Introduction

The earth is currently facing numerous environmental crises, including unsustainable consumption and production that exacerbate climate change, natural degradation, loss of biodiversity, environmental pollution, and increased waste [1]. The climate crisis has led to increasing extreme weather events and has caused trillions of dollars in economic losses. Ecosystem degradation has affected 40% of the world’s population, causing approximately one-third of the world’s farmland to deteriorate, approximately 87% of inland wetlands to disappear, and one-third of commercial fishing resources to be overfished. Air pollution causes approximately seven million premature deaths each year, and the amount of plastic waste in aquatic ecosystems continues to increase [2]. Meanwhile, the United Nations Environment Program has announced that the world is using resources equivalent to 1.6 earths to maintain its current way of life. Ecosystems are no longer able to sustainably meet global demands, which is believed to be the root cause of the current environmental crisis, constraining the sustainable development of the global economy. To address the new challenges faced globally in sustainable development, it is important to understand whether the intensity of human activities exceeds the ecosystem’s capacity to withstand and regulate before implementing targeted countermeasures and measures [3]. Therefore, it is important to apply relevant data to evaluate and analyze the spatiotemporal evolution trends of ECC at the regional scale and propose tailored decision-making solutions to support the sustainable development of global and regional socioeconomic development.

After Park et al. proposed the concept of bearing capacity in 1921 [4], researchers conducted a series of related studies. With changes in the ecological environment, ECC has become a popular research topic. This research has predominantly focused on the construction of an ECC evaluation index system and the selection of evaluation methods.

To date, most researchers have constructed evaluation index systems from the perspective of single and multiple factors. Single factors have mainly focused on resources such as water, ocean, and land. Jia, Cai et al. established a water environment carrying capacity indicator system including the water environment carrying capacity, environmental pressure, water environment vulnerability, and development and use potential [5]. Du and Wang constructed a resource environmental carrying capacity evaluation index system for marine ranches from the perspectives of pressure and support [6]. An ecological geological environment carrying capacity evaluation index system has also been constructed, including the geological environment, ecological geological environment, ecological environment, and social environment subsystems [7]. The in-depth study of single factor carrying capacity by scholars provides a basis for further clarifying the connotation of ECC. However, the ecological environment is a complex system influenced by various factors such as economy, society, and resources, and studying ECC solely from a single factor has become increasingly limited. Therefore, scholars have begun to evaluate ECC from multiple factors. The specific research results include studying the intrinsic mechanism of ECC from the perspective of state pressure response based on the DPSIRM framework [8]. An evaluation index system for the resource and environmental carrying capacity (RECC) was based on water resources, land resources, environment, and ecosystem, using the square difference decision-making method [9]. An indicator system for urban resource and environmental carrying capacity was established based on ecological civilization, including water carrying capacity, land carrying capacity, atmospheric environmental carrying capacity, energy carrying capacity, and solid waste environmental carrying capacity [10, 11]. The ECC index system of economy, resources, and environment uses the entropy weight method to calculate indicator weights, and the TOPSIS model was used to calculate ECC [2]. Based on the elements and structural characteristics of urban ecosystems, ECC was examined and explored from natural, social, and economic perspectives [12]. The research results on the construction of an ECC evaluation index system have laid a solid theoretical foundation for further delineation of the theoretical boundaries of ECC and for the construction of a suitable index system in this article.

In the field of evaluation methods research, scholars have used various evaluation methods to evaluate the level of regional ECC comprehensively. Specifically, they have used the carbon footprint and biomass capacity indicators within the framework of the environmental carrying capacity [13]. Utilizing a modified dynamic capacity model [14]. Wang and Liu determined the state of the regional carrying capacity using the ratio of positive and negative contribution values [15]. Calculating the model using the RECC index [6, 16]. A combination of an ecological environment pressure index model and state space method was constructed [17], and different methods were used to evaluate the ECC. With the emergence of the ecological footprint method, detailed and diverse evaluation methods have been developed, including the indicator system method and the system dynamics model. Researchers have begun to use detailed evaluation methods to study ECC. proposed a detailed model to determine the carrying capacity for resources and the environment based on the ecological footprint method [18, 19]. Further research was also conducted using an improved ecological footprint model [20]. The entropy weight method has been used to calculate the weight of indicators, and the TOPSIS model is then used to calculate the ECC [2, 7]. The ECC and its subsystems were evaluated using the Shannon entropy theory, fuzzy synthesis method, and analytic hierarchy process [3, 21]. Meanwhile, Nakajima and Ortega evaluated the carrying capacity using energy value assessment [22]. Other scholars used a system dynamics model to simulate its historical conditions and future scenarios, emphasizing the connection between socioeconomic and ecological environmental factors [23, 24]. The series of evaluation methods used by scholars have enriched the pathway of a comprehensive evaluation of ECC and provided a reliable basis for selecting reasonable research methods in this study.

