Gradient transformation and influencing factors of urban green economy efficiency in the Yangtze River Economic Belt

LI Ru-zi, CHEN Qiao-juan, GAO Xiong-yuan, MENG Si-xian, WEI Guo-en, LIU Yao-bin

JOURNAL OF NATURAL RESOURCES ›› 2024, Vol. 39 ›› Issue (1) : 125-139.

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JOURNAL OF NATURAL RESOURCES ›› 2024, Vol. 39 ›› Issue (1) : 125-139. DOI: 10.31497/zrzyxb.20240107
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Gradient transformation and influencing factors of urban green economy efficiency in the Yangtze River Economic Belt

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Abstract

Improving the efficiency of regional green economy is not only the key to achieving high-quality regional development, but also an important means to promote coordinated regional development. Revealing the gradient transformation law and influencing factors of regional green economy efficiency is of great significance to promote regional green comprehensive transformation and coordinated development. This paper uses the super-efficient EBM-GML model to measure the urban green economy efficiency of the Yangtze River Economic Belt, compares and analyzes the changes of regional green economy efficiency spatial gradient pattern before and after the strategy of "promoting well-coordinated environmental conservation and avoiding excessive development" in the Yangtze River Economic Belt in 2016, and then uses the spatial Markov chain and spatial econometric model to reveal the transformation law and influencing factors of green economy efficiency gradient. The results show that: (1) From 2003 to 2020, the overall green economy efficiency of the study area showed a continuous upward trend, and since 2016, the green economy efficiency has increased significantly, and technological progress is the main source. (2) The green economy efficiency showed a vertical "~" gradient strengthening trend along the upper, middle and lower streams, while from the horizontal perspective from the main stream and its sides, it showed a flattened to inverted "U" gradient transformation. (3) Since 2016, the green economy efficiency has developed well, and the positive spatial spillover effect of high-gradient cities has been highlighted, which has significantly enhanced the "path breakthrough" effect of low-gradient cities. (4) Economic development, urban scale, investment intensity, and environmental regulations have positive externalities for the gradient transformation of green economy efficiency, while industrial structure and opening-up have negative spillover effects. Comparing the two periods, we found that the driving factors for the gradient transformation of green economy efficiency have undergone significant changes. Among them, the indirect effects of economic development level, urban scale, industrial structure, and government intervention on green economy efficiency have been strengthened, while the direct effects of environmental regulations have been enhanced.

Key words

green economy efficiency / gradient transformation / spatial Markov chain / spatial spillover effects / Yangtze River Economic Belt

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LI Ru-zi, CHEN Qiao-juan, GAO Xiong-yuan, MENG Si-xian, WEI Guo-en, LIU Yao-bin. Gradient transformation and influencing factors of urban green economy efficiency in the Yangtze River Economic Belt[J]. JOURNAL OF NATURAL RESOURCES, 2024, 39(1): 125-139 https://doi.org/10.31497/zrzyxb.20240107
自1978年改革开放40多年来，中国独特的区域梯度发展模式为经济持续增长奠定了重要的空间基础。随着资源环境约束、区域发展不平衡等问题凸显[1,2]，亟需转变发展方式，提升区域绿色经济效率，促进低梯度地区向高梯度地区跃迁，重塑区域梯度格局[3,4]。长江经济带作为中国重要的经济支撑带[5]，上中下游地区经济梯度差异较为突出，尤其是上游地区面临复杂的生态环境约束，而中游地区成为绿色经济效率凹地[6]。优化长江经济带绿色经济效率梯度格局成为促进区域协调发展的关键。2016年中央提出长江经济带“共抓大保护、不搞大开发”战略，将其建设成为生态文明建设的先行示范带、区域互动合作的协调发展带。那么，在此背景下，长江经济带城市绿色经济效率梯度格局是否发生转换?其影响因素为何?回答上述问题，对提升长江经济带绿色经济效率、促进区域协调发展具有重要意义。
绿色经济效率是衡量区域发展方式转换方向与发展潜力的重要指标[6]。近年来，伴随快速城市化与工业化进程中的区域资源环境承载力减退，绿色经济效率成为人文经济地理学者关注的热点话题[7]。当前相关研究主要集中于绿色经济效率测度[8,9]、时空演变[10,11]、影响因素及作用路径等[12,13]。从研究区域来看，主要集中于中国东中西三大地带、主要城市群以及重要流域等。在区域协调发展战略和节能减排政策导向下，中国东中西三大地带绿色经济效率逐渐呈现分化格局[14]。而由于发育阶段差异，城市群绿色经济效率呈现出一定的梯度层级，东部地区沿海城市群的绿色经济效率表现优于西北地区城市群[15,16]。尤其是随着流域生态环境保护与高质量发展，长江经济带、黄河流域绿色经济效率梯度变化成为研究热点[7,17]。长江经济带作为生态文明建设先行示范带，由于规模效率、绿色技术、资源配置等差异，其绿色经济效率呈现显著的梯度分异特征[6,18]。虽然长江经济带城市绿色经济效率存在中游塌陷现象[19,20]，但其局部空间结构尚不稳定，仍存在较大的时空跃迁可能性[21]。尤其是伴随“共抓大保护、不搞大开发”战略实施以来，长江经济带经济格局变化呈现出新趋势[1]。从影响因素来看，以往研究多运用地理探测器、空间计量模型等方法识别绿色经济效率的影响因子[22,23]。诸多研究表明，由于经济效率的投入产出特征，其时空变化表现出显著的空间溢出关联效应[24]，主要受到产业结构[25]、技术创新[26]、环境规制[27]、经济集聚[28]、投资强度[17]、政府干预[29]等因素的空间传导影响。就长江经济带而言，流域内合作网络更加系统化、复杂化，绿色经济效率空间溢出效应尤其突出[30,31]。
综上所述，关于绿色经济效率的研究成果颇为丰富，但有必要从以下方面进行拓展和完善：（1）绿色经济效率梯度特征有待进一步揭示。以往研究多聚焦于绿色经济效率区域差异，从梯度视角认识绿色经济效率格局仍有待深入。（2）绿色经济效率梯度转换规律有待深入探讨。既有文献已经对碳排放、生态、科技金融等效率的动态变迁过程进行探讨[32⇓-34]，缺乏对绿色经济效率动态的梯度转换规律研究。
为此，本文首先采用超效率EBM-GML指数模型对2003—2020年长江经济带108个地级及以上城市的绿色经济效率进行测度；其次，将长江经济带发展阶段划分为“2003—2015”“2016—2020”两个时期，对比分析长江经济带城市绿色经济效率梯度格局的变化，进一步从梯度转换的视角对城市绿色经济效率进行等级划分，探究其梯度的转换规律；最后，运用空间杜宾模型识别绿色经济效率梯度转换的影响因素。

1 理论框架

空间梯度是指地理现象沿某一具体方向有规律变化的空间特征，常用来刻画流域、湖泊、交通等景观格局变化。经济社会要素作为地理空间的重要组成部分，同样呈现出空间梯度特征。尤其是流域地区，既是重要的生态环境廊道，又是经济活动重要载体。流域经济活动不仅沿流域上下游位移而形成纵向的空间梯度格局，也会以流域为轴线向两侧形成横向梯度格局。伴随经济社会转型和各类因子作用，流域经济梯度逐步发生转变，成为要素流动的源动力，进而驱动经济地理格局变化。绿色经济效率作为衡量区域发展方式转换方向与发展潜力的重要指标，其梯度变化与传统经济梯度有所差异。这主要是由于各类要素投入和产出在不同区域间流动，使得区域间技术效率与技术进步均表现出更为显著的空间关联特征，进而影响区域绿色经济效率梯度。基于此，本文以绿色经济效率的两大源泉，即技术效率和技术进步作为间接传导途径，构建绿色经济效率梯度转换的影响因素框架（图1）。
Fig. 1 Theoretical framework of green economy efficiency gradient transformation in Yangtze River Economic Belt

图1 长江经济带绿色经济效率梯度转换的理论框架

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（1）技术效率影响路径
技术效率驱动绿色发展，主要通过资源要素集聚和资源优化配置两方面实现。就资源要素集聚而言，区域经济发展和城市规模的扩张有利于经济要素集聚，带来集聚红利[28]，对技术效率产生积极作用。但集聚规模效应可能导致非期望产出增加，进而抑制绿色经济效率。从资源优化配置视角来看，产业结构是“资源转换器”，产业结构升级有利于资源的优化配置。而资本的有效积累引导资金向绿色产业流入，可以促进产业结构升级[17]。区域对外开放带来先进的管理经验[26]，有利于要素管理制度体系和技术的更新。而技术创新又有利于引导要素配置，推动绿色技术效率的提升[26]。政府管制方面，适当的政策干预促进资源的合理配置[7]，同时政府合理的环境规制倒逼企业采用先进的技术和管理，提高资源利用率[27]。
（2）技术进步影响路径
技术进步驱动绿色经济效率改进主要通过引入先进技术和自主绿色技术创新实现。经济发展和城市规模带来的要素集聚，使不同产业间知识和技术的交流、共享更加便捷[35]，为产业结构绿色化、高级化提供技术支撑。而产业结构的转型升级又有利于促进产业创新活力，进一步增强区域产业绿色技术创新[36]。在此过程中，随着区域合作日渐频繁，城市对外开放程度逐步加深，通过对引进技术的消化吸收，提高自主创新能力[37]。同时，随着对科学技术投入增加和资本深化，可能会引致技术进步向绿色化方向转变[29,38]。但过于依赖资本投入带来的规模效应，可能会挤出研发投入，导致绿色经济效率损失。此外，政府一方面可以通过合理的干预营造良好的创新环境，有效促进区域绿色技术创新[39]，另一方面通过制定适当的环境规制刺激绿色技术创新[40]，进而产生创新补偿效应。
（3）空间溢出效应
各类要素投入和产出在不同区域间流动，使得区域间绿色技术效率与绿色技术进步均表现出更为显著的空间关联特征，进而对邻近城市的绿色经济效率梯度转换形成溢出效应，驱动效率梯度协同转换，改变经济地理格局。要素集聚对相邻地区存在正辐射作用，但过度集聚会产生虹吸效应[41]。区域间的合作交流使得产业结构模式、资金、技术、劳动、政策红利及制度产生外溢效应。而对外开放虽然在一定程度上强化要素外溢[37]，但可能伴随高能耗、环境污染性企业进入成为“污染天堂”，抑制绿色经济效率提升。

2 研究方法与数据来源

2.1 超效率EBM-Malmquist Luenberger指数模型

本文在“超效率”模型基础上[42]，运用EBM模型[43]，与Global Malmquist Luenberger指数相结合[44]，将绿色经济效率变化（GEE）分解为技术效率变化（EC）和技术进步变化（TC），即GEE=EC×TC。限于篇幅，仅给出最终表达式如下：
GEExt+1,yt+1,bt+1;xt,yt,bt=D0t→xt+1,yt+1,bt+1D0t→xt,yt,bt×D0t+1→xt+1,yt+1,bt+1D0t+1→xt,yt,bt12=D0t→xt+1,yt+1,bt+1D0t→xt,yt,bt×(D0t→xt+1,yt+1,bt+1D0t+1→xt+1,yt+1,bt+1×D0t→xt,yt,btD0t+1→xt,yt,bt)1/2=EC(xt+1,yt+1,bt+1;xt,yt,bt)×TC(xt+1,yt+1,bt+1;xt,yt,bt)
（1）
式中：EC是由于政策制度变革等环境因素引起的资源配置效率变化；TC是绿色技术创新推动的效率变化[29]。同时，为了更准确地反映长江经济带绿色经济效率及其分解的变化趋势[29]，本文将GEE、EC和TC以2003年为基期连乘处理，分别得到各个年份的GEE指数、EC指数和TC指数。

2.2 趋势面分析

趋势面分析常用来揭示地理要素在空间上的分布规律和变化趋势[45]。本文利用趋势面分析来刻画长江经济带城市绿色经济效率在纵向和横向的梯度格局。根据趋势面原理，可将公式设为：
Zi(Xi,Yi)=Ti(Xi,Yi)+εi
（2）
式中：(Xi, Yi) 为地理坐标，X轴为东西方向，Y轴为南北方向；Zi(Xi, Yi) 为包含地理要素的实际观测值；Ti(Xi, Yi) 为运用最小二乘法拟合二维非线性函数所得出的趋势拟合值；εi表示剩余值。

2.3 空间马尔可夫链

马尔可夫随机过程是将区域现象量化的连续值大小离散成K种状态类型，构造成K×K的状态概率转移矩阵，分析事件状态发生和转换的概率[32]。假设矩阵中元素Pij为区域t时刻处于状态i转移到t+1时刻处于状态j的概率，其计算公式如下：
Pij=nij/ni
（3）
式中：nij为整个研究期内t时刻处于i状态转移到t+1时刻处于j状态的城市数量之和（个）；ni表示整个研究期内处于i状态的城市数量总和（个）。
空间马尔可夫分析则是空间滞后值与传统马尔可夫相结合的结果，按照空间滞后值划分的K种类型，将传统K×K的概率转移矩阵分解成K个K×K矩阵，计算在K类型条件下，区域从t时刻处于状态i转移到t+1时刻处于状态j的概率，弥补了传统马尔可夫对地理要素空间关联性影响的忽视。本文在探究长江经济带绿色经济效率梯度重构的基础上，运用空间马尔可夫对其效率梯度的转换规律进行分析，空间马尔可夫的相关具体公式详见参考文献 [32]。

