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Analysis of carbon emission intensity, urbanization and energy mix: evidence from China

Abstract

The Chinese government committed to reduce carbon dioxide emissions per unit gross domestic product by 60–65 % from the 2005 level in a document submitted to the Secretariat of the United Nations Framework Convention on Climate Change. China has also proposed a strategy for a new type of urbanization. We employ static spatial econometrics and panel co-integration models to investigate the relationship between regional carbon emission intensity (CEI), and the level of urbanization, energy mix (EM) in China. The results suggest that spatial distributions for CEI exhibit a regional spillover effect in 29 provinces. A spatial lag model indicates that urbanization and EM have a positive impact on CEI. Co-integration analysis presents both CEI–urbanization elasticity and CEI–EM elasticity are greatest in Central China, followed by Western China and Eastern China.

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References

  • Andersson FNG, Karpestam P (2013) CO2 emissions and economic activity: short-and long-run economic determinants of scale, energy intensity and carbon intensity. Energy Policy 61:1285–1294

    Article  Google Scholar 

  • Anselin L, Bera AK, Florax R, Yoon MJ (1996) Simple diagnostic tests for spatial dependence. Reg Sci Urban Econ 26:77–104

    Article  Google Scholar 

  • Anselin L, Varga A, Acs Z (2000) Geographic spillovers and university research: a spatial econometric perspective. Growth Change 31:501–516

    Article  Google Scholar 

  • CNBSa (China’s National Bureau of Statistics) (2000, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011) China Energy Statistic Yearbook 1997–1999, 2000–2002, 2004, 2005, 2006, 2007, 2008, 2009, 2010. China Statistical Press, Beijing

  • CNBSb (China’s National Bureau of Statistics) (1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011) China Statistic Yearbook 1998–2010. China Statistical Press, Beijing

  • CNBSc (China’s National Bureau of Statistics) (2015) Statistical Communique of The People’s Republic of China on the 2014 National Economic and Social Development [EB/OL]. China’s National Bureau of Statistics. http://www.stats.gov.cn/tjsj/zxfb/201502/t20150226_685799.html/

  • Dietz T, Rosa EA (1994) Rethinking the environmental impacts of population, affluence and technology. Hum Ecol Rev 1:277–300

    Google Scholar 

  • Dong F, Li XH, Long RY, Liu XY (2013a) Regional carbon emission performance in China according to a stochastic frontier model. Renew Sustain Energy Rev 28:525–530

    Article  Google Scholar 

  • Dong F, Long RY, Chen H, Li XH, Yang QL (2013b) Factors affecting regional per-capita carbon emissions in China based on an LMDI factor decomposition model. PLoS One 8:e80888

    Article  Google Scholar 

  • Fan Y, Liu LC, Wu G, Tsai HT, Wei YM (2007) Changes in carbon intensity in China: empirical findings from 1980–2003. Ecol Econ 62:683–691

    Article  Google Scholar 

  • Harris DF, Tzavalis E (1999) Inference for unit roots in dynamic panels where the time dimension is fixed. J Econom 91:201–226

    Article  Google Scholar 

  • IEA (2011) 2011 Key world energy statistics. OECD/IEA, Paris

    Book  Google Scholar 

  • Kasman A, Duman YS (2015) CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Econ Model 44:97–103

    Article  Google Scholar 

  • Li K, Qi SZ (2011) Trade openness, economic growth and carbon dioxide emission in China. Econ Res J 11:60–72

    Google Scholar 

  • Li Y, Zhao R, Liu T, Zhao J (2015) Does urbanization lead to more direct and indirect household carbon dioxide emissions? Evidence from China during 1996–2012. J Clean Prod 102:114–203

    Article  Google Scholar 

  • Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data and a new simple test: evidence from simulation and the bootstrap. Oxford Bull Econ Stat 61:631–652

    Article  Google Scholar 

  • Martínez-Zarzoso I, Maruotti A (2011) The impact of urbanization on CO2 emissions: evidence from developing countries. Ecol Econ 70:1344–1353

