Global land-use change hidden behind nickel consumption
Bibliographic record
Abstract
Economic growth is associated with a rapid rise in the use of natural resources within the economy, and has potential environmental impacts at local and/or global scales. In today's globalized economy, each country has indirect flows supporting its economic activities, and natural resource consumption through supply chains influences environmental impacts far removed from the place of consumption. One way to control environmental impacts associated with consumption of natural resources is to identify the consumption of natural resources and the associated environmental impacts through the global supply chain. In this study, we used a global link input–output model (GLIO, a hybrid multiregional input–output model) to detect the linkages between national nickel consumption and mining-associated global land-use changes. We focused on nickel, whose global demand has risen rapidly in recent years, as a case study. The estimated area of land-use change around the world caused by nickel mining in 2005 was 1.9 km2, and that induced by Japanese final demand for nickel was 0.38 km2. Our modeling also revealed that the areas of greatest land-use change associated with nickel mining were concentrated in only a few countries and regions far removed from the place of consumption. For example, 57.7% of the world's land-use changes caused by nickel mining were concentrated in five countries in 2005: Australia, 13.7%; Russia, 12.9%; Indonesia, 12.5%; New Caledonia, 10.4%; and Colombia, 8.2%. The mining-associated land-use change induced by Japanese final demand accounted for 19.5% of the total area affected by land-use change caused by nickel mining. The top three countries accounted for 70.6% (Indonesia: 47.0%, New Caledonia: 16.0%, and Australia: 7.7%), and the top five accounted for 82.4% (the Philippines: 7.5%, and Canada: 4.3%, in addition to the top three countries and regions).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".