The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
-
Qinnan Zhang
,
Chunhai Tao
Abstract
The United Nations sustainable development goals aim to promote global industrialization and innovation and achieve sustainable and inclusive economic development by 2030. The sustainable growth of provincial economies has emerged as a critical component of the national plan as China’s economy transitions to a new normal. This article builds an assessment index system encompassing the four "economy–society–ecology–innovation" dimensions to better assess the degree of sustainable economic development in China’s regions. It then uses the gray correlation analysis method to measure and analyze the sustainable economic development of China’s regions from 2006 to 2017. Additionally, the spatial evolution characteristics of each region’s sustainable economic development and spatial pattern are investigated through exploratory spatial data analysis and GIS spatial analysis techniques. This essay seeks to support the region’s economy in achieving sustainable development by offering theoretical underpinnings and useful advice for the creation of successful regional development plans. The study’s findings indicate that in 2017, the assessed value of the level of economic sustainability of China’s regions averaged 0.7156, an increase of 7.3% compared to 2006 while the degree of sustainable economic development in the rest of China, except Inner Mongolia, Shaanxi, and Xinjiang, has steadily increased between 2006 and 2017, the gap between the regions has also widened, with the eastern region having a much higher level of sustainable economic development than the central and western regions. An analysis of the spatial evolution reveals that the degree of sustainable economic development in every region of China generally increases in positive spatial correlation. Its spatial agglomeration is becoming increasingly apparent, with more regions becoming high-high-type and low-low-type agglomerations. Regional wealth disparities, ecological governance capabilities, and technological innovation levels were identified at the local level as essential factors for provincial economic sustainable development, with varying impacts across different regions.
1 Introduction
After issues like resource depletion, excessive population growth, environmental pollution, and ecological damage gained international attention in the 1990s, nations began looking for ways to develop economically in a way that would not endanger social justice and ecological harmony. As a result, the theoretical community is now paying close attention to the debate around sustainable economic development. The modernization of China in the new era is a comprehensive modernization that includes the coordinated development of economy, ecology, and innovation, as well as the complete construction of material, political, spiritual, social, and ecological civilizations. Sustainable economic development is a strategy put forward from a new historical position, adapted to the changes in society’s main contradictions, and a strategy that must be followed in the long term to promote the country’s modernization in the new era [1].
Economic sustainability is an important, but not the only, component of measuring an economy’s economic sustainability. Gross domestic product (GDP) is generally chosen as an important indicator of a country or region’s economy’s overall size. However, the degree of an economy’s sustainable economic progress is not entirely reflected by the GDP metric [2,3,4,5]. Factors such as income inequality [6,7,8], social unrest [9,10,11], ecological development [12,13,14], and the level of innovative development are integral to assessing sustainable development [15,16,17]. For example, in terms of income inequality, narrowing the gap between the rich and the poor of the nation and a sound and comprehensive social security system will be conducive to promoting an economy’s sustainable development [6]. Gender differences in occupations and industries, as well as differences in gender roles and the gender division of labor, remain essential [7]. At the top of the distribution, the gender wealth gap is the largest in this asset class. Gender wealth gaps vary considerably by household type [8]. Regarding social stability, the urbanization process should be compatible with the stage of economic development, and it is necessary to maintain high economic growth sustainably, combine economic growth with social equity and the three rural issues, and maintain social smoothness [9]. The stability and sustainability of social development are predicted by establishing social unrest indicators and analyzing the frequency of social unrest. Food price increases lead to increased social unrest, while fluctuations are not associated with increased social unrest [11]. Regarding the ecological environment, China is on the road to sustainability in economic development, and strengthening ecological construction is an integral part of it. More funds and resources should be invested in ecological environmental protection [12,14]. The coupled coordination of economic development, logistics development, and ecological environment in most provinces and cities in China is at the medium coupling level, and only Shanghai, Anhui, and Fujian in the eastern region have reached the high-quality coupling level [13]. The decoupling between economic growth and resource-environmental pressures shows weak decoupling from 2006 to 2010 and strong decoupling from 2011 to 2015 [18]. Innovation is one of the essential distinguishing marks between middle-income and high-income countries. It provides an important fulcrum for supply-side structural reform. The innovation-driven development strategy is being promoted by improving the innovation system and environment internally and promoting open innovation externally. The established indicator system mainly measures domestic and foreign scholars’ evaluation of sustainable economic development. However, the diversity of the research results is due to the data’s variability and the research’s regional level. To circumvent this problem and maintain the accuracy and truthfulness of the research, it is necessary to select indicators that can accurately reflect the level of economic quality development. Jia used the literature research method and the deductive induction method to construct the evaluation index system of fundamental government services across different areas of Guangdong Province and then measured and assessed the degree of basic public services in Guangdong Province. Wei and Sun [19] constructed an index system for assessing the eco-city’s degree of development including the economic and social environment from the perspective of eco-city construction and carried out a comprehensive evaluation of the ecological development of Qingdao City. Peng et al. [20] build a three-dimensional quantity–quality–benefit model to better evaluate the water resources environment. This model introduces a coupled degree of coordination model to thoroughly evaluate the sustainability of urban water in terms of water quantity, water quality, and water benefits, making the sustainability assessment of water resources more scientific. Wang [21] measured the degree of development of economic innovation among 31 Chinese areas and comprehensively evaluated the economic innovation development level of each region in China. The construction of the indicator system of sustainable development level has not yet had a unified standard. Liu and Sun [22] analyzed the thorough assessment of Inner Mongolia’s economic expansion and the sustainable development of the economy of western provinces from five dimensions. Dai [23] established Wenzhou’s sustainable development index system from three aspects of economy, society, and ecology and analyzed the realization path of sustainable development in Wenzhou. However, none of its sustainable development indicator systems cover the degree of the financial system’s development and the urban–rural wealth gap.
The following are the innovations presented in this article: (1) The indicator system. In contrast to a single GDP index, this article’s innovation is developing a comprehensive evaluation index system for sustainable economic development. It combines the social status of China’s 31 provinces, municipalities, and autonomous regions and condenses sustainable economic development into ten essential components. This system comprises 10 first-level indicators, 21 second-level indicators, and 6 third-level indicators, including economic growth, wealth distribution, social services, urbanization, employment, social stability, financial stability, ecological quality, and innovation capacity. The scientific and reasonableness of the comprehensive evaluation are guaranteed by using the global principal component analysis method to weight each of the secondary indicators of the level of public social services, the social unease index, the steady state value of the financial system (SSVFS), the level of eco-development, and the level of innovation and development. This multifaceted assessment approach offers a more thorough foundation for developing policies. It opens up new perspectives for the study of sustainable economic development, helping to gain a deeper understanding of an economy’s overall quality of development and its long-term sustainability. (2) Methodology of research. The per capita gross national income (GNI) figures of China’s provinces and regions were corrected using the Atlas conversion factor to identify each province’s and region’s income classification more precisely. Even though the World Bank has extensively used the Atlas technique to evaluate the income levels of national economies, domestic studies have yet to use it at the province level adequately. Adopting the Atlas conversion factor can significantly lessen the effect of exchange rate variations on the GNI per capita computation, increasing the comparability of provinces’ income levels in cross-border comparisons.
In conclusion, researchers have examined the sustainable growth of regional economies from several angles, including the appraisal of rural economic development, the coordination of China’s economic development, and the sustainable development of ecological and economic systems. Nevertheless, the assessment of national province panel data has received little attention. Following extensive research and exploration, the author contends that the ten dimensions encompassing economic development level, balanced wealth distribution, corruption, social public service level, urbanization level, employment level, social unrest level, financial system stability, ecological development level, and innovation development level offer a more comprehensive reflection of the region and the country’s sustainable development level. Based on China’s administrative provincial units, this article aims to create an assessment index system for the sustainable development level according to these measurements. The goal is to gauge each Chinese region’s degree of sustainable development from 2006 to 2017, providing comprehensive evaluations and assessments based on sub-indicators. Building on this foundation, GIS spatial analysis techniques and exploratory spatial data analysis (ESDA) will delve deeper into the spatial evolution profile and the spatial features of each Chinese region’s sustainable development level.
2 Measurement of the level of sustainable development of provincial economies
2.1 A theoretical exploration of the World Bank’s Atlas method
The World Bank determines whether a nation is low, middle, or high income by using the Atlas technique to calculate its per capita GNI value. This study uses the Atlas approach to adjust the per capita GNI numbers to identify whether China’s provinces and regions are low, middle, or high income. This correction process can effectively reduce the impact of exchange rate fluctuations and inflation on cross-regional income comparisons, make the comparison of economic development levels among provinces and regions more scientific and objective, and provide a reliable data basis for the subsequent analysis of the level of sustainable economic development of China’s regions.
2.1.1 A theoretical exploration of the World Bank’s classification of economies
When scholars at home and abroad classify an economy, they generally use the World Bank’s classification criteria for economies in previous years as a tool without exploring the underlying mechanisms. To understand why the World Bank uses per capita GNI indicators to group economies and why it uses Atlas-adjusted per capita GNI indicators for international comparisons is a prerequisite for us to apply the classification standard and to adjust the per capita GNI indicators of China’s regions by the World Bank’s caliber of measurement.
