The convergence of carbon emissions in important regions is a key prerequisite for reaching China’s carbon emissions peak target. The Yangtze River Economic Belt (YREB) is the latest strategic economic area in China. In this study, a method of investigating the convergence of per capita carbon emissions in the YREB is proposed, and it includes the following models: convergence, stochastic convergence and convergence. Besides, the five control variables per capita Gross Domestic Product (GDP), energy consumption structure, industrial structure, population density and urbanization rate are considered in the test models. In addition to testing the whole sample of 74 cities belonging to the Economic Belt, the convergence of three subgroups (municipalities, capital and sub-provincial cities and prefecture-level cities) is also tested by considering spatial effects. The following results are obtained: (1) although convergence and conditional convergence are observed, stochastic convergence is not observed for the 74 cities; (2) the per capita GDP and energy structure are the key factors influencing the cities’ convergence, whereas the urbanization rate has a limited impact; and (3) improving the industrial structure and reducing the population density are effective methods of promoting the convergence of per capita carbon emissions among the capital and sub-provincial cities and municipalities, respectively.
NBSC. National Bureau of Statistics. Available at: www.stats.gov.cn/ (2017, accessed 7 November 2018).
3.
KarakayaEAlataşSYılmazB.Replication of Strazicich and List (2003): are CO2 emission levels converging among industrial countries?Energy Econ2017, http://dx.doi.org/10.1016/j.eneco.2017.08.033
4.
LiJHuangXYangHet al.
Convergence of carbon intensity in the Yangtze River Delta, China. Habitat International2017;
60: 58–68.
5.
LiXLinB.Global convergence in per capita CO2 emissions. Renew Sustain Energ Rev2013;
24: 357–363.
6.
SolowRM.A contribution to the theory of economic growth. Q J Econ1956;
70: 65–94.
LiK-WZhouXPanZ.Cross-country output convergence and growth: evidence from varying coefficient nonparametric method. Econ Model2016;
55: 32–41.
9.
QuahDT.Empirics for economic growth and convergence. Eur Econ Rev1996;
40: 1353–1375.
10.
YangJZhangTShengPet al.
Carbon dioxide emissions and interregional economic convergence in China. Econ Model2016;
52: 672–680.
11.
Ben-DavidD.Trade and convergence among countries. J Int Econ1996;
40: 279–298.
12.
JobertTKaranfilFTykhonenkoA.Convergence of per capita carbon dioxide emissions in the EU: Legend or reality?Energy Econ2010;
32: 1364–1373.
13.
LeeC-CChangC-P.Stochastic convergence of per capita carbon dioxide emissions and multiple structural breaks in OECD countries. Econ Model2009;
26: 1375–1381.
14.
Romero-ÁvilaD.Convergence in carbon dioxide emissions among industrialised countries revisited. Energy Econ2008;
30: 2265–2282.
15.
StrazicichMCListJA.Are CO2 emission levels converging among industrial countries?Environ Resour Econ2003;
24: 263–271.
16.
HaoYLiaoHWeiY-M.Is China’s carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence. Appl Energy2015;
142: 229–239.
17.
WangJZhangK.Convergence of carbon dioxide emissions in different sectors in China. Energy2014;
65: 605–611.
18.
WuJWuYGuoXet al.
Convergence of carbon dioxide emissions in Chinese cities: a continuous dynamic distribution approach. Energy Pol2016;
91: 207–219.
BrännlundRLundgrenTSöderholmP.Convergence of carbon dioxide performance across Swedish industrial sectors: an environmental index approach. Energy Econ2015;
51: 227–235.
21.
MoutinhoVRobaina-AlvesMMotaJ.Carbon dioxide emissions intensity of Portuguese industry and energy sectors: a convergence analysis and econometric approach. Renew Sustain Energ Rev2014;
40: 438–449.
22.
YuSHuXFanJLet al.
Convergence of carbon emissions intensity across Chinese industrial sectors. J Clean Prod2018;
194: 179–192.
23.
BarassiMRColeMAElliottRJR.Stochastic divergence or convergence of per capita carbon dioxide emissions: re-examining the evidence. Environ Res Econ2008;
40: 121–137.
LeeC-CChangC-P.New evidence on the convergence of per capita carbon dioxide emissions from panel seemingly unrelated regressions augmented Dickey–Fuller tests. Energy2008;
33: 1468–1475.
26.
PanopoulouEPantelidisT.Club convergence in carbon dioxide emissions. Environ Resource Econ2009;
44: 47–70.
