Decoupling of economic growth and resources-environmental pressure in the Yangtze River Economic Belt, China
Bibliographic record
Abstract
The increase in the consumption of resources required for social progress and the continuous deterioration of the ecological environment has brought heavy pressure to the sustainable development of the Yangtze River Economic Belt (YREB). This article applied the Hybrid Entropy-TOPSIS method to evaluate the resources-environmental pressure (REP), used the Logarithmic Mean Divisa Index (LMDI) method to identify driving factors, and utilized the Tapio decoupling model to analyze the relationship between economic growth and REP in 110 cities in the YREB. The main contribution of this study is the dynamic evolution of the decoupling relationship from perspective of the city unit, and profoundly depicts the interaction between resources-environmental factors and economic growth. The main results are as follows: (1) In the time dimension, the REP of 74.55% of the YREB's cities has continued to increase over 15 years, and the numerical differences between different cities are significant. In 2019, 24.55% of cities were under medium-pressure and high-pressure levels. In the spatial dimension, the city's REP is more prominent in the eastern part of the YREB and more decentralized in the central and western regions. The internal composition analysis of REP shows that the pressure caused by pollutant discharge is slowly reduced, and the pressure caused by unreasonable consumption of resources is increasing year by year. (2) The economic effect (EE) is the most important driving factor to influence REP in YREB, while the pressure intensity effect (PIE) is one of the main driving factors to influence the REP. In the temporal dimension, the development structural effect (DSE) is not significant. However, in the spatial dimension, it has more prominent characteristics in the west. The effect of population effects (PE) on REP is very weak. (3) The decoupling relationship between economic growth and REP shows a trend of weak decoupling from 2006 to 2010, strong decoupling from 2011 to 2015, and the coexistence of multiple types of decoupling from 2016 to 2019. In terms of spatial distribution characteristics in 2019, the strong decoupling (48 cities), weak decoupling (37 cities), and deteriorating decoupling (25 cities) showed uniform distribution in the YREB. In addition, the decoupling stability of 21 cities is poor, mainly resource-dependent cities distributed in Guizhou, Anhui, and Jiangsu Provinces. This article will provide a reference for the high-quality development and ecological environment protection of regions in the YREB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".