Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment

ZHANG Ming-dou, XI Sheng-jie

JOURNAL OF NATURAL RESOURCES ›› 2023, Vol. 38 ›› Issue (1) : 220-237.

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JOURNAL OF NATURAL RESOURCES ›› 2023, Vol. 38 ›› Issue (1) : 220-237. DOI: 10.31497/zrzyxb.20230114
Green Low-carbon and Hhigh-quality Development

Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment

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Abstract

Based on the dual pressures of economic transformation and energy conservation and emission reduction faced by resource-based cities, this paper empirically measures the carbon emission efficiency of 114 resource-based cities in China from 2004 to 2019 by using a three-stage super-efficiency SBM model, and discusses the efficiency differences, spatio-temporal evolution and sources of differences by using the methods of Dagum Gini coefficient, kernel density estimation and QAP regression analysis. The results show that: (1) After the environmental factors and random interference is removed, the average carbon emission efficiency of resource-based cities decreases to 0.230, but the overall trend is on the rise. (2) The areas with high carbon emission efficiency are concentrated in regenerating and mature cities, and the distribution is relatively scattered. The low value areas are mainly growing and declining cities, and they are clustered in northeast, central and other regions. (3) The carbon emission efficiency of resource-based cities varies greatly, among which the contribution rate of inter-group difference is the highest, but it shows a downward trend; The differences within the groups of regenerating and mature cities are relatively stable, while the differences within the groups of growing and declining cities are gradually expanding. (4) The differences of population density, per capita disposable income and the number of large-scale enterprises will significantly expand the differences of carbon emission efficiency of resource-based cities, and the differences in population density, population structure and disposable income of residents have a significant heterogeneous impact on the differences in carbon emission efficiency of different types of resource-based cities. Considering the characteristics of carbon emission efficiency of resource-based cities, this paper puts forward the following policy implications: Firstly, according to the types of resource-based cities, we should adopt emission reduction schemes that suit local conditions. Secondly, we should increase investment in low-carbon innovation and promote green transformation of industry. Resource-based cities can increase R&D investment by imitating innovation, and make low-carbon innovation more directional and targeted. At the same time, resource-based cities should encourage enterprises to carry out green technological transformation and upgrading. Thirdly, we will promote green consumption mode and strengthen emission reduction of residents. The government should enhance residents' cognitive level of consumption emission reduction ability and encourage consumers to make low-carbon consumption. Moreover, the government should explore green financial innovation, establish individual carbon credit and carbon account system, and encourage residents' participation in green financial market.

Key words

resource-based cities / carbon emission efficiency / source of difference / three stage super-efficiency SBM model

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ZHANG Ming-dou, XI Sheng-jie. Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment[J]. JOURNAL OF NATURAL RESOURCES, 2023, 38(1): 220-237 https://doi.org/10.31497/zrzyxb.20230114
2020年9月,习近平总书记在第七十五届联合国大会上郑重宣布:“中国将提高国家自主贡献力度,采取更加有力的政策和措施,二氧化碳排放力争于2030年前达到峰值,努力争取2060年前实现碳中和”。淘汰高能耗、高排放的落后产能,提高碳排放效率也就成为国家当前的重要任务。资源型城市作为践行降碳减排政策的重要主体,面临着更为严峻的挑战。由于资源型城市以自然资源开发、加工为主导产业,产业结构过于单一,而且资源型产品生产难以避免较高的碳排放,因此,其需要面对经济发展转型与节能减排带来的双重压力,在落实相关低碳政策时往往力不从心。此种困境下,从碳排放效率视角出发,明确其时空演变、内在差异以及影响因素,也就成为探索资源型城市绿色转型的重要出路。
关于资源型城市,已有研究主要集中于其演化阶段的识别[1]、产业结构转型[2]、资源诅咒[3]以及生态文明建设[4]等方面,低碳经济方面的内容涉及较少。当前,国内外学者对于低碳经济的研究主要围绕二氧化碳排放的估算方法[5]、作用机理[6]、碳交易[7]以及碳排放效率[8,9]等视角展开,其中,碳排放效率作为环境绩效评估的重要内容,成为学界关注的焦点。有关碳排放效率的研究主要包含以下三个方面:一是碳排放效率的测度方法。目前,数据包络分析是使用最广泛的效率测度方法,但由于其忽略非期望产出、外生环境等因素,测度结果存在偏差。对此,部分学者采用SBM(Slack Based Model)模型[10]、超效率SBM模型[8]、三阶段DEA(Data Envelopment Analysis)模型[11]等改进后的测算方法,所得结果更加符合现实状态,尤其是三阶段测度方法与超效率SBM模型的有效结合能够同时消除上述影响[9,12]。二是碳排放效率的描述性分析。相关研究主要采用马尔可夫链、泰尔指数、基尼系数与核密度指数等方法对碳排放效率的时空特征进行探索分析,并发现中国碳排放效率区域差异较大,而且呈现扩大趋势,上海、广东、福建等东部沿海地区碳排放效率始终较高[13-15];从长期演变的趋势预测看,碳排放效率整体呈现上升趋势,并逐渐向高值区集中[8]。三是碳排放效率的影响因素。相关学者采用空间计量、QAP(Quadratic Assignment Procedure)回归以及Tobit回归等分析方法,着重探讨了产业结构、技术创新、经济发展、对外经济等对碳排放效率的影响。结果表明,产业结构升级、技术进步以及公共支出等因素均对碳排放效率产生正向影响,而经济发展差距会减弱碳排放效率的空间联系[16-20];外商直接投资根据不同地区FDI规模、结构效应和技术溢出对碳排放效率产生异质性影响,进出口贸易则与碳排放效率存在倒“U”型关系[21,22]。此外,人口特征、基础设施、城镇化等均是碳排放效率的重要影响因素[23-25]
综合已有成果可以看出,关于碳排放效率的研究已颇为全面,尤其在定量测度、描述分析、影响因素等方面均开展了一定探索,但少有研究从资源型城市视角出发,探究其碳排放效率的时空演变趋势与机理。此外,由于中国资源型城市拥有的资源类型、所处开发阶段均存在一定差异,同时这种差异形成的现实基础和内在成因并不是完全统一,而是由资源型城市自身经济发展、产业结构特点所导致。因此,实证分析中国资源型城市碳排放效率差异,归纳总结其演化规律,并在此基础上探寻差异成因,不但有助于缩小资源型城市碳排放效率差距,也能够有效提升资源型城市经济环境的协调发展水平,为国家低碳城市建设提供保障。
本文的边际贡献主要体现在以下三个方面:第一,研究样本上,为挖掘资源型城市碳排放效率的基本状况,借助ODIAC数据库克服城市尺度的碳排放数据缺失问题,为研究奠定数据基础。第二,测算方法上,本文借助三阶段超效率SBM模型对资源型城市碳排放效率进行测算,在强调“全要素”思想的基础上,考虑经济生产过程中其他投入要素的替代效应,将资本存量、劳动力、能源消耗作为投入变量,GDP和二氧化碳排放分别作为期望和非期望产出,有效剔除了非期望产出、环境因素以及统计噪声对研究结果的干扰,使得碳排放效率测算更为科学精准。第三,研究视角上,为深入探究资源型城市内在因素对碳排放效率的影响,根据《全国资源型城市可持续发展规划(2013—2020年)》中所划分的成熟型(资源开发处于稳定阶段)、再生型(基本摆脱资源依赖)、成长型(资源开发处于上升阶段)和衰退型(资源开发趋于枯竭)四种资源型城市的分类标准,借助Dagum基尼系数、核密度分析以及QAP回归模型等方法,对中国资源型城市碳排放效率的差异特征、时序演变及其成因进行探讨分析,使得对差异来源的认识更加客观合理。

1 研究方法与数据来源

1.1 研究方法

1.1.1 三阶段超效率SBM模型

DEA模型是一种线性规划模型,用于评价多投入多产出时各决策单元(Decision Making Unit,DMU)的效率状况[26]。由于传统DEA模型受到管理无效率、环境因素以及统计噪声的影响,所得结果缺乏现实性和精准度,因此,本文引入三阶段DEA模型,用以剔除环境因素和随机误差项对决策单元效率的干扰[27]
(1)第一阶段。传统三阶段DEA模型一般在第一和第三阶段选取CCR或BCC模型,两者均为径向模型,忽略松弛变量对效率值的影响,即当存有非零松弛变量时,径向DEA会高估DMU的效率值。为克服上述缺陷,Tone[28,29]先后提出了非径向、非角度的SBM模型与包含非期望产出的超效率SBM模型,后者着重考虑了要素投入与非期望污染物产出的同步性特征,并解决了各DMU效率的上限问题。为此,本文采用基于非期望产出的超效率SBM模型进行后续的实证测度与分析。具体计算公式见文献 [8]。
(2)第二阶段。SFA(Stochastic Frontier Approach)为随机前沿模型,可用于剔除环境因素和统计噪声。由于第一阶段各投入变量的松弛值受管理无效率、环境因素以及统计噪声影响,因此,此处采用面板SFA,对第一阶段得出的松弛变量进行分解[30]。具体计算公式见文献 [30]。
(3)第三阶段。利用调整后的投入变量再次进行效率的测算,模型同第一阶段。

