Evaluation of linkage relationships between carbon emissions and economic development based on the decoupling model and the VAR model: a case study of Shanxi Province (China)

Xing, Qinfeng; He, Zhaowei; Li, Wei

doi:10.1007/s11356-023-26924-3

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…It is an important indicator of whether a region is developing well [3]. Economic development represents the output Land 2024, 13, 537 2 of 20 characterization of regional development, primarily manifested in the speed, quality, scale, and output efficiency of economic development [4,5]. In short, space exploitation and economic development (SEcon) represent the development degree of a region, and the process of SEcon significantly affects the evolution of the ecological environment, which in turn constrains SEcon [6].…”

Section: Introductionmentioning

confidence: 99%

How to Coordinate the Relationship between Urban Space Exploitation, Economic Development, and Ecological Environment: Evidence from Henan Province, China

Xie,

Wu,

et al. 2024

Land

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With the rapid development of urbanization, China is facing problems, such as uncoordinated regional development, imbalanced land space development, and ecological environment pollution. This poses a huge threat to the sustainable development of China’s economy and society. Therefore, there is an urgent need to determine how to coordinate the relationship between the space exploitation, economic development, and ecological environment (SEE) of urban areas. In this study, taking the Henan Province as an example, long time-series data (2000–2020) were used, at a city scale. Then, we developed a logical framework to reveal the interrelationship and intrinsic mechanism between SEE. Next, we explored the spatiotemporal coupling characteristics of SEE using a linear weighting method and a coupling coordination analysis. We found that, from 2000 to 2020, the comprehensive level of SEE showed an obvious trend of change, and different cities have different coupling coordination degrees. However, the overall coupling coordination level is steadily developing and tending to improve. Furthermore, with the spatial autocorrelation method, we analyzed spatial correlation patterns and collaboration/trade-off relationships for SEE. Through the analysis, positive correlation types (HH, LL) cluster significantly and negative correlation types (HL, LH) have low clustering. Meanwhile, we found significant spatial differences in cooperation/trade-off relationships between different years. This research can serve as a reference and as methodological guidance for achieving coordination and sustainable development of the economy, space, and environment.

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