Spatio-temporal evolution analysis of the coupling situation of economic-social-ecological system in Guangdong

Abstract

The economic and social structures of Chinese cities are constantly transforming in recent years. The coordinated development of economic, social, and ecological environment is an important path to achieving the construction of high-quality development. Taking Guangdong Province, the largest economic province in China, as an example, the evaluation index systems of economic development system (ED), social development system (SD), and ecological environment system (EE) are constructed, respectively. The entropy weight method and comprehensive evaluation method are applied to measure the evaluation indexes of economic, social, and ecological environment levels of each city in Guangdong Province from 2010 to 2020. The coupled coordination model is used to measure the spatial and temporal evolution of the coupled ED-SD-EE coordination of Guangdong cities and explore the impact of the epidemic on the coupling coordination degree. The results concluded that (i) the economic, social, and ecological environment of each city in Guangdong Province will be more harmonious from 2010 to 2020. ED-SD-EE coupling coordination of Guangdong cities shows a “rising and then declining” trend, but it is still in a “high coupling-low coordination” development state. (ii) The lagging development of the coupled ED-SD-EE system in Guangdong cities is mainly the ecological environment system. (iii) Epidemic harms the coupling and coordination of Guangdong cities, with the most negative effect on the coordination development of the EE. The paper findings clarify the current state of ED-SD-EE coupling and coordination in Guangdong cities, with a view to providing a reference basis for policy formulation and research on quality urban development.

Introduction

How to achieve the harmonization of city economy, society, and ecological environment has become a major theoretical and practical issue common to the construction of city ecosystems in the world. Since the reform and opening-up, China’s city construction and economic construction have taken off, and China’s industrialization and urbanization have made great achievements. But China’s pre-crude economic development model and huge industrial system have consumed numerous resources and energy, and the impact and pressure on the ecological environment have become more obvious, while the shortage of resources and the deterioration of the ecological environment have also brought constraints and limitations to city economic and social development. The contradiction between ecological environment and development has become increasingly prominent in cities (Zhang and Liu 2019; Jiang et al. 2017).

Therefore, the Chinese government also attaches great importance to the harmonization of urban ecosystems and economic\social development. Since 2012, China has successively proposed new urbanization, ecological civilization construction, and new development concept. The core requirement and objective of these plans and concepts is to achieve the harmonization of economic, social, and ecological development. But high-quality coordinated urban development studies require continuous optimization according to the cities’ current economic, social, and ecological environment and an accurate grasp of the evolutionary trends and directions of urban ecosystems. Timely and well coordinating the relationship between the cities’ economic, social, and ecological environment is a necessary path to promote the cities’ green and sustainable development, which is important for accelerating China’s ecological civilization construction, ensuring sustainable urban development and improving the cities’ liable environment (Wang et al. 2016).

There has been a rise in research into the coordination of the various subsystems of the city in recent years. Some researchers have used mathematical models such as system dynamics (Wang et al. 2012) and discrete mathematics (Dou et al. 2021) to study the urban ecosystem’s coordinated development. In the study of geography, the main ones are watersheds (Ariken et al. 2020; Liu et al. 2021), provinces (Zuo et al. 2021; Liu et al. 2020), and rural areas (Yang Y et al. 2020; Zhu et al. 2021). And the use of coupled coordination models for the coordinated development of cities is increasingly being studied (Wang et al. 2020; Li 2019; Yang C et al. 2020; Xie et al. 2021; Sun and Zhang 2021). This indicates the widespread recognition of the coupling coordination degree model use for system coordination studies in various regions, especially at the city scale.

However, urban ecosystems are complex nature-economy-society complex systems (Sun et al. 2017). In the available research, the cities’ coupling coordination has been mainly studied between ecological and economic and social integrated systems (Wang et al. 2020; Yang C et al. 2020; Xie et al. 2021). Meantime, China’s rapid economic growth and urbanization have led to continuous changes in social and economic structures as well as production and lifestyle (Li et al. 2012b). Studies by Chiaravalloti et al. (2021), Laurent (2021) has shown that economic and social drivers of various types of systems do not coincide and that developments between the two are not necessarily synchronized. Therefore, the distinction between economic and social development, and thus the coupling and coordination of multiple systems, is also increasingly being considered by researchers (Zuo et al. 2021; Chiaravalloti et al. 2021).

Based on this, the paper conducts an empirical study on the basis of existing research based on data availability, scientific validity, and consideration of the specificity of the study area, using city-level data from 2010 to 2020 for the largest economic province in China, Guangdong Province. In constructing a three-system indicator system for economic development (ED), social development (SD), and ecological environment (EE) for 21 prefecture-level cities, the entropy method measures the comprehensive development level index of each subsystem and analyzes the comprehensive index to derive the relative development type of each municipality and the relative lagging system. Combined with the coupled coordination model to measure the temporal development characteristics of coupled ED-SD-EE coordination in each city of Guangdong, ArcGIS10.4 software was used to visualize and analyze the spatial and temporal evolution characteristics and types of coupled coordinated development in each city. The paper also explores the impact of the COVID-19 epidemic on the coupling and coordination among urban social, economic, and ecological systems. It is expected to provide reference suggestions for the construction of coordinated and high-quality development of economy, society, and ecological environment in Guangdong at this stage and to lay the foundation for governments to carry out scientific decision-making and seek optimal solutions for the interests of ecological, economic, and social parties.

Subject and data

Study area

Guangdong Province is located in the south of China, where downstream of the Pearl River, at latitude 20°22′–25°52′ north and longitude 109°66′–117°30′ east. The climate is mid-subtropical, south-subtropical, and tropical from north to south, dominated by mountains and hills. Guangdong Province, with 21 prefecture-level cities under its jurisdiction, is a pioneering region in China’s reform and opening up and the introduction of foreign investment, and it is at the forefront of the urbanization process in China. It can be said that the transition to coordinated urban development in Guangdong Province in the context of “high-quality development” and “new urbanization” has received national attention. However, from a regional perspective, the PRD region (nine prefecture-level cities) will account for 80.82% of the province’s GDP in 2020, amounting to 8,952.3 billion CNY. In contrast, the development of the eastern, western, and northern of Guangdong eco-development zones is relatively backward and polarized, and the different resource endowments and economic structures of Guangdong’s municipalities create significant disparities in economic development between them. Due to the “Matthew effect,” these disparities have been widening over time, and the problem of regional economic-social-ecological disparities has become increasingly prominent, becoming a shortcoming that restricts Guangdong from achieving high-quality economic and social development (Cai et al. 2013). Therefore, the study on the coordination of economic-social-ecological development of Guangdong will help Guangdong to optimize the allocation of resources and the spatial layout of productive forces, accelerate the formation of a regional economic layout with obvious main functions, complementary advantages and high-quality development, build a higher level of modernized regional coordinated development system, and realize green economic and social transformation.

Indicator system

System construction

In the paper, based on the systematic, regional, scientific, operable, quantitative, and data disclosure degree of each city in Guangdong Province, a total of 38 indicators are selected to build a coupling and coordination of ED-SD-EE evaluation indicator system for cities, and the indicators are classified according to the classification, definition, and characteristics of Chinese cities by relevant researchers [10,18,22–31]. The 38 indicators were properly classified according to the classification, definition, and characteristics of Chinese cities (Table 1) (Zuo et al. 2021; Sun and Zhang 2021; Lee and Goh 2016; Li 2019; Alfaro et al. 2004; Stuetzer et al. 2018; Li et al. 2012a; Shi et al. 2019; Ke et al. 2020; Qiu et al. 2020; Xia et al. 2021; Chaerun et al. 2020).

