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Article

Interlinkages among County-Level Construction Indicators and Related Sustainable Development Goals in China

by
Jiawei Zhong
* and
Xun Li
Department of Urban and Regional Planning, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(11), 2008; https://0-doi-org.brum.beds.ac.uk/10.3390/land11112008
Submission received: 19 October 2022 / Revised: 2 November 2022 / Accepted: 2 November 2022 / Published: 10 November 2022
(This article belongs to the Special Issue Regional Solutions to Urban-Rural Coordinated Development in the Megacity Regions)
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Abstract

:
Counties are the fundamental platforms of urban–rural integration in China. Indicators at the county level, however, are insufficiently investigated for their interlinkage with each other. This study focuses on the indicators in the China Statistical Yearbook (county level) and China County Seat Construction Statistical Yearbook based on the classification of Sustainable Development Goals (SDGs). Average weighted degree and modularity are adopted to reveal the indicators with high connections to others, as well as the trends of indicator connections and network divisions as the total index increases. Construction indicators regarding green space, water supply and wastewater treatment are found to be most influential in the indicator correlation network. The inverted U curve of modularity indicates that a county may encounter a bottleneck when the total index is at the middle level, as the indicators become more isolated. This study also compares the results with those in the Sustainable Development Report 2020 and Rural Construction Evaluation Report 2020 to verify the findings at the county/county-seat level. Additional indicators regarding public services and environment protection are required in further statistics to track the comprehensive performance of SDGs at this level.

1. Introduction

Sustainable Development Goals (SDGs) are the 17 goals and 169 targets announced by the United Nations (UN) in the 2030 Agenda for Sustainable Development, which was passed at the United Nations Sustainable Development Summit 2015. These goals encourage development that balances the economy, the society and the environment. They cover the multiple challenges in diversified areas faced by developed and developing countries in the new century. China recently stated its position on supporting the SDGs at the country level in “China’s National Plan on Implementation of the 2030 Agenda for Sustainable Development”. In 2021, the government released the first five-year report (2016–2020) on its performance regarding the SDGs, which demonstrates the Chinese government’s concrete actions to achieve the UN goals.
Urban–rural integration is an important national strategy in sustainable development in China [1,2]. Sustainable development in both urban and rural regions can lead to their support of each other [3]. In China, the county, as the basic unit of the government system, ensures rural autonomy [4]. County seats are the connection points between cities and the countryside. They can absorb rural migration and the spillovers of urban resources [5]. County seats can work as fundamental platforms supporting the coordination of urban–rural development [6]. Hence, an investigation of county-level indicators, especially on the correlation relationship among different indicators, can contribute to the discussion of urban–rural integration and sustainable development.
However, research about how the SDGs are implemented in China is mainly conducted at the national, provincial and municipal levels [7]. This is due to the fact that the SDG system announced by the UN focuses on development at the country and municipal levels. For example, Bertelsmann Stiftung and the Sustainable Development Solutions Network [8] conducted an assessment of the SDG scores in China. The results show that the SDG scores in China have risen from 2016 to 2021, with 72.1% of the SDGs achieved. On the other hand, the unbalanced SDG scores among different provinces is still an important problem that remains to be solved [9]. At the smaller scale, such as counties, no operable instructions are supplied in the guideline. Although the SDG targets for cities can be applied indiscriminately to the counties, this may not be a good solution for two reasons. First, the statistics data for counties are usually insufficient in comparison to those for municipalities. Some statistics adopted at the municipal level cannot be estimated at the county level. Second, the general development level in a county is usually lower compared to that in a city. The low achievement of SDGs in counties may lead to significantly different phenomena in the correlation among the SDGs compared with the case in municipalities. Hence, the interlinkage among SDGs at the county level is worth being investigated, even when the cases on larger levels have been discussed.
The 17 SDGs have potential interactions with each other [10,11]. For example, No Poverty (SDG1) should have a synergistic relationship with Good Health and Well-being (SDG3), Clean Water and Sanitation (SDG6) and Decent Work and Economic Growth (SDG8). If the government takes action to improve the quality of life related to SDG3, SDG6 or SDG8, this will usually help to achieve SDG1. On the contrary, a trade-off relationship between SDG8 and Climate Action (SDG13) or other environmental goals is usually believed to exist. The developing countries may have difficulties in balancing SDG8 and other SDGs related to long-term environmental protection. Detecting the connections of different indicators, targets and SDGs will foster recognition of essential indicators and help governments formulate feasible policies.
Complex network analysis is an essential tool to investigate the interlinkages among multiple variables. Ospina-Forero et al. [12] have summarized the related approaches for SDGs through network analysis, including qualitative methods, such as experts’ assessments, to construct the correlation matrix [13,14] or to conduct correlation analysis based on existing criteria [15,16]. These studies are generally conducted to evaluate the diffusion effects from policies on the other SDGs [17,18].
Miola et al. [19] organized a methodology to match the indicators and the SDGs, including the methods of the linguistic approach [17], literature approach [20,21], expert judgement approach [22,23], quantitative approach [24] and modeling complex system interaction [25,26]. These approaches still focus on the national or international level, but some methodology can be extended to the county level.
This paper focuses on the existing statistical data at the county level or in the county seats. Indicators corresponding to the SDGs are filtered from these statistical data. In addition to the descriptive statistics for all the indicators, the network among different indicators is also discussed by adopting complex network analysis. Average weighted degrees and modularity are applied to assess the most important indicators, indicators that have less influence on the others in the network and the level of division among these indicators. This helps to discover which indicators are crucial in the assessment of development in the counties or county seats and their corresponding SDGs.

