基于GWR模型的中国水资源绿色效率驱动机理

doi:10.11821/dlxb202005010

摘要/Abstract

摘要：

遵循“以人文本”的绿色发展理念,综合考虑水资源利用的经济、社会及环境效益,运用地理加权回归模型,综合选取自然、社会经济、环境、科技四大因素共19个指标,测度2000—2016年中国水资源绿色效率,并对其驱动机理进行探究。结果表明：中国水资源绿色效率存在着显著的空间正相关性和空间分布不均衡性;各因素对水资源绿色效率的影响存在着空间差异,且随着时间而变化;总体来看,产业结构、工农业发展水平、交通基础设施建设、环保投入和科技转化率等因素对中国水资源绿色效率的提高具有显著影响,人均水资源量、人口规模和污染物排放等因素的增加则限制了水资源绿色效率的提高;就其空间差异而言,水资源禀赋、人口规模等因素对西部地区水资源绿色效率的促进作用大于东部,而产业结构、用水结构、交通基础设施建设等因素对水资源绿色效率的影响程度东部大于西部。研究结果对于解决中国水资源短缺问题,因地制宜地制定适合各地区发展的水资源政策,具有重要的实际意义。

关键词: 超效率SBM模型, GWR模型, 水资源绿色效率, 驱动机理

Abstract:

Water shortage, which can greatly affect both natural systems and social development, has drawn extensive attention from governing authorities around the world, and become an urgent issue to be addressed. How to improve the green efficiency of water resources and alleviate the present situation of water shortage is the content of this study. In the concept of "people-oriented" green development, the evaluation system of green efficiency is constructed, which includes four aspects: nature, social economy, environment, science and technology. This paper, based on the panel data of 31 provincial-level regions of China obtained from 2000 to 2016, aims to calculate the green efficiency of water resources by applying the Super-efficiency SBM model, to analyze its spatial correlation pattern, and to explore the driving mechanism of green efficiency of water resources by making use of the geographical weighted regression (GWR) model. The results show that there was ignificant positive spatial correlation and uneven spatial distribution of the green efficiency of water resources in China. Compared with the OLS model (the regression coefficient obtained by the model just represents the average characteristics of the overall green efficiency of water resources), the GWR model proves that there were spatial differences in the influence of various factors on the green efficiency of water resources. Generally speaking, industrial structure, industrial and agricultural development level, transportation infrastructure construction, environmental protection investment and science and technology conversion rate had a positive effect on the green efficiency of water resources, while the factors, including per capita water resources, population size and pollutant emissions, had a restraining influence. Social factors and environmental factors had a particularly obvious effect on the green efficiency of water resources. In addition, due to the large potential of the technology market turnover, the regions should step up efforts to increase investment of scientific research and focus on the transformation of scientific research results so as to make technology a new growth pole of green efficiency of water resources. In terms of spatial differences, water endowment and population size had significant impact on the green efficiency of water resources in the western region, and industrial structure, water utilization structure and transportation infrastructure posed a positive effect in the eastern region. This study is of great practical significance to solve water shortage and to formulate relevant policies suitable for the development of all regions.

Key words: SE-SBM model, GWR model, green efficiency of water resources, driving mechanism

孙才志, 马奇飞, 赵良仕. 基于GWR模型的中国水资源绿色效率驱动机理[J]. 地理学报, 2020, 75(5): 1022-1035.

SUN Caizhi, MA Qifei, ZHAO Liangshi. Analysis of driving mechanism based on a GWR model of green efficiency of water resources in China[J]. Acta Geographica Sinica, 2020, 75(5): 1022-1035.

