中国县域碳排放的时空演变及影响因素

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王少剑^,, 谢紫寒, 王泽宏中山大学地理科学与规划学院广东省城市化与地理环境空间模拟重点实验室,广州 510275

The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China

WANG Shaojian^,, XIE Zihan, WANG ZehongGuangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

收稿日期:2021-03-15修回日期:2021-08-17

基金资助:

教育部人文社会科学研究规划基金项目(21YJAZH087)

Received:2021-03-15Revised:2021-08-17

Fund supported:

Ministry of Education Research in the Humanities and Social Sciences Planning Fund(21YJAZH087)

作者简介 About authors
王少剑(1986-), 男, 河南驻马店人, 博士, 副教授, 博士生导师, 中国地理学会会员(S110011019M), 研究方向为城市地理、城市与区域规划。E-mail: 1987wangshaojian@163.com

摘要
县域是实现新型城镇化的重要平台和关键地域单元,揭示县域碳排放的时空格局演变及其驱动因素对于完善中国新型城镇化战略和促进生态文明建设、绿色转型发展具有重要意义。本文使用2000—2017年中国县域碳排放数据,分析了县域人均碳排放的总体变化、区域差异、时空格局及集聚特征,并在STIRPAT模型和环境库茨涅茨曲线（EKC）假说下,运用面板分位数回归解释社会经济发展对县域人均碳排放的动态影响。结果表明：① 中国县域人均碳排放呈现先急后缓的增长趋势。人均碳排放水平差异加大,且呈上升趋势,西部地区县域人均碳排放差距悬殊。② 县域人均碳排放总体上呈现“北高南低”的空间格局,经济发达地区的人均碳排放远高于其他地区,空间极化效应明显。③ 县域人均碳排放具有显著的空间正相关性,高—高集聚的区县数量逐渐增多且分布重心向西北移动,而低—低集聚的区县数量不断减少,主要集中于中南地区,县域人均碳排放集聚类型具有空间锁定效应。④ 人口密度、政府财政支出对县域人均碳排放具有抑制作用,第二产业产值规模、碳排放强度则存在显著的正相关性,中低碳排放水平区县的经济发展和人均碳排放之间呈现倒“N”型曲线关系,社会经济发展结构的调整是实现整体碳减排的关键。因此,政府减排策略的落实应考虑区县碳排放的阶段性差异,实现落后地区发展和转型“两手抓”的同时发挥重点城市群、都市圈在碳减排中的先导作用。此外,通过技术创新提高能源利用效率应作为现阶段县域碳减排的主要手段。
关键词： 县域人均碳排放;时空格局;区域差异;影响因素;面板分位数回归

Abstract

County is the pivotal platform and region unit to realize the new-type urbanization. The study of county-level CO₂ emissions is of great significance to improve China's urbanization strategy, accelerate the achievement of ecological civilization and low-carbon transformation. Based on the data of China's county-level CO₂ emissions from 2000 to 2017, this paper analyzed the overall tendency, regional differences, spatiotemporal pattern and agglomeration characteristics of per capita CO₂ emissions. Meanwhile, under the STIRPAT model and EKC hypothesis, this study employed the panel quantile regressions to explain the dynamic impact of socio-economic development on per capita CO₂ emissions. The main conclusions show that: (1) China's county-level CO₂ emissions show an increasing trend of rapid growth followed by slow growth. The regional disparity of per capita CO₂ emissions is distinct and shows a more uneven trend. (2) On the whole, China's county-level CO₂ emissions present a spatial pattern of "high in the north and low in the south". The per capita CO₂ emissions level in economically developed areas is much higher than that in other areas, thus brings about an obvious spatial polarization effect. (3) There is a significant positive spatial correlation of per capita CO₂ emissions within counties. The number of counties with High-High concentration gradually increases and the distribution center gradually moves to Northwest China, while the number of Low-Low concentration counties decreases continuously and they were mainly distributed in the central and southern regions. The agglomeration type of county-level per capita CO₂ emissions presents a spatial locking effect. (4) Population density and government expenditure have an inhibitory effect on county-level per capita CO₂ emissions, while the scale of secondary industry output value and carbon emission intensity have significant promotive influence. And there is an inverted "N-shaped" relationship between economic development and per capita CO₂ emissions in the counties with low- and middle-level emissions. The adjustment of socio-economic development structure plays a critical role in achieving China's total CO₂ emission reduction target. Therefore, the policy makers of emission reduction strategy should consider the regional disparity to realize the development and transformation of backward counties. And the key urban agglomerations should play a leading role in carbon emission reduction simultaneously. In addition, improving energy use efficiency through technological innovation should be the key way to the reduction of carbon emissions in China's counties at the present stage.

Keywords：per capita CO₂ emissions at county level;spatiotemporal pattern;regional disparity;influencing factor;panel quantile regression

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本文引用格式
王少剑, 谢紫寒, 王泽宏. 中国县域碳排放的时空演变及影响因素. 地理学报, 2021, 76(12): 3103-3118 doi:10.11821/dlxb202112016
WANG Shaojian, XIE Zihan, WANG Zehong. The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China. Acta Geographica Sinice, 2021, 76(12): 3103-3118 doi:10.11821/dlxb202112016

1 引言

碳排放增多引发的全球气候变化已成为各界关注的焦点,推动碳减排政策措施落实到区县一级,是中国促进绿色低碳循环发展和实现可持续城镇化的重要举措。碳排放增多主要源于经济规模扩张^[1],而经济增长导致的大规模城镇化进一步刺激了中国能源消费和碳排放的持续增长^[2,3],中国已在2009年成为世界第一大碳排放国,2017年碳排放总量占全球比重达23.87%,减排压力巨大。尽管中国正稳步推进碳减排工作,但在保持经济中高速增长的同时实现低碳循环发展,对中国而言依旧是严峻的挑战^[4,5]。县域是经济发展和产业转移的基本空间单元和承载体^[6],对于实现中国经济结构和发展方式的调整及转变具有重要意义^[7],同时也是未来城镇化发展的重要环节^[8]。开展县域碳排放研究,精细刻画县域尺度下碳排放时空演变规律及其影响因素,对于促进区域低碳协调发展,提高节能减排政策的科学性、针对性、可操作性具有重要参考价值^[9,10]。

已有研究在不同视角和地理尺度下测算了碳排放的总量^[11]、足迹、强度^[12]和绩效,并揭示了碳排放的时空格局演变特征和驱动因素^[13]。碳排放测算方面,学界关注的焦点不仅集中于工业^[14]、农业^[15]、旅游业^[13]、交通^[16]等不同行业部门,而且近年来能源消费碳排放、土地利用碳排放和贸易隐含碳排放^[17,18]方面的文章也逐渐增多,极大丰富了碳排放领域的研究。省域尺度下的碳排放时空格局演变研究表明,中国碳排放具有显著的空间分异和集聚效应,能源消费和交通碳排放的重心均分布在东部沿海地区^[19,20]。地级市尺度的研究同样表明碳排放的时空分布具有明显的空间集聚特征,北京、上海、天津、苏州、重庆、成都等大城市的碳排放远高于其他城市^[21]。与此同时,马尔科夫转移矩阵表明中国城市碳排放及碳排放强度均具有空间溢出效应,邻近城市间的变动会产生相互影响^{[12, 22]}。碳排放的社会经济影响因素复杂多样^[23],****们在扩展STIRPAT框架^[24]和引入环境库兹尼茨曲线（EKC）^[25]的基础上,主要通过指数分解模型（如对数平均迪氏指数法（LMDI）^[26]）、结构分解模型（如投入产出分析法^[27]）和空间计量经济模型^[28]综合探讨不同视角下社会经济发展对碳排放的影响。学界主要观点认为经济增长无论在长期上还是在短期上都促进碳排放的增加^[29],第二产业为主导的产业结构是碳排放增多的主要原因^[1],而技术进步能够有效减少碳排放^[30],且相对于发达国家关注碳排放绝对量的减少,中国现阶段的碳减排应主要在于碳排放强度的下降,因此提升能源效率是现阶段保持经济增长的同时实现碳减排目标的主要途径^[31],城镇化引发的人口集聚对碳排放同样具有显著的促进作用^[32]。此外,交通运输^[33]、对外开放^[22]和资本投入^[34]等也被证实是抑制或促进碳排放的因素。

