Study on agricultural carbon emission efficiency calculation and driving path of grain production department in China
Main Article Content
Keywords
agricultural carbon emission efficiency; configurational analysis; fuzzy-set Qualitative Comparative Analysis (fsQCA) Method; Technology–Organization–Environment (TOE) framework
Abstract
Agriculture plays a pivotal role in China’s environment, economy, and society, standing as a pillar in the country’s shifts toward lower carbon emissions. This is important, especially in food production, where sustainable prac-tices protect the environment and ensure food security and quality. Therefore, understanding the factors affect-ing carbon emission efficiency is highly practical for speeding up emission reductions and improving efficiency throughout the entire food supply chain. This study uses the Super Slacks Based Measure (SBM) model to evalu-ate the efficiency of carbon emissions in 30 provinces (including municipalities and autonomous regions) in China from 2013 to 2022, emphasizing the food production sector. Through the Technology Organization Environment (TOE) framework, an integrated analysis is crafted to explore ways to enhance carbon emission efficiency. The set Qualitative Comparative Analysis (fsQCA) method is applied to examine these pathways from a perspective offering unique insights tailored to the food production field. The findings reveal that agricultural carbon emis-sion efficiency surpasses the average in half of the provinces studied, yet notable discrepancies exist among them. In the eastern regions, efficiency values tend to be higher compared to the western areas, impacting the sustain-ability of regional food production. The research identifies four patterns that drive agricultural carbon emission efficiency: those led by technical conditions, attention structure synergy, agriculture support structure synergy, and overall development synergy. Enhancing efficiency involves factors such as adopting technologies promoting digital economy development, investing in agriculture financially, embracing eco-friendly agricultural practices, and optimizing the agricultural industrial structure. These aspects have implications for food science and the broader agricultural sector. Additionally, the study uncovers a substitution relationship between technological and environmental conditions that influence efficiency. These findings provide an overview of the pathways that enhance provincial agricultural carbon emission efficiency from an interactive perspective. This study is helpful to expand the understanding of the TOE framework, enrich the research results in the field of low-carbon agri-culture, provide insights for provinces in the stage of efficiency improvement, and provide theoretical support for carbon emission reduction in grain production in various provinces. The research aims to guide policymakers, food scientists, and agricultural stakeholders in China toward optimizing carbon efficiency in food production systems to support global climate change mitigation efforts while ensuring food supply.
References
Chen, H., Zheng, C.Y., Chen, F., Qiao, Y.Q., Du, S.Z., Cao, C.F., et al. 2021. Less N2O emission from newly high-yielding cultivars of winter wheat. Agriculture Ecosystems & Environment 320: 107557. https://doi.org/10.1016/j.agee.2021.107557
Chen, M.H., Xiao, H.Y., Zhao, H. and Liu, L.A. 2024. The power of attention: Government climate-risk attention and agricultural-land carbon emissions. Environmental Research 251(Pt 2): 118661. https://doi.org/10.1016/j.envres.2024.118661
Cheng, C.M., Li, J.Q., Sun, M.Y., Cao, Q.W. and Gao, Q. 2023. Nonlinear effect of farmland management scale expansion on agricultural eco-efficiency: A moderating effect of service outsourcing. Polish Journal of Environmental Studies 32(6): 5527–5541. https://doi.org/10.15244/pjoes/169897
Deng, Y. and Zhang, S. 2024. Green finance, green technology innovation and agricultural carbon emissions in China. Applied Ecology and Environmental Research 22(2): 1415–1436. https://doi.org/10.15666/aeer/2202_14151436
Dul, J. 2016. Identifying single necessary conditions with NCA and fsQCA. Journal of Business Research 69(4): 1516–1523. https://doi.org/10.1016/j.jbusres.2015.10.134
Edenhofer, O., Pichs-Madruga, R., Sokona, Y. and Intergovernmental Panel Climate Change, W.G., III. 2014. Climate Change 2014 Mitigation of Climate Change Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Preface.