2. Methodology

The goals and targets in the SDGs are not isolated. Even before the introduction of the network analysis approach, scholars had already discussed the interactions among the different goals and targets [27,28,29,30]. Some goals were found consistent with each other, while some were found in conflict with one another.
In the indicator structure of the SDGs, indicators related to economic, social and environmental areas are usually cross-sectional. To illustrate the interlinkages among different goals and targets, substantial research work is required. Interdisciplinary experts have to take a deep dive into the discussion about the impacts of each goal or target [19]. However, the correlation network developed by expert discussions is usually not replicable. The network developed by the literature reviews may be incomplete, as previous works usually depend on prior knowledge that some interactions should exist. Network analysis, instead, gains the scholars’ attention, as it does not depend on prior knowledge.
The advantage of network analysis in this area is that it is a data-driven approach, which does not rely on the existing understanding of the massive indicators. Network analysis has proven its feasibility in the fields of finance [31] and genetics [32], which contain massive datasets and interactions that cannot be completely described by the literature and expert discussions. Network analysis can be operated automatically without prior knowledge. Network analysis can also be employed in the analysis of SDGs or detailed indicators, such as the construction indicators at the county level in this study [14,33,34]. Additionally, the interactions among indicators may diverge in different countries [28] because these interactions in socio-economic areas are not universal in a way that the physical phenomena are.
As with other data-driven methodology, an explanation of network analysis should be based on existing knowledge about the indicators. If a significantly conflicting phenomenon is detected from the network analysis, further investigations, such as robustness tests or comparisons between different groupings or different sources, should be conducted. Results from network analysis should be treated deliberately.
In the network analysis process, several statistics are adopted to describe the effect of each indicator on the others. The first statistic describes connectivity. The connectivity of a graph is usually defined as the proportion of the actual number of links among the nodes in that graph to the maximum number of all possible links among the nodes whose number is the same as that in the graph. High connectivity of the synergy network means that more SDGs can be improved simultaneously. Low connectivity of the synergy network indicates that greater efforts are required to achieve the same number of SDGs.
In an undirected graph, the degree of a node is the number of links of that node. If each link contains a weight, the weighted degree of a node is the weighted sum of its links. The average weighted degree of this undirected graph is the arithmetic mean of the weighted degrees of all nodes.
The value of connectivity is positively proportional to the average weighted degree. In fact, the sum of weighted degrees is the product of the weighted connectivity and the number of maximum links in the undirected graph. In this research, the number of nodes is fixed, as is the number of maximum links. As a result, the average weighted degree is a substitute for connectivity.
The second statistic describes modularity. The modularity of a graph is defined as the degree to measure how a network is separated into different modules. In each module, the nodes within it are strongly connected with each other but are loosely connected to nodes outside that module. If the modularity of a network is higher, this network can be divided into several groups of nodes with weak connections among different groups. If the modularity of a network is lower, the nodes in this network are closer to each other.
The mathematical definition of modularity is as follows. Suppose there is another random network, which has the same number of nodes and the same degree of each node as the original network. Then, if the degrees of all nodes are ordered in the original network, the list of degrees should be the same as that in the random network. The modularity is then defined as the probability of a link, which is completely located inside a group/module in the random network. It is not difficult to prove that this mathematical definition is consistent with the former definition. When the modularity of a network is higher, the probability of a link being in a specific group is also higher.
Connectivity and modularity are two common network metrics. However, some disadvantages still require attention. The order and division of indicators obtained from connectivity and modularity are not equal to causality. The geographical and temporal range of samples would also affect the analysis results. The order of priority for the indicators should be treated as the direction of further research rather than a unique standard answer. The conclusions should also be compared with those in other studies related to sustainable development.

