中国绿色经济效率空间关联网络演变特征及影响因素

doi:10.18402/resci.2021.10.01

摘要/Abstract

摘要：

厘清绿色经济效率空间关联网络结构的演变特征及驱动因素,可为推进区域协同绿色发展提供参考依据。本文通过构建绿色经济效率评价体系,采用考虑非期望产出的Super-EBM模型对2000—2018年中国省域绿色经济效率进行了测度,运用修正的引力模型和社会网络分析方法分析了绿色经济效率的空间关联网络结构演变特征与关联网络效应,借助二次指派程序方法识别了其影响因素。研究表明：①2000—2018年中国绿色经济效率呈先下降后上升的“S”型变化趋势,空间上呈现东部>西部>中部的地区间不均衡特征。②中国绿色经济效率逐渐呈现出以京津、长三角为极核的复杂网络结构形态,省际间空间关联性日益提升,网络结构的稳定性逐渐增强,但整体网络仍存在较为森严的等级结构,网络结构尚待进一步优化。③京津、长三角和珠三角位于关联网络的中心位置,并兼具“中介”和“桥梁”功能;西北和东北地区处在关联网络的边缘位置;江西、重庆、甘肃和贵州在关联网络中具有“承东启西、贯南通北”的纽带功能。④绿色经济效率空间关联网络受到多种因素的综合作用,地理空间邻近和经济发展水平差异对空间关联强度具有正向效应,科技创新差异对空间关联网络具有抑制作用,资源禀赋和对外开放差异的正向效应趋于减弱。优化绿色经济效率空间关联网络,对于提高区域绿色经济效率和缩小绿色发展的区域差异具有重要意义。

关键词: 绿色经济效率, 空间关联网络, 影响因素, 效应, 社会网络分析, Super-EBM模型, 中国

Abstract:

Clarifying the characteristics of change and driving factors of the spatial correlation network structure of green economic efficiency can provide some reference for promoting regional coordination of green development. By constructing a green economic efficiency evaluation system, this study used the Super-Epsilon-Based Measure (Super-EBM) model that considers undesired output to measure the green economic efficiency in China’s provinces from 2000 to 2018. The structural change characteristics of green economic efficiency and its effect were analyzed by using a modified gravity model and the social network analysis method, and the influencing factors were identified by the quadratic assignment procedure method. The results show that: (1) From 2000 to 2018, China’s green economic efficiency showed a S-shaped trend that declined first and then increased, showing spatial imbalance between the regions with east > west > central region. (2) China’s green economic efficiency has gradually developed a complex network structure with the Beijing-Tianjin area and the Yangtze River Delta as the polar core. The spatial correlation between provinces has been increasing, and the stability of the network structure has gradually enhanced. However, there is still a relatively strict hierarchical structure of the overall network, and the network structure needs to be further optimized. (3) The Beijing-Tianjin area, the Yangtze River Delta, and the Pearl River Delta are located in the core areas of the network and have both “intermediary” and “bridge” functions. Northwest and northeast regions are located in the periphery areas of the network. Jiangxi, Chongqing, Gansu, and Guizhou have played the role of “connecting the east and the west, and connecting the south and the north” in the spatial correlation network. (4) The spatial correlation network of green economic efficiency is affected by the joint action of multiple factors. Geographical proximity and the difference in economic development levels have significant positive impact on the improvement of the spatial correlation strength of green economic efficiency. The difference in scientific and technological innovation levels has an inhibitory effect on the spatial correlation network. The positive effects of the differences in resource endowment and opening up tend to weaken. Optimizing the spatial correlation network of green economic efficiency is of great significance for improving the regional green economic efficiency and reducing the regional differences of green development.

Key words: green economic efficiency, spatial correlation network, influencing factors, effects, social network analysis, Super-EBM model, China

赵林, 曹乃刚, 韩增林, 高晓彤. 中国绿色经济效率空间关联网络演变特征及影响因素[J]. 资源科学, 2021, 43(10): 1933-1946.

