Coordination of the Industrial-Ecological Economy in the Yangtze River Economic Belt, China
Bibliographic record
Abstract
The Yangtze River Economic Belt (YREB) is an important growth pole of China’s economy, but it is also one of the most environmentally polluted basins in China. Maintaining the vitality of economic development while at the same time realizing the coordinated development of industry and ecosystems, is an important issue that needs in-depth discussion and research. This paper analyzes the degree of coordination regarding the industrial-ecological economy in the YREB, identifies important influencing factors, and puts forward measures for improvement. First, an evaluation model of the industrial-ecological economy is constructed. Second, a model is constructed for the measurement of the coordination degree of the industrial economy and industrial ecology based on the Lotka-Volterra Model. Third, the relationship is assessed with respect to competition versus cooperation. Finally, the important factors affecting coordination are identified using a Neural Network Model. Four main conclusions can be drawn: 1) The comprehensive development of the industrial economy and industrial ecology in 11 provinces and cities in the YREB is generally trending upward. 2) The coordination level of the industrial-ecological economy in the midstream area is high. The provinces Jiangsu, Jiangxi, Sichuan, and Guizhou are in a coordinated state. 3) The midstream area has a more balanced industrial-ecological economy with significant symbiosis between the industrial economy and industrial ecology. Jiangsu, Jiangxi, Sichuan, and Guizhou Provinces show a symbiotic relationship; Shanghai City, Chongqing City, and Anhui Province show a partially symbiotic relationship; and Zhejiang, Hubei, Hunan, and Yunnan Provinces show a mutually inhibitory relationship. 4) The industrial ecosystem is the largest factor in the degree of coordination, and intensity of R&D investment, regional GDP per capita, and proportion of tertiary-industry added-value in GDP also have a great impact. Based on this analysis, this paper proposes measures for high-quality development of the industrial-ecological economy of the YREB with regard to balanced development of the industrial economy, transformation and upgrading of the surrounding environment, along with coordinated and integrated development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".