Fan, B. and Li, M.Y., 2022. The effect of heterogeneous environmental regulations on carbon emission efficiency of the grain production industry: Evidence from China’s inter-provincial panel data. Sustainability 14(21): 14492. https://doi.org/10.3390/su142114492
Grimsby, S. 2024. Knowledge bases, innovation and sustainability—When tradition meets novelty in the food industry. Trends in Food Science & Technology 144: 104307. https://doi.org/10.1016/j.tifs.2023.104307
Gu, R.L., Duo, L., Guo, X.F., Zou, Z.L. and Zhao, D.X. 2023. Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China. Environmental Science and Pollution Research 30(17): 49470–49486. https://doi.org/10.1007/s11356-023-25821-z
Guo, L.L., Guo, S.H., Tang, M.Q., Su, M.Y. and Li, H.J. 2022. Financial support for agriculture, chemical fertilizer use, and carbon emissions from agricultural production in China. International Journal of Environmental Research and Public Health 19(12): 7155. https://doi.org/10.3390/ijerph19127155
Hong, R.L., Zhan, M.L. and Wang, F.X. 2023. What determines the development of a rural collective economy? A fuzzy set qualitative comparative analysis (fsQCA) approach. China Agricultural Economic Review 15(3): 506–533. https://doi.org/10.1108/caer-12-2021-0244
Huang, X.Q., Xu, X.C., Wang, Q.Q., Zhang, L., Gao, X. and Chen, L.H. 2019. Assessment of agricultural carbon emissions and their spatiotemporal changes in China, 1997–2016. International Journal of Environmental Research and Public Health 16(17): 3105. https://doi.org/10.3390/ijerph16173105
Huang, Y.J., Huang, X.K., Xie, M.N., Cheng, W. and Shu, Q. 2021. A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model. Scientific Reports 11(1): 9953. https://doi.org/10.1038/s41598-021-89293-2
Jiang, Y., van Groenigen, K.J., Huang, S., Hungate, B.A., van Kessel, C., Hu, S.J., et al. 2017. Higher yields and lower methane emissions with new rice cultivars. Global Change Biology 23(11): 4728–4738. https://doi.org/10.1111/gcb.13737
Khojasteh, D., Haghani, M., Shamsipour, A., Zwack, C.C., Glamore, W., Nicholls, R.J., et al. 2024. Climate change science is evolving toward adaptation and mitigation solutions. Wiley Interdisciplinary Reviews-Climate Change 15(4): 1–18. https://doi.org/10.1002/wcc.884
Kwon, D.S., Cho, J.H. and Sohn, S.Y. 2017. Comparison of technology efficiency for CO2 emissions reduction among European countries based on DEA with decomposed factors. Journal of Cleaner Production 151: 109–120. https://doi.org/10.1016/j.jclepro.2017.03.065
Li, F.S., Hou, J., Yu, H.Y., Ren, Q.Z. and Yang, Y.F. 2024. Harnessing the digital economy for sustainable agricultural carbon productivity: A path to green innovation in China. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-024-02158-7
Liu, C.J., Ma, C.B. and Xie, R. 2020. Structural, innovation and efficiency effects of environmental regulation: Evidence from China’s carbon emissions trading pilot. Environmental & Resource Economics 75(4): 741–768. https://doi.org/10.1007/s10640-020-00406-3
Majiwa, E., Lee, B.L. and Wilson, C. 2018. Increasing agricultural productivity while reducing greenhouse gas emissions in sub-Saharan Africa: Myth or reality? Agricultural Economics 49(2): 183–192. https://doi.org/10.1111/agec.12407
Mei, B.J., Khan, A.A., Khan, S.U., Ali, M.A. and Luo, J.C. 2023. Variation of digital economy's effect on carbon emissions: Improving energy efficiency and structure for energy conservation and emission reduction. Environmental Science and Pollution Research 30(37): 87300–87313. https://doi.org/10.1007/s11356-023-28010-0
Raza, M.Y., Qiu, Z.P. and Wang, P.J. 2023. Agriculture-related energy consumption, food policy, and CO2 emission reduction: New insights from Pakistan. Frontiers in Environmental Science 10: 2022. https://doi.org/10.3389/fenvs.2022.1099813
Rosegrant, M.W. and Cline, S.A. 2003. Global food security: Challenges and policies. Science 302(5652): 1917–1919. https://doi.org/10.1126/science.1092958
Schneider, C.Q. and Rohlfing, I. 2013. Combining QCA and process tracing in set-theoretic multi-method research. Sociological Methods & Research 42(4): 559–597. https://doi.org/10.1177/0049124113481341
