广东省农业绿色发展潜力影响因素及空间格局演变

doi:10.16258/j.cnki.1674-5906.2024.10.012

生态环境学报 ›› 2024, Vol. 33 ›› Issue (10): 1600-1611.DOI: 10.16258/j.cnki.1674-5906.2024.10.012

• 研究论文【环境科学】 • 上一篇下一篇

广东省农业绿色发展潜力影响因素及空间格局演变

胡韵菲¹(), 陶亮²^,^*(), 罗旖文¹

1.广东省农业科学院农业经济与信息研究所/农业农村部华南都市农业重点实验室，广东广州 510640
2.广东省科学院生态环境与土壤研究所/华南土壤污染控制与修复国家地方联合工程研究中心/广东省农业环境综合治理重点实验室，广东广州 510650

收稿日期:2024-07-12 出版日期:2024-10-18 发布日期:2024-11-15
通讯作者: *陶亮。E-mail: taoliang@soil.gd.cn
作者简介:胡韵菲（1990年生），女，助理研究员，博士，研究方向为农业区域发展。E-mail: huyunfei2015@qq.com
基金资助:
广东省基础与应用基础研究基金自然科学基金面上项目(2023A1515011018);广东省科技计划项目(2023B1212060044);广东省农业农村厅2024年省级乡村振兴战略专项“耕地种植用途管控全域试点、动态监测与管理体系构建”

The Influencing Factors and Spatial Pattern Evolution of the Potential for Green Agricultural Development in Guangdong Province

HU Yunfei¹(), TAO Liang²^,^*(), LUO Yiwen¹

1. Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences / Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510640, P. R. China
2. National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China/Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management/Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, P. R. China.

Received:2024-07-12 Online:2024-10-18 Published:2024-11-15

摘要/Abstract

摘要：

面向广东以新质生产力支撑新一轮高质量发展的机遇，为下一阶段新质生产力提升农业绿色发展水平寻找一条可行的实施路径，采用专家咨询法评估了广东省30年来（1990－2020）农业生产水平、资源环境保障度以及农业绿色发展潜力，建立了农业绿色发展潜力解释模型并探讨了其时空演变格局。结果表明，1990－2020年，广东省农业生产水平综合评分提升15%，但80%的城市农业资源保障度有所下降，2020年，农业绿色发展潜力较高的深圳、东莞和江门3个城市为全省贡献了大部分“盈余”。对农业绿色发展潜力的影响上，农业资源使用效率>农业生态环境>农产品供给能力>农业资源保障度>农业物质装备水平>农业生产效率，且6个指标的影响存在区域异质性。从空间格局看，广东农业生产水平呈现从珠三角沿海向周边递增的梯度变化；农业资源环境保障度则呈高、低保障度城市分别局部集中、中等保障度城市东西向大范围连片格局。全省农业绿色发展低潜力区从1990年的区域性分布转变为2020年的范围性分布。对于人口压力和资源约束“双高”的城市，亟需从依靠消耗资源向创造资源转变；对于资源环境保障能力强的城市，则需将农业绿色发展潜力转化为优质农产品的有效供给。基于以上分析，为了满足广东农业绿色高质量发展的迫切需求，建议从强化生态环境分区管控、健全重点领域政策法规、加快绿色生产科技创新以及建立健全乡村生态资源价值实现机制等4个方面，着力提升农业绿色生产水平与资源利用效率，以新质生产力推进广东高质量建设现代农业强省。

关键词: 农业生产, 资源环境, 发展潜力, 聚类分析, 时空格局

Abstract:

