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The changes of spatial pattern in energy consumption have an impact on global climate change. Based on the spatial autocorrelation analysis and the auto-regression model of spatial statistics, this study has explored the spatial disparities and driving forces in energy consumption changes in China. The results show that the global spatial autocorrelation of energy consumption change in China is significant during the period 1990–2010, and the trend of spatial clustering of energy consumption change is weakened. The regions with higher energy consumption change are significantly distributed in the developed coastal areas in China, while those with lower energy consumption change are significantly distributed in the less developed western regions in China. Energy consumption change in China is mainly caused by transportation industry and non-labor intensive industry. Rapid economic development and higher industrialization rate are the main causes for faster changes in energy consumption in China. The results also indicate that spatial autoregressive model can reveal more influencing factors of energy consumption changes in China, in contrast with standard linear model. At last, this study has put forward the corresponding measures or policies for dealing with the growing trend of energy consumption in China.

The changes in energy consumption have obvious impacted on the pattern of carbon dioxide emission and then on the process of global climatic change [

Spatial analysis is statistically important because it enhances the inference accuracy, and at the same time it reduces estimated bias with paying enough attention to consider spatial proximity and dependence. Spatial autocorrelation, defined as the situation in which the value of a variable at a location is related to the values of the same variable at the locations nearby, is a statistic method being used to describe spatial interaction of regional social economic phenomena [

Spatial autocorrelation, the measurement of clustering degree in the spatial domain, serves as the proxy for the correlation of same variables in the different spatial position [

The economic relationship between adjacent regions in China is very obvious, especially frequent mobility of labor, capital and other factors between neighboring provinces [

The main purposes of this study are: (1) to explore the spatial correlation and spatial heterogeneity of energy consumption change in China; (2) to find the main influencing factors of energy consumption change in China; (3) and to test the superiority of spatial autoregressive model by comparison with the traditional linear regressive model.

Energy consumption data and social-economical data at the province level in this study were derived from the Chinese energy statistics yearbook and the Chinese statistics yearbook from 1991 to 2011, respectively. With the missing data of energy consumption in the Tibet province, Taiwan, Hong Kong and Macao, the final number of spatial analysis unit totals 30 in this study. In this paper 2000 was the year used as the breakpoint, the study period are divided into two periods 1990–2000 and 2000–2010. This is mainly because 2000 is not only the starting point of China’s 10th five-year plan, but also the key year of China's rapid economic development and energy consumption change.

Global spatial autocorrelation can be used to measure the global correlation and disparity degree of some social economic phenomena. Statistic indices measuring global spatial autocorrelation include Moran’s _{i}_{j}^{2 }is the mean square deviation; _{ij} is the spatial weight value, which is expressed by the

The Getis’s _{i}_{j}_{i}_{, j} is the spatial weight between feature

The significance level of Moran’s _{I}-

The significance level of Getis’s _{G}-

The null hypothesis H_{0} refers to the spatial correlation of energy consumption change do not exist. With a significance level of 0.05, if the absolute value of _{I}-_{G}-_{0} can be rejected. It is assumed that the

The local indicators of spatial association (LISA) are a series of indices decomposed directly by global spatial autocorrelation indicator. It is expressed by the distribution state of local heterogeneity and can be used to measure the spatial disparities degree between the regional _{i}^{*} statistic for each feature in a dataset. The resultant _{i}^{*} is given as:
_{j} is the attribute value for feature _{i,j}

The _{i}^{*} statistic is a Z-score so no further calculations are required. The G_{i}^{*} statistic returned for each feature in the dataset is a Z score. For statistically significant positive Z scores, the larger the Z score is, the more intense the clustering of high values (hot spot). For statistically significant negative Z scores, the smaller the Z score is, the more intense the clustering of low values (cold spot).

Moran’s

According to the spatial correlations between the dependent variable and the independent variables, the most general formulation of the spatial autoregressive model is Equation (6) [

_{1}_{2}_{ii}_{i}_{i}_{1}y, _{2 }_{2}

Based on the general formulation of the spatial autoregressive model, we can derive the spatial lag model and spatial error model. Spatial lag model takes into account the spatial correlation between dependent variables. The spatial lag model is Equation (7).

Spatial error model reflects the error process through the covariance of different. Spatial error model is Equation (8).

Traditional goodness of fit index R^{2} is not suitable for spatial regression model. Instead, a so-called goodness of fit index pseudo R^{2} can be computed. In the spatial statistics, the pseudo R^{2} is defined as the ratio of the variance of the predicted values over the variance of the observed values for the dependent variable [^{2} [

The GIS9.3 and OpenGeoDa softwares are used to conduct the exploratory spatial data analysis (ESDA) in this study. Based on the GIS9.3 and OpenGeoDa softwares, we have got the results about Global Moran’s

Getis’s

As can be seen from

Under the 95% confidence interval, the Moran’s

Getis’s

In order to explore the local spatial disparities of energy consumption change from 1990 to 2009 in China, we use the OpenGeoda software to obtain the results of local Moran’s

Related parameters of local Moran’s I for energy consumption change in China from 1990 to 2010.

