Building Resilient Water Supply Systems Through Economic Instruments: Evidence from a Water Resource Fee-to-Tax Reform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Water supply systems (WSS) face various threats such as climate change, declining freshwater availability, and over-extraction of groundwater. To improve the resilience and sustainability of WSS, both technological innovation and effective institutional and economic mechanisms are required. This study evaluates China’s recent water resource fee-to-tax reform as a quasi-natural experiment. It analyzes panel data from 222 prefecture-level cities between 2012 and 2023 and applies a multi-period difference-in-differences model to assess the impact of this reform on water use structure and efficiency. The two main research goals are to examine whether the reform has enhanced the structural resilience of WSS in terms of the shift from groundwater dependence to surface water, and whether it has improved water use efficiency to ensure sustainable water use. Our results show that the reform significantly reduced reliance on groundwater and increased the proportion of surface water use, thereby enhancing the structural resilience of urban water supply systems. Further analyses confirm that these effects are most pronounced in eastern and central regions, where water stress is higher. On the other hand, while the reform improved water use patterns, its positive impact on water use efficiency remains limited due to the current tax design. Overall, our research results demonstrate how fiscal instruments can be leveraged to improve sustainability of WSS. They provide policy insights for strengthening resilience of WSS against resource scarcity and environmental risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it