ZHAO Lin, CAO Naigang, HAN Zenglin, GAO Xiaotong. Spatial correlation network and influencing factors of green economic efficiency in China[J]. Resources Science, 2021, 43(10): 1933-1946.

图/表 7

表1

图1

图2

表2

2000—2018年中国绿色经济效率空间关联的网络中心性分析

地区	点入度/点出度				度数中心度				中介中心度
地区	2000年	2006年	2012年	2018年	2000年	2006年	2012年	2018年	2000年	2006年	2012年	2018年
北京	8/22	6/25	4/24	6/24	79.310	86.207	86.207	82.759	17.858	11.728	8.405	8.233
天津	8/21	4/24	3/26	5/24	79.310	82.759	89.655	82.759	7.795	4.536	8.774	5.154
河北	4/3	3/2	4/3	5/3	13.793	10.345	13.793	17.241	0.139	0.239	6.705	1.053
山西	5/3	3/3	5/3	4/7	17.241	13.793	17.241	24.138	0.180	0.239	7.522	1.617
内蒙古	3/2	4/2	6/14	5/2	10.345	17.241	55.172	20.690	0.139	0.108	28.478	0.489
辽宁	4/5	4/1	5/4	4/3	24.138	13.793	24.138	17.241	6.789	0.000	6.650	1.053
吉林	5/2	5/1	5/2	6/1	17.241	17.241	17.241	20.690	3.448	0.062	0.000	0.000
黑龙江	5/1	3/0	8/2	9/1	17.241	10.345	27.586	31.034	0.000	0.000	2.833	0.452
上海	4/28	4/27	4/26	4/26	96.552	93.103	89.655	89.655	21.618	6.500	9.011	11.680
江苏	2/14	2/16	3/20	3/24	48.276	55.172	68.966	82.759	0.247	0.592	4.269	10.382
浙江	2/13	2/19	3/18	4/18	44.828	65.517	62.069	65.517	0.216	0.777	7.480	2.713
安徽	3/7	5/3	5/3	5/5	24.138	17.241	17.241	24.138	0.680	2.401	2.517	1.236
福建	3/8	3/6	6/9	5/12	34.483	27.586	44.828	48.276	0.442	0.308	3.533	5.913
江西	5/4	7/0	8/4	8/6	20.690	24.138	27.586	27.586	21.000	0.000	12.277	26.450
山东	4/7	4/4	3/4	6/5	27.586	17.241	17.241	27.586	0.303	0.412	0.683	1.209
河南	5/3	6/2	7/1	7/0	17.241	20.690	24.138	24.138	0.303	0.570	0.343	0.000
湖北	6/0	6/0	6/2	8/2	20.690	20.690	20.690	31.034	0.000	0.000	2.258	0.070
湖南	7/1	7/1	8/1	8/2	24.138	24.138	27.586	27.586	2.535	0.302	0.873	1.662
广东	7/12	5/14	7/9	7/9	48.276	51.724	37.931	34.483	25.225	7.759	17.620	27.225
广西	6/2	7/1	8/1	9/1	20.690	24.138	27.586	31.034	2.670	0.302	0.873	5.354
海南	4/2	5/1	7/1	7/3	13.793	17.241	24.138	27.586	1.478	0.240	0.086	1.243
重庆	8/1	7/1	6/4	8/5	27.586	24.138	27.586	31.034	0.000	0.000	3.918	16.626
四川	7/0	6/0	9/1	8/1	24.138	20.690	31.034	27.586	0.000	0.000	0.924	0.179
贵州	8/2	8/2	9/2	8/2	27.586	27.586	31.034	27.586	5.983	1.964	4.835	5.533
云南	7/0	7/0	9/0	8/2	24.138	24.138	31.034	27.586	0.000	0.000	0.000	5.533
陕西	7/0	6/0	6/1	7/1	24.138	20.690	24.138	24.138	0.000	0.000	0.000	0.021
甘肃	7/1	7/1	14/2	12/4	27.586	24.138	48.276	41.379	0.041	0.000	22.098	14.095
青海	9/0	7/1	7/1	6/1	31.034	24.138	24.138	20.690	0.000	0.000	0.000	0.000
宁夏	8/0	8/0	7/2	6/1	27.586	27.586	24.138	20.690	0.000	0.000	22.626	0.000
新疆	3/0	6/0	8/0	7/0	10.345	20.690	27.586	24.138	0.000	0.000	0.000	0.000
均值	5.5/5.5	5.2/5.2	6.3/6.3	6.5/6.5	30.805	30.805	36.322	36.092	3.970	1.301	6.186	5.173

