greater Bay Area Based on Using the Entropy and Gray Correlation Methods. Entropy 2021, 23, 1253. [Google Scholar]
- Jamin, A.; Humeau-Heurtier, A. (Multiscale) Cross-Entropy Methods: A Review. Entropy 2019, 22, 45. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jia, X.; Zhou, Y.; Yan, J.; Wei, Y. Sustainable development of an ecological-economic system in the Taihu Lake city cluster based on emergy analysis. Acta Ecol. Sin. 2019, 39, 6487–6499. [Google Scholar]
- Li, J.P.; Chen, F.P.; Wang, Z.S. The emergy synthesis and sustainability analysis of city’s environment and economy. Acta Ecol. Sin. 2006, 02, 439–448. [Google Scholar]
- Li, K.; Cui, L.J.; Li, W.; Kang, X.M.; Zhang, Y.Q. Emoving double counting in wetland ecosystem services valuation based on emergy alge-bra. China J. Ecol. 2016, 35, 1108–1116. [Google Scholar] [CrossRef]
- Chen, W.; Geng, Y.; Huang, B.; Wu, F.; You, W.; Song, X. Emergy accounting of a western underdeveloped eco-economic system: A case study of Haixi in Qinghai. Acta Ecol. Sin. 2019, 39, 7904–7913. [Google Scholar]
- González-Mejía, A.M.; Ma, X. The Emergy Perspective of Sustainable Trends in Puerto Rico from 1960 to 2013. Ecol. Econ. 2017, 133, 11–22. [Google Scholar] [CrossRef]
- Song, G.F.; Liu, Y.Z.; Zhu, X.N.; Chen, Y.; Zhang, Z. Study on Dynamic Change of Ecological Efficiency in Wuhan Based on Emergy Analysis. Ecol. Econ. 2019, 35, 103–109. [Google Scholar]
- Qiao, T.; Kong, F.L.; Li, Y.; Chi, M.; Yang, L. Eco-Economic system sustainable development assessment in Qingdao city based on emergy analysis. Urban Environ. Urban Ecol. 2015, 28, 4–8. [Google Scholar]
- Fang, C.; Ren, Y. Analysis of emergy-based metabolic efficiency and environmental pressure on the local coupling and telecoupling between urbanization and the eco-environment in the Beijing-Tianjin-Hebei urban agglomeration. Sci. China Earth Sci. 2017, 60, 1083–1097. [Google Scholar] [CrossRef]
- Chu, F.F.; Jiang, D.F. Evolutionary Tendency of Ecological-economic System in Chang-Zhu-Tan City Cluster Based on Emergy Analysis. Econ. Geogr. 2012, 32, 143–148. [Google Scholar] [CrossRef]
- Gao, H.L.; Tu, J.J.; Yang, L. Evaluation of the comprehensive carrying capacity of cities. J. Southwest Univ. (Nat. Sci. Ed.) 2010, 32, 148–152. [Google Scholar] [CrossRef]
- Wang, D.W.; Liang, X.C.; Wang, P.X. A Study on Sustainable Development of Ecological-Economy-Society System of Urban Agglomeration Along Yangtze River Delta Based on Emergy Theory. Bull. Soil Water Conserv. 2020, 40, 228–236, 243. [Google Scholar] [CrossRef]
- Amaral, L.P.; Martins, N.; Gouveia, J.B. A review of emergy theory, its application and latest developments. Renew. Sustain. Emergy Rev. 2016, 54, 882–888. [Google Scholar] [CrossRef]
- Liu, G.Y.; Yang, Z.F.; Su, M.R.; Chen, B. The structure, evolution and sustainability of urban socio-economic system. Ecol. Inform. 2012, 10, 2–9. [Google Scholar] [CrossRef]
- Dong, X.B.; Yan, M.C.; Dong, Y.; Yang, L.Z.; Zhang, Y.F.; Zhang, X.S. Emergy Evaluation of the Eco- economic System of Inner Mongolia and Study on Its Sustainable Development Strategy. Prog. Geogr. 2007, 26, 47–57. [Google Scholar]