1.1.2 Dagum基尼系数

Dagum基尼系数能够考虑子群分布状况,可将资源型城市碳排放效率分解为组内差异贡献、组间差异贡献以及受组间交叉项影响的超变密度贡献。本文采用Dagum基尼系数分解法测度四种类型资源型城市碳排放效率的基尼系数,并刻画发展差异及其来源。具体计算公式见文献 [14]。

1.1.3 核密度估计

核密度估计属于非参数检验方法,可以直观揭示资源型城市碳排放效率的演变特征。本文以资源型城市碳排放效率测度值为基础,选用高斯核函数,并通过考察主峰的位置、形状及其延展性等来揭示资源型城市碳排放效率的时序演进特征。具体计算公式见文献 [15]。

1.1.4 QAP回归模型

QAP回归模型是研究一个矩阵与多个矩阵因果效应的分析方法,其采用重排法对矩阵的各行各列同时随机置换,再进行回归检验,多次重复以估计统计量的标准误。而且,QAP回归分析不需要自变量之间相互独立的假设条件,能减少多重共线性问题对实证的影响,回归结果更加稳健。本文采用差值矩阵进行回归分析,能有效探索资源型城市碳排放效率的差异来源[31]

1.2 变量选取

1.2.1 投入变量

(1)资本存量(K)。本文采用永续盘存法进行估算,其中,基期资本存量参考张军等[32]的研究,并按照2004年城市固定资产投资占比对省级资本存量进行分配。资本折旧借鉴张少辉等[33]的做法,将相对效率定义为4.00%,将建筑和设备的使用寿命分别定义为38年和16年,其他类型投资假定为25年,计算所得折旧率分别为8.12%、18.22%、12.08%。在此基础上,再根据各年度各省份建筑、设备器具和其他费用的固定资产投资所占比例估算该省份的加权折旧率,得到分年度差异化折旧率。城市固定资产投资则按照2004年价格为基准进行平减,并参考柯善咨等[34]的做法,引入固定资产平均建设周期概念,将周期设定为三年。具体计算公式见文献 [32-34]。
(2)就业人数(L)。本文选取城镇单位就业与私营个体从业人数之和进行来衡量。
(3)能源投入(E)。由于中国在城市能耗统计层面缺少统一规范,故选取市辖区全社会用电量、天然气以及液化石油气使用量表示城市能耗数据,并根据IPCC提供的各燃料与标准煤的转换系数,换算为统一单位(吨标准煤)计算能源消耗总量[8,35]

1.2.2 产出变量

(1)期望产出。选取城市GDP作为期望产出,并以2004年为基准进行平减。
(2)非期望产出。将二氧化碳排放量视为经济活动的非期望产出。由于中国城市尺度的能源消耗数据不够全面,无法进行二氧化碳排放核算。为分析城市这一微观层面的碳排放效率,本文采用ODIAC数据库对中国资源型城市碳排放进行提取。原始ODIAC是一个全球高分辨率(约1 km)月排放数据集,其使用点源概况(发电厂排放量估计和地理定位剖面相结合)和卫星观测的夜间灯光数据对二氧化碳排放量进行估计[36]。该数据集具备较高精度且有利于以一致的方式追溯碳排放的历史特征,目前被广泛应用于碳排放测度、反演建模等多方面研究[37,38]。由于该数据统计具有统一尺度,其处理过程主要包含月度数据加总、中国区域掩膜处理以及进行表格分区统计。

1.2.3 环境变量

外部环境变量选取要求对二氧化碳排放效率具有显著性影响,同时又无法主观可控。
(1)产业结构。产业结构影响能源消耗总量,从而间接影响二氧化碳排放,因此,地区产业结构优化与其碳排放效率关联密切。同时考虑到第二产业对碳排放影响较大,第三产业减排效应并不明显[39],故采用第二产业产值占GDP比例来衡量资源型城市产业结构。
(2)科技投入水平。技术进步能够提高能源利用效率,促进清洁能源的发展,从而改善能源结构,有利于降碳减排。同时,科技发展阶段决定各类要素投入强度,在技术发展初期,会形成巨大的要素需求,而在后期,则能够通过享受技术红利提升碳排放效率。本文采用科学支出占财政支出比例衡量科技投入水平。
(3)环境规制水平。波特假说认为环境规制会刺激企业进行技术创新,从而提高能源利用效率,降低碳排放。成本约束理论则认为环境规制会增加企业生产成本,降低企业在行业中竞争能力,这可能会降低碳排放强度,却不具备可持续性[11]。根据数据的可得性,本文参考叶琴等[40]的做法,采用命令型环境规制,即计算城市废水和二氧化硫排放量的综合指数来表示城市的环境规制强度。考虑到当年环境规制强度受上年污染物排放的影响,且政策落实具有一定滞后性,故将上年综合指标定义为当年环境规制强度。具体计算公式见文献 [40]。
(4)经济开放水平。污染天堂假说认为发达国家或地区会通过向欠发达国家或地区转移高污染产业而降低规制成本,从而引起东道国环境污染,造成碳排放增强。污染光环假说则认为投资国会为东道国带来绿色清洁的生产技术,并通过示范、竞争效应提升东道国的环境保护水平,进而可能降低碳排放强度[21]。本文选取外国直接投资与GDP比值来衡量经济开放水平。

1.3 数据来源

资源型城市共包含116个地级市,由于莱芜、毕节行政区划的调整,本文最终以 114个地级市为研究样本,研究时段为2004—2019年。相关数据主要来源于2003— 2020年《中国城市统计年鉴》《中国固定资产投资统计年鉴》、ODIAC数据库以及统计公报,其中,二氧化碳排放数据来自ODIAC数据库,不同构成的固定资产投资数据来自2005—2020年《中国固定资产投资统计年鉴》,对于缺失的2017—2019年城市固定资产投资数据借助统计公报补齐。

2 结果分析

2.1 资源型城市碳排放效率分析

2.1.1 第一阶段超效率SBM测度结果

第一阶段超效率SBM的测算结果显示,中国资源型城市碳排放效率的中位数均处于0.550以下(图1),总体水平较低,仍有较大的降碳减排空间。结合中位数变化过程可知,2004—2007年资源型城市碳排放效率呈现上升趋势,并于2007年达到峰值0.527,随后,2007—2015年下降至0.443,基本又回归到2004年水平,而2015—2019年再次恢复上升趋势,但表现为波动上涨,且涨幅较小。由此可见,2008—2015年作为金融危机的恢复期,资源型产业面临较多经营困境,低碳建设逐渐让步于经济发展;2015—2019年,随着国家对降碳减排的重视程度提高,政府相关政策开始生效,促使资源型城市持续进行产业绿色转型升级,从而提升了碳排放效率。
Fig. 1 The box diagram of carbon emission efficiency of resource-based cities in the first stage

图1 资源型城市第一阶段碳排放效率箱线图

Full size|PPT slide

2014年后,箱线图中箱体与拖尾明显缩短,资源型城市碳排放效率分异特征有所减弱,自2013年《全国资源型城市可持续发展规划(2013—2020年)》下发后,资源型城市低碳经济建设可能具有趋同特征,使得碳排放效率离散程度缩小。同时,2014年后,破坏箱体上下限的离群值有所增多,资源型城市碳排放效率存在极点,这与中国对资源型城市的分类情况相契合,成熟型和再生型城市一般具有较高的碳排放效率,反观成长型和衰退型城市的碳排放效率较低,其在政策落实、技术普及以及产业转型等方面还有较大提升空间。

2.1.2 第二阶段SFA回归结果

将第一阶段所求投入变量的松弛值作为被解释变量,将四个环境变量作为解释变量,并使用Frontier 4.1进行SFA分析。结果显示,三组回归γ值均较大且接近1,这说明管理无效率在复合误差项中占主导作用。因此,实际投入值和目标投入值之间的差距受外部环境因素的影响,借助SFA模型对要素投入进行调整具备较高适用性,实证结果见表1
Table 1 Second stage SFA regression results

