Study on the spatial correlation network structure of agricultural carbon emission efficiency in China

Jieqiong Yang; Panzhu Luo; Jieqiong Yang; Panzhu Luo

doi:10.3934/era.2023368

Electronic Research Archive

2023, Volume 31, Issue 12: 7256-7283. doi: 10.3934/era.2023368

Previous Article Next Article

Research article Special Issues

Study on the spatial correlation network structure of agricultural carbon emission efficiency in China

Jieqiong Yang ¹,
Panzhu Luo ^{2
,
,}

1.
College of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China
2.
College of Business, Central South University of Forestry and Technology, Changsha 410004, China

Received: 13 September 2023 Revised: 24 October 2023 Accepted: 29 October 2023 Published: 13 November 2023

Achieving carbon neutrality requires high efficiency in agricultural carbon emissions. This study employs a super efficiency Slack Based Measure-Data Envelopment Analysis (SBM-DEA) model to measure the Agricultural Carbon Emission Efficiency (ACEE) of 31 provinces, cities, and autonomous regions within the Chinese Mainland from 2001 to 2021. Additionally, it utilizes the modified gravity model and a social network analysis to establish the spatial correlation relationship of ACEE, and extensively investigates the characteristics and transmission mechanism of China's spatial correlation network structure regarding ACEE. The findings reveal the following: 1) The spatial correlation relationship of China's ACEE from 2001 to 2021 exhibits a complex network structure; 2) in terms of the overall network structure characteristics of the spatial correlation, the ACEE network demonstrates a high degree of correlation and displays a stable temporal evolution trend; 3) concerning the centrality network structure characteristics of the spatial correlation, most provinces in China experience a continuous decline in point centrality and near centrality, while the interdependence of the ACEE between provinces increases; and 4) regarding the clustering characteristics of the spatial correlation, variations exist in the correlation among the four plates of the ACEE. However, they mostly assume a mediating role, and in 2021, the ACEE network sectors witnessed a robust interoperability.
Citation: Jieqiong Yang, Panzhu Luo. Study on the spatial correlation network structure of agricultural carbon emission efficiency in China[J]. Electronic Research Archive, 2023, 31(12): 7256-7283. doi: 10.3934/era.2023368

Related Papers:

Abstract

Achieving carbon neutrality requires high efficiency in agricultural carbon emissions. This study employs a super efficiency Slack Based Measure-Data Envelopment Analysis (SBM-DEA) model to measure the Agricultural Carbon Emission Efficiency (ACEE) of 31 provinces, cities, and autonomous regions within the Chinese Mainland from 2001 to 2021. Additionally, it utilizes the modified gravity model and a social network analysis to establish the spatial correlation relationship of ACEE, and extensively investigates the characteristics and transmission mechanism of China's spatial correlation network structure regarding ACEE. The findings reveal the following: 1) The spatial correlation relationship of China's ACEE from 2001 to 2021 exhibits a complex network structure; 2) in terms of the overall network structure characteristics of the spatial correlation, the ACEE network demonstrates a high degree of correlation and displays a stable temporal evolution trend; 3) concerning the centrality network structure characteristics of the spatial correlation, most provinces in China experience a continuous decline in point centrality and near centrality, while the interdependence of the ACEE between provinces increases; and 4) regarding the clustering characteristics of the spatial correlation, variations exist in the correlation among the four plates of the ACEE. However, they mostly assume a mediating role, and in 2021, the ACEE network sectors witnessed a robust interoperability.