    Article  Google Scholar 

  • Moutinho V, Robaina-Alves M, Mota J (2014) Carbon dioxide emissions intensity of Portuguese industry and energy sectors: a convergence analysis and econometric approach. Renew Sustain Energy Rev 40:438–449

    Article  Google Scholar 

  • Pedroni P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull Econ Stat 61:653–670

    Article  Google Scholar 

  • Pedroni P (2000) Fully modified OLS for heterogenous cointegrated panels. Adv Econom 15:93–130

    Article  Google Scholar 

  • Pedroni P (2004) Panel Cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econom Theory 20:597–625

    Article  Google Scholar 

  • Poumanyvong P, Kaneko S (2010) Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecol Econ 70:434–444

    Article  Google Scholar 

  • Roberts JT, Grimes PE (1997) Carbon intensity and economic development 1962—91: a brief exploration of the environmental Kuznets curve. World Dev 25:191–198

    Article  Google Scholar 

  • Sadorsky P (2014) The effect of urbanization on CO2 emissions in emerging economies. Energy Econ 41:147–153

    Article  Google Scholar 

  • Song Y, Zhang M, Dai S (2015) Study on China’s energy-related CO2 emission at provincial level. Nat Hazards 77:89–100

    Article  Google Scholar 

  • Su B, Ang BW (2015) Multiplicative decomposition of aggregate carbon intensity change using input–output analysis. Appl Energy 154:13–20

    Article  Google Scholar 

  • Wang S, Fang C, Guan X, Pang B, Ma HT (2014) Urbanisation, energy consumption, and carbon dioxide emissions in China: a panel data analysis of China’s provinces. Appl Energy 136:738–749

    Article  Google Scholar 

  • Wang Y, Zhang X, Kubota J et al (2015) A semi-parametric panel data analysis on the urbanization-carbon emissions nexus for OECD countries. Renew Sustain Energy Rev 48:704–709

    Article  Google Scholar 

  • Xu B, Lin B (2015) How industrialization and urbanization process impacts on CO2 emissions in China: evidence from nonparametric additive regression models. Energy Econ 48:188–202

    Article  Google Scholar 

  • Yao HQ, Xu ZY (2013) The economic development in Western China (2013). China Renmin University Press, Beijing

    Google Scholar 

  • Zhang C, Lin Y (2012) Panel estimation for urbanization, energy consumption and CO2 emissions: a regional analysis in China. Energy Policy 49:488–498

    Article  Google Scholar 

  • Zhang YJ, Liu Z, Zhang H, Tan TD (2014) The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China. Nat Hazards 73:579–595

    Article  Google Scholar 

  • Zhu Q, Peng X (2012) The impacts of population change on carbon emissions in China during 1978–2008. Environ Impact Asses 36:1–8

    Article  Google Scholar 

  • Zhu HM, You WH, Zeng Z (2012) Urbanization and CO2 emissions: a semi-parametric panel data analysis. Econ Lett 117:848–850

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71573254, 41101569, 71273258 and 71473247), the Fundamental Research Funds for the Central Universities (Grant Nos. 2013W01 and 2015XKMS091), Jiangsu Qing Lan Project, Jiangsu Education Science Project (Grant No. B-b/2015/01/027) and Scholar Fund of China Scholarship Council (Grant No. 201308320084). The authors also would like to thank the anonymous reviewers for their helpful suggestions on the earlier draft of this study, and upon which the content have been improved.

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Correspondence to Feng Dong.