表1 第二阶段SFA回归结果

变量 资本存量松弛量 就业人数松弛量 能源投入松弛量
常数项 -525.046***
(-8.190)
-3.292
(-1.214)
275.703***
(15.631)
产业结构 678.162***
(5.628)
12.788***
(2.767)
-11.597
(-0.496)
科技投入水平 19341.987***
(16.094)
152.493***
(3.090)
994.766***
(664.017)
环境规制水平 -34.888**
(-2.553)
0.237
(0.493)
14.955***
(9.237)
经济开放水平 18.909***
(3.707)
0.072
(0.462)
-0.730
(-1.102)
σ2 2787277.900***
(238411.520)
1161.639***
(8.339)
21086.387***
(18927.674)
γ 0.920***
(362.727)
0.766***
(26.415)
0.777***
(106.443)
对数似然函数值 -14385.034 -7817.007 -10643.103
LR单边检验 2826.814*** 560.952*** 866.380***
注:******分别表示10%、5%、1%显著性水平,括号内为T统计量。
产业结构对资本和劳动投入松弛值的影响均为1%水平显著正相关,表明中国资源型城市第二产业的发展对资本和劳动仍具有较高依赖性,还未进入人力资本和知识资本赋能生产效率的发展阶段,不利于碳排放效率的提升。而能源投入系数非显著为负,表明资源型城市清洁能源的使用未对化石燃料呈现明显替代作用。
科技投入水平对资本、劳动和能源投入松弛值的影响均为1%水平显著正相关,表明中国资源型城市技术创新处于对研发资本、技术人才以及能源的高需求阶段,更多要素投入虽可以有效适配创新水平,但会拉低资源型城市碳排放效率。
环境规制水平对资本投入松弛值的影响为5%水平显著负相关,对劳动和能源投入则分别为非显著和显著性正相关,表明现阶段中国资源型城市对环境污染治理未过度依赖资本投入。而且,环境规制没有对节省能源消耗产生积极影响,一方面资源型城市可能通过相关技术创新,在不减少能源投入基础上,降低碳排放强度,另一方面环境规制可能还未呈现较好效果,清洁能源未真正替代传统能源并占据主导地位。
经济开放水平对资本投入松弛值的影响为1%水平显著正相关,对劳动和能源投入分别为非显著正相关和负相关,表明外资进入带来的生产、环境等问题可能造成更高的国内规制成本(污染天堂假说),不利于提升碳排放效率。能源方面则可能由于政府对外资进入能源板块进行管控,故回归系数未显著。

2.1.3 第三阶段超效率SBM测度结果

基于第二段SFA分析对投入变量做出的处理,本文利用调整后的投入变量再次计算资源型城市碳排放效率,图2为第一和第三阶段碳排放效率均值的折线图。由图2可知,第三阶段资源型城市碳排放效率显著低于第一阶段,均值由0.534降至0.230,这是因为在第二阶段SFA分析中,环境变量系数多为正数,整体提升了要素投入水平。由此可见,忽略环境因素及随机误差项的干扰,造成对资源型城市碳排放效率的高估。在第三阶段,资源型城市碳排放效率呈现再生型>成熟型>衰退型和成长型(两者基本相同)的发展格局,表明再生型城市通过产业结构转型,在低碳经济建设方面具备一定优势,而衰退型和成长型城市有必要继续加大在减碳工作方面的投入。时序上,第一阶段碳排放效率表现为先上升再下降,最后逐渐平稳的发展趋势;而第三阶段碳排放效率则整体为上升趋势,涨幅达17.56%,其中,94个城市出现增长,仅少数城市出现下降的情况。因此,第一阶段的实证测算低估了中国资源型城市碳排放效率的提升速度,也再次说明借助三阶段分析方法排除环境因素及随机干扰的必要性。此外,四大类型资源型城市碳排放效率均表现为上升趋势,再生型城市提升最为缓慢,衰退型和成长型城市上升较快,表明成长型城市在加快能源开发的同时,能有效控制碳排放强度,而衰退型城市得利于国家的大力扶持,在降碳减排方面已初见成效。
Fig. 2 The average carbon emission efficiency of resource-based cities in the first and third stages

图2 资源型城市第一和第三阶段碳排放效率均值

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为更深入地分析资源型城市碳排放效率的空间演变特征,本文使用ArcGIS 10.3软件将第三阶段测算的碳排放效率可视化,得到空间分布图(图3)。图3根据时间中心对称原则列出2004年、2009年、2014年和2019年碳排放效率分布图,并利用自然断点法对2004—2019年碳排放效率均值进行分类,以此为依据展开各年份分析。
Fig. 3 The carbon emission efficiency of resource-based cities in the third stage

图3 资源型城市第三阶段碳排放效率

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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图3可知,资源型城市碳排放效率高值区较为分散,仅存在区域性“极点”,未出现高值集聚特征,这与资源型城市改革困境密切相关,单点城市的“爆发”,并没有足够“能量”带动周边资源型城市共同发展。而低值区在东北、中部等地区存在广泛的集聚特性,诸多资源型城市在低碳经济建设方面无法做到兼顾经济发展与降碳减排,甚至部分城市两方面均发展受阻,可见资源型城市还需在两者的协调性方面寻找突破口。4个年份中,淄博、唐山、自贡、鞍山、济宁、临沂碳排放效率均处于前10名,其中,淄博、唐山、临沂和鞍山为再生型城市;济宁、自贡为成熟型城市,在经济发展、能源利用效率、控制碳排放强度等方面具备优势。后10名多为衰退型城市,资源枯竭、经济发展放缓以及产业转型困难是其碳排放效率水平较低的主要原因。此外,观察期内,部分城市碳排放效率变化较大,如萍乡、六盘水、鄂尔多斯等城市出现明显上涨,唐山、本溪、克拉玛依等城市则下降较多。其中,萍乡为衰退型城市,自身碳排放效率水平较低,近几年随着相关政策的落实,在节能减排方面有所进步。六盘水、鄂尔多斯作为成长型城市,处于资源开发的增速期,碳排放效率上升表明其在发展资源型产业的同时,十分重视碳排放强度,能够做到统筹兼顾。唐山为再生型城市,虽摆脱了资源依赖,却还没有形成真正的低碳经济发展模式,一方面部分高排放产业仍未淘汰,另一方面相关新兴产业可能依然会造成较高的碳排放。本溪和克拉玛依均为成熟型城市,其具备相对完善的资源型产业链,在资源开采、加工、制造等方面基础良好,但是成熟的发展模式并不一定符合低碳经济的标准,成熟型城市仍需寻找接续替代产业,探索新的发展路径。

2.2 资源型城市碳排放效率差异分析

2.2.1 资源型城市碳排放效率总体及组内差异分析

图4可知,资源型城市碳排放效率总体差异呈波动下降趋势,并于2014年达到最低点0.451,2014—2019年则基本趋于稳定,整体下降幅度为9.69%,但由于其均值为0.475,数值偏大,碳排放效率的非均衡特征显著。从四大城市类型的对比分析可以看出,2015年前,资源型城市碳排放效率差异的层级特征明显,表现为再生型>成熟型>衰退型>成长型;而2015年后,成长型和衰退型城市差异有所上升,再生型和成熟型则分别出现不同程度的下降,并于2019年最终形成衰退型>再生型>成长型>成熟型的发展格局。国家于2013年下达有关资源型城市的发展规划后,再生型和成熟型城市能够借助自身发展优势和基础,不断缩小碳排放效率差异。衰退型城市则面临产业结构方面的改革困境,组内差异难以有效改善,而成长型城市处于资源开发的上升阶段,由于其资源型产业的准入门槛、排放标准、能源禀赋等方面存有差异,故于2015—2017年基尼系数出现较大幅度上升。
Fig. 4 The overall and intra-group Gini coefficient of carbon emission efficiency of resource-based cities

图4 资源型城市碳排放效率的总体及组内基尼系数

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2.2.2 资源型城市碳排放效率组间差异分析

表2可知,再生型城市分别与衰退型、成长型以及成熟型城市形成了较高的组间差异,其基尼系数均值分别为0.632、0.626和0.543,而成熟型—成长型、成熟型—衰退型城市基尼系数分别为0.429、0.442,处于中间位置,成长型—衰退型城市之间差异最小,基尼系数均值仅为0.383。由此可见,资源型城市碳排放效率与其成长阶段显著相关,成长型城市处于资源开发上升期,资源保障潜力大,对低碳经济建设还处于摸索阶段,碳排放效率较低;而成熟型城市资源开发处于稳定阶段,资源保障能力强,经济社会发展水平较高,能够有效控制碳排放,与其他类型城市碳排放效率形成差异。在资源开发后期,经济转型成功的再生型城市基本摆脱了资源依赖,经济社会发展步入良性轨道,能够在不断培育战略新兴产业的同时,有效兼顾降碳减排工作,从而与其他类型城市形成较大的碳排放效率差距,而转型受阻的衰退型城市则存在经济发展滞后、生态环境压力大等发展难题,碳排放效率水平较低。
Table 2 Inter-regional Gini coefficient of carbon emission efficiency of resource-based cities

