# Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emissions of European transport activities for the period 2001–2008.

_{2}emissions, and identified the impact factors quantitatively by using LMDI method. Zha et al. [16] used the LMDI method to decompose the residential CO

_{2}emissions in urban and rural China. The generalized LMDI technique was used by Wang et al. [17] to analyze the respective contributions of changes of energy consumption in China from 1991 to 2011. Zhang et al. [18] used the LMDI method to study the difference of residential energy consumption between urban and rural Jiangsu areas.

_{2}emission for world regions and OECD countries. By comparing PDA with the well-known IDA and SDA techniques, they pointed out the highlights of PDA in CO

_{2}emission decomposition. Li [22] assessed productive efficiency through output distance function for desirable output sub vector while holding bad output and inputs constant, which avoided symmetric scaling on good and bad outputs. The proposed approach was used to investigate the sources of CO

_{2}emission changes in China from 1991 to 2006. Zhang et al. [23] proposed an alternative PDA decomposition model and applied it to empirically analyze 20 developed countries. Kim and Kim [24] concentrated on the production technology to analyze worldwide CO

_{2}emissions. They provided more detailed information about the influence of both production technical efficiency and technological change on CO

_{2}emissions, and they showed that the relative degree of each country’s energy efficiency paradox phenomenon can be identified empirically. Wang et al. [25] investigated the challenge of the infeasibility of (date envelopment analysis) DEA liner programming by modifying the PDA approach and applying it to analyze the driving factors of carbon dioxide emissions in China, using data from 2005 to 2010.

## 2. Methodology

#### 2.1. Production Technology

#### 2.2. Decomposition Approach

#### 2.3. Joint Decomposition Approach

## 3. Date Description

^{6}tons of coal equivalent (Mtce) in calorific value calculation. The labor input data are calculated by the average number of employed persons at the beginning and end of the year, and its unit is 10,000 persons. Fixed asset investment data are total investment in fixed assets in the whole country measured in billion yuan in constant 1991 price terms. GDP data are in billion yuan in constant 1991 price terms. The data has been collected from various issues from the China Energy Statistical Yearbook (CESY, 1991–1996, 1997–1999, 2000–2002, 2003, 2004. 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013) [28] and China Statistical Yearbook (CSY 2013) [29].