Facing the opportunity for Guangdong to support a new round of high-quality development with new quality productive forces and to find a feasible implementation path for improving the level of agricultural green development with new quality productive forces in the next stage, the Delphi method was used to evaluate the agricultural production level, resource and environmental protection degree, and agricultural green development potential in Guangdong Province over the past 30 years (1990-2020). An explanatory model for agricultural green development potential was established and its spatiotemporal evolution pattern was explored. The results indicated that between 1990 and 2020, the comprehensive score of the agricultural production level in Guangdong Province increased by 15%, but the agricultural resource guarantee of 80% of the cities decreased. In 2020, three cities with a high potential for agricultural green development contributed the most potential surplus to the province. In terms of the impact on the potential for green development in agriculture, the efficiency of agricultural resource utilization is greater than the agricultural ecological environment, the supply capacity of agricultural products, the degree of agricultural resource security, the level of agricultural material equipment, and the efficiency of agricultural production. Moreover, the impact of the six indicators varied across regions. From the perspective of spatial patterns, the agricultural production level in Guangdong presents a gradient change from the coastal areas of the Pearl River Delta to the surrounding areas, and the degree of guarantee of agricultural resources and environment shows a pattern of local concentration in cities with high and low guarantee degrees, respectively, and a large-scale east-west contiguous pattern in cities with medium guarantee degrees. Areas with low potential for agricultural green development in the province shifted to a regional distribution from 1990 to 2020. For cities with high population pressures and resource constraints, it is crucial to shift from relying on resource consumption to generating resources. For cities with solid resources and environmental protection capabilities, it is necessary to convert their potential for green agricultural development into a steady supply of high-quality agricultural products. Based on the above analysis, to address the pressing need for green and high-quality agricultural development in Guangdong, it is recommended to prioritize strengthening ecological environment zoning control, improving policies and regulations in key areas, accelerating technological innovation in green production, and establishing a sound mechanism for realizing the value of rural ecological resources. Efforts should be made to improve green agricultural production and resource utilization efficiency to promote Guangdong's high-quality construction of a modern agriculturally strong province with new productivity.

Key words: agricultural production, resource environment, development potential, cluster analysis, spatiotemporal pattern

中图分类号:

胡韵菲, 陶亮, 罗旖文. 广东省农业绿色发展潜力影响因素及空间格局演变[J]. 生态环境学报, 2024, 33(10): 1600-1611.

HU Yunfei, TAO Liang, LUO Yiwen. The Influencing Factors and Spatial Pattern Evolution of the Potential for Green Agricultural Development in Guangdong Province[J]. Ecology and Environment, 2024, 33(10): 1600-1611.

图/表 13

参考文献 37

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目标层	准则层- 代码	指标层	权重
农业生产水平	农业物质装备水平-fac_l (0.28)	耕种收综合机械化率/%	0.04
		农机总动力/(10000 kW∙h)	0.05
		有效灌溉指数 ¹⁾	0.04
		节水灌溉指数 ²⁾	0.03
		人均农林牧渔业固定资产投资/万元	0.06
		每公顷农作物播种固定资产投资/万元	0.06
	农业生产效率- prod_e (0.38)	农村居民人均农林牧渔业产值/万元	0.10
		每公顷农地产值指数/万元³⁾	0.08
		粮食单产/(kg∙hm⁻²)	0.04
		农民人均农业经营收入贡献指数 ⁴⁾	0.08
		第一产业从业人员占常住人口比重/%	0.02
		农民人均纯收入/元	0.06
	农产品供给能力- supl_c (0.34)	人均粮食产量/kg	0.08
		人均猪牛羊肉产量/t	0.06
		人均牛奶产量/kg	0.05
		人均蔬菜产量/kg	0.07
		人均水产产量/kg	0.05
		人均油料产量/kg	0.03
农业资源环境保障度	农业资源保障度- resc_g (0.31)	耕地保有率 ⁵⁾	0.09
		人均水资源/m³	0.05
		农业用水保障度/(10⁵ m³∙hm⁻²)	0.06
		人均耕地面积/(1000 m²)	0.07
		农业减灾指数 ⁶⁾	0.04
	农业资源使用效率- reuti_e (0.38)	化肥使用经济效率/(10⁴ yuan ∙t⁻¹) ⁷⁾	0.09
		农业用水经济效率/(yuan∙m⁻³) ⁸⁾	0.08
		农业加工指数 ⁹⁾	0.08
		人均经营面积/(667 m²)	0.08
		农村用电经济效率/(yuan∙kW∙h⁻¹) ¹⁰⁾	0.05
	农业生态环境-eco_e (0.31)	总氮减排指数/(hm²∙t⁻¹) ¹¹⁾	0.06
		总磷减排指数/(hm²∙t⁻¹)	0.06
		复合肥减施指数/(hm²∙t⁻¹)	0.07
		农药控制指数/(hm²∙t⁻¹)	0.07
		农膜污染减排指数/(hm²∙t⁻¹)	0.05