表2 资源型城市碳排放效率的组间基尼系数

年份 再生型—
成熟型
再生型—
成长型
再生型—
衰退型
成熟型—
成长型
成熟型—
衰退型
成长型—
衰退型
2004 0.563 0.674 0.683 0.465 0.477 0.385
2005 0.567 0.672 0.673 0.446 0.464 0.372
2006 0.559 0.663 0.662 0.440 0.457 0.370
2007 0.568 0.665 0.667 0.449 0.465 0.374
2008 0.576 0.664 0.673 0.438 0.450 0.365
2009 0.562 0.648 0.655 0.440 0.444 0.363
2010 0.539 0.632 0.618 0.424 0.427 0.366
2011 0.528 0.617 0.606 0.422 0.426 0.365
2012 0.531 0.616 0.610 0.420 0.423 0.362
2013 0.535 0.616 0.614 0.417 0.428 0.366
2014 0.516 0.591 0.599 0.404 0.421 0.366
2015 0.534 0.607 0.616 0.404 0.427 0.374
2016 0.526 0.587 0.610 0.415 0.431 0.408
2017 0.542 0.603 0.621 0.432 0.444 0.429
2018 0.528 0.591 0.614 0.427 0.444 0.430
2019 0.507 0.574 0.596 0.426 0.445 0.430
均值 0.543 0.626 0.632 0.429 0.442 0.383

2.2.3 资源型城市碳排放效率差异来源分解

表3可知,资源型城市组间差异贡献率均值高达44.51%,组内差异和超变密度平均贡献率分别为31.25%和24.25%,表明组间差异是资源型城市碳排放效率差异的主要来源。研究期内,组间差异和超变密度贡献率分别呈现下降和上升趋势,变动幅度为10.15%和9.52%,而组内差异贡献基本稳定。说明资源型城市碳排放效率组间差异总体在缩小,主要在于成熟型、成长型、衰退型城市碳排放效率增长速率超过再生型城市,从而减小了与再生型城市之间的差距,导致再生型—成熟型、再生型—成长型以及再生型—衰退型城市的基尼系数下降。
Table 3 Source decomposition of carbon emission efficiency difference in resource-based cities

表3 资源型城市碳排放效率差异来源分解

年份 区域内 贡献率/% 区域间 贡献率/% 超变密度 贡献率/%
2004 0.157 31.24 0.245 48.73 0.101 20.03
2005 0.153 30.89 0.241 48.62 0.102 20.49
2006 0.152 30.99 0.235 47.99 0.103 21.02
2007 0.156 31.28 0.235 47.17 0.108 21.55
2008 0.151 30.48 0.241 48.64 0.104 20.88
2009 0.153 31.27 0.230 47.06 0.106 21.67
2010 0.146 31.22 0.210 44.96 0.111 23.82
2011 0.147 31.77 0.202 43.66 0.114 24.58
2012 0.147 31.72 0.203 43.99 0.112 24.30
2013 0.147 31.61 0.205 44.04 0.113 24.35
2014 0.143 31.67 0.194 43.05 0.114 25.28
2015 0.143 31.07 0.202 43.88 0.115 25.05
2016 0.142 30.88 0.190 41.44 0.127 27.68
2017 0.147 30.93 0.191 40.21 0.137 28.85
2018 0.145 31.10 0.187 40.08 0.134 28.83
2019 0.146 31.87 0.177 38.58 0.135 29.55
均值 0.148 31.25 0.212 44.51 0.115 24.25

2.3 资源型城市碳排放效率动态演进分析

本文通过观测核密度分析中主峰的中心位置、形状及其延展性等来探究资源型城市碳排放效率的时序演变特征,如图5所示。
Fig. 5 The estimation results of carbon emission efficiency and kernel density of resource-based cities

图5 资源型城市碳排放效率核密度估计结果

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从分布位置看,资源型城市整体以及成熟型、成长型和衰退型城市的主峰位置均出现一定程度右移,碳排放效率得到明显提升,这与前文客观事实相符。再生型城市主峰位置变化不大,碳排放效率趋于稳定。
从主峰形态看,资源型城市整体的主峰经历了持续缓慢下降的演变过程,宽度基本不变。成长型和衰退型城市则形态相仿,自2009年开始,其主峰高度明显下降,宽度也有所增大,这意味着资源型城市整体以及成长型和衰退型城市碳排放效率绝对差异呈现扩大趋势。再生型和成熟型城市主峰演进规律基本一致,其主峰高度于2009年、2017年等多个年份出现波动,但总体稳定,而且再生型和成熟型城市主峰宽度基本恒定,组内绝对差异趋于稳态。
从分布延展性看,资源型城市整体及四大类型城市碳排放效率分布曲线均呈现向右拖尾现象,碳排放效率差异较大。此外,资源型城市整体以及再生型和成熟型城市碳排放效率均呈现收敛趋势,这表明组内碳排放效率较高的城市与平均水平更加接近。而成长型和衰退型城市碳排放效率延展性具有拓宽特征,组内差异逐渐扩大。
从极化趋势看,资源型城市整体以及再生型和成熟型城市分布主要呈现单峰特征,侧峰隆起幅度较小,表明碳排放效率均未出现明显的多极化现象。2015年前,成长型城市碳排放效率由多峰构成,且多个侧峰变化幅度较大,并于2015年后逐渐演变为单峰。衰退型城市则在2009年之前由主峰和侧峰构成,2009年后,侧峰逐渐消失,这表明衰退型城市呈现由两极化向单极化演变的发展趋势。

2.4 资源型城市碳排放效率差异来源分析

2.4.1 变量选取与模型构建

为探寻资源型城市碳排放效率的差异来源,本文借助非参数方法QAP对城市碳排放效率差异矩阵进行回归分析,并参考相关文献[16,23-25,41],选取六个影响因素展开讨论。其中,人口因素造成的集聚效应与拥挤效应是影响碳排放效率的正反两面,而人口结构的年轻化更为产业发展和城市人力资本补给提供可能性支撑,尤其0~14岁少年儿童比例是反映人口年轻化结构与社会经济发展的重要潜在指标(受限于数据,选用中小学生在校人数替代),因此,选取人口密度和人口结构同时表征资源型城市的人口特征。
居民消费行为作为碳排放的重要来源,对碳排放效率产生特定影响,本文选取居民可支配收入表示居民消费能力;政府行为和公共服务环境对碳排放效率的影响则分别使用财政支出和基础设施建设表示;规模企业数量则强调企业主体在碳排放效率中的影响作用。具体回归模型如下:
R=fPop, Ps, Dpi, Fis, Road, Co
(1)
式中:R表示资源型城市碳排放效率差异关系矩阵;PopPsDpiFisRoadCo分别为人口密度差异、人口结构差异、居民可支配收入差异、财政支出差异、基础设施差异和规模企业数量差异,具体变量说明如表4所示。
Table 4 The description of influencing factors and variables of carbon emission efficiency difference in resource-based cities

表4 资源型城市碳排放效率差异的影响因素及变量说明

变量名称 变量含义 指标计算 数据来源
Pop 人口密度差异 城市i和城市j户籍人数与行政区域面积比值差值矩阵 中国城市统计年鉴
Ps 人口结构差异 城市i和城市j中小学生人数占户籍人口比例差值矩阵 中国区域经济统计年鉴
Dpi 居民可支配收入差异 城市i和城市j城镇居民可支配收入差值矩阵 中国区域经济统计年鉴
Fis 财政支出差异 城市i和城市j财政支出占GDP比例差值矩阵 中国城市统计年鉴
Road 基础设施建设差异 城市i和城市j公路里程与行政区域面积比值差值矩阵 中国区域经济统计年鉴
Co 规模企业数量差异 城市i和城市j规模以上工业企业数量差值矩阵 中国城市统计年鉴

2.4.2 基准回归分析

表5可知,(1)研究期间内,人口密度差异和规模企业数量差异系数显著为正,表明两者均为资源型城市碳排放效率差异的重要来源。其中,人口密度差异系数最大,人口密度虽然可通过人口集聚效应和规模效应提升公共服务共享价值,发挥人力资本优势,形成技术创新,进而提高能源利用效率,但也会形成集聚不经济,人类活动会直接或间接产生大量碳排放,因此,人口密度的悬殊会明显扩大碳排放效率的差距。作为碳排放的重要主体,企业的用能、技术创新等行为均会影响碳排放强度,故规模企业数量差异扩大了资源型城市碳排放效率差异。(2)人口结构差异仅2019年系数负向显著,说明2019年人口结构差异有利于缩小资源型城市碳排放效率差异。年轻化的人口结构是吸引产业的关键区位因素,也是重要的潜在人力资本,由于资源型城市自身发展受阻与人口流失,其未真正通过人口结构年轻化水平形成有效的产业吸引力和人力资本积累,进而未形成正向影响。(3)2009年居民可支配收入差异系数正向非显著,其余年份均为正向显著,表明居民可支配收入差异会扩大资源型城市碳排放效率差异。居民可支配收入增加,会提高商品服务消费水平,进而提升各类消费性碳排放,从而造成差异扩大化。(4)基础设施建设差异系数均为负向,且仅有2009年和2019年系数显著,这说明基础设施差异会缩小碳排放效率差异。基础设施差异会形成公共服务环境异质化,这种异质化一般会导致碳排放差异化,因此负向系数可能是由于资源型城市基础设施建设差异化程度较低,且对产业吸引能力总体偏弱。(5)政府财政支出差异系数各年份均不显著,表明政府财政支出差异与资源型城市碳排放效率差异无明显关系。这可能是因为财政支出对碳排放效率的影响主要来自于环境规制强度,本文所测算的碳排放效率已将环境规制这一环境因素剔除,导致两者差异不具备相关性。
Table 5 Benchmark regression results