Time stage | Minimum | Maximum | Mean | Moran’s _{i} |
Moran’s _{i} |
Range |
---|---|---|---|---|---|---|

1990–2000 | −1.0771 | 1.9914 | 0.2178 | 74.1935 | 25.8065 | 3.0685 |

2000–2010 | −0.4994 | 2.7136 | 0.1822 | 51.6129 | 48.3871 | 3.2130 |

1990–2010 | −0.5502 | 2.8206 | 0.2164 | 58.0650 | 41.9350 | 2.2704 |

As can be seen from

If variable _{z} at each research unit are calculated to be used as the lateral axis and longitudinal axis, we can get the Moran scatter plot of energy consumption. In other words, standardized value (Std-I_{clc}) of research observation is used as the lateral axis, and spatial lagged value (Lag-I_{clc}) as the longitudinal axis. Moran scatter plot of energy consumption change is composed of local Moran’s _{i}

Moran scatter plot of energy consumption at province in China during 1990–2010.

When the Std-I_{clc} value is positive in the Moran scatter plot, it means research unit belongs to those regions with faster energy consumption change. Otherwise it belongs to those areas with slow energy consumption changes. Those areas with positive Std-I_{clc} value account for 40% at the whole time stage (see _{clc} value is both 40% during 1990–2000 and during 2000–2010 and, which means that there is no change about the number of those areas with faster energy consumption change in the view of spatial association at the whole time stage. The Lag-I_{clc} value is positive in the Moran scatter plot, which means surrounding regions of research unit belonged to those regions with faster energy consumption change. While the number of those areas with positive Lag-I_{clc} value remains 63% at the whole time stage, the number of those regions with positive Lag-I_{clc} value increased from 50% during 1990–2000 to 63% during 2000–2010, which means a number of surrounding regions of research unit with faster energy consumption change increased in the view of spatial association.

According to the composite attribute of the Std-I_{clc} index and Lag-I_{clc }one, four area types of energy consumption change were divided by positive or negative spatial association (see

As can be seen from

Related parameters and disparities type of standardized variable

Time Stage | Std-I_{clc }> 0 |
Std-I_{clc }< 0 |
Lag-I_{clc }> 0 |
Lag-I_{clc }< 0 |
H–H | H–L | L–L | L–H | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Ratio | Ratio | Ratio | Ratio | Comparison Ratio | Ratio | Comparison Ratio | Ratio | Comparison Ratio | Ratio | Comparison Ratio | Ratio | |

1990–2000 | 40.00 | 60.00 | 50.00 | 50.00 | S_{+}L_{+} |
30.00 | S_{+}L_{−} |
10.00 | S_{−}L_{−} |
26.67 | S_{−}L_{+} |
23.33 |

2000–2010 | 40.00 | 60.00 | 63.33 | 36.67 | S_{+}L_{+} |
23.33 | S_{+}L_{−} |
16.67 | S_{−}L_{−} |
16.67 | S_{−}L_{+} |
33.33 |

1990–2010 | 40.00 | 60.00 | 63.33 | 36.67 | S_{+}L_{+} |
23.33 | S_{+}L_{−} |
16.67 | S_{−}L_{−} |
30.00 | S_{−}L_{+} |
30.00 |

Notes: S_{+}—Std-I_{clc} > 0, S_{−}—Std-I_{clc} < 0, L_{+}—Lag-I_{clc} > 0, L_{−}—Lag-I_{cl}.

In order to effectively explore more spatial characteristics of energy consumption change in China during 1990–2010, LISA clustering map of two time stages were formed by matching the proper type to corresponding spatial location of each province during 1990–2000 and during 2000–2010 (see

From _{clc} > 0 and Lag-I_{clc} > 0. This indicates the local spatial disparities of energy consumption change are smaller and stronger for local homogeneity in the research unit while the changes of energy consumption in their surrounding units are relatively higher.

_{clc} < 0 and Lag-I_{clc} < 0. This means that the local spatial disparities of energy consumption change are smaller and stronger in local homogeneity in the research units and relatively slow in their surrounding units. The number of those type regions decreased from 12 during 1999–2000 to 8 during 2000–2010. The ratio of energy consumption change in L–L type area is lower than average. 26.67% type region is lower than average level during 1999–2000. All provinces are significantly distributed in the north-western regions with low-level development of economy in China. Xinjiang Province particularly, has significantly positive relationship during 1990–2009 and during 2000–2010.