3. Data Analyses

3.1. Data Source and Descriptions of Indicators

The original data in this research are from the China Statistical Yearbook (county level) and the China County Seat Construction Statistical Yearbook (CCSCSY). The analysis includes 1495 county-level administrative units, including counties, county-level cities and autonomous counties. The permanent resident population in a county seat is derived from the county seat population and the county seat temporary population in CCSCSY 2021 (which contains the data from 2020). The permanent resident population in a county-level administrative unit is from the seventh national census in 2020. The area of a county seat is from the areas of built districts listed in CCSCSY 2021.
The advantage of adopting these two yearbooks is that they provide the most comprehensive coverage of county-level construction statistics. Their inclusion of statistics from a large number of counties can reduce the bias of conclusions in the network analysis. However, as mentioned later, the statistical index systems in these two yearbooks were not established based on the SDGs. For some SDGs, no corresponding indicators are provided in the present version of these yearbooks. Hence, even if the most influential indicators are detected in the correlation network, it does not follow that the related SDGs must be the most important SDGs. The following discussions will focus on the available indicators in these yearbooks. The conclusions cannot be simply extended to the interlinkages among SDGs.
In CCSCSY, the following indicators are used directly from the yearbook: Population Density, Daily Water Consumption Per Capita, Water Coverage Rate, Public Water Coverage Rate, Gas Coverage Rate, Density of Water Supply Pipelines in Built District, Road Surface Area Per Capita, Density of Road Network in Built District, Surface Area of Roads Rate of Built District, Density of Sewers in Built District, Wastewater Treatment Rate, Centralized Treatment Rate of Wastewater Treatment Plants, Public Recreational Green Space Per Capita, Green Coverage Rate of Built District, Domestic Garbage Treatment Rate and Domestic Garbage Harmless Treatment Rate. Some indicators are divided by population, including Newly Added Fixed Assets of This Year, Total Quantity of Water Supply, Total Natural Gas Supplied, Total Liquefied Petroleum Gas Supplied, Total Quantity of Wastewater Treated, Surface Area of Roads Cleaned and Maintained and Number of Latrines. Additionally, the Length of Gas Supply Pipeline and Number of Bridges are divided by the area of the built district.
In the China Statistical Yearbook (county level) (CSY), the following indicators are divided by population: Regional Domestic Product, Revenue in Local Government General Public Budgets, Expenditures in Local Government General Public Budgets, Household Savings Balance, Loan Balance of Financial Institution at Year-End, Number of Fixed Line Telephone Users, Number of Beds in Health Institutions and Number of Beds in Social Welfare Institutions with Accommodation. The number of enterprises above the designated size is adopted directly from the CSY.
Table 1 lists the converted indicators (after the division by population or by area), with the corresponding SDGs and targets.
The correlation coefficient matrix based on the complete samples is colored as follows (Figure 1). The range is from −1 (red) to 1 (green). The number of green cells is significantly larger than that of red cells. Red cells concentrate on the coefficients of indicators S11.3.1 (Population Density) and S11.3.2 (Expenditures in Local Government General Public Budgets Per Capita), neither of which is usually explained as the direct measure of development. The high proportion of positive correlation suggests that it is reasonable to focus on the synergy network. The following analyses will pay attention to the synergy network of the indicators in Table 1. Figure A1 in Appendix A provides a graph of the synergy network of these indicators.
The indicators are then normalized by the following formula:
X ′ = X − X m i n X m a x − X m i n ∗ 100
The upper bound Xmax and the lower bound Xmin are the maximum value and the minimum value of the original indicator X in the dataset. This does not change the correlation coefficient between two indicators.