表2

表3

表4

图3

参考文献 46

[1]	康蕾, 宋周莺. 中国区域投入产出效率的研究框架与实证分析[J]. 地理科学, 2020, 40(11):1868-1877. doi: 10.13249/j.cnki.sgs.2020.11.012
	[ Kang L, Song Z Y. Research framework and empirical study of input-output efficiency of resources and environment in China[J]. Scientia Geographica Sinica, 2020, 40(11):1868-1877.]
[2]	王圣云, 韩亚杰, 任慧敏, 等. 中国省域生态福利绩效评估及其驱动效应分解[J]. 资源科学, 2020, 42(5):840-855.
	[ Wang S Y, Han Y J, Ren H M, et al. Evaluation of provincial ecological well-being performance and its driving effect decomposition in China[J]. Resources Science, 2020, 42(5):840-855.]
[3]	林伯强, 谭睿鹏. 中国经济集聚与绿色经济效率[J]. 经济研究, 2019, 54(2):119-132.
	[ Lin B Q, Tan R P. Economic agglomeration and green economy efficiency in China[J]. Economic Research Journal, 2019, 54(2):119-132.]
[4]	刘华军, 贾文星. 中国区域经济增长的空间网络关联及收敛性检验[J]. 地理科学, 2019, 39(5):726-733. doi: 10.13249/j.cnki.sgs.2019.05.003
	[ Liu H J, Jia W X. Spatial network correlation and convergence test of regional economic growth in China[J]. Scientia Geographica Sinica, 2019, 39(5):726-733.]
[5]	Pearce D, Markandya A, Barbier E B. Blueprint for a Green Economy[M]. London: Earthscan Publication Limited, 1989.
[6]	雷敏, 张兴榆, 曹明明. 资源型城市绿色GDP核算研究: 以陕西省榆林市为例[J]. 自然资源学报, 2009, 24(12):2046-2055.
	[ Lei M, Zhang X Y, Cao M M. Accounting research of green GDP of resource dependent cities: A case study of Yulin City in Shaanxi Province[J]. Journal of Natural Resources, 2009, 24(12):2046-2055.]
[7]	Weng Q, Qin Q, Li L. A comprehensive evaluation paradigm for regional green development based on “Five-Circle Model”: A case study from Beijing-Tianjin-Hebei[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.124076.
[8]	郭付友, 佟连军, 仇方道, 等. 黄河流域生态经济走廊绿色发展时空分异特征与影响因素识别[J]. 地理学报, 2021, 76(3):726-739. doi: 10.11821/dlxb202103016
	[ Guo F Y, Tong L J, Qiu F D, et al. Spatio-temporal differentiation characteristics and influencing factors of green development in the eco-economic corridor of the Yellow River Basin[J]. Acta Geographica Sinica, 2021, 76(3):726-739.]
[9]	程钰, 王晶晶, 王亚平, 等. 中国绿色发展时空演变轨迹与影响机理研究[J]. 地理研究, 2019, 38(11):2745-2765. doi: 10.11821/dlyj020190057
	[ Cheng Y, Wang J J, Wang Y P, et al. A comparative research of the spatial-temporal evolution track and influence mechanism of green development in China[J]. Geographical Research, 2019, 38(11):2745-2765.]
[10]	Merino-Saum A, Baldi M G, Gunderson I, et al. Articulating natural resources and sustainable development goals through green economy indicators: A systematic analysis[J]. Resources, Conservation & Recycling, 2018, 139:90-103.
[11]	Tong C, Ding S, Wang B, et al. Assessing the target-availability of China’s investments for green growth using time series prediction[J]. Physica A: Statistical Mechanics and its Applications, 2020, DOI: 10.1016/j.physa.2019.122724.
[12]	吴传清, 周西一敏. 长江经济带绿色经济效率的时空格局演变及其影响因素研究[J]. 宏观质量研究, 2020, 8(3):120-128.
	[ Wu C Q, Zhou X Y M. Research on the spatiotemporal evolution and the influencing factors of green economic efficiency in the Yangtze River Economic Belt[J]. Journal of Macro-Quality Research, 2020, 8(3):120-128.]
[13]	胡博伟, 周亮, 王中辉, 等. 干旱区资源型城市绿色经济效率时空分异特征[J]. 资源科学, 2020, 42(2):383-393.
	[ Hu B W, Zhou L, Wang Z H, et al. Spatiotemporal differentiation of green economic efficiency of resource-based cities in arid area[J]. Resources Science, 2020, 42(2):383-393.]
[14]	Shuai S, Fan Z. Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model[J]. Journal of Environmental Management, 2020, DOI: 10.1016/j.jenvman.2020.110227.
[15]	钱争鸣, 刘晓晨. 中国绿色经济效率的区域差异与影响因素分析[J]. 中国人口·资源与环境, 2013, 23(7):104-109.
	[ Qian Z M, Liu X C. Regional differences in China’s green economic efficiency and their determinants[J]. China Population, Resources and Environment, 2013, 23(7):104-109.]