Table 1.

ED-SD-EE indicator system

Target layer	Criteria layer	Index layer	Attribute
Economic development system (ED)	Economic development structure	Percentage of added value of primary industry* (%)	−
		Percentage of added value of secondary industry* (%)	−
		Percentage of added value of tertiary industry* (%)	+
	Economic development situation	Urbanization rate* (%)	+
		GDP per capita* (yuan)	+
		Total gross domestic product* (100,000,000 CNY)	+
		Consumer price index*	+
		Total investment in fixed assets* (100,000,000 CNY)	+
		Foreign direct investment* (10,000 CNY)	+
		Total import and export volume of foreign enterprises* (100,000,000 USD)	+
	Economic development potential	Deposits and loans in renminbi in all financial institutions* (billion CNY)	+
		Savings deposit by household in all financial institutions* (100,000,000 CNY)	+
Social development system (SD)	Population situation	Natural population growth rate** (%)	+
		Population density *(p/sq. km)	−
	Education and technology	Expenditure for education per capita* (yuan/p)	+
		Proportion of educational practitioners in the whole society* (%)	+
		Internal expenditure on R&D of industrial enterprises* (100,000,000 CNY)	+
		Collections of public libraries per 10 persons*** (copy)	+
	Public health	Number of medical beds per 10,000 people* (bed)	+
		Number of medical technical personnel* (person)	+
	Social insurance	Proportion of environmental and public facilities’ practitioners in the whole society*** (%)	+
		Proportion of public management and social organization practitioners in the whole society*** (%)	+
		Local government general budgetary revenue* (100,000,000 CNY)	+
	Social welfare	Public transportation vehicles per 10,000 people* (unit)	+
		Area of parks and green land per capita* (sq. m)	+
		Local government general budgetary expenditure* (100,000,000 CNY)	+
		Average wages of fully employed staff and workers in urban units* (yuan)	+
Ecological environment system (EE)	Environmental pressure	Electricity consumption* (100 million kwh)	−
		Volume of waste water discharged *(100 million tons)	−
		Total volume of industrial waste gas emission* (100 million cu·m)	−
		Volume of industrial soot (dust) emission* (10,000 tons)	−
		Volume of industrial solid wastes produced* (10,000 tons)	−
		Water use per capita ~ (cu·m)	−
	Environmental state	Water resource per capita ~ (cu·m)	+
		Average annual rainfall ~ (mm)	+
		Cultivated land area** (ha)	+
	Environmental response	Rate of sewage treatment* (%)	+
		Rate of consumption waste treatment* (%)	+

^*Guangdong Provincial Statistical Yearbook or Municipal Statistical Yearbooks

^**Municipal Statistical Bulletins or Annual Survey Reports

^***China Urban Statistical Yearbook

^~Guangdong Water Resources Bulletin

⁺Positive indicator

⁻Negative indicator

There are 12 indicators in the economic development system. Based on existing research, the paper divides the 12 indicators into three categories: economic development structure, status, and potential. There are 15 indicators in the social development system. Based on the internal division of the Guangdong Provincial Statistical Yearbook and related studies, this paper divides them into five parts: social demographic status, education, and science and technology services, public health services, social security, and social welfare, with the aim of building a relatively complete subsystem of cities’ social development. There are 11 indicators of cities’ ecological environment system. The paper refers to the environmental pressure-state-response model (Rapport 1989; Chuan 2000), which is often used to construct the ecological environment quality evaluation index system and selects the complete and available cities’ ecological environment data to represent the current situation and development of cities’ ecological environment.

Indicator description

Among the indicators of the economic development subsystem constructed in the paper, foreign direct investment (FDI), deposits, and loans in renminbi in all financial institutions and savings deposit by household in all financial institutions are not much applied and classified in various studies. But some researchers have defined FDI as part of GDP (Li 2019), and some researchers have studied the role of FDI on economic development (Alfaro et al. 2004). Therefore, this paper includes FDI in the ED system, together with related indicators to indicate the state of economic development. Meanwhile, some researchers regard the deposits and loans in renminbi in all financial institutions as the financial capital of regional economic development (Stuetzer et al. 2018), while household deposits are part of it, but household deposits can better reflect the consumption potential of regional residents. So the paper regards them as potential financial capital that can promote regional economic development, and the two together constitute the development potential layer of the economic development subsystem.

Among the social development subsystem, average wages of fully employed staff and workers in urban units are gradually considered by Chinese scholars to reflect the level of talent skills (Peijian and Jing 2021) and the fairness of social distribution (Binbin 2021). Some Chinese scholars believe that a good average wage level and rate of increase can reflect the first advantage of high-quality development of cities (Li 2021). Therefore, based on data availability, the paper adopts the average wages of fully employed staff and workers in urban units to reflect the overall average wage level of the region, which is used to represent the degree of high-quality development of talents in regional social development. A large scale of government expenditure and fully functional government functions can effectively promote social harmony and stability. Existing studies have mostly used public expenditure to measure the scale of local governments (Li et al. 2012a); the paper uses local government general budgetary expenditure to measure the scale of local governments and reflect the government’s ability to sustain support for social welfare. The paper also uses local government general budgetary revenue as a measure of the government’s ability to sustain healthy development and to cover spending on various social services. In addition, indicators such as number of medical technical personnel (Shi et al. 2019), internal expenditure on R&D of industrial enterprises (Ke et al. 2020), expenditure for education per capita (Qiu et al. 2020), and collections of public libraries per 10 persons (Xia et al. 2021; Chaerun et al. 2020) have been classified into the social development system through relevant literature studies.

The indicators of the cities’ ecosystem are mainly selected in terms of the state of regional water and soil resources, energy consumption, pollutant emission pressure, and the effectiveness of cities’ environmental management, while combining the PSR model and data availability to filter out the appropriate indicators.

Data sources and declarations

The data for the paper were obtained from the statistical yearbooks, statistical bulletins, and annual survey reports of Guangdong Province and municipalities from 2011 to 2021, as well as the China Urban Statistical Yearbook and the Guangdong Water Resources Bulletin. Where some data differed between the provincial and municipal yearbooks and where the data differed from year to year, the Guangdong Statistical Yearbook and the latest yearbook data prevail. Due to the outbreak of the COVID-19 epidemic in 2020, some data for that year (natural population growth rate, public library collections, the proportion of environmental and public facilities’ practitioners in the whole society, proportion of public management and social organizations’ practitioners in the whole society, etc.) are not available. The missing data were predicted by index smoothing projections using Excel 2019 software.

Empirical framework

Determination of indicator weights

Due to the different levels of development of each municipality, the degree of influence of each indicator on urban development varies at different times. The paper uses the entropy method (Qiyue 2010) to determine the indicator weights based on the development characteristics of each city and then carry out the calculation of the coupling and coordination degree of each system. The indicator weights calculated in the paper for each city are detailed in Appendix 1.

Table 4.

