Agricultural total factor productivity and influencing factors in Guangdong Province: Based on triple perspectives of carbon source, carbon sink and surface pollution

YANG Lin; LI Hangfei

doi:10.7671/j.issn.1001-411X.202407004

Journal of South China Agricultural University > 2024 > 45(6): 975-981. > DOI: 10.7671/j.issn.1001-411X.202407004

YANG Lin, LI Hangfei. Agricultural total factor productivity and influencing factors in Guangdong Province: Based on triple perspectives of carbon source, carbon sink and surface pollution[J]. Journal of South China Agricultural University, 2024, 45(6): 975-981. DOI: 10.7671/j.issn.1001-411X.202407004

Citation:

PDF (615 KB)

Agricultural total factor productivity and influencing factors in Guangdong Province: Based on triple perspectives of carbon source, carbon sink and surface pollution

YANG Lin,
LI Hangfei^,

College of Tourism and Geography, Shaoguan University, Shaoguan 512005, China

More Information

Received Date: July 01, 2024
Revised Date: July 28, 2024
Accepted Date: August 01, 2024
Available Online: September 22, 2024
Published Date: September 24, 2024

Graphical Abstract

Abstract

Abstract

Objective
Based on the triple perspectives of carbon source, carbon sink and non-point source pollution in the process of agricultural production, to measure the agricultural total factor productivity of Guangdong Province, scientifically grasp the basis of high-quality development of agriculture in Guangdong Province, and promote rural revitalization.
Method
The DEA-Malmquist model was used to effectively measure agricultural total factor productivity in Guangdong Province from 2006 to 2021 and analyze its spatio-temporal differences, meanwhile the panel regression model was used to explore the influencing factors.
Result
From 2006 to 2021, the agricultural total factor productivity of Guangdong Province was in an overall growth trend, with an average annual growth rate of 1.7%, although there were some inter-year fluctuations. The difference of agricultural total factor productivity in 21 prefecture-level cities in Guangdong Province was obvious, among which the highest was 1.070 in Zhuhai and the lowest was only 0.988 in Chaozhou. The region difference was presented in “Pearl River Delta region > Western Guangdong region > East Guangdong region > Northern Guangdong mountainous region” pattern. Technological progress was the key to the growth of agricultural total factor productivity in Guangdong Province. The urbanization level and agricultural industrial structure were the main factors affecting the agricultural total factor productivity in Guangdong Province.
Conclusion
In order to improve the agricultural total factor productivity in Guangdong Province, it is suggested to increase the investment in agricultural science and technology, strengthen the popularization and application of green agricultural science and technology, optimize the financial support mechanism, vigorously cultivate and develop efficient three-dimensional ecological agriculture, and take the road of green agricultural development.
- Agricultural total factor productivity,
- DEA-Malmquist model,
- Panel regression model,
- Guangdong Province

FullText(HTML)

References (36)