表5 基准回归结果

变量名称 2004年 2009年 2014年 2019年
Intercept -0.272*** -0.261*** -0.312*** -0.219***
Pop 1.132** 1.432** 1.256** 3.028***
Ps 0.051 0.286 -0.304 -0.525*
Dpi 0.017** 0.006 0.123*** 0.021***
Fis 0.108 0.050 -0.494 -0.035
Road -0.027 -0.093** -0.045 -0.114***
Co 0.059*** 0.064*** 0.054*** 0.041***
R2 0.194 0.231 0.215 0.195
随机置换次数/次 5000 5000 5000 5000

2.4.3 异质性回归分析

表6可知,(1)再生型、成熟型以及成长型城市的人口密度差异回归系数显著为正,衰退型城市则显著为负,表明人口密度与碳排放效率关系密切,但其差异水平并非衰退型城市碳排放效率差异的主要来源,人口的集聚效应所产生的环境外部性对衰退型城市作用不明显。(2)人口结构差异与再生型城市碳排放效率差异显著负相关,其他类型城市回归系数均不显著,这与基准回归解释类似,再生型城市未通过人口结构年轻化水平形成有效的产业吸引力和人力资本积累。(3)居民可支配收入差异与成熟型城市碳排放效率差异显著正相关,其他类型城市回归系数均不显著,表明居民可支配收入所导致的消费性碳排放差异会在成熟型城市中体现,这可能与成熟型城市资源开发稳定,经济社会发展水平相对较高有关。(4)财政支出差异、基础设施建设差异以及规模企业数量差异的回归系数基本与基准回归相同,且不存在明显异质性特征。
Table 6 Heterogeneous regression results

表6 异质性回归结果

变量名称 再生型 成熟型 成长型 衰退型
Intercept -0.056*** -0.095*** -0.217*** 0.036***
Pop 4.928** 5.182*** 6.978** -1.159**
Ps -0.353** -0.577 0.710 0.171
Dpi 0.019 0.017** 0.017 -0.008
Fis 0.124 0.092 -0.159 -0.186
Road -0.309** -0.249*** -0.134*** -0.254***
Co 0.037* 0.021*** 0.030*** 0.016**
R2 0.175 0.213 0.381 0.338
随机置换次数/次 5000 5000 5000 5000

3 结论与讨论

3.1 结论

本文以中国114个资源型城市为研究样本,在“全要素”视角下,运用三阶段超效率SBM模型实证测度了2004—2019年的碳排放效率,并利用Dagum基尼系数、核密度分析、QAP回归等方法对效率差异、演变趋势以及成因来源进行深入探讨。结果表明:
(1)剔除环境因素和随机干扰后,资源型城市碳排放效率均值降至0.230,但整体呈现上升趋势。第一阶段中,资源型城市碳排放效率具有较强的分异特征,且未呈现特定发展趋势。第二阶段SFA回归分析表明,产业结构、科技投入水平、环境规制水平以及经济开放水平对投入要素的松弛变量多为正向影响,总体需求更高的要素投入来适配外部环境造成的冲击,故第三阶段碳排放效率下降明显,且效率均值降为0.230,呈现较低的碳排放效率水平。第三阶段剔除外生环境因素和随机干扰后,资源型城市碳排放效率总体转变为上升趋势,且高值区呈现“零星”分布,低值区在东北、中部地区存在集聚性特征。此外,高值区主要集中于再生型与成熟型城市,低值区则集中于衰退型与成长型城市,其中,再生型城市碳排放效率增长缓慢,成熟型、成长型以及衰退型城市则呈现较快增长。
(2)不同类型的资源型城市碳排放效率呈现显著分异特征。资源型城市碳排放效率总体差异波动式下降。2015年之前,四大类型城市碳排放效率呈现再生型城市差异>成熟型>衰退型>成长型的分布格局;2015年之后,逐渐向衰退型城市差异>再生型>成长型>成熟型的分布格局转变。组间差异主要来源于再生型城市与其他类型城市碳排放效率发展差距。从贡献率大小看,组间差异贡献率最大,且呈现下降趋势,超变密度贡献率最低,并有所上升,组内差异贡献则基本恒定。
(3)成长型和衰退型城市碳排放效率内部差异化特征扩大,再生型和成熟型城市差异化趋于稳定。资源型城市整体以及四大类型资源型城市碳排放效率呈现上升趋势,且存在极化现象。其中,再生型和成熟型城市碳排放效率表现为“单极”结构,组内绝对差异相对稳定,而成长型和衰退型城市则逐渐由多极向单极演进,组内绝对差异逐渐扩大。
(4)人口密度、居民可支配收入和规模企业数量差异是资源型城市碳排放效率分异的重要来源。人口密度、居民可支配收入和规模企业数量差异会显著扩大资源型城市碳排放效率差异,人口结构和基础设施建设差异则会有效降低碳排放效率差异。而且人口密度、人口结构以及居民可支配收入差异对不同类型资源型城市碳排放效率差异呈现显著的异质性影响。

3.2 讨论

结合中国双碳战略背景,并考虑资源型城市碳排放效率特征,提出如下政策启示:
(1)依据资源型城市类型,采取因地制宜的减排方案。不同类型资源型城市的资源特征与发展阶段存在差异,因地制宜地采取减排方案势在必行。结合资源型城市碳排放效率测度结果,成长型和衰退型城市始终是碳排放效率的低值区,其中,成长型城市应重视资源开发增速阶段的减排工作,着力提升资源深加工、绿色加工水平,同时严控资源开发型企业的进入门槛,控制资源开发强度,形成开发、治理同步进行的发展模式;而衰退型城市则需借助技术创新、财政转移支付等手段,通过向再生型城市学习改革经验,实现完全替代或链条延伸式的产业转型升级。
(2)增加低碳创新投入,促进产业绿色转型。对于资源型城市而言,其自身科研基础薄弱,因此,可通过模仿创新的方式增加研发投入,使得低碳创新更具方向性和针对性。同时资源型城市应激励企业主体进行绿色化的技术改造和升级,推广清洁的生产工艺、技术和生产设备,并加强产学研深度合作,深化科技成果转化,进而实现资源开发数字化、智能化以及低碳化的转变,促进产业的绿色转型升级。
(3)推广绿色消费方式,加强居民端减排。政府应提升居民端对消费减排能力的认知水平,从气候变化、高碳消费的影响结果着手进行宣传教育,鼓励消费者进行低碳消费。此外,政府还应探索绿色金融创新,建立个人碳信用、碳账户体系,对购买节能产品的消费行为提供优惠的信贷支持,以此提升居民绿色金融市场的参与度。同时,政府还需提供各类低碳消费的应用场景,在绿色出行、低碳产品在线购买等方面提供智慧化、便民化服务。

References

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Abstract
资源型城市作为一类特殊城市,发展过程具有明显的阶段性特征。本文以灯光影像等数据为基础,采用门槛面板模型、非参数估计等方法,从城市空间结构演化角度,对资源型城市发展阶段进行定量划分,并分析了不同发展阶段中心城区空间集聚对城市经济增长、城市转型的差异性特征,揭示了资源型产业对城市空间结构演变的作用以及资源型城市转型关键时间节点和政策着力点。结果表明:资源型城市空间相对分散,但不同资源类型城市之间存在较大差异;采掘业从业人员占比1.9%和31.0%可以作为资源型城市不同发展阶段的重要标志性指标;不同发展阶段中,社会经济要素在中心城区的集聚程度与城市经济增长的相关关系明显不同,成熟发展期成为资源型城市转型发展的重要转折期。
[LU S, ZHANG W Z, YU J H, et al. The identification of spatial evolution stage of resource-based cities and its development characteristics. Acta Geographica Sinica, 2020, 75(10): 2180-2191.]

Resource-based cities refer to the cities where the exploiting and processing of natural resources, such as minerals and forests, dominate industrial development. This special type of city is obviously staged during its development. From a new perspective of urban spatial structure, this paper quantitatively splits the development stages of resource-based cities based on the data of night lights images and uses the methods, such as the threshold panel model and non-parametric estimation. To delve into the impacts of resource-based industries on the evolution of urban spatial structure, as well as the timing of transformation policy design, different effects of the spatial agglomeration of central urban areas on urban growth and transformation by development stages are also analyzed in this paper. The results suggest that: the resource-based cities are relatively internally fragmented with noticeable differences among resource types and individuals, such as oil and gas resource type, and ferrous metal resource type. This paper attempts to adopt the proportion of mining employees as the indicator. Some 1.9% and 31.0% can act as the tipping points to divide different development stages of resource-based cities. The resource-based cities are split into four stages of development given two threshold values. In various stages of development, the correlation between the concentration of social and economic factors in the central urban area and the urban economic growth is noticeably different. Furthermore, 'the mature stage' has acted as a critical turning phase in the transformation of resource-based cities.

