DILEMMA OF CO2 MITIGATION: EFFECTS OF CLIMATE, CO2, AND TECHNOLOGICAL PROGRESS ON RICE YIELDS

Yu-Kai Huang et al.

Climate Change Economics2025https://doi.org/10.1142/s2010007825500071article
ABDC B
Weight
0.50

Abstract

Rice feeds billions of populations, and its yields are sensitive to climate change, carbon dioxide (CO[Formula: see text], and technological progress. This study estimates a model of these effects using Asian rice yield data merged with free air carbon dioxide enrichment (FACE) experimental data. A three-stage generalized least squares model is developed to quantify their effects on mean rice yields and their variance. The results show that CO 2 concentration has significantly contributed to increased rice yields, accounting for 16–26% of observed yield growth over 1990–2015. This indicates that if significant CO 2 mitigation occurs, the CO 2 fertilization effect will diminish, inducing a yield reduction. This finding suggests that increased agricultural research and development investments are needed to overcome not only adverse climate change effects but also to counteract CO 2 mitigation effects on rice yields.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1142/s2010007825500071

Or copy a formatted citation

@article{yu-kai2025,
  title        = {{DILEMMA OF CO2 MITIGATION: EFFECTS OF CLIMATE, CO2, AND TECHNOLOGICAL PROGRESS ON RICE YIELDS}},
  author       = {Yu-Kai Huang et al.},
  journal      = {Climate Change Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s2010007825500071},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

DILEMMA OF CO2 MITIGATION: EFFECTS OF CLIMATE, CO2, AND TECHNOLOGICAL PROGRESS ON RICE YIELDS

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.