Leveraging Integrated Assessment Models to access climate feedbacks on Water, Energy, and Land Systems: An Evaluation of Regional and Sectoral Transformations for Achieving the Sustainable Development Goals
Bibliographic record
Abstract
This research investigates the interconnections between water, energy, and land systems in the context of a long-term assessment of transition paths to achieve the Sustainable Development Goals (SDGs). It highlights the importance of integrated methods and addresses the complexity, interdependence, and uncertainty of climate change's impacts on natural systems and technology in the water, energy, and land sectors. The research utilizes two Integrated Assessment Models (MESSAGEix-GLOBIOM and IMAGE) to assess the long-term resources, supply, and demand of these sectors, together with the regional and sectoral reforms required to achieve the SDGs. It demonstrates how various locations and sectors would be affected by climate feedback under various climate mitigation scenarios. The study concludes that changes in water availability, that influence agriculture, water and sanitation access, hydropower potential, and power plant cooling technologies, constitute the largest proportion of climate impacts and the prime source of uncertainty. Furthermore, scenario analysis is used to understand the relationship between the SDGs and climate impacts in the absence of climate policies. The findings demonstrate that considerable progress towards the trajectories of the nexus SDGs resulted in strong synergies and interactions across the energy-water and land nexus components, irrespective of climate factors. Additionally, the study demonstrates that ambitious and healthy dietary modifications and a reduction in food waste can result in a decrease in global food demand, irrigation withdrawals, and emissions. Changes in the land sector can reduce overall SDG policy costs and energy and water expenditures, while strengthening the needs of the poor. Improving wastewater treatment and establishing more efficient water management technologies has socioeconomic and environmental advantages and can alleviate stress on freshwater withdrawals in locations that are water stressed. The study also shows that some regions, such as the Middle East and South Asia, are more vulnerable to climate impacts on the water sector and may require more extensive investments in water efficiency. In addition, it stresses that supplying households with electricity and clean cooking services can stimulate energy demand in emerging economies, but widespread adoption would require an increase in household incomes, notably in South Asia and Sub-Saharan Africa. Overall, the study highlights the importance of exploring the effects of climate change on natural and technological systems in the water, energy, and land sectors, as well as the relevance of implementing a coordinated strategy to achieving the Sustainable Development Goals. It also demonstrates the inter - dependencies and potentials of various sectors to achieve the SDGs while addressing the challenges they face because of climate change.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".