Indicator weights for Guangdong Province and municipalities

Region	U1	U2	U3	U4	U5	U6	U7	U8	U9
Guangdong	0.02487	0.03367	0.02284	0.02835	0.02591	0.02390	0.03536	0.03396	0.02104
Guangzhou	0.02754	0.03499	0.01665	0.03269	0.02309	0.02549	0.02899	0.03272	0.04033
Shenzhen	0.03000	0.02451	0.02452	0.01932	0.01821	0.02362	0.02276	0.04431	0.03305
Zhuhai	0.03408	0.02187	0.02761	0.04866	0.02357	0.02757	0.01899	0.02752	0.03013
Shantou	0.02530	0.03405	0.02831	0.02174	0.02618	0.02566	0.02667	0.03371	0.02565
Foshan	0.02223	0.04594	0.03518	0.02953	0.02971	0.02526	0.03480	0.03092	0.03055
Shaoguan	0.02096	0.02708	0.01138	0.03502	0.02077	0.02109	0.02806	0.01838	0.03781
Heyuan	0.01822	0.03893	0.02933	0.04104	0.02158	0.02043	0.02028	0.03529	0.02128
Meizhou	0.03340	0.02501	0.02240	0.03158	0.02347	0.02179	0.03995	0.03737	0.02293
Huizhou	0.01551	0.04677	0.01821	0.03050	0.01913	0.02078	0.05723	0.02735	0.02847
Shanwei	0.02384	0.03713	0.04109	0.03080	0.02356	0.02185	0.01916	0.03214	0.03104
Dongguan	0.01682	0.02813	0.02219	0.04170	0.02910	0.02341	0.02644	0.03075	0.03134
Zhongshan	0.02663	0.03320	0.01398	0.02954	0.01253	0.01740	0.02791	0.02027	0.02221
Jiangmen	0.01816	0.03113	0.02931	0.03308	0.02734	0.02807	0.05088	0.03369	0.02176
Yangjiang	0.01907	0.01968	0.03729	0.03323	0.01600	0.01648	0.04582	0.01587	0.05478
Zhanjiang	0.02755	0.03829	0.01805	0.02834	0.01851	0.01833	0.03327	0.03015	0.04256
Maoming	0.01788	0.02704	0.03388	0.02940	0.02098	0.02190	0.02893	0.02784	0.04487
Zhaoqing	0.02568	0.02604	0.01595	0.04430	0.02259	0.02289	0.02443	0.02868	0.04200
Qingyuan	0.01222	0.02279	0.01970	0.04648	0.02359	0.02402	0.03210	0.02821	0.05082
Chaozhou	0.01289	0.03602	0.02065	0.02439	0.01930	0.01791	0.03413	0.02866	0.03464
Jieyang	0.01837	0.03065	0.04637	0.02961	0.01880	0.01786	0.02957	0.03192	0.05674
Yunfu	0.02421	0.02511	0.03724	0.03705	0.02416	0.02415	0.05003	0.02191	0.03603
	U10	U11	U12	U13	U14	U15	U16	U17	U18
Guangdong	0.02015	0.03374	0.02690	0.01589	0.03034	0.01972	0.05540	0.02403	0.03326
Guangzhou	0.01203	0.03581	0.02735	0.02836	0.03229	0.01843	0.02231	0.02515	0.04341
Shenzhen	0.02489	0.03515	0.02898	0.01633	0.02747	0.01846	0.04874	0.02490	0.01964
Zhuhai	0.03748	0.03841	0.02545	0.01709	0.02004	0.01909	0.03451	0.03184	0.02889
Shantou	0.02102	0.02946	0.02789	0.03165	0.03569	0.02176	0.02421	0.02055	0.03884
Foshan	0.02331	0.02867	0.02881	0.03387	0.03197	0.01587	0.01658	0.01843	0.05543
Shaoguan	0.01279	0.02586	0.02776	0.01636	0.01510	0.02106	0.01526	0.01770	0.02360
Heyuan	0.03750	0.02699	0.02364	0.01381	0.02614	0.02507	0.03088	0.01852	0.05281
Meizhou	0.01651	0.02944	0.02513	0.05243	0.04172	0.02275	0.01251	0.01145	0.04333
Huizhou	0.02401	0.03633	0.02642	0.04588	0.02990	0.01510	0.04727	0.02056	0.02767
Shanwei	0.02207	0.03084	0.02374	0.03007	0.03622	0.02387	0.01980	0.01412	0.05003
Dongguan	0.01402	0.02729	0.02140	0.05038	0.04061	0.01526	0.06341	0.03018	0.01487
Zhongshan	0.01509	0.03060	0.02739	0.05193	0.03566	0.01393	0.01961	0.01522	0.06175
Jiangmen	0.01775	0.03115	0.02726	0.02157	0.02280	0.02179	0.03397	0.02179	0.04192
Yangjiang	0.01513	0.02552	0.02568	0.02267	0.02345	0.01732	0.01667	0.01930	0.03150
Zhanjiang	0.03387	0.02546	0.02257	0.03405	0.01914	0.02029	0.02082	0.02238	0.05743
Maoming	0.03098	0.03086	0.02754	0.01153	0.02068	0.02501	0.01188	0.01860	0.06063
Zhaoqing	0.01496	0.03070	0.02739	0.01794	0.02177	0.01918	0.05156	0.01875	0.02443
Qingyuan	0.02949	0.03088	0.02859	0.03685	0.02791	0.02258	0.06523	0.02057	0.05112
Chaozhou	0.01958	0.02277	0.02297	0.03652	0.03723	0.02312	0.02645	0.01317	0.09794
Jieyang	0.01711	0.02077	0.02240	0.02141	0.02958	0.02034	0.01279	0.02175	0.02877
Yunfu	0.01707	0.02754	0.02666	0.01860	0.01878	0.02842	0.02398	0.01378	0.03943
	U19	U20	U21	U22	U23	U24	U25	U26	U27
Guangdong	0.02005	0.02741	0.02674	0.05338	0.02386	0.01138	0.01596	0.02695	0.03028
Guangzhou	0.01787	0.03166	0.02556	0.04278	0.02334	0.02429	0.01406	0.03068	0.03592
Shenzhen	0.02052	0.02722	0.03776	0.02780	0.02666	0.04436	0.02214	0.03125	0.03351
Zhuhai	0.01574	0.03727	0.01663	0.02188	0.02557	0.01328	0.01953	0.03404	0.02398
Shantou	0.02740	0.02614	0.01977	0.05810	0.01713	0.04345	0.01728	0.02564	0.02559
Foshan	0.01106	0.02870	0.01515	0.04780	0.02524	0.01034	0.03364	0.03175	0.02802
Shaoguan	0.02593	0.02532	0.02583	0.05027	0.02075	0.03062	0.06346	0.02635	0.03137
Heyuan	0.03153	0.03064	0.03296	0.03266	0.01857	0.02854	0.03835	0.02423	0.02318
Meizhou	0.02981	0.02991	0.02277	0.03637	0.01986	0.03966	0.03732	0.02535	0.02572
Huizhou	0.01818	0.02234	0.01857	0.02859	0.02063	0.02329	0.01514	0.02358	0.02337
Shanwei	0.03244	0.02500	0.01263	0.02984	0.02298	0.05306	0.01762	0.02787	0.02678
Dongguan	0.01385	0.02443	0.04084	0.05614	0.02154	0.02035	0.02665	0.02315	0.02556
Zhongshan	0.02218	0.02839	0.02332	0.06522	0.01684	0.03590	0.01793	0.02348	0.02776
Jiangmen	0.02196	0.02691	0.01758	0.03763	0.02695	0.01407	0.01664	0.02922	0.03006
Yangjiang	0.02401	0.02224	0.04198	0.01735	0.01441	0.03912	0.06977	0.02385	0.02436
Zhanjiang	0.02920	0.02277	0.04491	0.03900	0.01503	0.02498	0.01599	0.02370	0.02914
Maoming	0.03128	0.02735	0.01974	0.04285	0.02029	0.04628	0.02499	0.02529	0.02514
Zhaoqing	0.02348	0.02826	0.01237	0.03000	0.02258	0.03683	0.02452	0.02720	0.03021
Qingyuan	0.02551	0.02235	0.01573	0.02144	0.01764	0.03094	0.02098	0.02824	0.02566
Chaozhou	0.01590	0.02291	0.01164	0.05606	0.01624	0.02451	0.02264	0.02777	0.02450
Jieyang	0.03743	0.02900	0.02057	0.03174	0.01456	0.04201	0.02936	0.02373	0.02203
Yunfu	0.04222	0.02476	0.01843	0.04758	0.01908	0.01436	0.04280	0.03298	0.02857
	U28	U29	U30	U31	U32	U33	U34	U35	U36
Guangdong	0.02341	0.02755	0.02688	0.01182	0.03676	0.02835	0.02064	0.02093	0.02405
Guangzhou	0.02754	0.02349	0.02648	0.01447	0.02462	0.03075	0.01921	0.01532	0.02344
Shenzhen	0.02382	0.02567	0.02246	0.01026	0.01041	0.04361	0.02223	0.02033	0.03030
Zhuhai	0.02545	0.01648	0.02534	0.02194	0.01360	0.08225	0.01809	0.01793	0.02280
Shantou	0.02807	0.02094	0.02147	0.01805	0.01374	0.01624	0.03033	0.02705	0.02522
Foshan	0.03368	0.03425	0.02028	0.02087	0.01879	0.02008	0.01912	0.01492	0.01364
Shaoguan	0.01852	0.04385	0.02386	0.02117	0.02224	0.03843	0.03637	0.03269	0.04676
Heyuan	0.02465	0.01570	0.01418	0.01459	0.00909	0.02270	0.04609	0.04337	0.02935
Meizhou	0.01903	0.01538	0.01456	0.01274	0.01684	0.01873	0.04307	0.03003	0.02662
Huizhou	0.02437	0.02208	0.03597	0.02234	0.03722	0.04177	0.02352	0.01691	0.02592
Shanwei	0.02124	0.01525	0.03046	0.01033	0.02641	0.02233	0.02928	0.02466	0.02841
Dongguan	0.01996	0.03329	0.01615	0.00995	0.03149	0.02163	0.02271	0.01969	0.02139
Zhongshan	0.02747	0.02172	0.03144	0.01932	0.03096	0.03234	0.02282	0.02287	0.02626
Jiangmen	0.02696	0.02075	0.01337	0.02309	0.04743	0.03928	0.02015	0.02434	0.02693
Yangjiang	0.02151	0.02013	0.02851	0.00976	0.02233	0.06535	0.02358	0.02702	0.02535
Zhanjiang	0.01859	0.02820	0.02598	0.01101	0.02390	0.01678	0.03032	0.03076	0.02759
Maoming	0.02157	0.01936	0.01039	0.01341	0.03018	0.01754	0.03759	0.02240	0.03092
Zhaoqing	0.03567	0.03701	0.02662	0.02601	0.02781	0.04885	0.01461	0.01658	0.02807
Qingyuan	0.01695	0.01421	0.01707	0.02712	0.02366	0.02753	0.01773	0.01718	0.02692
Chaozhou	0.01898	0.01472	0.04319	0.01549	0.01113	0.03542	0.02512	0.02689	0.02300
Jieyang	0.03587	0.03874	0.02305	0.00951	0.02636	0.04648	0.02615	0.02052	0.02001
Yunfu	0.02535	0.01234	0.03845	0.01419	0.01793	0.03832	0.01587	0.01824	0.02734
	U37	U38
Guangdong	0.02119	0.01307
Guangzhou	0.02418	0.01669
Shenzhen	0.02289	0.01194
Zhuhai	0.00905	0.02632
Shantou	0.03068	0.00937
Foshan	0.01457	0.02106
Shaoguan	0.00931	0.01088
Heyuan	0.00960	0.00827
Meizhou	0.00897	0.01409
Huizhou	0.00962	0.00951
Shanwei	0.02189	0.01018
Dongguan	0.01469	0.00920
Zhongshan	0.00967	0.01923
Jiangmen	0.01048	0.01278
Yangjiang	0.00849	0.01542
Zhanjiang	0.02251	0.00860
Maoming	0.03219	0.01081
Zhaoqing	0.01004	0.01402
Qingyuan	0.01391	0.01606
Chaozhou	0.02588	0.00972
Jieyang	0.01865	0.00944
Yunfu	0.00999	0.01705

