Sectoral investments to achieve water, energy and land SDG under climate change uncertainty
Bibliographic record
Abstract
Abstract Achieving the sustainable development goals (SDGs) requires a deep understanding of the intricate relationships between water, energy, food, and land resources, particularly in the context of a growing global population and climate change. Previous studies have either explored individual SDG investment needs or analyzed climate impacts independently, but few have integrated these aspects across multiple sectors. This paper addresses this gap by exploring how climate impacts alter investment needs for key SDGs related to water, energy, and food security, using the MESSAGEix-GLOBIOM-Nexus model to analyze optimal multi-sector investment strategies. By comparing scenarios with and without SDG targets and climate impacts based on radiative concentration pathway 6.0 forcing, and across different water availability assumptions, we identify regions with the highest uncertainties in development costs due to climate change. Developing countries in Asia and sub-Saharan Africa will need to increase their spending by 10%–30%, compared to current trends, to meet their SDGs for water. Climate-related uncertainties lead to a spread in investment needs of 30% in the water sector and 5% in the energy sector in the most affected regions, amounting to billions of dollars. Our findings show that cross-sectoral policies, such as those aimed at reducing food waste and improving nutrition, can yield significant cost savings by reducing water demand, especially in water-scarce regions such as South Asia. The study also highlights the importance of considering long-term costs and uncertainties to maintain the standards of SDG targets throughout the century, with large variations in expected investment requirements in Asia under climate change scenarios after 2040. The study provides a framework for understanding the economic implications of climate impacts on SDG achievement and highlights the need for dedicated financing strategies that incorporate resilience in development finance.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".