27.
PhillipsPCBSulD.Transition modeling and econometric convergence tests. Econometrica2007;
75: 1771–1855.
28.
BurnettJW.Club convergence and clustering of U.S. energy-related CO2 emissions. Res Energy Econ2016;
46: 62–84.
29.
YavuzNCYilanciV.Convergence in per capita carbon dioxide emissions among G7 countries: a TAR panel unit root approach. Environ Resource Econ2013;
54: 283–291.
BarassiMRColeMAElliottRJR.The stochastic convergence of CO2 emissions: a long memory approach. Environ Res Econ2011;
49: 367–385.
32.
ApergisNPayneJE.Per capita carbon dioxide emissions across U.S. states by sector and fossil fuel source: evidence from club convergence tests. Energy Econ2017;
63: 365–372.
33.
PresnoMJLandajoMFGonzálezP.Stochastic convergence in per capita CO2 emissions. An approach from nonlinear stationarity analysis. Energy Econ2018;
70: 563–581.
34.
WuC-QHuangL. Research on the performance and synergistic effect of green development of Yangtze River Economic Belt’s industry. J China Univ Geosci2018. (In Chinese.)
35.
WangX-XPengLSuC-Jet al.
Eco-environment health assessment of cities in the Yangtze River in economic zone based on urbanization approach. J Hunan Univ2015;
42: 132–140. (In Chinese.)
36.
JiangH.A study on environmental justice evaluation of the Yangtze River Economic Belt. Rural Econ Sci Technol2016;
27: 1–2. (In Chinese.)
37.
ChenNXuLChenZ.Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and Tobit models. Energy2017;
134: 659–671.
38.
WuC-QDuY. Research on the effect of biased technical change on total factor energy efficiency of the Yangtze River Economic Belt. China Soft Sci2018. (In Chinese.)
39.
WangKMengXYangBet al.
The industrial eco-efficiency of the Yangtze River Economic Zone based on environmental pressure. Resour Sci2015;
37: 1491–1501. (In Chinese).
40.
ZhaoXSunXTaoR.Energy eco-efficiency evaluation and convergence analysis of Yangtze River Economic Belt from the perspective capacity reduction. J Taiyuan Univ Technol2016;
34: 45–50. (In Chinese.)
41.
XingZ-CJ.-GWZhangJ. Research on spatial-temporal variation and evolution of total-factor ecological efficiency in the Yangtze River Economic Belt. ResourceEnviron Yangtze Basin. 2018; 27, (In Chinese).
42.
LiJHuangX. Analysis of influential factors of carbon emission based on spatial panel models: a case study for Yangtze River Economic Belt. Resource Environ Yangtze Basin. 2015; 24, 1665–1671. (In Chinese.)
43.
LuSJiangHLiuYet al.
Regional disparities and influencing factors of average CO2 emissions from transportation industry in Yangtze River Economic Belt. Transp Res D Transp Environ2017;
57: 112–123.
44.
QiSLinS.The influencing factors on the carbon emission of electric power industry: an empirical study based on Yangtze Economic Belt spatial dynamic panel. J Environ Econ2016; 1: 91–105. (In Chinese.)
45.
SunCChenLTianY.Study on the urban state carrying capacity for unbalanced sustainable development regions: evidence from the Yangtze River Economic Belt. Ecol Indic2018;
89: 150–158.
46.
TianYSunC.Comprehensive carrying capacity, economic growth and the sustainable development of urban areas: a case study of the Yangtze River Economic Belt. J Clean Prod2018;
195: 486–496.
47.
TianYSunC.A spatial differentiation study on comprehensive carrying capacity of the urban agglomeration in the Yangtze River Economic Belt. Reg Sci Urban Econ2018;
68: 11–22.
48.
TanFBiJ.An inquiry into water transfer network of the Yangtze River Economic Belt in China. J Clean Prod2018;
176: 288–297.
49.
AnselinL.Spatial econometrics: methods and models. Econ Geogr1988;
65: 160–162.
50.
AnselinL.Spatial effects in econometric practice in environmental and resource economics. Am J Agri Econ2001;
83: 705–710.
51.
LeeL-FYuJ.Estimation of spatial autoregressive panel data models with fixed effects. J Econom2010;
154: 165–185.
52.
LiK.Fixed-effects dynamic spatial panel data models and impulse response analysis. J Econom2017;
198: 102–121.
53.
MarbuahGAmuakwa-MensahF.Spatial analysis of emissions in Sweden. Energy Econ2017;
68: 383–394.