图/表 9

表1

表2

中国水资源绿色效率测度结果

地区	2000	2002	2004	2006	2008	2010	2012	2014	2016
北京	1.007	0.916	0.883	1.006	0.884	1.031	1.023	0.964	1.043
天津	1.035	1.003	1.003	1.004	0.986	0.940	0.955	0.895	1.016
河北	0.194	0.192	0.185	0.186	0.187	0.182	0.175	0.177	0.182
山西	0.497	0.513	0.506	0.511	0.421	0.387	0.345	0.311	0.298
内蒙古	1.015	0.848	0.464	0.421	0.372	0.363	0.336	0.307	0.309
辽宁	0.669	0.644	0.532	0.512	0.446	0.366	0.378	0.346	0.337
吉林	0.732	0.541	0.475	0.466	0.382	0.343	0.331	0.312	0.312
黑龙江	0.375	0.391	0.392	0.397	0.393	0.348	0.347	0.333	0.317
上海	0.718	0.701	0.794	0.860	0.953	0.969	1.010	0.883	1.125
江苏	0.254	0.266	0.266	0.288	0.278	0.251	0.251	0.261	0.288
浙江	0.460	0.494	0.460	0.435	0.402	0.414	0.388	0.379	0.417
安徽	0.241	0.263	0.257	0.249	0.260	0.271	0.234	0.208	0.210
福建	0.383	0.429	0.422	0.479	0.470	0.400	0.374	0.370	0.350
江西	0.362	0.325	0.291	0.277	0.262	0.260	0.257	0.248	0.254
山东	0.371	0.248	0.182	0.197	0.201	0.179	0.167	0.170	0.178
河南	0.145	0.150	0.144	0.142	0.132	0.127	0.120	0.121	0.115
湖北	0.274	0.275	0.272	0.278	0.275	0.261	0.239	0.234	0.244
湖南	0.332	0.267	0.227	0.226	0.236	0.207	0.184	0.184	0.194
广东	0.338	0.376	0.553	0.817	0.947	1.010	0.573	0.297	0.242
广西	0.298	0.324	0.304	0.285	0.267	0.251	0.221	0.226	0.230
海南	0.991	0.970	0.903	0.893	0.920	0.883	0.666	0.595	0.586
重庆	1.009	0.548	0.434	0.464	0.447	0.456	0.447	0.449	0.452
四川	0.665	0.689	0.454	0.572	0.282	0.264	0.290	0.230	0.210
贵州	0.319	0.293	0.272	0.275	0.282	0.293	0.289	0.280	0.267
云南	0.253	0.254	0.254	0.244	0.222	0.226	0.210	0.196	0.194
西藏	1.057	1.003	0.852	0.731	0.680	0.801	0.814	0.862	0.802
陕西	0.377	0.368	0.333	0.324	0.321	0.318	0.305	0.290	0.286
甘肃	1.005	0.579	0.452	0.432	0.404	0.355	0.347	0.331	0.315
青海	1.024	0.834	0.745	0.703	0.679	0.764	0.926	1.010	1.006
宁夏	1.049	1.011	1.003	0.670	0.639	0.652	0.601	0.807	1.008
新疆	0.403	0.375	0.350	0.323	0.320	0.323	0.310	0.280	0.255
均值	0.576	0.519	0.473	0.473	0.450	0.448	0.423	0.405	0.421

表2

图1

表3

表4

表5

图2

图3

图4

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因素分类	变量描述	解释变量
自然因素	水资源禀赋	人均水资源量(m3)
自然因素	水资源禀赋	降水量(mm)
社会经济因素	经济水平	人均GDP(元)
	产业结构	第三产业比重(%)
	产业结构	三产劳均增加值(万元)
	工业发展水平	规模以上工业企业主要产品利润率(%)
	农业发展水平	谷物单位面积产量(kg/hm²)
	人口规模	总人口(亿人)
	人口素质	平均受教育年限(a)
	用水结构	工业用水比重(%)
	用水结构	农业用水比重(%)
	对外开放程度	外商直接投资(亿元)
	交通基础设施	单位面积交通里程(km/km2)
环境因素	污染程度	COD排放总量(万t)
	污水处理	城市污水处理率(%)
	环保投入	污染治理投资占地方预算百分比(%)
科技因素	科技成果	万人专利授权数(件)
	科技转化率	科技市场成交额(亿元)
	科技投入	R&D经费投资总额(亿元)