References

[1]	C. M. Juan, S. Amparo, Cost and performance of carbon risk in socially responsible mutual funds, Quant. Finance Econ., 7 (2023), 50–73. https://doi.org/10.3934/QFE.2023003 doi: 10.3934/QFE.2023003
[2]	I. Ayodele, M. O. Obaika, A. C. Munem, Does industrialization trigger carbon emissions through energy consumption? Evidence from OPEC countries and high industrialised countries, Quant. Finance Econ., 7 (2023), 165–186. https://doi.org/10.3934/QFE.2023009 doi: 10.3934/QFE.2023009
[3]	S. Mark, International cooperation on climate research and green technologies in the face of sanctions: The case of Russia, Green Finance, 5 (2023), 102–153. https://doi.org/10.3934/GF.2023006 doi: 10.3934/GF.2023006
[4]	Z. Li, G. Liao, K. Albitar, Does corporate environmental responsibility engagement affect firm value? The mediating role of corporate innovation, Bus. Strategy Environ., 29 (2020), 1045–1055. https://doi.org/10.1002/bse.2416 doi: 10.1002/bse.2416
[5]	IPCC, Climate Change 2007: The Fourth Assessment Report of the Intergovernmental Panel on Climate Change, New York: Cambridge University Press, 2007.
[6]	J. Sui, W. Lv, Crop production and agricultural carbon emissions: Relationship diagnosis and decomposition analysis, Int. J. Environ. Res. Public Health, 18 (2021), 8219. https://doi.org/10.3390/ijerph18158219. PMID: 34360511 doi: 10.3390/ijerph18158219.PMID:34360511
[7]	Z. Li, Z. Huang, Y. Su, New media environment, environmental regulation and corporate green technology innovation: Evidence from China, Energy Econ., 119 (2023), 106545. https://doi.org/10.1016/j.eneco.2023.106545 doi: 10.1016/j.eneco.2023.106545
[8]	Z. Huang, H. Dong, S. Jia, Equilibrium pricing for carbon emission in response to the target of carbon emission peaking, Energy Econ., 112 (2022), 106160. https://doi.org/10.1016/j.eneco.2022.106160 doi: 10.1016/j.eneco.2022.106160
[9]	Z. Li, H. Chen, B. Mo, Can digital finance promote urban innovation? Evidence from China, Borsa Istanbul Rev., 23 (2022), 285–296. https://doi.org/10.1016/j.bir.2022. 10.006 doi: 10.1016/j.bir.2022.10.006
[10]	Z. Li, F. Zou, B. Mo, Does mandatory CSR disclosure affect enterprise total factor productivity? Econ. Res.-Ekonomska Istraživanja, 35 (2022), 4902–4921. https://doi.org/10.1080/1331677X.2021.2019596 doi: 10.1080/1331677X.2021.2019596
[11]	S. Menegat, A. Ledo, R. Tirado, Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture, Sci. Rep., 12 (2022), 14490. https://doi.org/10.1038/s41598-022-18773-w doi: 10.1038/s41598-022-18773-w
[12]	L. Lassaletta, G. Billen, J. Garnier, L. Bouwman, E. Velazquez, N. D. Mueller, et al., Nitrogen use in the global food system: Past trends and future trajectories of agronomic performance, pollution, trade, and dietary demand, Environ. Res. Lett., 11 (2016), 095007. https://doi.org/10.1088/1748-9326/11/9/095007 doi: 10.1088/1748-9326/11/9/095007
[13]	I. Shcherbak, N. Millar, G. P. Robertson, Global metaanalysis of the nonlinear response of soil nitrous oxide (N₂O) emissions to fertilizer nitrogen, PNAS, 111 (2014), 9199–9204. https://doi.org/10.1073/pnas.1322434111 doi: 10.1073/pnas.1322434111
[14]	Y. Gan, C. Liang, Q. Chai, R. L. Lemke, C. A. Campbell, R. P. Zentner, Improving farming practices reduces the carbon footprint of spring wheat production, Nat. Commun., 5 (2014), 5012. https://doi.org/10.1038/ncomms6012 doi: 10.1038/ncomms6012
