Exploring the dynamic equilibrium relationship between urbanization and ecological environment -- A case study of Shandong Province, China

Sui, Yunyun; Hu, Jiangshan; Zhang, Naiqian; Ma, Fang

doi:10.1016/j.ecolind.2023.111456

Cited by 8 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the formula, R represents the degree of relative development [44]. Combined with the classification of the coupling degree, the judgment criteria are as shown in Table 2.…”

Section: Relative Degree Of Development Modelmentioning

confidence: 99%

The Coupling Mechanism between Railway Alignment Design and Resource Environment in the Southwestern Mountainous Areas of China

Wan,

Bao,

2024

Sustainability

View full text Add to dashboard Cite

Faced with the characteristics of multifactor coupling and interweaving along the railway lines in the southwestern mountainous areas of China, researching the coupling mechanism and optimizing the collaborative development path between alignment designs and the resource environment is conducive to promoting the green and sustainable development of railways in these southwestern mountainous areas. In this study, first, regarding the environmental characteristics of engineering along the railway lines in the southwestern mountainous area, the key elements of the interaction and coercion between the alignment design (internal system) and the resource environment (external system) are identified, and the interactive impact mechanism of the “alignment design–resource environment” complex is revealed. Accordingly, a nonlinear coupling mechanism between the alignment design system and the resource environment system is established using the deviation coefficient coupling degree model. Second, from a methodological perspective, following a technical path of “analyzing the coercive effects of internal and external factors–identifying coupling relationships–discovering coupling laws–screening driving factors–proposing optimization plans–achieving collaborative development goals”, we propose an overall optimization plan to solve the problem. Finally, the Chengdu Changdu section of the X railway, which is located in a southwestern mountainous area, is taken as an example for this study. The results indicate that if the state of the external system of the KL scheme is continuously improved through the regulation of the u22 (crossing the ecological protection red line length), u23 (biodiversity impact), and u24 (ecosystem impact) indicators and that the internal structure of the line design is improved by optimizing the l12 (total length of bridges and tunnels) and l13 (number of stations) indicators, effectively driving the improvement in the u31 (land resource occupation) and u43 (land development intensity) indicators, the alignment design and resource environment will exhibit a mutually reinforcing coupling evolution trend and ultimately achieve an “alignment design–resource environment” composite system with higher quality coupling.

show abstract

“…In the formula, R represents the degree of relative development [44]. Combined with the classification of the coupling degree, the judgment criteria are as shown in Table 2.…”

Section: Relative Degree Of Development Modelmentioning

confidence: 99%

The Coupling Mechanism between Railway Alignment Design and Resource Environment in the Southwestern Mountainous Areas of China

Wan,

Bao,

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…(3) Urbanization, which enhances resource utilization efficiency and accelerates economic growth [46], is not only a key driver of economic modernization but also a significant factor in altering carbon sources and sinks [47]. In this study, urbanization is evaluated across three dimensions: population, economy, and living standards of urban residents [48]. To establish the weight of each indicator, the entropy method is utilized, which computes both the objective weight and the entropy weight by considering the variances among indicators.…”

Section: Construction Of the Indicator System For The Three Moderniza...mentioning

confidence: 99%

Spatial Effects of Economic Modernization on Carbon Balance in China

Huang,

Liu,

Liu

et al. 2024

Land

View full text Add to dashboard Cite

Exploring the impact of economic modernization on carbon balance is an essential endeavor to achieve carbon neutrality and combat climate change. However, the spatial impact of economic modernization on carbon balance remains ambiguous. Therefore, this study aims to explore the spatial spillover effects of agricultural modernization, industrialization, and urbanization on carbon balance during the economic modernization process in China, taking 30 provinces and cities in China as examples from 2010 to 2021. This study utilizes the spatial Durbin model to derive the following results: In the past decade, the carbon balance ratio has shown a fluctuating and decreasing dynamic evolution trend. There is an increase in regions with serious carbon deficits. Further investigation into the spatial spillover effect of carbon balance unveils that for every 1% increase in the carbon balance ratio of a province, neighboring provinces experience a decrease of 0.833%. Additionally, the spatial spillover effects of the three modernizations in China on the carbon balance ratio behave differently. Agricultural modernization and urbanization demonstrate negative spatial spillover effects on the carbon balance in neighboring regions, while industrialization exerts a significant positive spatial spillover effect on the carbon balance of neighboring regions. Regarding control variables, the level of innovation solely contributes to local carbon balance realization without generating a trickle-down effect, whereas infrastructure development operates inversely. At the same time, there are differences in the spatial effects of agricultural modernization and industrialization on the carbon balance between the eastern region and the central and western regions. The study underscores the importance of economic modernization and development processes focusing on fostering synergistic growth between economic and environmental benefits within both local and neighboring areas.

show abstract

“…Human activities and natural factors collectively impact EEQ dynamics [40]. The significance of human activities [41][42][43] and natural factors [44][45][46][47] for EEQ varies notably across different research scales. Anchored in the natural environment and socio-economic context of the Jiaodong Peninsula, we identify six driving factors as independent variables, using RSEI as the dependent variable to explore the driving mechanisms (Table 4).…”

Section: Selection and Calculations Of Driving Factorsmentioning

confidence: 99%

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Guo,

Wang

2024

Sustainability

View full text Add to dashboard Cite

To monitor the Ecological Environment Quality (EEQ) of the Jiaodong Peninsula and provide a scientific basis for ecological environment governance and sustainable development in the region, this study evaluates the EEQ of Jiaodong using the Remote Sensing-based Ecological Index (RSEI) model and analyzes its spatiotemporal evolution patterns, building upon single-factor correlation analysis using the Random Sample Consensus (RANSAC) algorithm, using GeoDetector to analyze the driving mechanisms of human activities and natural factors in EEQ. The results indicate the following: (1) The average RSEI values for 2000, 2010, and 2020 are 0.60, 0.57, and 0.66, with Good or Excellent areas accounting for 56.48%, 51.02%, and 67.17%. From 2000 to 2020, RSEI strong improvement areas were predominantly distributed in the eastern hilly areas of the Jiaodong Peninsula. The RSEI of the Jiaodong Peninsula showed significant spatial autocorrelation. (2) The RANSAC algorithm effectively reduces noise interference in remote sensing data, thereby improving the accuracy of single-factor correlation analysis. (3) In 2000, the importance of natural factors exceeds that of human activity factors. The Standardized Precipitation Evapotranspiration Index (SPEI) is the most important driving factor; while in 2010 and 2020, human activity factors surpass natural factors in importance, with the Land Use Composite Index (LUCI) being the most significant driving factor. The driving factors exhibited double-factor and nonlinear enhancement. The most significant interaction factors affecting the EEQ of the Jiaodong Peninsula in 2000, 2010, and 2020 are SPEI∩GDP, LUCI∩SPEI, and LUCI∩GDP.

show abstract

Exploring the dynamic equilibrium relationship between urbanization and ecological environment -- A case study of Shandong Province, China

Cited by 8 publications

References 55 publications

The Coupling Mechanism between Railway Alignment Design and Resource Environment in the Southwestern Mountainous Areas of China

The Coupling Mechanism between Railway Alignment Design and Resource Environment in the Southwestern Mountainous Areas of China

Spatial Effects of Economic Modernization on Carbon Balance in China

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Contact Info

Product

Resources

About