3.2. Connectivity

Connectivity analysis is the fundamental part of network analysis in the SDG network [34,35,36]. The connectivity analysis is based on the work by Wu et al. [35]. This method has the advantage of answering the question of whether the interlinkages among different goals or indicators vary in different development phases. This method can also detect the indicators, which show the greatest impact on the correlation network at different development levels. Internal comparisons will be conducted to verify whether the findings are consistent as a whole and in different parts. Meanwhile, this method can still reveal the synergy or trade-off relationships among different indicators as other methods. These advantages are the reasons to adopt this method.
The disadvantage is that only the correlation, not the causality, is obtained from the connectivity analysis. Panel data analysis is required for the causal analysis. However, the CSY (county level) and CCSCSY are still improving. Some indicators are supplied for only a few years. For example, the public water coverage rate has been provided since 2017. The wastewater treatment rates for some counties have been provided after 2018. The temporal range of some indicators is too short to pass the stationary test in the causal analysis. Hence, the results in this study are explained as undirected correlation, not directed causality. The conclusions of the cross-section data in this study will be compared with those in the Sustainable Development Report 2020 and the Rural Construction Evaluation Report 2020.
This is a data-driven method. By the internal comparison among the different groups in the cross-section data and the external comparison with other reports, consistent interlinkages will be discovered for further investigation.
The statistics in the network analysis are generated from the Pearson correlation coefficient among different indicators. Here, only positive coefficients are adopted. One dataset can only generate one set of statistics (i.e., one average weighted degree and one modularity). If we want to discuss the trend of the statistics as the total index increases, we need to choose samples to form a sub-dataset and generate the network analysis statistics for this sub-dataset. Then, we can repeat this process to achieve a series of statistics to study their trend.
The moving-window approach is adopted to choose the sub-dataset. The total index value for each sample is the arithmetical average of the values of all indicators. Additionally, this research does not aim to construct a new indicator system to assess the development of counties. The major target is to find the most/least important indicators among the indicators in the yearbooks. Hence, we do not generate the arithmetical average of the values related to the same SDG first. Instead, the arithmetical average of all the indicators is calculated directly.
After the total index value for each county is generated, all the index values are ordered from lowest to highest. The window width is defined as the fixed number of the continuous samples, which follow this order. The step size is defined as the fixed number between two starting points in two continuous windows. For example, suppose the window width is 200, and the step size is 5. Then, the first sub-dataset is No. 1–200, the second is No. 6–205, the third is No. 11–210, and so on.
No predetermined criteria are set to choose the window width. In order to maintain the representativeness, the window width is set as 10–20% of the total samples. When the window width is 200, and the step size is 5, 260 sets of samples are generated. After calculating the average weighted degree (AWD) for all indicators in each window, we can obtain the trend of each indicator and compare different indicators in a graph.
However, 30 indicators is too many to be compared in one graph. To discuss the values of AWD of different indicators, we choose to generate the average value, the maximum and minimum values, and the range of AWD in the 260 sets of samples as four different statistical standards for each indicator. We use the order of these four standards to capture the indicators with high or low AWD in a quantitative way in most scenarios. Figure 2 shows the orders of the indicators in the above four standards based on AWD. When the lowest value is zero, all indicators with the zero value are included.
This section will only provide a view of the trends of indicators. Detailed discussions will be conducted in an individual section below. In the whole trend, Population Density (S11.3.1), Number of Beds in Social Welfare Institutions with Accommodation (S11.1), Number of Beds in Health Care Institutions Per Thousand Persons (S3.8), Density of Water Supply Pipelines in Built District (S6.1.1), Density of Gas Supply Pipeline (S7.1.1), Density of Bridges (S11.2.1) and Newly Added Fixed Assets of This Year Per Capita (S9.1) contribute to the network the least, as the AWDs of these indicators are usually the lowest. These indicators usually have not only the lowest maximum values but also the lowest minimum values. The curves of these indicators are usually below the curves of other indicators.
On the contrary, Gross Domestic Product Per Capita (S8.1.1), Revenue in Local Government General Public Budgets Per Capita (S8.1.2), Green Coverage Rate of Built District (S11.7.2), Green Space Rate of Built District (S11.7.3), Expenditures in Local Government General Public Budgets Per Capita (S11.3.2), Water Coverage Rate (S6.1.4), Public Water Coverage Rate (S6.1.5) have the highest AWDs and show greater impacts on the whole network.
The above comparison only provides a rough view. Even if an indicator has the highest maximum and minimum values, it may still be less important between the maximum and minimum values. To illustrate a more accurate comparison among the indicators, this study divides the 260 sets into five groups, each containing 20% of sets. Similarly, the orders of the average value, the maximum and minimum values, and the ranges of AWD in five groups are all generated. Figure 3 focuses on the average value and provides the order of indicators across five groups, respectively.
Across the five groups at different levels of the total index, Population Density (S11.3.1), Number of Beds in Social Welfare Institutions with Accommodation (S11.1), Number of Beds in Health Care Institutions Per Thousand Persons (S3.8) and Newly Added Fixed Assets of This Year Per Capita (S9.1) still belong to the indicators, which contribute the least to the general network, on average. On the other hand, Expenditures in Local Government General Public Budgets Per Capita (S11.3.2), Green Space Rate of Built District (S11.7.3) and Green Coverage Rate of Built District (S11.7.2) are the most important indicators in the network at different levels of the total index. Compared with the results for the maximum/minimum values in 260 sets of samples, the importance of the above indicators at different levels of the total index is consistent with the whole trend.
Moreover, some indicators become the most or the least important factors at certain levels. For example, Density of Gas Supply Pipeline (S7.1.1) and Density of Water Supply Pipelines in Built District (S6.1.1) show the least influence at the 40–80% level. Wastewater Treatment Rate (S6.3.3) is one of the most important indicators at the 0–20% and 40–60% levels, as is Water Coverage Rate (S6.1.4) at the 40–60% and 80–100% levels. Density of Gas Supply Pipeline (S7.1.1) and Density of Water Supply Pipelines in Built District (S6.1.1) also appear in the general trend as the least important indicators, whereas Water Coverage Rate (S6.1.4) appears as the most important indicator.
The above analyses are all based on a window width of 200. As mentioned before, the window width is not predetermined. In order to examine whether the window width would change the network structure based on correlation coefficients, the same calculations are conducted when the window width is set as 150 and 250. The average value of AWD of all indicators are generated in five groups based on each window width. Then, the five highest and five lowest values of average AWDs are listed in Figure 3.
In the case of indicators with the lowest values of AWD, Population Density, Number of Beds in Social Welfare Institutions with Accommodation, Number of Beds in Health Care Institutions Per Thousand Persons and Newly Added Fixed Assets of This Year Per Capita still perform as the least important indicators in most cases, regardless of window width. When the window width is 250, although the Number of Beds in Social Welfare Institutions with Accommodation and the Number of Beds in Health Care Institutions Per Thousand Persons are not found in the five lowest values at some levels, they can still be found in the sixth or seventh lowest values. Density of Gas Supply Pipeline and Density of Water Supply Pipelines in Built District are also detected as showing the least influence in several groups based on different window widths.
In the case of indicators with the highest values of AWD, Expenditures in Local Government General Public Budgets Per Capita, Green Space Rate of Built District and Green Coverage Rate of Built District occupy the five highest values in most groups. If the values for Expenditures in Local Government General Public Budgets Per Capita, Revenue in Local Government General Public Budgets Per Capita (S8.1.2) and Green Coverage Rate of Built District fall outside the five highest ones, they usually hold the sixth or seventh position (Figure 3). Wastewater Treatment Rate ranks in the five most important indicators from the low level to the middle level of the total index, while Water Coverage Rate appears from the middle level to the high level based on different window widths. Surface Area of Roads Cleaned and Maintained Per Capita (S6.2.1) only has an important impact at the 0–20% level across different window widths. In general, the window widths do not significantly change the network structure. The findings about the AWD are stable. The indicators with the greatest and weakest influence on other indicators are consistent when the window width is changed.
In the next section, we will focus on the trends of indicators with higher importance along the total index. By comparing the orders of indicators in Figure 2 and Figure 3, 14 indicators are identified as ones with a higher influence on the network. Figure 4 demonstrates their trends along the total index. Table 2 provides the descriptions of their trends and the corresponding SDG targets.