总体而言,学界针对碳排放已开展了较为系统的研究并取得丰富的成果,对本文具有重要的借鉴作用。但现有研究主要集中于国家和省域尺度,由于能源利用统计数据的缺乏,对县域尺度的研究较少,且已有县域碳排放研究仅仅关注特定区域^{[9, 35]},尚未有研究从全国视角来探讨县域碳排放。其次,多数现有研究仅简单揭示碳排放与社会经济因素间的线性关系,中国区县数量庞大、社会经济背景复杂,延续已有研究的方式难免忽略发展阶段和区域差异,不利于国家及各级政府在提高经济发展质量和稳步推动城镇化进程的基础上,因地制宜地制定切实可行的降碳减污方案。

基于此,本文以中国1908个区县的人均碳排放为研究对象,在分析人均碳排放的整体变化的基础上,通过空间自相关、时空跃迁矩阵等方法在县域尺度下更为精细地刻画碳排放的时空格局演变,完善碳排放时空分布格局在不同尺度下的差异性认识;进一步基于STIRPAT理论框架和EKC假说,采用面板分位数回归模型量化社会经济因素对县域人均碳排放的非线性影响,以揭示不同碳排放水平下节能减排措施实施效果的差异性,为推行阶段化的碳减排措施提供理论基础。

2 研究方法与数据来源

2.1 IPAT模型及其扩展

IPAT（Impact, Population, Affluence, Technology）模型最初由Ehrlich和Holdren提出,用于分析人类发展与环境问题之间的因果关系^[36]。在实际运用过程中,IPAT模型被****们不断地补充扩展,并衍生出ImPAT模型和STIRPAT模型。STIRPAT模型是IPAT等式的随机形式表示,可用于EKC曲线等非线性假说的验证并支持对相关变量进行分解和改进^[37],其基本表达式为：

（1）

I = a \times P^{b} \times A^{c} \times T^{d} \times e

式中：P、A、T分别表示对环境压力因子I产生影响的人口、财富和技术因素;a代表模型系数;b、c、d代表影响因素指数;e为误差项。

参考已有研究,本文对STIRPAT模型进行相应扩展。研究证实人口因素为碳排放的主要驱动力之一^[38,39,40],人口通过生产与消费行为对碳排放产生影响^[24]且具有双向效应^[41]。相比较人口规模、结构和城市化率等指标,人口密度更反映城镇化进程下人口变动对碳排放水平的影响^[42],因此本文选取人口密度（PopDen）表征人口因素。国民富裕程度的提升将使得生产与消费模式产生变动,进而改变碳排放水平^[24],本文选取人均GDP（PGDP）和政府一般公共财政支出（PFE）表征影响环境压力的富裕度水平。此外,人均GDP的二次项和三次项被纳入模型中用于检验经济发展与人均碳排放的关系是否符合EKC假说。工业化对于减少碳排放具有决定性作用,而能源利用效率的提升是实现碳减排的关键手段之一^[43,44],本文选取碳排放强度（CI）用于表征技术进步对碳排放的影响。由于偏重第二产业的经济结构是中国碳排放的助长因素^[22],因此进一步引入第二产业产值规模（IS）指标用于探讨第二产业对县域人均碳排放的影响。为消除通货膨胀等价格因素的影响,人均GDP、财政支出和第二产业产值规模折算成2000年不变价。主要社会经济因素如表1所示,对各变量取自然对数后等式为：

（2）

\begin{array}{l} \ln (PC O_{2})_{it} = α_{0} + β_{1} \ln PopDe n_{it} + β_{2} \ln PGD P_{it} + β_{3} {(\ln PGD P_{it})}^{2} + \\ β_{4} {(\ln PGD P_{it})}^{3} + β_{5} \ln PF E_{it} + β_{6} \ln C I_{it} + β_{7} \ln I S_{it} + ε_{it} \end{array}

式中：

\ln (PC O_{2})_{it}

为环境质量因子的自然对数;β₁, β₂, …, β₇为解释变量弹性系数;ε_it为模型的随机误差项。从系数β₂、β₃和β₄符号的正负可以判断经济发展与人均碳排放的线性或非线性关系,具体可归纳为以下5种：① 当β₂≠ 0、β₃= 0、β₄= 0时,经济发展与人均碳排放之间呈现简单线性关系,即随着经济发展,人均碳排放水平越来越高;② 当β₂< 0、β₃> 0、β₄= 0时,呈现“U”型曲线关系,伴随着经济发展,人均碳排放水平呈现先下降经过拐点后上升的趋势;③ 当β₂> 0、β₃< 0、β₄= 0时,呈现倒“U”型曲线关系,即随着经济水平的提高人均碳排放先升后降;④ 当β₂> 0、β₃< 0、β₄> 0时,呈现“N”型曲线关系,随经济水平的提高人均碳排放呈现先上升后下降再上升的趋势;⑤ 而当β₂< 0、β₃> 0、β₄< 0时,两者关系为倒“N”型,即呈现先降后升再降的趋势。不同于“U”型曲线关系,“N”型曲线关系表明碳排放变化随经济发展阶段存在两个拐点。

Tab. 1
表1
表1研究数据概况及来源(2000—2017年)
Tab. 1Research data explaination and sources (2000-2017)

变量维度	变量名称	缩写	单位	区县数量(个)	来源
环境压力(I)	人均碳排放量	PCO₂	t/人	1908	中国碳核算数据库
城镇化水平(P)	人口密度	PopDen	人/km²	1908	人口规模/城市行政面积
经济发展(A)	人均地区生产总值	PGDP	元	1908	《中国县域统计年鉴》
财政投入(A)	一般公共预算支出	PFE	万元	1908	《中国县域统计年鉴》
产业结构(T)	第二产业产值规模	IS	万元	1908	《中国县域统计年鉴》
技术进步(T)	碳排放强度	CI	-	1908	碳排放量/地区生产总值
变量对数	最小值	最大值	均值	标准差	样本量
lnPCO₂	-15.7614	5.2985	1.2207	1.1359	34344
lnCI	-15.0003	11.4358	1.0121	0.9231	34344
lnPopDen	-2.2965	9.0840	5.1626	1.3639	34344
lnPGDP	6.4935	13.0268	9.0633	0.7902	34344
lnIS	4.6052	16.0759	11.6039	1.5056	34344
lnPFE	6.1070	15.9838	10.4047	0.9487	34344

注：lnPCO₂表示变量PCO₂取自然对数后的数据。

新窗口打开|下载CSV

2.2 面板分位数回归

通过面板分位数回归旨在量化主要影响因素对不同发展阶段和排放水平区县的人均碳排放影响程度。一般面板回归遵循普通最小二乘法（OLS）估计原理,其本质是条件均值回归^[30]。当回归的随机误差项符合正态分布、零均值和齐次方差的古典计量经济学假设时,OLS才满足方差最小的无偏估计条件。当变量存在时空异质性时,数据容易在分布上呈现高峰值、异方差和首尾两端数据偏离大的非正态分布,这种情况在OLS估计过程中容易被忽视从而形成有偏估计^[45]。当数据分布为非正态分布或不同分位上回归系数波动较大时,采用分位数回归能够捕捉到被解释变量y在不同分位点水平上受影响的程度,使估计结果更稳健。

