中国城市碳排放强度的空间溢出效应及驱动因素

doi:10.11821/dlxb201906005

摘要/Abstract

摘要：

采用核密度估计、空间自相关、空间马尔科夫链和面板分位数回归等方法对1992-2013年全国283个城市碳排放强度的空间溢出效应和驱动因素进行了分析。① 核密度估计结果表明,中国城市碳排放强度总体均值下降,差异在逐步缩小。② 空间自相关Moran's I指数表明城市碳排放强度存在显著的空间集聚性且空间集聚性在逐渐增强,但空间集聚水平的变化逐年缩小。③ 空间马尔科夫链分析结果表明：第一,中国城市碳排放强度存在马太效应,低强度与高强度的城市在相邻年份转移过程中呈现维持初始状态的特征。第二,城市碳排放“空间溢出”效应明显,且不同区域背景下溢出效应存在异质性,即若与碳排放强度低的城市为邻,该城市的碳强度能够增加向上转移的概率,反之亦然。④ 面板分位数结果显示：在碳排放强度低的城市,经济增长、技术进步、适当的人口密度起到减排作用;外商投资强度与交通排放是使碳强度增大的主要因素。在碳排放强度高的城市,人口密度是重要的减排因素,技术进步暂时没起减排作用;工业排放、粗放式的资本投资以及城市土地蔓延则是碳强度上升的主要因素。

关键词: 中国城市尺度, 碳排放强度, 空间溢出, 空间马尔科夫链, 面板分位数回归

Abstract:

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.

Key words: city level, carbon emission intensity, spatial spillover effect, spatial Markov-chain, quantile regression panel model

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[J]. 地理学报, 2019, 74(6): 1131-1148.

WANG Shaojian,HUANG Yongyuan. Spatial spillover effect and driving forces of carbon emission intensity at city level in China[J]. Acta Geographica Sinica, 2019, 74(6): 1131-1148.