Researchers have conducted research on the ECC in various regions at different temporal and spatial scales with a range of results that have provided a solid foundation for this study. The evaluation index system and methods for ECC have been continuously iterated and improved, but further improvements can be made in the following areas:

(1) The complex and diverse ecological environment is the result of the mutual influences between the regional society, economy, and resource environment. Therefore, it is important to select key social, economic, and resource environmental indicators that affect regional ECC and reduce the impact of subjective factors on the key indicators.

(2) In the evaluation of the ECC, a single method is often used. However, many factors affect the ECC evaluation results, and there are differences in the results obtained using different methods. Therefore, it is necessary to construct a data-driven method for measuring and evaluating regional ECC to predict its dynamic changes and regional ECC trends.

(3) Due to regional differences, the universality of relevant research findings needs to be improved, and support for regional decision-making has remained limited. Therefore, using data-driven methods to evaluate regional ECC can provide full process and dynamic monitoring for different regions, thereby forming differentiated and suitable precise countermeasures for different regions.

To address these challenges, this study has drawn on the research of Liu et al. [25]. and proposes a data-driven evaluation method for regional ECC [26, 27]. This has been in line with the spatiotemporal evolution trend of regional ECC to alleviate the contradictions between regional resource use, environmental protection, and economic development. It has also provided suggestions for policy formulation to achieve sustainable economic development.

The theoretical contributions and practical applications of this study are as follows:

Theoretical importance: Based on the relationship between regional society, economy, and resource environment and the characteristics of the regional ecological environment, a detailed evaluation index system for regional ECC was constructed from the three levels of society, economy, and resource environment. This was tailored to local conditions. By using the entropy weight method, TOPSIS method, and a data-driven approach, high-quality dynamic data were collected to analyze the dynamic evolution of regional ECC. Based on the results, the evaluation theory for regional ECC was improved.

Practical importance: This has provided a basis for decision-makers in regional ecological environment management to quantify the level of ECC and identify development trends, to allow practitioners to establish themselves in the new stage of development, implement the concept of sustainable development, examine the dynamic evolution of regional ECC, adjust development strategies, and provide relevant suggestions for researchers and national policymakers to enhance ECC. To achieve these research objectives, the second section presents the methods, the third presents a case analysis, and the fourth presents the conclusion.

Methods

This section provides a detailed introduction to the measurement, evaluation, and identification methods, including method flow, data collection, data processing, data models, and data applications.

Method Processes

Advancements in urbanization and industrialization have led to increasingly prominent environmental pollution issues, which have severely constrained sustainable development [28]. To achieve harmonious coexistence between humans and nature, the ecological economy should form a key focus to promote high-quality economic development. It is necessary to deploy and promote green development, focus on solving prominent environmental problems, and increase efforts toward ecosystem protection. It is necessary to evaluate the mutual constraints between the regional society, economy, and ecology and quantify the level of ECC. This is also the motivation for this study.

However, the ecological environment is complex and diverse. In this context, the coupling and coordination relationships among society, the economy, and the resource environment remain unclear [6, 29]. Regional ECC is a complex system with regional differences in the influencing factors. There are substantial differences in the establishment of different regional datasets and the selection of evaluation methods [30]. Therefore, constructing a universal evaluation index system and accurate measurement of the dynamic evolution of regional ECC is a challenge for researchers.

To address this challenge, this study established a data-driven evaluation method that measures, evaluates, and identifies regional ECC [25]. Data-driven applications mainly include data collected on indicators from the perspectives of society, economy, resources, and the environment [20] (see Fig. 1). The data processing involves using a normalization method for dimensionless standardization and an entropy method to determine the weights of indicators. Data modeling is then used in the construction of TOPSIS models for regional ECC [7]. Data analysis was based on TOPSIS, which can be used to evaluate the dynamic evolution trend of regional ECC. Targeted methods can then be proposed to improve regional ECC implementation practices. Data-driven applications are used to construct a more universal and detailed method for measuring, evaluating, analyzing, and optimizing regional ECC identification. This method can provide support for the evaluation and improvement of carrying capacity in different regions. This can also help management decision-makers who focus on carrying capacity, strengthen ecological environment protection, and formulate high-quality development strategies in different regions. This can improve the practical importance of ECC research.

