Agricultural infrastructure, spatial spillover, and green total factor productivity of grain in the Yangtze River Economic Belt: a panel data analysis

doi:10.16178/j.issn.0528-9017.20240953

Abstract

Abstract:

This paper took the 11 provinces and cities along the Yangtze River Economic Belt as the research subjects. First, it employed an ultra-efficient SBM-GML framework to evaluate the green total factor productivity (GTFP) of grain from 2011 to 2023. Finally, the spatial Durbin model was used to analyze the impact of agricultural infrastructure on GTFP of grain. The results indicated that there was a significant spatial correlation between agricultural infrastructure and GTFP of grain in the Yangtze River Economic Belt. The improvement of agricultural infrastructure in the Yangtze River Economic Belt not only enhanced the GTFP of grain within the region but also had a spatial spillover effect, accelerating the improvement of GTFP of grain in surrounding areas. Increased fiscal support and higher urbanization levels were beneficial for enhancing the GTFP of grain in the Yangtze River Economic Belt, while the adjustment of the agricultural industry structure, and urban-rural income gap hindered the improvement of GTFP of grain in the region. Based on this, in order to further enhance the driving force of agricultural infrastructure on GTFP of grain in the Yangtze River Economic Belt, it was proposed to implement differentiated strategies, promote high-quality development of agricultural infrastructure, strengthen spatial spillover effects of agricultural infrastructure, increase the potential for green production of grain, improve the quality of agricultural finance, and orderly promote new urbanization.

Key words: agricultural infrastructure, green total factor productivity of grain, spatial Durbin model

CLC Number:

MA Chenglong, KONG Lingcheng. Agricultural infrastructure, spatial spillover, and green total factor productivity of grain in the Yangtze River Economic Belt: a panel data analysis[J]. Journal of Zhejiang Agricultural Sciences, 2026, 67(1): 251-259.

Figures/Tables 7

References 24

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一级指标	二级指标	计算方法	单位
投入指标	劳动力投入	第一产业从业人数×B	10⁴人
	土地投入	粮食种植面积	10³ hm²
	农业机械动力投入	农业机械总动力×A	10⁴ kW·h
	化肥用量	化肥用量×A	10⁴ t
	农药用量	农药用量×A	10⁴ t
	农膜用量	农膜用量×A	10⁴ t
	有效灌溉面积	农业有效灌溉面积×A	10³ hm²
产出指标	期望产出	农业总产值×A	10⁸元
	非期望产出	农业碳排放量×A	10⁴ t

一级指标	二级指标	计算方法	单位
投入指标	劳动力投入	第一产业从业人数×B	10⁴人
	土地投入	粮食种植面积	10³ hm²
	农业机械动力投入	农业机械总动力×A	10⁴ kW·h
	化肥用量	化肥用量×A	10⁴ t
	农药用量	农药用量×A	10⁴ t
	农膜用量	农膜用量×A	10⁴ t
	有效灌溉面积	农业有效灌溉面积×A	10³ hm²
产出指标	期望产出	农业总产值×A	10⁸元
	非期望产出	农业碳排放量×A	10⁴ t

变量	样本数	均值	标准差	最小值	最大值
GTFP	143	2.003	0.955	0.967	6.701
AI	143	1.678	0.726	0.197	3.219
CYJG	143	0.222	0.103	0.066	0.553
SZL	143	0.114	0.096	0.010	0.643
SRCJ	143	2.523	0.381	1.897	3.648
CZLD	143	0.295	0.179	0.065	0.728
CZH	143	0.596	0.131	0.350	0.896

变量	样本数	均值	标准差	最小值	最大值
GTFP	143	2.003	0.955	0.967	6.701
AI	143	1.678	0.726	0.197	3.219
CYJG	143	0.222	0.103	0.066	0.553
SZL	143	0.114	0.096	0.010	0.643
SRCJ	143	2.523	0.381	1.897	3.648
CZLD	143	0.295	0.179	0.065	0.728
CZH	143	0.596	0.131	0.350	0.896

年份	Moran's I	p值
2011—2012	-0.107	0.468
2012—2013	-0.100	0.499
2013—2014	-0.112	0.442
2014—2015	-0.054	0.272
2015—2016	-0.053	0.267
2016—2017	-0.008	0.010
2017—2018	0.045	0.024
2018—2019	0.043	0.028
2019—2020	0.056	0.020
2020—2021	0.086	0.010
2021—2022	0.086	0.010
2022—2023	0.071	0.022