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参考文献 59

[1]	Liu Yanhua, Ge Quansheng, He Fanneng , et al. Countermeasures against international rressure of reducing CO₂ emissions and analysis on China's potential of CO₂ emission reduction. Acta Geographica Sinica, 2008,63(7):675-682.
	[ 刘燕华, 葛全胜, 何凡能 , 等. 应对国际CO₂减排压力的途径及我国减排潜力分析. 地理学报, 2008,63(7):675-682.]
[2]	Jotzo F , Pezzey J C V. Optimal intensity targets for greenhouse gas emissions trading under uncertainty. Environmental & Resource Economics, 2007,38(2):259-284.
[3]	Zhao Qiaozhi, Yan Qingyou, Zhao Hairui . Research on spatial characteristics and influencing factors of provincial carbon emissions in China. Journal of Beijing Institute of Technology, 2018,20(1):9-16.
	[ 赵巧芝, 闫庆友, 赵海蕊 . 中国省域碳排放的空间特征及影响因素. 北京理工大学学报(社会科学版), 2018,20(1):9-16.]
[4]	Sun Yaohua, Zhong Weizhou, Qing Dongrui . Analysis on differences of carbon emission intensity of each province in China based on Theil Index. Finance and Trade Research, 2012,2(3):1-7.
	[ 孙耀华, 仲伟周, 庆东瑞 . 基于Theil指数的中国省际间碳排放强度差异分析. 财贸研究, 2012,2(3):1-7.]
[5]	Zhao Yuntai, Huang Xianjin , Zhong Taiyang, at el. Spatial pattern evolution of carbon emission intensity from energy consumption in China. Environmental Science, 2011,32(11):3145-3152.
	[ 赵雲泰, 黄贤金, 钟太洋 , 等. 1999-2007年中国能源消费碳排放强度空间演变特征. 环境科学, 2011,32(11):3145-3152.]
[6]	Li Shantong, Hou Yongzhi, Liu Yunzhong , et al. The analysis on survey of local protection in China domestic market. Economic Research Journal, 2004(11):78-84.
	[ 李善同, 侯永志, 刘云中 , 等. 中国国内地方保护问题的调查与分析. 经济研究, 2004(11):78-84.]
[7]	Pan Wenqing . Regional linkage and the spatial spillover effects on regional economic growth in China. Economic Research Journal, 2012(1):54-65.
	[ 潘文卿 . 中国的区域关联与经济增长的空间溢出效应. 经济研究, 2012(1):54-65.]
[8]	Gu Chaolin, Tan Zongbo, Liu Wan , et al. A study on climate change, carbon emissions and low-carbon city planning. Urban Planning Forum, 2009(3):38-45.
	[ 顾朝林, 谭纵波, 刘宛 , 等. 气候变化、碳排放与低碳城市规划研究进展. 城市规划学刊, 2009(3):38-45.]
[9]	Pettersson F, Maddison D , Acar Sevil et al. Convergence of carbon dioxide emissions: A review of the literature. International Review of Environmental & Resource Economics, 2014,7(2):141-178.
[10]	Xu Guangyue . The convergence in carbon dioxide emissions: Theoretical hypotheses and empirical research in China. The Journal of Quantitative & Technical Economics, 2010(9):31-42.
	[ 许广月 . 碳排放收敛性: 理论假说和中国的经验研究. 数量经济技术经济研究, 2010(9):31-42.]
[11]	Lin Boqiang, Huang Guangxiao . Dynamics of China's regional carbon emissions under gradient economic developing mode: A view of spatial analysis. Journal of Financial Research, 2011(12):35-46.
	[ 林伯强, 黄光晓 . 梯度发展模式下中国区域碳排放的演化趋势: 基于空间分析的视角. 金融研究, 2011(12):35-46.]
[12]	Wu Jianxin, Guo Zhiyong . Research on the convergence of carbon dioxide emissions in China: A continuous dynamic distribution approach. Statistical Research, 2016,33(1):54-60.
	[ 吴建新, 郭智勇 . 基于连续性动态分布方法的中国碳排放收敛分析. 统计研究, 2016,33(1):54-60.]
[13]	Ma Dalai, Chen Zhongchang, Wang Ling . Spatial econometrics research on inter-provincial carbon emissions efficiency in China. China Population, Resources and Environment, 2015,25(1):67-77.
	[ 马大来, 陈仲常, 王玲 . 中国省际碳排放效率的空间计量. 中国人口·资源与环境, 2015,25(1):67-77.]
[14]	Zhao Guimei, Chen Lizhen, Sun Licheng , et al. Markov steady state prediction of carbon emission intensity in China based on the perspective of spatial differentiation. Science and Technology Management Research, 2017,37(22):228-233.
	[ 赵桂梅, 陈丽珍, 孙立成 , 等. 空间分异视角下中国碳排放强度的Markov稳态预测. 科技管理研究, 2017,37(22):228-233.]
[15]	Cheng Yeqing, Zhang Shouzhi, Ye Xinyue , 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.
	[ 程叶青, 张守志, 叶信岳 , 等. 中国能源消费碳排放强度及其影响因素的空间计量. 地理学报, 2013,68(10):1418-1431.]
[16]	Yan Yanmei, Wang Zheng, Wu Leying , et al. Analysis of the determinants of carbon emission intensity on regional differences. Acta Scientiae Circumstantiae, 2016,36(9):3436-3444.
	[ 颜艳梅, 王铮, 吴乐英 , 等. 中国碳排放强度影响因素对区域差异的作用分析. 环境科学学报, 2016,36(9):3436-3444.]
[17]	Zhou Jieqi, Wang Tongsan . Research on the convergence and mechanism of regional economic growth and carbon emissions intensity differences: An empirical analysis based on provincial panel data in China. Social Science Research, 2014(5):66-73.
	[ 周杰琦, 汪同三 . 地区经济增长与碳强度差异的收敛性及其机理: 基于中国省际面板数据的实证分析. 社会科学研究, 2014(5):66-73.]
[18]	Iea International Energy Agency. World Energy Outlook 2009 Factsheet, 2010.
[19]	Cai Bofeng, Cao Dong, Liu Lancui , et al. China transport CO₂ emission study. Advances in Climate Change Research, 2011,7(3):197-203.