Fig. 1.

Data driven flowchart.

Case Study

Using these research methods, focusing on the ECC of the Yangtze River Delta region from the perspective of the social economy resource environment, using a data-driven approach, we quantified the dynamic evolution trend of ECC in the region. We have also proposed policy recommendations to enhance the ECC of the Yangtze River Delta region, optimize the ecological security pattern, achieve high-quality economic development, and summarize management insights.

Case Study Background

Encompassing Shanghai, Jiangsu, Zhejiang, and Anhui provinces (see Fig. 3), the Yangtze River Delta region is the most developed region of China’s economy. In 2021, the region had a regional gross domestic product of 27.59 trillion yuan. This represents an increase of 8.4% compared to the previous year, accounting for 24% of the national total. This has played an important role in the high-quality development of the Chinese economy. However, there is a regional imbalance in the economic development of the Yangtze River Delta. Some regions rely on large-scale investments to drive economic growth. This has resulted in high resource consumption, intensive pollutant emissions, and severe air pollution. The 2021 China Environmental Status Bulletin disclosed that although the proportion of excellent days in the Yangtze River Delta region increased in 2021, there are still days when pollutants such as O₃, PM2.5, PM10, and NO₂ exceed the standard, severely affecting public health. Therefore, the Yangtze River Delta is the most developed and urbanized region in China, with the highest degree of urban agglomeration. Studying regional ECC can alleviate the contradictions between resource use, environmental protection, and economic development in the Yangtze River Delta region, achieve high-quality economic development, and have a certain typicality. This study also provides a reference for other regions.

Fig. 2.

Data application flowchart.

Fig. 3.

The position of the Yangtze River Delta in China.

Results

Using the entropy weight TOPSIS method, the ECC size of the Yangtze River Delta region from 2011 to 2020 was obtained. To further analyze and describe the trends and reasons for ECC changes, the carrying capacity of the three carrying subsystems of society, economy, and resource environment was calculated. Based on this, the characteristics and dynamic evolution trends of the ECC of the Yangtze River Delta region and each subsystem were analyzed. The specific changes are shown in Fig. 4–7.

ECC Analysis From the Yangtze River Delta Region

Fig. 4 shows that the ECC in the Yangtze River Delta region continued to improve from 2011 to 2020. In 2011, the per capita gross domestic product (GDP) of the Yangtze River Delta region was relatively low. However, the level of urbanization was relatively low, and an industrial-added value of 10,000 yuan resulted in large amounts of wastewater, exhaust gas, and solid waste emissions. The use rate for industrial solid waste and the urban domestic sewage treatment capacity were relatively low. This has reduced the ECC of the region and led to a relatively low ECC level in the Yangtze River Delta region. By 2020, the ECC index in the Yangtze River Delta region reached new highs, with Zhejiang Province (0.637) reaching a relatively advanced level. Anhui Province (0.59) and Jiangsu Province (0.47) performed well, while the ECC in Shanghai (0.388) required further improvement.

Fig. 4.

Comprehensive evaluation results of ECC in the Yangtze River Delta region (2011-2020).

From the perspective of zoning, in the last ten years, the Yangtze River Delta region has achieved successful results in terms of social, economic, and ecological environmental protection and governance. In the Yangtze River delta region, ECC has improved the fastest in Zhejiang Province, followed by Anhui Province and Jiangsu Province. Meanwhile, Shanghai required further improvement. This is due to the insufficient retention of resources in Shanghai. Comparative analysis of ECC in the Yangtze River Delta region showed a spatial distribution pattern of high in Zhejiang, medium in Anhui and Jiangsu, and low in Shanghai. The main reasons for the spatial differentiation of regional ECC were that Zhejiang Province has abundant resources and a high level of socio-economic development. Although Anhui Province is relatively rich in resources, its economic development level is relatively low. Jiangsu Province has a relatively high level of economic development, but has insufficient resources. Although Shanghai has the highest level of economic development among the three provinces and one city, its lack of resources severely limits its ECC level.