The World Bank uses the GNI per capita indicator to categorize economies into four income groups. While it is generally recognized that the GNI per capita indicator does not adequately capture the level of well-being or sustainable development in a country, it is a useful and accessible indicator that has a tight relationship with other non-financial metrics, such as quality of life. Therefore, there is a practical and theoretical basis for using the GNI per capita indicator as a criterion for classifying economies.
Before conducting international comparisons of GNI per capita indicators across countries, local currencies should be converted to United States dollars. In this regard, the World Bank has proposed two methods. The first involves using the purchasing power parity conversion factor from the international comparison program. However, this method faces limitations in accurately converting local currencies to US dollars due to constraints in calculation methodology, geographic coverage, timeliness, data quality, and derivation techniques. The second method is the use of the chart-set methodology known as the Atlas conversion factor. An average of 3 years’ worth of a nation’s exchange rate, modified to take into consideration the discrepancy between that nation’s inflation rate and the global inflation rate, is represented by the Atlas conversion factor for any given year. By making this adjustment, the effects of inflation-related changes in exchange rates are reduced. Cross-country comparisons of national income are made more relevant and trustworthy by the Atlas conversion factor, which reduces the impact of exchange rate variations.
2.1.2 Atlas method
The steps for calculating the regional GNI per capita indicator as measured by the Atlas method, derived from the Atlas method published by the World Bank, are as follows.
Calculation of national or regional inflation. Given that the average of a nation’s exchange rates for years t, t – 1, and t – 2 equals the Atlas conversion factor for year t. Therefore, GDP is first deflated to calculate the inflation rate for years t – 1 and t – 2, as follows:
Among them,
Calculate the international inflation rate. Changes in exchange rates caused by inflation will overstate or understate real GNI indicators per capita in different countries or regions. Therefore, the Special Drawing Rights Deflator (SDR deflator) is used to blunt this effect and calculate the international inflation rate for years
Among them,
Calculate the exchange rate of the Atlas method
Among them,
Calculate the total income of residents in each region after exchange rate adjustment by the Atlas method as follows:
Among them,
The GNI per capita index of each region adjusted by the Atlas method was calculated as follows:
Among them,
2.1.3 Level of regional economic development
The World Bank adjusts its economic classification standards and per capita GNI indicators using the Atlas conversion factor. This article employs the Atlas method to measure regional per capita GNI indicators and assigns weights to achieve consistency with the World Bank’s economic classification standards.
Making use of China’s four main economic regions: eastern, northeastern, central, and western, the article utilizes regional per capita GNI indicators measured by the Atlas method to indicate each region’s degree of economic growth, as illustrated in Figure 1.
Per capita GNI indicator values by region in China, as measured by the chart set method, 2006–2017.
As depicted in Figure 1, spanning from 2000 to 2017, China’s regional economic development exhibited a distinctive "S"-shaped growth trajectory, characterized by robust growth in the per capita GNI indicator. Notably, the eastern region demonstrated the highest overall economic development, followed by the northeastern and central regions, while the western region exhibited a slightly lower economic development level. In 2017, the per capita GNI indicator in the eastern region, except for Hebei, Hainan, and Shandong, significantly surpassed that of the northeastern, central, and western regions, indicating a superior economic development level. The northeastern and central regions shared similar per capita GNI values, reflecting an average economic development level. In contrast, the western regions, excluding Chongqing, Inner Mongolia, and Shaanxi, all reported GNI per capita values below $6,000, signaling a comparatively lower level of economic development. Based on the World Bank’s 2017 Classification of Economies by Level of Development, China’s regions are evaluated as separate economies and divided into four income groups: high-, upper middle-, lower middle-, and low-income groups. In 2017, of China’s 31 provinces, municipalities, and autonomous regions, there were seven high-income groups, namely Beijing, Tianjin, Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong, all in the eastern part of the country, and 23 upper middle-income groups, namely Heilongjiang, Jilin, Liaoning, Hebei, Shandong, Hainan, Shanxi, Henan, Hubei, Hunan, Jiangxi, Anhui, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, and Inner Mongolia, Ningxia, Xinjiang, Qinghai, and Tibet, while the lower-middle-income group has only Gansu, with no low-income areas.
2.2 Construction of an indicator system for the level of sustainable development
The GDP indicator alone falls short of fully capturing the nuanced aspects of an economy’s sustainable development. It is clear that this indicator alone cannot be used to determine the degree of sustainable development. Considering the diverse social landscape of China’s 31 provinces, municipalities, and autonomous regions, the level of sustainable development can be comprehensively assessed through the following ten dimensions: economic growth, balanced wealth distribution, corruption status, public social service quality, urbanization level, employment rate, social unrest status, financial system stability, ecological quality, and innovation and development level. To create a robust evaluation framework for an economy’s sustainable development, we selected 10 primary indicators, complemented by 21 secondary indicators and 6 tertiary indicators, culminating in the establishment of the comprehensive indicator system depicted in Figure 2.
GDP growth rate (RGDP). To be in line with international standards, this article chooses the growth rate of the GDP indicator adjusted by the chart set method, RGDP = (regional GDP of the current period/regional GDP of the base period) − 1 to represent each region’s rate of economic growth.
GINI. The Gini coefficient is an internationally used indicator to measure wealth inequality. Two indicators, the per capita net income of rural inhabitants and the per capita disposable income of urban residents, were used, and the simple Gini coefficient calculation method proposed by Zhang [24] was used to obtain the Gini coefficient used to measure the level of urban–rural wealth balance.
Corruption Index (CI). Uses the number of job-related offenses/number of employees in state-owned units to reflect the level of corruption in the provinces, municipalities, and autonomous regions.
Urbanization rate (UR). Reflects urbanization using the number of urban population/total population.
Overall Index of the Level of Public Social Services. In this article, seven secondary indicators are employed to gauge the overall index of the level of social public services, namely the proportion of education expenditure to GDP, the proportion of healthcare expenditure to GDP, the percentage of involvement in pension insurance, the percentage of involvement in medical insurance, the percentage of involvement in unemployment insurance, the percentage of involvement in industrial injury insurance, and the percentage of involvement in maternity insurance. The global principal component analysis method [25,26,27] is applied for this measurement, which serves to assess the state of social public services in each region.
Employment rate (ER). This is a globally recognized indicator for assessing the employment situation. In this context, the urban registered unemployment rate was selected as the specific indicator. The urban registered employment rate was derived by employing the formula: employment rate = 1 – unemployment rate.
Social Unease Index (SUI). Based on the composition of the Social Unrest Index indicators within the Social Risk Indicator System introduced by Zhang [28], the author calculated the SUI by utilizing three secondary indicators. These include the traffic accident fatality rate, fire accident fatality rate, and criminal offense rate (number of criminal offenses/total population). This measurement is employed to assess social unrest in each region.
Financial System Steady State Value (SSVFS). This is assessed in this article with three secondary indicators. These indicators include the fiscal deficit corresponding to GDP, stock turnover as a share of GDP, and the deposit and loan ratios of financial institutions (local and foreign currency loans/local and deposits made by Chinese and international financial organizations in foreign currencies). The aim is to gauge the SSVFS, providing a measure of the stability degree within the financial system.
Overall Index of Ecological Development Level. This article uses four secondary indicators, namely the ratio of wastewater emissions to total water resources, forest coverage, the ratio of natural reserves to the jurisdiction’s area, and the total index of exhaust gas quality level to measure the total index of ecological development level. It is used to measure the degree of ecological damage suffered during the course of economic growth.
Overall Index of Innovation Development Level. This article draws on the innovation development level index system proposed by Wang Hui and uses four secondary indicators, namely total factor productivity (total factor productivity is measured using capital stock [29] and urban unit employment as inputs and regional GDP as outputs, and the approach utilized to measure total factor productivity is the DEA-Malmquist index method), R&D expenditure as a share of GDP, the ratio of R&D personnel to employment, and the number of patent applications received per capita (number of patent applications received/total population) were used to measure the overall index of its innovation development level.
Framework diagram of the indicator system for evaluating the level of sustainable development.
The overall index of the degree of public social services, the index of social insecurity, the value of financial system stability, the overall index of the degree of ecological development, and the overall index of the degree of innovation and development are all analyzed using global principal component analysis to ascertain the weights of each of the second-level indicators, to obtain the value of their composite indicators.
The framework diagram of the indicator system for evaluating the level of sustainable development is shown in Figure 2.
2.3 Data sources
The study encompasses the period from 2006 to 2017 for each indicator. Data for the analysis were sourced from various reputable outlets, including the National Bureau of Statistics, provincial statistical yearbooks, local governments, and autonomous regions, China Procuratorate yearbooks, work reports from provincial procuratorates, municipalities, and independent areas, the Cathay Pacific database, the CEEC statistical database, and the World Bank. In instances where data were missing, a consistent approach was applied to estimate values using the linear fitting method. For reasons of data limitations, provincial data in this article exclude Taiwan Province, Hong Kong, and Macao.