	[ 蔡博峰, 曹东, 刘兰翠 , 等. 中国交通二氧化碳排放研究. 气候变化研究进展, 2011,7(3):197-203.]
[20]	Zhang Taoxin, Zeng Aozhi . Spatial econometrics analysis on China transport carbon emissions. Urban Development Studies, 2013,20(10):14-20.
	[ 张陶新, 曾熬志 . 中国交通碳排放空间计量分析. 城市发展研究, 2013,20(10):14-20.]
[21]	Glaeser E L, Kahn M E . The greenness of cities: Carbon dioxide emissions and urban development. Journal of Urban Economics, 2010,67(3):404-418. doi: 10.1016/j.jue.2009.11.006
[22]	Xie R, Fang J Y, Liu C J . The effects of transportation infrastructure on urban carbon emissions. Applied Energy, 2017,196
[23]	Cong Jianhui, Liu Xuemin, Zhao Xueru . Demarcation problems and the corresponding measurement methods of the urban carbon accounting. China Population, Resources and Environment, 2014,24(4):19-26.
	[ 丛建辉, 刘学敏, 赵雪如 . 城市碳排放核算的边界界定及其测度方法. 中国人口·资源与环境, 2014,24(4):19-26.]
[24]	Wang S J, Liu X P . China's city-level energy-related CO₂ emissions: Spatiotemporal patterns and driving forces. Applied Energy, 2017,200.
[25]	Xu Jianhua. Mathematical Methods in Contemporary Geography. Beijing: Higher Education Press, 1996.
	[ 徐建华 . 现代地理学中的数学方法. 北京: 高等教育出版社, 1996.]
[26]	Le Gallo J . Space-time analysis of GDP disparities among European regions: A Markov chains approach. International Regional Science Review, 2004,27(2):138-163. doi: 10.1177/0160017603262402
[27]	Chen Peiyang, Zhu Xigang . Regional convergence at county level in China. Scientia Geographica Sinica, 2013,33(11):1302-1308.
	[ 陈培阳, 朱喜钢 . 中国区域经济趋同: 基于县级尺度的空间马尔可夫链分析. 地理科学, 2013,33(11):1302-1308.]
[28]	Chen Qiang. Advanced Econometrics and Stata Application. Beijing: Higher Education Press, 2010.
	[ 陈强 . 高级计量经济学及Stata应用. 北京: 高等教育出版社, 2010.]
[29]	Koenker R, Bassett Jr G . Regression quantiles. Econometrica: Journal of the Econometric Society, 1978,46(1):33-50. doi: 10.2307/1913643
[30]	Luo Youxi, Tian Maozai . Quantile regression for panel data and its simulation study. Statistical Research, 2010,27(10):81-87.
	[ 罗幼喜, 田茂再 . 面板数据的分位回归方法及其模拟研究. 统计研究, 2010,27(10):81-87.]
[31]	Koenker R . Quantile regression for longitudinal data. Journal of Multivariate Analysis, 2004,91(1):74-89. doi: 10.1016/j.jmva.2004.05.006
[32]	York R, Rosa E A, Dietz T . STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 2003,46(3):351-365. doi: 10.1016/S0921-8009(03)00188-5
[33]	Jiao Wenxian, Chen Xingpeng . Environmental impact analysis of Gansu Province based on the STIRPAT model: A case study of energy consumption during 1991-2009. Resources and Environment in the Yangtze Basin, 2012,21(1):105.
	[ 焦文献, 陈兴鹏 . 基于STIRPAT模型的甘肃省环境影响分析: 以1991-2009年能源消费为例. 长江流域资源与环境, 2012,21(1):105.]
[34]	Dong F, Yu B, Hadachin T , et al. Drivers of carbon emission intensity change in China. Resources, Conservation and Recycling, 2018,129:187-201. doi: 10.1016/j.resconrec.2017.10.035
[35]	Wang Yuan, Cheng Xi, Yin Peihong , et al. Research on regional characteristics of China's carbon emission performance based on entropy method. Journal of Natural Resources, 2013,28(7):1106-1116. doi: 10.11849/zrzyxb.2013.07.003
	[ 王媛, 程曦, 殷培红等 . 影响中, 国碳排放绩效的区域特征研究: 基于熵值法的聚类分析. 自然资源学报, 2013,28(7):1106-1116.] doi: 10.11849/zrzyxb.2013.07.003
[36]	Wang Zheng, Ma Cuifang, Wang Ying , et al. A geographical investigation into knowledge spillovers between regions. Acta Geographica Sinica, 2003,58(5):773-780.
	[ 王铮, 马翠芳, 王莹 , 等. 区域间知识溢出的空间认识. 地理学报, 2003,58(5):773-780.]
[37]	Zeng Gang, Shang Yongmin, Si Yuefang . The convergent evolution of China's regional economic development models. Geographical Research, 2015,34(11):2005-2020.
	[ 曾刚, 尚勇敏, 司月芳 . 中国区域经济发展模式的趋同演化: 以中国16种典型模式为例. 地理研究, 2015,34(11):2005-2020.]
[38]	Long G Y . Understanding China's recent growth experience: A spatial econometric perspective. Annals of Regional Science, 2003,37(4):613-628. doi: 10.1007/s00168-003-0129-x
[39]	Zhang Xueliang . Has transport infrastructure promoted regional economic growth? With an analysis of the spatial effects of transport infrasturcture. Social Sciences in China, 2012(3):60-77.
	[ 张学良 . 中国交通基础设施促进了区域经济增长吗: 兼论交通基础设施的空间溢出效应. 中国社会科学, 2012(3):60-77.]
[40]	Li Guozhang, Wang Shuang . Regional factor decompositions in China's energy intensity change: Based on LMDI technique. Journal of Finance and Economics, 2008,34(8):52-62.
	[ 李国璋, 王双 . 中国能源强度变动的区域因素分解分析: 基于LMDI分解方法. 财经研究, 2008,34(8):52-62.]
[41]	Li Yanmei, Zhang Lei, Cheng Xiaoling . A decomposition model and reduction approaches for carbon dioxide emissions in China. Resources Science, 2010,32(2):218-222.
