This paper builds an econometric model to analyze the income elasticity and price elasticities of sectoral natural gas demand and forecasts China’s natural gas demand up to 2030. The findings indicate that there is a long-term equilibrium relationship among sectoral natural gas demand, sectoral income and various fuel prices. The results also indicate that most price elasticities are smaller relative to developed countries; the effect of fuel prices on natural gas demand is partly offset by the government regulation. In the Business As Usual (BAU) scenario, China’s natural gas demand will reach 340 bcm and 528 bcm and its foreign dependence will reach 27.9% and 43.2% in 2020 and 2030, respectively. The forecast and discussion in this paper provide important insights into China’s energy policy design and pricing mechanism reform, and into the potential impact of China’s growing natural gas demand on global energy market dynamics.
AguileraR.F.InchauspeJ.RippleR.D. (2014). “The Asia Pacific natural gas market: Large enough for all?” Energy Policy65: 1-6. https://doi.org/10.1016/j.enpol.2013.10.014.
2.
Andrews-SpeedP.LenC. (2015). China shale gas: Can the pace be sustained? Policy Brief, Energy Studies Institute, National University of Singapore.
3.
AscheF.NilsenO.B.TveterasR. (2008). “Natural gas demand in the European household sector.” The Energy Journal29(3): 27-6. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol29-No3-2.
4.
BaltagiB.H.BressonG.PirotteA. (2002). “Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption.” Economics Letters76: 375-382. https://doi.org/10.1016/S0165-1765(02)00065-4.
5.
BaltagiB.H.GriffinJ.M. (1997). “Pooled estimators v.s. their heterogeneous counterparts in the context of dynamic demand for gasoline.” Journal of Econometrics77: 303-327. https://doi.org/10.1016/S0304-4076(96)01802-7.
6.
BiancoV.ScarpaF.TagliaficoL.A. (2014). “Scenario analysis of nonresidential natural gas consumption in Italy.” Applied Energy113: 392-03. https://doi.org/10.1016Zj.apenergy.2013.07.054.
7.
DalfardV.M.AsliM.N.AsadzadehS.M.SajjadiS.M.Nazari-ShirkouhiA. (2013). “A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting.” Applied Mathematical Modelling37: 5664-5679. https://doi.org/10.10167j.apm.2012.11.012.
8.
DuanZ. (2011). China's Natural Gas Market Outlook. The 4th CNPC/IEEJ Press Conference of Oil Market Research, Research Institute of Economics and Technology CNPC.
9.
Energy Information Administration (2013). International Energy Outlook 2013.
10.
EngstedT.GonzaloJ.HaldrupN. (1997). “Testing for multicointegration.” Economics Letters56: 259-266. https://doi.org/10.1016/S0165-1765(97)00167-5.
11.
ErdogduE. (2010). “Natural gas demand in Turkey.” Applied Energy87: 211-219. https://doi.org/10.1016Zj.apen-ergy.2009.07.006.
12.
EscarioR.GadeaM.D.SabateM. (2012). “Multicointegration, seigniorage and fiscal sustainability. Spain 1857-2000.” Journal of Policy Modeling34: 270-283. https://doi.org/10.1016/j.jpolmod.2011.09.001.
13.
FanY.XiaY. (2012). “Exploring energy consumption and demand in China.” Energy40: 23-30. https://doi.org/10.1016/j.energy.2011.09.049.
14.
FcoE.UbedaS.BerzosaA. (2007). “Modeling and forecasting industrial end-use natural gas consumption.” Energy Economics29: 710-742. https://doi.org/10.1016/j.eneco.2007.01.015.
15.
ForouzanfarM.DoustmohammadiA.MenhajM.B.HasanzadehS. (2010). “Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran.” Applied Energy87: 268-274. https://doi.org/10.1016/j.apenergy.2009.07.008.
16.
GrangerC.W.J.LeeT.H. (1989). “Investigation of Production, sales and inventory relationships using multicointegration and non-symmetric error correction models.” Journal of Applied Econometrics4: 145-159. https://doi.org/10.1002/jae.3950040508.
17.
GrangerC.W.J.LeeT.H. (1990). Multicointegration. In Advances in Econometrics, 8. G.F. Rhodes and T.B. Fomby (Eds.), JAI Press, Greenwich, CT, pp. 71-84.
18.
GregoryA.W.HansenB.E. (1996). “Residual-based tests for cointegration in models with regime shifts.” Journal of Econometrics70: 99-126. https://doi.org/10.1016/0304-4076(69)41685-7.
19.
GriffinJ.M., (1977). “Inter-fuel substitution possibilities: a translog application to intercountry data.” International Economic Review18: 755-770. https://doi.org/10.2307/2525959.
20.
HaasR.SchipperL. (1998). “Residential energy demand in OECD countries and the role of irreversible efficiency improvements.” Energy Economics20(4): 421-442. https://doi.org/10.1016/S0140-9883(98)00003-6.
21.
HofflerF.KublerM. (2007). “Demand for storage of natural gas in northwestern Europe: Trends 2005-30.” Energy Policy35: 5206-5219. https://doi.org/10.1016/j.enpol.2007.04.039.
22.
HuntingtonH.G., (2007). “Industrial natural gas consumption in the United States: an empirical model for evaluating future trends.” Energy Economics29: 743-759. https://doi.org/10.1016/j.eneco.2006.12.005.
23.
International Energy Agency (2012). Golden rules for a golden age of gas. World Energy Outlook special report on unconventional gas.
24.
International Energy Agency (2016). World Energy Outlook 2016.
25.
JamilF.AhmadE. (2011). “Income and price elasticities of electricity demand: aggregate and sector-wise analyses.” Energy Policy39: 5519-5527. https://doi.org/10.1016/j.enpol.2011.05.010.