本文使用含固定效应的面板分位数回归,回归结果不易受异常值的影响且能够更全面地涵盖个体异质性,具体表达式如下：

（3）

Quan t_{Y_{i}} (θ_{k} | X_{it}) = β {(θ_{k})}^{'} X_{it} + α_{i} (i = 1,2, \cdot \cdot \cdot N; t = 1,2, \cdot \cdot \cdot T)

式中：Y_i为人均碳排放;

Quan t_{Y_{it}} (θ | X_{i})

表示第θ分位数上的人均碳排放;X_it为区县i在t年的外生变量向量;θ_k表示为第k分位数水平;β(θ_k)代表估计系数向量;i为研究的区县;t为研究年份;α_i为未观测到的个体效应。基于STIRPAT框架和EKC理论假说,本文在特定的分位数水平上衡量社会经济因素对县域碳排放的影响,所使用的回归公式为：

（4）

\begin{array}{l} Q_{(PC O_{2})_{i, t}} (θ | X_{it}) = α_{i} + β_{1, θ} \ln PopDe n_{it} + β_{2, θ} \ln PGD P_{it} + β_{3, θ} \ln PGD {P_{it}}^{2} + \\ β_{4, θ} \ln PGD {P_{it}}^{3} + β_{5, θ} \ln PF E_{it} + β_{6, θ} \ln C I_{it} + β_{7, θ} \ln I S_{it} + ε_{t} \end{array}

Koenker^[46]通过在模型中引入带有调整参数λ的惩罚项

\sum_{i = 1} |α_{i}|

,从而提高系数β的估计可信度和消除个体效应,使得残差绝对值的加权平均作为最小化的目标函数^[12]以解决存在大量参数时的计算问题。因此面板分位数模型中的回归参数在相应的分位点的估计为：

（5）

\begin{array}{l} θ_{k} = \underset{β}{argmin} \sum_{k = 1} \sum_{t = 1} \sum_{i = 1} w_{k} P_{θ_{k}} \{Y_{(PC O_{2})_{i, t}} - β_{1, θ} \ln PopDe n_{i, t} - β_{2, θ} \ln PGD P_{i, t} - β_{3, θ} \ln PGD {P_{i, t}}^{2} - \\ β_{4, θ} \ln PGD {P_{i, t}}^{3} - β_{5, θ} \ln PF E_{i, t} - β_{6, θ} \ln C I_{i, t} - β_{7, θ} \ln I S_{i, t} - α_{i}\} + λ \sum_{i = 1} |α_{i}| \end{array}

式中：K为分位数指数;T为变量的观测数量;N为样本区县;X为解释变量矩阵;w_k是第k分位数上的权重;

P_{θ_{k}}

代表分位数损失函数。通过设置不同的θ值可以获得相应分位点上的回归情况,回归中参数的标准差采用Buchinsky^[47]提出的Bootstrap方法获取。

2.3 数据来源

基于数据获取的可行性和完整性,本文选取中国1908个区县作为研究对象,涵盖30个省、直辖市和自治区（受限于数据的可得性,暂未包括西藏自治区、台湾省、香港和澳门特别行政区）。县域碳排放数据来源于中国碳核算数据库（CEAD）（ https://www.ceads.net/data/county/）,该数据由国家物理地球数据中心（NGDC）提供的DMSP/OLS和NPP/VIIRS夜间灯光数据反演得出,数据具有统计口径一致、连续性强等优势。社会经济变量数据主要来源于2000—2017年《中国县域统计年鉴》以及区县的《国民经济与社会发展统计公报》,对个别年份的缺失数据采用了插值法补齐。为减少异方差,消除变量量纲影响,文中所有变量进行取自然对数标准化处理,表1为各变量的基本统计情况及来源。

3 中国县域碳排放的时空演变与集聚特征

3.1 县域碳排放的时间序列变化分析

2000—2017年中国县域人均碳排放整体上呈现出以2011年为分界点的“先急后缓”的持续增长状态（图1）。2000—2017年中国平均县域人均碳排放从2.41 t/人增长至8.47 t/人,总体增长率达251.45%,年均增长14.79%。尤其是2001—2011年增长速度较快,年均增长率达21.66%。而“十二五”规划的节能减排具体目标为淘汰落后产能以及促进污染企业升级改造提供了强有力的政策约束,使得2011年后县域人均碳排放增速明显放缓,且在2015年出现明显的回落,2011—2017年的年均增长率仅为1.50%。

图1

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图12000—2017年中国人均碳排放及区域碳排放总量变化趋势

Fig. 1The tendency of per capita CO₂ emissions and the total CO₂ emissions in the four economic regions of China, 2000-2017

根据四大经济区域的地理划分,核算各区县所在地区碳排放的占比以揭示区域差异（暂未包括西藏自治区、台湾省、香港和澳门特别行政区）（图1）。2000—2017年东部地区碳排放占全国总量的比例维持在40%左右波动,是中国主要的碳排放来源地。相对而言,中部地区和东北地区的碳排放占全国总量的比例在研究期间略微降低;东北地区碳排放总量占全国总量比例在2000年为12%,2017年降至8%,且下降趋势较为平缓,主要原因在于重工业的衰退、生态环境修复的推进与产业结构调整^[48];自2006年起,中部地区排放总量占全国总量比例一直维持在18%。伴随着西部大开发战略和丝绸之路经济带的建设,西部地区近年来发展机会与经济投资增多^[49],碳排放总量占全国总量的比例在研究期间上升8%。总体来看,中西部地区对中国碳排放总量的贡献越来越大,而东北和东部地区呈现下降趋势。

本文采用基尼系数^①(①基尼系数计算公式为：

GIN I_{PC O_{2}} = 1 - \sum_{i = 1} (po p_{i} - po p_{i - 1}) (em i_{i} - em i_{i - 1})

。式中：

GIN I_{PC O_{2}}

表示人均碳排放的基尼系数;

po p_{i}

表示区域历史累计人口占比;

em i_{i}

表示区域的历史累计排放占比。计算过程参考He等^[2]的研究。)测算各经济区域的县域人均碳排放水平差异情况（图2）。2000—2017年中国县域人均碳排放的差异较大,县域人均碳排放的基尼系数由2000年的0.47增长至2017年的0.51,差距稳定扩大。东部和东北地区县域人均碳排放基尼系数在研究期间均未超过0.4的警戒值,碳排放差异相对合理。东部地区的排放差距略有上升,东北地区县域人均碳排放的基尼系数波动明显,基尼系数先逐年下降,在2011年迅速提升后再次逐渐下降。中部地区县域人均碳排放基尼系数具有明显的下降趋势,分异水平稍有减轻,2008年后基尼系数低于全国水平但仍高于0.4的警戒值^②(②按经济学和联合国开发计划署等组织对基尼系数的解释,0.4为收入分配公平与不公平的警戒线^[4],人均碳排放的基尼系数大于0.4意味着区域碳排放差距较大。)。西部地区县域人均碳排放基尼系数的变动与全国整体水平一致,且远高于其他地区和全国水平,2007年后西部地区县域人均碳排放的基尼系数突破0.6并随后持续上涨,县域人均碳排放水平差异悬殊。