因变量	回归系数	标准差	t值	显著性
常数	2.3349^***	0.4301	5.43	0.000
人均水资源量	-0.0169^**	0.0110	-1.54	0.025
降水量	-0.0135	0.0165	-0.82	0.415
人均GDP	0.1536^***	0.0266	5.78	0.000
第三产业比重	0.4806^***	0.1499	3.21	0.001
规模以上工业企业主要产品利润率	-0.4073^***	0.1125	-3.62	0.000
谷物单位面积产量	-0.2489^***	0.0547	-4.55	0.000
总人口	-0.4681^***	0.0592	-7.90	0.000
平均受教育年限	-0.0159	0.0137	-1.16	0.245
工业用水比重	-0.5865^***	0.1248	-4.70	0.000
农业用水比重	-0.5372^***	0.1054	-5.10	0.000
单位面积交通里程	0.0671^*	0.0372	1.80	0.072
COD排放总量	-0.0007^*	0.0004	-1.90	0.058
城市污水处理率	-0.5114	0.0490	-10.43	0.109
污染治理投资占地方预算百分比	-0.4572^*	0.2428	-1.88	0.060
万人专利授权数	-0.0115	0.0023	-0.05	0.960
科技市场成交额	0.0047^**	0.0037	1.25	0.011
R&D经费投资总额	0.0069	0.0056	1.22	0.222
调整R²	0.650

模型参数	2000年	2005年	2010年	2016年
Bandwidth	396939.049	410258.124	396070.654	125358.383
Residual Squares	0.878045	0.412876	0.548912	0.457864
Effective Number	27.723	27.708	27.904	31
Sigma值	0.168297	0.115406	0.133067	0.121531
AIC值	31.738798	8.347751	17.176235	11.553877
R²	0.817	0.860	0.861303	0.935754
调整R²	0.701	0.777	0.779	0.810

变量	最小值	下四分位数	中位数	上四分位数	最大值	平均值
人均水资源量	-0.468/-15.232	-0.027/-0.134	0.060/-0.061	0.261/0.230	1.425/0.812	0.129/-0.450
人均GDP	-3.449/-2.091	-0.356/-0.283	-0.159/0.060	0.094/0.206	1.480/5.017	-0.137/1.554
第三产业比重	-22.896/-10.798	-4.874/-0.941	-1.748/0.000	1.171/1.528	8.942/5.098	-2.554/4.725
工业企业主要产品利润率	-8.227/-36.330	-2.570/-2.054	-0.443/0.000	-0.006/1.280	5.596/28.083	-0.874/3.175
谷物单位面积产量	-5.125/-6.914	-0.741/-0.374	-0.186/0.375	0.061/1.567	2.174/20.411	-0.348/-0.757
总人口	-7.417/-6.151	-2.917/-0.868	-1.109/-0.589	-0.330/0.000	1.553/32.017	-1.701/3.831
工业用水比重	-19.377/-36.937	-1.971/-2.680	-0.527/-0.322	0.903/0.000	2.678/4.978	-1.633/12.798
农业用水比重	-10.932/-29.492	-2.223/-2.203	-0.738/0.000	2.531/0.764	5.940/19.401	-0.658/5.029
单位面积交通里程	-3.171/-6.629	-1.096/-0.230	0.260/0.164	2.438/0.347	5.237/1.705	0.571/-1.329
COD排放总量	-0.015/-0.503	-0.001/-0.009	0.005/-0.001	0.016/0.005	0.049/0.030	0.008/-0.030
污染治理投资百分比	-7.938/-8.398	0.592/-0.630	1.814/0.279	2.981/3.498	44.268/50.999	3.440/1.147
科技市场成交额	-0.029/-1.852	-0.004/-0.073	0.006/0.027	0.011/0.075	0.044/0.001	0.005/0.089