- Cao, W.; Sun, C. Improvement of the emergy ecological footprint model: A case study of Hainan Province. Acta Ecol. Sin. 2019, 39, 216–227. [Google Scholar] [CrossRef]
- Lin, C.S. Planning for the Development of the City-Regions in the Guangdong-Hong Kong-Macao Greater Bay Area: What Can and Cannot be Done. Trop. Geogr. 2017, 37, 755–756, 761. [Google Scholar] [CrossRef]
- Zhang, R.X.; Gu, Z.T. The Origin and Next Move of Guangdong-Hong Kong-Macao Greater Bay. Reform 2017, 279, 64–73. [Google Scholar]
- Mei, F.M. Economic Spatial Connection and Spatial Structure of Guangdong-Hong KongMacao Greater Bay and the Surrounding Area Cities—An Empirical Analysis Based on Improved Gravity Model and Social Network Analysis. Econ. Geogr. 2017, 37, 57–64. [Google Scholar] [CrossRef]
- Chen, Y.Z. Strategic Thinking on Innovation-driven Development in Guangdong-Hong Kong-Macao Greater Bay. Think Tank Theory Pract. 2019, 4, 52–57. [Google Scholar] [CrossRef]
- Lu, H.F.; Ye, Z.; Zhao, X.F.; Peng, S.L. A new emergy index for urban sustainable development. Acta Ecol. Sin. 2003, 23, 1363–1368. [Google Scholar]
- Lu, H.F.; Lan, S.F.; Li, L.; Peng, S.L. Studies on emergy indices for evaluating system sustainable development property. China Environ. Sci. 2002, 22, 93–97. [Google Scholar]
- Chen, Y.L. The Evaluation of Regional Ecological and Economic Development is Studied Based on Entropy Method. J. Chongqing Univ. Technol. Nat. Sci. 2020, 34, 232–237. [Google Scholar]
- Ma, W.J.; Liu, J. Evaluation of sustainable development of eco-economic systems in China based on emergy analysis. Chin. J. Appl. Ecol. 2020, 31, 2029–2038. [Google Scholar] [CrossRef]
- Brown, M.T.; Ulgiati, S. Emergy-based indices and ratios to evaluate sustainability: Monitoring economies and technology toward environmentally sound innovation. Ecol. Eng. 1997, 9, 51–69. [Google Scholar] [CrossRef]
- Awosusi, A.A.; Adebayo, T.S.; Altuntas, M.; Agyekum, E.B.; Zawbaa, H.M.; Kamel, S. The dynamic impact of biomass and natural resources on ecological footprint in BRICS economies: A quantile regression evidence. Emergy Rep. 2022, 8, 1979–1994. [Google Scholar] [CrossRef]
- Girdzijauskas, S.; Streimikiene, D.; Griesiene, I.; Mikalauskiene, A.; Kyriakopoulos, G.L. New Approach to Inflation Phenomena to Ensure Sustainable Economic Growth. Sustainability 2022, 14, 518. [Google Scholar]
- Liu, G.; Yang, Z.; Chen, B. Urban metabolism process based on emergy synthesis: Theory and method. Acta Ecol. Sin. 2013, 33, 4539–4551. [Google Scholar]
- Brown, M.T.; Campbell, D.E.; Vilbiss, C.D.; Ulgiati, S. The geobiosphere emergy baseline: A synthesis. Ecol. Model. 2016, 339, 92–95. [Google Scholar] [CrossRef]
Indicator System | Index | Symbol (Unit) | Expression for Computation | Hong Kong | Macau | Guangzhou | Shenzhen | Zhuhai | Foshan | Huizhou | Dongguan | Zhongshan | Jiangmen | Zhaoqing |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Emergy Flow Indicators | Renewable resources and products | ×1018 R (sej) | R | 5.20 | 0.15 | 32.80 | 9.44 | 7.89 | 16.50 | 71.00 | 12.00 | 8.28 | 42.10 | 64.70 |
Nonrenewable resources and products | ×1022 N (sej) | N | 8.69 | 0.34 | 14.90 | 6.62 | 2.01 | 3.40 | 3.55 | 5.97 | 2.06 | 3.66 | 2.04 | |
