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This paper develops a model in which a country, which only has access to a dirty technology for producing electric power in the short run, looks to expand its production in the long run by only permitting new power plants based on clean technology. The model mimics current reality in which major developing countries are being pushed by factors, such as the Paris Climate agreement of 2015 and the large burden of mortality and morbidity resulting from use of fossil fuels, to rely more on clean technologies. Our model shows how emissions and emission intensity of power output after the adoption of clean technologies are increasing in the targets for power production set by the government before availability of such technology and supply variables such as the wage rate and expenses on fixed capital, and decreasing in the tax on power production before the availability of clean technologies. Finally, it is seen that for low enough cost of the clean resource input, a country with a higher demand is able to set a higher target for production with the dirty technology when the clean technology is not available and yet achieves lower emissions and emission intensity in the long run. [ABSTRACT FROM AUTHOR]

Background and Objectives: To enrich micronutrients in rice through breeding and to identify biofortified donor lines, screening a large diversity panel of major subspecies of rice accessions for their ionomes is necessary. Findings: Inductively coupled plasma optical emission spectroscopy was deployed to profile grain ionomes from 1100 rice accessions. Classification models derived for multidimensional ionomics data using a random forest model, support vector machine, and artificial neural network predicted three distinct groups that differ in ionomic levels with higher prediction accuracy (76.8%–85.1%). While class A showed inferior lines in mineral content, class B and C represent superior lines with enriched multiple minerals. The identified contrasting lines from the modeling classifications were milled and profiled for minerals and toxic elements using inductively coupled plasma mass spectrometry and revealed donors with an elevated mineral content even higher than the Zn‐biofortified rice lines. Conclusion: Overall, the result of this study provided new insights into ionomic variation among subspecies with a preferential enrichment of minerals in temperate japonica and admixed populations coincided with increased grain protein content, while in indica median mineral content is low while selected for increased amylose content. Significance and Novelty: Artificial intelligence models were developed to predict grains enriched with multiple minerals. [ABSTRACT FROM AUTHOR]