目标层	准则层- 代码	指标层	权重
农业生产水平	农业物质装备水平-fac_l (0.28)	耕种收综合机械化率/%	0.04
		农机总动力/(10000 kW∙h)	0.05
		有效灌溉指数 ¹⁾	0.04
		节水灌溉指数 ²⁾	0.03
		人均农林牧渔业固定资产投资/万元	0.06
		每公顷农作物播种固定资产投资/万元	0.06
	农业生产效率- prod_e (0.38)	农村居民人均农林牧渔业产值/万元	0.10
		每公顷农地产值指数/万元³⁾	0.08
		粮食单产/(kg∙hm⁻²)	0.04
		农民人均农业经营收入贡献指数 ⁴⁾	0.08
		第一产业从业人员占常住人口比重/%	0.02
		农民人均纯收入/元	0.06
	农产品供给能力- supl_c (0.34)	人均粮食产量/kg	0.08
		人均猪牛羊肉产量/t	0.06
		人均牛奶产量/kg	0.05
		人均蔬菜产量/kg	0.07
		人均水产产量/kg	0.05
		人均油料产量/kg	0.03
农业资源环境保障度	农业资源保障度- resc_g (0.31)	耕地保有率 ⁵⁾	0.09
		人均水资源/m³	0.05
		农业用水保障度/(10⁵ m³∙hm⁻²)	0.06
		人均耕地面积/(1000 m²)	0.07
		农业减灾指数 ⁶⁾	0.04
	农业资源使用效率- reuti_e (0.38)	化肥使用经济效率/(10⁴ yuan ∙t⁻¹) ⁷⁾	0.09
		农业用水经济效率/(yuan∙m⁻³) ⁸⁾	0.08
		农业加工指数 ⁹⁾	0.08
		人均经营面积/(667 m²)	0.08
		农村用电经济效率/(yuan∙kW∙h⁻¹) ¹⁰⁾	0.05
	农业生态环境-eco_e (0.31)	总氮减排指数/(hm²∙t⁻¹) ¹¹⁾	0.06
		总磷减排指数/(hm²∙t⁻¹)	0.06
		复合肥减施指数/(hm²∙t⁻¹)	0.07
		农药控制指数/(hm²∙t⁻¹)	0.07
		农膜污染减排指数/(hm²∙t⁻¹)	0.05

变量	facl_l	prod_e	supl_c	resc_g	ru_e	eco_e	P_{_gda}
faci_l	1
prod_e	0.21*³⁾	1
supl_c	0.174	0.655	1
resc_g	−0.088	0.1	0.355***¹⁾	1
ru_e	−0.102	−0.03	−0.31***	0.006	1
eco_e	−0.204*	−0.154	0.246** ²⁾	0.364***	0.131	1
P_{_gda}	−0.411***	−0.497***	−0.485***	0.185*	0.586***	0.463***	1

变量	facl_l	prod_e	supl_c	resc_g	ru_e	eco_e	P_{_gda}
faci_l	1
prod_e	0.21*³⁾	1
supl_c	0.174	0.655	1
resc_g	−0.088	0.1	0.355***¹⁾	1
ru_e	−0.102	−0.03	−0.31***	0.006	1
eco_e	−0.204*	−0.154	0.246** ²⁾	0.364***	0.131	1
P_{_gda}	−0.411***	−0.497***	−0.485***	0.185*	0.586***	0.463***	1

变量	VIF	1/VIF
faci_l	1.115	0.897
prod_e	2.547	0.393
supl_c	3.305	0.303
resc_g	1.291	0.775
ru_e	1.380	0.725
eco_e	1.644	0.608
Mean	1.880	0.617

广东省农业绿色发展潜力影响因素及空间格局演变

The Influencing Factors and Spatial Pattern Evolution of the Potential for Green Agricultural Development in Guangdong Province