2.4 Research methods
2.4.1 Gray correlation analysis (GRA)
GRA [30,31,32] is a multi-factor statistical method that describes and compares the development state of a system quantitatively. GRA is typically applied to answer difficulties related to thorough evaluation that are subject to change. The main idea is to calculate the correlation degree between curves based on the geometric shape similarity degree of the reference data column and several comparative data columns to see if they are closely related. Then, based on the correlation degree, calculate the comprehensive evaluation value of the curves and perform additional analysis. The specific steps are as follows:
2.4.1.1 Determine the parent sequence
Its original data matrix is
In the above formula,
2.4.1.2 Dimensionless processing of variables
Since the dimensions of each index value are different, dimensionless processing of data is generally required before correlation degree analysis. In this article, dimensionless processing by means of average value is selected for each index as follows:
Among them,
2.4.1.3 Calculated correlation coefficient
The correlation coefficient formula is as follows:
where
2.4.1.4 Calculated correlation degree
The information is too dispersed to allow for an overall comparison, and the correlation coefficient – which measures the degree of correlation between the reference and comparison series at each instant – has a sequence value. Therefore, it is necessary to average them to analyze the whole information of reference series and comparison series. The formula of correlation degree
Among them,
2.4.1.5 Calculated weight
After normalization of the correlation degree, the gray correlation weight value
2.4.1.6 Calculate the comprehensive assessment value
Each index is weighted by the gray correlation weight value, and the comprehensive evaluation value
2.4.2 Geographical weighted regression
An enhanced version of the spatial linear regression model via ordinary least squares (OLS) is the geographic weighted regression model. The geographical weighted regression model took spatial heterogeneity into account and integrated the data’s spatial position into the regression parameters. At the same time, the spatial correlation is also taken into account in this model because the observation points at different locations have different influence on the regression parameters. The general form is shown as follows: [33,34,35]:
where
In the above formula,
3 Results and analyses
3.1 Characteristics of the temporal evolution of sustainable development
3.1.1 Characteristics of the time evolution of the combined level of sustainable development of economies
Building upon the previously proposed evaluation index system for the sustainable development level of the economy, we employed the GRA method to assess China’s regional sustainable development from 2006 to 2017. The assessment value for each economic subregion’s degree of sustainable development was determined by averaging the comprehensive evaluation values of individual regions within the four economic subregions. Table 1 presents the detailed measurement findings.
Results of measuring the level of sustainable development by region in China over the years
| Area | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 |
|---|---|---|---|---|---|---|---|
| Beijing | 0.7525 | 0.7631 | 0.7642 | 0.7500 | 0.7636 | 0.7800 | 0.7958 |
| Tianjin | 0.7254 | 0.7403 | 0.7407 | 0.7383 | 0.7510 | 0.7662 | 0.7801 |
| Hebei | 0.6416 | 0.6671 | 0.6556 | 0.6602 | 0.6633 | 0.6629 | 0.6736 |
| Shanxi | 0.6561 | 0.6797 | 0.6508 | 0.6430 | 0.6564 | 0.6633 | 0.6730 |
| Inner Mongolia | 0.7061 | 0.7184 | 0.7084 | 0.7029 | 0.7134 | 0.7158 | 0.7197 |
| Liaoning | 0.7054 | 0.6930 | 0.6987 | 0.6970 | 0.6990 | 0.7121 | 0.7247 |
| Jilin | 0.6580 | 0.6880 | 0.6651 | 0.6675 | 0.6804 | 0.6770 | 0.6849 |
| Heilongjiang | 0.6870 | 0.6931 | 0.6836 | 0.6849 | 0.6944 | 0.6997 | 0.7029 |
| Shanghai | 0.7546 | 0.7587 | 0.7524 | 0.7640 | 0.7753 | 0.7839 | 0.7785 |
| Jiangsu | 0.7005 | 0.7295 | 0.7099 | 0.7208 | 0.7343 | 0.7447 | 0.7569 |
| Zhejiang | 0.6904 | 0.7068 | 0.6756 | 0.6800 | 0.6888 | 0.7040 | 0.7219 |
| Anhui | 0.6382 | 0.6629 | 0.6489 | 0.6532 | 0.6661 | 0.6806 | 0.6938 |
| Fujian | 0.6640 | 0.6911 | 0.6690 | 0.6724 | 0.6892 | 0.6979 | 0.7091 |
| Jiangxi | 0.6859 | 0.6600 | 0.6811 | 0.6839 | 0.6953 | 0.7020 | 0.7074 |
| Shandong | 0.6802 | 0.7025 | 0.6786 | 0.6834 | 0.6903 | 0.6962 | 0.7059 |
| Henan | 0.6239 | 0.6626 | 0.6187 | 0.6300 | 0.6408 | 0.6492 | 0.6655 |
| Hubei | 0.6722 | 0.6956 | 0.6805 | 0.6860 | 0.7152 | 0.7116 | 0.7138 |
| Hunan | 0.6416 | 0.6742 | 0.6550 | 0.6567 | 0.6802 | 0.6883 | 0.7063 |
| Guangdong | 0.7096 | 0.7223 | 0.7106 | 0.7016 | 0.7241 | 0.7325 | 0.7427 |
| Guangxi | 0.6306 | 0.6677 | 0.6234 | 0.6313 | 0.6417 | 0.6631 | 0.6715 |
| Hainan | 0.6715 | 0.6809 | 0.6680 | 0.6693 | 0.6601 | 0.6681 | 0.6712 |
| Chongqing | 0.6549 | 0.6755 | 0.6590 | 0.6473 | 0.6556 | 0.6877 | 0.7146 |
| Sichuan | 0.6723 | 0.6862 | 0.6634 | 0.6638 | 0.6722 | 0.6883 | 0.6969 |
| Guizhou | 0.5859 | 0.6138 | 0.5925 | 0.5880 | 0.6022 | 0.6232 | 0.6376 |
| Yunnan | 0.6086 | 0.6279 | 0.6084 | 0.6083 | 0.6122 | 0.6108 | 0.6439 |
| Xizang | 0.5987 | 0.6117 | 0.5980 | 0.6131 | 0.6150 | 0.6326 | 0.6322 |
| Shanxi | 0.7008 | 0.6746 | 0.6645 | 0.6645 | 0.6782 | 0.6819 | 0.7033 |
| Gansu | 0.6485 | 0.6596 | 0.6372 | 0.6337 | 0.6362 | 0.6383 | 0.6543 |
| Qinghai | 0.6407 | 0.6590 | 0.6598 | 0.6438 | 0.6589 | 0.6613 | 0.6780 |
| Ningxia | 0.6236 | 0.6499 | 0.6367 | 0.6263 | 0.6378 | 0.6523 | 0.6789 |
| Xinjiang | 0.6461 | 0.6555 | 0.6504 | 0.6477 | 0.6724 | 0.6647 | 0.6687 |
| Eastern Region | 0.7011 | 0.7178 | 0.7051 | 0.7063 | 0.7166 | 0.7267 | 0.7366 |
| Northeastern Region | 0.6835 | 0.6914 | 0.6825 | 0.6832 | 0.6912 | 0.6962 | 0.7042 |
| Central Region | 0.6530 | 0.6725 | 0.6558 | 0.6588 | 0.6757 | 0.6825 | 0.6933 |
| Western Region | 0.6431 | 0.6583 | 0.6418 | 0.6392 | 0.6497 | 0.6600 | 0.6750 |
| Area | 2013 | 2014 | 2015 | 2016 | 2017 | Amplification (%) | Amplification variance (%) |
|---|---|---|---|---|---|---|---|
| Beijing | 0.8015 | 0.8018 | 0.8134 | 0.7914 | 0.8037 | 5.113 | 0.245 |
| Tianjin | 0.7973 | 0.8061 | 0.8026 | 0.8037 | 0.7866 | 6.125 | 1.257 |
| Hebei | 0.6764 | 0.6713 | 0.6693 | 0.6878 | 0.6918 | 5.019 | 0.151 |
| Shanxi | 0.6784 | 0.6720 | 0.6617 | 0.6681 | 0.6841 | 2.797 | −2.071 |
| Inner Mongolia | 0.7246 | 0.7200 | 0.7085 | 0.7021 | 0.6988 | −0.733 | −5.601 |
| Liaoning | 0.7216 | 0.7178 | 0.7098 | 0.6985 | 0.7169 | 1.157 | −3.711 |
| Jilin | 0.6868 | 0.6872 | 0.6805 | 0.6773 | 0.6805 | 2.251 | −2.617 |
| Heilongjiang | 0.6998 | 0.6969 | 0.6986 | 0.7112 | 0.7052 | 1.818 | −3.050 |
| Shanghai | 0.7705 | 0.7702 | 0.7800 | 0.7798 | 0.7760 | 2.136 | −2.732 |
| Jiangsu | 0.7551 | 0.7572 | 0.7724 | 0.7736 | 0.7785 | 7.793 | 2.925 |
| Zhejiang | 0.7431 | 0.7487 | 0.7487 | 0.7627 | 0.7839 | 9.347 | 4.479 |
| Anhui | 0.7078 | 0.7102 | 0.7031 | 0.7154 | 0.7127 | 7.444 | 2.576 |
| Fujian | 0.7214 | 0.7218 | 0.7186 | 0.7253 | 0.7491 | 8.511 | 3.643 |
| Jiangxi | 0.7095 | 0.7104 | 0.7042 | 0.7148 | 0.7297 | 4.376 | −0.492 |
| Shandong | 0.7154 | 0.7048 | 0.7110 | 0.7210 | 0.7220 | 4.179 | −0.689 |
| Henan | 0.6703 | 0.6884 | 0.6663 | 0.6679 | 0.6913 | 6.739 | 1.871 |
| Hubei | 0.7254 | 0.7220 | 0.7167 | 0.7276 | 0.7374 | 6.523 | 1.655 |
| Hunan | 0.7113 | 0.7121 | 0.7022 | 0.7151 | 0.7224 | 8.079 | 3.211 |
| Guangdong | 0.7275 | 0.7210 | 0.7252 | 0.7342 | 0.7449 | 3.535 | −1.333 |
| Guangxi | 0.6670 | 0.6738 | 0.6871 | 0.6699 | 0.7035 | 7.290 | 2.422 |
| Hainan | 0.6792 | 0.6831 | 0.6837 | 0.6857 | 0.6952 | 2.369 | −2.499 |