References

[1]	田云, 张俊飚. 中国农业生产净碳效应分异研究[J]. 自然资源学报, 2013, 28(8): 1298-1309. doi: 10.11849/zrzyxb.2013.08.003
[2]	陈罗烨, 薛领, 雪燕. 中国农业净碳汇空间集聚与分异[J]. 生态环境学报, 2015, 24(11): 1777-1784.
[3]	杨果, 陈瑶. 中国农业源碳汇估算及其与农业经济发展的耦合分析[J]. 中国人口·资源与环境, 2016, 26(12): 171-176.
[4]	丘雯文, 钟涨宝, 李兆亮, 等. 中国农业面源污染排放格局的时空特征[J]. 中国农业资源与区划, 2019, 40(1): 26-34.
[5]	范丽霞, 李谷成. 全要素生产率及其在农业领域的研究进展[J]. 当代经济科学, 2012, 34(1): 109-119.
[6]	REZEK J P, PERRIN R K. Environmentally adjusted agricultural productivity in the Great Plains[J]. Journal of Agricultural and Resource Economics, 2004, 29(2): 346-369.
[7]	全炯振. 中国农业全要素生产率增长的实证分析: 1978—2007年: 基于随机前沿分析(SFA)方法[J]. 中国农村经济, 2009(9): 36-47.
[8]	林青宁, 毛世平. 农业全要素生产率的演化过程、测算方法与未来展望[J]. 中国农业大学学报, 2023, 28(4): 248-256. doi: 10.11841/j.issn.1007-4333.2023.04.22
[9]	CHARNES A, COOPER W W, RHODES E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2(6): 429-444. doi: 10.1016/0377-2217(78)90138-8
[10]	TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130(3): 498-509. doi: 10.1016/S0377-2217(99)00407-5
[11]	CHENG Y S, ZHANG D Y, WANG X. Agricultural total factor productivity based on farmers’ perspective: An example of CCR, BCC, SBM and technology optimization Malmquist-Luenberger index[J]. Journal of Resources and Ecology, 2024, 15(2): 267-279.
[12]	王紫露, 张玮, 杨丽. 长三角城市群农业全要素生产率时空演化特征分析[J]. 江苏农业科学, 2023, 51(8): 255-260.
[13]	曹玲娟. 长江经济带农业绿色全要素生产率测度与区域异质性分析[J]. 生态经济, 2024, 40(1): 95-102.
[14]	刘战伟. 中国农业全要素生产率的动态演进及其影响因素分析[J]. 中国农业资源与区划, 2018, 39(12): 104-111.
[15]	BAI Z M, WANG T Y, XU J B, et al. Can agricultural productive services inhibit carbon emissions? Evidence from China[J]. Land, 2023, 12(7): 1313. doi: 10.3390/land12071313
[16]	HUAN M, LI Y, CHI L, et al. The effects of agricultural socialized services on sustainable agricultural practice adoption among smallholder farmers in China[J]. Agronomy, 2022, 12(9): 2198. doi: 10.3390/agronomy12092198
[17]	OLESEN J E, BINDI M. Consequences of climate change for European agricultural productivity, land use and policy[J]. European Journal of Agronomy, 2002, 16(4): 239-262. doi: 10.1016/S1161-0301(02)00004-7
[18]	付伟, 李梦柯, 罗明灿, 等. 我国农业绿色全要素生产率时空演变与区域异质性分析[J]. 江苏农业科学, 2023, 51(23): 227-235.
[19]	罗玉波, 朱晨曦, 王春云. 基于共同前沿理论的中国农业绿色全要素生产率测度及“追赶”效应解析[J]. 农林经济管理学报, 2024, 23(1): 30-40.
[20]	吴昊玥, 黄瀚蛟, 何宇, 等. 中国农业碳排放效率测度、空间溢出与影响因素[J]. 中国生态农业学报(中英文), 2021, 29(10): 1762-1773.
[21]	刘亦文, 欧阳莹, 蔡宏宇. 中国农业绿色全要素生产率测度及时空演化特征研究[J]. 数量经济技术经济研究, 2021, 38(5): 39-56.
[22]	穆佳薇, 乔保荣, 余国新. 新疆塔里木河流域县域农业低碳生产率时空格局及影响效应研究[J]. 干旱区地理, 2023, 46(6): 968-981.
[23]	臧俊梅, 张思影, 唐春云. “双碳”目标下广东省农业生态效率时空演变及影响因素研究[J]. 中国农业资源与区划, 2023, 44(10): 135-146.
[24]	李航飞. 基于数据包络分析的我国农业生产效率区域差异分析[J]. 科技管理研究, 2020, 40(1): 59-66. doi: 10.3969/j.issn.1000-7695.2020.01.010
[25]	王宝义, 张卫国. 中国农业生态效率的省际差异和影响因素: 基于1996—2015年31个省份的面板数据分析[J]. 中国农村经济, 2018(1): 46-62.
[26]	侯孟阳, 姚顺波. 空间视角下中国农业生态效率的收敛性与分异特征[J]. 中国人口·资源与环境, 2019, 29(4): 116-126.
[27]	田云, 王梦晨. 湖北省农业碳排放效率时空差异及影响因素[J]. 中国农业科学, 2020, 53(24): 5063-5072.
[28]	高孟菲, 郑晶. 中国农业全要素生产率测算及其时空差异分析: 基于碳汇视角的再检验[J]. 生态经济, 2021, 37(12): 98-104.
[29]	王修兰. 二氧化碳、气候变化与农业[M]. 北京: 气象出版社, 1996.
[30]	韩召迎, 孟亚利, 徐娇, 等. 区域农田生态系统碳足迹时空差异分析: 以江苏省为案例[J]. 农业环境科学学报, 2012, 31(5): 1034-1041.
[31]	常青, 蔡为民, 谷秀兰, 等. 河南省农业碳排放时空分异、影响因素及趋势预测[J]. 水土保持通报, 2023, 43(1): 367-377.
[32]	李波, 杜建国, 刘雪琪. 湖北省农业碳排放的时空特征及经济关联性[J]. 中国农业科学, 2019, 52(23): 4309-4319. doi: 10.3864/j.issn.0578-1752.2019.23.011
[33]	陈红, 王浩坤, 秦帅. 农业碳排放的脱钩效应及驱动因素分析: 以黑龙江省为例[J]. 科技管理研究, 2019, 39(17): 247-252. doi: 10.3969/j.issn.1000-7695.2019.17.033
[34]	吴小庆, 王亚平, 何丽梅, 等. 基于AHP和DEA模型的农业生态效率评价: 以无锡市为例[J]. 长江流域资源与环境, 2012, 21(6): 714-719.
[35]	伍国勇, 刘金丹, 杨丽莎. 中国农业碳排放强度动态演进及碳补偿潜力[J]. 中国人口·资源与环境, 2021, 31(10): 69-78. doi: 10.12062/cpre.20210606
[36]	张霞. 供给侧结构改革背景下的西南地区农业全要素生产率分析[J]. 中国农业资源与区划, 2019, 40(10): 147-154.

Cited By

Get Citation

PDF

XML

Article views (558) PDF downloads (29)

Turn off MathJax

Article Contents

Abstract

References

Agricultural total factor productivity and influencing factors in Guangdong Province: Based on triple perspectives of carbon source, carbon sink and surface pollution

Abstract

References

Catalog

Export File

Citation

Format

Content