China’s Agricultural Irrigation and Water Conservancy Projects: A Policy Synthesis and Discussion of Emerging Issues
Bibliographic record
Abstract
The United Nations (UN) has identified 17 Sustainable Development Goals (SDGs) to tackle major barriers to sustainable development by 2030. Achieving these goals will rely on the contribution of all nations and require balancing trade-offs among different sectors. Water and food insecurity have long been the two major challenges facing China. To address these challenges and achieve the SDGs, China needs to safeguard its agricultural irrigation and water conservancy projects. Although China is making efforts to transition its agricultural development to a sustainable trajectory by promoting water-saving irrigation, a number of issues are emerging, both with policy reforms and technological innovations. Through synthesizing the historical development of agriculture and its relationship with policy and political regimes, this paper identifies four major issues that are challenging the sustainability transformation of China’s agricultural irrigation system and water conservancy projects: (1) problems with financial policy coordination between central and local governments; (2) the lack of incentives for farmers to construct and maintain irrigation infrastructure; (3) conflicts between decentralized operation of land and benefits from shared irrigation infrastructure; and (4) deterioration of small-scale irrigation infrastructure calls for action. In addressing these challenges, policy changes are required: government financial accountability at all levels needs to be clarified; subsidies need to be raised for the construction and management of small-scale irrigation and water conservancy projects; local non-profit organizations need to be established to enhance co-management between farmers and government.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".