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.

Appendices

Appendix 1: Equations of Moran’s I

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

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$$S^{2} = \frac{1}{n}\sum\limits_{i = 1}^{n} {(Y_{i} - \overline{Y} )}$$

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$$\overline{Y} = \frac{1}{n}\sum\limits_{i = 1}^{n} {Y_{i} }$$

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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

$${\text{Panel}}\;{\text{v}}\;{\text{statistic}}:Z_{\text{v}}^{\text{w}} = \left( {\sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } \hat{e}_{i,t - 1} } \right)^{ - 1}$$

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$${\text{Panel}}\;{\text{Rho}}\;{\text{statistic: }}Z_{\text{P}}^{\text{w}} = \left( {\sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } \hat{e}_{i,t - 1} } } \right)^{ - 1} \sum\limits_{i - 1}^{N} {\sum\limits_{t = 1}^{T} {\hat{L}_{11i}^{ - 2} } } (\hat{e}_{i,t - 1}\Delta \hat{e}_{it} - \hat{\lambda }_{i} )$$

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

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

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$${\text{Group}}\;{\text{Rho}}\;{\text{statistic}}:Z_{\text{P}}^{\text{B}} = \sum\limits_{i - 1}^{N} {\left( {\sum\limits_{t = 1}^{T} {\hat{e}_{i,t - 1} } } \right)^{ - 1} \sum\limits_{t = 1}^{T} {(\hat{e}_{i,t - 1}\Delta \hat{e}_{it} - \hat{\lambda }_{i} )} }$$

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$${\text{Group PP statistic}}:Z_{\text{PP}}^{\text{B}} = \sum\limits_{i - 1}^{N} {\left( {\sum\limits_{t = 1}^{T} {\hat{s}_{i}^{*2} } \hat{e}_{{_{i,t - 1} }}^{*2} } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} } \sum\limits_{t = 1}^{T} {\hat{e}_{{_{i,t - 1} }}^{*} \varDelta \hat{e}_{{_{i,t - 1} }}^{*} \varDelta \hat{e}_{{_{it} }}^{*} }$$

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$${\text{Group}}\;{\text{ADFstatistic}}:Z_{T}^{B} = \sum\limits_{i - 1}^{N} {\left( {\hat{\sigma }_{i}^{2} \sum\limits_{t = 1}^{T} {\hat{e}_{{_{i,t - 1} }}^{2} } } \right)^{{ - {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} } \sum\limits_{t = 1}^{T} {\hat{e}_{i,t - 1} } (\varDelta \hat{e}_{{_{it} }}^{*} - \hat{\lambda }_{i} )$$

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