Unconventional shale gas development: challenges for environmental policy and EA practice
Bibliographic record
Abstract
The growth of unconventional shale gas development has been accompanied by controversy over its environmental and social impacts. This paper reviews recent literature to clarify what is known and not known about the physical, chemical and toxicological properties of the process chemicals and wastewaters generated in hydraulic fracturing, the mechanisms and pathways by which they enter surface water and groundwater aquifers and the risks posed to human and ecosystem health. Assessing the impacts of unconventional shale gas development is clearly constrained by a lack of baseline information, complex hydrogeological histories for natural migration of hydrocarbons, lack of tracers to monitor and verify the source, timing and mechanism of contaminant migration into water resources. This is compounded by lack of transparency and accountability in policy decisions. The paper argues that managing the social and environmental risks of unconventional shale gas development calls for a new generation of impact assessment, one that marries the ideals of strategic environmental assessment, cumulative effects assessment, backcasting and deliberative and inclusive processes of community engagement towards collective risk management.
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 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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".