[16]	Lu X, Qu Y, Sun P L, et al. Green transition of cultivated land use in the Yellow River Basin: A perspective of green utilization efficiency evaluation[J]. Land, 2020, DOI: 10.3390/land9120475.
[17]	岳立, 薛丹. 黄河流域沿线城市绿色发展效率时空演变及其影响因素[J]. 资源科学, 2020, 42(12):2274-2284.
	[ Yue L, Xue D. Spatiotemporal change of urban green development efficiency in the Yellow River Basin and influencing factors[J]. Resources Science, 2020, 42(12):2274-2284.]
[18]	车磊, 白永平, 周亮, 等. 中国绿色发展效率的空间特征及溢出分析[J]. 地理科学, 2018, 38(11):1788-1798. doi: 10.13249/j.cnki.sgs.2018.11.006
	[ Che L, Bai Y P, Zhou L, et al. Spatial pattern and spillover effects of green development efficiency in China[J] Scientia Geographica Sinica, 2018, 38(11):1788-1798.]
[19]	Wang Q, Yi H T. New energy demonstration program and China’s urban green economic growth: Do regional characteristics make a difference?[J]. Energy Policy, 2021, DOI: 10.1016/j.enpol.2021.112161.
[20]	Ren Y F, Fang C L, Li G D. Spatiotemporal characteristics and influential factors of eco-efficiency in Chinese prefecture-level cities: A spatial panel econometric analysis[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.120787.
[21]	任嘉敏, 马延吉. 地理学视角下绿色发展研究进展与展望[J]. 地理科学进展, 2020, 39(7):1196-1209. doi: 10.18306/dlkxjz.2020.07.012
	[ Ren J M, Ma Y J. Research progress and prospects of green development from the perspective of geography[J]. Progress in Geography, 2020, 39(7):1196-1209.]
[22]	Zhang H, Geng Z R, Yin R S, et al. Regional differences and convergence tendency of green development competitiveness in China[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2019.119922.
[23]	Wang Z B, Wang X M, Liang L W. Green economic efficiency in the Yangtze River Delta: Spatiotemporal evolution and influencing factors[J]. Ecosystem Health and Sustainability, 2019, 5(1):20-35. doi: 10.1080/20964129.2018.1559000
[24]	Sun C X, Tong Y L, Zou W. The evolution and a temporal-spatial difference analysis of green development in China[J]. Sustainable Cities and Society, 2018, 41:52-61. doi: 10.1016/j.scs.2018.05.006
[25]	Cheng C Y, Ge C Z. Green development assessment for countries along the Belt and Road[J]. Journal of Environmental Management, 2020, DOI: 10.1016/j.jenvman.2020.110344.
[26]	Zhao P J, Zeng L E, Lu H Y, et al. Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the Super-SBM model with undesirable outputs and spatial Durbin model[J]. Science of the Total Environment, 2020, DOI: 10.1016/j.scitotenv.
[27]	辛龙, 孙慧, 王慧, 等. 基于地理探测器的绿色经济效率时空分异及驱动力研究[J]. 中国人口·资源与环境, 2020, 30(9):128-138.
	[ Xin L, Sun H, Wang H, et al. Research on spatial-temporal differentiation and driving forces of green economic efficiency based on geographic detector[J]. China Population, Resources and Environment, 2020, 30(9):128-138.]
[28]	Ma L, Long H, Chen K, et al. Green growth efficiency of Chinese cities and its spatio-temporal pattern[J]. Resources, Conservation & Recycling, 2019, 146:441-451.
[29]	周亮, 车磊, 周成虎. 中国城市绿色发展效率时空演变特征及影响因素[J]. 地理学报, 2019, 74(10):2027-2044. doi: 10.11821/dlxb201910006
	[ Zhou L, Che L, Zhou C H. Spatio-temporal evolution and influencing factors of urban green development efficiency in China[J]. Acta Geographica Sinica, 2019, 74(10):2027-2044.]
[30]	Tone K, Tsutsui M. An epsilon-based measure of efficiency in DEA: A third pole of technical efficiency[J]. European Journal of Operational Research, 2010, 207(3):1554-1563. doi: 10.1016/j.ejor.2010.07.014
[31]	刘华军, 贾文星. 不同空间网络关联情形下中国区域经济增长的收敛检验及协调发展[J]. 南开经济研究, 2019, (3):104-124.
	[ Liu H J, Jia W X. Convergence test and coordinated development of China’s regional economic growth under the background of different spatial network[J]. Nankai Economic Studies, 2019, (3):104-124.]