图2

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图22000—2017年中国四大经济区域人均碳排放基尼系数变化

Fig. 2The change in Gini coefficients of per capita CO₂ emissions in the four economic regions of China, 2000-2017

3.2 县域碳排放的空间分布与集聚特征分析

2000—2017年中国各区县的人均碳排放量均有提升,空间分布逐渐趋于稳定,并具有显著的南北分异和空间集聚特征（图3）。区别于已有研究认为中国碳排放总量大致为以胡焕庸线为界呈“东南高西北低”的空间分布格局^[50,51],本文发现县域人均碳排放总体上呈现北高南低的空间分布格局。人均碳排放水平高的区县主要集中于内蒙古、新疆、青海、陕西、东北三省等地区。其原因可能在于这些地区煤炭和石化能源丰富,属于传统能源开采和消耗多的省份,城市发展高度依赖碳密集型产业,且相对于东南地区的人口密集县域,上述地区的人口外流情况严重,加上以煤炭为主的能源利用方式相对粗放,使得人均排放保持较高水平。另一方面,相比现有研究认为碳排放高的城市主要集中于中国北部地区和东部沿海城市群^[22],在县域尺度下的人均碳排放时空分布表明,除京津冀地区、山东半岛城市群、长三角地区、珠三角地区的人均碳排放水平较高之外,大部分区域人均碳排放水平较高的区县主要以省会城市及其相邻的区县为主。伴随着经济发展和人口集聚,能源需求也不断提升,经济发达区县的人均碳排放水平也逐步上涨,碳排放的“核心—边缘”结构愈发突显。因此,经济发达地区的低碳发展对于中国整体碳减排目标的实现和带动周边地区实现绿色低碳转型的作用越来越重要。

图3

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图32000—2017年中国县域碳排放的时空演变

注：基于自然资源部标准地图服务网站GS(2020)3184号标准地图制作,底图边界无修改。
Fig. 3Spatiotemporal evolution of CO₂ emissions in China's counties, 2000-2017

本文采用全局Moran's I指数和LISA分布检验县域碳排放的空间相关性,并刻画其空间集聚特征（图4）。2000—2017年中国县域人均碳排放具有显著的空间自相关性,且相关性不断增强并趋于稳定,空间集聚的模式较为固定。中国县域人均碳排放的Moran's I指数均大于零且在95%的水平上通过显著性检验,人均碳排放水平相似的区县倾向于集中分布（图4）。同时,2000—2017年Moran's I指数逐渐递增,且指数递增速度前期高于后期（Moran's I指数从2000年的0.214上涨至2010年的0.318,2017年为0.324）,即县域人均碳排放的空间集聚水平不断提升,并在后期逐渐趋于稳定状态。

图4

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图42000—2017年中国县域人均碳排放LISA和莫兰散点图

注：基于自然资源部标准地图服务网站GS(2020)3184号标准地图制作,底图边界无修改。
Fig. 4The LISA distribution and Moran's I scatter plot of per capita CO₂ emissions in China's counties, 2000-2017

进一步采用LISA图分析县域人均碳排放在局部空间上的集聚和分散情况（图4）,发现县域人均碳排放空间聚类分布与地级市人均碳排放^[52]及省域人均碳排放^[28]的空间聚类分布类似度高。总体上看,县域人均碳排放的空间集聚模式较为固定,高—高集聚（HH）和低—低集聚（LL）是主要的局部空间自相关类型,且高—高集聚的区县数量明显增加,而低—低集聚的区县数量则逐渐减少。一方面,高—高集聚（HH）的区县的分布重心在研究期间逐渐向西北转移,原有的环渤海高—高集聚（HH）区消失,高—高集聚（HH）的区县在后期主要分布在宁夏、山西、内蒙古、新疆、青海等省份;另一方面,低—低集聚（LL）区则在研究期间逐步趋于集中,低—低集聚（LL）的区县在研究后期主要集中于四川、重庆、湖南和江西等地。主要原因可能在于,随着京津冀地区“退二进三”的工业结构调整和环境污染治理工作的展开,碳排放水平正逐步得到有效地控制;而中南地区相对清洁的能源消费结构和碳密集程度较低的制造工业是该地区人均碳排放水平较低的主要原因;但由于东部地区向中西部地区的产业转移主要以高消耗、高排放产业为主,且西部地区逐渐成为中国重要的能源基地,因此使得高—高集聚（HH）的区县的分布重心向西北转移的同时低—低集聚（LL）的区县数量也在减少。此外,西北地区能源消费水平随着城市发展得到较大提高,但能源利用和降碳技术的改进相对发达地区的区县具有滞后性,在一定时期内的碳排放水平突破路径依赖的瓶颈具有难度。

为揭示县域人均碳排放集聚类型的转移情况,采用LISA时空跃迁矩阵追踪县域人均碳排放空间关联类型的相互转移过程。时空跃迁的类型分为4类,类型Ⅰ为仅县域城市本身发生跃迁;类型Ⅱ为县域城市本身未跃迁,邻域县市发生跃迁;类型Ⅲ是县域城市和邻域县市均发生跃迁;类型Ⅳ是县域城市本身和邻域县市均未发生跃迁。空间的凝聚程度越高,则发生跃迁的概率越小,人均碳排放的路径依赖效应也就越显著。从表2判断,2000—2017年以Ⅳ类型跃迁为主,不同类型间的时空跃迁发生次数很少。2000—2005年空间凝聚度达99.005%,2005—2017年人均碳排放的时空结构存在很强的空间锁定效应和“俱乐部趋同”现象,并且呈现相当稳定的状态。

Tab. 2
表2
表22000—2017年中国县域尺度人均碳排放的时空跃迁矩阵
Tab. 2Space-time transition matrices of per capita CO₂ emissions in China at county level, 2000-2017

年份	空间关联模式	HH_t₊₁	HL_t₊₁	LL_t₊₁	LH_t₊₁
2000—2005	HH_t	Ⅳ(147)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(2)	Ⅳ(7)	Ⅰ(1)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(1)	Ⅳ(227)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(17)
2005—2010	HH_t	Ⅳ(136)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(0)	Ⅳ(10)	Ⅰ(0)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(0)	Ⅳ(207)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(13)
2010—2017	HH_t	Ⅳ(110)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(0)	Ⅳ(5)	Ⅰ(0)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(0)	Ⅳ(163)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(5)

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4 中国县域人均碳排放的影响因素分析

4.1 模型检验

在回归分析前对变量的相关性、多重共线性、正态分布及序列单位根情况进行检验,以确保模型的稳健性和回归结果的可信度。变量间的方差膨胀因子（VIF）的最大值为4.50,均值为2.41,低于10这一警戒值,说明不存在严重的共线性问题。数据正态检验方面,所有变量的偏度值在偏离程度和方向上均符合正态分布假设。在分位数Q-Q图中,部分变量与Y = X的正态分布线拟合程度较差,分位数回归相比OLS回归更具适用性,变量一阶差分后呈现平稳状态且不存在单位根。

4.2 人口,财富和技术因素对人均碳排放的效应分析

参考Lin等^[53]的研究,本文设置10^th、25^th、50^th、75^th和90^th 5个分位数指数来评估影响因素之间的关系和人均碳排放的不同条件分布。表3为人均碳排放的影响因素面板分位数回归结果,图5为影响因素的弹性系数分布。

Tab. 3
表3
表3人均碳排放的影响因素面板分位数模型回归估计结果
Tab. 3The quantile regression model estimation results of influencing factors of per capita CO₂ emissions