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变量	POOL模型	FE模型	RE模型	时间固定效应	双向固定效应
faci_l	−0.036* ³⁾(−1.991)	−0.046 (−1.287)	−0.036* (−1.991)	−0.043** (−2.524)	−0.054** (−2.040)
prod_e	−0.023** (−2.008)	−0.033** (−2.452)	−0.023** ²⁾(−2.008)	−0.032** (−2.582)	−0.085*** (−2.712)
supl_c	−0.047***¹⁾(−4.041)	−0.042*** (−2.748)	−0.047*** (−4.041)	−0.039*** (−3.827)	−0.004 (−0.167)
resc_g	0.037*** (6.379)	0.036*** (4.849)	0.037*** (6.379)	0.039*** (3.970)	0.059*** (3.893)
ru_e	0.053*** (5.506)	0.036*** (2.741)	0.053*** (5.506)	0.061*** (4.317)	0.044*** (3.673)
eco_e	0.050*** (4.629)	0.056*** (4.203)	0.050*** (4.629)	0.049*** (5.325)	0.068*** (5.203)
r²	0.779	0.752	0.779	0.772	0.540
r²(within)	0.704	0.718	0.704	0.701	0.628
N	83	83	83	83	83
检验	F(6, 76)=30.610, p=0.000	F(6, 56)=12.777, p=0.000	χ²(6)=183.658, p=0.000	F(6, 73)=40.104, p=0.000	F(6, 53)=16.609, p=0.000

检验类型	检验目的	检验值	检验结论
F检验	FE模型和POOL 模型比较选择	F (20, 56)=0.917, p=0.569	POOL 模型
BP检验	RE模型和POOL 模型比较选择	χ²(1)=0.371, p=0.271	POOL 模型
Hausman 检验	FE模型和RE 模型比较选择	χ²(5)=2.344, p=0.800	RE模型

变量	基准模型 (POOL模型)	OLS回归模型	Robust回归模型
faci_l	−0.036* (−1.991)	−0.036*** (−3.113)	−0.026*** (−6.497)
prod_e	−0.023** (−2.008)	−0.023 (−1.714)	−0.036*** (−7.464)
supl_c	−0.047*** (−4.041)	−0.047*** (−4.044)	−0.024*** (−5.906)
resc_g	0.037*** (6.379)	0.037*** (2.992)	0.030*** (6.867)
ru_e	0.053*** (5.506)	0.053*** (6.133)	0.042*** (13.851)
eco_e	0.050*** (4.629)	0.050*** (5.545)	0.030*** (9.343)
r²	0.779	0.779	0.715
r²(within)	0.704	0.761	0.692
N	83	83	83
检验	F(6, 76)=30.610, p=0.000	F(6, 76)=44.535, p=0.000	F(6, 76)=31.770, p=0.000

变量	广东	珠三角	粤东	粤西	粤北
faci_l	−0.036* (−1.991)	−0.048 (−1.855)	−0.026** (−2.539)	−0.019* (−2.213)	−0.025 (−0.954)
prod_e	−0.023** (−2.008)	−0.027 (−0.911)	−0.031** (−2.496)	−0.006 (−0.579)	−0.036 (−0.870)
supl_c	−0.047*** (−4.041)	−0.036 (−1.550)	−0.042** (−2.347)	−0.040** (−3.317)	−0.055 (−1.693)
resc_g	0.037*** (6.379)	0.045* (2.020)	0.044** (3.033)	0.038*** (3.878)	0.040 (0.978)
ru_e	0.053*** (5.506)	0.062*** (3.928)	0.030 (1.709)	0.017* (2.249)	0.020 (0.607)
eco_e	0.050*** (4.629)	0.059*** (2.877)	0.044*** (4.155)	0.021*** (4.139)	0.048*** (3.293)
r²	0.779	0.766	0.955	0.976	0.887
r²(within)	0.704	0.718	0.925	0.957	0.812
N	83	36	16	15	16
检验	F(6, 76)=30.610, p=0.000	F(6, 29)=15.831, p=0.000	F(6, 9)=31.786, p=0.000	F(6, 8)=53.171, p=0.000	F(6, 9)=11.787, p=0.001

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