	[ 李艳梅, 张雷, 程晓凌 . 中国碳排放变化的因素分解与减排途径分析. 资源科学, 2010,32(2):218-222.]
[42]	Zhang Youguo . Economic development pattern change impact on China's carbon intensity. Economic Research Journal, 2010(4):120-133.
	[ 张友国 . 经济发展方式变化对中国碳排放强度的影响. 经济研究, 2010(4):120-133.]
[43]	Liu Y H, Gao C C, Lu Yingying . The impact of urbanization on GHG emissions in China: The role of population density. Journal of Cleaner Production, 2017,157:299-309. doi: 10.1016/j.jclepro.2017.04.138
[44]	Chai Zhixian . Density effects, development level and China's urban carbon dioxide emission. On Economic Problems, 2013(3):25-31.
	[ 柴志贤 . 密度效应、发展水平与中国城市碳排放. 经济问题, 2013(3):25-31.]
[45]	Chen Liangwen, Yang Kaizhong . Productivity, city size and economic density: A empirical study on effect of urban agglomeration economies. Social Sciences in Guizhou, 2007(2):113-119.
	[ 陈良文, 杨开忠 . 生产率、城市规模与经济密度: 对城市集聚经济效应的实证研究. 贵州社会科学, 2007(2):113-119.]
[46]	Cai Yinyin, Sun Bindong . Spatial dispersion of population and economic growth: Based on empirical analysis of megacities. Urban Insight, 2013,27(5):94-101.
	[ 蔡寅寅, 孙斌栋 . 城市人口空间分散与经济增长: 基于特大城市的实证分析. 城市观察, 2013,27(5):94-101.]
[47]	Li Lianshui, Zhou Yong . Can technological advance improve energy effect: A empirical study based on industrial sectors in China. Management World, 2006(10):82-89.
	[ 李廉水, 周勇 . 技术进步能提高能源效率吗? 基于中国工业部门的实证检验. 管理世界, 2006(10):82-89.]
[48]	Li Kaijie, Qu Ruxiao . Impact of technological change on carbon dioxide emission: An empirical analysis based on provincial dynamic panel data model. Journal of Beijing Normal University (Social Sciences), 2012(5):129-139.
	[ 李凯杰, 曲如晓 . 技术进步对碳排放的影响: 基于省际动态面板的经验研究. 北京师范大学学报(社会科学版), 2012(5):129-139.]
[49]	Zhang Lifeng . Relations among the industry structure, energy structure and carbon emissions. Journal of Arid Land Resources and Environment, 2011,25(5):1-7.
	[ 张丽峰 . 我国产业结构、能源结构和碳排放关系研究. 干旱区资源与环境, 2011,25(5):1-7.]
[50]	Zhang Changde, Liu Shuai . Industrial structure and carbon emissions: A analysis based on provincial panel data in China. Research on Development, 2011(2):26-33.
	[ 郑长德, 刘帅 . 产业结构与碳排放: 基于中国省际面板数据的实证分析. 开发研究, 2011(2):26-33.]
[51]	Zhao Ruyu, Qiu Zhenzhuo . Review on the relationship between industrial structure and carbon emissions. Economic Review, 2014(10):110-113.
	[ 赵儒煜, 邱振卓 . 产业结构与碳排放关系研究述评. 经济纵横, 2014(10):110-113.]
[52]	Xiao Yanfei, Wan Zijie, Liu Hongguang . An empirical study of carbon emission transfer and carbon leakage in regional industrial transfer in China: Analysis based on inter-regional input-output model in 2002 and 2007. Journal of Finance and Economics, 2014,40(2):75-84.
	[ 肖雁飞, 万子捷, 刘红光 . 我国区域产业转移中“碳排放转移”及“碳泄漏”实证研究: 基于2002年、2007年区域间投入产出模型的分析. 财经研究, 2014,40(2):75-84.]
[53]	Guo Chaoxian . An analysis of the increase of CO₂ emission in China: Based on SDA technique. China Industrial Economics, 2010(12):47-56.
	[ 郭朝先 . 中国二氧化碳排放增长因素分析: 基于SDA分解技术. 中国工业经济, 2010(12):47-56.]
[54]	Li Zihao, Liu Huihuang . FDI, technology progress and emission of CO₂: Evidence from Chinese provincial data. Studies in Science of Science, 2011,29(10):1495-1503.
	[ 李子豪, 刘辉煌 . 外商直接投资、技术进步和二氧化碳排放: 基于中国省际数据的研究. 科学学研究, 2011,29(10):1495-1503.]
[55]	Zhao Rongqin, Huang Xianjin, Xu Hui , et al. Progress in the research of carbon cycle and management of urban system. Journal of Natural Resources, 2009,24(10):1847-1859. doi: 10.11849/zrzyxb.2009.10.019
	[ 赵荣钦, 黄贤金, 徐慧等. 城市系统碳循环与碳管理研究进展. 自然资源学报, 2009,24(10):1847-1859.] doi: 10.11849/zrzyxb.2009.10.019
[56]	Tian Li . Urbanization of land in urbanization process of China: Boon or bane? City Planning Review, 2011(2):11-12.
	[ 田莉 . 我国城镇化进程中喜忧参半的土地城市化. 城市规划, 2011(2):11-12.]
[57]	She Qiannan, Jia wenxiao, Pan Chen , et al. Spatial and temporal variation characteristics of urban forms' impact on regional carbon emissions in the Yangtze River Delta. China Population, Resources and Environment, 2015,25(11):44-51.
	[ 佘倩楠, 贾文晓, 潘晨 , 等. 长三角地区城市形态对区域碳排放影响的时空分异研究. 中国人口·资源与环境, 2015,25(11):44-51.]
[58]	Peng Mi, Lu Bin, Zhang Chun , et al. On the spatial differentia of carbon emissions in energy and its influencing factors on provincial level in China. Urban Studies, 2010(7):6-11.
	[ 彭觅, 吕斌, 张纯 , 等. 中国能源碳排放的区域差异及其影响因素分析. 城市发展研究, 2010(7):6-11.]
[59]	Park Juhee . The effects of compact city form on transportation energy consumption and air pollution[D]. Beijing: Tsinghua University, 2014.
	[ 朴珠希 . 紧凑城市形态对交通能耗及大气污染的影响实证研究[D]. 北京: 清华大学, 2014.]

