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Energy consumption, CO2 emissions, and agricultural disaster efficiency evaluation of China based on the two-stage dynamic DEA method

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Abstract

With a large agricultural sector, China is greatly affected by natural disasters caused by extreme weather events. Because the occurrence of natural disasters is closely related to the sharp increased consumption of energy and the massive emissions of carbon dioxide, this research examines relevant data from 2013 to 2017 in four major regions of China that cover 30 provincial administrative regions. Using the two-stage dynamic DEA model, we evaluate total efficiency value, two-stage efficiency value, and the efficiencies of energy consumption, CO2 emissions, and crop disaster areas, setting CO2 as the link between the production stage (first stage) and the crop damage stage (second stage). The research findings show that overall efficiency in China is generally low, whereby the total efficiencies of eastern and northeastern China are higher than those of central and western China. The efficiency value of the first stage (production stage) is greater than that of the second stage (crop damage stage), and the efficiency of most administrative regions’ second stage is below 0.3, which is the main reason for the country’s low overall efficiency. There is little difference between China’s CO2 and energy consumption efficiency scores, but the efficiency values of crop disaster areas fluctuate greatly. The efficiency scores of various indicators in the eastern region are generally higher and more balanced, and the total efficiency scores exhibit a decreasing trend from east to west. Therefore, it is necessary to implement the environmental policy of controlling energy consumption and early warning of natural disasters in the central and western regions, and promote the R&D industry and technological innovation of carbon dioxide emission reduction and disaster control in the economically developed eastern regions.

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Notes

  1. 2018 statistical bulletin on China’s national economic and social development.

  2. http://www.stats.gov.cn/

Abbreviations

CCR:

Charnes & Cooper & Rhodes

BCC:

Banker & Charnes & Cooper

SBM:

Slacks-based measure

DMU:

Decision-making unit

MPI:

Malmquist productivity index

ARDL:

Autoregressive distributed lag

VECM:

Vector error correction model

CPI:

Consumer Price Index

St. dev.:

Standard deviation

CNY:

China yuan

AVE:

Average

LMDI:

Logarithmic mean Divisia index

PDA:

Production decomposition analysis

DEA:

Data envelopment analysis

References

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Funding

This study was supported by the Fundamental research funds for the central universities (2019B35814).

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Authors and Affiliations

Authors

Contributions

Conceptualization, ZT; methodology, F-RR; software, ZT; validation, F-RR and Y-TS; formal analysis, H-SC; investigation, H-SC; resources, ZT; data curation, Y-TS; writing original draft preparation, F-RR; writing, review, and editing, F-RR; visualization, H-SC; supervision, F-RR; project administration, ZT; funding acquisition, ZT.

Corresponding author

Correspondence to Fang-rong Ren.

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Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Responsible editor: Eyup Dogan

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix section

Appendix section

$$ \mathrm{Labor}\ \mathrm{efficiency}=\frac{\mathrm{Target}\ \mathrm{labor}\ \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\kern0.5em \mathrm{labor}\ \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{Energy}\ \mathrm{efficiency}=\frac{\mathrm{Target}\ \mathrm{energy}\ \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\ \mathrm{energy}\ \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{Ecological}\ \mathrm{investment}\ \mathrm{efficiency}=\frac{\mathrm{Target}\ \mathrm{investment}\kern0.5em \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\ \mathrm{investment}\kern0.5em \mathrm{input}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{GDP}\ \mathrm{efficiency}=\frac{\mathrm{Actual}\ \mathrm{GDP}\ \mathrm{desirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Target}\ \mathrm{GDP}\ \mathrm{desirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{CPI}\ \mathrm{efficiency}=\frac{\ \mathrm{Actual}\ \mathrm{CPI}\ \mathrm{desirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Target}\ \mathrm{CPI}\ \mathrm{desirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{Crop}\ \mathrm{disaster}\ \mathrm{area}\ \mathrm{efficiency}=\frac{\ \mathrm{Target}\ \mathrm{disaster}\ \mathrm{area}\ \mathrm{undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\ \mathrm{disaster}\ \mathrm{area}\ \mathrm{undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{Direct}\ \mathrm{economic}\ \mathrm{loss}\ \mathrm{efficiency}=\frac{\mathrm{Target}\ \mathrm{loss}\ \mathrm{Undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\ \mathrm{loss}\ \mathrm{Undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)} $$
$$ \mathrm{CO}2\ \mathrm{efficiency}=\frac{\mathrm{Target}\ \mathrm{CO}2\ \mathrm{Undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)}{\mathrm{Actual}\ \mathrm{CO}2\kern0.5em \mathrm{Undesirable}\ \mathrm{output}\ \left(\mathrm{i},\mathrm{t}\right)} $$

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Ren, Fr., Tian, Z., Chen, Hs. et al. Energy consumption, CO2 emissions, and agricultural disaster efficiency evaluation of China based on the two-stage dynamic DEA method. Environ Sci Pollut Res 28, 1901–1918 (2021). https://doi.org/10.1007/s11356-020-09980-x

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