[2]
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Abstract
城市能源消费碳排放核算方法的选择对核算结果具有一定影响。本研究参照IPCC温室气体排放清单编制方法,根据中国能源统计现状,利用能源表观消费量数据和现行的能源消费碳排放核算方法,将能源消费碳排放核算方法分为3种核算方式:①基于能源平衡表的能源消费碳排放核算;②基于一次能源消费量的能源消费碳排放核算;③基于终端能源消费量的能源消费碳排放核算,并分别根据3种能源消费核算方法构建城市能源消费碳排放核算体系;以北京市为案例对比3种方法的能源消费碳排放核算结果。研究结果表明,能源消费碳排放核算方法的选择对核算结果有很大影响,通过分析误差产生的原因,认为排放因子、碳氧化水平及加工转换过程是产生不确定性的3个主要原因。基于能源平衡表的修正后的能源消费碳排放核算方法,可以在一定程度上避免在能源加工转换过程中的二次能源消费遗漏及重复计算,然而,由于当前的国民经济核算体系尚不能满足能源消费碳排放计算的需要,需要尽快建立更为细致的统计核算体系。
[LIU Z, GENG Y, XUE B, et al. A calculation method of CO2 emission from urban energy consumption. Resources Science, 2011, 33(7): 1325-1330.]
Energy-related CO<sub>2</sub> emission is one of the largest drivers for accelerating atmosphere radiative forcing which contribute to global warming, and thus needs to be taken in high priority for Greenhouse-gas (GHG) emission abatement. Cities, with its aggregation of economic activities and associated energy use, are the main contributors of energy consumption CO<sub>2</sub> emissions and may be critical for CO<sub>2</sub> mitigation and adaptation to climate change. However, uncertainty exists among urban energy consumption CO<sub>2</sub> emissions, as the accounting methods of carbon emissions for urban energy consumption would affect accounting results, hindering climate adaptation and CO<sub>2</sub> mitigation policies. Here, we analyzed the discrepancy between different CO<sub>2</sub> accounting methods, and illustrated the underlying reasons. The study is based on the reference approach of IPCC Guideline for National Greenhouse-gas Emission Inventory, adopted China’s energy statistical data, and categorized energy consumption into three accounting methods based on apparent energy consumption data, i.e., 1) final energy use of carbon emission from an energy input-output perspective, 2) total energy consumption account by major energy category, and 3) final use account by energy category. Solid, liquid, and gas fuel are accounted for. Furthermore, an accounting system of carbon emissions from urban energy consumption was established based on the three types of accounting methods. Accounting results of carbon emission from energy consumption in Beijing were obtained with the three methods. Results show that discrepancies exist among the three types of accounting methods. By further analyzing the causes of discrepancy, it was concluded that 1) the discrepancy is generally caused by differing account methods and associated data error; 2) the accounting method of CO<sub>2</sub> emissions based on the energy balance sheet may avoid the omission and double counting of the secondary energy consumption due to its direct reflection on the input and output status of energy production and use, and lead to a smaller error compared with other methods in theory. It was indicated that the accounting method based on the energy balance sheet needs to be placed in high priority for urban energy consumption CO<sub>2</sub> emission accounting. The current statistics system needs to be more explicit to satisfy the calculation requirement of carbon emissions from energy consumption.
[6]
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Abstract
碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO<sub>2</sub>排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。
[CHENG Y Q, WANG Z Y, ZHANG S Z, et al. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China. Acta Geographica Sinica, 2013, 68(10): 1418-1431.]
The economic and social development has been facing with serious challenge brought by global climate change due to carbon emissions. As a responsible developing country, China pledged to reduce its carbon emission intensity by 40%-45% below 2005 levels by 2020. The realization of this target depends on not only the substantive transition of society, economy and industrial structure in national scale, but also the specific action and share of energy saving and emissions reduction in provincial scale. Based on the method provided by the IPCC, this paper examines the spatio-temporal dynamic patterns and domain factors of China's carbon emission intensity from energy consumption in 1997-2010 using spatial autocorrelation analysis and spatial panel econometric model. The aim is to provide scientific basis for making different policies on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt in 1997-2010, with an annual rate of 7.15%, which was much slower than that of annual growth rate of GDP (11.72%); therefore, China's carbon emission intensity tended to decline. Secondly, the changing curve of Moran's I indicated that China's carbon emission intensity from energy consumption has a continued strengthening tendency of spatial agglomeration at provincial scale. The provinces with higher and lower values appeared to be path-dependent or space-locked to some extent. Third, according to the analysis of spatial panel econometric model, it can be found that energy intensity, energy structure, industrial structure and urbanization rate were the domain factors that have impact on the spatio-temporal patterns of China's carbon emission intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, we should improve the utilizing efficiency of energy, and optimize energy and industrial structure, and choose the low-carbon urbanization way and implement regional cooperation strategy of energy conservation and emissions reduction.
[7]
耿文欣, 范英. 碳交易政策是否促进了能源强度的下降? 基于湖北试点碳市场的实证. 中国人口·资源与环境, 2021, 31(9): 104-113.
[GENG W X, FAN Y. Does a carbon trading policy contribute to energy intensity reduction? Evidence from the Hubei carbon trading pilot. China Population, Resources and Environment, 2021, 31(9): 104-113.]
[8]
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.
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 prediction of urban carbon emission performance in China based on super-efficiency SBM model. Acta Geographica Sinica, 2020, 75(6): 1316-1330.]

Climate change caused by CO2 emissions has become an environmental issue globally in recent years, and improving carbon emission performance is an important way to reduce carbon emissions. Although some scholars have discussed the carbon emission performance at the national scale and industry level, literature lacks studies at the city- level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we used the super-efficiency SBM model to measure the urban carbon emission performance, and the traditional Markov probability transfer matrix and spatial Markov probability transfer matrix are constructed to explore the spatio-temporal dynamic evolution characteristics of urban carbon emission performance in China for the first time and to predict its long-term evolution trend. The study shows that urban carbon emission performance in China presents a trend of steady increase in the fluctuation, but the overall level is still at a low level, so there is still a great improvement space in urban carbon emission performance, with huge potential for energy conservation and emission reduction. The spatial pattern of national urban carbon emission performance shows the characteristics of "high in the south and low in the north", and there is a significant difference in the level of carbon emission performance between cities. The spatial Markov probabilistic transfer matrix results show that the transfer of carbon emission performance type in Chinese cities is stable, thus it forms the "club convergence" phenomenon, and the geographical background plays an important role in the process of the transfer. From the perspective of long-term trend prediction, the future evolution of urban carbon emission performance in China is relatively optimistic. The carbon emission performance will gradually improve over time, and the distribution of carbon emission performance presents a trend of high concentration. Therefore, in the future, China should continue to strengthen research and development to improve the performance level of urban carbon emissions and achieve the national target of energy conservation and emission reduction. At the same time, neighboring cities with different geographical backgrounds should establish a sound linkage mechanism of economic cooperation to pursue coordinated development between economic growth, energy conservation and emission reduction, so as to realize low-carbon city construction and sustainable development.