Input emergy | ×1023 I (sej) | I | 605.00 | 2.29 | 3.11 | 8.95 | 0.10 | 0.75 | 0.99 | 3.49 | 0.369 | 0.039 | 0.11 | |
Total emergy used | ×1023 U (sej) | U = R + N + I | 606.00 | 2.33 | 4.74 | 9.73 | 1.21 | 1.16 | 1.39 | 4.15 | 0.06 | 0.71 | 0.36 | |
Output emergy | ×1023 O (sej) | O | 886.00 | 0.06 | 1.16 | 6.54 | 0.80 | 0.42 | 0.75 | 4.00 | 0.30 | 0.28 | 0.01 | |
Waste emergy | ×1021 W (sej) | W | 7.42 | 0.67 | 14.80 | 15.80 | 5.38 | 8.95 | 0.400 | 12.70 | 3.58 | 5.18 | 0.36 | |
Social Subsystem Indicators | Emergy density | ×1015 ED (sej/m2) | U/A | 54.80 | 7.08 | 638.00 | 48.70 | 697.00 | 305.00 | 0.01 | 0.16 | 0.03 | 0.001 | 0.002 |
Emergy use per capita | ×1016 EUC (sej/Person) | U/P | 814.00 | 34.90 | 3.18 | 7.47 | 6.40 | 1.47 | 2.88 | 4.95 | 1.82 | 1.54 | 1.07 | |
Population-carrying capacity of current environmental resources | ×102 PCC (ten thousand people) | (R + I)/U∙P | 7.44 | 0.66 | 9.78 | 12.00 | 1.56 | 5.13 | 3.45 | 7.04 | 2.01 | 2.38 | 1.07 | |
Electric emergy per capita | ×1015 EEC (sej/Person) | Electric/P | 3.42 | 4.81 | 3.62 | 4.01 | 5.36 | 2.13 | 4.87 | 5.54 | 5.05 | 3.53 | 2.98 | |
Economic Subsystem Indicators | Emergy power ratio | ×10−1 EPR | Electric/U | 0.004 | 0.14 | 1.14 | 0.54 | 0.84 | 1.45 | 1.69 | 1.12 | 2.81 | 2.28 | 2.77 |
Emergy yield ratio | EYR | EYR = U/I | 1.00 | 1.02 | 1.52 | 1.09 | 1.21 | 1.54 | 1.40 | 1.19 | 1.65 | 2.36 | 3.13 | |
Emergy money ratio | ×1012 EMR (sej/USD) | U/GDP | 21.30 | 4.23 | 1.37 | 2.66 | 2.76 | 0.78 | 2.24 | 3.32 | 1.09 | 1.62 | 25.10 | |
Emergy exchange ratio | EER | I/O | 0.68 | 35.50 | 2.67 | 1.37 | 1.26 | 1.81 | 1.32 | 0.87 | 1.22 | 1.09 | 12.60 | |
Natural Subsystem Indicators | Emergy self-sufficiency ratio | ESR | (R + N)/U | 0.001 | 0.01 | 0.31 | 0.07 | 0.17 | 0.29 | 0.26 | 0.14 | 0.34 | 0.52 | 0.57 |
Environment loading ratio | ×104 ELR | (I + N)/R | 1170.00 | 155.00 | 1.45 | 10.30 | 1.53 | 0.70 | 0.20 | 3.46 | 0.73 | 0.17 | 0.06 | |
Emergy waste ratio | ×10−2 EWR | W/U | 0.01 | 0.29 | 3.12 | 1.62 | 4.45 | 7.72 | 2.91 | 3.06 | 5.96 | 7.31 | 10.10 | |
Emergy renewable resource radio | ×10−5 ERR | R/U | 0.001 | 0.06 | 6.92 | 0.97 | 6.52 | 14.20 | 51.10 | 0.03 | 13.80 | 59.40 | 181.00 | |
Ratio of waste to renewable resource | ×102 EWI | W/R | 14.30 | 44.90 | 4.51 | 16.70 | 6.820 | 5.420 | 0.57 | 10.60 | 4.32 | 1.23 | 0.56 | |
Sustainability Indicators | Emergy sustainable index | ×10−6 ESI | EYR/ELR | 0.09 | 0.07 | 105.00 | 10.50 | 79.20 | 219.00 | 716.00 | 34.50 | 228.00 | 1400.00 | 569.00 |
Emergy index for sustainable development | ×10−5 EISD | (EYRX EER)/ (ELR + EWI) | 0.006 | 2.32 | 37.30 | 1.42 | 9.54 | 36.80 | 92.10 | 2.91 | 26.20 | 142.00 | 6520.00 | |
System Health Indicators | Emergy-based city ecosystem health index | ×103 EUEHI | EYRX EERXED)/(ELRX EMR) | 0.15 | 38.80 | 12.70 | 2.60 | 2.41 | 14.50 | 3.60 | 1.43 | 8.11 | 6.55 | 6180.00 |
Modified emergy-based city ecosystem health index | ×10−10 EUEHI’ | (EYRX EERX ESR)/ (ELRX EWI) | 0.0006 | 0.75 | 1970.00 | 5.87 | 243.00 | 2140.00 | 4,260,000.00 | 40.80 | 221.00 | 64,100.00 | 739,000,000.00 |
