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.
Similar content being viewed by others
Notes
2018 statistical bulletin on China’s national economic and social development.
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
Abbas A, Waseem M, Yang M (2020) An ensemble approach for assessment of energy efficiency of agriculture system in Pakistan. Energy Efficiency 13:683–696
Acheampong AO, Adams S, Boateng E (2019) Do globalization and renewable energy contribute to carbon emissions mitigation in Sub-Saharan Africa? Sci Total Environ 677:436–446. https://doi.org/10.1016/j.scitotenv.2019.04.353
Ali A, Ilhan O (2010) On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35(12):5412–5420. https://doi.org/10.1016/j.energy.2010.07.009
Asumadu-Sarkodie S, Owusu PA (2016) The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model. Environ Sci Pollut Res 23:10968–10982. https://doi.org/10.1007/s11356-016-6252-x
Battese GE, Rao DSP (2002) Technology potential, efficiency and a stochastic metafrontier func-tion. Int J Bus Econ 1(2):1–7
Battese GE, Prasada Rao DS, O'Donnell CJ (2004) A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. J Product Anal 21:91–103
Boontome P, Therdyothin A, Chontanawa J (2017) Investigating the causal relationship between non-renewable and renewable energy consumption, CO2 emissions and economic growth in Thailand. Energy Procedia 138:925–930. https://doi.org/10.1016/j.egypro.2017.10.141
Callendar GS (1938) The artificial production of carbon dioxide and its influence on temperature. Q J R Meteorol Soc 64(275):223–240. https://doi.org/10.1002/qj.49706427503
Cassman KG, Dobermann A, Walters DT, Yang H (2003) Meeting cereal demand while protecting natural resources and improving environmental quality. Annu Rev Environ Resour 28:315–358. https://doi.org/10.1146/annurev.energy.28.040202.122858
Chel A, Kaushik G (2011) Renewable energy for sustainable agriculture. Agron Sust Dev 31:91–118
Chen JD, Cheng SL, Song ML (2008) Changes in energy-related carbon dioxide emissions of the agricultural sector in China from 2005 to 2013. Renew Sust Energ Rev 94:748–761. https://doi.org/10.1016/j.rser.2018.06.050
Chen YL, Zhao JC, Lai ZZ, Wang Z, Xia HB (2019) Exploring the effects of economic growth, and renewable and non-renewable energy consumption on China’s CO2 emissions: evidence from a regional panel analysis. Renew Energy 140:341–353. https://doi.org/10.1016/j.renene.2019.03.058
Cheng C, Ren XH, Wang Z (2019) The impact of renewable energy and innovation on carbon emission: an empirical analysis for OECD countries. Energy Procedia 158:3506–3512. https://doi.org/10.1016/j.egypro.2019.01.919
Ekwueme DC, Zoaka JD (2020) Effusions of carbon dioxide in MENA countries: inference of financial development, trade receptivity, and energy utilization. Environ Sci Pollut Res 27:12449–12460. https://doi.org/10.1007/s11356-020-07821-5
Elum Z, Modise D, Nhamo G (2017) Climate change mitigation: the potential of agriculture as a renewable energy source in Nigeria. Environ Sci Pollut Res 24:3260–3273. https://doi.org/10.1007/s11356-016-8187-7
Fan DD (2013) Research on energy efficiency in China from the perspective of low carbon. Dongbei university of finance and economics
Fei RL, Lin BQ (2017, Energy) Estimates of energy demand and energy saving potential in China’s agricultural sector. 135:865–875. https://doi.org/10.1016/j.energy.2017.06.173
Hao Y, Huang YN (2018) Exploring the nexus of energy consumption structure and CO 2 emissions in China: empirical evidence based on the translog production function. Pol J Environ Stud 27(6):2541–2255. https://doi.org/10.15244/pjoes/81071
Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D (2011) Climate impacts on agriculture: implications for crop production. Agron J 103(2):351–370. https://doi.org/10.2134/agronj2010.0303
Hussain S, Peng S, Fahad S, Khaliq A, Huang J, Cui K, Nie L (2015) Rice management interventions to mitigate greenhouse gas emissions: a review. Environ Sci Pollut Res 22:3342–3360. https://doi.org/10.1007/s11356-014-3760-4
Jalil A, Mahmud SF (2009) Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy 37:5167–5172. https://doi.org/10.1016/j.enpol.2009.07.044
Jane MF, Johnson AJ, Franzluebbers SL et al (2007) Agricultural opportunities to mitigate greenhouse gas emissions. Environ Pollut 150(1):107–124. https://doi.org/10.1016/j.envpol.2007.06.030