Evaluation of the Ecological Subsystem Carrying Capacity in the Yangtze River Delta Region

Evaluation of the Social Subsystem of the ECC in the Yangtze River Delta Region

As shown in Fig. 6, the carrying capacity of the social system in the Yangtze River Delta region shows an upward trend from 2011 to 2020. In 2011, except for Shanghai (0.275), which was at a lower stage, the carrying capacity index of the other three provinces exceeded 0.4. This is mainly because of the population density of 3715 people/km² in Shanghai, which is substantially higher than that in other regions. The social system’s carrying capacity improved significantly in 2016. The carrying capacity of the system is expected to improve further by 2020. Jiangsu Province (0.719), Zhejiang Province (0.714), and Anhui Province (0.674) have relatively high states. The social system carrying capacity index of Shanghai also exceeded 0.5. This indicated that the per capita disposable income and medical and health conditions in the Yangtze River Delta region have substantially improved along with quality of life.

From the perspective of spatial layout, based on the average social system carrying capacity in the past 10 years, the social system carrying capacity in the Yangtze River Delta region has a distribution that is relatively high in Jiangsu and Zhejiang provinces, medium in Anhui Province, and low in Shanghai city. This is because the per capita disposable income in Jiangsu and Zhejiang provinces is relatively high and the population density is moderate. Anhui Province has insufficient per capita disposable income and medical and health performance. Although Shanghai has relatively high per capita disposable income and healthcare performance, its population density is relatively high.

Fig. 5.

Spatial evolution of ECC evaluation in the Yangtze River Delta region.

Fig. 6.

Evaluation results of the social subsystem of ECC in the Yangtze River Delta region (2011-2020).

Fig. 7.

Spatial evolution of ECC social subsystem evaluation in the Yangtze River Delta region.

Evaluation of ECC Economic Subsystems in the Yangtze River Delta Region

Fig. 8 shows that owing to the rapid decline in the GDP energy consumption rate, continuous optimization of the industrial structure, and continuous improvement in the urbanization level, the carrying capacity index of the Yangtze River Delta regional economic system has shown a clear upward trend. In 2011, the carrying capacity index of the economic subsystem in Shanghai reached 0.677, indicating a strong carrying capacity. The indices of Jiangsu and Zhejiang provinces are both 0.367. Meanwhile, the carrying capacity index of Anhui Province is only 0.085, indicating that its economic subsystem carrying capacity is at a relatively low level. In 2015, except for Anhui Province, the carrying capacity index of the economic subsystem exceeded 0.5, which is relatively high. In 2020, the carrying capacity indices of Shanghai, Jiangsu, and Zhejiang exceeded or approached 0.7. Meanwhile, that of Anhui Province also approached 0.5, especially Shanghai, which reached 0.992. This indicates that the carrying capacity index of the regional economic subsystems of the Yangtze River Delta is already high, but there are regional differences.

From the perspective of spatial distribution, from 2011 to 2020, the carrying capacity of the economic subsystem showed a trend of being high in Shanghai, medium in Jiangsu and Zhejiang, and low in Anhui. The carrying capacity of the economic subsystem in Shanghai has always been ahead of that of the other regions, and the carrying capacity index of Anhui Province is lower. This is because Shanghai’s per capita GDP, that is, 155,800 yuan in 2020, and urbanization level (89%) are substantially higher than those in other regions, and industrial structure adjustment and upgrading are more prominent. The amounts of wastewater, exhaust gas, and solid waste generated by the industrial added value per 10,000 yuan were relatively low.

Fig. 8.

Evaluation results of the economic subsystem of ECC in the Yangtze River Delta region (2011-2020).

Fig. 9.

Spatial evolution of ECC economic subsystem evaluation in the Yangtze River Delta region.

Evaluation of ECC Resource and Environmental Subsystems in the Yangtze River Delta Region

Fig. 10 shows that the carrying capacity of the resource and environment subsystems in the Yangtze River Delta region exhibited a fluctuating growth trend from 2011 to 2020, with 2016 being the turning point. This is because investment in industrial pollution control in various provinces and cities in 2016 was the highest in the last decade. In 2020, the carrying capacity index for the resource and environmental subsystems in Zhejiang Province (0.615) and Anhui Province (0.594) both exceeded 0.5. Meanwhile, the carrying capacity index of Shanghai was 0.206. This indicated that the carrying capacity of resources and the environment in the region improved, but the overall level was not high and the difference was significant.