## 4. Results and Discussions

#### 4.1. China’s Energy Situation

#### 4.2. Empirical Analysis

#### 4.2.1. Temporal Decomposition Analysis

#### 4.2.2. Sectoral Decomposition Analysis

## 5. Conclusions

## Acknowledgments

## Author Contribution

## Conflicts of Interest

## References

- Ang, B.W.; Zhang, F.Q. A survey of index decomposition analysis in energy and environmental analysis. Energy
**2000**, 25, 1149–1176. [Google Scholar] [CrossRef] - Rose, A.; Casler, S. Input-output structure structural decomposition analysis: A critical appraisal. Econ. Syst. Res.
**1996**, 8, 33–62. [Google Scholar] [CrossRef] - Wang, C.; Chen, J.; Zou, J. Decomposition of energy-related CO
_{2}emission in China: 1957–2000. Energy**2005**, 32, 1326–1333. [Google Scholar] [CrossRef] - Zhang, H.; Qi, Y. A structure decomposition analysis of China’s production-source CO
_{2}emissions: 1992–2002. Environ. Resour. Econ.**2011**, 49, 65–77. [Google Scholar] [CrossRef] - Ang, B.W.; Liu, N. Handling zero values in the logarithmic mean Divisia index decomposition approach. Energy Policy
**2007**, 35, 238–246. [Google Scholar] [CrossRef] - Xu, X.; Ang, B.W. Index decomposition analysis applied to CO
_{2}emission studies. Ecol. Econ.**2013**, 93, 313–329. [Google Scholar] [CrossRef] - Su, B.; Ang, B.W. Structural decomposition analysis applied to energy and emissions: Some methodological developments. Energy Econ.
**2012**, 34, 177–188. [Google Scholar] [CrossRef] - Su, B.; Ang, B.W. Attribution of changes in the generalized Fisher index with application to embodied emission studies. Energy
**2014**, 69, 778–786. [Google Scholar] [CrossRef] - Wang, H.; Ang, B.W.; Su, B. Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues. Energy
**2017**, 123, 47–63. [Google Scholar] [CrossRef] - Ang, B.W. Decomposition analysis for policy making in energy: Which is the preferred method? Energy Policy
**2004**, 32, 1131–1139. [Google Scholar] [CrossRef] - Hirst, E.; Marlay, R.; Greene, D. Recent changes in US energy consumption: What happened and why. Annu. Rev. Energy
**1983**, 8, 193–245. [Google Scholar] [CrossRef] - Sun, J.W. Changes in energy consumption and energy intensity: A complete decomposition model. Energy Econ.
**1998**, 20, 85–100. [Google Scholar] [CrossRef] - Andreoni, V.; Galmarini, S. European CO
_{2}emission trends: A decomposition analysis for water and aviation transport sectors. Energy**2012**, 45, 595–602. [Google Scholar] [CrossRef] - Zhang, M.; Mu, H.L.; Ning, Y.D. Accounting for energy-related CO
_{2}emission in China, 1991–2006. Energy Policy**2009**, 37, 767–773. [Google Scholar] [CrossRef] - Kim, S.Y. LMDI decomposition analysis of energy consumption in the Korean manufacturing sector. Sustainability
**2017**, 2, 202. [Google Scholar] [CrossRef] - Zha, D.L.; Zhou, D.Q.; Zhou, P. Driving forces of residential CO
_{2}emissions in urban and rural China: An index decomposition analysis. Energy Policy**2010**, 38, 3377–3383. [Google Scholar] - Wang, W.W.; Liu, X.; Zhang, M.; Song, X.F. Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China’s energy consumption. Energy
**2014**, 67, 617–622. [Google Scholar] [CrossRef] - Zhang, M.; Song, Y.; Li, P.; Li, H.N. Study on affecting factors of residential energy consumption in urban and rural Jiangsu. Renew. Sustain. Energy Rev.
**2016**, 53, 330–337. [Google Scholar] [CrossRef] - Pasurka, J.A. Decomposing electric power plant emissions within a joint production framework. Energy Econ.
**2006**, 28, 26–43. [Google Scholar] [CrossRef] - Wang, C. Decomposing energy productivity change: A distance function approach. Energy
**2007**, 32, 1326–1333. [Google Scholar] [CrossRef] - Zhou, P.; Ang, B.W. Decomposition of aggregate CO
_{2}emissions: A production-theoretical approach. Energy Econ.**2008**, 30, 1054–1067. [Google Scholar] [CrossRef] - Li, M. Decomposing the change of CO
_{2}emissions in China: A distance function approach. Ecol. Econ.**2010**, 70, 77–85. [Google Scholar] [CrossRef] - Zhang, X.P.; Tan, Y.K.; Tan, L.Q.; Yuan, J.H. Decomposition of aggregate CO
_{2}emissions within a joint production framework. Energy Econ.**2012**, 34, 1088–1097. [Google Scholar] [CrossRef] - Kim, K.; Kim, Y. International comparison of industrial CO
_{2}emission trends and energy efficiency paradox utilizing production-based decomposition. Energy Econ.**2012**, 34, 1724–1741. [Google Scholar] [CrossRef] - Wang, Q.W.; Chiu, Y.H.; Chiu, C.R. Driving factors behind carbon dioxide emissions in china: A modified production-theoretical decomposition analysis. Energy Econ.
**2015**, 51, 252–260. [Google Scholar] [CrossRef] - Färe, R.; Primont, D. Multi-Output Production and Duality: Theory and Applications; Kluwer Academic Publishers: Boston, MA, USA, 1995. [Google Scholar]
- Färe, R.; Grosskopf, S.; Norris, M. Productivity growth, technical progress and efficiency change in industrialized countries. Am. Econ. Rev.
**1994**, 84, 66–83. [Google Scholar] - National Bureau of Statistics of China. China Energy Statistical Yearbook; National Bureau of Statistics of China: Beijing, China, 2013.
- National Bureau of Statistics of China. China Statistical Yearbook; National Bureau of Statistics of China: Beijing, China, 2013.
- Lin, B.Q.; Du, K.R. Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis. Appl. Energy
**2014**, 129, 158–165. [Google Scholar] [CrossRef] - Fan, J.L.; Zhang, Y.J.; Wang, B. The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis. Renew. Sustain. Energy Rev.
**2017**, 75, 220–233. [Google Scholar] [CrossRef]