4. Modularity

To illustrate the trend of modularity, 20% of the sets of samples with window widths of 150, 200, 250 are input one by one into Gephi to generate the modularity. The sets of samples are chosen every five sets in order (e.g., Set 1, Set 6, Set 11, and so on). Gephi adopts the algorithm by Blondel Guillaume et al. [37] to assess the network modularity based on the Pearson correlation coefficient matrix. Figure 5 shows the trends of modularity in three cases of window width. The trends in different window widths are similar. It starts from a slowly increasing trend and then turns to a slowly decreasing trend (i.e., the inverted U curve). The modularity falls within the range of 0.45–0.70. A high modularity indicates that any linkage has a high probability to fall into a division. In other words, when a network has high modularity, it has a high density of linkages within each division of nodes but a low density of linkages among different divisions. As a result, the divisions become more isolated. As the total index increases, the indicators first become several isolated divisions and then return to a more interactive network again. Different from the AWD, the modularity is a statistic for the whole network, not a statistic for a specific node or a specific link. Hence, we cannot order the indicators by their modularity.

5. Discussion and Conclusions

5.1. Discussions about the Trends of AWD and Modularity along the Total Index

This section discusses the trends of the indicators themselves and their corresponding SDG targets in order to capture the characteristics of development in Chinese counties and county seats.
  • The indicators in the CCSCSY and CSY (county level) highly concentrate on only a few SDG targets. All the indicators belong to the targets from SDG3 (Good Health and Well-Being), SDG6 (Clean Water and Sanitation), SDG7 (Affordable and Clean Energy), SDG8 (Decent Work and Economic Growth), SDG9 (Industry, Innovation and Infrastructure) and SDG11 (Sustainable Cities and Communities). In particular, SDG6, SDG7, SDG8 and SDG11 are more favored by the county-level governments in China. Financial and fiscal, water supply, gas supply, transportation, sewerage, sanitation and greening are the development programs, which the government is most concerned about. Medical service is only recorded as the Number of Beds in Health Care Institutions. Housing is only recorded as Social Welfare Housing, which provides the housing support to the elderly, the orphans and the physically challenged and meets their minimum living standards. For industries, CCSCSY and CSY also only report the Number of Industrial Enterprises above Designated Size. Compared to the comprehensive indicators in urban regions in the provincial statistical yearbook, the indicators in the CCSCSY and CSY (county level) tend to emphasize the basic living standards for residents.
  • Within the above SDG targets, the indicators with high average weighted degrees (AWD) are related to targets 6.1, 8.1, 11.3 and 11.7, while those with low AWD are related to targets 3.8, 6.1, 7.1, 9.1, 11.1, 11.2 and 11.3. The high/low AWD is explained by the fact that this indicator is highly/lowly connected to other indicators. In other words, if the total index is generated from the existing indicators/targets, the indicators/targets with high AWDs are the dominant ones in the index.
  • Here, 6.1 and 11.3 have several different indicators, and the influences of them diverge significantly. In general, safe and affordable drinking water, economic growth, the public budget expenditures for sustainable development and the supply of green space are treated as the most important detailed targets. On the other hand, the supply of basic health care services, the supply of modern energy, the newly acquired fixed assets for infrastructure and basic housing services are not set as key statistics at the county level.
  • The setting of targets is performed in relation to the general development level of the counties. During 2016–2021 (the Thirteenth Five-Year Plan), the fight against poverty in China came to the final stage. In 2020, the goal was to remove the 832 impoverished counties from the country’s poverty list. Based on the data from 2020 collected in this research, most counties have met the standard of poverty alleviation. Few indicators would be set to measure the minimum living standards. For example, the housing need in the countryside is not a serious problem, as rural residents are supplied with homesteads to satisfy their basic needs. The health care services at the county level are positioned to cover the treatment of common diseases. Patients with difficult or severe diseases are usually transferred to city hospitals. Therefore, the development targets on the county level are not being paid much attention.
  • For the water supply, the importance of two indicators diverges. Density of Water Supply Pipelines in Built District evaluates the water supply facilities but does not consider the population density. A county with a low density may still satisfy the need for water if its population density is also low. Instead, the Public Water Coverage Rate can accurately measure the proportion of households that have benefited from the public water supply. The rates in 249 counties are lower than 90%, and those in 94 are lower than 80%. A government with a low rate of public water supply would face great pressure to improve this indicator. This indicator supplements a much more stated objective for the local government than the Density of Water Supply Pipelines in Built District and should have a high correlation with other indicators in the assessment network. On the contrary, Density of Water Supply Pipelines in Built District is not regarded as a clear indicator and has weak correlations with other indicators.