Social–Economic–Natural Subsystems | Index System | Hong Kong | Macau | Guangzhou | Shenzhen | Zhuhai | Foshan | Huizhou | Dongguan | Zhongshan | Jiangmen | Zhaoqing | Weight |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Social Subsystem Indicators | ED | 0.000 | 0.871 | 1.000 | 0.990 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.040 |
EUC | 1.000 | 0.042 | 0.003 | 0.008 | 0.007 | 0.001 | 0.002 | 0.005 | 0.001 | 0.001 | 0.000 | 0.270 | |
PCC | 0.170 | 0.000 | 9.160 | 11.200 | 1.460 | 4.800 | 3.230 | 6.590 | 1.880 | 2.220 | 1.000 | 0.060 | |
EEC | 0.380 | 0.786 | 0.437 | 0.551 | 0.947 | 0.000 | 0.804 | 1.000 | 0.856 | 0.411 | 0.250 | 0.020 | |
Economic Subsystem Indicators | EPR | 0.000 | 0.049 | 0.405 | 0.191 | 0.297 | 0.515 | 0.601 | 0.398 | 1.000 | 0.813 | 0.990 | 0.040 |
EYR | 0.000 | 0.008 | 0.245 | 0.040 | 0.100 | 0.254 | 0.188 | 0.090 | 0.305 | 0.639 | 1.000 | 0.070 | |
EMR | 0.000 | 0.811 | 0.947 | 0.886 | 0.881 | 0.975 | 0.906 | 0.854 | 0.960 | 0.935 | 1.000 | 0.040 | |
EER | 0.000 | 1.000 | 0.057 | 0.020 | 0.017 | 0.032 | 0.018 | 0.005 | 0.016 | 0.012 | 0.340 | 0.170 | |
Natural Subsystem Indicators | ESR | 0.000 | 0.025 | 0.548 | 0.119 | 0.290 | 0.512 | 0.446 | 0.251 | 0.598 | 0.902 | 1.000 | 0.040 |
ELR | 0.000 | 0.867 | 0.999 | 0.991 | 0.999 | 0.999 | 1.000 | 0.997 | 0.999 | 1.000 | 1.000 | 0.040 | |
EWR | 1.000 | 0.971 | 0.690 | 0.839 | 0.559 | 0.235 | 0.712 | 0.697 | 0.409 | 0.275 | 0.000 | 0.020 | |
REE | 0.000 | 0.000 | 0.038 | 0.005 | 0.036 | 0.079 | 0.282 | 0.016 | 0.076 | 0.328 | 1.000 | 0.130 | |
EWI | 0.690 | 0.000 | 0.911 | 0.635 | 0.859 | 0.890 | 1.000 | 0.774 | 0.915 | 0.985 | 1.000 | 0.050 |
Eco-Economic System and Coordinated Development | Hong Kong | Macau | Guangzhou | Shenzhen | Zhuhai | Foshan | Huizhou | Dongguan | Zhongshan | Jiangmen | Zhaoqing |
---|---|---|---|---|---|---|---|---|---|---|---|
Social Subsystems | 0.256 | 0.025 | 0.007 | 0.004 | 0.004 | 0.005 | 0.013 | 0.004 | 0.004 | 0.013 | 0.033 |
Economic Subsystem | 0.000 | 0.092 | 0.016 | 0.006 | 0.007 | 0.010 | 0.010 | 0.008 | 0.012 | 0.017 | 0.055 |
Natural Subsystems | 0.006 | 0.006 | 0.015 | 0.011 | 0.008 | 0.013 | 0.017 | 0.010 | 0.012 | 0.024 | 0.061 |
Sustainable Development Index | 0.262 | 0.123 | 0.038 | 0.021 | 0.019 | 0.028 | 0.040 | 0.021 | 0.028 | 0.054 | 0.150 |
Coordination Degree | 0.000 | 0.203 | 0.845 | 0.727 | 0.859 | 0.801 | 0.927 | 0.853 | 0.730 | 0.908 | 0.905 |
Coordinated Development Degree | 0.000 | 0.158 | 0.179 | 0.124 | 0.127 | 0.150 | 0.192 | 0.135 | 0.143 | 0.222 | 0.369 |
City Name | Category | Degree of Coherent Development | Nature of the City | Emergy Characteristics |
---|---|---|---|---|
Hong Kong | 1 | 0.000 | International financial, shipping, and trade center and international aviation hub | Hong Kong has the best import and export emergy levels, ESR, and high sustainable development capacity in the GBA, but the excessive total consumption emergy leads to the highest ED, EUC, EPR, and ELR values among the GBA city groups, while the EYR, EER, and coordination level are the lowest. The overreliance on import emergy leads to Hong Kong’s economic growth lacking sustainable and solid support, and its development space faces bottleneck constraints. |