2.4 空间面板回归模型

空间面板回归模型可以分为空间杜宾模型、空间误差模型以及空间滞后模型，选择合适的模型是正确估计参数的前提。本文经过模型检验最终选用固定效应空间杜宾模型检验长江经济带绿色经济效率梯度转换的影响因素，具体公式如下：
Yit=ρwYit+βXit+θwXit+μit+εit
（4）
式中：Yit表示被解释变量，在本文为GEE指数、EC指数和TC指数；w为空间权重矩阵；Xit为控制变量；μit表示固定效应；εit为误差项；ρ、β、θ分别表示被解释变量的空间自回归系数、控制变量的待估计系数、控制变量的空间回归系数。

2.5 指标数据来源与处理

本文选取2003—2020年长江经济带108个地级及以上城市为研究对象，绿色经济效率包含投入、期望产出和非期望产出三类指标。投入变量包括劳动力、资本和自然资源等。其中资本采用永续盘存法进行核算[46,47]；劳动投入用年末单位从业人数表示[3]；自然资源投入包括水、土地和能源等，分别选取城市供水总量、城市建成区面积[19]、DMSP/OLS夜间灯光等代理[48]。期望产出为各地级市GDP，并以2003年为基期用GDP指数进行平减；非期望产出选取工业废水、工业SO2[6]以及PM2.5平均浓度[17]作为衡量指标。DMSP/OLS夜间灯光数据来自NOAA地球观测组织（https://www.ngdc.noaa.gov/eog/download.html），空间分辨率1000 m×1000 m；PM2.5平均浓度来源于大气成分分析组织利用气溶胶遥感数据反演得出的数据。影响因素层面，在参考以往文献基础上，将经济发展（eco）、城市规模（cs）[28]、产业结构（is）[49]、对外开放（fi）[26]、投资强度（ii）[17]、技术创新（ti）[38]、政府干预（gi）[32]、环境规制（er）[50]等纳入模型（表1）。
Table 1 Descriptive statistics of variables

表1 变量描述性统计

类型 要素 指标 均值 最小值 最大值 预期结果
被解释变量 绿色经济效率指数 GEE指数 1.17 0.91 1.94
技术效率指数 EC指数 0.94 0.57 1.54
技术进步指数 TC指数 1.26 0.78 1.95
影响因素 经济发展 ln(人均GDP)lneco 10.28 7.85 12.20 不确定
城市规模 ln(城市市辖区人口)lncs 4.74 2.66 7.94 不确定
产业结构 第二产业占GDP比例is/% 47.29 14.7 75.86 负
对外开放 ln(实际利用外商投资额)lnfi 9.96 2.08 14.52 不确定
投资强度 ln(固定投资资产总额)lnii 15.88 12.42 19.14 不确定
技术创新 财政性科学技术支出占GDP比例ti/% 3.30 0.04 6.65 正
政府干预 公共财政预算支出占GDP比例gi/% 16.12 1.02 68.76 正
环境规制 工业废弃物综合利用率er/% 78.65 7.32 100 正
除特殊说明外，以上数据均来源于各省（市）统计年鉴、《中国城市统计年鉴》及相关地级市统计公报。个别缺失数据采用相邻均值插补法处理。

3 结果分析

3.1 长江经济带绿色经济效率时间演变

如图2所示，长江经济带绿色经济效率水平均值总体呈现持续上升的阶段性变化特征。2003—2015年间，城市绿色经济效率整体上升但相对平缓，该阶段长江经济带着眼于流域开发，经济发展依赖要素投入的粗放型生产方式，导致环境污染问题严重，绿色增长缓慢。2016—2020年间，绿色经济效率大幅度上扬，说明2016年“共抓大保护、不搞大开发”战略得到充分贯彻和落实，长江经济带城市绿色发展取得积极成效。从分解结果来看，绿色技术效率整体呈持续减小趋势，而绿色技术进步持续提升，与绿色经济效率变化趋势一致。意味着与技术进步相比，长江经济带资源配置效率整体偏低，其绿色经济效率变化主要源自绿色技术进步驱动。
Fig. 2 Changes of green economy efficiency index and decomposition of cities along the Yangtze River Economic Belt

图2 长江经济带城市绿色经济效率指数及其分解的变化

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3.2 长江经济带绿色经济效率梯度演变

本文利用趋势面分析，进一步刻画不同阶段长江经济带城市绿色经济效率（GEE指数）空间梯度特征。绿色经济效率空间梯度是其沿着某一方向有规律地逐渐变化的特征。从流域纵向来看（图3），长江经济带绿色经济效率在2003—2015年、2016—2020年均呈现“~”型空间分布趋势。与2003—2015年相比，2016—2020年间绿色经济效率梯度差异扩大，上游围绕成渝地区表现为显著的中心—外围结构，而长江中游城市群与上游和下游地区相比表现出显著的“凹地”特征。
Fig. 3 Vertical change of green economy efficiency gradient in Yangtze River Economic Belt

图3 长江经济带绿色经济效率梯度纵向变化

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从流域横向来看（图4），2003—2015年间，长江经济带沿江、远江城市绿色经济效率梯度差异较小。而到2016—2020年间，从Z轴来看，长江经济带城市绿色经济效率指数整体上升，但沿江城市绿色经济效率逐步成为高梯度区域，远江城市绿色经济效率与沿江城市差距扩大，整体表现为倒“U”型特征。表明随着长江经济带“生态优先、绿色发展”战略实施，沿江城市绿色发展格局发生显著变化。
Fig. 4 Horizontal change of green economy efficiency gradient in Yangtze River Economic Belt

图4 长江经济带绿色经济效率梯度横向变化

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3.3 长江经济带绿色经济效率梯度转换规律

上述分析表明，长江经济带绿色经济效率梯度特征变化明显，且表现出较明显的空间集聚特征。在此基础上，本文进一步利用空间马尔可夫链揭示其梯度转换规律。将长江经济带绿色经济效率指数分为低水平增长、较低水平增长、较高水平增长、高水平增长4个梯度层级，分别对应K=1、2、3、4，进而据此构造不同时间段的空间马尔可夫概率转移矩阵（表2）。具体来看，长江经济带绿色经济效率高梯度城市的正向溢出效应较明显，使得低效率梯度城市“路径突破”概率增大。与2003—2015年相比，2016—2020年间本地绿色经济效率在与较高水平增长、高水平增长的城市邻近时，该城市绿色经济效率梯度向上转换的概率明显增大，而向下转换概率明显降低。这表明2016年后长江经济带11省市建立的省际协商合作机制得到有效运行，使得高梯度城市的辐射带动作用成为区域路径突破的重要外生力量。
Table 2 Markov probability transfer matrix of the green economy efficiency in the Yangtze River Economic Belt

表2 长江经济带绿色经济效率空间马尔可夫概率转移矩阵

空间滞后 t/(t+1) 2003—2015年 2016—2020年
n 1 2 3 4 n 1 2 3 4
1 1 133 0.74 0.23 0.02 0.01 55 0.62 0.31 0.07 0.00
2 44 0.07 0.50 0.41 0.02 24 0.08 0.54 0.33 0.04
3 17 0.00 0.06 0.71 0.24 7 0.00 0.14 0.71 0.14
4 4 0.25 0.25 0.00 0.50 4 0.00 0.00 0.00 1.00
2 1 121 0.69 0.30 0.00 0.01 58 0.69 0.28 0.03 0.00
2 107 0.07 0.58 0.34 0.01 49 0.06 0.61 0.33 0.00
3 67 0.00 0.06 0.75 0.19 34 0.00 0.09 0.56 0.35
4 25 0.00 0.04 0.16 0.80 11 0.00 0.00 0.00 1.00
3 1 48 0.58 0.38 0.02 0.02 39 0.51 0.44 0.05 0.00
2 108 0.04 0.68 0.29 0.00 57 0.04 0.56 0.37 0.04
3 133 0.02 0.11 0.65 0.23 56 0.00 0.02 0.59 0.39
4 95 0.00 0.00 0.09 0.91 65 0.00 0.00 0.06 0.94
4 1 14 0.64 0.29 0.07 0.00 2 0.00 1.00 0.00 0.00
2 46 0.07 0.65 0.28 0.00 13 0.00 0.31 0.62 0.08
3 73 0.00 0.05 0.68 0.26 30 0.00 0.03 0.73 0.23
4 153 0.00 0.00 0.07 0.93 36 0.00 0.03 0.03 0.94
从长江经济带绿色经济效率梯度转换空间分布格局来看（图5），2003—2015年间，邻域城市绿色经济效率梯度向上转移时，本区域向上转移的城市仅有2个；邻域城市效率梯度转移平稳时，区域平稳的城市75个。此阶段城市绿色经济效率的梯度转移具有明显的时空惯性，难以打破低梯度等级的路径依赖。而在“共抓大保护、不搞大开发”战略实施后，城市绿色经济效率梯度转移均转变为平稳或者向上状态。区域向上、邻域向上的城市上升到了24个，区域平稳、邻域平稳的城市84个。这表明，通过绿色转型，提升了长江经济带整体绿色经济效率，推动了区域协同发展。
Fig. 5 Spatial pattern of urban green economy efficiency gradient transfer in the Yangtze River Economic Belt

图5 长江经济带城市绿色经济效率梯度转移空间格局

注：本图基于自然资源部标准地图服务系统下载的标准地图制作，底图无修改。

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3.4 长江经济带绿色经济效率梯度转换影响因素分析

3.4.1 空间计量检验

基于上述分析，以GEE指数、EC指数和TC指数为被解释变量，构建空间计量模型，识别长江经济带绿色经济效率梯度转换的影响因素。首先对变量进行共线性分析，最高的方差膨胀因子（VIF）为6.06，说明变量间不存在多重共线性。LM-lag、LM-error检验均在1%的置信水平下拒绝原假设，故选择空间计量模型（表3）；而Hausman检验结果均通过显著性检验，满足固定效应模型条件。LR检验和Wald检验均达到显著性水平，即空间杜宾模型不会退化成空间误差模型和空间滞后模型。因此，本文选择空间杜宾固定效应模型进行实证分析。
Table 3 Testing of spatial econometric models

表3 空间计量模型检验

模型检验 GEE指数 EC指数 TC指数
统计量 P值 统计量 P值 统计量 P值
LM-lag 22.643 0.0000 8.684 0.0030 111.055 0.0000
R-LM-lag 5.955 0.0150 23.990 0.0000 54.882 0.0000
LM-error 125.65 0.0000 63.355 0.0000 198.008 0.0000
R-LM-error 108.965 0.0000 78.661 0.0000 141.834 0.0000
Hausman test 28.3 0.0004 22.93 0.0035 31.72 0.0163
LR-lag 128.47 0.0000 112.70 0.0000 190.57 0.0000
LR-error 198.70 0.0000 111.52 0.0000 304.47 0.0000
Wald-lag 131.31 0.0000 116.24 0.0000 194.83 0.0000
Wald-error 161.84 0.0000 107.47 0.0000 248.87 0.0000

3.4.2 空间计量结果分析

由表4可知，GEE指数、EC指数和TC指数的空间自回归系数分别为0.34、0.1585和0.3778，均通过了显著性检验，说明长江经济带绿色经济效率存在正向空间溢出效应。从影响因素的空间回归系数来看，经济发展、城市规模、投资强度、环境规制对绿色经济效率梯度转换具有正外部性，而产业结构、对外开放则有负向溢出效应，与预期结果相符。进一步从分解结果来看，环境规制主要通过促进技术效率进而提升邻地绿色经济效率，经济发展、城市规模主要通过促进技术进步的正向空间溢出，从而实现绿色发展的正外部性；产业结构、对外开放主要通过影响技术进步，对绿色经济效率产生抑制作用。同时，投资强度则可以通过技术效率、技术进步的“双轮驱动”实现绿色发展的正向外溢。
Table 4 Spatial Dubin model regression estimation