影响因素	分位数					固定效应
影响因素	10^th	25^th	50^th	75^th	90^th	固定效应
lnPopDen	-0.18218^*** (-28.555)	-0.15187^*** (-21.685)	-0.17282^*** (-27.010)	-0.24991^*** (-54.692 )	-0.29087^*** (-39.553)	-0.20069^*** (-76.88)
lnCI	0.90664^*** (158.487)	0.90433^*** (171.892)	0.89386^*** (205.112)	0.85964^*** (153.908)	0.83368^*** (113.389)	0.95526^*** (342.30)
lnIS	0.26706^*** (25.618)	0.16169^*** (19.225)	0.14599^*** (18.188)	0.21041^*** (30.355)	0.25208^*** (34.522)	0.30189^*** (78.71)
lnPFE	0.05451^*** (-10.758)	0.08925^*** (26.564)	0.15641^*** (24.127)	0.18372^*** (29.201)	0.15736^*** (13.609)	-0.05577^*** (-9.77)
lnPGDP	-5.78257^*** (12.350)	-4.82953^*** (-3.245)	-2.84177 ^*** (-4.162)	0.65888 (0.555)	2.23077 (1.620)	1.48360^*** (3.06)
lnPGDP²	0.74913^*** (-12.793)	0.65329^*** (3.775)	0.43963 ^*** (5.697)	0.06170 (0.456)	-0.12378 (-0.788)	-0.10288 (-1.95)
lnPGDP³	-0.02913^*** (6.010)	-0.02523^*** (-3.775)	-0.01738 ^*** (-5.991)	-0.00433 (-0.847)	0.00265 (0.447)	0.00335 (1.76)

注：括号中的数值为t检验值;^***,^**和^*分别代表模型结果在1%,5%和10%的置信水平上显著。

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图5

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图5人均碳排放的影响因素回归弹性系数分布

Fig. 5Quantile regression flexibility coefficients distribution of independent variables on per capita CO₂ emissions

人口密度的回归系数为负,且在1%的置信水平上显著,表明人口集聚对于人均碳排放具有抑制作用,相对中低分位区县,人口密度对碳排放的抑制作用在高分位区县更为显著。城镇化带动了人口和其他资源的集聚,人口的集聚使能源利用和交通方式产生改变^[54],通过规模效应降低了边际减排成本^[55],因此,当经济正外部性溢出明显时,人口密度的提升有利于能源资源的有效配置和集约利用^[32],进而降低县域人均碳排放水平。碳排放强度与第二产业产值规模对县域人均碳排放在不同分位数上均存在1%显著水平的正效应,表明第二产业产值和碳排放强度的提升会促进县域人均碳排放的增多。第二产业产值规模反映了地区工业产业的发达程度,碳排放强度反映了能源利用效率。一方面,碳排放强度回归系数随分位提高而变小,碳排放强度的正效应逐渐减弱,表明在区县碳排放水平由低变高过程中通过改进生产技术能有效抑制碳排放量增速。工业化在带动经济发展的同时也刺激了碳排放,工业部门对高分位和低分位区县的碳排放具有更明显的驱动作用,产业结构调整是处于高分位和低分位区县实现碳减排和发展转型的关键^[44]。另一方面,碳排放强度对于县域人均碳排放的影响在各个分位上均远高于其他社会经济因素,即相对于单一地调整产业结构,现阶段中国大部分区县应该通过提高能源利用效率,降低单位产值的碳排放量,才能够在维持自身发展的同时通过技术创新、降低高耗高排产业比重、能源消费结构调整等手段逐步实现绿色转型发展。

政府公共财政投入对于县域人均碳排放具有显著的负效应,模型回归系数表明,财政投入对中高分位区县碳排放的抑制作用尤其关键。财政对公共环境卫生事业和基础设施建设的支出,如公共交通设施、水电设施和重大生态环境项目投资等方面具有正外部经济效应时,有利于社会和生态环境的优化。人均GDP与县域人均碳排放水平呈非线性关系。在10^th、25^th和50^th分位上人均GDP与人均GDP的二次方、三次方弹性系数分别为负、正、负,且在1%的可信度水平上显著,表明中低分位区县的经济发展水平与县域碳排放之间存在典型的倒“N”型曲线关系,文章结果与邵帅等^[5]的发现一致。在地区发展的早期阶段,产业集聚具有正外部性,经济活动集聚产生的规模经济效应能够有效抑制县域人均碳排放的增加。当经济发展越过第一个拐点时,产业规模的扩大和能源消费的增长明显刺激了碳排放水平的上升,尤其是在能源密集型产业为主导产业的地区;并且,此阶段的环境规制尚未完善,企业用能和污染排放较为随意,将进一步加重资源紧缺和环境压力。当经济发展水平到达第二个拐点后,由于原有经济增长的传统动力衰竭,在新兴产业和节能环保技术迅速发展背景下,产业集聚的正外部性再次现显,加之更加严苛和完善的环境政策,从而倒逼经济发展转型和企业生产技术更新,因此经济发展水平的提高在此阶段对人均碳排放又呈现抑制效应。

与此同时,分位数回归系数分布图（图5）揭示了社会经济因素对于县域人均碳排放的影响具有阶段性差异：人口密度和政府财政投入对中高分位区县的人均碳排放的抑制效应大于低分位区县;碳排放强度和第二产业产值规模对低分位区县的碳排放增加有更显著的促进作用。相比较面板分位数回归的结果,OLS固定效应回归所估计的人口密度、碳排放强度和第二产业产值规模对县域人均碳排放的正负效应相同,政府财政投入对县域人均碳排放的作用相反,且经济发展水平与县域人均碳排放间呈不显著的“N”型曲线关系,这与分位数回归模型结果有所差异。

5 结论与讨论

5.1 结论

本文以县域人均碳排放为研究对象,首先分析中国县域人均碳排放变化趋势和区域差异,其次探讨县域人均碳排放的时空分布格局和空间集聚特征,最后基于STIRPAT模型和EKC假说通过面板分位数回归讨论了县域人均碳排放的影响因素。主要结论如下：

（1）2000—2017年中国县域人均碳排放整体上呈现“先急后缓”的持续增长状态,2011年为分界点;中部地区与东北地区碳排放量占比所有减少,西部地区碳排放量占比稳步提升,东部地区碳排放量占比维持在40%左右;全国县域人均碳排放基尼系数高于0.4的警戒值,且呈上升趋势,东部地区与东北地区的县域人均碳排放基尼系数较低,内部差异小,中部地区人均碳排放基尼系数逐步下降但内部差距仍较大,西部地区县域人均碳排放差距悬殊,且上升趋势明显。

（2）中国县域人均碳排放具有明显的南北分异,整体上北部区县的人均碳排放高于南部地区,且县域人均碳排放的空间极化趋势显著,东部沿海各大城市群和省会城市等经济发达区县的人均碳排放远高于其他区县;县域人均碳排放具有显著的空间正相关性,高—高集聚（HH）地区数量逐渐增多且重心向西北移动,低—低集聚（LL）地区数量有所减少,主要分布于中南地区;此外,LISA时空跃迁矩阵表明人均碳排放存在“俱乐部趋同”效应。相比已有研究认为高碳排放地区主要集中于中国北部地区和东部沿海城市群,本文通过县域人均碳排放研究完善了碳排放时空分布格局在不同尺度下的差异性认识,发现东部沿海各大城市群和省会城市等经济发达区域的人均碳排放更高,这些区域对中国现阶段和未来碳减排的作用不容忽视。