[9]
李金铠, 马静静, 魏伟. 中国八大综合经济区能源碳排放效率的区域差异研究. 数量经济技术经济研究, 2020, 37(6): 109-129.
[LI J K, MA J J, WEI W. Study on regional differences of energy carbon emission efficiency in eight economic areas of China. The Journal of Quantitative & Technical Economics, 2020, 37(6): 109-129.]
[10]
王凯, 张淑文, 甘畅, 等. 中国旅游业碳排放效率的空间网络结构及其效应研究. 地理科学, 2020, 40(3): 344-353.
Abstract
基于中国30个省区2001-2016年面板数据,采用SBM模型测度各省区旅游业碳排放效率,借助修正的引力模型和社会网络分析方法,厘清中国旅游业碳排放效率的空间网络结构及其效应。研究表明:① 中国旅游业碳排放效率的空间关联渐趋紧密,网络发育程度日益完善,但距理想状态仍有差距;② 各省区网络中心性指标分异性逐步减小,上海、北京、江苏等省区排名稳居前列,重庆、福建、内蒙古等省区排名波动上升,宁夏、青海、山西等省区排名相对滞后;③ 网络整体呈核心区由东部沿海向中部及西南地区持续扩展,而边缘区范围逐步收缩态势;④ 网络密度与旅游业碳排放效率呈正相关,与旅游业碳排放效率差异构成负相关关系,网络等级度和网络效率则与之相反,网络中心性各指标的提升均能显著增强旅游业碳排放效率。
[WANG K, ZHANG S W, GAN C, et al. Study on spatial network structure and effect of carbon emission efficiency of China's tourism industry. Scientia Geographica Sinica, 2020, 40(3): 344-353.]
[11]
胡剑波, 闫烁, 韩君. 中国产业部门隐含碳排放效率研究: 基于三阶段DEA模型与非竞争型I-O模型的实证分析. 统计研究, 2021, 38(6): 30-43.
[HU J B, YAN S, HAN J. Study on the carbon emissions efficiency embodied in China's industrial sector-empirical analysis based on three-stage DEA model and non-competitive I-O model. Statistical Research, 2021, 38(6): 30-43.]
[12]
盖美, 朱静敏, 孙才志, 等. 中国沿海地区海洋经济效率时空演化及影响因素分析. 资源科学, 2018, 40(10): 1966-1979.
Abstract
为展现中国沿海地区海洋经济效率时空演变现状,并根据其具体原因提出针对性对策建议,促进中国海洋经济可持续发展,本文基于考虑非期望产出的三阶段超效率SBM-Global模型、标准差椭圆和重心坐标方法,对中国沿海11个省市2000&#x02014;2015年海洋经济效率进行测算和时空对比分析。研究发现:①与一阶段相比,三阶段海洋经济效率整体上升,各省市排名出现不同程度变动;②时间演变上,全国各沿海地区海洋经济效率呈上升趋势,绝对差异和相对差异都不断变大;③空间演变上,全国标准差椭圆呈东北-西南格局分布,面积由小变大,效率分布由收缩趋势变为分散趋势,最终呈北、中、南三级格局分布,与三大海洋经济圈分布相吻合;三大海洋经济圈标准差椭圆面积都逐渐缩小,效率分布出现极化现象;④海洋经济区位熵、海洋科研人力资本、海洋环保技术水平与海洋经济效率之间存在非线性关系;陆域经济发展水平、海洋产业结构水平、政府对海洋科技支持力度、海洋经济政策力度对海洋经济效率呈显著正影响;对外开放水平对其影响不显著。
[GAI M, ZHU J M, SUN C Z, et al. Spatio-temporal evolution and influencing factors of marine economic efficiency in coastal areas of China. Resources Science, 2018, 40(10): 1966-1979.]

Based on the three stage super-efficient SBM-Global model, standard deviation ellipse and centroid coordinate method, the marine economic efficiency of 11 coastal provinces and cities of China in 2000-2015 years is calculated and analyzed with the time and space. Research discovery: ① Compared with the first stage, the efficiency of marine economy in the third stage has increased overall, and the ranking of provinces and cities has changed differently. ② In the evolution of time: The efficiency of marine economy in all coastal areas of China has been on the rise, and the absolute and relative difference has been increasing and the relative difference changes relatively smooth. ③ In space evolution: The national standard deviation ellipse was distributed in the Northeast-Southwest pattern and its area increased from small to large. The efficiency distribution changes from the contraction trend to the dispersion trend and finally in the three level pattern of North, Middle and South, which is in accordance with the three major marine economic circles. The standard deviation ellipse area of the three major marine economic circles is gradually reduced, and polarization appears in the efficiency distribution. ④ There is a nonlinear relationship between development level of land area economy, marine economic location entropy, human capital of marine scientific research, marine environmental protection technology level and marine economic efficiency; land economic development level, the level of marine industrial structure, the government's support for marine science and technology has a significant positive impact on it, and the level of opening to the outside world has no significant impact on it.

[13]
李博, 张文忠, 余建辉. 碳排放约束下的中国农业生产效率地区差异分解与影响因素. 经济地理, 2016, 36(9): 150-157.
[LI B, ZHANG W Z, YU J H. Decomposition and influence factors of district difference of China agricultural production efficiency under the constraint of carbon emission. Economic Geography, 2016, 36(9): 150-157.]
[14]
张卓群, 张涛, 冯冬发. 中国碳排放强度的区域差异、动态演进及收敛性研究. 数量经济技术经济研究, 2022, 39(4): 67-87.
[ZHANG Z Q, ZHANG T, FENG D F. Study on regional differences, dynamic evolution and convergence of carbon emission intensity in China. The Journal of Quantitative & Technical Economics, 2022, 39(4): 67-87.]
[15]
屈小娥. 中国省际全要素CO2排放效率差异及驱动因素: 基于1995—2010年的实证研究. 南开经济研究, 2012, 28(3): 128-141.
[QU X E. Total factor efficiency differences of CO2 emissions and driving factors in China's inter-provincial: Based on the 1995-2010 years of empirical research. Nankai Economic Studies, 2012, 28(3): 128-141.]
[16]
莫惠斌, 王少剑. 黄河流域县域碳排放的时空格局演变及空间效应机制. 地理科学, 2021, 41(8): 1324-1335.
Abstract
利用空间面板模型、空间自相关分析和以区域背景与最近邻状况为空间滞后的空间马尔科夫链对2000—2017年黄河流域县域碳排放时空格局与空间效应进行分析,结果表明:① 2000年以来黄河流域碳排放量激增,由山东全域和陕甘宁蒙交界的高值区向外圈层与轴向扩张,形成东高西低碳排放格局;② 存在“俱乐部趋同”现象,高碳排放县集聚于山东全域和陕甘宁蒙交界,低碳排放县集聚于西南部;2000年与2017年对比发现县域碳排放类型稳定性强,较高碳排放变为较低碳排放的县集中在东南部区域,而相反方向转变的县集中在内蒙古;③ 高碳溢出效应与低碳锁定效应是塑造时空格局的重要作用力,前者作用力更强;区域背景增强了“俱乐部趋同”与被包围异常值趋同,作用力强于最近邻状况,不显著区域内碳排放类型转变概率提高。④ 空间面板模型结果显示年轻人口结构、大经济规模、二产为主产业结构、高生活水平和高公共支出促进了碳排放量增加与空间效应作用,其中经济规模与产业结构是重要驱动因素。
[MO H B, WANG S J. Spatio-temporal evolution and spatial effect mechanism of carbon emission at county level in the Yellow River Basin. Scientia Geographica Sinica, 2021, 41(8): 1324-1335.]

Carbon emission control is the main problem and measure of ecological protection and high-quality development in the Yellow River Basin. Carbon emission at county level research can provide more accurate theoretical support for collaborative governance and sustainable development of the Yellow River Basin. Spatial panel model, spatial autocorrelation analysis and spatial Markov chain with regional background and nearest neighbor as spatial lags were used to analyze the spatiotemporal pattern and spatial effect of carbon emissions in counties of the Yellow River Basin from 2000 to 2017, the results showed that: 1) the carbon emission in the Yellow River basin has increased dramatically since 2000; the high carbon emissions areas, Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia, expands to the outer circle layer and the axial direction, forming the spatial pattern of high in the east and low in the west; 2) there is a phenomenon of “club convergence”; the high carbon emission counties converge in Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia; the low carbon emission counties converge in the southwest; the comparison between 2000 and 2017 shows that county carbon emission type has strong stability; counties which tranfered from higher carbon emission type to lower carbon emission type are concentrated in the southeast region, while counties that change in the opposite direction are concentrated in Inner Mongolia. 3) high carbon spillover effect and low carbon locking effect are important forces to shape the spatiotemporal pattern and the former is stronger; the regional background enhances “club convergence” and the convergence of surrounded outliers and its acting force was stronger than the nearest neighbor; the probability of carbon emission type transition in insignificant regions increased; 4) the spatial panel model shows that increase of carbon emissions and its spatial effect are promoted by young population structure, large economy, industrial structure dominated by the secondary industry, high living standard and high public expenditure; economy and industrial structure are important driving factors.