表4 空间杜宾模型回归结果

变量 GEE指数模型 EC指数模型 TC指数模型
lneco -0.0109(0.0198) -0.0283*(0.0166) 0.0616***(0.0198)
lncs 0.0104(0.0081) 0.0026(0.0067) 0.0184**(0.0080)
is -0.1211*(0.0672) -0.1697***(0.0562) 0.1552**(0.0674)
lnfi 0.0045(0.0032) 0.0038(0.0027) -0.0022(0.0032)
lnii -0.0932***(0.0114) -0.0516***(0.0095) -0.0297***(0.0114)
ti 1.3586*(0.6935) 1.7045***(0.5800) -1.2616*(0.6933)
gi -0.0804(0.0499) -0.1465***(0.0417) 0.1150**(0.0499)
er -0.0087(0.0146) 0.0166(0.0122) -0.0300**(0.0146)
lneco×w 0.1004***(0.0270) -0.0357(0.0224) 0.0796***(0.0274)
lncs×w 0.0336**(0.0161) -0.0131(0.0134) 0.0351**(0.0161)
is×w -0.4949***(0.0812) 0.3813***(0.0660) -0.9847***(0.0824)
lnfi×w -0.0108**(0.0047) -0.0022(0.0039) -0.0080*(0.0047)
lnii×w 0.1030***(0.0159) 0.0731***(0.0133) 0.0275*(0.0159)
ti×w -0.1149(0.8061) 0.3117(0.6775) 0.5754(0.8053)
gi×w -0.0609(0.0621) 0.1165**(0.0518) -0.2285***(0.0619)
er×w 0.0410**(0.0190) 0.0280*(0.0159) 0.0176(0.0190)
ρ 0.3400***(0.0267) 0.1585***(0.0291) 0.3778***(0.0244)
Log-likelihood 1903.3913 2253.7053 1896.5846
R2 0.2878 0.0069 0.4164
固定效应 控制 控制 控制
样本数/个 1836 1836 1836
注：*、**、***分别表示0.1、0.05、0.01显著性水平，下同。
将绿色经济效率空间溢出总效应分解为直接效应和间接效应，并对比分析两个时期变化可以发现（表5），长江经济带城市绿色经济效率转换影响因素发生显著变化。首先，2016—2020年间，经济发展水平、城市规模、产业结构、政府干预对绿色经济效率的间接效应更为突出，说明长江经济带要素流动日益密切，使得区域之间的技术扩散效应、示范效应加强[28]。同时随着部分城市产业链趋同现象加剧，区域第二产业的过度集聚和同质化严重使得环境污染扩散转移，不利于邻地城市绿色经济效率提升[50]。此外，随着政府干预程度逐渐加大，可能带来不同城市间政府“逐底竞争”，加剧环境污染，进而抑制绿色经济效率向上转换[51]。而环境规制对绿色经济效率直接效应增强，经历了由不显著到显著负效应的转变。说明过度的环境治理和规制措施，可能会导致企业生产经营成本过高，反而抑制了企业技术创新和绿色生产[40]。
Table 5 Comparison of spatial effect of urban GEE index in different periods of the Yangtze River Economic Belt

表5 不同时期长江经济带城市绿色经济效率空间效应对比

变量 2003—2020年 2003—2015年 2016—2020年
直接效应 间接效应 直接效应 间接效应 直接效应 间接效应
lneco -0.0008
(0.0196)		 0.1404***
(0.0328)		 -0.0117
(0.0178)		 0.1706***
(0.0281)		 -0.0529
(0.0328)		 0.2414***
(0.0587)
lncs 0.0136*
(0.0082)		 0.0529**
(0.0233)		 0.0073
(0.0066)		 0.0368**
(0.0186)		 0.0437**
(0.0212)		 0.0927*
(0.0511)
is -0.1678***
(0.0622)		 -0.7682***
(0.0851)		 -0.2685***
(0.0562)		 -0.0064
(0.0813)		 0.2753***
(0.1036)		 -0.5852***
(0.1883)
lnfi 0.0036
(0.0031)		 -0.0128*
(0.0068)		 0.0000
(0.0028)		 -0.0136**
(0.0063)		 0.0025
(0.0049)		 -0.0171
(0.0121)
lnii -0.0868***
(0.0105)		 0.0989***
(0.0199)		 -0.0728***
(0.0100)		 0.0288*
(0.0172)		 0.0330*
(0.0179)		 0.0808
(0.0497)
ti 1.4436**
(0.6686)		 0.5043
(1.0011)		 1.2452***
(0.4624)		 0.6371
(0.6860)		 4.9775*
(2.5799)		 4.1727
(6.1197)
gi -0.0891*
(0.0497)		 -0.1274*
(0.0734)		 -0.0936**
(0.0429)		 0.0500
(0.0686)		 -0.0793
(0.0677)		 -0.2229*
(0.1231)
er -0.0052
(0.0136)		 0.0558**
(0.0245)		 0.0046
(0.0104)		 0.0816***
(0.0178)		 -0.0654**
(0.0317)		 0.1354
(0.0869)

4 结论与启示

本文运用超效率EBM-GML模型测算长江经济带108个地级及以上城市绿色经济效率，探究“生态优先、绿色发展”背景下，绿色经济效率梯度格局变迁规律，并识别其影响因素，以期对长江经济带城市绿色协同发展提供参考。主要研究结论如下：
（1）研究期内，长江经济带城市绿色经济效率整体呈现持续上升的阶段性变化趋势。2003—2015年间的绿色经济效率变化缓慢，在“共抓大保护，不搞大开发”战略实施后，2016—2020年间的绿色经济效率进入了大幅度提升阶段。趋势面分析发现，长江经济带城市绿色经济效率沿上中下游呈现“~”型的梯度特征，从沿江到远江对比来看，则呈现扁平化趋势向倒“U”型梯度转换，整体梯度差异扩大。
（2）2003—2015年间长江经济带绿色经济效率梯度的转换存在明显的路径依赖现象。“生态优先，绿色发展”战略实施后，长江经济带绿色经济效率向上转换概率显著提升。尤其是低效率梯度城市向上转换的可能性显著增强，实现路径突破。
（3）经济发展、城市规模、对外开放、投资强度、技术创新、教育水平、环境规制、政府干预程度等多方面因素通过影响技术效率和技术进步，间接驱动绿色经济效率梯度转换。对比2003—2015年、2016—2020年两个阶段，绿色经济效率梯度转换的影响因素发生了明显变化。经济发展水平、城市规模、产业结构、政府干预对绿色经济效率的间接效应更为突出，环境规制直接效应增强。
根据上述研究结论，可得出以下研究启示：（1）推进经济社会的全面绿色转型，充分利用城市间的效率梯度势能差，发挥核心城市的辐射带动作用，打造绿色发展协同提升经济带。不同绿色经济增长类型的城市之间建立健全合作交流机制，以成渝城市群、长江中游城市群和长三角城市群为增长极，充分发挥高水平增长型城市的正向空间溢出效应。低水平增长型城市则需进一步加大对外开放程度，积极引进和承接人才、技术等优势资源，充分借鉴高水平增长型城市的成功经验。（2）推动技术创新、增强绿色发展动能，基于比较优势优化产业结构和区域分工格局，促进城市绿色经济效率梯度向上转换。明确企业技术创新的主体地位，引导支持企业自主研发创新，提高技术创新对绿色经济发展的转化效率和服务价值；进一步加强对绿色投资的力度，鼓励支持节能减排、清洁能源产业的发展，引导资金从高污染行业向创新型、环保型的高技术行业流动，加快区域产业结构高极化和合理化。就上游地区而言，四川可以发挥国家全面创新改革试验区域的先行先试效应加快创新驱动发展，贵州则可以借助大数据推动产业转型升级。而下游地区作为绿色技术进步实现跃迁的中心地区，应把技术创新方向朝节能环保等领域扩张，并将重要创新成果向中游地区辐射，推动上中下游城市间科技领域的合作联系。

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
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Abstract
运用Malmquist 指数模型测度了1990-2009 年中国省域的经济效率和全要素生产率, 并对其影响因素进行了分析。研究发现:在这20 年间, 中国的全要素生产率在全国层面及不同空间尺度的区域层面呈现波动状态, 且全要素生产率平均下降了1.35%。按照东、中、西和东北进行划分, 只有东部地区平均全要素生产率得到改善, 平均上升了2.37%, 上升的原因在于技术进步率显著改善;其余区域的全要素生产率均呈现不同程度的下降, 其中, 中部地区下降最多, 平均下降了4.45%;西部地区和东北地区的全要素生产率的平均增长率分别下降了2.78%和1.84%;原因在于中国技术效率、纯技术效率和规模效率的增长率在大部分年份出现了下降。从全要素生产率的影响因素上看, 经济集聚、人力资本、信息化、基础设施、经济开放度及制度因素对全要素生产率的影响为正;产业结构、政府干预和土地投入对全要素生产率的影响为负;但基础设施水平对全要素生产率的影响在统计学上并不显著。文章讨论了主要结果赋予的政策含义。
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https://doi.org/10.11821/xb201208006
Cited in this article [2] Abstract
The paper, using Malmquist index model, estimates China's economy efficiency and TFP (total factor productivity) during 1990-2009 years, and analyzes its affecting factors. The main result shows that China's TFP was fluctuant and fell at an annual rate of 1.35% in the past 20 years. According to the regional division, China can be divided into East, Middle, West, and Northeast regions, among which eastern region's TFP increased with an average of 2.37%, showing a promotion for the significant rise of technological progress efficiency. However, for the rest of China, the total factor productivity shows a decline in varying degrees, and the TFP for Middle, West and Northeast regions decreased by 4.45%, 2.78% and 1.84%, respectively; the TFP decline was due to the decline of technical efficiency, pure technical efficiency and scale efficiency in most years. The paper also finds that economic aggregation, human capital, information, infrastructure, open and institution are positive on total factor productivity, while industry, government intervention and land scale are negative on it. However, infrastructure is not significant in statistics. The key findings of the paper have important policy implications.
[2]
李汝资, 刘耀彬, 谢德金. 中国产业结构变迁中的经济效率演进及影响因素. 地理学报, 2017, 72(12): 2179-2198.
https://doi.org/10.11821/dlxb201712005
Abstract
现代经济增长过程表明,产业结构变迁与经济效率演进关系密不可分,具体反映在区域发展的阶段性与异质性上。运用DEA-BCC模型、Malmquist生产率指数分析中国三次产业静态综合效率与动态全要素生产率（TFP）的部门与区域变动情况,并基于DEA-Tobit两阶段分析框架构建面板计量模型,探究中国不同地区三次产业经济效率变动影响因素。研究表明：中国三次产业具备一定的静态规模效率,但仍有待优化;1978-2014年间,中国三次产业TFP均有提升,但一、二、三产业TFP增长对其部门经济贡献率依次递减,经济增长粗放型特征仍很明显;TFP增长主要源于技术进步,技术效率改进开始由以纯技术效率为主转向以规模效率为主;将三次产业TFP变动划分为四个阶段,制度、结构、要素、政策等红利对经济增长均有贡献,但在结构调整阶段,制度与结构红利让位于技术进步;三次产业TFP变动表现出显著的区域差异特征,总体上东部地区具有相对优势,中部地区表现为经济效率“凹地”,东北地区二、三产业TFP变动反映出严峻的结构转型升级问题。由于不同产业内在发展规律差异,其经济效率影响因素表现出区内相对一致性及部门差异性特征,其中一、三产业结构变动、非农化水平、对外开放程度、人力资源禀赋等对第一产业经济效率产生显著正向作用,对外开放程度显著促进第二产业经济效率提升,而对外开放程度、人力资源禀赋对第三产业经济效率产生显著的负面影响。最后讨论了结论的主要政策启示。
[LI R Z, LIU Y B, XIE D J. The evolution of economic efficiency in China's industrial structure change and its influencing factors. Acta Geographica Sinica, 2017, 72(12): 2179-2198.]
Cited in this article [1]
[3]
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. 地理学报, 2019, 74(10): 2027-2044.
https://doi.org/10.11821/dlxb201910006
Abstract
绿色发展作为化解自然环境约束、破解经济转型难题、支撑和实现全球可持续发展目标（SDGs）关键。正逐渐成为中国生态文明建设、美丽中国建设和全球经济转型与重构的重要指导理念。在梳理绿色发展概念与内涵基础上,采用SBM-Undesirable模型、泰尔指数和空间马尔科夫链等方法,对2005-2015年中国城市绿色发展效率时空分异特征及其演变过程进行了测度与刻画,并进一步耦合自然与人文因素定量探讨了人地关系地域系统下的影响机制。研究表明：① 2005-2015年中国城市绿色发展效率稳步提升,由0.475增加到0.523,总体提高了10%,时序上呈现“W”型波动增加的阶段性演变特征。② 中国城市绿色发展效率呈现出“东中西”阶梯状递减的区域差异规律,不同类型城市群具有“国家级>区域性>地方性”倒金字塔式集群增长特征,形成了“超大城市>特大城市>大城市>中等城市>小城市”稳定等级规模结构。③ 中国城市绿色发展效率空间集聚特征显著,高效率城市存在正向溢出效应,低效率城市则负向溢出影响,“高高集聚、高带动低”的空间俱乐部趋同现象较为凸显,不同类型城市演化存在显著的路径依赖与时空惯性。④ 人地关系地域系统视角下,人文社会因素对城市绿色发展效率影响程度大于自然本底要素,其中经济实力、产业结构、开放程度和城市气温呈现积极促进作用。
[ZHOU L, CHE L, ZHOU C H. Spatio-temporal evolution and influencing factors of urban green development efficiency in China. Acta Geographica Sinica, 2019, 74(10): 2027-2044.]
https://doi.org/10.11821/dlxb201910006
Cited in this article [2] Abstract