The regions belonging to the L–H type have a negative spatial autocorrelation, where Std-I_{clc} < 0 and Lag-I_{clc} > 0 (see

LISA clustering of energy consumption change in China during the period 1990–2000.

LISA clustering of energy consumption change in China during the period 2000–2010.

The regions belonging to the H–L type have a negative spatial autocorrelation, where Std-I_{clc} > 0 and Lag-I_{clc} < 0. This means that the local spatial disparities of energy consumption change are larger and stronger in local heterogeneity in the research unit and slower in their surrounding units. Energy consumption changes in the research unit are relatively slow, which forms a hot spot of the local heterogeneity. The ratio of energy consumption changes in the H–L type is higher than average. The number in the H–L type is 3 during 1990–2000 and 5 during 2000–2010.

Studies show that energy demand is dominated by the level of economic development, pricing, change of industry structure, population growth, technological progress, the level of urbanization and other factors [^{2} between the independent variables is in the range of 0.12 to 0.71, lower than the critical value of 0.8 [

Moran’s I values and the results of their statistic test of six independent variables in the regression model are listed in

Moran’s I values and their statistic test of independent variables of regression model.

Independent Variable | Moran’s I | E (I) | Mean | SD | Z_{I-score} |
---|---|---|---|---|---|

Population growth rate | 0.3476 * | −0.0345 | −0.0326 | 0.1127 | 3.3904 |

GDP growth rate | 0.4580 ** | −0.0345 | −0.0363 | 0.1160 | 4.2456 |

Urbanization rate | 0.3781 * | −0.0345 | −0.0319 | 0.1145 | 3.6035 |

Industrialized rate | 0.0022 | −0.0345 | −0.0264 | 0.1133 | 0.3239 |

Percentage of industry production value change | 0.2073 * | −0.0345 | −0.0302 | 0.0997 | 2.4252 |

Percentage of transportation industry production value change | 0.4439 ** | −0.0345 | −0.0337 | 0.1135 | 4.2149 |

*

The Akaike Information Criterion (AIC), maximized log likelihood (LIK) and Schwartz Criterion [

The results of three regression models for energy consumption change in China during 2000–2010 are listed in ^{2} or Pseudo R^{2}), estimated coefficient, standard error,

Statistical tests of three regression models.

Model type | R^{2} or Pseudo R^{2} |
LIK | AIC | SC |
---|---|---|---|---|

Linear regression model | 0.8065 | 3.7631 | 6.4737 | 16.5117 |

Spatial lag model (SLM) | 0.8425 | 6.5164 | 2.9673 | 14.4392 |

Spatial error model (SEM) | 0.8075 | 3.7948 | 6.4103 | 16.4482 |

Compared with the significance level of the parameters for three models, this study concludes that significance level of the parameters increases, and that six variables—

From

At the 0.05% significance level, _{i}

The variable

From

Parameters of three different regression models for energy consumption change in China during 2000–2010.

Variable | Coefficient | Std. Error | t Statistic | Probability |
---|---|---|---|---|

(A) Linear regression model R^{2} = 0.8065 |
||||

Constant | 0.0251 | 0.2425 | 0.1034 | 0.9185 |

Population growth rate | 0.3109 | 0.0826 | 3.7640 | 0.0010 |

GDP growth rate | 0.6732 | 0.2250 | 2.9924 | 0.0063 |

Urbanization rate | −1.1445 | 0.2193 | −5.2181 | 0.0000 |

Industrialized rate | 0.3300 | 0.2028 | 1.6269 | 0.1168 |

Percentage of industry production value change | 0.1439 | 0.0805 | 1.7887 | 0.0863 |

Percentage of transportation industry production value change | 0.3321 | 0.1804 | 1.8407 | 0.0781 |

Variable | Coefficient | Std. Error | Z-value | Probability |

(B) Spatial lag model Pseudo R^{2} = 0.8425 |
||||

ρ | −0.3713 | 0.1427 | −2.6011 | 0.0093 |

Constant | 0.1942 | 0.2002 | 0.9700 | 0.3321 |

Population growth rate | 0.3268 | 0.0656 | 4.9787 | 0.0000 |

GDP growth rate | 0.7766 | 0.1814 | 4.2811 | 0.0000 |

Urbanization rate | −1.2822 | 0.1779 | −7.2057 | 0.0000 |

Industrialized rate | 0.3868 | 0.1628 | 2.3763 | 0.0175 |

Percentage of industry production value change | 0.1783 | 0.0654 | 2.7268 | 0.0064 |

Percentage of transportation industry production value change | 0.4302 | 0.1446 | 2.9745 | 0.0029 |