[17]
李顺成, 肖卫东, 王志宝. 家庭部门能源消费影响因素及碳排放结构研究: 基于PLS结构方程模型的实证解析. 软科学, 2020, 34(2): 117-123.
[LI S C, XIAO W D, WANG Z B. Study on factors affecting energy consumption and CO2 emissions structure in household sector: An empirical analysis based on mode of PLS-SEM. Soft Science, 2020, 34(2): 117-123.]
[18]
刘志华, 徐军委, 张彩虹. 科技创新、产业结构升级与碳排放效率: 基于省际面板数据的PVAR分析. 自然资源学报, 2022, 37(2): 508-520.
[LIU Z H, XU J W, ZHANG C H. Technological innovation, industrial structure upgrading and carbon emissions efficiency: An analysis based on PVAR model of panel data at provincial level. Journal of Natural Resources, 2022, 37(2): 508-520.]
[19]
禹湘, 陈楠, 李曼琪. 中国低碳试点城市的碳排放特征与碳减排路径研究. 中国人口·资源与环境, 2020, 30(7): 1-9.
[YU X, CHEN N, LI M Q. Research on carbon emission characteristics and reduction pathways of low-carbon pilot cities in China. China Population, Resources and Environment, 2020, 30(7): 1-9.]
[20]
邵海琴, 王兆峰. 中国交通碳排放效率的空间关联网络结构及其影响因素. 中国人口·资源与环境, 2021, 31(4): 32-41.
[SHAO H Q, WANG Z F. Spatial network structure of transportation carbon emissions efficiency in China and its influencing factors. China Population, Resources and Environment, 2021, 31(4): 32-41.]
[21]
周杰琦, 韩颖, 林洪. FDI对中国工业碳排放效率的影响机理及其效应: 理论构建与经验分析. 软科学, 2016, 30(1): 76-80.
[ZHOU J Q, HAN Y, LIN H. The effects and mechanisms of FDI on industrial carbon efficiency of China: Theory and empirical research. Soft Science, 2016, 30(1): 76-80.]
[22]
朱德进, 杜克锐. 对外贸易、经济增长与中国二氧化碳排放效率. 山西财经大学学报, 2013, 35(5): 1-11.
[ZHU D J, DU K R. Foreign trade, economic growth and the efficiency of carbon emission in China. Journal of Shanxi University of Finance and Economics, 2013, 35(5): 1-11.]
[23]
何文举, 张华峰, 陈雄超, 等. 中国省域人口密度、产业集聚与碳排放的实证研究: 基于集聚经济、拥挤效应及空间效应的视角. 南开经济研究, 2019, 35(2): 207-225.
[HE W J, ZHANG H F, CHEN X C, et al. An empirical study about population density, economic agglomeration and carbon emission state of Chinese provinces: Based on the perspective of agglomeration economy effects, congestion effects and spatial effects. Nankai Economic Studies, 2019, 35(2): 207-225.]
[24]
李在军, 尹上岗, 姜友雪, 等. 长三角经济增长与碳排放异速关系及形成机制. 自然资源学报, 2022, 37(6): 1507-1523.
[LI Z J, YIN S G, JIANG Y X, et al. Analysis of allometric relationship and formation mechanism between economic growth and carbon emissions in the Yangtze River Delta. Journal of Natural Resources, 2022, 37(6): 1507-1523.]
[25]
王睿, 张赫, 强文丽, 等. 基于城镇化的中国县级城市碳排放空间分布特征及影响因素. 地理科学进展, 2021, 40(12): 1999-2010.
Abstract
论文选择中国1897个县级城市作为研究单元,基于CHRED-online碳排放公开数据库以及县、县级市社会经济统计数据,采用空间自相关分析和地理探测器方法,探究中国县级城市碳排放空间分布格局及人口、经济、土地多维度城镇化水平对碳排放的影响。结果表明:① 中国县级城市碳排放量非均衡性较高,碳排放总量高值地区数量少,但数值较大。② 碳排放总量空间分布主要呈现东高西低格局,高值地区主要集中于东部、中部大城市周边和内蒙古中部、北部地区,呈&#x0201c;簇状&#x0201d;分布结构。人均碳排放强度和经济碳排放强度则呈现北高南低格局,主要聚集于内蒙古中部、北部和新疆青海交界地区。③ 经济和土地城镇化水平的空间异质性对县级城市碳排放总量差异具有较强的解释力,人口城镇化对碳排放总量影响不明显。经济城镇化及土地城镇化各指标之间交互作用对碳排放影响最为剧烈,并呈现非线性增强作用。④ 在分地区差异性比较中,城镇化水平对西部欠发达地区影响作用最为剧烈。在同一指标的解释力和关键影响因素指标的选取方面,东、中、西部地区也存在明显的空间分异特征。应结合高碳排放区域和城镇化影响作用机制,进行差异化控碳路径选择。
[WANG R, ZHANG H, QIANG W L, et al. Spatial characteristics and influencing factors of carbon emissions in county-level cities of China based on urbanization. Progress in Geography, 2021, 40(12): 1999-2010.]

This study selected 1897 county-level cities in China as the research unit to examine the spatial distribution pattern of carbon emissions and the effects of population, economy, and multi-dimensional urbanization levels on carbon emissions. The data sources are CHRED-online carbon emission public database and social and economic statistics of counties and county-level cities. Spatial autocorrelation and geographic detector methods were used in the empirical research. The results show that: 1) County-level cities of China are relatively highly different in carbon emissions, and the number of areas with high carbon emissions is relatively small, but the emission value is relatively large. The carbon emissions per unit of GDP are higher than the national average. 2) The spatial distribution of total carbon emissions mainly presents a pattern of high in the east and low in the west. The high-value areas of total carbon emissions are mainly concentrated in the east, the periphery of large cities in central China, and the central and northern regions of Inner Mongolia, showing a clustered distribution structure. Per capita carbon emissions and carbon intensity show a pattern of high in the north and low in the south, mainly concentrated in the central and northern parts of Inner Mongolia and the border areas of Xinjiang and Qinghai. 3) The spatial heterogeneity of the level of economic and land urbanization has a strong explanatory power for the difference in the total carbon emissions of county-level cities, and the impact of population urbanization on the total carbon emissions is not obvious. The interaction between economic urbanization and land urbanization has the most dramatic impact on carbon emissions, and shows a nonlinear enhancement effect. 4) With regard to regional differences, the level of urbanization has the most dramatic effect on the underdeveloped areas in the west. The eastern, central, and western regions also show different spatial characteristics in terms of the explanatory power of the same indicators and the key influencing factors.

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Abstract
以江苏省为研究对象,选取2007~2016年数据资料,探讨城市紧凑发展与能耗间的作用关系。首先计算城市紧凑度和能耗数值,紧凑度通过构建评价指标体系测算,包括土地利用、经济、人口、基础设施、公共服务5个一级指标以及12个二级指标,城市能源消耗以全社会用电总量、天然气供气总量、液化石油气供气总量折算标准煤方式获取。其次,将江苏省城市紧凑度和能耗数值进行空间可视化,观察两者在4个时间节点,地域和时空上的演变。最后,运用灰色关联分析法和计量经济学中的普通最小二乘法及一阶差分广义矩估计,对城市紧凑度与能耗的相关关系、作用机制进行定量研究。通过研究发现,江苏省城市紧凑度和能耗皆存在空间上的集聚分布特征,且两者具有较强的相关性,紧凑度的提升对能耗有显著的抑制作用,但这种作用表现出较为典型的时效性和滞后性,城市空间结构调整所带来的效用增长会缓慢作用于能源利用结构。
[HAN G, YUAN J D, ZHANG X, et al. The mechanism of compact city spatial structure on energy consumption: An empirical research based on Jiangsu province. Scientia Geographica Sinica, 2019, 39(7): 1147-1154.]

The concept of compact city provides an efficient urban development model with a fine fabric and organic connection between urban spatial structures. It achieves intensive land utilization pattern by controlling the urban sprawl with urban growth boundary, as well as the linked communities by the advanced public transportation system. In addition, as one of the most recommendable urban development ways advocated by sustainability science, the greatest value of compact city is considered to reduce energy consumption significantly in the process of urban economic and social development. Taking Jiangsu Province as an example, this study selects mass of statistical data from 2007 to 2016, to explore whether a compact city can contribute to reducing power consumption and emission, and testing the quantitative relationship, how does compact urban structure affect energy consumption as well its force direction. Firstly, the urban compactness and energy consumption value are calculated. An assessment index system with five primary indicators (land utilization, economy, population, infrastructure, and public services) and twelve secondary indicators is built, and the urban compactness is measured by the value of these indicators. It should be noted that, subject to the limitation of the complexity of urban energy consumption statistics in China, energy consumption value is obtained by converting the original row data of the whole society’s electricity, total natural gas supply and total liquefied petroleum gas (LPG) supply into the standard coal then adding them together. Such processing method can greatly reduce the error of results. Secondly, the representation of urban compactness and energy consumption value are visualized spatially by prefecture-level city, and then the temporal and spatial evolution of these two variables are observed at four time points. Finally, the correlation between urban compactness and energy consumption is investigated by employing the grey relational analysis method, and the interactive mechanism of them is quantified by using the ordinary least squares method and the first-order difference generalized moment estimation in econometrics. This study finds that both urban compactness and energy consumption in Jiangsu Province have significant characteristics of spatial concentration, and the northern and southern parts of Jiangsu are significantly different by taking the Yangtze River as the boundary. Moreover, urban compactness and energy consumption are strongly correlated. The increase in compactness promotes its inhibitory effect on energy consumption, however, this effect shows typical timeliness and hysteresis. The utility of adjusting urban spatial structure will conduce to the changes of energy utilization structure gradually. Clearly, the results show that compact city plays an important role in the transformation process in terms of urban spatial development from incremental expansion to inventory optimization in the eastern China. Besides, from the perspective of practical application in spatial planning, compact city enhances the integration of production, living and ecological spaces in urban area, and promotes the sustainable and coordinated development.

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