（3）人口集聚、政府公共财政支出有助于抑制县域人均碳排放,而碳排放强度和第二产业产值规模同人均碳排放具有显著的正向关系,碳排放强度对县域人均碳排放的影响远高于第二产业产值规模,因此相比单一地进行产业结构调整,通过生产技术改进降低碳排放强度对于碳减排具有更重要的作用;中低分位区县的经济发展与碳排放之间存在倒“N”型曲线关系,长远来看,通过政策调控和市场手段相互配合,加快经济发展经过第二次拐点有利于提前实现社会可持续的低碳发展模式。人口集聚和财政投入对中高分位区县碳排放的抑制作用相对低分位区县更明显,碳排放强度和第二产业产值规模对低分位区县的影响大于中高分位区县。碳减排政策的制定和实施应考虑县域发展的阶段性差异,社会经济发展结构的调整是实现整体碳减排的关键。一方面,科学合理的促进人口向城镇集聚以及增加政府公共财政投入对于人均碳排放水平较高的区县实现绿色低碳发展具有显著的推动作用;另一方面,应通过技术创新降低高耗高排产业比重、优化能源消费结构等手段实现低人均碳排放区县的可持续低碳发展。

5.2 讨论

本文所使用的CEAD夜间灯光反演碳排放数据为区县级尺度研究提供了更精准的数据基础,通过采用粒子群优化—反向传播（PSO—BP）算法实现了DMSP/OLS和NPP/VIIRS两套卫星影像数据的衔接^[56],将能源相关碳排放的测算推进至2017年,提高了数据的时效性。该数据填补了统计数据在微观尺度的能源数据的空白,被国内外****广泛应用于城市扩张、人口和交通等社会经济因素评估、污染物和碳排放量的测算等研究中^[50,51]。但由于缺乏真实县域碳排放数据,故该数据也缺乏稳健性验证。

“十四五”时期是实现碳达峰碳中和目标的重要机遇期,推动能源消费结构性改善和减污降碳政策落实到区县,是改善生态环境和可持续发展的关键举措。县域城市因社会经济发展阶段差异,碳排放水平存在明显的空间异质性,且社会经济活动向大都市圈、城市群的集聚趋势愈发明显,经济发达区县的碳排放量也将持续大幅度增加,因此需要重视这些地区在碳减排中的先导作用。首先,通过区县合作和地方政策解锁“俱乐部趋同”效应,促进东部发达区县对西北后发区县的技术和资金溢出和带动,实现区域间社会经济和生态环境的协调发展。其次,经济发展与县域人均碳排放之间的倒“N”型关系启示,应发挥集聚效应和规模效应的优势,加快越过第二次拐点,实现社会经济发展与碳排放的解耦。再次,碳排放强度的提高促进碳排放量增加,在生产活动中需加快传统能源利用技术的转换升级,降低单位产值的能耗和排放水平。提高清洁能源在能源构成中的比重,通过引导能源消费结构的多元化来优化能源生产结构达到碳减排目的。此外,人口政策的制定需考虑地区差异,引导人均碳排放水平高的区县人口适当向城镇集聚,提高城市建设的紧凑度,实现公共资源和设施的集约利用。最后,财政支持需从中央落实到区县政府乃至乡镇政府,推动城镇基础设施服务的绿色化和鼓励企业改进生产技术,实现高碳排放区县社会经济发展的低碳转型。中国的大部分县域处于碳排放的中分位区,是采取碳减排行动最有效的时期,由于在短期内难以改变以煤炭为主的能源结构,发挥市场规模优势,降低碳减排技术推广成本,通过技术创新降低碳排放强度是当前中国碳减排的主要路径。

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Since the Paris Climate Change Conference in 2015, reducing carbon emission and lowering carbon intensity has become a global consensus to deal with climate change. Due to different economic development stages, carbon intensity is regarded as a better index to measure regional energy-related carbon emissions. Although previous scholars have made great efforts to explore the spatiotemporal patterns and key driving factors of carbon intensity in China, the results lack the perspective from city level because of limited availability of statistical data of city-level carbon emission. In this study, based on carbon intensity of 283 cities in China from 1992-2013, we used the kernel density estimation, spatial autocorrelation, spatial Markov-chain and quantile regression panel model to empirically reveal its spatial spillover effects and explore the critical impact factors of carbon intensity at the city level. Our result indicates that although the total carbon emission increased during the study period, carbon intensity saw a gradual decline and regional differences were shrinking. Secondly, the city-level carbon intensity presented a strong spatial spillover effect and diverse regional backgrounds exerted heterogeneous effects on regions. Thirdly, quantile panel data analysis result showed that for low-intensity cities, on the one hand, FDI and transport sector were main contributing factors, and economic growth, technical progress and high population density negatively affected carbon intensity. On the other hand, industrial activity, extensive growth of investment and urban sprawl were key promoting factors for high-intensity cities, and population density was beneficial to emission reduction task. Furthermore, technological advance has not exerted negative influence on carbon intensity in high-intensity cities. At last, we suggested that Chinese government should take different carbon intensity levels into full consideration before policy making.

[王少剑, 黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素
地理学报, 2019, 74(6): 1131-1148.]

[本文引用: 3]

[13]

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Yuhuan

, Yang

Guangchun

. Factor decomposition and decoupling effect of Chinese tourism-derived carbon emissions
China Environmental Science, 2020, 40(12): 5531-5539.

[本文引用: 2]

[孙玉环, 杨光春. 中国旅游业碳排放的影响因素分解及脱钩效应
中国环境科学, 2020, 40(12): 5531-5539.]

[本文引用: 2]

[14]

Xiaogang

, Zhang

Yaohui

. Influence factors and Environmental Kuznets Curve relink effect of Chinese industry's carbon dioxide emission: Eempirical research based on STIRPAT model with industrial dynamic panel data
China Industrial Economics, 2012(1): 26-35.

[本文引用: 1]

[何小钢, 张耀辉. 中国工业碳排放影响因素与CKC重组效应: 基于STIRPAT模型的分行业动态面板数据实证研究
中国工业经济, 2012(1): 26-35.]

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Gao

Ming

, Song

Hongyuan

. Dynamic changes and spatial agglomeration analysis of the Chinese agricultural carbon emissions performance
Economic Geography, 2015, 35(4): 142-148, 185.

[本文引用: 1]

[高鸣, 宋洪远. 中国农业碳排放绩效的空间收敛与分异: 基于Malmquist-luenberger指数与空间计量的实证分析
经济地理, 2015, 35(4): 142-148, 185.]

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[16]

Yang

Wenyue

, Cao

Xiaoshu

. The influence mechanism of travel-related CO₂ emissions from the perspective of residential self-selection: A case study of Guangzhou
Acta Geographica Sinica, 2018, 73(2): 346-361.