Factor | Value (Mtce) | Ratio |
---|---|---|

$\Delta {E}_{csef}$ | 74.32 | 4.27% |

$\Delta {E}_{is}$ | 3.30 | 0.19% |

$\Delta {E}_{pei}$ | −989.01 | −56.87% |

$\Delta {E}_{py}$ | 3436.40 | 197.61% |

$\Delta {E}_{eue}$ | −311.93 | −17.94% |

$\Delta {E}_{yoe}$ | 124.51 | 7.16% |

$\Delta {E}_{tces}$ | −37.99 | −2.19% |

$\Delta {E}_{tcyo}$ | −560.58 | −32.24% |

**Table 2.**Complete decomposition of energy consumption changes for seven sectors, 2001–2012 (Unit: Mtce).

Sector | $\Delta {\mathit{E}}_{\mathit{c}\mathit{s}\mathit{e}\mathit{f}}$ | $\Delta {\mathit{E}}_{\mathit{i}\mathit{s}}$ | $\Delta {\mathit{E}}_{\mathit{p}\mathit{e}\mathit{i}}$ | $\Delta {\mathit{E}}_{\mathit{p}\mathit{y}}$ | $\Delta {\mathit{E}}_{\mathit{e}\mathit{u}\mathit{e}}$ | $\Delta {\mathit{E}}_{\mathit{y}\mathit{o}\mathit{e}}$ | $\Delta {\mathit{E}}_{\mathit{t}\mathit{c}\mathit{e}\mathit{s}}$ | $\Delta {\mathit{E}}_{\mathit{t}\mathit{c}\mathit{y}\mathit{o}}$ |
---|---|---|---|---|---|---|---|---|

1 | 0.01 | −0.40 | −25.17 | 72.40 | 0.97 | −0.01 | 18.08 | −22.35 |

2 | 2.97 | 19.72 | −598.06 | 1720.63 | 23.04 | −0.13 | 429.61 | −531.06 |

3 | 0.22 | 1.37 | −17.13 | 49.27 | 0.66 | −0.01 | 12.30 | −15.21 |

4 | 4.81 | 35.74 | −92.08 | 264.92 | 3.54 | −0.02 | 66.14 | −81.76 |

5 | 0.97 | −0.63 | −18.26 | 52.52 | 0.70 | −0.01 | 13.11 | −16.21 |

6 | 3.85 | −11.30 | −97.86 | 281.55 | 3.77 | −0.02 | 70.30 | −86.89 |

7 | 1.71 | −2.20 | −37.06 | 106.63 | 1.43 | −0.01 | 26.62 | −32.91 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhou, D.; Liu, X.; Zhou, P.; Wang, Q. Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method. *Sustainability* **2017**, *9*, 685.
https://doi.org/10.3390/su9050685

**AMA Style**

Zhou D, Liu X, Zhou P, Wang Q. Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method. *Sustainability*. 2017; 9(5):685.
https://doi.org/10.3390/su9050685

**Chicago/Turabian Style**

Zhou, Dequn, Xiao Liu, Peng Zhou, and Qunwei Wang. 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method" *Sustainability* 9, no. 5: 685.
https://doi.org/10.3390/su9050685