Assessing the Economic Contribution of Ocean-Based Activities Using the Pacific Coast of British Columbia as a Case Study
Bibliographic record
Abstract
Global obligations to achieve sustainable oceans by 2030 require countries to commit to solutions that balance ocean use and protection. To do so necessitates baseline understanding of the ocean’s contribution to socio-economic well-being, which we do by measuring the economic activity of ocean-related sectors. Economic assessments tend to be data intensive and are typically reliant on professional economists, yet they are increasingly relevant to non-economists who engage in ocean management and communication, where they are integral in facilitating trade-off analysis of future ocean change. Thus, there is a need to make ocean economic assessment more accessible to nonspecialists. We fill this need by providing a pragmatic framework for conducting an economic assessment using British Columbia’s ocean sector as a case study. Our results show the impact of the province’s ocean sectors on four economic indicators and indicate that the ocean contributed almost $5 billion (or about 2%) to provincial gross domestic product (GDP) and generated about 106,120 jobs (over 4% of the province’s total) in 2015. Of these, the marine transport sector made the highest overall contribution followed by cruise lines, with GDP impacts of 66% and 13%, respectively. It should be noted that this estimated economic value is not representative of the full value of the ocean as it excludes oil and gas, research and education, and other activities that do not meet our criteria for inclusion, and it does not account for cultural and ecological values. Nonetheless the study highlights the substantial number of economic benefits generated by the blue economy. More significantly, the framework provides a simplified procedure for quantifying economic benefits, and can be applied by nonspecialists to perform rapid economic assessments in a variety of contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".