| Chongqing | 0.7278 | 0.7401 | 0.7397 | 0.7528 | 0.7549 | 9.996 | 5.128 |
| Sichuan | 0.7053 | 0.7104 | 0.7021 | 0.7114 | 0.7149 | 4.260 | −0.608 |
| Guizhou | 0.6479 | 0.6584 | 0.6690 | 0.6581 | 0.6892 | 10.336 | 5.468 |
| Yunnan | 0.6296 | 0.6412 | 0.6337 | 0.6371 | 0.6545 | 4.585 | −0.282 |
| Xizang | 0.6348 | 0.6317 | 0.6276 | 0.6518 | 0.6685 | 6.978 | 2.110 |
| Shanxi | 0.7089 | 0.7131 | 0.6975 | 0.6969 | 0.7006 | −0.021 | −4.889 |
| Gansu | 0.6616 | 0.6704 | 0.6457 | 0.6566 | 0.6739 | 2.537 | −2.331 |
| Qinghai | 0.6750 | 0.6699 | 0.6560 | 0.6726 | 0.6842 | 4.348 | −0.520 |
| Ningxia | 0.6781 | 0.6795 | 0.6811 | 0.6906 | 0.7042 | 8.058 | 3.190 |
| Xinjiang | 0.6626 | 0.6371 | 0.6242 | 0.6342 | 0.6257 | −2.040 | −6.908 |
| Eastern Region | 0.7413 | 0.7424 | 0.7460 | 0.7494 | 0.7566 | 5.550 | 0.682 |
| Northeastern Region | 0.7027 | 0.7006 | 0.6963 | 0.6957 | 0.7009 | 1.742 | −3.126 |
| Central Region | 0.7005 | 0.7025 | 0.6923 | 0.7015 | 0.7129 | 5.993 | 1.125 |
| Western Region | 0.6769 | 0.6788 | 0.6727 | 0.6778 | 0.6894 | 4.633 | −0.235 |
With the measurement results in Table 1, the following conclusions can be obtained.
3.1.1.1 In the majority of China’s regions, the degree of sustainable development has steadily improved
Between 2006 and 2017, the level of sustainable development in all regions of China showed a gradual improvement, apart from Inner Mongolia, Shanxi, and Xinjiang. In 2017, the mean value of sustainable development across all regions of China reached 0.7156, reflecting a 7.3% increase compared to 2006. Notably, the top five regions with the highest assessed values for sustainable development are Beijing (0.8037), Tianjin (0.7866), Zhejiang (0.7839), Jiangsu (0.7785), and Shanghai (0.7760). All these regions are situated in the eastern part of China and are characterized by high-income economies. On the contrary, the bottom five regions, with lower assessed values for sustainable development, are Xinjiang (0.6257), Yunnan (0.6545), Tibet (0.6685), Gansu (0.6739), and Jilin (0.6805). Xinjiang, Yunnan, Tibet, and Gansu are located in the west, while Jilin is situated in the northeast.
3.1.1.2 Based on their degree of sustainable development, China’s four main economic sub-regions are ranked as follows: Eastern Region, Northeastern Region, Central Region, and Western Region
From 2006 to 2017, the evaluation value for the level of sustainable development in the Eastern region consistently ranked first among the four major economic subregions, averaging 0.7288 over the 12-year period, significantly surpassing the others. Except for the years 2014, 2016, and 2017, the Northeast region’s assessed value for sustainable development level slightly exceeded that of the Central region, securing the second position with an average of 0.6940. The Central region’s assessed value for sustainable development level averaged 0.6834. In contrast, the West region consistently lagged behind the other three major economic subregions, with an average assessed value of sustainable development level at 0.6636. These findings align with the results of measuring the economic development levels of the four major economic subregions using the regional GNI per capita indicator of the Atlas method.
3.1.1.3 Growing gaps in the level of sustainable development between China’s regions
Comparing the regions with the highest and lowest assessed values of sustainable development levels from 2006 to 2017, the differences between them were 0.1688, 0.1514, 0.1717, 0.1761, 0.1731, 0.1731, 0.1637, 0.1719, 0.1745, 0.1892, 0.1695, and 0.1780. In 2007, the smallest gap in the evaluation value of sustainable development level was observed at 0.1514. Conversely, in 2015, the largest gap considering the level of sustainable development was recorded at 0.1892. In 2017, the gap in the evaluation value was 0.1780, the second highest after 2015. This indicates that during the period 2006–2017, the gap in the level of sustainable development between Chinese regions exhibited an overall upward trend amid oscillations.
3.1.1.4 Differences in the rate of increase in the level of sustainable development among China’s regions are becoming increasingly apparent.
From 2006 to 2017, significant variations in the increase in the level of sustainable development were observed among China’s regions, with Guizhou, Chongqing, Zhejiang, Fujian, and Hunan ranking among the top five regions in terms of the magnitude of increase. Despite a substantial 10.336% increase, Guizhou’s sustainable development level has not yet surpassed the 0.7 mark, primarily due to its initially low value. Zhejiang, on the contrary, was already at a high level of sustainable development. With a growth rate of 9.347%, it ascended to the third position in the ranking of sustainable development level, trailing only Beijing and Tianjin.
Analyzing from the perspective of the four major economic subregions: the central region and the eastern region experienced the most significant increases in the level of sustainable development, with 5.993 and 5.550%, respectively. The western region had a sustainable development level increase of 4.663%, slightly below the average increase. In contrast, the northeastern region recorded the lowest increase in the level of sustainable development at 1.742%, deviating from the average increase by 3.126%.
3.1.1.5 China’s sustainable development levels differ significantly from its GNI per capita rankings.
As can be seen from Table 2, in 2017, the rankings of China’s regions in terms of their level of sustainable development compared with their rankings in terms of per capita GNI indicators as measured by the chart set method, the rankings of the regions on average changed considerably, and the rankings of six regions showed the biggest changes: the rankings of Heilongjiang, Jiangxi, and Anhui went up 11, 11, and 7 places, respectively, and the rankings of Jilin, Xinjiang, and Inner Mongolia went down by 13, 12, and 9 places, respectively.
Ranking table of GNI per capita indicators and sustainable development levels by region in China, 2017
| Ranking | Regional GNI per capita | Sustainable development level | Ranking | Regional GNI per capita | Sustainable development level |
|---|---|---|---|---|---|
| 1 | Shanghai | Beijing | 17 | Hunan | Ningxia |
| 2 | Beijing | Tianjin | 18 | Henan | Guangxi |
| 3 | Tianjin | Zhejiang | 19 | Xinjiang | Shanxi |
| 4 | Jiangsu | Jiangsu | 20 | Sichuan | Inner Mongolia |
| 5 | Guangdong | Shanghai | 21 | Jiangxi | Hainan |
| 6 | Zhejiang | Chongqing | 22 | Anhui | Hebei |
| 7 | Fujian | Fujian | 23 | Hebei | Henan |
| 8 | Shandong | Guangdong | 24 | Guangxi | Guizhou |
| 9 | Chongqing | Hubei | 25 | Qinghai | Qinghai |
| 10 | Liaoning | Jiangxi | 26 | Shanxi | Shanxi |
| 11 | Inner Mongolia | Hunan | 27 | Heilongjiang | Jilin |
| 12 | Hubei | Shandong | 28 | Xizang | Gansu |
| 13 | Shanxi | Liaoning | 29 | Guizhou | Xizang |
| 14 | Jilin | Sichuan | 30 | Yunnan | Yunnan |
| 15 | Hainan | Anhui | 31 | Gansu | Xinjiang |
| 16 | Ningxia | Heilongjiang |
3.1.2 Characteristics of the time evolution of sub-indicators of the level of sustainable development of an economy
The GNI per capita indicator singularly mirrors the economic development level of a region or country. Conversely, the level of sustainable development stands as a comprehensive indicator, assessing an economy across 10 different dimensions. For the six regions noted earlier, which have undergone substantial shifts in both sustainable development and GNI per capita rankings, a detailed exploration of the reasons behind these significant changes will be conducted by examining their 10 sub-indicators.