Khattak SI, Ahmad M, Khan ZU et al (2020) Exploring the impact of innovation, renewable energy consumption, and income on CO2 emissions: new evidence from the BRICS economies. Environ Sci Pollut Res 27:13866–13881. https://doi.org/10.1016/j.enpol.2017.03.009
Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H (2013) Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 58:588–593. https://doi.org/10.1016/j.energy.2013.06.030
Lu CC, Chen X, Hsieh CL, Chou KW (2019) Dynamic energy efficiency of slack-based measure in high-income economies. Energy Sci Eng 7:943–961. https://doi.org/10.1002/ese3.324
Lulie ML, Ryusuke H, Goh KJS (2005) CO2 flux from three ecosystems in tropical peatland of Sarawak, Malaysia. Acad J 57(1):1–11. https://doi.org/10.1111/j.1600-0889.2005.00129.x
Lv LD (2016) Evaluation and analysis of regional energy conservation and emission reduction efficiency in China based on DEA method. University of science and technology of China
Martel JC (2016) Exploring the integration of energy efficiency and disaster management in public policies and programs. Energy Efficiency 9:533–543. https://doi.org/10.1007/s12053-015-9379-6
Mita B, Sefa AC, Sudharshan RP (2017) The dynamic impact of renewable energy and institutions on economic output and CO2 emissions across regions. Renew Energy 111:157–167. https://doi.org/10.1016/j.renene.2017.03.102
Moradi M, Nematollahi MA, Mousavi KA et al (2018) Comparison of energy consumption of wheat production in conservation and conventional agriculture using DEA. Environ Sci Pollut Res 25:35200–35209. https://doi.org/10.1007/s11356-018-3424-x
O'Donnell CJ, Prasada Rao DS, Battese G (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratioS. Empirical Economics 3(2):231–255. https://doi.org/10.1007/s00181-007-0119-4
Osana Y, Tissue DT, Bange MP et al (2017) Interactive effects of elevated CO2, temperature and extreme weather events on soil nitrogen and cotton productivity indicate increased variability of cotton production under future climate regimes. Agric Ecosyst Environ 246:343–353. https://doi.org/10.1016/j.agee.2017.06.004
Pervanchon F, Bockstaller C, Girardin P (2002) Assessment of energy use in arable farming systems by means of an agro-ecological indicator: the energy indicator. Agric Syst 72(2):149–172. https://doi.org/10.1016/S0308-521X(01)00073-7
Qu KD (2016) Carbon dioxide emission efficiency evaluation in China based on super-efficiency DEA model. Jiangnan university
Ramanathan R (2005) An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy 30:2831–2842. https://doi.org/10.1016/j.energy.2005.01.010
Robredo AI, López UP, Apodaca JM et al (2011) Elevated CO2 reduces the drought effect on nitrogen metabolism in barley plants during drought and subsequent recovery. Environ Exp Bot 71(3):399–408. https://doi.org/10.1016/j.envexpbot.2011.02.011
Shahbaz M, Nasir MA, Roubaud D (2018) Environmental degradation in France: the effects of FDI, financial development, and energy innovations. Energy Econ 74(August 2018):843–857. https://doi.org/10.1016/j.eneco.2018.07.020
Singh S, Stewart RB (1991) Potential impacts of a CO2-induced climate change using the GISS scenario on agriculture in Quebec, Canada. Agric Ecosyst Environ 35(4):327–347. https://doi.org/10.1016/0167-8809(91)90082-9
Tone K, Tsutsui M (2014) Dynamic DEA with network structure: A slacks-based measure approach, Omega, 42:124–131. https://doi.org/10.1016/j.omega.2013.04.002
Wang JQ, Liu XY, Zhang XH, Liu X, Zhang X, Smith P, Li L, Filley TR, Cheng K, Shen M, He Y, Pan G (2016) Size and variability of crop productivity both impacted by CO2 enrichment and warming—a case study of 4-year field experiment in a Chinese paddy. Agric Ecosyst Environ 21:40–49. https://doi.org/10.1016/j.agee.2016.01.028
Zhao LLD (2016) Research on energy and environmental efficiency based on data envelopment analysis (DEA). University of science and technology of China
Funding
This study was supported by the Fundamental research funds for the central universities (2019B35814).
Author information
Authors and Affiliations
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
Ethics declarations
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
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-09980-x