[32]	冯颖, 侯孟阳, 姚顺波. 中国粮食生产空间关联网络的结构特征及其形成机制[J]. 地理学报, 2020, 75(11):2380-2395. doi: 10.11821/dlxb202011008
	[ Feng Y, Hou M Y, Yao S B. Structural characteristics and formation mechanism of spatial correlation network of grain production in China[J]. Acta Geographica Sinica, 2020, 75(11):2380-2395.]
[33]	刘佳, 宋秋月. 中国旅游产业绿色创新效率的空间网络结构与形成机制[J]. 中国人口·资源与环境, 2018, 28(8):127-137.
	[ Liu J, Song Q Y. Space network structure and formation mechanism of green innovation efficiency of tourism industry in China[J]. China Population, Resources and Environment, 2018, 28(8):127-137.]
[34]	孙才志, 马奇飞. 中国省际水资源绿色效率空间关联网络研究[J]. 地理研究, 2020, 39(1):53-63. doi: 10.11821/dlyj020180926
	[ Sun C Z, Ma Q F. Spatial correlation network of water resources green efficiency between provinces of China[J]. Geographical Research, 2020, 39(1):53-63.]
[35]	李晖, 刘卫东, 唐志鹏. 全球贸易隐含碳净转移的空间关联网络特征[J]. 资源科学, 2021, 43(4):682-692. doi: 10.18402/resci.2021.04.04
	[ Li H, Liu W D, Tang Z P. Spatial correlation network of net carbon transfer in global trade[J]. Resources Science, 2021, 43(4):682-692.]
[36]	Liu S N, Xiao Q T. An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model[J]. Energy, 2021, DOI: 10.1016/j.energy.
[37]	张鹏, 于伟. 基于社会网络分析的我国绿色全要素生产率增长空间特征及其动因研究[J]. 经济问题探索, 2017, (2):39-46.
	[ Zhang P, Yu W. Research on the spatial characteristics and motivation of green total factor productivity growth in China based on social network analysis[J]. Inquiry into Economic Issues, 2017, (2):39-46.]
[38]	刘小瑜, 余海华. 中国省际绿色发展的空间关联及溢出效应[J]. 江西财经大学学报, 2020, (3):14-24.
	[ Liu X Y, Yu H H. Spatial correlation and spillover effect of China’s inter-provincial green development[J]. Journal of Jiangxi University of Finance and Economics, 2020, (3):14-24.]
[39]	秦腾, 佟金萍, 章恒全. 环境约束下中国省际水资源效率空间关联网络构建及演化因素[J]. 中国人口·资源与环境, 2020, 30(12):84-94.
	[ Qin T, Tong J P, Zhang H Q. Construction and evolutionary factors of spatial correlation network of China’s provincial water resource efficiency under environmental constraints[J]. China Population, Resources and Environment, 2020, 30(12):84-94.]
[40]	刘耀彬, 袁华锡, 王喆. 文化产业集聚对绿色经济效率的影响: 基于动态面板模型的实证分析[J]. 资源科学, 2017, 39(4):747-755.
	[ Liu Y B, Yuan H X, Wang Z. Dynamic panel data modeling of the effects of cultural industry clusters on green economic efficiency[J]. Resources Science, 2017, 39(4):747-755.]
[41]	单豪杰. 中国资本存量K的再估算: 1952-2006年[J]. 数量经济技术经济研究, 2008, (10):18-32.
	[ Shan H J. Reestimating the capital stock of China: 1952-2006[J]. The Journal of Quantitative & Technical Economics, 2008, (10):18-32.]
[42]	汪克亮, 刘悦, 史利娟, 等. 长江经济带工业绿色水资源效率的时空分异与影响因素: 基于EBM-Tobit模型的两阶段分析[J]. 资源科学, 2017, 39(8):1522-1534.
	[ Wang K L, Liu Y, Shi L J, et al. Yangtze River Economic Zone spatial and temporal disparities in industrial green water resource efficiency and influencing factors based on two-step analysis of EBM-Tobit model[J]. Resources Science, 2017, 39(8):1522-1534.]
[43]	樊鹏飞, 冯淑怡, 苏敏, 等. 基于非期望产出的不同职能城市土地利用效率分异及驱动因素探究[J]. 资源科学, 2018, 40(5):946-957.
	[ Fan P F, Feng S Y, Su M, et al. Differential characteristics and driving factors of land use efficiency in different functional cities based on undesirable outputs[J]. Resources Science, 2018, 40(5):946-957.]
[44]	刘军. 整体网分析: UCINET软件实用指南. 2版[M]. 上海: 格致出版社, 2014.
	[ Liu J. Lecture on Whole Network Approach: A Practical Guide to UCINET. 2nd ed.[M]. Shanghai: Gezhi Publishing House, 2014.]
[45]	Wasserman S, Faust K. Social Network Analysis: Methods and Applications[M]. London: Cambridge University Press, 1994.
[46]	黄建欢, 吕海龙, 王良健. 金融发展影响区域绿色发展的机理: 基于生态效率和空间计量的研究[J]. 地理研究, 2014, 33(3):532-545. doi: 10.11821/dlyj201403012
	[ Huang J H, Lv H L, Wang L J. Mechanism of financial development influencing regional green development: Based on eco-efficiency and spatial econometrics[J]. Geographical Research, 2014, 33(3):532-545.]