U1, percentage of added value of primary industry; U2, percentage of added value of secondary industry; U3, percentage of added value of tertiary industry; U4, urbanization rate; U5, GDP per capita; U6, total gross domestic product; U7, price index; U8, total investment in fixed assets; U9, foreign direct investment; U10, total import and export volume of foreign enterprises; U11, deposits and loans in renminbi in all financial institutions; U12, savings deposit by household in all financial institutions; U13, natural population growth rate; U14, population density; U15, expenditure for education per capita; U16, proportion of educational practitioners in the whole society; U17, internal expenditure on R&D of industrial enterprises; U18, collections of public libraries per 10 persons; U19, number of medical beds per 10,000 people; U20, number of medical technical personnel; U21, proportion of environmental and public facilities practitioners in the whole society; U22, proportion of public management and social organizations practitioners in the whole society; U23, local government general budgetary revenue; U24, public transportation vehicles per 10,000 people; U25, area of parks and green land per capita; U26, local government general budgetary expenditure; U27, average wages of fully employed staff and workers in urban units; U28, electricity consumption; U29, volume of waste water discharged; U30, total volume of industrial waste gas emission; U31, volume of industrial soot (dust) emission; U32, volume of industrial solid wastes produced; U33, water use per capita; U34, water resource per capita; U35, average annual rainfall; U36, cultivated land area; U37, rate of sewage treatment; U38, rate of consumption waste treatment

