Decoupling Analysis of Water Footprint and Economic Growth: A Case Study of Beijing–Tianjin–Hebei Region from 2004 to 2017
Bibliographic record
Abstract
The Beijing-Tianji-Hebei region (BTHR) is economically developed and densely populated, but its water resources are extremely scarce. A clear understanding of the decoupling relationship between water footprint and economic growth is conducive to facilitating and realizing the coordinated development of water resources and economic growth in this region. This study calculated the water footprint and other related indicators of BTHR from 2004 to 2017, and objectively evaluated the utilization of water resources in the region. Then, logarithmic mean divisia index (LMDI) method was applied to study the driving factors that resulted in the change of water footprint and their respective effects. Finally, Tapio decoupling model was used to research the decoupling relationships between water footprint and economic growth, and between the driving factors of water footprint and economic growth. There are three main results in this research. (1) The water utilization efficiency in BTHR continues to improve, and the water footprint shows a gradually increasing trend during the research period, among which the agricultural water footprint accounts for a relatively high proportion. (2) The change of water footprint can be attributed to efficiency effect, economic effect, and population effect. Furthermore, efficiency effect is the decisive factor of water footprint reduction and economic effect is the main factor of water footprint increase, while population effect plays a weak role in promoting the increase in water footprint. (3) The decoupling status between water footprint and economic growth show a weak decoupling in most years, while the status between water footprint intensity and economic growth always remains strong decoupling. Moreover, population size and economic growth always show an expansive coupling state. In sum, it is advisable for policy makers to improve water utilization efficiency, especially agricultural irrigation efficiency, to raise residents' awareness of water conservation, and increase the import of water-intensive products, so as to alleviate water shortage and realize the coordinated development of water resources and economic growth in BTHR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".