Characterizing China’s energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach

Lei JIANG, Minhe JI, Ling BAI

PDF(837 KB)
PDF(837 KB)
Front. Earth Sci. ›› 2015, Vol. 9 ›› Issue (2) : 355-368. DOI: 10.1007/s11707-014-0469-0
RESEARCH ARTICLE

Characterizing China’s energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach

Author information +
History +

Abstract

Coupled with intricate regional interactions, the provincial disparity of energy-resource endowment and other economic conditions in China have created spatially complex energy consumption patterns that require analyses beyond the traditional ones. To distill the spatial effect out of the resource and economic factors on China’s energy consumption, this study recast the traditional econometric model in a spatial context. Several analytic steps were taken to reveal different aspects of the issue. Per capita energy consumption (AVEC) at the provincial level was first mapped to reveal spatial clusters of high energy consumption being located in either well developed or energy resourceful regions. This visual spatial autocorrelation pattern of AVEC was quantitatively tested to confirm its existence among Chinese provinces. A Moran scatterplot was employed to further display a relatively centralized trend occurring in those provinces that had parallel AVEC, revealing a spatial structure with attraction among high-high or low-low regions and repellency among high-low or low-high regions. By a comparison between the ordinary least square (OLS) model and its spatial econometric counterparts, a spatial error model (SEM) was selected to analyze the impact of major economic determinants on AVEC. While the analytic results revealed a significant positive correlation between AVEC and economic development, other determinants showed some intricate influential patterns. The provinces endowed with rich energy reserves were inclined to consume much more energy than those otherwise, whereas changing the economic structure by increasing the proportion of secondary and tertiary industries also tended to consume more energy. Both situations seem to underpin the fact that these provinces were largely trapped in the economies that were supported by technologies of low energy efficiency during the period, while other parts of the country were rapidly modernized by adopting advanced technologies and more efficient industries. On the other hand, institutional change (i.e., marketization) and innovation (i.e., technological progress) exerted positive impacts on AVEC improvement, as always expected in this and other studies. Finally, the model comparison indicated that SEM was capable of separating spatial effect from the error term of OLS, so as to improve goodness-of-fit and the significance level of individual determinants.

Keywords

per capita energy consumption / economic growth / energy endowment / spatial autocorrelation / spatial econometric model

Cite this article

Download citation ▾
Lei JIANG, Minhe JI, Ling BAI. Characterizing China’s energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach. Front. Earth Sci., 2015, 9(2): 355‒368 https://doi.org/10.1007/s11707-014-0469-0

References

[1]
Anselin L (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer, 1988
[2]
Anselin L (1995). Local indicators of spatial association-LISA. Geogr Anal, 27(2): 77–104
[3]
Anselin L (2003). Spatial externalities, spatial multipliers, and spatial econometrics. Int Reg Sci Rev, 26(2): 153–166
CrossRef Google scholar
[4]
Anselin L, Bera R A K, Florax R, Yoon M J (1996). Simple diagnostic tests for spatial dependence. Reg Sci Urban Econ, 26(1): 77–104
CrossRef Google scholar
[5]
Anselin L, Getis A (1992). Spatial statistical analysis and geographic information systems. The annuals of Regional Science 26: 19–33
[6]
Bosetti V, Carraro C, Galeotti M (2006). The dynamics of carbon and energy intensity in a model of endogenous technical change. The Energy Journal, Engogenous technological change and the economics of atmospheric stablisation (special issue): 93–107
[7]
Bosetti V, Carraro C, Massetti E, Tavoni M (2007). International energy R&D spillovers and the economics of greenhouse gas atmospheric stabilization. http://www.feem.it/Feem/Pub/Publications/WPapers/default.htm
[8]
Cheng B S (1999). Causality between energy consumption and economic Growth in India: an Application of cointegration and error correction modeling. Indian Econ Rev, 34(1): 39–49
[9]
Elhorst J P (2010). Applied spatial econometrics: raising the bar. Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1): 9–28
[10]
Fan G, Wang X L, Zhu H P (2009). NERI Index of Marketization of China’s Provinces 2009 Report. Beijing: Economic Science Press, 2009 (in Chinese)
[11]
Fisher-Vanden K, Jefferson G H, Liu H M, Tao Q (2004). What is driving China’s decline in energy intensity? Resour Energy Econ, 26(1): 77–97
CrossRef Google scholar
[12]
Fisher-Vanden K, Jefferson G H, Ma J, Xu J (2006). Technology development and energy productivity in China. Energy Econ, 28: 690–705
[13]
Garbaccio R F (1995). Price reform and structural change in the Chinese economy: policy simulations using a CGE model. China Econ Rev, 6(1): 1–34
CrossRef Google scholar
[14]
Hondroyiannis G, Lolos S, Papapetrou E (2002). Energy consumption and economic growth: assessing the evidence from Greece. Energy Econ, 24(4): 319–336
CrossRef Google scholar
[15]
Jumbe C B L (2004). Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi. Energy Econ, 26(1): 61–68
CrossRef Google scholar
[16]
Khazzoom J D (1980). Economic implications of mandated efficiency in standards for household appliances. Energy J (Camb Mass), 1(4): 21–40
[17]
Khazzoom J D, Miller S (1982). Economic implications of mandated efficiency standards for household appliances: response to Besen and Johnson’s comments. Energy J, 3(1): 117–124
[18]
LeSage J P, Pace R K (2009). Introduction to Spatial Econometrics. Boca Raton: CRC Press
[19]
Manski C F (1993). Identification of endogenous social effects—the reflection problem. Rev Econ Stud, 60(3): 531–542
CrossRef Google scholar
[20]
Moran P A P (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2): 17–23
CrossRef Google scholar
[21]
Pao H T, Fu H C, Tseng C L (2012). Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400–409
CrossRef Google scholar
[22]
Soytas U, Sari R (2003). Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Econ, 25(1): 33–37
CrossRef Google scholar
[23]
Yang H Y (2000). A note on the causal relationship between energy and GDP in Taiwan. Energy Econ, 22(3): 309–317
CrossRef Google scholar
[24]
Yuan J H, Kang J G, Zhao C H, Hu Z G (2008). Energy consumption and economic growth: evidence from China at both aggregated and disaggregated levels. Energy Econ, 30(6): 3077–3094
CrossRef Google scholar
[25]
Yuan X L, Qu E (2009). Difference in energy consumption between regions in China and its influencing factors. Journal of Business Economics, 215(9): 58–64 (in Chinese)
[26]
Zhang X H, Han J, Zhao H, Deng S H, Xiao H, Peng H, Li Y W, Yang G, Shen F, Zhang Y Z (2012). Evaluating the interplays among economic growth and energy consumption and CO2 emission of China during 1990‒2007. Renew Sustain Energy Rev, 16(1): 65–72
CrossRef Google scholar
[27]
Zhang Z G, Jin X C, Yang Q X, Zhang Y (2013). An empirical study on the institutional factors of energy conservation and emissions reduction: evidence from listed companies in China. Energy Policy, 57: 36–42
CrossRef Google scholar

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 40671074) and China Scholarship Council (No. 2011614110).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(837 KB)

Accesses

Citations

Detail

Sections
Recommended

/