Dimensionless treatment of the indicator system:

$$\begin{array}{c}\begin{array}{cc}{\mathrm{g}}_{\mathit{ij}}^{{}^{\prime }}=\frac{{\mathrm{g}}_{\mathit{ij}}-\min \left({\mathrm{g}}_j\right)}{\max \left({\mathrm{g}}_j\right)-\min \left({\mathrm{g}}_j\right)},& \mathrm{Positive}\mathrm{indicator}\end{array}\\ \begin{array}{cc}{\mathrm{g}}_{\mathit{ij}}^{{}^{\prime }}=\frac{\max \left({\mathrm{g}}_j\right)-{\mathrm{g}}_{\mathit{ij}}}{\max \left({\mathrm{g}}_j\right)-\min \left({\mathrm{g}}_j\right)},& \mathrm{Negative}\mathrm{indicator}\end{array}\\ \left(i,=,1,2,,,\cdots ,,,m,,,j,=,1,2,,,\cdots ,,,n\right)\end{array}$$ 1

where it is assumed that there are m evaluation objects and n evaluation indicators, and g_ij denotes the j indicator of the i object. Calculate the weight of the j sample indicator value under the i indicator P_ij (Eq. (2)), determine the entropy value $e_i$ (Eq. (3)), and when $P_{\mathit{ij}}=0$, let $P_{\mathit{ij}}\times \ln P_{\mathit{ij}}=0$. Finally, calculate the entropy weight ${\omega }_j$ of the i ${\omega }_j$ of the i indicator (Eq. (4)).

$$Pij=\frac{{\mathrm{g}}_{\mathit{ij}}^{{}^{\prime }}}{{\sum }_{i=1}^m{\mathrm{g}}_{\mathit{ij}}^{{}^{\prime }}}$$ 2

$$e_i=-\frac{1}{\ln m}\times {\sum }_{i=1}^m\left(P_{\mathit{ij}},\times ,\ln ,,P_{\mathit{ij}}\right)$$ 3

$${\omega }_j=\frac{1-e_i}{{\sum }_{i=1}^m\left(1,-,e_i\right)}$$ 4

Coupling and coordination degree model

The study of urban system coupling helps to clarify the coupling process and the evolution law of ED-SD-EE systems, find the shortcomings of cities’ economic, social, and ecological development, and has important practical significance for establishing a sustainable and efficient model of green, sustainable, and high-quality urban development. As a popular method for the analysis of the coordinated development of various systems, the coupled coordination model is widely used in the coupling analysis of ecological and other systems (Ariken et al. 2020; Liu et al. 2021; Zuo et al. 2021; Liu et al. 2020; Yang Y et al. 2020; Zhu et al. 2021; Wang et al. 2020; Li et al. 2019; Yang C et al. 2020; Xie et al. 2021). The coupling degree and coordination degree are used to quantify the coupling and coordination degree of each indicator within the system, which can reflect the degree of coordination and the relative development level of the system (Zhang et al. 2019). It can visually and quantitatively reveal the interaction between cities’ ED-SD-EE systems and identify the relative lagging systems of cities’ development, which helps cities focus on the shortcomings of development and promote comprehensive economic-social-ecological to coordinated development.

Referring to the three-system coupling degree formulae of some scholars (Jianhua et al. 2015; Zhong and Liu 2012), a three-system economic-social-ecological coupling degree measurement model is constructed, which is calculated as follows.

$$C={\left[\left(F,\times ,G,\times ,H\right),/,{\left[\left(F,+,G,+,H\right),/,3\right]}^3\right]}^{\left(1,/,3\right)}$$ 5

where C is the coupling degree; the F\G\H are the comprehensive indices of the social, economic, and ecological subsystems, respectively. To further reflect the degree of coupling and coordination of cities’ ED-SD-EE system, the coupling degree of ED-SD, SD-EE, and ED-EE systems is calculating and combines the coupling and coordination degree of the three composite systems to comprehensively reflect the measurement model of coupling degree of cities’ ED-SD-EE systems (Liu et al. 2005). Calculations are as in Eq. (6).

$$\begin{array}{c}C={\left[\left(F,\times ,G\right),/,{\left[\left(F,+,G\right),/,2\right]}^2\right]}^{\left(1,/,2\right)}\\ C={\left[\left(F,\times ,H\right),/,{\left[\left(F,+,H\right),/,2\right]}^2\right]}^{\left(1,/,2\right)}\\ C={\left[\left(H,\times ,G\right),/,{\left[\left(H,+,G\right),/,2\right]}^2\right]}^{\left(1,/,2\right)}\end{array}$$ 6

T is the integrated economic-social-ecological evaluation index, and the calculation is shown in Eq. (7), where $\alpha$, $\beta$, and $\gamma$ are the weights. In the paper, we believe that the three aspects of ED-SD-EE in sustainable urban development in the context of new urbanization are equally important. So the weights in the comprehensive evaluation index T for the three systems are $\alpha =\beta =\gamma =1/3$. The weights in the calculation of the comprehensive evaluation index for the two systems are 0.5.

$$\begin{array}{c}\begin{array}{c}T=\alpha F+\beta G+\gamma H\end{array}\\ \begin{array}{c}T=\alpha F+\beta G\end{array}\\ \begin{array}{c}\begin{array}{c}T=\alpha F+\gamma H\end{array}\\ \begin{array}{c}T=\beta G+\gamma H\end{array}\end{array}\end{array}$$ 7

Finally, the coupling coordination is calculated as in Eq. (8), where $D$ is the degree of coupling coordination.

$$D=\sqrt{C\times T}$$ 8

Classification method of coupling and coordination degree in city

In the paper, the degree of coupling coordination is classified according to relevant studies and also according to the characteristics of the calculation results, on the premise that the spatial and temporal evolution of the coupling coordination development can be more clearly shown (Table 2).

Table 2.

Determination of coupling coordination standard

Coupling degree ($C$)	Coupling phase	Coordination degree (D)	Coordination phase
$0<C\le 0.25$	Not coupling	$0<D\le 0.25$	Incoordination
$0.25<C\le 0.40$	Low coupling	$0.25<D\le 0.40$	Low coordination
$0.40<C\le 0.55$	Moderate coupling	$0.40<D\le 0.55$	Moderate coordination
$0.55<C\le 0.70$	Basic coupling	$0.55<D\le 0.70$	Basic coordination
$0.70<C\le 0.85$	High coupling	$0.70<D\le 0.85$	High coordination
$0.85<C\le 1.00$	Excellent coupling	$0.85<D\le 1.00$	Excellent coordination

At the same time, based on the comparison between the comprehensive evaluation indices of the ED-SD-EE subsystems of each city, the lagging systems of each city are identified, and the degree and type of lagging of each subsystem are determined (Table 3). In order to clarify the importance and urgency of the balanced ED-SD-EE system development of cities, the paper assigns scores to the relative lagging degree among the subsystems, identifies the shortcomings of the balanced development of cities, and provides a basis for such plans as “new urbanization,” “green and sustainable,” and “high-quality development.” This will provide reasonable and scientific suggestions for governments to formulate targeted policies.

Table 3.