  • The comparison among the 14 indicators with high values shows that the indicators related to greening are stable at different levels of the total index. For counties at various development stages, the construction of green space is always taken into account. Even in counties at the 0–20% level of the total index, the residents’ minimum requirement for housing is already satisfied, and they are looking for an improvement in the quality of life, such as greening. The high AWD of this indicator is explained as the desire to live with the natural environment of the county residents, especially those who lived in the countryside and moved to nearby county seats.
  • If the samples are ordered based on the water supply rate (not the public water supply rate) and divided into five equal parts, the average of each part should be 91.1%, 95.6%, 97.0%, 98.0% and 98.6%, respectively. On the one hand, even the lowest 20% of counties still supply water to more than 90% of residents, on average. The fundamental demand is realized for most residents. On the other hand, it requires great effort to further improve this rate. For counties with rates of over 97%, the rest of the residents usually live in isolated areas and under poor conditions to achieve stable water supply. This requires an active investment by the government to answer these residents’ demand, which may be much higher than the investment to supply the same number of households with drinking water. This can explain why the Water Coverage Rate becomes an important indicator again when the total index grows to the middle and high level. The improvement of the water supply for the rest of the residents may require a comprehensive construction project to improve their housing quality. Hence, this indicator becomes highly correlated with others.
  • Wastewater Treatment Rate is an indicator with unstable AWD. In three types of window widths, it has a high AWD at levels of 0–20%, 40–60% and 80–100% along the total index. However, the AWD falls to the middle of all indicators at the levels of 20–40% and 60–80%. Here, the sewerage includes the domestic sewage, industry wastewater and wastewater in the public space, such as wastewater from catering in the commercial regions. It does not represent the treatment of domestic sewage. Additionally, when the Water Coverage Rate is lower than 100%, this indicates that the water supply system does not fully cover the whole population. When a county’s Wastewater Treatment Rate is lower than 100%, this county lacks the capacity to collect or to deal with the total amount of wastewater. When the Wastewater Treatment Rate is over a certain level, the untreated wastewater would be diluted, so that the harm of water pollution is not evident in the short term. Moreover, the precipitation and hydrological conditions are diversified in different counties, which may also change the damage from the polluted water. Hence, an upgrade of the wastewater treatment system may sometimes incur a high cost but produce a low return in the development, and it is not regarded as a major target in some cases. In fact, more than 500 counties have achieved a water supply rate of 100%, but only 119 counties have reached a Wastewater Treatment Rate of 100%.
  • The inverted U curve of the modularity states that when the total index is at the middle level, the indicators tend to split into several separate parts. It is difficult to improve the total index by just enhancing some key indicators. When the total index is at the low level or at the high level, most indicators have significant correlations with each other. The change of some indicators is more likely to spread to other indicators. This indicates that a county would run into a development bottleneck at the middle development level. To break this bottleneck, a comprehensive development strategy has to be designed to deal with multiple targets simultaneously.
  • As the major economic indicators, it is not surprising that Gross Domestic Product Per Capita, Expenditures in Local Government General Public Budgets Per Capita and Revenue in Local Government General Public Budgets Per Capita are captured as important indicators. However, the AWD of Expenditures in Local Government General Public Budgets Per Capita falls significantly at the 0–20% level. This phenomenon suggests that when the total index is low, a simple strategy to increase the Revenue in General Public Budgets can promote a reinforcement of most indicators. This usually occurs in poverty alleviation. However, the efficiency of increasing the Expenditures in General Public Budgets first drops sharply and then remains stable as the total index rises to a certain level. The county cannot just follow its previous strategy of eliminating extreme poverty but has to discover the key development problem and invest. Of course, continuous expenditures are still necessary. On the other hand, as a capacity to maintain the fiscal income, Revenue in Local Government General Public Budgets Per Capita is a foundation for further development at all stages, especially when the county is above the average level. Fiscal subsidy is only provided to counties with the lowest fiscal income. Sustainable development in most counties depends on their own fiscal capacity.

