A strategic framework for social impact assessment : an application to greenhouse gas mitigation strategies in Canadian prairie agriculture
Bibliographic record
Abstract
Social Impact Assessment (SIA) is the process of assessing the social consequences that are likely to follow specific policy actions or project development. SIA has not been widely adopted and is said to be the ‘orphan’ of the assessment process. Using Environmental Assessment (EA) however, there are two primary limitations to EA: first, EA is inherently biased toward the biophysical environment, and social impacts, when considered, are only considered in an indirect or secondary manner; second, EA is targeted at the project level, where many alternatives that may have met the larger goals have been rejected. These limitations are reflected in Canada’s agricultural sector where SIAs are rarely, if ever, undertaken. Agriculture is responsible for approximately ten percent of total greenhouse gas (GHG) emissions in Canada, and several better management practices (BMP) have been suggested for managing these emissions in Canadian agriculture. However, there has not been a strategic assessment of the on-farm socioeconomic effects of such programs, nor the geographic implications of a ‘one-size-fits-all’ policy solution. This paper presents a ‘higher level’ strategic assessment of alternative policy options for managing greenhouse gas emissions in Canadian agriculture. Data are collected using a stakeholder survey assessment, and the process is guided by a seven-phase strategic environmental assessment framework. Using this strategic framework, the on-farm social impacts of alternative greenhouse gas mitigation programs are assessed. Data are aggregated using multi-criteria weighting techniques. Stakeholder preference structures for the alternatives set are identified as well, the results of the SIA identified adoption of zero till practices as the most socially acceptable alternative. The research results suggest that a ‘one-size-fits-all’ GHG mitigation policy would not be acceptable from a social perspective. The implications of include such issues as: the applicability of regional policies based on soil zone, alternatives to governmental ‘top down’ hierarchical’ policies, and the necessity for collaboration and meaningful dialogue between on-farm individuals and policy makers. Adoption of a GHG mitigation policy in Canada will require education and collaboration between all affected stakeholders and decision makers. The application of a strategic framework illustrates how the SIA process is enhanced when an assessment is completed at the plan, policy, and program level – it enables proactive consideration of the social effects on par with the biophysical effects, and it facilitates consideration of a broad range of alternatives, in support of sustainable development principles.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".