Green development is pivotal to resolving natural environmental constraints, solving national economic transition, and supporting and realizing the United Nations sustainable development goals. It is gradually growing into a crucial guideline for China's ecological civilization construction, "Beautiful China" development, and global economic transition and restructure. Based on a thorough review of the concept of green development, this paper accurately depicts a full picture of China's spatio-temporal patterns of urban green development efficiency (UGDE) in 2005-2015 by using SBM-Undesirable, the Theil index and the Spatial Markov Chain methods. Moreover, the influencing mechanism has been further discussed based on a quantitative analysis of both natural and human factors. Our results demonstrate that: (1) UGDE increased steadily by 10% from 0.475 in 2005 to 0.523 in 2015. And temporally, it shows a pattern of "W"-shaped fluctuated growth. (2) Spatially, UGDE decreased from eastern to central China, and further to western China. Besides, there is an inverted pyramid pattern of "national level > regional level > local level" urban agglomeration in UDGE growth. Moreover, there is a steady urban scale structure from super city to small city in UDGE. (3) There is an evident concentration of cities with high-level and low-level UDGE, indicating a significant influence of path dependence. (4) Quantitatively speaking, compared to natural factors, human factors such as economy size, industry structure, and openness level play a more important role in influencing the UDGE.
[4]
盖美, 秦冰, 郑秀霞. 经济增长动能转换与绿色发展耦合协调的时空格局演化分析. 地理研究, 2021, 40(9): 2572-2590.
https://doi.org/10.11821/dlyj020200702
Abstract
绿色发展与动能转换之间联系密切,研究根据二者内涵构建综合评价指标体系,基于耦合协调相对发展度模型、空间自相关、重心和标准差椭圆模型分析中国31个省市（不含港澳台）2008—2018年两系统耦合协调的时空格局演变特征,得出以下结论：① 绿色发展和动能转换综合指数均呈波动上升趋势,但空间异质性显著,东部沿海地区水平最高。② 各省绿色发展与动能转换的耦合协调度逐年提升,逐步形成东部沿海地区为轴带向中西部地区扩散的“川”字阶梯型发展格局。③ 绿色发展与动能转换的耦合协调度具有显著的空间正相关性,位于“低-低”类型区域的省份最多,高值和低值区域趋于两极集聚分布。④ 2008—2018年耦合协调度的重心主要向西南方向迁移,耦合协调度呈向西南方向分散的态势。东、中、西部地区绿色发展与动能转换耦合协调的空间格局演变特征存在差异,东部地区呈西南方向迁移的分散态势,中部地区呈西南方向迁移的集聚态势,西部地区呈东南方向迁移的集聚态势。
[GAI M, QIN B, ZHENG X X. The evolution of the spatio-temporal pattern of the coupling and coordination between economic growth kinetic energy conversion and green development. Geographical Research, 2021, 40(9): 2572-2590.]
Cited in this article [1]
[5]
陆大道. 长江大保护与长江经济带的可持续发展: 关于落实习总书记重要指示, 实现长江经济带可持续发展的认识与建议. 地理学报, 2018, 73(10): 1829-1836.
https://doi.org/10.11821/dlxb201810001
Abstract
中国国土开发与经济布局的“T”字型架构仍然是中国今后经济增长潜力最大的两大地带。本文对长江经济带的战略地位与落实习近平总书记关于“共抓大保护,不搞大开发”指示的重大意义作了初步阐述,指出了近20年来长江经济带在实现了高速经济增长的同时却忽视了保护的重要性;认为贯彻习总书记的“共抓大保护,不搞大开发”最为关键的是落实一个“共”字,即“共抓”,并提出了各地区各部门要长时期采取协调一致的具体行动的几个主要领域。
[LU D D. Conservation of the Yangtza River and sustainable development of the Yangtze River Economic Belt: An understanding of General Secretary Xi Jinping's important instructions and suggestions for their implementation. Acta Geographica Sinica, 2018, 73(10): 1829-1836.]
Cited in this article [1]
[6]
李汝资, 刘耀彬, 王文刚, 等. 长江经济带城市绿色全要素生产率时空分异及区域问题识别. 地理科学, 2018, 38(9): 1475-1482.
https://doi.org/10.13249/j.cnki.sgs.2018.09.010
Abstract
运用非期望产出的Malmquist-Luenberger指数、ESDA方法探讨长江经济带城市绿色全要素生产率变化时空格局,以GIS空间叠加方法对城市绿色TFP变动主要类型进行划分,识别不同类型城市存在的发展问题。研究结果显示：考虑非期望产出的长江经济带城市绿色TFP提升更明显,污染物减排效应反映出的技术进步对绿色TFP改善贡献突出;区域差异表现为上、中、下游城市绿色TFP增长率依次递减;长江经济带城市绿色TFP变化具有显著空间自相关性,局部热点区域表现为上、中、下游“哑铃”型分布,并开始由下游地区逐步向上游地区转移。最后将长江经济带城市划分为绿色TFP增长严重滞后型、技术进步引发绿色TFP增长滞后型、综合效率引发TFP增长滞后型、技术进步滞后型、综合效率滞后型、绿色TFP增长稳定型等6种类型区域,并从提升区域协同发展水平、明确主体功能、强化城市群辐射功能、加快绿色发展动力转换等方面提出长江经济带实现保护与开发协调发展的主要途径。
[LI R Z, LIU Y B, WANG W G, et al. Spatial-temporal evolution of green total factor productivity and identification of area problems in the Yangtze River Economic Belt. Scientia Geographica Sinica, 2018, 38(9): 1475-1482.]
https://doi.org/10.13249/j.cnki.sgs.2018.09.010
Cited in this article [4] Abstract

In this article, the Malmquist-Luenberger index based on directional distance function and ESDA method are used to study the characteristics of GTFP change stages, regional differences and spatial agglomeration patterns of 108 cities along the Yangtze River Economic Belt from 2003 to 2014. Then the article divides the main types by GIS spatial superposition method to identify the problems of different areas. The results suggest the following findings: 1) The GTFP promotion considering undesired outputs of the Yangtze River Economic Belt is more obvious. The technological progress reflected by the pollutant reduction effect contributes to the improvement of TFP. 2) GTFP changes show great regional differences and significantly spatial clustering characteristics, and the GTFP promotions of the lower, the middle, the upper reaches of the Yangtze River Economic Belt decrease sequentially. The hotspot region distributes “dumbbell-shape”, and shifts from the lower reaches to the upper reaches. 3) The Yangtze River Economic Belt is divided into six types as GTFP growth lagging type, GTFP growth lagging due to technical change type, GTFP growth lagging due to efficiency change type, technical change lagging type, efficiency change lagging Type and GTFP steady growth type. Finally, the article puts forward the main ways to realize the coordinated development of protection and development in the Yangtze River Economic Belt from the aspects of promoting regional coordinated development, clarifying the main function, strengthening the radiation function of urban agglomeration and speeding up the transformation of green development power.
[7]
郭付友, 高思齐, 佟连军, 等. 黄河流域绿色发展效率的时空演变特征与影响因素. 地理研究, 2022, 41(1): 167-180.
https://doi.org/10.11821/dlyj020200895
Abstract
基于2005—2017年黄河流域61个地级市数据,构建了黄河流域绿色发展效率投入产出指标体系,并运用多种计量方法研究了黄河流域绿色发展效率时空格局特征与驱动因素。结果如下：① 黄河流域绿色发展效率的区域差距不断扩大,研究期限内绿色发展效率呈现出由“高效率小差距”向“低效率大差距”演进,说明黄河流域绿色发展效率的俱乐部收敛特征不断凸显。② 黄河流域绿色发展效率增长主要来源于规模效率的贡献,科学技术尚未发挥重要驱动作用。③ 研究期限内黄河流域绿色发展效率存在较为明显的空间依存关系,绿色发展效率水平相近的地区空间集聚现象显著。④ 黄河流域绿色发展效率空间分异性显著,高效率地区的东西分布、南北分布的地域差异性突出,集中表现在以城市群为载体呈集群式发展。最后采用Tobit回归模型具体分析了产业结构、经济发展、科学技术、政府调控与市场化水平对于黄河流域及上中下游地区绿色发展效率的作用强度与作用效果。
[GUO F Y, GAO S Q, TONG L J, et al. Spatio-temporal evolution track and influencing factors of green development efficiency in the Yellow River Basin. Geographical Research, 2022, 41(1): 167-180.]
Cited in this article [3]
[8]
郭付友, 佟连军, 仇方道, 等. 鲁南经济带城乡绿色发展效率时空分异及驱动因素识别. 自然资源学报, 2020, 35(8): 1972-1985.
https://doi.org/10.31497/zrzyxb.20200816
Abstract
城乡绿色发展效率对高效低耗绿色发展模式构建、社会经济可持续发展以及实现城乡互动融合具有重要意义。基于鲁南经济带35个县（市、区）13 a面板数据,综合采用DEA-Malmquist指数法、城乡绿色发展效率脱钩状态模型、地理探测器等多种计量模型,对2005—2017年鲁南经济带城乡绿色发展效率时空分异特征与驱动因素进行综合研究,结果表明：（1）技术进步是鲁南经济带绿色TFP增长的主要来源,但其贡献度不断下降,而纯技术效率与规模效率对于绿色TFP增长的促进作用逐年增强;（2）研究期内鲁南经济带绿色TFP增长具有空间分异性,绿色TFP的高效率区和次高效率区有向东北和西南地区集中的趋势;（3）鲁南经济带城乡绿色发展效率的脱钩关系出现反复,由负脱钩（耦合）到脱钩再到负脱钩,整体上城乡绿色发展处于动态变化与非协调（或低级耦合）阶段;（4）普通中学在校学生数、粮食产量、城乡居民储蓄存款余额是城乡绿色发展效率的高作用力影响指标,因子交互作用后对城乡绿色发展效率解释力远超单因子,反映出鲁南经济带城乡绿色发展效率的驱动因素具有复杂性特征。
[GUO F Y, TONG L J, QIU F D, et al. Spatial-temporal pattern and driving forces of urban-rural green development efficiency in Lunan Economic Belt. Journal of Natural Resources, 2020, 35(8): 1972-1985.]
https://doi.org/10.31497/zrzyxb.20200816
http://www.jnr.ac.cn/EN/10.31497/zrzyxb.20200816
Cited in this article [1]
[9]
林晓, 徐伟, 杨凡, 等. 东北老工业基地绿色经济效率的时空演变及影响机制: 以辽宁省为例. 经济地理, 2017, 37(5): 125-132.
[LIN X, XU W, YANG F, et al. Spatio-temporal characteristics and driving forces of green economic efficiency in old industrial base of Northeast China: A case study of Liaoning province. Economic Geography, 2017, 37(5): 125-132.]
Cited in this article [1]
[10]
申丹虹, 刘锦叶, 师王芳. 黄河流域绿色全要素生产率及影响因素研究. 调研世界, 2023, (3): 3-10.
[SHEN D H, LIU J Y, SHI W F. Research on green total factor productivity and influencing factors in the Yellow River Basin. The Word of Survey and Research, 2023, (3): 3-10.]
Cited in this article [1]
[11]
姜磊, 陈元, 黄剑, 等. 财政支出效率对绿色全要素生产率影响的实证分析: 基于中国284个城市的面板数据. 经济地理, 2022, 42(11): 28-36.
[JIANG L, CHEN Y, HUANG J, et al. Impact of fiscal expenditure efficiency on green total factor productivity: Based on panel data from 284 cities in China. Economic Geography, 2022, 42(11): 28-36.]
Cited in this article [1]
[12]
苏华, 杨帆, 张亚力. 中国市域绿色全要素生产率空间计量分析. 经济地理, 2022, 42(9): 138-146.
[SU H, YANG F, ZHANG Y L. Temporal-spatial evolution characteristics and spatial economic analysis of green total factor productivity in Chinese cities. Economic Geography, 2022, 42(9): 138-146.]
Cited in this article [1]
[13]
刘雨婧, 唐健雄. 中国旅游业绿色发展效率时空演变特征及影响机理. 自然资源学报, 2022, 37(3): 681-700.
https://doi.org/10.31497/zrzyxb.20220309
Abstract
绿色发展是旅游业可持续发展理念的重要组成部分,是旅游业奉行以人为本、生态至上和全面发展的新价值观。在梳理旅游业绿色发展概念及内涵基础上,构建旅游业绿色发展效率评价体系,运用SBM-Undersirable模型、核密度估计、空间马尔科夫链等方法,探讨2008—2018年中国31个省（市、自治区）旅游业绿色发展效率（TGDE）时空演化特征及影响机理。研究发现：（1）时间和空间变化方面,TGDE总体处于中等偏下水平,时间上呈“W”型变化形态,“下降—上升—调整”阶段特征显著;空间呈“东—中—西”递减分布,内部差异为西部地区>东部地区>中部地区,低、中、高效率由“金字塔”向“菱形”结构转变,高效率地区集中于东部沿海,中等效率多分布于中西部地区,低效率位于胡焕庸线两侧。（2）动态演进方面,TGDE始终存在两极分化现象,但区域协调性逐步增强,具有较强平稳性,难以实现跨越式发展,空间向上转移省份比较集中,以中西部为主,向下调整省份较少,且存在明显的空间溢出效应,溢出影响具有不对称性。（3）影响机理方面,总体上,经济水平、产业结构、政府规制、教育水平和旅游资源影响因子与TGDE间存在显著的正向关系,对外开放程度的作用不显著,但各因子的影响程度、作用机理及条件具有较强地域性。
[LIU Y Q, TANG J X. Spatio-temporal evolution characteristics and influencing mechanism of green development efficiency of tourism industry in China. Journal of Natural Resources, 2022, 37(3): 681-700.]
https://doi.org/10.31497/zrzyxb.20220309
http://www.jnr.ac.cn/EN/10.31497/zrzyxb.20220309
Cited in this article [1]
[14]
杨志江, 文超祥. 中国绿色发展效率的评价与区域差异. 经济地理, 2017, 37(3): 10-18.
[YANG Z J, WEN C X. Evaluation on China's green development efficiency and regional disparity. Economic Geography, 2017, 37(3): 10-18.]
Cited in this article [1]
[15]
刘杨, 杨建梁, 梁媛. 中国城市群绿色发展效率评价及均衡特征. 经济地理, 2019, 39(2): 110-117.
[LIU Y, YANG J L, LIANG Y. The green development efficiency and equilibrium features of urban agglomerations in China. Economic Geography, 2019, 39(2): 110-117.]
Cited in this article [1]
[16]
黄跃, 李琳. 中国城市群绿色发展水平综合测度与时空演化. 地理研究, 2017, 36(7): 1309-1322.
https://doi.org/10.11821/dlyj201707010
Abstract
绿色发展日益重要,党的十八届五中全会把绿色发展确立为“十三五”时期的一项重要发展理念,而作为中国发展核心区域的城市群必然对绿色发展的推进起着至关重要的作用。以中国城市群为研究对象,构建绿色发展综合评价体系,采用投影寻踪模型、Pearson相关、变异系数、Theil指数等方法,综合分析中国城市群绿色发展时空特征及异质性。结果表明：① 2005年以来中国城市群绿色发展水平波动上升,且呈一定的层级格局;城市群绿色发展差异显著,分为：高度推进区、快速推进区、稳步推进区、初步推进区、初始起步区5类。② 经济发展要素为中国城市群绿色发展主导支撑、社会进步要素次之,生态文明要素逐步加快;不同层级城市群不同阶段主导要素演化不同;同一层级内差异同样显著。③ 不同层级城市群中心城市与城市群绿色发展等级匹配存在异质性,需区别对待。④ 中国城市群绿色发展水平差异呈一定发散趋势,层级间差异成为绿色发展差异的最主要原因。⑤ 最后,提出全面提升中国城市群绿色发展水平的建议：加强城市群交流与合作、实现资源共享,加大环境政策的创新以及推行力度。
[HUANG Y, LI L. A comprehensive assessment of green development and its spatial-temporal evolution in urban agglomerations of China. Geographical Research, 2017, 36(7): 1309-1322.]
https://doi.org/10.11821/dlyj201707010
Cited in this article [1] Abstract