变量类型	变量名称	变量单位	变量解释
被解释变量	碳排放强度(CEI)	t/万元	城市碳排放总量/GDP
解释变量	城市富裕程度(A)	元	人均GDP
	人口密度(P)	人/km²	总人口数/市域面积
	产业结构(IS)	%	第二产业增加值/GDP
	投资强度(CI)	%	固定资产投资总额/GDP
	外资强度(FDI)	万美元/万元	实际利用外资额/GDP
	技术进步(T)	万元/tce	能源强度(能源消费总量/GDP)的倒数
	土地城市化(UB)	%	道路500 m缓冲区面积/市域面积
	道路密度(RD)	km/百km²	公里里程数/市域面积

年份	Moran's I	Z值	年份	Moran's I	Z值
1992	0.359	8.717^**	2003	0.434	11.018^**
1993	0.416	10.459^**	2004	0.435	10.942^**
1994	0.336	8.807^**	2005	0.498	12.424^**
1995	0.325	8.653^**	2006	0.478	11.903^**
1996	0.292	7.565^**	2007	0.464	11.564^**
1997	0.460	11.741^**	2008	0.443	11.003^**
1998	0.316	8.143^**	2009	0.489	12.150^**
1999	0.431	11.005^**	2010	0.448	11.124^**
2000	0.411	10.504^**	2011	0.435	10.880^**
2001	0.414	10.453^**	2012	0.428	10.599^**
2002	0.417	10.516^**	2013	0.380	9.534^**