Table 2 reveals that Heilongjiang, Jiangxi, and Anhui are ranked 27th, 21st, and 22nd, respectively, in terms of per capita GNI indicators. However, these rankings do not fully capture the nuanced picture, as Heilongjiang exhibits a level of social stability and ecological quality significantly above the average, Jiangxi outperforms the average in economic growth, social stability, and wealth distribution, and Anhui excels in economic growth, wealth distribution, urbanization, employment, social stability, ecological quality, innovation and development, and corruption control, all surpassing the average (refer to Figure 3). Consequently, the rankings for the level of sustainable development in these three regions have experienced more substantial improvements. On the contrary, Jilin lags below the average in economic growth, wealth distribution, financial system stability, urbanization, employment, social public services, and innovative development. Xinjiang exhibits lower-than-average levels of economic growth, social public services, urbanization, social stability, financial system stability, ecological quality, innovation development, and faces higher corruption levels. Inner Mongolia falls below average in economic growth, social public services, urbanization, employment, financial system stability, and innovation development. As a result, these three regions have witnessed more significant declines in their rankings for the level of sustainable development.
Evaluation of sub-indicators of sustainable development in six regions of China in 2006 (up) and 2007 (down).
As illustrated in Figure 3, there are similarities and differences between the six regions in ten aspects of development and change, including the rate of expansion of the economy, the level of wealth distribution equilibrium, and the rate of expansion of corruption, from 2006 to 2017: The common point is that compared with the other 21 regions, Heilongjiang, Jiangxi, Anhui, Jilin, Xinjiang, and Inner Mongolia have significantly improved the level of social public services and urbanization. It shows that local governments have made progress in improving the population’s quality of life and promoting urbanization. In addition, the economic growth level, wealth distribution level, and ecological quality level of Heilongjiang, Jiangxi, and Anhui regions have improved, but the stability of their financial system has decreased slightly compared with other regions. It shows that economic growth, wealth distribution, and ecological quality are the key indicators of economic sustainable development level and significantly affect the evaluation value of regional economic sustainable development level. It also shows that these regions have achieved steady economic growth while improving wealth distribution and environmental quality. However, the SSVFS in these regions has slightly decreased compared to other regions, indicating that the stability of the financial system needs to be further strengthened despite good economic and ecological development. The difference is that compared with Anhui, the employment level and innovation level of Heilongjiang and Jiangxi have decreased slightly, which shows that the technological innovation ability of these two regions has insufficient momentum, which may pose a challenge to future technological upgrading and economic restructuring.
3.2 Spatial patterns of China’s provincial sustainable development level
3.2.1 An examination of how the degree of sustainable development varies spatially in China’s provinces
The preceding article employed GRA to assess the sustainable development level of Chinese regions spanning the years 2006 to 2017. Building upon this analysis, GIS spatial analysis techniques were applied to visualize the sustainable development level of 31 Chinese regions in the years 2006, 2010, 2013, and 2017. This allowed for the illustration of the spatial evolution profile of the sustainable development level for each Chinese region, as depicted in Figure 4.
Spatial distribution of the level of sustainable development by region in China in different years.
In order to accomplish regional stratification and visually represent the spatial evolution features of the sustainable development level in each of China’s regions, this study utilizes the quartile method. It divides the sustainable development level of the 31 regions into four gradients: the first gradient (0.585–0.642) designates a low-level region; the second gradient (0.642–0.699) represents a middle-to-low-level region; the third gradient (0.699–0.756) characterizes an upper-middle-level region, and the fourth gradient (0.756–0.814) identifies a high-level region.
As illustrated in Figure 4, from 2006 to 2017, the sustainable development level in all regions of China exhibited a gradual improvement, particularly noticeable in the eastern and central regions. The western regions also showed signs of enhancement, and a distinctive feature of this period is the regional aggregation in sustainable development levels.
In 2006, the degree of sustainable development in the key areas was primarily distributed in the low and medium-low levels. Specifically, Anhui, Henan, Shanxi, Jiangxi, and Hubei were situated in the low to medium-low levels. The eastern region displayed a broader distribution encompassing the low, medium-low, and medium-high levels. Hebei was the sole region in the low level, while Zhejiang, Fujian, Shandong, and Hainan were in the lower-middle level, and Beijing, Tianjin, Shanghai, Jiangsu, and Guangdong were in the upper-middle level. By 2017, the sustainable development level in the central region shifted toward the lower-middle and upper-middle levels, with Henan and Shanxi in the lower-middle level and Anhui, Jiangxi, Hubei, and Hunan in the upper-middle level. In the eastern region, the distribution expanded across the lower-middle, upper-middle, and high levels. Specifically, Hebei and Hainan were in the lower-middle level, Fujian, Guangdong, and Shandong were in the upper echelon, and Beijing, Tianjin, Shanghai, Jiangsu, and Zhejiang were at a high level.
The spatial distribution of China’s sustainable development level by region in 2006, 2010, 2013, and 2017 indicates a more dispersed pattern in 2006 and 2010. However, in 2013 and 2017, the distribution became more aggregated, with higher levels of sustainable development clustered in the east-central and northeastern regions. In contrast, regions with lower levels of sustainable development were mainly concentrated in the western part of the country, particularly evident in Xinjiang.
3.2.2 Spatial characteristics of China’s provincial sustainable development level
The previous article used GIS spatial analysis technology to visualize the sustainable development level of China’s regions, and the analysis found that the sustainable development level of China’s regions has obvious aggregation characteristics. The author further investigated the spatial features of the degree of sustainable development of Chinese regions using ESDA in order to corroborate this phenomenon. This investigation revealed the spatial heterogeneity and agglomeration of the level of sustainable development of Chinese regions. Using Open GeoDa software, the spatial characteristics of the level of sustainable development of Chinese regions from 2006 to 2017 were analyzed.
3.2.2.1 Global spatial autocorrelation analysis
Using Moran’s I, the spatial clustering properties of each region’s sustainable development level in China between 2006 and 2017 were examined. Table 3 presents the findings.
Moran’s I on the level of sustainable development in Chinese regions, 2006–2017
| Year | Moran’s I | P-value | Z | Year | Moran’s I | P-value | Z |
|---|---|---|---|---|---|---|---|
| 2006 | 0.2728*** | 0.005 | 2.560 | 2012 | 0.3385*** | 0.002 | 3.047 |
| 2007 | 0.3550*** | 0.002 | 3.448 | 2013 | 0.4090*** | 0.001 | 3.667 |
| 2008 | 0.3506*** | 0.003 | 3.207 | 2014 | 0.4216*** | 0.001 | 3.779 |
| 2009 | 0.4017*** | 0.001 | 3.734 | 2015 | 0.4653*** | 0.001 | 4.384 |
| 2010 | 0.3334*** | 0.002 | 3.013 | 2016 | 0.4814*** | 0.001 | 4.409 |
| 2011 | 0.3404*** | 0.001 | 3.173 | 2017 | 0.4964*** | 0.001 | 4.729 |
***, **, and * represent significant at the 1, 5, and 10% levels, respectively.
Table 3 allows for the following conclusions to be made.
The sustainable development level among China’s regions exhibits a notable and positive spatial correlation. From 2006 to 2017, Moran’s I for the sustainable development level in China’s regions consistently showed positive values, all of which passed the significance test at the 1% level. This indicates a significant and positive spatial correlation among the sustainable development levels of China’s regions, emphasizing a distinct spatial clustering pattern. In other words, regions with higher levels of sustainable development tend to be proximate to each other, while those with lower levels cluster together. This underscores the spatial distribution pattern of China’s sustainable development level, affirming the regional development characteristics of strength in the east and relative weakness in the west, aligning with the findings of previous analyses.
The general pattern of the degrees of sustainable development’s spatial correlation among different regions in China exhibits an increasing pattern with wave-like fluctuations. From 2006 to 2012, Moran’s I value for sustainable development levels in various regions showed a periodic decrease every other year but demonstrated a consistent upward trajectory. Specifically, Moran’s I rose from 0.2728 in 2006 to 0.3385 in 2012, indicating a continual enhancement in the spatial autocorrelation of sustainable development levels across China. During the period from 2012 to 2017, Moran’s I experienced a substantial and continuous increase, highlighting a more evident spatial autocorrelation of sustainable development levels among regions.
3.2.2.2 Local spatial autocorrelation analysis
Due to the limitations of global spatial autocorrelation analysis, which only reflects the overall spatial clustering of sustainable development levels among the 31 regions in China and fails to display spatial correlation patterns between a specific region and its neighboring areas. The author further employs Moran’s I scatter plots for the years 2006 and 2017 to conduct a local spatial autocorrelation analysis of sustainable development levels in various regions of China. The results are illustrated in Figure 5.
