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


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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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.


Appendix 1: Equations of Moran’s I

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

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

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

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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).

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