Green development is becoming increasingly important. The Fifth Plenary Session of the 18th CPC Central Committee established green development as an important development concept of the 13th Five-Year Plan (2016-2020). Urban agglomerations in China that perform as core areas are likely to play vital roles in the promotion of green development. This study focuses on Chinese urban agglomerations as the research object, and proposes a comprehensive index system for the assessment of green development. In addition, methods such as the projection pursuit model, Pearson correlation, coefficient of variation, and Theil index are used to measure the level of green development, spatial and temporal evolution characteristics, and heterogeneity in urban agglomerations of China. Results show that: (1) since 2005, the level of green development has been increasing, although by fluctuating amounts. When divided into layers, the green development results indicate a certain degree of hierarchical structure. Based on the overall performance of China's urban agglomerations over the past decade, their green development can be divided into five grades: highly advancing area, rapidly advancing area, steadily advancing area, preliminarily advancing area, and starting area. (2) The economic development element dominates the green development, followed by the social progress element, and then the ecological civilization element, which is improving gradually. Different levels of urban agglomerations show diverse characteristics at different stages when it comes to the evolution of dominant element. National urban agglomerations are dominated by economic development element, the dominant element in regional urban agglomerations is complex, and the provincial ones generally lack dominant element. Besides, there are also significant differences in the same hierarchy urban agglomeration. (3) The green development level of urban agglomerations mismatch the level of central cities and this phenomenon needs to be treated differently. (4) According to the Theil index value, differences in green development reveal a divergent trend, mainly owing to variations among the layers. (5) Finally, several policies are proposed to raise the level of such green development in China, including the exchange and cooperation among urban agglomerations, resource sharing, and the innovation and promotion of environmental policy.
[17]
曹乃刚, 赵林, 高晓彤. 黄河三角洲县域绿色经济效率的时空演变与驱动机制. 应用生态学报, 2021, 32(9): 3299-3310.
https://doi.org/10.13287/j.1001-9332.202109.039
Abstract
综合测度黄河三角洲地区的绿色经济效率,可为实现黄河三角洲地区生态保护和高质量发展提供参考依据。本研究依托多源遥感数据构建了县域绿色经济效率评价体系,采用考虑非期望产出的Super-EBM模型对黄河三角洲县域绿色经济效率进行了综合测度,运用核密度函数估计等方法刻画了时空演变特征,最后利用系统广义矩估计法识别其影响因素。结果表明: 2000—2015年黄河三角洲县域绿色经济综合效率和纯技术效率呈现波动上升态势,规模效率呈快速提升后保持平稳的类“Γ”型趋势,综合效率的提升由规模-技术驱动向技术主导转变;黄河三角洲县域绿色经济综合效率和纯技术效率呈现由“俱乐部收敛”向“整体收敛”演进趋势,低效率县区对高效率县区形成“追赶效应”,规模效率趋向均衡平稳发展;绿色经济综合效率及其分解效率空间上形成中部高、两翼低的“山”字型格局,高值区集中于黄河三角洲岬角处和莱州湾沿岸,且高值区呈西北-东南向偏移的特征,黄河三角洲东西两翼形成低值塌陷区;产业结构、人口集聚水平、固定资产投资强度对绿色经济效率具有正向影响,人口城镇化率对绿色经济效率具有负向作用,绿色经济效率与经济发展水平之间存在明显的“环境库兹涅茨”效应。
[CAO N G, ZHAO L, GAO X T. Spatio-temporal evolution and driving mechanism of green economic efficiency at counties level in the Yellow River Delta, China. Journal of Applied Ecology, 2021, 32(9): 3299-3310.]
Cited in this article [5]
[18]
陆玉麒, 董平. 新时期推进长江经济带发展的三大新思路. 地理研究, 2017, 36(4): 605-615.
https://doi.org/10.11821/dlyj201704001
Abstract
国家重视长江经济带的规划建设非始今日,而是基本上十年就有一次,这充分说明了长江经济带在全国特别重要的战略地位,由此也使得研究长江经济带的成果特别丰富。通过对国内外宏观发展态势和长江经济带现状特征、存在问题、发展态势的总体把握,植根以往区域发展领域既有的创新性理论与方法研究积累,以及长江经济带相关研究基础,长江经济带未来发展的新思路应立足于三大视角：基于流域视角的长江经济带要素耦合研究,基于双核视角的长江经济带空间布局研究,以及基于宜居视角的长江经济带人居环境研究。具体而言,长江经济带的发展,应立足流域经济本质,以三级流域为基本单元,通过社会经济类要素与资源环境类要素的综合集成分析和空间匹配耦合分析,提炼长江经济带的空间开发类型与特色,谋划经济社会和资源环境的协调发展。与此同时,应以“一带一路”战略为背景,理清长江经济带在全国发展的宏观战略地位;通过对外通道、对外门户及其与中心城市内在关系的分析,以双核结构模式为理论工具理清长江经济带的基础设施格局和城镇演化机理,细化三大城市群的演化过程与内在机理。在上述基础上进一步从环境宜居角度,理清长江经济带的人居环境类型并进行环境宜居性评价,为成为全国环境宜居的先导区和示范区提供相应的对策建议。
[LU Y Q, DONG P. Three innovative thoughts on promoting the development of Yangtze River Economic Belt in the New Era. Geographical Research, 2017, 36(4): 605-615.]
https://doi.org/10.11821/dlyj201704001
Cited in this article [1] Abstract

China has been highlighting the planning and construction of the Yangtze River Economic Belt (YREB), radically every ten years. This indicates the important strategic roles of the YREB in China, which has resulted in great academic achievements in studying the YREB. These achievements raise an academic question: what is the new perspective on studying the YREB in the new era? This paper will systematically review the macro development trend at national and international levels and the general characteristics, issues and trends of the YREB. Based on the previously developed innovative theories and methods in the areas of regional development and the relevant studies on the YREB, this paper aims to propose three new perspectives on its future development. The first perspective of river basin is focused on coupling all the elements of the YREB. The second perspective of dual-nuclei concentrates on the spatial layout of the YREB. The third perspective of livability will look at the human settlement environment of the YREB. Specifically, the development of this region should be dependent on the economic strength of the river basin, using the three-level basin as a basic unit. Its socio-economy and resource and environment elements should be systematically integrated and coupled and spatially matched. Such analyses are used to refine the spatial development types and patterns, and particularly to achieve the coordination between socio-economic and resource and environment systems. Meanwhile, the macro strategic roles of the YREB in China should be explicitly positioned within the background of 'One Belt One Road' national strategy. The internal relationships between the YREB and other central cities should be clearly analyzed through its external pathways and doors. The patterns of infrastructure and evolution of cities and towns within the YREB should be clarified using the theory of dual-nuclei structure. Furthermore, the evolution process and essential mechanisms of the three urban agglomerations can be analyzed in details. These analyses facilitate the classification of human settlement types and the assessment of environmental livability from the perspective of environmental livability, which will provide policy suggestions for the pilot and demonstrative zones of environmental livability at the national level.
[19]
卢丽文, 宋德勇, 黄璨. 长江经济带城市绿色全要素生产率测度: 以长江经济带的108个城市为例. 城市问题, 2017, (1): 61-67.
[LU L W, SONG D Y, HUANG C. Measurement on the green total factor productivity of the Yangtze River Economic Belt: Taking 108 cities for example. Urban Problems, 2017, (1): 61-67.]
Cited in this article [2]
[20]
卢丽文, 宋德勇, 李小帆. 长江经济带城市发展绿色效率研究. 中国人口·资源与环境, 2016, 26(6): 35-42.
[LU L W, SONG D Y, LI X F. Green efficiency of urban development in the Yangtze River Economic Belt. China Population, Resources and Environment, 2016, 26(6): 35-42.]
Cited in this article [1]
[21]
方世敏, 黄琰. 长江经济带旅游效率与规模的时空演化及耦合协调. 地理学报, 2020, 75(8): 1757-1772.
https://doi.org/10.11821/dlxb202008014
Abstract
区域旅游效率和旅游规模的空间差异明显,动态把握两者时空演化特征和耦合协调关系对推动旅游高质量可持续发展具有重要的理论意义和实践价值。测度长江经济带126个市域单元2001—2018年的旅游规模,引入DEA-MI模型对旅游效率进行测算和分解,运用探索性时空数据分析方法探讨区域旅游差异和空间结构的时空动态特征,构建旅游效率与规模的耦合协调度模型,分析两者的耦合优良性和协同一致性。结果表明:① 长江经济带旅游综合效率空间差异明显,平均情况呈现东西高中间低的分布特征,年际变动呈现波动下降态势,规模效率对综合效率起支撑作用,技术效率起影响和制约作用;② 旅游效率和旅游规模局部空间结构波动幅度较小,依赖方向较为稳定,后者波动性稍强于前者,且空间依赖方向变化相似,旅游效率局部结构竞争态势强于协作,旅游规模协作整合性较强;③ 旅游规模局部空间结构较稳定,市域单元相对位置变动较困难,旅游效率局部空间结构尚不稳定,市域单元存在较大的变动可能性;④ 旅游效率与规模的整体耦合度和耦合协调度逐渐提高,具有相似的时空分异特征,局部演进存在空间异质性和波动性,耦合协调度高值区扩散范围更广、速度较缓。
[FANG S M, HUANG Y. Spatio-temporal evolutions and coordination of tourism efficiency and scale in the Yangtze River Economic Belt. Acta Geographica Sinica, 2020, 75(8): 1757-1772.]
Cited in this article [1]
[22]
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. 中国人口·资源与环境, 2020, 30(9): 128-138.
[XIN L, SUN H, WANG H, et al. Research on the spatial-temporal differentiation and driving forces of green economic efficiency based on geographic detector model. China Population, Resources and Environment, 2020, 30(9): 128-138.]
Cited in this article [1]
[23]
赵林, 刘焱序, 曹乃刚, 等. 中国包容性绿色效率时空格局与溢出效应分析. 地理科学进展, 2021, 40(3): 382-396.
https://doi.org/10.18306/dlkxjz.2021.03.003
Abstract
包容性绿色发展旨在保障经济持续增长的同时促进社会公平和资源环境改善,是建设生态文明、保障和改善民生的必然选择。论文基于包容性绿色效率评价体系,采用考虑非期望产出的Super-EBM模型综合测度了中国省域包容性绿色效率,刻画了时空格局特征,最后采用空间杜宾模型识别了空间溢出效应与影响因素。研究表明：① 2000—2017年包容性绿色综合效率与规模效率呈缓慢波动上升趋势,纯技术效率呈先下降后上升的“V”型演变,综合效率的提升由规模效应驱动向技术驱动转变。② 包容性绿色效率空间格局由低水平均衡向高水平不均衡演进,高值区集中于“胡焕庸线”向东一侧,低值区以西北、西南和东北地区为主,综合效率与纯技术效率形成京津、长三角和珠三角3个高水平集聚区,规模效率的高水平区呈“H”型分布特征。③ 包容性绿色效率的同类型地区存在空间集聚特征,且空间集聚性不断增强,热点区呈向东北移动的趋势且逐渐稳定于长三角地区,次热点以京津、珠三角地区为主,西北、西南和东北基本为冷点区。④ 包容性绿色综合效率及其分解效率存在正向空间溢出效应,经济发展水平、产业结构、工业化、政府行政能力、财政分权、环境规制、科技创新及市场化对包容性绿色效率具有显著影响,其中政府行政能力、财政分权、环境规制、科技创新和市场化具有一定溢出效应。
[ZHAO L, LIU Y X, CAO N G, et al. Spatio-temporal pattern and spillover effect of inclusive green efficiency in China. Progress in Geography, 2021, 40(3): 382-396.]
https://doi.org/10.18306/dlkxjz.2021.03.003
Cited in this article [1] Abstract