省区	绿色经济效率值					省区	绿色经济效率值
省区	2000年	2006年	2012年	2018年	均值	省区	2000年	2006年	2012年	2018年	均值
北京	0.418	0.600	0.798	1.008	0.718	湖南	0.721	0.501	0.490	0.535	0.525
天津	0.575	0.705	0.846	1.003	0.773	广东	0.637	0.968	0.834	0.860	0.853
河北	0.559	0.438	0.377	0.505	0.450	广西	0.693	0.450	0.480	0.592	0.528
山西	0.598	0.457	0.485	1.001	0.549	海南	0.680	0.796	0.771	1.002	0.785
内蒙古	0.653	0.382	0.596	0.622	0.519	重庆	0.568	0.523	0.749	0.794	0.653
辽宁	0.744	0.478	0.729	0.907	0.678	四川	0.571	0.453	0.587	0.660	0.538
吉林	0.607	0.394	0.537	0.540	0.515	贵州	0.506	0.410	0.423	0.635	0.457
黑龙江	0.774	0.573	0.394	0.412	0.509	云南	0.573	0.384	0.453	0.623	0.474
上海	0.755	0.936	0.957	1.002	0.948	陕西	0.511	0.411	0.537	0.690	0.508
江苏	0.674	0.594	0.680	0.823	0.651	甘肃	0.496	0.456	0.330	0.489	0.406
浙江	0.536	0.622	0.726	0.781	0.661	青海	0.362	0.390	0.577	0.743	0.499
安徽	0.741	0.362	0.353	0.441	0.430	宁夏	0.342	0.306	0.409	0.637	0.393
福建	0.724	0.723	0.831	1.001	0.805	新疆	0.457	0.435	0.470	0.556	0.451
江西	0.768	0.388	0.383	0.693	0.522	东部	0.634	0.663	0.732	0.865	0.713
山东	0.673	0.428	0.501	0.624	0.517	中部	0.699	0.449	0.445	0.596	0.508
河南	0.733	0.437	0.385	0.502	0.478	西部	0.521	0.418	0.510	0.640	0.493
湖北	0.646	0.480	0.535	0.647	0.536	全国	0.610	0.516	0.574	0.711	0.578