[15]	J. M. F. Johnson, A. J. Franzluebbers, S. L. Weyers, D. C. Reicosky, Agricultural opportunities to mitigate greenhouse gas emissions, Environ. Pollut., 15 (2007), 107–124. https://doi.org/10.1016/j.envpol.2007.06.030 doi: 10.1016/j.envpol.2007.06.030
[16]	J. Pei, Z. Niu, L. Wang, X. Song, N. Huang, J. Geng, et al., Spatial-temporal dynamics of carbon emissions and carbon sinks in economically developed areas of China: a case study of Guangdong Province, Sci. Rep., 8 (2018), 13383. https://doi.org/10.1038/s41598-018-31733-7 doi: 10.1038/s41598-018-31733-7
[17]	S. Frank, P. Havlík, E. Stehfest, H. V. Meijl, P. Witzke, I. Pérez-Domínguez, et al., Agricultural non-CO₂ emission reduction potential in the context of the 1.5 ℃ target, Nat. Clim. Change, 9 (2019), 66–72. https://doi.org/10.1038/s41558-018-0358-8 doi: 10.1038/s41558-018-0358-8
[18]	K. H. Nguyen, M. Kakinaka, Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis, Renewable Energy, 132 (2019), 1049–1057. https://doi.org/10.1016/j.renene.2018.08.069 doi: 10.1016/j.renene.2018.08.069
[19]	R. Gao, J. Du, X. Li, Dynamic analysis of agricultural growth and environmental pollution-verification based on panel data from 2006 to 2015 (In Chinese), Chin. J. Agric. Resour. Reg. Plann., 39 (2018), 138–145. https://doi.org/10.7621/cjarrp.1005-9121.20181219 doi: 10.7621/cjarrp.1005-9121.20181219
[20]	T. O. West, G. Marland, Net carbon flux from agricultural ecosystems: methodology for full carbon cycle analyses, Environ. Pollut., 116 (2002), 439–444. https://doi.org/10.1016/S0269-7491(01)00221-4 doi: 10.1016/S0269-7491(01)00221-4
[21]	R. Lal, Carbon emission from farm operations, Environ. Int., 30 (2004), 981–990. https://doi.org/10.1016/j.envint.2004.03.005 doi: 10.1016/j.envint.2004.03.005
[22]	R. Rebolledo-Leiva, L. Angulo-Meza, A. Iriarte, M. C. González-Araya, Joint carbon footprint assessment and data envelopment analysis for the reduction of greenhouse gas emissions in agriculture production, Sci. Total Environ., 593 (2017), 36–46. https://doi.org/10.1016/j.scitotenv.2017.03.147 doi: 10.1016/j.scitotenv.2017.03.147
[23]	Z. Shen, T. Balezentis, J. Streimikis, Capacity utilization and energy-related GHG emission in the European agriculture: A data envelopment analysis approach, J. Environ. Manage., 318 (2022), 115517. https://doi.org/10.1016/j.jenvman.2022.115517 doi: 10.1016/j.jenvman.2022.115517
[24]	H. A. A. Sayed, Q. Ding, Z. M. Hendy, J. O. Alele, O. H. Al-Mashhadany, M. A. Abdelhamid, Improving energy efficiency and greenhouse gas emissions in small farm wheat production scenarios using data envelopment analysis, Agronomy, 13 (2023), 1973. https://doi.org/10.3390/agronomy13081973 doi: 10.3390/agronomy13081973
[25]	K. Tone, Slacks-based measure of efficiency in data envelopment analysis, Eur. J. Oper. Res., 130 (2001), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5 doi: 10.1016/S0377-2217(99)00407-5
[26]	J. Shang, X. Q. Ji, R. Shi, M. R. Zhu, Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China, Chin. J. Eco-Agric., 30 (2022), 543–557. https://doi.org/10.12357/cjea.20210607 doi: 10.12357/cjea.20210607
[27]	T. Shan, Y. Xia, C. Hu, S. Zhang, J. Zhang, Y. Xiao, et al., Analysis of regional agricultural carbon emission efficiency and influencing factors: Case study of Hubei Province in China, PLoS One, 17 (2022), e0266172. https://doi.org/10.1371/journal.pone.0266172 doi: 10.1371/journal.pone.0266172