Inclusive green development aims to ensure sustainable economic growth while promoting social equity and resource and environmental improvement. It is an inevitable choice for building ecological civilization and improving people's livelihood. This study constructed an evaluation system of inclusive green efficiency, and used the Super-Epsilon-Based Measure (EBM) model considering the undesirable outputs to comprehensively measure China's inclusive green efficiency. On this basis, spatial autocorrelation analysis was used to analyze the spatiotemporal characteristics of inclusive green efficiency. By using a spatial Durbin model, the spatial spillover effects and influencing factors were analyzed. The results show that: 1) From 2000 to 2017, China's comprehensive inclusive green efficiency and scale efficiency fluctuated slowly, and the pure technical efficiency showed a "V-shaped" trend. The improvement of comprehensive efficiency gradually changed from scale effect to technology driven. 2) The spatial pattern of inclusive green efficiency in China has evolved from low-level equilibrium to high-level imbalance. The high-value areas are concentrated to the east of the Hu Huanyong Line, while the low-value areas are mainly in the northwestern, southwestern, and northeastern areas. Comprehensive efficiency and pure technical efficiency form three high-value areas in Beijing-Tianjin, the Yangtze River Delta, and the Pearl River Delta at the national level, and the high-value areas of scale efficiency show a "H" shape. 3) The same type of inclusive green efficiency shows the characteristics of spatial agglomeration, which is also continuously enhanced. The hotspots tend to move to the northeast. The hotspots are gradually stabilized in the Yangtze River Delta region, and the secondary hotspots are mainly in the Beijing-Tianjin and Pearl River Delta regions. The northwest, southwest, and northeast are basically cold spots. 4) The regression results of spatial Durbin model show that there is a positive spatial spillover effect of comprehensive inclusive green efficiency and its components. Economic development level, industrial structure, industrialization, government administrative ability, fiscal decentralization, environmental regulation, scientific and technological innovation, and marketization have significant influences on inclusive green efficiency, among which government administrative ability, fiscal decentralization, environmental regulation, scientific and technological innovation, and marketization have some spillover effects. Finally, some related policies, such as strengthening inter-regional exchanges and cooperation, accelerating the optimization and upgrading of industrial structure, optimizing the government's fiscal expenditure structure, promoting the transformation and application of scientific and technological achievements, and letting the market play a decisive role in the allocation of resources, were put forward based on the empirical analysis. This study provides a new perspective than the traditional assessment of economic efficiency, and the conclusions can provide some reference for China's ecological civilization construction and green transformation development.
[24]
刘建国, 张文忠. 中国区域全要素生产率的空间溢出关联效应研究. 地理科学, 2014, 34(5): 522-530.
https://doi.org/10.13249/j.cnki.sgs.2014.05.522
Abstract
运用空间计量模型对1990~2011年中国全要素生产率进行研究,发现：此间中国省域全要素生产率在大部分年份呈现了空间自相关性,表明这22 a间中国省域全要素生产率并不是完全的随机状态,受其它区域的影响。进一步运用空间计量经济模型从空间维度探究了区域全要素生产率的影响因素,研究表明：经济的集聚水平越高,全要素生产率会得到显著改善;人力资本对经济增长与效率的提升有着显著地积极作用,并表现一定程度的溢出;政府干预和产业结构对全要素生产率的影响为负,说明政府部门要减少对经济的干预;同时表明了中国的产业结构可能存在不合理的地方;信息化水平、基础设施水平对全要素生产率的影响为正,但基础设施水平在统计学意义上并不显著;民营化所占比重的提升对全要素生产率的改进是显著的,因为私有化致使企业的权力下放有助于提高技术效率;经济开放水平显著提升了中国的区域全要素生产率;中国部分省份土地投入规模过大而出现规模不经济的问题。从全要素生产率在各个地区间溢出的证据出发,各个地方政府在统筹区域经济发展的过程中不仅需要考虑本地区经济发展的实际,需要打破目前行政区经济的界限,实现跨区域的协调与合作,实现共赢,最终实现所有地区全要素生产率的提高。
[LIU J G, ZHANG W Z. The spatial spillover effects of regional total factor productivity in China. Scientia Geographica Sinica, 2014, 34(5): 522-530.]
Cited in this article [1]
[25]
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. 地理科学, 2018, 38(11): 1788-1798.
https://doi.org/10.13249/j.cnki.sgs.2018.11.006
Abstract
提高绿色发展效率是建设生态文明、促进经济转型发展的重要途径。基于Super-SBM模型对中国（除港、澳、台、西藏地区）2005~2015年绿色发展效率进行测度,从空间异质、空间关联与空间机理3个方面分析绿色发展效率的空间特征,运用空间杜宾模型验证绿色发展效率的溢出效应并探讨各要素的空间传导机制。结果表明：① 2005~2015年,中国绿色发展效率表现为“先平稳再快速再稳定”的阶段性变化规律,地区间差异较大,形成了“东-中-西”阶梯式递减和“南-中-北”对称式分布的空间分异特征,“T”字型发展格局逐渐凸显。② 绿色发展效率存在显著的空间正相关,空间集聚程度逐步降低,热点区域增加,东部沿海地区形成稳定的热点区,中西部形成稳定的冷点区。③ 绿色发展效率的空间自组织性逐渐增强,空间差异不断扩大,由空间自相关导致的结构化分异更加明显,随机成分引起的空间异质性正逐渐减弱,西北-东南是空间差异的主要方向。④ 绿色发展效率存在较强的空间溢出效应,经济水平、技术创新和能源强度产生明显正向效应,产业结构则具有显著负向效应。
[CHE L, BAI Y P, ZHOU L, et al. Spatial pattern and spillover effects of green development efficiency in China. Scientia Geographica Sinica, 2018, 38(11): 1788-1798.]
https://doi.org/10.13249/j.cnki.sgs.2018.11.006
Cited in this article [1] Abstract

：The concepts of innovation, coordination, green, open and sharing are the keys to the development of China and even the future. Green development is the main tone of the 13th Five-Year Plan of China, and it emphasizes on the mutual unity and coordinated development between economic growth and environmental protection. It is a kind of human-oriented way of sustainable development. Improving the efficiency of green development is an important way to achieve the ecological civilization construction and transformation of economic development the important way. This study used the spatial analysis methods, such as the Super-SBM model, spatial autocorrelation, spatial variation functions and spatial durbin model to measure the green development efficiency from 2005 to 2015 in China (Tibet, Hong Kong, Macao and Taiwan are excluded), by building an input and output index system of green development efficiency. In addition, from the perspective of geography space, it revealed the spatial pattern and spillover effects of green development efficiency in China. The results showed that: 1) From 2005 to 2015, the efficiency of China’s green development is characterized by the stage characteristics of ‘stable at beginning, then fast and last stable again’. It shows an overall upward trend with large differences among regions. The regional differentiation of the ‘East-Central-West’ stepwise decreasing and the ‘South-Central-North’ symmetrical distribution, and ‘T’ shaped shaft development pattern is particularly evident. 2) There is a positive correlation between green development efficiency, the degree of spatial agglomeration gradually decreases, the hot spots increase, the eastern coastal areas form stable hot spots, and the central and western parts form stable cold spots. 3) The spatial self-organization of green development efficiency is more and more strong, the space difference is gradually increased, the structural differentiation caused by spatial autocorrelation is more obvious, the spatial heterogeneity caused by random components is gradually weakened, and the space between northwest and southeast Significant difference. 4) There is a significant spillover effect of green development efficiency, a significant positive effect on the level of economic development, and a significant negative effect on industrial structure, urbanization and technological innovation. Trying hard to explore the law of spatial evolution of green development and provide a reference for the coordinated green development of the three systems of regional economy, society and environment.
[26]
曾刚, 胡森林. 技术创新对黄河流域城市绿色发展的影响研究. 地理科学, 2021, 41(8): 1314-1323.
https://doi.org/10.13249/j.cnki.sgs.2021.08.002
Abstract
创新是推动黄河流域高质量发展的重要途径之一。利用2006—2018年黄河流域79个地级以上城市的面板数据，首先构建指标体系对各城市技术创新及绿色发展水平进行分析，其次通过面板计量模型深入探究技术创新对城市绿色发展的作用机理。研究表明：① 2006—2018年，黄河流域城市技术创新与绿色发展水平均得到明显提升，但空间差异显著，总体呈“下游>中游>上游”阶梯式递减特征。② 黄河流域城市技术创新对绿色发展总体上没有显著影响，但在加入技术创新的二次项后，两者之间呈现显著的“U”型非线性关系，即技术创新先抑制后促进城市绿色发展，这也验证了技术的“回弹效应”假说；③ 技术创新对黄河流域城市绿色发展的影响可以通过直接效应和间接效应共同体现，但这两种效应正好相反，即一个城市技术创新能力的提升对该城市绿色发展存在显著的“U”型（先抑制后促进）关系，但对邻近城市的作用呈现倒“U”型相反的关系。根据研究结论，从技术创新对城市绿色发展的直接效应和间接效应2个方面提出了相应的政策建议。
[ZENG G, HU S L. The impact of technological innovation on urban green development in the Yellow River Basin. Geographic Science, 2021, 41(8): 1314-1323.]
Cited in this article [4]
[27]
蔡乌赶, 周小亮. 中国环境规制对绿色全要素生产率的双重效应. 经济学家, 2017, (9): 27-35.
[CAI W G, ZHOU X L. Dual effect of Chinese environmental regulation on green total factor productivity. Economist, 2017, (9): 27-35.]
Cited in this article [2]
[28]
丁玉龙. 城市规模对绿色经济效率的影响及空间效应研究: 基于我国285个地级及以上城市数据的实证分析. 城市问题, 2021, (12): 58-68.
[DING Y L. Study on the influence of city scale on the efficiency of green economic and its spatial effect: Empirical evidence from 285 prefecture-level and above in China. Urban Problems, 2021, (12): 58-68.]
Cited in this article [4]
[29]
童昀, 刘海猛, 马勇, 等. 中国旅游经济对城市绿色发展的影响及空间溢出效应. 地理学报, 2021, 76(10): 2504-2521.
https://doi.org/10.11821/dlxb202110012
Abstract
在“生态优先、绿色发展”战略背景下,针对旅游经济绿色产业外部性及其空间溢出的科学认识缺乏,论证中国旅游经济能否促进绿色发展并揭示其空间溢出特征具有理论和现实意义。选取绿色全要素生产率（GTFP）作为城市绿色发展水平评价指标;融合多源数据并利用EBM-GML模型测算并分解中国284个地级以上城市2005—2016年GTFP;利用空间分析方法刻画地市尺度下GTFP时空格局及聚类情况;依托空间杜宾模型揭示旅游经济对绿色发展的影响及空间溢出效应。结果表明：① 东部、中部、西部、东北城市GTFP年度均值呈现总体上升态势,但“中部塌陷”特征明显;地市尺度中国GTFP格局与经济版图存在空间错位。② 旅游经济具有良好的绿色发展效应,能够同时促进绿色技术效率和绿色技术进步,进而驱动目的地本地GTFP增长。③ 旅游经济对GTFP存在不显著的正向空间溢出,但对绿色技术效率具有显著正向空间溢出。④ 政策上应加强区域内旅游经济联动发展,构建旅游目的地创新溢出机制,推动旅游目的地与邻地产业分工协同发展,打造旅游业深度参与的区域产业生态圈和综合体等,以期强化中国旅游经济对绿色技术进步的空间溢出。
[TONG J, LIU H M, MA Y, et al. The influence and spatial spillover effects of tourism economy on urban green development in China. Acta Geographica Sinica, 2021, 76(10): 2504-2521.]
https://doi.org/10.11821/dlxb202110012
Cited in this article [4] Abstract