Moran’s I scatter plots of sustainable development levels across different regions in China for various years.
It is clear from Figure 5 that the first and third quadrants contain the bulk of China’s 31 provinces, municipalities, and autonomous areas. In 2006, 2010, 2013, and 2017, the regions distributed in the first and third quadrants amounted to 19, 23, 19, and 26, respectively, accounting for 61.29, 74.19, 61.29, and 83.87% of all regions. In different parts of China, the geographical clustering of sustainable development levels has been steadily increasing. Over these 4 years, the number of regions located in the first quadrant was 9, 10, 9, and 11, respectively, while the regions in the third quadrant were 10, 13, 10, and 15, indicating that the fluctuation in the number of regions with high-value clustering was relatively small, whereas regions with low-value clustering exhibited more significant fluctuations. The data indicate that in regions with high-value clustering, the sustainable development levels of adjacent areas fluctuate but consistently maintain a relatively high level, while in regions with low-value clustering, the sustainable development levels of adjacent areas undergo continuous changes.
Variations regarding the way China’s regions cluster different years’ degrees of sustainable development
| Year | Quadrant I (HH aggregation) | Quadrant II (HH aggregation) | Quadrant III (HH aggregation) | Quadrant IV (HH aggregation) |
|---|---|---|---|---|
| 2006 | Beijing, Tianjin Shanghai, Zhejiang, Jiangsu, Jiangxi, Hainan, Heilongjiang Liaoning | Fujian, Jilin, Anhui, Hebei, Ningxia, Shanxi | Chongqing, Hunan, Henan, Yunnan, Qinghai, Guizhou, Guangxi, Gansu, Xizang, Xinjiang | Guangdong, Shandong Hubei, Sichuan, Shanxi, Inner Mongolia |
| 2010 | Beijing, Tianjin Shanghai, Zhejiang, Jiangsu, Jiangxi, Heilongjiang, Ningxia, Fujian, Jilin | Anhui, Hainan, Hebei | Shanxi, Ningxia, Sichuan, Shanxi, Henan, Yunnan, Qinghai, Guizhou, Guangxi, Gansu, Xizang, Xinjiang, Chongqing | Guangdong, Shandong, Hubei, Inner Mongolia, Hunan, Hubei |
| 2013 | Beijing, Tianjin Shanghai, Zhejiang, Jiangsu, Jiangxi, Fujian, Anhui, Hubei | Heilongjiang, Jilin, Hainan, Hebei | Ningxia, Shanxi, Henan, Yunnan, Qinghai, Guizhou, Guangxi, Gansu, Xizang, Xinjiang | Sichuan, Inner Mongolia, Chongqing, Guangdong, Liaoning, Shanxi, Shandong, Hunan |
| 2017 | Beijing, Tianjin Shanghai, Zhejiang Jiangsu, Jiangxi, Fujian, Shandong, Hubei, Guangdong, Hunan | Anhui, Hainan, Hebei | Shaanxi, Inner Mongolia, Ningxia, Heilongjiang, Sichuan, Shanxi, Jilin, Henan, Yunnan, Qinghai, Guizhou, Guangxi, Gansu, Xizang, Xinjiang | Chongqing, Liaoning |
Drawing on the methodology of He et al. [36], Moran’s I scatterplot has been analyzed to discern spatial correlation patterns in different quadrants, illustrating changes in each region over various years, as presented in Table 4. Across the four selected years, Beijing, Tianjin, Shanghai, Jiangsu, and Jiangxi consistently occupy the first quadrant, showcasing high-high (HH) agglomeration. Conversely, Henan, Yunnan, Qinghai, Guizhou, Guangxi, Gansu, Tibet, and Xinjiang consistently reside in the third quadrant, indicating low-low (LL) agglomeration. HH-type agglomeration predominantly occurs in the eastern region, extending partially into the northeastern and central regions but has yet to extend to the western region. Conversely, LL-type agglomeration is primarily observed in the western region, with minimal extension into the northeastern and central regions. Notably, the spatial agglomeration of sustainable economic development levels in each Chinese region will be analyzed, focusing on changes from 2006 to 2017.
Sichuan, Shaanxi, Inner Mongolia, and Hainan have changed from HL-type agglomeration to LL-type agglomeration and HH-type agglomeration to LH-type agglomeration, respectively. The general level of their own sustainable economic development and the increase is smaller than the overall average level, causing them to become areas where sustainable economic development is less advanced, thus transforming into LL-type agglomeration and LH-type agglomeration. Jilin, Ningxia, Shanxi, and Liaoning regions change from LH-type agglomeration to LL-type agglomeration and HH-type agglomeration to HL-type agglomeration, respectively. Because the level of sustainable economic development of their neighboring regions is average and the increase is smaller than the overall average level, these neighboring regions have a lower level of sustainable economic development compared with other regions, and when spatial agglomeration analysis is carried out, the Jilin, Ningxia, Shanxi, and Liaoning regions are shown to be surrounded by low-level regions.
Fujian and Chongqing regions have changed from LH-type agglomeration to HH-type agglomeration and LL-type agglomeration to HL-type agglomeration, respectively. Due to their own better and larger increase in the level of sustainable economic development, the level of sustainable economic development is comparable to that of developed regions, so their spatial agglomeration characteristics change to HH-type agglomeration and HL-type agglomeration. Guangdong, Shandong, and Hubei regions change from HL-type agglomeration to HH-type agglomeration. Given that their adjacent regions’ levels of sustainable economic development have significantly increased; they are transformed into HH-type agglomeration.
Between 2006 and 2017, there are two sudden changes in the spatial agglomeration of the level of sustainable economic development of each region, respectively, Heilongjiang from HH-type agglomeration to LL-type agglomeration and Hunan from LL-type agglomeration to HH-type agglomeration. 2006–2017, the level of sustainable economic development of other regions has increased significantly, but the level of sustainable economic development of Heilongjiang and its neighboring regions (Jilin and Inner Mongolia) has not increased significantly, or even some regional indicators have declined, resulting in the spatial agglomeration characteristics of Heilongjiang and its neighboring regions are at a lower level compared with other regions. During 2006–2017, the sustainable development level of other regions increased significantly, but the economic sustainable development level of Heilongjiang and its neighboring regions (Jilin and Inner Mongolia) did not increase significantly or even declined in some areas, which resulted in the economic sustainable development level of Heilongjiang and its neighboring regions being at a reduced rate in contrast to other areas, and its spatial agglomeration characteristics changed from HH-type agglomeration to LL-type agglomeration. Since 2006, Hunan and its neighboring regions (Guangdong, Jiangxi, Hubei, Chongqing, Guangxi, and Guizhou) have experienced a significant increase in the level of sustainable economic development, with an average increase of 7.16%, which is much higher than the overall average increase of the 31 regions in China (4.87%). As of 2017, the economic sustainability level of Hunan and its neighboring regions is at a high level compared with other regions, so its spatial agglomeration characteristics have changed from LL-type agglomeration to HH-type agglomeration.
Observing the spatial characteristics of the sustainable economic development levels in each region from 2006 to 2017 reveals that as each region enhances its own sustainable economic development, it not only influences the overall spatial agglomeration but also instigates significant shifts in both its own spatial agglomeration and that of its neighboring regions. Consequently, in the pursuit of advancing the sustainability of their economies, regions experience inevitable fluctuations in spatial agglomeration. Nevertheless, the overarching trend indicates a general increase in the degree of agglomeration.
3.3 Identification of factors influencing the spatio-temporal evolution of sustainable economic development
From the spatial and temporal analyses above on the sustainable economic development of Chinese regions, it becomes evident that the level of sustainable economic development among provinces and regions in China exhibits significant spatial agglomeration. This research uses both the spatial autoregressive model and the geographically weighted regression (GWR) model for a detailed analysis in order to get deeper into the spatial pattern of sustainable economic development in Chinese areas. This choice is made with the aim of gaining a comprehensive understanding of the spatial intricacies that contribute to the overall landscape of sustainable economic development across Chinese regions.
3.3.1 Analysis of the global law of sustainable economic development
In this article, considering the current situation of sustainable economic development in all regions of China in 2017, we initially selected 30 indicators, such as GDP growth rate, Gini coefficient, corruption coefficient, and the proportion of education expenditure to GDP and then applied principal component analysis to calculate their correlation coefficient matrices and their cumulative contribution rates, and from the cumulative contribution rate of more than 85%, i.e., the Gini coefficient, the CI, the pension insurance participation rate, and the criminal crime rate, and other 11 indicators, and further carry out the diagnosis of multicollinearity to exclude the indicators with VIF values greater than 10, and finally determine eight indicators as the regression model’s independent variables, such as the Gini coefficient, the criminal offence rate, the share of stock turnover in GDP, and the rate of wastewater discharge (Table 5).