Comparison relation of F/G/H and assigned to a score

$F>G$	$G/F\ge 0.8$	$0.5\le G/F<0.8$	$G/F<0.5$
Basic types	The ED system lags behind the SD system	The ED system lags significantly behind the SD system	The ED system is extremely lagging behind the SD system
$G>F$	$F/G\ge 0.8$	$0.5\le F/G<0.8$	$GF/G<0.5$
	SD system is lagging	SD system is seriously lagging	SD system is extremely lagging
$G>H$	$H/G\ge 0.8$	$0.5\le H/G<0.8$	$H/G<0.5$
	EE system is lagging	EE system is seriously lagging	EE system is extremely lagging
$H>G$	$G/H\ge 0.8$	$0.5\le G/H<0.8$	$G/H<0.5$
	ED system is lagging	ED system is seriously lagging	ED system is extremely lagging
$H>F$	$F/H\ge 0.8$	$0.5\le F/H<0.8$	$F/H<0.5$
	SD system is lagging	SD is seriously lagging	SD systems is extremely lagging
$F>H$	$H/F\ge 0.8$	$0.5\le H/F<0.8$	$H/F<0.5$
	EE system is lagging	EE system is seriously lagging	EE system is extremely lagging
Score	1	2	3

In the paper, the relative lagging systems of each subsystem in Table 3 are evaluated and scored, and then the scores are summed to give a final score for each subsystem, ranging from 0 to 6, which indicates the priority of each subsystem for coordinated development. Higher scores are being given priority in the coordinated development of the city. The scores are analyzed on a step-by-step basis to judge the urgency of optimizing a particular system in a three-system city. When the final score of a subsystem is 0/1, it means that the system has a low impact on the overall urban ecosystem lag and requires only a little attention in the subsequent cities’ development process. When subsystem A has a final score of 2, two scenarios can be distinguished as follows: (i) it lags badly compared to subsystem B + subsystem C has no impact (i.e., 2 + 0). (ii) it lags relatively well for both systems (i.e., 1 + 1), meaning that the city needs to invest some effort in focusing on the coordinated development of the system. When the final score is 3 (i.e., there are 1 + 2 and 3 + 0) or 4 (i.e., 1 + 3 and 2 + 2), local governments should be aware that at this point the system is already affecting the balanced development of the city more seriously and more effort needs to be invested to properly deal with the lag between this system and other systems. When the final score is 5 (i.e., 2 + 3) or 6 (i.e., 3 + 3), it means that the development of the system is severed from the other systems and has seriously affected the balanced development of the city, and it is urgent to deal with the lagging problem of the system.

Empirical results and analysis

Coupling analysis

In terms of the degree of coupling between the ED-SD-EE systems, most of Guangdong Province and its subordinate cities are in an extremely coupling state from 2010 to 2020 (Fig. 1a). The coupling degree of Guangdong Province as a whole in 2010 is 0.73002, which is in the basic coupling state, while the coupling degrees of Guangzhou, Huizhou, Yangjiang, and Dongguan in the rest of 2010 are 0.75665, 0.80887, 0.84014, and 0.84869, respectively. While the coupling degree of Dongguan in 2019 is 0.84803, and the coupling degrees of Shanwei, Meizhou, and Dongguan in 2020 are 0.80333, 0.83710, and 0.82613, respectively, and all are in a highly coupled state. Although there are slight differences in the temporal changes of the coupling degree of different cities, the overall trend is similar, and the overall trend of coupling degree is inverted U-shaped (Fig. 2), showing an increasing trend, followed by a decreasing trend. Since the onset of the COVID-19, strong epidemic prevention and control measures in China have brought about tangible epidemic prevention and control effects, while also having a huge impact on resource consumption and economy and society (Chen et al. 2021). The paper therefore presents a comparative analysis of urban coupling coordination in 2019 and 2020 to explore the impact of the COVID-19 on the coordination development and coupling mechanism of cities.

Fig. 1.

a Coupling degree of ED-SD-EE, b coupling degree of ED-SD, c coupling degree of SD-EE, d coupling degree of ED-EE

Fig. 2.

Trends in panel data based on coupling between Guangdong and municipalities

In terms of coupling degree, it can be concluded that the COVID-19 has a negative impact on the coupling between the ED-SD-EE subsystems of cities (Fig. 1a), with most of the coupling decreasing in 2020 (Shenzhen, Zhuhai, and Chaozhou’s coupling has increased, compared to 2019). But it is still in an extremely coupled state. An analysis of Fig. 1b shows that the epidemic does not have a significant impact on the coupling between the city’s SD and ED subsystems; the degree of ED-SD coupling is mostly at excellent coupling (except for Guangzhou, which is at high coupling in 2010). And the degree of ED-SD coupling does not change much after 2014, implying a high degree of city economic-social coupling. This also matches the fact that many researchers usually consider the economic and social combinations when researching (Chen et al. 2019). It is also in Fig. 1c, the SD-EE coupling of Shenzhen, Zhuhai, and Chaozhou increased, while this coupling of other cities showed a decreasing trend, and the coupling of Guangdong, Shantou, Heyuan, Meizhou, Huizhou, and Yangjiang decreased from excellent coupling to highly coupling, and even Shantou decreased from excellent coupling to highly coupling, indicating that the epidemic would have a negative impact on the SD-EE system coupling in most cities in Guangdong. An analysis of Fig. 1d shows that the ED-SD coupling decreases in all cities except Zhuhai, implying that the epidemic also has a negative impact on the coupling of ecology and economy.

Coupling coordination analysis

From the change trend of the coupling and coordination degree of each city (Fig. 3), the development of the coupling and coordination degree of the two systems and the three systems basically shows a similar time change pattern, and the coupling and coordination degree of Guangdong Province and each city basically shows a stable upward trend. Combined with Fig. 4, the degree of coupling coordination of the ED-SD-EE systems has increased from uncoordinated (Guangzhou, Heyuan, and Meizhou) or low coordination in 2010 to moderate coordination in 2020. The degree of coupling coordination of the ED-SD system improves from uncoordinated (Guangzhou, Heyuan, Meizhou, and Zhanjiang) or low coordination in 2010 to basic coordination in 2020. The degree of coupling coordination of the SD-EE system, on the other hand, has increased from low or moderate coordination (Shenzhen, Zhongshan, Qingyuan, and Jieyang) to moderate coordination or basic coordination in general, with Zhongshan and Shenzhen being at the moderate coordination level for a long time. The analysis of the visualized coordination between 2019 and 2020 shows that the epidemic has a negative impact on the SD-EE coupling coordination of cities, but the overall change of coupling coordination degree is not significant. With the SD-EE in Maoming rising from moderate coordination in 2019 to basic coordination, the coupling coordination of ED-EE increased or maintained from low or moderate coordination (Qingyuan, Zhaoqing, Yunfu, Yangjiang, Jiangmen, Zhongshan, Jieyang, and Chaozhou) in 2010 to basic coordination (Guangzhou, Qingyuan, Zhaoqing, Yunfu, Jiangmen, Jieyang, Heyuan, and Shaoguan) or moderate coordination in 2019; and Guangdong Province was also in basic coordination in 2019. However, in 2020, except for Shenzhen and Zhuhai, which improve to a state of basic coordination, the coupling coordination of ED-EE in other cities decreases or is in a state of moderate coordination (Foshan and Dongguan are still in a state of moderate coordination in 2020, although their ED-EE coordination has improved). In general, the coupling coordination degrees of each city do not vary much, and the trends of change are similar.

Fig. 3.

a Coupling and coordination degree of ED-SD-EE. b Coupling and coordination degree of ED-EE. c Coupling and coordination degree of ED-SD. d Coupling and coordination degree of SD-EE. (*Calculated results for the 2010–2020 dataset)

Fig. 4.

Spatial distribution of coupling and coordination degree in Guangdong from 2010 to 2020 (calculated results for the 2010–2020 dataset)

At the same time, in 2016, the coupling and coordination of most cities rose from low to moderate coordination after the requirement of new urbanization, and construction of ecological civilization was introduced in 2012. It is easy to see that in 2017, after the introduction of high-quality development, the coupling coordination also rose towards basic coordination, but the arrival of the COVID-19 interrupted this upward trend.