Ecological priority and green development has become one of China's national strategies. Additionally, the scientific understanding of the green externality of tourism economy and its spatial spillover is still insufficient. Therefore, in terms of theoretical and practical significance, it is necessary to demonstrate whether China's tourism economy can promote green development and reveal its spatial spillover characteristics. On the basis of constructing the spatial spillover mechanism of green development effect of tourism economy, this paper selects green total factor productivity (GTFP) as the evaluation index of urban green development level based on bibliometric analysis; integrates multi-source data and uses EBM-GML model to calculate and decompose the GTFP of 284 cities at prefecture level in China from 2005 to 2016; uses the spatial analysis method to describe the spatio-temporal pattern and spatial clustering of GTFP at prefecture level. Based on the spatial Durbin model, this paper reveals the impact of tourism economy on green development and spatial spillover effect. The results show that: (1) the annual average of GTFP in eastern, central, western and northeastern China showed an overall upward trend. Eastern China has the largest improvement in GTFP (accumulated growth of 48.08%), followed by the western region (accumulated growth of 44.18%) and the northeastern region (accumulated growth of 36.05%), while the central region has the lowest improvement (accumulated growth of 26.56%), so that the "Central Collapse" feature is obvious. Moreover, there is a spatial dislocation between China's GTFP pattern and its economic map at the prefecture level. (2) The tourism economy could significantly promote the growth of local GTFP in tourist destinations by simultaneously promoting green efficiency change (GEC) and green technological change (GTC). (3) The spatial spillover mechanism of tourism economy on green development is reflected in the fact that tourism economy can significantly improve the GEC in neighboring cities, but it cannot significantly promote the GTC in neighboring cities. (4) In terms of policy, it is necessary to strengthen the linkage development of tourism economy within the region, and build an innovative spillover mechanism for tourism destinations. In addition, it is feasible to promote the coordinated development of tourism destinations and neighboring industries, and create a regional industrial ecosystem and complex with deep participation in the tourism industry.
[30]
郝国彩, 徐银良, 张晓萌, 等. 长江经济带城市绿色经济绩效的溢出效应及其分解. 中国人口·资源与环境, 2018, 28(5): 75-83.
[HAO G C, XU Y L, ZHANG X M, et al. Spillover effect and decomposition of green economic performance of the city in the Yangtze River Economic Belt. China Population, Resources and Environment, 2018, 28(5): 75-83.]
Cited in this article [1]
[31]
陈明华, 刘文斐, 王山, 等. 长江经济带城市生态效率的时空分异及其驱动因素. 中国人口·资源与环境, 2020, 30(9): 121-127.
[CHEN M H, LIU W F, WANG S, et al. Spatio-temporal differentiation of urban eco-efficiency in the Yangtze River Economic Belt and its driving factors. China Population, Resources and Environment, 2020, 30(9): 121-127.]
Cited in this article [1]
[32]
王少剑, 高爽, 黄永源, 等. 基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测. 地理学报, 2020, 75(6): 1316-1330.
https://doi.org/10.11821/dlxb202006016
Abstract
由CO<sub>2</sub>排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。
[WANG S J, GAO S, HUANG Y Y, et al. Spatio-temporal evolution and trend of urban carbon emission performance in China based on super-efficiency SBM model. Acta Geographica Sinica, 2020, 75(6): 1316-1330.]
Cited in this article [4]
[33]
侯孟阳, 姚顺波. 1978—2016年中国农业生态效率时空演变及趋势预测. 地理学报, 2018, 73(11): 2168-2183.
https://doi.org/10.11821/dlxb201811009
Abstract
基于1978-2016年中国各省市面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列分析和空间相关性分析的基础上,构建传统和空间马尔可夫概率转移矩阵,探讨中国农业生态效率的时空动态演变特征,并预测其长期演变的趋势。研究发现：① 中国农业生态效率呈现出在波动中稳定上升的&ldquo;双峰&rdquo;分布特征,且波峰高度的差距在缩小,但整体仍处于较低水平,农业生态效率仍存在较大提升空间,东部地区农业生态效率提升较中西部地区更加显著;② 中国农业生态效率整体上向高水平方向转移的趋势显著,但农业生态效率的演变具有维持原有状态的稳定性,且较难实现跨越式转移。地理空间格局在农业生态效率时空演变过程中发挥着重要作用,空间集聚特性显著,农业生态效率较高的省市具有正向的溢出效应,而农业生态效率较低的省市具有负的溢出效应,从而在空间格局上逐渐形成&ldquo;高高集聚、低低集聚、高辐射低、低抑制高&rdquo;的&ldquo;俱乐部收敛&rdquo;现象;③ 从长期演变的趋势预测来看,多数省市农业生态效率逐渐向上转移为较高水平,并逐渐演变为由低到高渐次递增的格局,在农业生态效率较低的地理背景下,其长期演变的稳定状态表现为偏&ldquo;单峰&rdquo;分布,而在农业生态效率较高的地理背景下,其长期演变为较高水平集聚的偏&ldquo;双峰&rdquo;分布。最后,分析当前研究需要改进的方向,并提出控制农业污染排放量、地区间农业生态政策联动、加强地区间农业生态合作交流与借鉴等能够有效提升中国农业生态效率及缩小省市间差距。
[HOU M Y, YAO S B. Spatial-temporal evolution and trend prediction of agricultural eco-efficiency in China: 1978-2016. Acta Geographica Sinica, 2018, 73(11): 2168-2183.]
https://doi.org/10.11821/dlxb201811009
Cited in this article [1] Abstract

Based on the panel data of 30 provinces in China from 1978 to 2016, the super efficiency SBM model was used to measure the inter-provincial agricultural eco-efficiency in our study. On the basis of time series analysis and spatial correlation analysis, traditional and spatial Markov probability transfer matrices were constructed to explore the spatial and temporal evolution of agricultural eco-efficiency of China, and the long-term trends were also predicted. The result shows that: (1) The agricultural eco-efficiency in China presents a "double-peak" distribution with stable rise in fluctuation, and the gap between peak heights is narrowing, but the overall level is still relatively low. Therefore, there is still room for improvement in agricultural eco-efficiency. Besides, the agricultural eco-efficiency improvement in the eastern region is more significant than that in the central and western regions. (2) The trend of China's agricultural eco-efficiency shifting to a higher level is significant, but the evolution of agricultural eco-efficiency has maintained the stability of the original state, and it is difficult to achieve a leap-forward shift. The geospatial structure plays an important role in the spatial-temporal evolution of agricultural eco-efficiency and the spatial agglomeration is significant. The provinces with higher agricultural eco-efficiency have positive spillover effects, while those with lower agricultural eco-efficiency have negative spillover effects. As a result, the "club convergence" phenomenon of "high agglomeration, low concentration, high radiates low, and low inhibits high" has been gradually formed in the spatial pattern. (3) From the long-term trend prediction, the agricultural eco-efficiency in most provinces gradually shifts upward to a relatively high level, and gradually evolves from a low-to-high incremental pattern. In the context of the low agricultural eco-efficiency, its long-term stable evolution is manifested as a "partial unimodal" distribution; while under the geographical background of higher agricultural eco-efficiency, it has evolved into a "double-peak" distribution of higher-level agglomeration for a long time. Finally, we analyze the shortcomings and what needs to be improved for current research. What's more, we propose that controlling agricultural pollution emissions, inter-regional agro-ecological policy linkages, and strengthening inter-regional agro-ecological cooperation, exchange, and learning can effectively improve China's agricultural eco-efficiency and narrow the gap between provinces.
[34]
李天籽, 韩沅刚. 武汉城市圈科技金融效率时空特征与趋同演化分析. 经济地理, 2022, 42(1): 61-69.
[LI T Z, HAN Y G. Spatio-temporal characteristics and convergent evolution analysis of sci-tech finance efficiency in Wuhan Urban Agglomeration. Economic Geography, 2022, 42(1): 61-69.]
Cited in this article [1]
[35]
邵帅, 张可, 豆建民. 经济集聚的节能减排效应: 理论与中国经验. 管理世界, 2019, 35(1): 36-60, 226.
[SHAO S, ZHANG K, DOU J M. Effects of economic agglomeration on energy saving and emission reduction: Theory and empirical evidence from China. Management World, 2019, 35(1): 36-60, 226.]
Cited in this article [1]
[36]
余振, 龚惠文, 胡晓辉. 可持续性转型地理研究综述与展望. 地理科学进展, 2021, 40(3): 498-510.
https://doi.org/10.18306/dlkxjz.2021.03.013
Abstract
可持续性转型是近20 a欧洲学界的新兴研究领域,它关注既有社会技术系统向更加可持续的生产与消费模式的根本性转变,对不少国家和地区的绿色转型政策实践已经产生了重要的影响。近年来,越来越多学者开始关注可持续性转型与经济地理的交叉融合,可持续性转型地理逐渐发展成为一个新兴的研究议题,着重从空间根植性与多尺度交互2个维度回答“可持续性转型在哪里发生”的问题。论文在简要总结可持续转型理论与分析框架的基础上,系统回顾和评述了转型地理研究进展与不足,并着重从中国的情境提出未来该议题的几个重点方向：① 基于中国语境下的的转型地理概念化和理论框架构建;② 后发地区可持续性转型与绿色产业追赶;③ 城市可持续性转型差异与联系;④ 多尺度交互下转型主体能动性与权力博弈;⑤ 人工智能等新兴技术对可持续转型的影响。
[YU Z, GONG H W, HU X H. Geography of sustainability transitions: A sympathetic critique and research agenda. Progress in Geography, 2021, 40(3): 498-510.]
https://doi.org/10.18306/dlkxjz.2021.03.013
Cited in this article [1] Abstract

Sustainability transitions focus on the fundamental transformation of the existing socio-technical system towards a more sustainable mode of production and consumption. Emerged in Europe two decades ago, this new research field has already exerted impacts on the green transition policy practices of many countries and regions. In recent years, transition studies have increasingly taken geography into account, resulting in a new paradigm of geography of sustainability transitions. This emerging paradigm focuses on the role of spatial embeddedness and multi-scalar interactions in explaining where transitions take place. This article provides a critical overview of the development in the geography of sustainability transitions research, and suggests five promising avenues for future transition research in the Chinese context: 1) to develop concepts and theorize from the Chinese context; 2) to link sustainability transitions with latecomer regions' industry catch-up; 3) to compare the sustainability transitions in cities with different leading industries; 4) to pay more attention to the role of local agency through the lens of multi-scalar interactions; and 5) to explore the impact of digitalization and artificial intelligence on sustainability transitions.
[37]
李晓钟, 张小蒂. 外商直接投资对我国技术创新能力影响及地区差异分析. 中国工业经济, 2008, (9): 77-87.
[LI X Z, ZHANG X D. Analysis of different regional effect of FDI on innovative capacity in China. China Industrial Economics, 2008, (9): 77-87.]
Cited in this article [2]
[38]
钱龙. 中国城市绿色经济效率测度及影响因素的空间计量研究. 经济问题探索, 2018, 38(8): 160-170.
[QIAN L. Spatial econometric research on China's urban green economic efficiency measurement and influencing factors. Inquiry into Economic Issues, 2018, 38(8): 160-170.]
Cited in this article [2]
[39]
孙燕铭, 谌思邈. 长三角区域绿色技术创新效率的时空演化格局及驱动因素. 地理研究, 2021, 40(10): 2743-2759.
https://doi.org/10.11821/dlyj020210102
Abstract
在长三角更高质量一体化的战略背景下,绿色技术创新作为绿色发展和创新驱动两大国家战略的结合点,已成为长三角区域绿色转型发展的重要引擎。通过构建包含非期望产出的超效率SBM-DEA模型,对2010—2017年长三角区域核心城市的绿色技术创新效率进行测度,并研究其时空演化格局和驱动因素。结果显示：① 在时序演变上,长三角区域的绿色技术创新效率呈现“W”型变化特征;② 在空间演变上,长三角东南部地区的绿色技术创新效率相对稳定,而中部、西南部变动明显,整体呈现连片集聚发展特征;③ 在空间关联上,长三角区域绿色技术创新效率的区域空间联系逐渐由“极化效应”转变为“涓滴效应”,泰尔指数和基尼系数整体表现为与时序演变相反的“M”型变化特征;④ 基于长三角区域绿色技术创新投入、产出及效率测算结果,将各城市划分为高高高、高高低、高低低、低高高、低低高和低低低六种类型,进一步揭示了长三角区域绿色技术创新发展路径的区域差异;⑤ 驱动因素分析结果表明,环境规制、经济发展、产业结构、对外开放和人力资本皆对长三角区域整体的绿色技术创新效率有着显著的正向促进影响,但创新支持具有显著的负向溢出效应。
[SUN Y M, CHEN S M. The spatio-temporal evolutionary pattern and driving forces mechanism of green technology innovation efficiency in the Yangtze River Delta Region. Geographical Research, 2021, 40(10): 2743-2759.]
https://doi.org/10.11821/dlyj020210102
Cited in this article [1] Abstract