Variable | Coefficient | Std. Error | Z-value | Probability |

(C) Spatial error model Pseudo R^{2} = 0.8075 |
||||

λ | −0.1227 | 0.2709 | −0.4529 | 0.6506 |

Constant | 0.0557 | 0.2122 | 0.2624 | 0.7930 |

Population growth rate | 0.3094 | 0.0723 | 4.2819 | 0.0000 |

GDP growth rate | 0.6730 | 0.1960 | 3.4345 | 0.0006 |

Urbanization rate | −1.1609 | 0.1941 | −5.9822 | 0.0000 |

Industrialized rate | 0.3287 | 0.1796 | 1.8297 | 0.0673 |

Percentage of industry production value change | 0.1423 | 0.0701 | 2.0294 | 0.0424 |

Percentage of transportation industry production value change | 0.3623 | 0.1589 | 2.2793 | 0.0226 |

From

In this study, when the influencing factors of energy consumption changes in China were analyzed by using the classical linear regression model, the effects of spatial autocorrelation were ignored. The spatial regression model provides a statistically reasonable solution. Compared to the classical linear regression model, there is no spatial autocorrelation of the residuals in the spatial error model, and it has a better goodness-of-fit. All the independent variables were tested significance level (

This study did not consider the impact of energy consumption structure and price on the energy consumption changes. In future studies, we should pay attention to the impact of energy consumption structure on global climate change due to coal and gas has different implications in terms of global Climate Change. It should also be focused on the effecting mechanism of energy prices on energy consumption in our future research work.

Traditional methods measuring the regional disparities ignored the factor of geographical position, which may not truly reflect the spatial characteristics of regional disparities. ESDA mainly measuring spatial association can solve the problem of spatial relationship between regions. It provides the stronger support for the quantitative analysis of spatial disparities of energy consumption change.

Energy consumption changes in China and its driving forces have shown a spatially positive correlation. The residuals of standard regression model also showed positive autocorrelation, indicating that stand multiple linear regression model failed to consider all the spatial dependencies.

The regional distribution of energy consumption change has significant clustering characteristics during 1990–2010 in China. This means that energy consumption change of research unit and its surrounding areas are higher.

Based on the composite attribute of Std-I_{clc} value and Lag-I_{clc} value, Moran’s scatter divides into four type regions and two spatial associations. Because the characteristics, causes and spatial disparities of energy consumption change in the four type regions are different, the strategies and measures of energy consumption should be put forward for each clustering regions in China.

The results of spatial autoregressive model show that higher industrialization rate and economic development level are the main causes for higher energy consumption change.

According to the conclusions of regression analysis, this study has proposed the following measures to deal with the growing trend of energy consumption changes in China. Firstly, during the process of China’s rapid urbanization, we should establish diversified energy consumption patterns, and improve the quality of the energy use. Secondly, in China’s economically developed eastern provinces, we should optimize the industrial structure and reduce the proportion of energy-intensive industries. Thirdly, governments need to actively promote the public transport system, reducing the proportion of energy consumption in the transportation sector. Energy demand in various regions of China will continue to grow in the coming periods, especially the energy consumption of second industrial, which is the most important factor in China’s energy consumption. The emphasis should be placed on energy policy in China to reduce the proportion of secondary industry in the national economy structure by optimizing adjustment, especially thereby improving industrial energy efficiency through technological innovation and other aspects. Second, insisting on population control policy will curb faster growth trend of energy demand to some extent. The transformation of economic growth mode and the regulation of the price mechanism in the area of energy demand are imperative. Meanwhile, the government should develop the spatial differentiated policies and measures of energy supply and demand. This study also shows that it needs to focus on the important role of geospatial factors in the area of adjustment localization policy for energy consumption behavior.

This article also shows that ESDA and spatial autoregressive model of spatial statistics are some effective methods to measure the spatial pattern and main driving forces of energy consumption change and to explore the distribution characteristics, local heterogeneity and homogeneity of many spatial social-economical phenomena by the comparison with general clustering analysis. This study also indicate that the spatial auto-regression model can reveal more influencing factors of energy consumption changes in China, in contrast with standard linear model.

We thank two anonymous reviewers for their constructive comments. This study was supported by the National Natural Science Foundation of China (No. 41361111), the Fok Ying Tung Foundation (No.141084), the Major Research Plan of National Social Science Foundation of China (No. 12&ZD213), the Natural Science Foundation of Jiangxi Province (No. 20122BAB203025 and No. 2008GQH0067), the Social Science Foundation of Jiangxi Province (No. 13GL05 and No. 13YJ53) and the Technology Foundation of Jiangxi Education Department of China (No. GJJ09559).

Hualin Xie and Peng Wang had the original idea for the study. Guiying Liu and Qu Liu were responsible for data collecting. Hualin Xie, Peng Wang and Guiying Liu carried out the analyses. All the authors drafted the manuscript, and approved the final one.

The authors declare no conflict of interest.

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