Taken together, the above analysis reveals that the coupling and coordination of Guangdong’s municipalities are by and large progressing towards a better trend until 2019. However, the epidemic in 2020 has a negative impact on the ED-SD-EE systems’ coupling in most of the cities in Guangdong, especially causing a fragmentation of the interactions between the ecological environment subsystem and the other two subsystems. However, it is also not difficult to find that Zhuhai’s coupling in 2019–2020 is on the rise in all categories, and the epidemic has a smaller impact on Zhuhai’s development coupling mechanism. Subsequent researchers can conduct research and analysis on the coupling in Zhuhai and try to identify the factors that condition the joint progress of the long-term epidemic prevention and control requirements and the high-quality coordinated development of the city. It can be seen that most cities, after experiencing the epidemic, have a reduced ability to interact between social development, economic development, and ecological environment systems. Subsequent governments need to consider ecological environment factors comprehensively when formulating relevant policies, especially policies on epidemic prevention and control and economic development and emergency response to major public health events.

In order to consider the change trend of the coupling coordination degree of Guangdong cities before the impact of the epidemic, the paper excludes the data of 2020 and uses the dataset of Guangdong cities from 2010 to 2019 to recalculate the index weights and coupling coordination degree, and the results are detailed in Figs. 5 and 6. An analysis of Fig. 5 shows that the coupling coordination degrees calculated from the 2010 to 2019 dataset show an increasing trend. The results of Fig. 5 show an increasing trend. Figure 6 shows that after excluding the 2020 data, the coupling coordination of ED-EE and SD-EE increases more significantly in 2019 (compared with Fig. 4, calculated for the 2010–2020 dataset), and the coupling coordination of the ED-EE system in Heyuan even reaches a high level of coordination in 2019. Therefore, it can be concluded that the impact of the COVID-19 on the ED-SD-EE coupling coordination of the city mainly acts on the coupled coordination between the ecological environmental systems and other systems. Combined with the analysis of Figs. 3 and 4, we can also learn that the level of coordination between the EE system and other systems is a long-term constraint affecting the coordination development of cities in Guangdong Province, even if the coordination between the EE system and economic development and social development systems still decreases when the epidemic also hinders the economy and society to a certain extent. Of course, although some data in the paper are supplemented by exponential smoothing predictions over 20 years, the data predicted have a relatively small weighting (see Appendix 1 for details) and have a small impact. So the conclusions drawn can be considered relatively reliable.

Fig. 5.

a Coupling and coordination degree of ED-SD-EE. b Coupling and coordination degree of ED-EE. c Coupling and coordination degree of ED-SD. d Coupling and coordination degree of SD-EE. (Calculated results for the 2010–2019 dataset)

Fig. 6.

Spatial distribution of coupling and coordination degree in Guangdong from 2010 to 2019 (calculated results for the 2010–2019 dataset)

Types of urban economic-social-ecological system development

By calculating $F/G/H$, the final scores were calculated by comparing each other and assigning scores to obtain the system lag score table (see Appendix 2 for details). From the results of the assignment, we can see that the lagging score of the city EE system shows an upward trend with time migration. The lagging of the EE system in all cities basically reaches more than 5 points in 2020 (3 points in Zhongshan and 4 points in Shenzhen). The overall development between the EE system and the ED and SD system shows extreme lagging (Zhongshan and Shenzhen are seriously lagging), and some cities (Guangzhou, Foshan, etc.) rose from no lagging influence to extreme lagging. At the same time, the scores of the SD system and the ED system vary from city to city. But except for Zhuhai, the final scores of ED system in Guangdong and in the rest of the cities basically show a decreasing trend, while the lagging impact of the SD system is relatively small (or basically no impact). Therefore, the lagging types of Guangdong cities (except Zhuhai) are mostly “seriously lagging in EE, relatively low impact of lagging SD, relatively leading in EE.” The lagging stage of Zhuhai is, however, in the lagging stage of “seriously lagging in EE, relatively leading in SD, relatively low impact of lagging EE.” From a comprehensive perspective, the relatively lagging EE system is an important reason limiting the coupling and coordination development of ED-SD-EE systems in various cities in Guangdong. To achieve high-quality and coordination development in subsequent places, it is necessary to focus on ecological and environmental management and make up for the ecological shortcomings.

Table 5.

Subsystem lagging assignment

Guangdong	ED	SD	EE	Guangzhou	ED	SD	EE	Shenzhen	ED	SD	EE	Zhuhai	ED	SD	EE
2010	3	5	0		3	5	0		2	3	1		3	5	0
2011	2	3	0		2	3	2		0	3	4		2	3	0
2012	2	3	0		2	3	2		0	2	4		2	3	1
2013	2	2	1		1	2	0		0	2	3		2	2	1
2014	1	1	1		1	2	1		1	1	1		0	2	3
2015	1	1	1		1	2	1		0	1	3		0	2	5
2016	0	1	2		0	1	2		1	0	2		0	1	4
2017	0	1	4		0	1	4		0	1	4		1	0	5
2018	0	1	6		0	1	5		1	0	4		1	0	6
2019	0	2	6		0	1	5		0	2	5		1	0	6
2020	0	2	6		0	1	6		0	1	4		2	0	5
Shantou	ED	SD	EE	Foshan	ED	SD	EE	Shaoguan	ED	SD	EE	Heyuan	ED	SD	EE
2010	3	4	0		4	3	0		3	3	0		3	5	0
2011	1	1	1		3	2	0		3	2	0		0	1	3
2012	2	3	0		2	1	0		4	3	0		4	3	0
2013	2	3	0		0	1	2		2	2	0		3	2	0
2014	1	2	1		0	1	3		1	2	0		1	0	3
2015	2	1	0		2	2	1		2	2	1		0	1	4
2016	1	1	1		0	1	3		2	2	1		1	2	2
2017	0	1	6		0	1	4		0	2	5		0	1	6
2018	0	2	5		0	1	6		0	2	5		0	2	6
2019	0	2	6		0	1	6		0	2	3		0	2	5
2020	0	2	6		0	1	6		0	2	5		0	2	6
Meizhou	ED	SD	EE	Huizhou	ED	SD	EE	Shanwei	ED	SD	EE	Dongguan	ED	SD	EE
2010	5	3	0		3	4	0		5	3	0		3	5	0
2011	4	2	0		3	2	0		3	2	0		0	1	2
2012	4	2	0		2	2	1		3	2	0		1	1	1
2013	3	2	0		0	2	3		2	4	0		0	1	3
2014	0	1	4		0	1	2		2	3	0		1	0	4
2015	0	2	5		1	2	2		0	2	3		0	1	3
2016	0	2	3		1	2	2		1	2	2		0	1	2
2017	0	2	6		0	1	6		0	2	5		0	2	5
2018	0	2	6		0	2	6		0	2	6		0	2	6
2019	0	2	6		0	1	6		0	2	6		0	3	6
2020	0	2	6		0	1	6		0	2	6		0	2	6
Zhongshan	ED	SD	EE	Jiangmen	ED	SD	EE	Yangjiang	ED	SD	EE	Zhanjiang	ED	SD	EE
2010	3	3	0		4	3	0		4	3	0		3	5	0
2011	1	2	2		1	0	2		4	2	0		3	2	0
2012	2	2	1		2	2	0		3	4	0		4	2	0
2013	2	3	2		2	2	0		2	3	0		3	2	0
2014	1	2	1		0	1	3		0	2	3		1	2	0
2015	2	2	1		1	2	1		0	2	3		1	2	1
2016	1	3	1		1	2	1		0	2	3		1	2	2
2017	0	2	4		0	2	3		0	2	5		0	2	5
2018	0	2	5		0	1	5		0	1	4		0	2	5
2019	0	2	5		0	1	5		0	2	5		0	1	6
2020	0	2	3		0	1	6		0	2	6		0	1	6
Maoming	ED	SD	EE	Zhaoqing	ED	SD	EE	Qingyuan	ED	SD	EE	Chaozhou	ED	SD	EE
2010	3	3	0		3	2	0		4	2	0		5	3	0
2011	1	2	1		3	2	0		2	0	5		4	3	0
2012	2	3	0		2	3	0		2	2	0		2	1	1
2013	2	3	0		2	2	0		2	3	0		2	2	0
2014	0	1	3		0	2	4		1	2	1		1	0	2
2015	0	1	2		0	2	4		2	2	1		0	2	3
2016	0	2	3		0	2	3		1	2	2		0	2	3
2017	0	2	5		0	1	6		0	1	5		0	2	6
2018	0	2	4		0	1	4		0	1	6		0	2	5
2019	0	2	5		0	1	4		0	2	5		0	2	5
2020	0	2	5		0	1	6		0	2	6		0	2	5
Jieyang	ED	SD	EE	Yunfu	ED	SD	EE
2010	3	3	0		3	3	0
2011	3	2	0		2	2	1
2012	2	2	0		3	2	0
2013	2	2	1		1	0	2
2014	1	0	4		0	1	4
2015	0	1	4		0	1	3
2016	0	1	3		0	2	5
2017	0	2	5		0	2	5
2018	0	2	6		0	2	5
2019	0	2	4		0	1	5
2020	0	2	5		0	2	6