Fig. 10.

Evaluation results of the resource and environment subsystem of ECC in the Yangtze River Delta region (2011-2020).

Fig. 11.

Spatial evolution of ECC resource and environment subsystem evaluation in the Yangtze River Delta region.

From the perspective of spatial layout, the carrying capacity index of the resource and environment subsystem in the Yangtze River Delta region shows a spatial distribution of high in Zhejiang and Anhui, medium in Jiangsu, and low in Shanghai. The means were 0.5845 in Zhejiang Province, 0.5563 in Anhui Province, 0.3509 in Jiangsu Province, and 0.1539 in Shanghai. This is because the water resources and forest cover in Zhejiang Province are relatively high. Meanwhile, the land and energy resources in Anhui Province are among the highest, and the air quality in the two provinces is relatively high. Although Shanghai has higher efficiency in environmental governance, the retention of various resources is relatively low, especially water and land resources, which are far lower than those of other regions.

Discussion and Management Recommendations

Building on prior literature in the field [45-47], this study has the following advantages: Based on theories such as carrying capacity and ecosystem theory and using literature analysis and survey analysis methods, an indicator system for regional ECC was developed from the perspective of the social economy resource environment. A dynamic database was then constructed. Based on this database, a data-driven regional ECC evaluation model and subsystem evaluation model were implemented. The entropy-weighted TOPSIS method was used to evaluate and identify the carrying capacity, thereby providing theoretical support for quantifying regional ECC. Based on data-driven methods, a tool for measuring, evaluating, and identifying regional ECC was developed, providing a theoretical basis for differentiated resource use, reduced pollution and waste emissions, and improved carrying capacities in different regions. This has become the basis for the sustainable development of regional economies tailored to local conditions.

Based on the study findings, we can provide the following management insights:

A precise evaluation of the ECC level was the main basis for the high-quality coordinated development of the regional economy. Although different regions have been striving to improve ECC and use efficiency, if relevant data-driven approaches can be used, it would be beneficial to deepen our understanding of the regional carrying capacity and make strategic use of regional resources and the environment.

The ECC evaluation is a complex system that involves economic, social, and resource environmental subsystems within the ecosystem. Any system affects the accuracy of the evaluation to a certain extent, and there are differences between different regions. Therefore, it is necessary to analyze, identify, and optimize the influencing factors based on an evaluation of the bearing capacity.

Based on the study findings, targeted suggestions have been proposed to improve the regional ECC level, leverage the comparative advantages of each region, promote strategic flow and efficient agglomeration of factors, form complementary advantages, and achieve coordinated, high-quality economic development between regions. The key is to fully understand the differences in the regional economy, society, resources, and environment and quantify the level of ECC.

Conclusions

With increasing global efforts to prevent ecological degradation and environmental pollution and the need to continuously promote regional ecological civilization construction and high-quality development [48-51], it is necessary to base all of this on ECC. Therefore, scientific evaluation of regional ECC has become extremely important. To this end, we have proposed a data-driven method to evaluate regional ECC and identify its key factors.

The specific innovation lies in the following:

(1) From the perspective of the mutual influence of the economy, society, resources, and environment, and their inherent interrelationships, based on the study of specific regions, an objective and critical regional ECC evaluation index system was constructed from the three major interaction subsystems of economy, society, resources, and the environment. (2) We have developed a data-driven method to measure, evaluate, and identify regional ECC. This method provides a more objective evaluation of regional ECC, reflecting the dynamic changes and prediction trends of regional ECC and improving decision-making accuracy.

(3) Based on the results, process and dynamic monitoring can be conducted for different regions, thereby forming precise strategies for differentiated and suitable resource use, environmental protection, and sustainable regional economic development.

In-depth research on ECC is a solid foundation for achieving ecological civilization construction. Therefore, ECC needs to integrate the concept of ecological civilization construction [52, 53], consider the influence of factors such as technology, culture, and the humanities, and achieve content exploration and innovation [54]. In the evaluation of ECC, it is necessary to continuously revise and improve the indicator system based on the development of content innovation and deepen research methods and statistical algorithms. These can be combined with modern big data technology, such as deep learning methods [9, 55], to evaluate and identify key influencing factors. Meanwhile, in-depth research should be conducted on relevant standards and norms. Relevant thresholds (intervals) and rate-related parameters should be defined, and a standardized evaluation method should be developed to support the practical implementation of ECC.