Results of the spatial autocorrelation model estimation
| Variable | OLS | SLM | SEM | |||
|---|---|---|---|---|---|---|
| Coefficient | T-value | Coefficient | Z-value | Coefficient | Z-value | |
| Constant | 0.7104*** | 21.3877 | 0.7064*** | 21.2922 | 0.6878*** | 27.9568 |
| B1 | −0.3607** | −2.8150 | −0.3679*** | −3.3005 | −0.2868*** | −3.4196 |
| G3 | −9.3695 × 10−5 | −0.3058 | −7.1786 × 10−5 | −0.2596 | 0.0002 | 0.5939 |
| H2 | 5.8487 × 10−5 | 0.1820 | 5.6971 × 10−5 | 0.2105 | −0.0002 | −0.7051 |
| I1 | 0.0400 | 1.2341 | 0.0413 | 1.4840 | 0.0387 | 1.5046 |
| I4 | 0.0093 | 0.1322 | 0.0077 | 0.1289 | −0.0109 | −0.1956 |
| J1 | 0.0101 | 0.5890 | 0.0106 | 0.7252 | 0.0216 | 1.5861 |
| J2 | 0.0367 | 1.4190 | 0.0343 | 1.4229 | 0.0297 | 0.0196 |
| J4 | 0.0609** | 2.1580 | 0.0598** | 2.4744 | 0.0696*** | 2.8690 |
| Rho | − | − | 0.0068 | 0.2358 | −0.6131** | −2.2780 |
| R 2 | 0.8140 | 0.8143 | 0.8305 | |||
| Log likelihood | 83.4754 | 83.5036 | 83.7264 | |||
| Aic | 148.951 | 147.007 | 149.453 | |||
| Sc | 136.045 | 132.667 | 136.547 | |||
***, **, and * represent significant at the 1, 5, and 10% levels, respectively.
Using the OLS model and a robustness test, this study examines the variables influencing the long-term expansion of province economies. The OLS model indicates that there is a noteworthy adverse effect of the Gini coefficient on sustainable economic development. The Gini coefficient’s regression coefficient, a crucial indicator of regional wealth disparity, is −0.3607. This implies that, holding all other factors constant, the level of sustainable economic development decreases by 0.36% for every 1% increase in the Gini coefficient. This suggests that, on a comprehensive scale, the widening wealth gap among regions stands as a pivotal factor hindering the achievement of sustainable economic development. Simultaneously, the number of patent applications per capita demonstrates a noteworthy positive influence on the sustainable development of the economy. The number of patent applications per capita regression coefficient, a key indicator of regional scientific and technological innovation capacity, is 0.0609. In other words, keeping all other conditions constant, a 1% increase in the number of patent applications per capita corresponds to a 0.06% rise in the level of sustainable economic development. A deficient level of scientific and technological innovation emerges as a significant factor impeding the economy’s capacity to attain sustainable development. Therefore, under the precondition of maintaining stable economic, social, and ecological development, all regions in China should strive to narrow the wealth gap, enhance levels of scientific and technological innovation, and propel the economy toward the realization of sustainable development.
3.3.2 Analysis of local patterns of sustainable economic development
The calculated coefficients as emerged based on OLS, SLM and SEM models are global coefficients, which are for China as a whole. In fact, for different regions, the degree of influence of different influencing factors on the coupling and coordination of their regional sustainable development varies. Therefore, in order to analyze the spatial non-stationarity, this research builds a GWR model between the sustainable development of Chinese provinces and regions and the influencing factors, and the most crucial part of the construction of the GWR model is to determine the weighting function. The Gaussian function is selected as the weighting function based on the “AIC minimization” approach, and 40.3923 km is the ideal bandwidth. The parameter estimation results of the GWR model calculated according to the optimal bandwidth are shown in Table 6.
Estimated results of GWR models
| Variable | Minimum | Upper quartile | Median | Lower quartile | Maximum | AIC | R 2 |
|---|---|---|---|---|---|---|---|
| Intercept | 0.6980 | 0.7142 | 0.7161 | 0.7185 | 0.7218 | 137.2156 | 0.8430 |
| B1 | −0.3808 | −0.3696 | −0.3639 | −0.3580 | −0.3427 | ||
| G3 | −0.0002 | −0.0002 | −0.0002 | −0.0001 | 0.0000 | ||
| H2 | 0.0000 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | ||
| I1 | 0.0368 | 0.0414 | 0.0425 | 0.0434 | 0.0450 | ||
| I4 | −0.0125 | −0.0076 | −0.0021 | 0.0049 | 0.0423 | ||
| J1 | 0.0032 | 0.0041 | 0.0053 | 0.0068 | 0.0167 | ||
| J2 | 0.0314 | 0.0324 | 0.0334 | 0.0349 | 0.0454 | ||
| J4 | 0.0586 | 0.0605 | 0.0614 | 0.0624 | 0.0645 |
According to Fotheringham’s evaluation criteria, the GWR model and AIC values are 0.8430 and 137.2160, respectively, which are better than the OLS, SLM, and SEM models, and the AIC value decreases significantly, which indicates that the GWR model with spatially localized information considered has a better fitting effect. As indicated in Table 6, the spatial heterogeneity of the four indicators, namely, the Gini coefficient, wastewater discharge rate, proportion of R&D expenditures to GDP, and the number of patent applications per capita, is relatively strong. Based on the regression results of the GWR model, this article plots the spatial distribution of the estimated coefficients of the Gini coefficient, wastewater discharge rate, the proportion of R&D expenditures in GDP, and the number of patent applications per capita in each region, as shown in Figure 6.
The key elements influencing changes in sustainable economic development and their spatial distribution.
It is clear from the GWR model’s coefficients that the 31 areas of China’s sustainable development are severely harmed by the Gini coefficient. Conversely, the wastewater discharge rate, the proportion of R&D expenditure to GDP, and the number of patent applications per capita exhibit significant positive impacts. This suggests that regional disparities in wealth, inefficient ecological management, insufficient R&D investment, and delayed scientific and technological innovation capacity are the primary factors contributing to changes in the level of sustainable development among the 31 regions in China. Among these factors, the number of patent applications per capita emerges as the most influential. Notably, the Gini coefficient, wastewater emission rate, proportion of R&D expenditure to GDP, and number of patent applications per capita exert varying impacts on China’s sustainable development level, increasing from north to south, northwest to southeast, southeast to northwest, and southwest to northeast, respectively. Therefore, each region in China must consider its unique developmental characteristics and implement effective measures to ensure coordinated and targeted advancement across the five major developmental levels. This approach aims to promote sustainable regional development throughout China.
The spatial autoregressive model provides a holistic and intuitive understanding of the rate of economic growth in China. On the contrary, the GWR model is adept at capturing the spatial non-stationarity in the economic sustainable development level across each region. This feature is beneficial for extracting localized information. The integration of these two models allows for a more comprehensive portrayal of the primary influencing elements impacting how the economy develops sustainably.
4 Discussion
4.1 Understand the degree of sustainable economic development in China
Sustainable development is a comprehensive concept involving economy, society, culture, technology, and natural environment, which mainly includes three aspects: sustainable development of natural resources and ecological environment, sustainable development of economy, and sustainable development of society [2]. A healthy ecological environment and the appropriate use of natural resources are the cornerstones of sustainable development. The second is the foundation of sustainable economic development; the third is to aim for the general advancement of society [37]. In order to achieve sustainable development, achieving a sustainable economy is essential. Clarifying the level of sustainable development of China’s economy is of great significance to promoting the coordination, equity, and sustainability of regional sustainable development. Based on the four-dimensional system of “economy, society, ecology, and innovation,” this research develops a framework for assessing sustainable economic growth, scientifically evaluates China’s proportion of sustainable economic development as a whole and in provinces, and defines the spatio-temporal pattern of sustainable economic development in China, which is the key to promoting regional sustainable development. The consistency of the development model and the differentiation of the mechanism are of reference significance to other countries and regions. The obstacles to sustainable economic development are closely related. In order to accomplish the healthy and sustainable development of China’s provincial and regional economies, it is imperative that obstacles be appropriately addressed, and the interconnected development of sustainable province and regional economies be strengthened [26].