[28]	X. Zhang, K. Liao, X. Zhou, Analysis of regional differences and dynamic mechanisms of agricultural carbon emission efficiency in China's seven agricultural regions, Environ. Sci. Pollut. Res. Int., 29 (2022), 38258–38284. https://doi.org/10.1007/s11356-021-16661-w doi: 10.1007/s11356-021-16661-w
[29]	L. Ning, W. Zheng, L. Zeng, Research on China's carbon dioxide emissions efficiency from 2007 to 2016: Based on two stages super efficiency SBM model and Tobit model, Acta Sci. Nat. Univ. Pekin., 57 (2021), 181–188. https://doi.org/10.13209/j.0479-8023.2020.111 doi: 10.13209/j.0479-8023.2020.111
[30]	P. Zhou, B. Ang, J. Han, Total factor carbon emission performance: A Malmquist index analysis, Energy Econ., 32 (2010), 194–201. https://doi.org/10.1016/j.eneco.2009.10.003 doi: 10.1016/j.eneco.2009.10.003
[31]	N. C. P. Edirisinghe, X. Zhang, Portfolio selection under DEA-based relative financial strength indicators: case of US industries, J. Oper. Res. Soc., 59 (2008), 842–856. https://doi.org/10.1057/palgrave.jors.2602442 doi: 10.1057/palgrave.jors.2602442
[32]	G. Rotondo, M. P. Ardeleanu, The challenges of agriculture between sustainability and efficiency, Calitatea, 16 (2015), 211.
[33]	R. Wang, Y. Feng, Research on China's agricultural carbon emission efficiency evaluation and regional differentiation based on DEA and Theil models, Int. J. Environ. Sci. Technol., 18 (2021), 1453–1464. https://doi.org/10.1007/s13762-020-02903-w doi: 10.1007/s13762-020-02903-w
[34]	Y. Qing, B. Zhao, C. Wen, The coupling and coordination of agricultural carbon emissions efficiency and economic growth in the Yellow River Basin, China, Sustainability, 15 (2023), 971. https://doi.org/10.3390/su15020971 doi: 10.3390/su15020971
[35]	R. Gu, L. Duo, X. Guo, Z. Zou, D. Zhao, Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China, Environ. Sci. Pollut. Res., 30 (2023), 49470–49486. https://doi.org/10.1007/s11356-023-25821-z doi: 10.1007/s11356-023-25821-z
[36]	X. Zhang, X. Zhou, K. Liao, Regional differences and dynamic evolution of China's agricultural carbon emission efficiency, Int. J. Environ. Sci. Technol., 20 (2023), 4307–4324. https://doi.org/10.1007/s13762-022-04196-7 doi: 10.1007/s13762-022-04196-7
[37]	H. Wu, H. Huang, Y. He, W. Chen, Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China, Chin. J. Eco-Agric., 29 (2021), 1762–1773. https://doi.org/10.13930/j.cnki.cjea.210204 doi: 10.13930/j.cnki.cjea.210204
[38]	Y. Zhu, C. Huo, The impact of agricultural production efficiency on agricultural carbon emissions in China, Energies, 15 (2022), 4464. https://doi.org/10.3390/en15124464 doi: 10.3390/en15124464
[39]	Q. Qin, H. Yan, J. Liu, X. Chen, B. Ye, China's agricultural GHG emission efficiency: regional disparity and spatial dynamic evolution, Environ. Geochem. Health, 44 (2022), 2863–2879. https://doi.org/10.1007/s10653-020-00744-7 doi: 10.1007/s10653-020-00744-7
[40]	H. Zhang, S. Guo, Y. Qian, Y. Liu, C. Lu, Dynamic analysis of agricultural carbon emissions efficiency in Chinese provinces along the Belt and Road, PLoS One, 15 (2020), e0228223. https://doi.org/10.1371/journal.pone.0228223 doi: 10.1371/journal.pone.0228223
[41]	P. Andersen, N. C. Petersen, A procedure for ranking efficient units in data envelopment analysis, Manage. Sci., 39 (1993), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261 doi: 10.1287/mnsc.39.10.1261

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)