Electricity consumption and economic growth in China: assessing Granger causality at provincial, electricitymarket, and national levels

Transcription

1 Electricity consumption and economic growth in China: assessing Granger causality at provincial, electricitymarket, and national levels Contributed presentation at the 60th AARES Annual Conference, Canberra, ACT, 2-5 February 2016 Copyright 2016 by Author(s). All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 Electricity consumption and economic growth in China: assessing Granger causality at provincial, electricity-market, and national levels

3 Motivation Data & Methodology Results Conclusions Which comes first? 20 China 20 Aggregate RGDP Growth Rate (%) 10 RGDP ELC Year 10 Electricity Consumption Growth Rate (%)

4 Motivation Data & Methodology Results Conclusions Why? Role of electricity consumption in economic development Planning infrastructure Nationwide carbon trading scheme

5 Motivation Data & Methodology Results Conclusions Most studies look at China as a whole Ürümqi Heilongjiang Harbin Changchun Xinjiang Jilin Shenyang Liaoning Hohhot Inner Mongolia (Nei Mongol) Beijing Hebei Tianjin Qinghai Yinchuan Ningxia Xining Lanzhou Shijiazhuang Taiyuan Shanxi Jinan Shandong Tibet (Xizang) Gansu Xi'an Shaanxi Zhengzhou Henan Jiangsu Nanjing Lhasa Chengdu Sichuan Chongqing Hubei Hefei Shanghai Anhui Hangzhou Wuhan Zhejiang Nanchang Changsha Guizhou Hunan Jiangxi Guiyang Fuzhou Fujian Kunming Yunnan Guangxi Nanning Guangdong Guangzhou Hong Kong SAR Macau SAR Haikou Hainan

6 Motivation Data & Methodology Results Conclusions Electricity markets in China Northwest Northeast North Tibet Central East Southern

7 Motivation Data & Methodology Results Conclusions No consensus in the literature Study Direction of Granger causality Shiu & Lam (2004) ELC RGDP Boqiang (2003) RGDP ELC Chen et al. (2007) RGDP ELC Table 1 : Previous studies

8 Motivation Data & Methodology Results Conclusions Data Chinese Statistical Yearbooks Electricity consumption and real gross regional product for 28 provinces Excluded: Hainan and Tibet Aggregated: Chongqing and Sichuan

9 Motivation Data & Methodology Results Conclusions Vector error correction model (VECM) ELC = Log Electricity Consumption RGRP = Log Real Gross Regional Product RGRP t = β 0 + β 1 ELC t + µ t ELC t = α 1 + α x µ t 1 + i=1 α 11 (i) RGRP t i + i=1 α 12 (i) ELC t i + ɛ xt RGRP t = α 2 +α y µ t 1 + i=1 α 21 (i) RGRP t i + i=1 α 22 (i) ELC t i +ɛ yt Shiu and Lam (2004)

10 Motivation Data & Methodology Results Conclusions Long run Granger causality map ELC RGRP RGRP ELC RGRP ELC RGRP ELC

11 Motivation Data & Methodology Results Conclusions Aggregation and robustness China Southern China Power Grid Guangdong Guangxi Guizhou Yunnan Long run Granger causal direction [1] [2] [3] [4] [5] [6] [7] [8] ELC RGRP RGRP ELC RGRP ELC RGRP ELC Model number and sample period key [1] [2] [3] [4] [5] [6] [7] [8]

12 Motivation Data & Methodology Results Conclusions Conclusions Regional clustering Limitations of highly aggregated data Case study for further analysis

13 Questions

14 References References Boqiang, L. (2003), Structural changes, efficiency improvement and electricity demand forecasting, Economic Research Journal 5, Chen, S.-T., Kuo, H.-I. and Chen, C.-C. (2007), The relationship between GDP and electricity consumption in 10 Asian countries, Energy Policy 35(4), Shiu, A. and Lam, P.-L. (2004), Electricity consumption and economic growth in China, Energy Policy 32(1),

15 Co-movement 20 Guangxi 20 Aggregate RGRP Growth Rate (%) RGRP ELC Year Electricity Consumption Growth Rate (%)

16 No co-movement 20 Xinjiang 40 Aggregate RGRP Growth Rate (%) RGRP ELC Year Electricity Consumption Growth Rate (%)

17 National level data Dependent variable Constant RGDP ELC (0.209) (0.894) ECT t *** (0.597) (0.008) RGDP L *** (0.001) (0.181) L *** (0.006) (0.987) L ** (0.910) (0.048) ELC L *** (0.652) (0.000) L (0.478) ** (0.030) L (0.288) 0.973*** (0.000) P-values in parentheses

18 Signs RGRP t = β 0 + β 1 ELC t + µ t µ t = RGRP t β 0 β 1 ELC t µ t > 0 if RGRP t > β 0 + β 1 ELC t Restore µ t to 0 by RGRP t OR ELC t RGRP t = β 0 + β 1 µ t ( )β 1 RGRP t helps restore equilibrium ELC t = β 0 + β 1 µ t (+)β 1 ELC t helps restore equilibrium

19 Short run Granger causality map ELC RGRP RGRP ELC RGRP ELC RGRP ELC (no lags)

20 Aggregation and robustness China Southern China Power Grid Guangdong Guangxi Guizhou Yunnan Short run Granger causal direction [1] [2] [3] [4] [5] [6] [7] [8] ELC RGRP RGRP ELC RGRP ELC RGRP ELC (no lags) Model number and sample period key [1] [2] [3] [4] [5] [6] [7] [8]

21 Procedure 1 Calculate optimal lag-length in underlying VAR (SBIC) 2 Check for stationarity (ADF Test) 3 Check for co-integration (Johansen Test) 4 Apply VECM 5 Check for serial correlation (LM Test)