4.2 Identification of impact factors of sustainable economic development in China
The sustainable development of economy is an important topic in the world today [3]. Promoting sustainable economic development can help reduce economic volatility and promote long-term prosperity [38]. Determining the elements that contribute to sustainable economic development is helpful in advancing sustainable development, preserving the planet’s natural balance, enhancing social justice, and fostering long-term economic growth and stability. Most prior research focuses on the three comprehensive aspects of economy, society, and ecology in the index system of sustainable economic development built by the article based on the four-dimensional system of "economy–society–ecology–innovation." In contrast, the factors affecting the sustainable development of the regional economy are more detailed in this article, and 10 first-level indicators, 21 second-level indicators, and 6 third-level indicators are established for the provincial scale. Regarding the elements influencing the sustainable growth of the regional economy, this study establishes 10 level 1 indicators, 21 level 2 indicators, and 6 level 3 indicators for the province level. The GNI per capita indicator measured by domestic and foreign scholars according to the World Bank Atlas method only reflects the level of economic development of a region or a country. In contrast, this article’s sustainable economic development level is a comprehensive indicator that evaluates an economy from ten different dimensions. A comparative analysis of the two regional rankings will help to understand the critical indicators in addition to the indicator of the level of economic development and the value of the impact of the other indicators, which is more significant or not. The study’s findings offer policymakers a valuable point of reference. Targeted policies for sustainable economic development should be developed in each region based on its unique socioeconomic features. While the western region, which is comparatively lagging in economic development, requires more investment and support to improve its infrastructure and raise the standard of living for its citizens, the eastern region, for instance, can further strengthen its public social services and innovation capacity to solidify its leading position. The government should simultaneously prioritize promoting sustainable development by bolstering the financial system’s stability and removing the financial hazards of fast economic expansion. In addition, policies should be formulated to take into account ecological and environmental protection, promote the development of a green economy, and ensure the sustainability of economic growth.
4.3 Limitations and future prospects
This study assesses China’s provinces’ degree of sustainable economic development between 2006 and 2017 using the gray correlation model, which is easy to operate, and the evaluation results objectively reflect the actual situation. First, the study’s scope might be broadened to cover more areas and time periods in order to obtain more thorough information on sustainable economic development and support its conclusions. Second, the sustainable economic development evaluation system selects both direct and related indirect indicators. However, some indicators, such as the CI and criminal crime rate, have not been updated in time, some indicators are difficult to obtain data on, and some positive indicators used to evaluate sustainable economic development have been passively abandoned. At the same time, due to the limitation of data, the flow of factors is not fully reflected. In addition, further research could be conducted on the mechanisms influencing sustainable economic development in different regions, particularly on how factors such as science, technology and innovation, resource allocation, and the policy environment play a role in different regions. Due to the diversification of paths to achieve sustainable economic development, it is necessary to further improve the corresponding indicator system according to China’s development stage in the future, for the purpose of offering China practical ways to attain sustainable economic development.
5 Conclusions
This article constructs an economic sustainable development indicator system based on the four-dimensional system of “economy–society–ecology–innovation.” It evaluates 31 Chinese regions’ degree of economically sustainable development between 2006 and 2017 using GRA and further analyzes each region’s temporal and spatial features of economic sustainable development using ArcGIS visualization. It also combines the spatial autoregressive and GWR models to examine the development patterns and essential factors of sustainable economic development in Chinese provincial regions. On this basis, GIS spatial analysis techniques and ESDA are used to analyze the spatial evolution profile and spatial characteristics of the level of sustainable economic development in each region of China in a more profound way. The analysis results show that: (1) The percentage of China’s economy that is developing sustainably is rising overall, with the exception of Inner Mongolia, Shaanxi, and Xinjiang. The gap in sustainable economic development between China’s regions is widening, and the difference in growth rates is becoming more pronounced. (2) The rankings of Chinese areas according to the per capita GNI index and their level of sustainable development fluctuate slightly. Economic growth, wealth distribution, technological innovation capacity, employment indicators, and ecological quality indicators are the key indicators of the level of sustainable economic development, which significantly affect the evaluation value of each region’s sustainable economic development level. (3) Here is a strong positive spatial association between each region in China’s level of sustainable economic development. The spatial correlation of the level of sustainable economic development of each region in China shows an overall growth trend under the wave movement. (4) The spatial clustering of Chinese areas according to their degree of sustainable economic development fluctuates up and down constantly, but the overall trend is that the degree of agglomeration is increasing, and the number of regions that have transformed into HH-type agglomeration and LL-type agglomeration is increasing day by day. (5) Globally speaking, the regional wealth disparity and the capacity for scientific and technological innovation are significant determinants of China’s economic sustainability, and China’s regions should make every effort to reduce the wealth disparity and raise their level of scientific and technological innovation to support the achievement of sustainable economic development. From the point of view of local laws, the Gini coefficient, wastewater discharge rate, the proportion of R&D expenditures to GDP, and the number of patent applications received per capita have spatial solid heterogeneity on the sustainable development of the economy of each region of China. Each region should combine its characteristics of regional development and take adequate measures to ensure that the level of economic, social, ecological, and innovation development is targeted to promote China’s sustainable economic development.
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Funding information: Authors state no funding involved.
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Author contributions: QZ: investigation, supervision, discussion, visualization, suggestions for data analysis, and writing original draft; CT: conceptualization, writing-reviewing; YX: suggestions for data analysis, and writing original draft. All the authors approved the final article.
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Conflict of interest: The contact author has declared that none of the authors has any competing interests.
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- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
- Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
- Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
- Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
- Flash flood potential index at national scale: Susceptibility assessment within catchments
- SWAT modelling and MCDM for spatial valuation in small hydropower planning
- Disaster risk perception and local resilience near the “Duboko” landfill: Challenges of governance, management, trust, and environmental communication in Serbia
Articles in the same Issue
- Research Articles
- Seismic response and damage model analysis of rocky slopes with weak interlayers
- Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
- Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
- GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
- Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
- Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
- Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
- Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
- The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
- Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
- Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
- Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
- Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
- Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
- Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
- Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
- Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
- Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
- Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
- Research on the variation in the Shields curve of silt initiation
- Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
- Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
- Analysis of the formation process of a natural fertilizer in the loess area
- Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
- Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
- Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
- Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
- Estimating Bowen ratio in local environment based on satellite imagery
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
- Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
- Study on the mechanism of plant root influence on soil properties in expansive soil areas
- Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
- Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
- Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
- Predicting coastal variations in non-storm conditions with machine learning
- Cross-dimensional adaptivity research on a 3D earth observation data cube model
- Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
- Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
- Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
- Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
- Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
- Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
- Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
- Identification of radial drainage networks based on topographic and geometric features
- Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
- Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
- Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
- Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
- Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
- An efficient network for object detection in scale-imbalanced remote sensing images
- Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
- Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
- A modified Hoek–Brown model considering softening effects and its applications
- Evaluation of engineering properties of soil for sustainable urban development
- The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
- Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
- Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
- Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
- 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
- Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
- Land use classification through fusion of remote sensing images and multi-source data
- Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
- Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
- Factors impacting spatial distribution of black and odorous water bodies in Hebei
- Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
- Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
- Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
- Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
- Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
- Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
- Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
- Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
- Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
- Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
- Estimating the travel distance of channelized rock avalanches using genetic programming method
- Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
- New age constraints of the LGM onset in the Bohemian Forest – Central Europe
- Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
- Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
- Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
- Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
- Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
- Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
- Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
- Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
- Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
- Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
- A test site case study on the long-term behavior of geotextile tubes
- An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
- Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
- Comparative effects of olivine and sand on KOH-treated clayey soil
- YOLO-MC: An algorithm for early forest fire recognition based on drone image
- Earthquake building damage classification based on full suite of Sentinel-1 features
- Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
- Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
- An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
- Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
- Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
- Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
- A metaverse-based visual analysis approach for 3D reservoir models
- Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
- Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
- Study on the spatial equilibrium of cultural and tourism resources in Macao, China
- Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
- Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
- The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
- Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
- Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
- Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
- Comparison of several seismic active earth pressure calculation methods for retaining structures
- Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
- Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
- Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
- Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
- InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
- Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
- Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
- Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
- Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
- Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
- Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
- Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
- Stability analysis of valley-type upstream tailings dams using a 3D model
- Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
- Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
- Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
- A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
- Study on the formation mechanism of the hard-shell layer of liquefied silty soil
- Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
- Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
- Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
- High-frequency cycles drive the cyclical enrichment of oil in porous carbonate reservoirs: A case study of the Khasib Formation in E Oilfield, Mesopotamian Basin, Iraq
- Reconstruction of digital core models of granular rocks using mathematical morphology
- Spatial–temporal differentiation law of habitat quality and its driving mechanism in the typical plateau areas of the Loess Plateau in the recent 30 years
- A machine-learning-based approach to predict potential oil sites: Conceptual framework and experimental evaluation
- Effects of landscape pattern change on waterbird diversity in Xianghai Nature Reserve
- Research on intelligent classification method of highway tunnel surrounding rock classification based on parameters while drilling
- River morphology and tectono-sedimentary analysis of a shallow river delta: A case study of Putaohua oil layer in Saertu oilfield (L. Cretaceous), China
- Dynamic change in quarterly FVC of urban parks based on multi-spectral UAV images: A case study of people’s park and harmony park in Xinxiang, China
- Review Articles
- Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
- Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
- Ore-controlling structures of granite-related uranium deposits in South China: A review
- Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
- A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
- Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
- Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
- Depopulation in the Visok micro-region: Toward demographic and economic revitalization
- Special Issue: Geospatial and Environmental Dynamics - Part II
- Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
- Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
- Minerals for the green agenda, implications, stalemates, and alternatives
- Spatiotemporal water quality analysis of Vrana Lake, Croatia
- Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
- Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
- Regional patterns in cause-specific mortality in Montenegro, 1991–2019
- Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
- Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
- Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
- Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
- Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
- Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
- Flash flood potential index at national scale: Susceptibility assessment within catchments
- SWAT modelling and MCDM for spatial valuation in small hydropower planning
- Disaster risk perception and local resilience near the “Duboko” landfill: Challenges of governance, management, trust, and environmental communication in Serbia
