Analysis of Environmental and Socio-Economic Impacts of Dams Through Sustainable Management Strategies
Bibliographic record
Abstract
The study aims to enrich the information of planners, policymakers, and water resources managers for planning and operating dams.This research aims to address the following question: What are the environmental, economic, and social impacts of the construction and operation of dams on the environment and society?The study assumes that good management is the ideal solution to solve the problems of negative effects resulting from the construction and operation of dams.The research relied on the descriptive analytical approach in studying the positive and negative impacts of Haditha Dam and the government's role.A questionnaire was conducted for 30 specialists in urban and regional planning to find out the most important strategies for sustainable management.The questionnaire was validated and reliable to ensure the validity of the questions selected for the study.Cronbach's alpha value ( = 0.82) was good and acceptable.The study concluded the importance of involving stakeholders in the planning and decision-making process, with a survey response rate of approximately 70%.Sixty percent of respondents believe that monitoring and decision support systems represent a challenge for decision-makers in the sustainable management process.The study recommends the importance of enhancing local community participation and improving focus on sustainability mechanisms to achieve maximum benefit from dams for society.The research recommends enhancing the contribution of the local community in the decision-making process and conducting a "follow-up" to assess the environmental impact, economic returns, and social impacts resulting from the operation and construction of the dam.
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.
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 | 0.001 | 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".