Integrated assessment of global water scarcity over the 21st century – Part 2: Climate change mitigation policies
Bibliographic record
Abstract
Abstract. We investigate the effects of emission mitigation policies on water scarcity both globally and regionally using the Global Change Assessment Model (GCAM), a leading community integrated assessment model of energy, agriculture, climate, and water. Three climate policy scenarios with increasing mitigation stringency of 7.7, 5.5, and 4.2 W m−2 in year 2095 (equivalent to the SRES A2, B2, and B1 emission scenarios, respectively), under two carbon tax regimes (a universal carbon tax (UCT) which includes land use change emissions, and a fossil fuel and industrial emissions carbon tax (FFICT) which excludes land use change emissions) are analyzed. The results are compared to a baseline scenario (i.e. no climate change mitigation policy) with radiative forcing reaching 8.8 W m−2 (equivalent to the SRES A1Fi emission scenario) by 2095. When compared to the baseline scenario and maintaining the same baseline socioeconomic assumptions, water scarcity declines under a UCT mitigation policy but increases with a FFICT mitigation scenario by the year 2095 particularly with more stringent climate mitigation targets. The decreasing trend with UCT policy stringency is due to substitution from more water-intensive to less water-intensive choices in food and energy production, and in land use. Under the FFICT scenario, water scarcity is projected to increase driven by higher water demands for bio-energy crops. This study implies an increasingly prominent role for water availability in future human decisions, and highlights the importance of including water in integrated assessment of global change. Future research will be directed at incorporating water shortage feedbacks in GCAM to better understand how such stresses will propagate across the various human and natural systems in GCAM.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".