Valuing damages and benefits of the altered global nitrogen cycle; lessons for national to global policy support
Bibliographic record
Abstract
Abstract Cost-benefit analysis (CBA) is increasingly used to inform environmental policy decisions by identifying interventions with the highest net societal benefits. Here we focus on CBAs for nitrogen (NCBA), explaining its history, presenting results of a recent first global NCBA and discussing opportunities and limitations. NCBAs have been conducted since the late 1990s for various geographic regions in Europe, the US, and China, primarily to support air quality and eutrophication policies. A first valuation of damages and benefits of the full nitrogen (N) cycle was conducted for the European Nitrogen Assessment in 2011, followed by NCBAs for the USA, the Netherlands and Germany. Here we present a first comprehensive global NCBA. Total global damage cost of N pollution in 2010 was estimated at US$1.1 trillion, primarily from increases in premature mortality by N derived PM2.5 (35%), terrestrial biodiversity loss by N deposition (33%), and marine eutrophication by N river loads (21%). Global benefits of N in 2010 were estimated at US$ 2.2 trillion with >95% from increased crop yields. By 2050, global N-related costs will rise faster than N benefits because underlying models project that economic growth (GDP) increases willingness-to-pay to prevent N pollution more than crop prices. The geographical distribution of N-related costs will also shift, with China and India surpassing Europe and North America as regions contributing most to global N-related costs. The estimated N cost range for 2010 was US$ 0.6–2.2 trillion with uncertainty largely in dose-impact and damage cost relations. Given the large uncertainties, when using valuation and NCBA to select a N mitigation option, the net benefits should be substantially higher than the costs and markedly better than for a rejected alternative option. Use of NCBA is discouraged to compare international policy options that involve regions with very different levels of GDP, cultures and political systems.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".