5.2. Comparison with Two Assessment Reports in China

The first comparison target is the Sustainable Development Report 2020 [38], which contains the assessment of SDG scores for 170 countries and regions. The indicators in the CCSCSY and CSY (county level) reflect concern regarding most SDGs, with significant challenges highlighted in the report, but not all. For indicators in these two yearbooks, SDG8 is achieved at the national level, while SDG3, 6, 7, 9 and 11 are still facing significant challenges. The above SDGs are increasing on the national level. SDG10, 14 and 15, which face significant challenges, have to be handled in co-ordination at the provincial or inter-provincial level. Achieving these SDGs is usually beyond the capacity of a county government. Hence, the lack of indicators related to these SDGs is not surprising. However, few indicators are related to SDG12, 13 or 16. Existing indicators can track the performance of county governments to take action to achieve the SDG targets, but indicators related to environmental protection, resource recycling, public health care services and public education are insufficient in the present yearbooks at the county level.
The second comparison target is the Rural Construction Evaluation Report 2020 [39,40]. This report focused on the development performance in the rural region and in the county seats, which can also assess the progress of urban–rural integration. The construction indicators in our study are mainly at the county-seat level. Hence, the construction indicators can be verified when compared to the Rural Construction Evaluation Report 2020 (RCER). In fact, the 12 counties in RCER are also included in the dataset of CCSCSY and CSY (county level). In the total index in this study, 1 of the 12 counties is ranked at the 0–20% level, 3 at the 20–40% level, 4 at the 40–60% level, 1 at the 60–80% level and 3 at the 80–100% level, respectively. For the three intervals with more samples, the Wastewater Treatment Rate, Water Coverage Rate, Green Space Rate and Green Coverage Rate of Built District are all highly connected indicators in the network. On the contrary, the rural regions are facing the quality of rural housing and the livability of the village environment in comparison to the county seats, according to RCER. More efforts are needed to improve the wastewater treatment, domestic garbage treatment, centralized water supply and the environment around houses in rural regions. The important indicators in this study are partially consistent with the problems of great concern in RCER. Unfortunately, indicators related to public services, residential housing quality and ecological environment are insufficient in this study, while RCER contains more indicators.

5.3. Summary of Conclusions

In summary, this study integrated the indicators in CSSCSY and CSY (county level) to investigate indicators, which show the greatest influence on the correlation network. The changes of the connections to other indicators for 14 highly connected indicators are further demonstrated as the total index increases. The inverted U curve of modularity indicates that county development would face a bottleneck in the middle level of the total index due to the reduction in network correlation at that level. Comprehensive development plans for multiple targets simultaneously are required to break the bottleneck. To verify the findings in this study, two comparisons with the Sustainable Development Report 2020 and the Rural Construction Evaluation Report 2020 are conducted in the conclusions. The indicators with high connections in this study are also related to SDGs with significant challenges at the national level and problems in the rural regions. Additional indicators regarding public services and environmental protection should be supplemented in the county-level statistics in order to track the performance of SDGs and to reveal the key indicators in the urban–rural integration.

Author Contributions

Conceptualization, X.L. and J.Z.; methodology, J.Z.; software, J.Z.; validation, X.L. and J.Z.; formal analysis, J.Z.; investigation, J.Z.; resources, X.L.; data curation, J.Z.; writing—original draft preparation, J.Z.; writing—review and editing, X.L.; visualization, J.Z.; supervision, X.L.; project administration, J.Z.; funding acquisition, X.L. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (Youth Programme) (Grant No. 42001177); National Social Science Foundation of China (Grant No. 21AZD034), National Natural Science Foundation of China (Grant No. 41971157), and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (Grant No. 22qntd2001). The APC was funded by National Natural Science Foundation of China (Youth Programme) (Grant No. 42001177).

Data Availability Statement

The original data in the China Statistical Yearbook (county level) and the China County Seat Construction Statistical Yearbook (CCSCSY) is provided by China Economic and Social Development Statistical Database (https://data.cnki.net/Yearbook, accessed on 15 August 2022).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. The correlation network among the indicators with different proportions of links. (a) All links included, (b) Only the links with the higher 50% of weights are included, (c) Only the links with the highest 25% of weights are included.
Figure A1. The correlation network among the indicators with different proportions of links. (a) All links included, (b) Only the links with the higher 50% of weights are included, (c) Only the links with the highest 25% of weights are included.
Land 11 02008 g0a1aLand 11 02008 g0a1b

