An Investigation on Crop Yield Prediction Using Machine Learning

Sajja, Guna Sekhar; Jha, Subhesh Saurabh; Mhamdi, Hicham; Naved, Mohd; Ray, Samrat; Phasinam, Khongdet

doi:10.1109/icirca51532.2021.9544815

Cited by 14 publications

(1 citation statement)

References 19 publications

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“…However, carbon emission efficiency is the result of the interaction of multiple factors, and not a single indicator can be interpreted. Therefore, some scholars proposed total factor energy efficiency based on the perspective of total factor productivity [13][14][15] , and then considered relaxation variables to integrate energy, economy and carbon emissions into the research framework of carbon emission efficiency, redefining the concept of carbon emission efficiency [16][17] . Compared with domestic and international carbon emission efficiency accounting standards, foreign research focuses more on the overall analysis and research on global or national organizations, such as ZofıÓ and Iftikhar conducted carbon emission efficiency accounting and evaluation for OECD member countries and major economies [18][19] , and found that India, Russia and China have huge carbon dioxide emission reduction potential.…”

Section: Introductionmentioning

confidence: 99%

Research on the spatial-temporal evolution pattern of China's industrial carbon emission efficiency-based on the super-efficiency SBM model

Samrat Ray

2023

World J. Adv. Res. Rev.

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This paper uses the super efficiency SBM model based on unexpected output to calculate the industrial carbon emission efficiency of 30 provinces in China from 2005 to 2019, and uses spatial autocorrelation and other methods to conduct an empirical analysis on the dynamic evolution process and temporal and spatial differences of carbon emission efficiency. The results show that China's industrial carbon emission efficiency is still in the low efficiency stage, with an average value of 0.34. In the study period, although the overall trend is rising, the spatial and temporal differences between provinces are obvious. The overall Moran's I index is subject to the impact of social policy environment on the time path, showing an overall "M" type fluctuation trend, and its spatial difference characteristics change in stages. At the same time, there is a significant global spatial agglomeration effect across the country, and there is a spatial dependency between provinces. The agglomeration trend is significant, forming a cluster with the eastern coastal area as the high value center, and the northwest inland area as the low value center. The Getis OrdGI * index reveals that the local spatial agglomeration characteristics of China's industrial carbon emission efficiency always maintain the pattern of "hot in the east and cold in the west", which echoes the overall spatial evolution pattern of China. The hot spots and sub hot spots of China's industrial carbon emission efficiency are mainly concentrated in the southeast coastal areas with relatively superior economic development, and present a "semi enclosed" pattern. Cold spot area and sub cold spot area gradually evolved into a "suppression" pattern dominated by provinces in northwest China, comprehensively suppressing hot spots. This paper conducts an all-round evaluation of China's industrial carbon emission efficiency from the perspective of regional differentiation, and deeply studies the regional differences and spatial evolution pattern of carbon emission efficien cy in various provinces. It is crucial to achieve the goal of carbon emission reduction in China, and has important practical value and practical significance.

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