长江经济带水-能源-粮食耦合效率的时空演化及影响因素

doi:10.18402/resci.2021.10.11

摘要/Abstract

摘要：

推动资源管理方式向“多资源”协同转变,保障水资源、能源和粮食关系的协同优化,是应对资源供需矛盾和安全风险的有效手段。基于构建的水-能源-粮食耦合协调指标体系,本文以长江经济带为研究对象,在考虑非期望产出的前提下测度了2003—2017年各省份水-能源-粮食耦合效率,进而借助核密度估计和空间计量模型探究了长江经济带水-能源-粮食耦合效率的时空演变规律及关键驱动因素。结果表明：①研究期内长江经济带水-能源-粮食耦合效率均值在0.65~0.95之间波动,未达到生产前沿,仍然具有一定的提升空间;②总体时序变化呈现为“下降—上升—下降”波动特征,且各省份呈现一定差异,2010年之后江西、湖南、贵州和云南呈现下降趋势,上海、浙江、安徽和四川趋于平稳,江苏、湖北和重庆呈现较大波动;③各地区之间的差异有所增强,两极分化现象初显,高值地区集中分布在长三角和川渝地区,而低值地区则不断向长江经济带上中游转移;④技术进步、人力资本水平、对外开放程度和产业结构是推动长江经济带水-能源-粮食耦合效率提升的重要正向因素,而经济发展和信息化水平的正向作用尚未显现;此外,空间计量模型的回归结果显示,邻近地区水-能源-粮食耦合效率的优化对本地区也有积极的正向影响。因此,除了要根据各省份的实际情况制定差异性的政策,还要加快创建“多资源”协同管理机制,充分发挥区域间的正向溢出效应,实现区域水-能源-粮食耦合效率的全面提升。

关键词: 长江经济带, 水-能源-粮食, 耦合效率, 超效率SBM模型, 空间计量模型

Abstract:

Promoting “multi-resources” synergy and optimization of the relationship of resources, energy, and food systems are effective ways to address resource supply and demand conflicts and security risks. Based on the water-energy-food coupling coordination indicator system established in this study and using the super-slacks-based measure (SBM) model, we measured the water-energy-food coupling efficiency of 11 provinces in the Yangtze River Economic Belt (YEB), and analyzed its spatial and temporal changes and driving factors with kernel density estimation and a spatial econometric model. The results show that the overall water-energy-food coupling efficiency fluctuated between 0.65-0.95, which presented a periodical trend of declining-rising-declining from 2003 to 2017. There were certain differences in different provinces. Taking 2010 as the time node, water-energy-food coupling efficiency declined in Jiangxi, Hunan, Guizhou and Yunnan, tended to be stable in Shanghai, Zhejiang, Anhui and Sichuan, fluctuated in Jiangsu, Hubei and Chongqing. The imbalance and polarization intensified with high-value regions concentrating in the Yangtze River Delta and Sichuan-Chongqing regions, while low-value regions shifting to the upper and middle reaches of the YEB. Technological progress, human capital, openness, and industrial structure were all important factors that can exert positive effect on improving water-energy-food coupling efficiency in the YEB. However, the positive effect of economic development and informatization had not yet appeared. Moreover, according to the regression results of the spatial econometric model, local water-energy-food coupling efficiency can also be improved due to spillover effects from neighboring areas. Therefore, in order to improve the overall water-energy-food coupling efficiency, it is necessary to develop a “multi-resources” synergy mechanism, exert positive spatial spillover effect, and formulate targeted policies based on the actual situation of each province.

Key words: Yangtze River Economic Belt, water-energy-food, coupling efficiency, Super-SBM model, spatial econometric model

秦腾, 佟金萍. 长江经济带水-能源-粮食耦合效率的时空演化及影响因素[J]. 资源科学, 2021, 43(10): 2068-2080.

QIN Teng, TONG Jinping. Spatiotemporal change of water-energy-food coupling efficiency and influencing factors in the Yangtze River Economic Belt[J]. Resources Science, 2021, 43(10): 2068-2080.

图/表 8

表1

水-能源-粮食耦合协调发展评价指标体系

目标层	指标层	单位	指标性质	权重
能源子系统	能源生产总量	万t标准煤	正	0.0308
	能源消费总量	万t标准煤	负	0.0126
	电力生产总量	亿kWh	正	0.0311
	全社会用电量	亿kWh	负	0.0103
	能源进口量	万t标准煤	正	0.0375
	能源出口量	万t标准煤	负	0.0086
	煤炭消费占比	%	负	0.0256
	石油消费占比	%	负	0.0150
	天然气消费占比	%	负	0.0151
	发电量中火电占比	%	负	0.0324
	能源工业投资占比	%	正	0.0457
	能源消费弹性系数	—	负	0.0103
	电力消费弹性系数	—	负	0.0113
	单位GDP能耗	万t标准煤/元	负	0.0096
	单位GDP电耗	亿kWh/元	负	0.0117
	人均生活能耗	万t标准煤/人	负	0.0132
	人均电力消费量	亿kWh/人	负	0.0152
	能源加工转换效率	%	正	0.0186
水资源子系统	用水总量	亿m³	负	0.0147
	降水量	亿m³	正	0.0262
	水资源总量	亿m³	正	0.0268
	废水排放量	万t	负	0.0145
	水利投资占比	%	正	0.0352
	农业用水占比	%	负	0.0257
	工业用水占比	%	负	0.0160
	生活用水占比	%	正	0.0255
	生态用水占比	%	正	0.0451
	人均用水量	m³/人	负	0.0180
	人均水资源量	m³/人	正	0.0251
	单位GDP耗水量	m³/元	负	0.0141
	水资源开发利用率	%	负	0.0073
	产水模数	m³/hm²	正	0.0225
	产水系数	%	正	0.0143
	城市污水处理能力	亿m³	正	0.0327
粮食子系统	粮食生产量	万t	正	0.0239
	食品消费支出	亿元	负	0.0134
	粮食种植面积	千hm²	正	0.0218
	农业财政支出	亿元	正	0.0295
	粮食播种面积比例	%	正	0.0137
	有效灌溉面积比例	%	正	0.0264
	农林牧渔投资占比	%	正	0.0316
	人均粮食产量	t/人	正	0.0178
	人均食品消费支出替代	万元/人	负	0.0130
	单位面积粮食产量	t/hm²	正	0.0212
	机械动力	kW/hm²	正	0.0285
	化肥负荷	t/hm²	负	0.0215
	粮食流动成本	—	负	0.0101
	粮食消费价格指数	—	负	0.0097

表1

图1

图2

图3

图4

图5

表2

表3

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检验	Moran’s I	LM-lag	Robust LM-lag	LM-error	Robust LM-error
统计量	1.933	3.197	4.556	0.006	0.939
概率值	0.053*	0.074*	0.033**	1.365	0.243

变量	普通面板回归		空间滞后模型（SLM）
变量	系数	p值	系数	p值
lnpergdp	-0.413***	0.000	-0.413***	0.000
lntech	0.153***	0.000	0.122***	0.007
lnhcap	1.738***	0.000	1.745***	0.000
lnopen	0.064**	0.014	0.201***	0.000
lninform	-0.089**	0.020	-0.229**	0.011
lnind	-0.264*	0.086	0.359**	0.023
ρ			0.328***	0.000
R²	0.411		0.409
logL			79.89
LR test			26.82***	0.000