Shenzhen | 1 | 0.124 | A modern city with high technology, finance, and logistics as pillars | Shenzhen’s EMR and ERR are high, which can make better use of renewable resources. The high emergy from waste and population-carrying capacity and the larger share of imported emergy than exported emergy indicate that the urban ecological environment is under great pressure, and the population far exceeds the urban land area and population-carrying capacity. |
Zhuhai | 1 | 0.127 | The gateway hub city of the GBA | Zhuhai’s electricity emergy use ratio, EEC, and EMR are in the top four, while the ESR is in the second-to-last position, which proves that Zhuhai is a city with a higher dependence on nonrenewable resources such as electricity, relies more on imported emergy, and needs to consume more emergy per unit of production. |
Dongguan | 1 | 0.135 | A city characterized by foreign trade with manufacturing as its main pillar | Dongguan’s nonrenewable resources and product emergy, ED, and total consumption emergy are all high. The city’s resource allocation is fragmented and consumed, and the product output efficiency is low; the land development intensity is as high as 49.4%, much higher than the international warning line of 30% [34]. |
Zhongshan | 2 | 0.143 | Important hub city of the coastal economic belt of the GBA city cluster | The overall emergy characteristics of Zhongshan are close to those of Foshan, but the overall emergy consumption is lower, indicating that the quality and standard of living of its residents are slightly lower than for Foshan, and the emergy is more derived from the self-generated natural ecosystem, which reduces the dependence on imported emergy to a certain extent, but the environmental pressure and population-carrying capacity are still larger. |
Foshan | 2 | 0.150 | The GBA’s western integrated hub city | The emergy characteristics of Foshan are very close to those of Guangzhou, with high land development intensity and obvious fragmentation and slightly lower economic development dynamics than the other cities [37], but the consumption of emergy and environmental pressure are less than Guangzhou’s, and the recycling rate of waste is higher than Guangzhou’s. |
Macau | 2 | 0.158 | World tourism and leisure center | Macau has the second highest ED, EMR, EEC, ELR, EWR, and ERR values after Hong Kong but has the highest ESR and EWI values and the lowest waste emergy in the GBA city cluster. Macau needs to maintain the city by reducing the total emergy consumption and the recycling of waste emergy under the situation of land space resource scarcity and high intensity levels of land development, integration, and sustainable development. |
Guangzhou | 2 | 0.179 | The capital of Guangdong Province, a cosmopolitan city integrating commerce, science, education, and culture | Guangzhou’s total emergy consumption is only lower than that of Hong Kong and Shenzhen, and its waste emergy and population-carrying capacity remain high. Guangzhou’s emergy consumption is high, while its waste reuse rate is too low, and its population base is large, resulting in excessive population-carrying pressure. |