26.
KhanM.A. (2015). “Modelling and forecasting the demand for natural gas in Pakistan.” Renewable and Sustainable Energy Reviews49: 1145-1159. https://doi.org/10.1016/j.rser.2015.04.154.
27.
LiJ.C.DongX.C.ShangguanJ.X.HookM. (2011). “Forecasting the growth of China’s natural gas consumption.” Energy36: 1380-1385. https://doi.org/10.1016/j.energy.2011.01.003.
28.
LinB.Q.WangT. (2012). “Forecasting natural gas supply in China: Production peak and import trends.” Energy Policy49: 225-233. https://doi.org/10.1016/j.enpol.2012.05.074.
29.
MaddalsG.S.TrostR.P.LiH.JoutzF. (1997). “Estimation of short-run and long-run elasticity of energy demand from panel data using shrinkage estimators.” Journal of Business &Economics Statistics15(1): 90-100.
30.
MelikogluM. (2013). “Vision 2023: Forecasting Turkey’s natural gas demand between 2013 and 2030.” Renewable and Sustainable Energy Reviews22: 393-00. https://doi.org/10.1016/j.rser.2013.01.048.
31.
MIT (2015). Energy &climate outlook 2015. MIT Joint Program on the Science and Policy of Global Change, USA Cambridge MA.
32.
MohrS.H.EvansG.M. (2011). “Long term forecasting of natural gas production.” Energy Policy39: 5550-5560. https://doi.org/10.1016/j.enpol.2011.04.066.
33.
MoroneyJ.BergD. (1999). “An integrated model of oil production.” The Energy Journal20(1): 105-124. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol20-No1-6.
34.
ParikhJ.PurohitP.MaitraP. (2007). “Demand projections of petroleum products and natural gas in India.” Energy32: 1825-1837. https://doi.org/10.10167j.energy.2007.02.008.
35.
PindyckR.S. (1979). “Interfuel substitution and the industrial demand for energy: an international comparison.” The Review of Economics and Statistics61: 169-179. https://doi.org/10.2307/1924584.
36.
PotocnikP.SoldoB.SimunovicG.SaricT.JeromenA.GovekarE. (2014). “Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia.” Applied Energy129: 94-103. https://doi.org/10.1016/j.apenergy.2014.04.102.
37.
Renou-MaissantP. (1999). “Interfuel competition in the industrial sector of seven OECD countries.” Energy Policy27: 99-110. https://doi.org/10.1016/S0301-4215(99)00006-3.
38.
SerletisA.TimilsinaG.R.VasetskyO. (2010). “Interfuel substitution in the United States.” Energy Economics32(3): 737-745. https://doi.org/10.1016Zj.eneco.2010.01.013.
39.
ShaikhF.JiQ. (2016). “Forecasting natural gas demand in China: Logistic modelling analysis.” Electrical Power and Energy Systems77: 25-32. https://doi.org/10.1016/j.ijepes.2015.11.013.
40.
ShaikhF.JiQ.ShaikhPH.MirjatN.H.UqailiM.A. (2017). “Forecasting China’s natural gas demand based on optimised nonlinear grey models.” Energy140: 941-951. https://doi.org/10.1016zj.energy.2017.09.037.
41.
SiemekJ.NagyS.RychlickiS. (2003). “Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation.” Applied Energy75: 1-7. https://doi.org/10.1016/S0306-2619(03)00013-8.
42.
SmithW. J. (2013). “Projecting EU demand for natural gas to 2030: A meta-analysis.” Energy Policy58: 163-176. https://doi.org/10.1016/j.enpol.2013.02.045.
43.
SoldoB. (2012). “Forecasting natural gas consumption.” Applied Energy92: 26-37. https://doi.org/10.1016/j.apen-ergy.2011.11.003.
44.
The Institute of Energy Economics, Japan (IEEJ)(2014). Asia/World Energy Outlook 2012—Increasing Significance of Asia and the Middle East, and their Interdependence.
45.
UN (2015). World Urbanization Prospects, the 2014 revision. United Nations, Department of Economic and Social Affairs.
46.
UrgaG.WaltersC. (2003). “Dynamic translog and linear logit models: a factor demand analysis of interfuel substitution in U.S. industrial energy demand.” Energy Economics25(1): 1-21. https://doi.org/10.1016/S0140-9883(02)00022-1.
47.
WadudZ.DeyH.S.KabirM.A.KhanS.I. (2011). “Modeling and forecasting natural gas demand in Bangladesh.” Energy Policy39: 7372-7380. https://doi.org/10.1016/j.enpol.2011.08.066.
48.
WangT.LinB.Q. (2014). “China’s natural gas consumption and subsidies-From a sector perspective.” Energy Policy65: 541-551. https://doi.org/10.1016/j.enpol.2013.10.065.
49.
WangT.LinB.Q. (2014). “Impacts of unconventional gas development on China’s natural gas production and import.” Renewable and Sustainable Energy Reviews39: 546-554. https://doi.org/10.1016/j.rser.2014.07.103.
50.
ZengB.LiC. (2016). “Forecasting the natural gas demand in China using a self-adapting intelligent grey model.” Energy112: 810-825. https://doi.org/10.1016/j.energy.2016.06.090.
51.
ZhangW.YangJ. (2015). “Forecasting natural gas consumption in China by Bayesian Model Averaging.” Energy Reports1: 216-220. https://doi.org/10.1016/j.egyr.2015.11.001.
52.
ZhangY.JiQ.FanY. (2018). “The price and income elasticity of China’s natural gas demand: A multi-sectoral perspective.” Energy Policy113: 332-341. https://doi.org/10.1016/j.enpol.2017.11.014.
