Building Resilient Water Supply Systems Through Economic Instruments: Evidence from a Water Resource Fee-to-Tax Reform
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.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".