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Figure 1. Correlation coefficient matrix.
Figure 1. Correlation coefficient matrix.
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Figure 2. The order of indicators based on AWD under four statistical standards.
Figure 2. The order of indicators based on AWD under four statistical standards.
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Land 11 02008 g003a 550Land 11 02008 g003b 550Land 11 02008 g003c 550
Figure 3. The order of indicators based on AWD at different index levels for three window widths (150, 200, 250). (a) Window width = 200, (b) Window width = 150, (c) Window width = 250.
Figure 3. The order of indicators based on AWD at different index levels for three window widths (150, 200, 250). (a) Window width = 200, (b) Window width = 150, (c) Window width = 250.
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Land 11 02008 g004a 550Land 11 02008 g004b 550Land 11 02008 g004c 550
Figure 4. The trends of AWD for 14 important indicators along the total index.
Figure 4. The trends of AWD for 14 important indicators along the total index.
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Figure 5. Trends of network modularity based on different window widths. Note: The number in the X-axis is the order of the sampling windows, which increases as the total index increases.
Figure 5. Trends of network modularity based on different window widths. Note: The number in the X-axis is the order of the sampling windows, which increases as the total index increases.
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Table
Table 1. Descriptions of indicators and related SDG targets.
Table 1. Descriptions of indicators and related SDG targets.
Target No.Description of TargetIndicators in This StudySerial Number
3.8Achieve universal health coverage, including financial risk protection, access to quality essential health care services and access to safe, effective, quality and affordable essential medicines and vaccines for allNumber of Beds in Health Care Institutions Per Thousand Persons (unit)S3.8
6.1By 2030, achieve universal and equitable access to safe and affordable drinking water for allDensity of Water Supply Pipelines in Built District (kilometer/square kilometer)S6.1.1
Daily Water Consumption Per Capita (liter)S6.1.2
Quantity of Water Supply Per Capita (m3)S6.1.3
Water Coverage Rate (%)S6.1.4
Public Water Coverage Rate (%)S6.1.5
6.2By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situationsSurface Area of Roads Cleaned and Maintained Per Capita (m3)S6.2.1
Number of Latrines Per Thousand Persons (unit)S6.2.2
6.3By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globallyQuantity of Wastewater Treated Per Capita (m3)S6.3.1
Density of Sewers in Built District (kilometer/square kilometer)S6.3.2
Wastewater Treatment Rate (%)S6.3.3
Centralized Treatment Rate of Wastewater Treatment Plants (%)S6.3.4
7.1By 2030, ensure universal access to affordable, reliable and modern energy servicesDensity of Gas Supply Pipeline (kilometer/square kilometer)S7.1.1
Gas Supplied Per Capita (m3)S7.1.2
Liquefied Petroleum Gas Supplied Per Capita (ton)S7.1.3
Gas Coverage Rate (%)S7.1.4
8.1Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countriesGross Domestic Product Per Capita (CNY 10,000)S8.1.1
Revenue in Local Government General Public Budgets Per Capita (CNY 10,000)S8.1.2
Balance of Household Saving Deposit Per Capita (CNY 10,000)S8.1.3
8.10Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for allLoans Balance of Financial Institutions at Year-End Per Capita (CNY 10,000)S8.10
9.1Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for allNewly Added Fixed Assets of This Year Per Capita (CNY 10,000)S9.1
9.2Promote inclusive and sustainable industrialization and, by 2030, significantly raise industry’s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countriesNumber of Industrial Enterprises above Designated Size (unit)S9.2
9.cSignificantly increase access to information and communications technology and strive to provide universal and affordable access to the internet in least developed countries by 2020Number of Fixed Line Telephones Per Capita (unit)S9.c
11.1By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slumsNumber of Beds in Social Welfare Institutions with Accommodation (unit)S11.1
11.2By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older personsDensity of Bridges (unit/square kilometers)S11.2.1
Road Surface Area Per Capita (m2)S11.2.2
Density of Road Network in Built District (kilometer/square kilometer)S11.2.3
Surface Area of Roads Rate of Built District (%)S11.2.4
11.3By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countriesPopulation Density (person/square kilometer)S11.3.1
Expenditures in Local Government General Public Budgets Per Capita (CNY 10,000)S11.3.2
11.6By 2030, reduce the per capita adverse environmental impact of cities, including by paying special attention to air quality and municipal and other waste managementDomestic Garbage Treatment Rate (%)S11.6.1
Domestic Garbage Harmless Treatment Rate (%)S11.6.2
11.7By 2030, provide universal access to safe, inclusive and accessible green and public spaces, in particular for women and children, older persons and persons with disabilitiesPublic Recreational Green Space Per Capita (m2)S11.7.1
Green Coverage Rate of Built District (%)S11.7.2
Green Space Rate of Built District (%)S11.7.3
Table
Table 2. The description of the trends of AWD for 14 important indicators along the total index.
Table 2. The description of the trends of AWD for 14 important indicators along the total index.
SDG TargetIndicatorTrend
6.1Public Water Coverage RateStable, and increasing after 80%
11.7Public Recreational Green Space Per CapitaStable, and increasing after 80%
6.1Water Coverage RateIncreasing
7.1Gas Coverage RateIncreasing
8.1Revenue in Local Government General Public Budgets Per CapitaIncreasing
6.1Quantity of Water Supply Per CapitaStable
8.1Gross Domestic Product Per CapitaStable
11.2Surface Area of Roads Rate of Built DistrictStable
11.7Green Coverage Rate of Built DistrictStable
11.7Green Space Rate of Built DistrictStable
9.cNumber of Fixed Line Telephones Per CapitaDecreasing
6.2Surface Area of Roads Cleaned and Maintained Per CapitaDecreasing, and stable after 20%
11.3Expenditures in Local Government General Public Budgets Per CapitaDecreasing, and stable after 20%
6.3Wastewater Treatment RateUnclear
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Zhong, J.; Li, X. Interlinkages among County-Level Construction Indicators and Related Sustainable Development Goals in China. Land 2022, 11, 2008. https://0-doi-org.brum.beds.ac.uk/10.3390/land11112008

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Zhong J, Li X. Interlinkages among County-Level Construction Indicators and Related Sustainable Development Goals in China. Land. 2022; 11(11):2008. https://0-doi-org.brum.beds.ac.uk/10.3390/land11112008

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Zhong, Jiawei, and Xun Li. 2022. "Interlinkages among County-Level Construction Indicators and Related Sustainable Development Goals in China" Land 11, no. 11: 2008. https://0-doi-org.brum.beds.ac.uk/10.3390/land11112008

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