ED economic development system, SD social development system, EE ecological environment system

Discussion

The paper presents an empirical analysis of the coupled coordination of the three systems in 21 cities of Guangdong Province from the perspectives of economic development, social development, and ecological environment. Meanwhile, an attempt is made in the paper to construct a system lag type assignment system based on the ratio of the composite index of each subsystem, which is used to study the shortcomings of the cities’ coordinated development. The results show that:

1. The spatial and temporal evolution of the coupling and coordination between 2010 and 2020 shows that the coupling and coordination between social, economic, and ecological systems in each city shows a trend of “increasing before decreasing.” However, the overall trend is still on the rise, and the cities’ ED-SD-EE system is at the stage of “high coupling-moderate coordination” in 2020. The degree of ED-SD-EE coordination among Guangdong’s cities is still insufficient, and the lagging impact of the ecological system is serious. The coupling of ED-SD-EE systems in Guangdong’s prefecture-level cities is inadequate. Subsequent government policies need to focus on the coordination between the ecological environment and economic and social.

2. The most economically developed region in Guangdong is the PRD region (Guangzhou, Shenzhen, Dongguan, Foshan, Zhongshan, Zhuhai, Jiangmen, Zhaoqing, and Huizhou), and there is no significant difference between its ED-SD-EE coupling and coordination degree and that of other non-PRD cities. The degree of coupling and coordination of “social-ecological” and “ecological-economic” is similar, and the degree of coupling and coordination of the two is slightly lower than that of “economic-social”.

3. In the Pearl River Delta region, especially in Guangzhou, Zhuhai, Shenzhen, Foshan, and Dongguan, many highly polluting and inefficient factories have been relocated since the introduction of plans and requirements such as “New Urbanization” in 2012 and “High Quality Development” in 2017. The introduction of enterprises with high technology content, for example, the introduction of Huawei in Dongguan and the creation of high-tech industrial clusters in Foshan mean that governments around the world are paying more and more attention to the harmonization of environmental and economic benefits. However, the economic, social, and ecological coordination of PRD cities is still in a relatively weak state, and the difference in the degree of coupling and coordination between ED, SD, and EE systems is not obvious.

4. Under the conditions defined by the data set, based on the results of the coupling coordination measurement combined with the analysis, it can be known that the epidemic has caused a certain negative impact on the coupling coordination of urban economy-society-ecology. In particular, it has caused a serious fragmentation of the urban ecological system from the economy and society, and ecological development has become a serious lagging factor in the high-quality and coordinated development of cities. Guangdong’s cities need to assess the extent to which previous policies have been implemented, how effective they have been, and the need for more scientific and better suited policy regulations for the long-term effects of the epidemic. However, Zhuhai was able to improve the degree of coupling and coordination after the onset of the epidemic. Subsequently, researchers can conduct relevant analyses of Zhuhai’s practices and industrial restructuring to provide lessons for other cities under the long-term effects of the epidemic.

Conclusion

The paper distinguishes between economic and social dimensions and constructs a three-system indicator system of urban economy-society-ecology and environment, which more comprehensively reflects the degree of coupled and coordinated development of social, economic, and ecological systems in cities. However, as the economy and society of cities are vast and complex systems, among which there are also the quality of public space for housing (Zhao et al. 2021), social media (Liu et al. 2022) and urban housing prices (Wu et al. 2018), which are all important components of the economic and social systems and can all have an impact on the economic and social development of cities.

Therefore, subsequent researchers can refer to the indicator system in the paper and add relevant available and scientific data from it to construct a more comprehensive indicator system for research on urban coupling and coordination and comprehensive evaluation of system development. The slow update of 2021 and subsequent yearbooks affects the author’s further research, and the article only tentatively points out the fragmentation of the coupling and coordination between ecological, environmental, social, and economic systems by the epidemic. Subsequent studies could construct a more comprehensive indicator system for cities (based on data availability) to explore the long-term effects of the epidemic on each system. This will help to eliminate the negative impact of the epidemic on the coordinated development of the city and to realize the potential of a good ecological environment to promote economic and social development.

To sum up, the possible marginal contributions of the paper are: (i) Through literature review, a comprehensive development evaluation index system for social, economic, and ecological systems at the municipal level in Guangdong Province is constructed. (ii) Distinguishing between economic and social development systems, the spatio-temporal evolutionary links between social, economic, and ecological systems are explored from the analysis of the coupling and coordination mechanism. (iii) By defining the temporal scope of the dataset, the impact of the epidemic on the degree of coupling and coordination among the social, economic, and ecological systems and the direction of its effects are explored. The results of the paper can provide scientific reference for the coordinated development of economic, social, and ecological systems in urban Guangdong Province.

Author contribution

All the authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Fei Chen, Na Wang, and Dandan Zhang. The first draft of the manuscript was written by Fei Chen and Guotong Qiao, and all the authors commented on the previous versions of the manuscript.

Data availability

In addition to the above sources, all the raw data in the paper can also be obtained from the FIGSHARE website: “Spatio-temporal evolution analysis of the coupling situation of economic-social-ecological system in Guangdong. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21430668.v1.”

Declarations

Ethical approval

Not applicable.

Consent to participate

In the paper, all the authors of the paper have agreed to be the authors of this study.

Consent for publication

All the authors have approved the manuscript and agreed with its submission to ESPR.

Competing interests

Not applicable.