Huizhou | 3 | 0.192 | An important node city in the Guangdong–Hong Kong–Macau Greater Bay Area | Huizhou’s emergy flow indicators are slightly higher than Jiangmen, except for N and W. The social subsystem indicators are higher than Jiangmen, while the economic subsystem indicators are lower than Jiangmen except for EMR, and the natural subsystem indicators are lower than Jiangmen except for the environmental load rate. This indicates that Huizhou’s ecological and economic benefits are slightly lower than those of Jiangmen, and it is under greater environmental pressure. |
Jiangmen | 3 | 0.222 | Hub gateway city of the western wing of the GBA | Jiangmen’s emergy characteristics are closer to those of Zhaoqing, and the emergy flow indicators are higher than those of Zhaoqing, except for R and the export emergy, which are lower. The social subsystem indicators are higher than those of Zhaoqing, while the economic subsystem indicators are lower and the natural subsystem indicators are higher except for ESR. This indicates that Jiangmen’s overall emergy consumption is higher than that of Zhaoqing, as well as its resident living standards. The overall emergy consumption of Jiangmen is higher than that of Zhaoqing, the quality and standard of living of the residents are higher than that of Zhaoqing, and there is a certain amount of economic development emergy, but the ecological and economic benefits are lower than those of Zhaoqing. |
Zhaoqing | 3 | 0.369 | An important node city in the GBA | Zhaoqing’s renewable resources and product emergy are the highest in the GBA city cluster, while the incoming and export emergy levels, total consumption emergy, ED, and EUC are the lowest, indicating that Zhaoqing’s emergy consumption structure is relatively more optimized, but the resident’s quality of life and living standards are the lowest among the 11 cities in the whole GBA; however, the EYR is the highest and the ELR is the lowest, indicating that the city’s ecological economy is high, while the pressure on the environment is relatively low. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 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
Li, H.; Huang, X.; Xu, Q.; Wang, S.; Guo, W.; Liu, Y.; Huang, Y.; Wang, J. A New Approach to Evaluate the Sustainability of Ecological and Economic Systems in Megacity Clusters: A Case Study of the Guangdong–Hong Kong–Macau Bay Area. Sustainability 2023, 15, 5881. https://doi.org/10.3390/su15075881
Li H, Huang X, Xu Q, Wang S, Guo W, Liu Y, Huang Y, Wang J. A New Approach to Evaluate the Sustainability of Ecological and Economic Systems in Megacity Clusters: A Case Study of the Guangdong–Hong Kong–Macau Bay Area. Sustainability. 2023; 15(7):5881. https://doi.org/10.3390/su15075881
Chicago/Turabian StyleLi, Hui, Xue Huang, Qing Xu, Shuntao Wang, Wanqi Guo, Yan Liu, Yilin Huang, and Junzhi Wang. 2023. "A New Approach to Evaluate the Sustainability of Ecological and Economic Systems in Megacity Clusters: A Case Study of the Guangdong–Hong Kong–Macau Bay Area" Sustainability 15, no. 7: 5881. https://doi.org/10.3390/su15075881