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An analysis of carbon emissions of crop production provides paths for global warming mitigation. Existing studies have focused on the magnitude of the carbon emissions from crop production, which is unreasonable for inter-location comparison due to neglecting regional variations in cultivation technologies and planting scale. Different from the conventional idea, this paper estimated the carbon-emission density of crop production (CEDCP) based on carbon emissions per hectare of crop production. With the 30 Chinese provinces between 2000 and 2020 as the study area, temporal dynamics and spatial patterns of the CEDCP were explored, regional disparities of the CEDCP were discussed based on the Theil index, and the possibility of regional coordinated optimization for the CEDCP was explored by relying on the convergence tests. The results show that the average annual CEDCP in China was 1.462 t/hm2, reaching a peak of 1.576 t/hm2 in 2015. The national carbon-emission densities of agricultural materials, rice fields, soil management, and straw burning were 0.492 t/hm2, 0.390 t/hm2, 0.189 t/hm2, and 0.391 t/hm2, respectively. In many provinces, the CEDCP increased first and then decreased, presenting a spatial pattern of high in the eastern region and low in the western region. Regional disparities of CEDCP shrank early but expanded later, and the disparities within the western region had always contributed considerably to the overall disparities. The CEDCP had shown σ- and β- convergence in both national and regional scales, and the convergence process had positive spillover effects. These findings suggest that inter-provincial cooperation may facilitate the CEDCP to converge.

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The Analytic hierarchy process (AHP) is a widely used multi-criteria decision theory, and most AHP relies on the judgments of experts to derive priority scales. However, the judgment of experts may be subjective, and different experts may give different judgments for a problem. In order to make the decision results more objective, machine learning algorithms can be used to make the judgment. However, machine learning algorithms are strongly related to the collected data and not being flexible enough. This paper tries to combine expert judgments with algorithmic judgments to improve the bias of experts' judgments while still making decision-making flexibility. The authors combine expert's judgments with the judgments of the machine learning algorithm into Ordered pair of normalized real numbers (OPNs) and then make decisions through OPNs. Experiments on real data sets show that the proposed algorithm can get reasonable decision results. Moreover, when the expert's judgments are wrong or invalid, the judgments given by the machine learning algorithm can correct the expert's judgments to obtain a reasonable decision-making result.

No abstract