DOI:10.11821/dlxb201802010 [本文引用: 1]

Numerous studies have examined the influencing factors of CO2 emissions from transportation at the national, city and community levels. However, fewer studies have considered the effect of residential self-selection. Ignoring this effect is likely to result in underestimating the role of the built environment, thus affecting relevant planning and policy development. Is the effect of residential self-selection in Chinese cities the same as in Western cities? How and to what extent does the built environment affect CO2 emissions from travel after controlling for the effect of residential self-selection? To address these questions, this paper first measures the CO2 emissions from travel on the basis of the Travel Intelligent Query System (TIQS) developed by us on the Baidu map LBS (Location Based Service) open platform, and 1239 questionnaires conducted in 15 communities in Guangzhou in 2015. It develops a structural equation model (SEM) to examine the effects of the influencing factors on CO2 emissions of trips with different purposes. The results show that the effect of residential self-selection also exists in Chinese cities. Changing residents' preference of travel mode will help reduce travel-related CO2 emissions. After controlling the effect of residential self-selection, the built environment still has significant effects on CO2 emissions from travel. Although some of them are direct effects, others are indirect effects that work through mediating variables, such as car ownership and travel distance. For different trip purposes, the mechanisms of CO2 emissions are not the same. Specifically, the distance to city public centers has a significant positive total effect on CO2 emissions from commuting trips, which is an indirect effect. Residential density significantly affects CO2 emissions from social, recreational and daily shopping trips, but it has no significant effect on CO2 emissions from commuting trips. Bus stop density is positively associated with CO2 emissions from commuting trips, and negatively associated with CO2 emissions from social and daily shopping trips. In addition, land-use mix has a negative effect on CO2 emissions from commuting, social and daily shopping trips, and metro station density and road network density have significant negative effects on CO2 emissions from all types of trip. These results suggest that it is necessary to comprehensively consider the effects of the built environment on CO2 emissions from different types of trip, and carry out targeted intervention on the built environment in related planning and policy development so as to guide the public to change their travel behavior and to promote low-carbon travel.

[杨文越, 曹小曙. 居住自选择视角下的广州出行碳排放影响机理
地理学报, 2018, 73(2): 346-361.]

[本文引用: 1]

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Mengyao

, Yao

Qiuhui

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Junming

, et al. China's intra- and inter-national carbon emission transfers by province: A nested network perspective
Science China Earth Sciences, 2020, 50(6): 748-764.

[本文引用: 1]

[韩梦瑶, 姚秋蕙, 劳浚铭, 等. 中国省域碳排放的国内外转移研究: 基于嵌套网络视角
中国科学, 2020, 50(6): 748-764.]

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Tang

Zhipeng

, Liu

Weidong

, Gong

Peiping

. Measuring of Chinese regional carbon emission spatial effects induced by exports based on Chinese multi-regional input-output table during 1997-2007
Acta Geographical Sinica, 2014, 69(10): 1403-1413.

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[唐志鹏, 刘卫东, 公丕萍. 出口对中国区域碳排放影响的空间效应测度: 基于1997—2007年区域间投入产出表的实证分析
地理学报, 2014, 69(10): 1403-1413.]

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Zeng

Xiaoying

, Qiu

Rongzu

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Danting

, et al. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China
China Environmental Science, 2020, 40(10): 4304-4313.

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[曾晓莹, 邱荣祖, 林丹婷, 等. 中国交通碳排放及影响因素时空异质性
中国环境科学, 2020, 40(10): 4304-4313.]

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Gao

Changchun

, Liu

Xianzhao

, Li

Chaokui

, et al. Spatiotemporal dynamics of carbon emissions by energy consumption in China from 1995 to 2014
Progress in Geography, 2016, 35(6): 747-757.

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Global warming due to greenhouse gases (such as CO2) emissions posts serious challenges to the sustainable development of the human society. Based on the reference method provided by the Intergovernmental Panel on Climate Change, this study calculated the CO2 emissions of energy consumption in 30 provinces of China (excluding Tibet) from 1995 to 2014. Using exploratory spatial data analysis (ESDA), LISA time path, spatiotemporal transitions, and standard deviation ellipse analysis methods, the authors analyzed the spatiotemporal dynamics of carbon emissions of energy consumption. The results show that: (1) there was a significant positive spatial correlation of carbon emissions of energy consumption in China’s provinces during 1995-2014. The inter-provincial carbon emissions differences first decreased then increased; (2) by means of LISA time path analysis, this study found that the majority of the Chinese provinces had a stable spatial structure of carbon emissions. Eighteen provinces had shorter than average time path lengths in 1995-2001 and 2002-2014. The majority of the southern provinces showed a fluctuating spatial dependence with increasing amplitude over time, while most of the northern provinces showed a relatively stable trend; (3) according to the directional Moran scatter plot, the number of provinces that showed the same trend decreased from 13 in 1995-2001 to 10 in 2002-2014, indicating that the spatial coherence of carbon emissions change at the provincial level had weakened; (4) spatial correlation patterns and clustering of carbon emissions by energy consumption at the provincial level were relatively stable and showed certain degree of path-dependence or lock-in character; (5) carbon emissions gravity center was between 113.739°~114.324°E, 34.475°~35.036°N and was moving to the northwest. The spatial distribution of provincial emissions presented a northeast-southwest pattern, and had the tendency of shifting to a north-south pattern. The focus of carbon emission reduction in China is to accelerate the development of clean energy, improve energy efficiency, promote the optimization and upgrading of energy and industrial structures in all provinces, make differentiated carbon emission reduction policies for different provinces, and establish carbon trading market.

[高长春, 刘贤赵, 李朝奎, 等. 近20年来中国能源消费碳排放时空格局动态
地理科学进展, 2016, 35(6): 747-757.]

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Liu Q

, Wu S

, Lei Y

, et al. Exploring spatial characteristics of city-level CO₂ emissions in China and their influencing factors from global and local perspectives
Science of the Total Environment, 2021, 754: 142206. DOI: 10.1016/j.scitotenv.2020.142206.

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[22]

Wang

Shaojian

, Su

Yongxian

, Zhao

Yabo

. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions
Acta Geographica Sinica, 2018, 73(3): 414-428.

DOI:10.11821/dlxb201803003 [本文引用: 4]

Carbon emissions are increasing due to human activities related with the energy consumptions for economic development. Thus, attention has been paid to the reduction of the growth of carbon emissions and formulation of policies for addressing climate change. Although most studies have explored the driving forces behind carbon emissions in China, 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 applied the spatial autocorrelation, spatial Markov-chain transitional matrices, dynamic spatial panel model and Sys-GMM to empirically estimate the key determinants of carbon emissions at the city-level and discuss its spatial spillover effects in consideration of spatiotemporal lag effects and different geographical and economic weighting matrices. Results indicated that the regional inequalities of city-level carbon emissions decreased over time and presented an obvious spatial spillover effect and high-emission "club" agglomeration. In addition, the evolution of the emission pattern has the characteristic of obvious path dependence. Panel data analysis results indicated that there was a significant U-shaped curve that can reflect the relationship between carbon emissions and GDP per capita. In addition, carbon emissions per capita are increasing with economic growth for most cities. High-proportion of secondary industry and extensive growth of investment exerted significantly positive effects on China's city-level carbon emissions. Conversely, rapid population agglomeration, the improvement of technology level, the increase of trade openness and road density play an inhibiting role in carbon emissions. Therefore, in order to reduce carbon emissions, the Chinese government should inhibit the effects of promotion factors and enhance the effects of mitigation factors. Combining with the analysis of results, we argued that optimizing the industrial structure, streamlining the extensive investment, increasing the level of technology and improving the road accessibility are the effective ways to increase energy savings and reduce carbon emissions in China.

[王少剑, 苏泳娴, 赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素
地理学报, 2018, 73(3): 414-428.]

[本文引用: 4]

[23]

Wang

Shaojian

, Liu

Yanyan

, Fang

Chuanglin

. Review of energy-related CO₂ emission in response to climate change
Progress in Geography, 2015, 34(2): 151-164.