54.
ShiWLeeL-F.Spatial dynamic panel data models with interactive fixed effects. J Econom2017;
197: 323–347.
55.
MengBWangJAndrewRet al.
Spatial spillover effects in determining China's regional CO2 emissions growth: 2007–2010. Energy Econ2017;
63: 161–173.
56.
SuWLiuYWangSet al.
Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China. J Geogr Sci2018;
28: 495–513.
57.
ChuaiXHuangXWangWet al.
Spatial econometric analysis of carbon emissions from energy consumption in China. J Geogr Sci2012;
22: 630–642.
58.
ZhangQYangJSunZet al.
Analyzing the impact factors of energy-related CO2 emissions in China: what can spatial panel regressions tell us?J Clean Prod2017;
161: 1085–1093.
59.
ZhangXZhaoY.Identification of the driving factors’ influences on regional energy-related carbon emissions in China based on geographical detector method. Environ Sci Pollut Res2018;
25: 9626–9635.
MengLHuangB.Shaping the relationship between economic development and carbon dioxide emissions at the local level: evidence from spatial econometric models. Environ Res Econ2018;
71: 127–156.
62.
TangZLiuWGongP.The measurement of the spatial effects of Chinese regional carbon emissions caused by exports. J Geogr Sci2015;
25: 1328–1342.
63.
LiMLiCZhangM.Exploring the spatial spillover effects of industrialization and urbanization factors on pollutants emissions in China's Huang-Huai-Hai region. J Clean Prod2018;
195: 154–162.
64.
ChenJXuCLiKet al.
A gravity model and exploratory spatial data analysis of prefecture-scale pollutant and CO2 emissions in China. Ecol Indic2018;
90: 554–563.
65.
HuangBMengL.Convergence of per capita carbon dioxide emissions in urban China: a spatio-temporal perspective. Appl Geogr2013;
40: 21–29.
66.
AdhikariDChenY.Energy productivity convergence in Asian countries: a spatial panel data approach. Int J Econ Financ2014;
6: 94–107.
67.
BernardABDurlaufSN.Interpreting tests of the convergence hypothesis. J Econom1996;
71: 161–173.
68.
HarrisRDFTzavalisE.Inference for unit roots in dynamic panels where the time dimension is fixed. J Econom1999;
91: 201–226.
69.
ImKSPesaranMHShinY.Testing for unit roots in heterogeneous panels. J Econom2003;
115: 53–74.
70.
NiuX.Research on regional difference and convergence of China's carbon emission efficiency from the perspective of total factors. Chengdu, China: Southwestern University of Finance and Economics, 2016 (In Chinese).
71.
LiddleB.Revisiting world energy intensity convergence for regional differences. Appl Energy2010;
87: 3218–3225.
72.
XuS-CHeZ-XLongR-Y.Factors that influence carbon emissions due to energy consumption in China: decomposition analysis using LMDI. Appl Energy2014;
127: 182–193.
73.
WangCWangFZhangXet al.
Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renew Sustain Energ Rev2017;
67: 51–61.
74.
ZhangNYuKChenZ.How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis. Energy Pol2017;
107: 678–687.
75.
YuSZhangJZhengSet al.
Provincial carbon intensity abatement potential estimation in China: a PSO–GA-optimized multi-factor environmental learning curve method. Energy Pol2015;
77: 46–55.
76.
WangYZhangPHuangDet al.
Convergence behavior of carbon dioxide emissions in China. Econ Model2014;
43: 75–80.
77.
SharmaSS.Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Appl Energy2011;
88: 376–382.
78.
ShengPGuoX.The long-run and short-run impacts of urbanization on carbon dioxide emissions. Econ Model2016;
53: 208–215.
79.
SadorskyP.The effect of urbanization on CO2 emissions in emerging economies. Energy Econ2014;
41: 147–153.
80.
RafiqSSalimRNielsenI.Urbanization, openness, emissions, and energy intensity: a study of increasingly urbanized emerging economies. Energy Econ2016;
56: 20–28.
81.
LiHMuHZhangMet al.
Analysis on influence factors of China's CO2 emissions based on Path–STIRPAT model. Energy Pol2011;
39: 6906–6911.
82.
LiuNMaZKangJ.Changes in carbon intensity in China's industrial sector: decomposition and attribution analysis. Energy Pol2015;
87: 28–38.
83.
YangZFanMShaoSet al.
Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis. Energy Econ2017;
68: 271–282.
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