被解释变量	绿色经济效率均值			绿色经济效率标准差
被解释变量	模型（1）	模型（2）	模型（3）	模型（4）	模型（5）	模型（6）	模型（7）
常数项	0.199	-0.966***	-0.880***	-0.025	-0.923***	2.125***	2.569***
网络密度	0.466**	—	—	1.115***	—	—	—
网络密度平方项	—	—	—	—	-0.345***	—	—
网络等级度	—	-5.975***	—	—	—	4.325**	—
网络效率	—	—	-0.734**	—	—	—	1.671***

变量	2000年	2002年	2004年	2006年	2008年	2010年	2012年	2014年	2016年	2018年
dis	0.193*** （0.000）	0.204*** （0.000）	0.187*** （0.000）	0.208*** （0.000）	0.214*** （0.000）	0.243*** （0.000）	0.271*** （0.000）	0.270*** （0.000）	0.271*** （0.000）	0.286*** （0.000）
pgdp	0.394*** （0.000）	0.441*** （0.000）	0.345*** （0.000）	0.416*** （0.000）	0.349*** （0.000）	0.462*** （0.000）	0.468*** （0.000）	0.483*** （0.000）	0.550*** （0.000）	0.601*** （0.000）
tec	-0.114** （0.030）	-0.183*** （0.003）	-0.031 （0.329）	-0.212*** （0.002）	-0.193** （0.016）	-0.114** （0.032）	-0.093* （0.070）	-0.100** （0.014）	-0.107*** （0.002）	-0.122*** （0.001）
inds	0.004 （0.467）	0.044* （0.097）	0.036 （0.138）	0.124** （0.014）	0.057 （0.197）	0.015 （0.379）	0.037 （0.277）	0.052 （0.142）	0.082** （0.015）	0.069** （0.042）
res	0.079** （0.011）	0.063** （0.026）	0.056** （0.043）	0.074*** （0.005）	0.085*** （0.002）	0.087*** （0.000）	0.078*** （0.001）	0.045** （0.041）	0.038** （0.039）	0.014 （0.281）
fina	0.053 （0.107）	0.019 （0.310）	0.012 （0.404）	0.061** （0.038）	0.075** （0.016）	0.028 （0.191）	0.010 （0.368）	-0.014 （0.329）	-0.017 （0.223）	-0.058** （0.013）
open	0.241*** （0.002）	0.282*** （0.000）	0.250*** （0.001）	0.245*** （0.000）	0.278*** （0.000）	0.129*** （0.006）	0.109*** （0.006）	0.096*** （0.004）	0.018 （0.243）	0.011 （0.408）
R²	0.296	0.312	0.320	0.353	0.321	0.312	0.318	0.315	0.338	0.343
Adj-R²	0.292	0.307	0.315	0.348	0.317	0.307	0.313	0.310	0.333	0.339
P值	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000