t\t+1	n	1	2	3	4
1	1439	0.8965	0.0980	0.0056	0
2	1457	0.1290	0.7714	0.0954	0.0041
3	1512	0.0013	0.1713	0.7798	0.0476
4	1535	0.0020	0.0007	0.1094	0.8879

邻域类型	t/t+1	n	1	2	3	4
1	1	659	0.9408	0.0561	0.0030	0
	2	357	0.1345	0.7703	0.0924	0.0028
	3	257	0.0039	0.2257	0.7237	0.0467
	4	176	0	0	0.1364	0.8636
2	1	478	0.8766	0.1192	0.0042	0
	2	467	0.1370	0.7687	0.0878	0.0064
	3	314	0	0.2070	0.7516	0.0414
	4	204	0.0098	0.0049	0.1422	0.8431
3	1	244	0.8361	0.1516	0.0123	0
	2	462	0.1364	0.7727	0.0887	0.0022
	3	483	0.0021	0.1843	0.7723	0.0414
	4	313	0.0032	0	0.1374	0.8594
4	1	58	0.8103	0.1724	0.0172	0
	2	171	0.0760	0.7778	0.1404	0.0058
	3	458	0	0.1026	0.8384	0.0590
	4	842	0	0	0.0855	0.9145

变量	(1)	(2)	(3)	(4)	(5)	(6)
变量	FE	q10	q25	q50	q75	q90
A	2.138^***	1.5515^***	1.6227^***	1.6329^***	1.4469^***	1.2669^***
	(8.85)	(4.97)	(5.58)	(5.97)	(5.16)	(5.08)
SA	-0.140^***	-0.1018^***	-0.1066^***	-0.1102^***	-0.1071^***	-0.1023^***
	(-10.60)	(-5.69)	(-6.14)	(-6.49)	(-6.15)	(-7.07)
P	-0.210^***	-0.0665^**	-0.0747^***	-0.0792^***	-0.0813^***	-0.0771^***
	(-2.76)	(-2.42)	(-2.65)	(-2.89)	(-2.97)	(-2.81)
IS	0.369^***	0.1758^**	0.1963^**	0.3458^***	0.4681^***	0.4350^***
	(3.21)	(2.25)	(2.16)	(2.85)	(4.88)	(5.22)
CI	0.159	0.0982^**	0.0954	0.1408	0.2881^**	0.4066^***
	(1.65)	(2.42)	(1.36)	(1.46)	(2.53)	(3.34)
FDI	5.427^**	8.6024^***	7.7062^***	6.3488^***	4.9094^***	3.3846^***
	(1.99)	(5.15)	(4.85)	(4.09)	(3.84)	(3.03)
T	-0.0221^***	-0.2610^***	-0.2528^***	-0.2087^***	-0.0922	-0.0182
	(-4.80)	(-4.48)	(-3.68)	(-2.71)	(-1.12)	(-0.41)
UB	0.0426^***	0.0347^***	0.0377^***	0.0474^***	0.0672^***	0.0836^***
	(4.79)	(4.77)	(4.9)	(5.4)	(5.98)	(6.77)
ROD	0.0435^***	0.0301^***	0.0248^***	0.0187^***	0.0092	0.00349
	(6.04)	(3.17)	(3.06)	(2.64)	(1.42)	(0.65)
Cons	-6.114^***	-4.4682^***	-4.5841^***	-4.3932^***	-3.0907^**	-1.9161^*
	(-4.91)	(-3.14)	(-3.58)	(-3.76)	(-2.54)	(-1.71)
N	6226	6226	6226	6226	6226	6226