Blockchain for environmental peacebuilding: application in water management
Bibliographic record
Abstract
Purpose The purpose of this paper is to spur further exploration of blockchain technologies for environmental peacebuilding, specifically through water management. Although the environmental peacebuilding field acknowledges the potentially transformative nature of frontier technologies, most existing studies do not address how such technologies can contribute to peacebuilding through improved natural resource governance. Using a theory synthesis research design, this conceptual paper connects these studies to discuss how blockchain technologies in water management may contribute to environmental peacebuilding through the efficient and transparent management of natural resources. Design/methodology/approach The authors use a conceptual approach and a theory synthesis research design to present potential mechanisms through which blockchain technology can potentially contribute to environmental peacebuilding. Findings The authors discuss the limitations in the literature on environmental peacebuilding, blockchain and water management, concluding that the third generation of studies should focus on the role of frontier technologies. This approach should especially address the negative consequences of technology for peacebuilding objectives. The authors argue that blockchain applications in water management can potentially contribute to environmental peacebuilding objectives in three ways: (i) creating a mechanism for confidence-building in low-trust contexts through automated and transparent water transactions, (ii) facilitating postconflict economic development through attracting capital and increasing the efficiency of water management and (iii) improving governance through greater transparency and local participation in natural resource management. Originality/value To the best of the authors’ knowledge, this study is among the first to conceptually explore the use of blockchain technology for water management in the context of environmental peacebuilding. The insights from this study can guide policymakers of conflict sides that focus on resolving issues such as lack of governance and low state agency.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".