Appendices

Appendix 1: Equations of Moran’s I

$${\text{Moran}}'{\text{s }}I = \frac{{\left[ {\sum\limits_{i = 1}^{n} {\sum\limits_{j = 1}^{n} {W_{ij} (Y_{i} - \bar{Y}} )} (Y_{j} - \bar{Y})} \right]}}{{\left[ {S^{2} \sum\limits_{i = 1}^{n} {\sum\limits_{j = 1}^{n} {W_{ij} } } } \right]}}$$
(6)
$$S^{2} = \frac{1}{n}\sum\limits_{i = 1}^{n} {(Y_{i} - \overline{Y} )}$$
(7)
$$\overline{Y} = \frac{1}{n}\sum\limits_{i = 1}^{n} {Y_{i} }$$
(8)

where \(Y_{i}\) is the result observed for province i, and n is the number of provinces. \(W_{ij}\) is a binary matrix and defines the spatial adjacency relationship between objects, in which adjacent regions take a value of 1, and 0 otherwise. \(S^{2}\) indicates variance, and \(\overline{Y}\) represents mean.

Appendix 2: Co-integration test statistics

$${\text{Panel}}\;{\text{v}}\;{\text{statistic}}:Z_{\text{v}}^{\text{w}} = \left( {\sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{i,t - 1} } \right)^{ - 1}$$
(9)
$${\text{Panel}}\;{\text{Rho}}\;{\text{statistic: }}Z_{\text{P}}^{\text{w}} = \left( {\sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } \hat{e}_{i,t - 1} } } \right)^{ - 1} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } (\hat{e}_{i,t - 1}\Delta \hat{e}_{it} - \hat{\lambda }_{i} )$$
(10)
$${\text{Panel}}\;{\text{PP}}\;{\text{statistic: }}Z_{\text{PP}}^{\text{w}} = \left( {\tilde{\sigma }_{NT}^{2} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{{_{i,t - 1} }}^{2} } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{{_{i,t - 1} }}^{*}\Delta \hat{e}_{{_{it} }}^{*} (\hat{e}_{i,t - 1}\Delta \hat{e}_{it} - \hat{\lambda }_{i} )$$
(11)
$${\text{Panel}}\;{\text{ADF}}\;{\text{statistic}}:Z_{T}^{\text{w}} = \left( {\tilde{s}_{NT}^{2} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{{_{i,t - 1} }}^{*2} } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{{_{i,t - 1} }}^{*} \hat{e}_{i,t - 1}\Delta \hat{e}_{{_{it} }}^{*}$$
(12)
$${\text{Group}}\;{\text{Rho}}\;{\text{statistic}}:Z_{\text{P}}^{\text{B}} = \sum\limits_{i - 1}^{N} {\left( {\sum\limits_{t = 1}^{T} {\hat{e}_{i,t - 1} } } \right)^{ - 1} \sum\limits_{t = 1}^{T} {(\hat{e}_{i,t - 1}\Delta \hat{e}_{it} - \hat{\lambda }_{i} )} }$$
(13)
$${\text{Group PP statistic}}:Z_{\text{PP}}^{\text{B}} = \sum\limits_{i - 1}^{N} {\left( {\sum\limits_{t = 1}^{T} {\hat{s}_{i}^{*2} } \hat{e}_{{_{i,t - 1} }}^{*2} } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} } \sum\limits_{t = 1}^{T} {\hat{e}_{{_{i,t - 1} }}^{*} \varDelta \hat{e}_{{_{i,t - 1} }}^{*} \varDelta \hat{e}_{{_{it} }}^{*} }$$
(14)
$${\text{Group}}\;{\text{ADFstatistic}}:Z_{T}^{B} = \sum\limits_{i - 1}^{N} {\left( {\hat{\sigma }_{i}^{2} \sum\limits_{t = 1}^{T} {\hat{e}_{{_{i,t - 1} }}^{2} } } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} } \sum\limits_{t = 1}^{T} {\hat{e}_{i,t - 1} } (\varDelta \hat{e}_{{_{it} }}^{*} - \hat{\lambda }_{i} )$$
(15)

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Dong, F., Long, R., Li, Z. et al. Analysis of carbon emission intensity, urbanization and energy mix: evidence from China. Nat Hazards 82, 1375–1391 (2016). https://doi.org/10.1007/s11069-016-2248-6

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  • DOI: https://doi.org/10.1007/s11069-016-2248-6

Keywords

  • Carbon emission intensity
  • Spatial econometrics
  • Panel co-integration