DOI:10.11820/dlkxjz.2015.02.004 [本文引用: 1]

Climate change has in recent years become an environmental issue globally. Through deepening research into and analysis of the phenomenon, most climate scientists now identify greenhouse gases, most notably CO2 emissions, as constituting the main cause of global warming. Based on literature study and comparative analysis, this article discusses the progress of the research of CO2 emissions from a multidisciplinary perspective and analyzes the calculation methods, intensity, performance, influencing factors, and forecasting methods of CO2 emissions. This article also discusses the remaining issues and future directions of the research on CO2 emissions. Results indicate that the research of CO2 emissions has gained fruitful achievements and has experienced rapid development in recent years. CO2 emission is an integrated, complex, interdependent system engineering of numerous variables interplay between different factors. Under the framework of multidisciplinary integration, geographical and spatial factors have however received insufficient attention. Research scale has mainly focused on global and international levels, although recent studies have begun to highlight multi-scale research and pay attention to scale effect. Panel study of provincial and city scales is still scarce. Panel data are gradually receiving attention; however, dynamic analysis and comparative assessment still need improvement. Future research should focus on creating more detailed and comprehensive datasets, exploring multi-scale integrated research, and highlighting city scale analysis. Furthermore, future study should emphasize adapting theories to localized practices, employing systemic thinking and methods for differentiated trend research.

[王少剑, 刘艳艳, 方创琳. 能源消费CO₂排放研究综述
地理科学进展, 2015, 34(2): 151-164.]

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Xizhe

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Qin

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Population Research, 2010, 34(1): 48-58.

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[彭希哲, 朱勤. 我国人口态势与消费模式对碳排放的影响分析
人口研究, 2010, 34(1): 48-58.]

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Jinchuan

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Chuanglin

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Geographical Research, 2003, 22(2): 211-220.

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[黄金川, 方创琳. 城市化与生态环境交互耦合机制与规律性分析
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Linlin

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Yuan

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Chen

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China Environmental Science, 2020, 40(5): 2312-2320.

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[黄琳琳, 王远, 张晨, 等. 闽三角地区碳排放时空差异及影响因素研究
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Changjian

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Hong'ou

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Yuyao

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[王长建, 张虹鸥, 叶玉瑶, 等. 广东省能源消费碳排放影响机理分析: 基于IO-SDA模型
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Jiajun

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Dan

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Chuan

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Journal of Natural Resources, 2015, 30(8): 1289-1303.

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[刘佳骏, 史丹, 汪川. 中国碳排放空间相关与空间溢出效应研究
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Hong

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Yumeng

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China Soft Science, 2013(3): 184-192.

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[赵红, 陈雨蒙. 我国城市化进程与减少碳排放的关系研究
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Chuanwang

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Jianshuang

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Lin

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Scientia Geographica Sinica, 2019, 39(4): 644-653.

DOI:10.13249/j.cnki.sgs.2019.04.014 [本文引用: 2]

Urbanization and real estate investment are important influencing factors of carbon emission in a region. Therefore, it is necessary to systematically analyze and test the internal impact mechanism of urbanization and real estate investment on carbon emission. Combining the deformation of Kaya identity and LMDI decomposition, this article decomposes the carbon emission changes of 30 provinces in China (due to data limitation, the data of Hong Kong, Macao, Taiwan and Tibet are not included in the study area) from 1997 to 2015. The spatial evolution characteristics of carbon emissions in Chinese provinces are described by Moran’s I index and LISA spatial agglomeration map. This article further uses the spatial panel data model to empirically test the effects of urbanization and real estate investment on carbon emission from both direct impact and spatial spillover effects. The results are as follow: First, from the time trend, China’s carbon emission has maintained a growth trend from 1997 to 2015. From the perspective of the decomposition factors of carbon emission, the carbon emission coefficient of real estate investment is the main driving factor to curb carbon emissions, while the intensity of urban real estate investment, the level of urbanization and the change of total population in the region have positive effects on carbon emission, and the effects are increasing year by year. Second, from the spatial distribution characteristics of carbon emission, there are significant differences for the carbon emission in the provincial-level, and the distribution characteristics are generally expressed as higher carbon emission in the east and lower in the west. The spatial concentration of provinces with less carbon emission has increased, and the regional differences among them have been shrinking. Third, from the regression results of the spatial panel data model, the direct impact of urbanization on carbon emission is significantly negative, but the spatial spillover effect is significantly positive. The direct effect of the intensity of urban real estate investment on carbon emission is positive, but its spillover effect is not significant. The direct effect and spatial spillover effect of the interaction between urbanization and the intensity of urban real estate investment are significantly negative. The direct effect and spatial spillover effect of economic development on carbon emission are significantly positive. The direct impact of government investment on carbon emission is significantly negative, but the spillover effect is not significant. Industrial structure has no significant direct effect on the carbon emission, but its spatial spillover effect is negatively negative. The degree of opening to the outside world has a significant negative direct effect on the carbon emission, but it has a positive effect on the carbon emission in the adjacent areas. With the improvement of the level of urbanization and economic development, there is a significant U-shaped and inverted U-shaped curve relationship between the two and carbon emission, respectively. This article further proposes countermeasures and suggestions to improve regional carbon emission reduction from the perspective of urbanization and real estate policies.

[范建双, 周琳. 城镇化及房地产投资对中国碳排放的影响机制及效应研究
地理科学, 2019, 39(4): 644-653.]

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Yongnian

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Acta Geographica Sinica, 2021, 76(1): 206-222.

DOI:10.11821/dlxb202101016 [本文引用: 2]

The global environment issue resulting from carbon emissions has aroused worldwide concern for governments, the public and scientific communities. A precise measurement of the time-resolved and spatial distribution characteristics of carbon dioxide (CO2) and carbon footprint as well as its long-period evolution mechanism, can help clarify the relationship between environmental carbon load and economic growth, and are critical references to the formulation of scientific carbon emission reduction targets with reasonable and differential emission reduction policies. In this study, the mainland of China is taken as the research object. According to the quantitative correlations between DMSP-OLS nighttime light image data and carbon emission statistics, the carbon emission panel data model was simulated for China's carbon emissions in the period 2000-2013, and then the spatiotemporal evolving trend and spatial distribution characteristics of carbon emissions in the 14-year research period were discussed using Theil-Sen Median trend analysis and Mann-Kendall test method. Based on the framework of exploratory spatial-temporal data analysis (ESTDA), the spatial pattern and spatiotemporal dynamic evolution of carbon footprint from 2001 to 2013 were analyzed from the perspective of spatiotemporal interaction. In the three periods, the decoupling effect between environmental carbon load and economic growth of 336 prefecture-level cities were analyzed using the improved Tapio decoupling model. The results show that the overall carbon emissions in China had been on the rise from 2000 to 2013, in which the stable-slow rise type was dominant. China's carbon footprint and carbon deficit increased year by year, and the central and western regions became the focus of the growth of carbon footprint and carbon deficit from 2001 to 2013. At different administrative city scales, the spatial distribution pattern of carbon footprint and carbon deficit show obvious administrative orientated and spatial zonal differentiation characteristics. The annual average of global Moran's I index of each level unit is 0.491, which indicates that there is a significant spatial auto-correlation feature in the carbon footprint of China's prefecture-level units. The relative length of the LISA time path is greater in the north than in the south, and it tends to increase from the coastal areas to the central and western regions. The curvature of LISA time path decreases from coastal areas to inland areas on the whole. The curvature of northeast and central regions is higher, while that of eastern and western regions is lower. There is a different trend of the decoupling effect of environmental carbon load in China. Meanwhile the expansion-connection and expansion of negative decoupling regions continuously increased and spatially agglomerated, presenting an "E"-shaped distribution pattern from the north to the south. The national average decoupling elastic value is gradually increasing, while the coefficient of variation continues to decline, and the decoupling type has a significant evolution trend. Therefore, the unbalanced trend of economic growth and carbon emissions in China will continue for a certain period.

[潘竟虎, 张永年. 中国能源碳足迹时空格局演化及脱钩效应
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