In the strategic background of high quality integration in the Yangtze River Delta (YRD), green technology innovation, as the combination of green development and innovation-driven national strategies, has become an important engine of green transformation and development in the region. This paper, by constructing a super-efficiency SBM-DEA model that includes undesired output, measures the efficiency of green technology innovation in core cities of the YRD from 2010 to 2017, and studies its spatio-temporal evolutionary pattern and driving forces mechanism. The results show that, (1) In terms of time series evolution, the green technology innovation efficiency in the study region shows a “W”-shape pattern. (2) In terms of spatial evolution, the green technology innovation efficiency in the southeast of the YRD is relatively stable, while changes in the central and southwestern parts are obvious, showing the characteristics of continuous agglomeration and development as a whole. (3) In terms of spatial correlation, the regional spatial relationship of green technology innovation efficiency in the YRD has gradually changed from the “polarization effect” to the “trickle down effect”. As a whole, Theil index and Gini coefficient show the characteristics of an “M” change opposite to the evolution of time series. (4) Based on the measured results of input, output and efficiency of green technology innovation in the delta region, all cities are identified into six types: high-high-high, high-high-low, high-low-low, low-high-high, low-low-high, and low-low-low This further reveals regional differences in the development path of green technology innovation in the YRD. (5) The research results of driving forces mechanism show that environmental regulation, economic development, industrial structure, opening degree to the outside world, human capital and urbanization all play significant positive roles in promoting the spatio-temporal evolution of green technology innovation efficiency in the YRD, while the innovation support has a significant negative spillover effect. (6) On the whole, the green technology innovation in the study region does have a significant “Porter Hypothesis” effect, but the “pollution paradise” effect mentioned in the literature has not been found.
[40]
张娟, 耿弘, 徐功文, 等. 环境规制对绿色技术创新的影响研究. 中国人口·资源与环境, 2019, 29(1): 168-176.
[ZHANG J, GENG H, XU G W, et al. Research on the influence of environmental regulation on green technology innovation. China Population, Resources and Environment, 2019, 29(1): 168-176.]
Cited in this article [2]
[41]
陈玉, 孙斌栋. 京津冀存在“集聚阴影”吗: 大城市的区域经济影响. 地理研究, 2017, 36(10): 1936-1946.
https://doi.org/10.11821/dlyj201710010
Abstract
在京津冀协同发展上升为国家战略以及雄安新区战略提出的背景下,检验京津冀城市群大小城市间的经济增长关系,可以为地区协调发展提供学术依据。研究发现：核心城市抑制了周边小城市的经济增长,存在集聚阴影和“环京津贫困带”;小城市之间也存在经济增长负面溢出效应;与长三角城市群相比,京津冀城市群内核心城市辐射和带动功能明显不足,区内城市间的发展差距更大。未来应实现多中心空间发展战略,抓住雄安新区建设的战略契机,在政府积极引导和尊重市场规律的前提下,打破行政分割,促进区域协调分工,形成大中小城市合理有序的城市群空间结构体系。
[CHEN Y, SUN B D. Does "agglomeration shadow" exist in Beijing-Tianjin-Hebei Region? Large cities' impact on regional economic growth. Geographical Research, 2017, 36(10): 1936-1946.]
https://doi.org/10.11821/dlyj201710010
Cited in this article [1] Abstract

In the context of the integrated development of Beijing-Tianjin-Hebei (BTH) region as a national strategy and the construction of Xiongan New Area, it is necessary to analyze the relationship of economic growth between large cities and small cities in this region. This paper examines the existence of "agglomeration shadow" and the "poverty belt around Beijing and Tianjin" suggested by the Asian Development Bank in 2005. The previous research of the "poverty belt around Beijing and Tianjin" did not conduct systematic analyses and empirical tests from the perspective of spatial interactions. The literature on spatial interactions has not reached an agreement on whether large cities are conducive to the economic growth of small cities. This paper aims to provide academic evidence for the coordinated development of the BTH region. The results reveal that the core cities in this region did curb the economic growth of small cities around them, supporting the "agglomeration shadow" proposed by new economic geography and the phenomenon "poverty belt around Beijing and Tianjin". Negative spillovers of economic growth exist among small cities as well, which could be attributed to the cut-throat competitions among small cities owing to the urgent desire for economic development of local governments. Compared with the Yangtze River Delta region where core cities benefit the economic growth of their adjacent cities, the radiating function and the trickle-down effect of core cities within the BTH region are obviously weak, and the development gap between large and small cities is greater. These conclusions indicate that to a great degree, the harmonious development of the BTH region is related to the radiation effects of the large cities, and the "agglomeration shadow" should be transformed into a sunshine zone of economic growth. In other words, a polycentric and reasonable urban hierarchy is crucial. From this point of view, the construction of the Xiongan New Area, as an anti-magnetic center just meets the need. This strategy will not only ease the pressure on Beijing, but also provide a new growth pole which helps to benefit the balance of regional economic growth and improve the "poverty belt around Beijing and Tianjin". The policy implications include: using the opportunity of constructing the Xiongan New Area to build a multi-centered spatial pattern with the government's active guidance and the function of the market mechanism; understanding the importance to form a rational and orderly spatial structure of urban system; breaking the obstacles owing to administrative boundaries among cities to promote the regional integration.
[42]
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https://doi.org/10.1287/mnsc.39.10.1261
https://pubsonline.informs.org/doi/10.1287/mnsc.39.10.1261
Cited in this article [1] Abstract
Data Envelopment Analysis (DEA) evaluates the relative efficiency of decision-making units (DMUs) but does not allow for a ranking of the efficient units themselves. A modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed. The procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.
[43]
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https://doi.org/10.1016/j.ejor.2010.07.014
https://linkinghub.elsevier.com/retrieve/pii/S0377221710005047
Cited in this article [1]
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ZOFIO J L. Malmquist productivity index decompositions: A unifying framework. Applied Economics, 2007, 39(18): 2371-2387.
https://doi.org/10.1080/00036840600606260
http://www.tandfonline.com/doi/abs/10.1080/00036840600606260
Cited in this article [1]
[45]
李强, 王士君, 梅林. 长春市中心城区大型超市空间演变过程及机理研究. 地理科学, 2013, 33(5): 553-561.
https://doi.org/10.13249/j.cnki.sgs.2013.05.553
Abstract
以长春市中心城区1998~2011年大型超市实地调研资料为基础,运用 GIS 空间分析和计量统计等方法,研究长春市中心城区大型超市空间演变过程、特征和机理。研究表明,长春市中心城区大型超市的空间布局演变遵循“随机-集中-分散”规律,存在整体日益分散,局部优势区域集中;空间分布不均衡,区际间差异较大;圈层发展日趋均匀,但南北方向分异明显;空间演变格局与城市发展方向一致,连锁超市布局日趋整体化等特征。研究认为消费者因素、企业自身因素、市场因素、城市发展因素是空间演变的内在机理。
[LI Q, WANG S J, MEI L. The spatial characteristics and mechanism of supermarkets in central district of Changchun, China. Scientia Geographica Sinica, 2013, 33(5): 553-561.]
https://doi.org/10.13249/j.cnki.sgs.2013.05.553
Cited in this article [1] Abstract

The commercial area is one of the most important study topics of urban geography. This article mainly studies on the spatial evolution characteristics and mechanism of super markets in Changchun by use of GIS spatial analysis and measurement statistics, which is based on the data of supermarkets in central district of Changchun from 1998 to 2011. The development of markets in Changchun started from 1994. The first supermarket was emerged in 1998, which named Yatai Supermarket. And then, the supermarkets are developed very rapidly, especially in quantity. At the end of 2011, there are 48 supermarkets in Changchun, and the total business area of supermarkets reaches 478 000 m2. According to the changes of supermarket number and distribution, the development of supermarkets in Changchun is divided into three stages: 1998-2001, 2002-2006 and 2007-2011. From 1998 to 2001, the location choice of supermarkets was random and disorderly. From 2002 to 2006, the distribution of supermarkets began to appear the phenomenon of dispersion. From 2003 to 2011, the new town trend of the distribution of supermarkets was very obviously. This study reaches some important and novel conclusions. First the spatial distribution change of supermarkets in Changchun follows the rules of “random-cluster-disperse”. The main evolution characteristics of super markets are as follows: The space directivity is very significant, and the distribution of super markets is mainly concentrated in dominant area. The Chongqing Road, Damalu Road, Dongsheng Street, Chuncheng Street and Hongqi Street, which are the hot zones of the spatial distribution of supermarkets. There is unevenly distribution in the different areas: the number of supermarkets in Nanguan District, Chaoyang District and Luyuan District is more than others areas. With the passage of time, the number of supermarkets in Changchun are more and more equal to each other in each ring road, but the distribution of change trend in north-south direction is more invariable than in west-east direction. The change trend of mean center of super markets is the same as the direction of urban development in past years and the distribution of Chain Supermarkets is more and more inclined to the whole strategy. Lastly, the research indicates that some factors are obviously affect the space evolution of supermarkets, such as the density and purchasing power of consumer, enterprise factors, market factors, the development of urban and so on.
[46]
YOUNG A. Gold into base metals: Productivity growth in the People's Republic of China during the reform period. NBRE Working Paper 7856, 2000.
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张军, 吴桂英, 张吉鹏. 中国省际物质资本存量估算: 1952—2000. 经济研究, 2004, 39(10): 35-44.
[ZHANG J, WU G Y, ZHANG J P. The estimation of China's provincial capital stock: 1952-2000. Economic Research Journal, 2004, 39(10): 35-44.]
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[48]
马勇, 童昀, 任洁. 多源遥感数据支持下的县域尺度生态效率测算及稳健性检验: 以长江中游城市群为例. 自然资源学报, 2019, 34(6): 1196-1208.
https://doi.org/10.31497/zrzyxb.20190606
Abstract
生态效率是评价区域生态文明水平的重要依据,也是地理学与经济学开展资源环境综合研究的常用指标和变量,县域尺度生态效率测度的研究匮乏,制约了上述问题在县域层面上开展。在长江经济带生态优先和绿色发展国家战略下,以长江中游城市群为研究区域,依托多源遥感数据构建县域尺度生态效率测算指标体系,应用非合意产出超效率EBM模型测算2000-2015年县域生态效率,利用GIS空间分析工具揭示其时空分异规律和空间关联特征。并设计稳健性检验方案,对县域尺度生态效率测度路径科学性及稳健性进行检验。结果表明：（1）各年份长株潭城市群和环鄱阳湖城市群所辖县域的生态效率均优于武汉城市圈所辖县域;（2）研究期内生态效率维持高位区域包括武汉市辖区等12个县市,生态效率持续低值区域包括瑞昌市等7个县市;（3）县域生态效率H-H集聚区由衡阳市、株洲市及其周边至2015年成片消失,L-L集聚区则按照顺时针走向,逐步形成围绕武汉市的闭合环形区域;（4）基于市域生态效率排名对比的稳健性检验表明,县域生态效率测度路径和结果具有较高可信度。
[MA Y, TONG Y, REN J. Calculation and robustness test of country-scale ecological efficiency based on multi-source remote sensing data: Taking the urban agglomeration in the Middle Reaches of the Yangtze River as an example. Journal of Natural Resources, 2019, 34(6): 1196-1208.]
https://doi.org/10.31497/zrzyxb.20190606
http://www.jnr.ac.cn/EN/10.31497/zrzyxb.20190606
Cited in this article [1]
[49]
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. 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. China Population, Resources and Environment, 2013, 23(7): 104-109.]
Cited in this article [1]
[50]
林伯强, 谭睿鹏. 中国经济集聚与绿色经济效率. 经济研究, 2019, 54(2): 119-132.
[LIN B Q, TAN R P. Economic agglomeration and green economy efficiency in China. Economic Research Journal, 2019, 54(2): 119-132.]
Cited in this article [2]
[51]
刘儒, 卫离东. 地方政府竞争、产业集聚与区域绿色发展效率: 基于空间关联与溢出视角的分析. 经济问题探索, 2022, (1): 79-91.
Abstract
&nbsp;地方政府竞争和产业集聚作为影响区域绿色发展的重要因素， 厘清三者之间的相互作用关系对于实现地方经济高质量发展有重要的现实意义。 本文从考虑地区经济增长和减排效应入手， 采用全要素非径向方向距离函数和 ＳＢＭ － ＤＥＡ 模型测算 ２００５ －２０１８ 年中国 ２８５ 个地级市的绿色发展效率， 从空间视角考察了地方政府竞争、 产业集聚对区域绿色发展效率的空间溢出效应。 研究结果发现： 地方绿色发展效率具有较强的空间自相关性， 且呈现空间集聚的特征； 地方政府竞争抑制了绿色发展效率的提升， 产业集聚起到促进作用， 而两者的交互作用有利于提高绿色发展效率； 时空异质性分析表明不同地理位置和城市规模下， 地方政府竞争和产业集聚对绿色发展效率的作用效果存在差异性。 因此， 应当进一步完善地方政府考核体系， 强化产业集聚的影响作用， 加强对企业技术创新的投入比例， 以推动实现地方经济高质量发展。
[LIU R, WEI L D. Local government competition, industrial agglomeration and regional green development efficiency: Analysis based on spatial correlation and spillover perspective. Inquiry into Economic Issues, 2022, (1): 79-91.]
Cited in this article [1]
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