Sethi, L., Behera, B. and Sethi, N. 2024. Do green finance, green technology innovation, and institutional quality help achieve environmental sustainability? Evidence from the developing economies. Sustainable Development 32(3): 2709–2723. https://doi.org/10.1002/sd.2811
Shi, H.X. and Chang, M. 2023. How does agricultural industrial structure upgrading affect agricultural carbon emissions? Threshold effects analysis for China. Environmental Science and Pollution Research 30(18): 52943–52957. https://doi.org/10.1007/s11356-023-25996-5
Tu, C.Y., Liang, Y.X. and Fu, Y. 2024. How does the environmental attention of local governments affect regional green development? Empirical evidence from local governments in China. Humanities & Social Sciences Communications 11(1): 1–14. https://doi.org/10.1057/s41599-024-02887-9
Wang, R. and Feng, Y. 2021. Research on China’s agricultural carbon emission efficiency evaluation and regional differentiation based on DEA and Theil models. International Journal of Environmental Science and Technology 18(6): 1453–1464. https://doi.org/10.1007/s13762-020-02903-w
Wang, R.X., Deng, X.Z., Fang, Y.L., Bai, W.T. and Chen, J.C. 2024. Examination of the relationship between agricultural carbon emission efficiency and food quality and safety: From the perspective of environmental regulation. Environmental Science and Pollution Research 31(1): 343–356. https://doi.org/10.1007/s11356-023-31214-z
Yang, Y.Y., Tian, Y., Peng, X.H., Yin, M.H., Wang, W. and Yang, H.W. 2022. Research on environmental governance, local government competition, and agricultural carbon emissions under the goal of carbon peak. Agriculture 12(10): 1703. https://doi.org/10.3390/agriculture12101703
Yin, S., Fan, Y.F. and Gao, X.Y. 2024. Transitioning to clean energy in rural China: The impact of environmental regulation and value perception on farmers’ clean energy adoption. Journal of Renewable and Sustainable Energy 16(5): 055904. https://doi.org/10.1063/5.0221788
Yin, S., Wang, M., Shi, Y.Q. and Zhao, Y.M. 2024. Assessing rural energy poverty and early warning based on long-run evolution for clean energy transition in China. Journal of Renewable and Sustainable Energy 16(4): 045901. https://doi.org/10.1063/5.0209376
Yin, S., Wang, Y.R., Liu, Y.J. and Wang, S. 2024. Exploring drivers of behavioral willingness to use clean energy to reduce environmental emissions in rural China: An extension of the UTAUT2 model. Journal of Renewable and Sustainable Energy 16(4): 045903. https://doi.org/10.1063/5.0211668
Yin, S., Zhao, Y.D., Hussain, A. and Ullah, K. 2024. Comprehensive evaluation of rural regional integrated clean energy systems considering multi-subject interest coordination with Pythagorean fuzzy information. Engineering Applications of Artificial Intelligence 138(2024): 109342. https://doi.org/10.1016/j.engappai.2024.109342
Zhang, L., Cai, C.Z., Singh, K. and Zhong, K.Y. 2024. Green technology innovation, trade deficit and carbon emission transfer in agriculture under the new “dual circulation” development pattern of China. Ecological Indicators 159: 111757. https://doi.org/10.1016/j.ecolind.2024.111757
Zhang, P.Y., He, J.J., Hong, X., Zhang, W., Qin, C.Z., Pang, B., et al. 2018. Carbon sources/sinks analysis of land use changes in China based on data envelopment analysis. Journal of Cleaner Production 204: 702–711. https://doi.org/10.1016/j.jclepro.2018.08.341
Zhou, Z., Duan, J.Q., Geng, S.Q. and Li, R. 2023. Spatial network and driving factors of agricultural green total factor productivity in China. Energies 16(14): 1–26. https://doi.org/10.3390/en16145380
Zhu, Y. and Huo, C.J. 2022. The impact of agricultural production efficiency on agricultural carbon emissions in China. Energies 15(12): 4464. https://doi.org/10.3390/en15124464
Zhu, Y.Y., Zhang, Y. and Piao, H.L. 2022. Does agricultural mechanization improve the green total factor productivity of China’s planting industry? Energies 15(3): 940. https://doi.org/10.3390/en15030940
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.