Renewable and Sustainable Energy...

Renewable and Sustainable Energy Reviews 53 (2016) 1149–1167

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China’s natural gas production and consumption analysis basedon the multicycle Hubbert model and rolling Grey model

Jianzhou Wang a, Haiyan Jiang b,n, Qingping Zhou b, Jie Wu b,c, Shanshan Qin b

a School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, Chinab School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, Chinac School of Software Engineering, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), PO Box 123, Broadway, NSW2007, Australia

a r t i c l e i n f o

Article history:Received 27 February 2014Received in revised form23 May 2015Accepted 17 September 2015Available online 10 November 2015

Keywords:Natural gasHubbert modelRolling GM(1,1)Grey relationship analysisSupply–demand gapEnergy policy

x.doi.org/10.1016/j.rser.2015.09.06721/& 2015 Elsevier Ltd. All rights reserved.

viations: GW, gigawatt; BP, British Petroleum; PCMACP, polynomial curve and moving aveaverage; ANNs, artificial neural networks; FISference system-stochastic frontier analysis; TSLP, self-organizing feature map and multilayerror; RMSE, root mean square error; NRMSEltaicesponding author. Tel.: þ86 13659462863; faail address: [email protected] (H. Jiang)

a b s t r a c t

As fossil fuels reserves deplete rapidly and the low-carbon economy develops expeditiously, especially inChina, natural gas as a clean and alternative energy is underway to help meet increased energy needsand climate needs. Therefore, accurate forecasts of natural gas production and consumption have been anecessary task for policy making in the coming years. This paper presents a review of natural gasforecasting models. The multicycle Hubbert model is employed to forecast China’s annual nature gasproduction and to determine the peak production, the peak year and the future production trends basedon several different URR scenarios. Moreover, a small-sample effective rolling GM(1,1) model is proposedfor the first time to forecast exponential natural gas consumption with different lengths of data sets.Then, the grey relationship analysis is used to select the best consumption curve in correspond withdifferent URR scenarios. The empirical result shows that the supply–demand gap will be larger and largerin the future, with a minimum gap of 22 bcm in 2011 and 225 bcm in 2050, with a maximum gap of31 bcm in 2011 and 807 bcm in 2050, which indicates that the natural gas production in China cannotmeet the rising consumption. Therefore, policy measures must be taken to ameliorate the situation,including expanding natural gas imports, increasing unconventional natural gas production, com-plementing the gap with other energy resources and combining energy saving with emission reduction.Accurate forecasting of natural gas production and consumption can provide the basis for decisionmaking and help the government generate new significant policies.

& 2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11502. Our contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11513. Data sources, reserves and determinant factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

3.1. Data sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11523.2. China’s natural gas reserves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11523.3. Determinant factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

4. A review of natural gas forecasting models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11534.1. Physical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11544.2. Statistical models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154

; Mtoe, million tons oil equivalent; tcm, trillion cubic meters; bcm, billion cubic meters; URR, ultimate recoverablerage combination projection; FTW estimation, Fermat–Torricelli–Weber estimation; ARIMA, autoregressive integrated, fuzzy inference system; RBF, radial basis function; OLS, ordinary least square; ANFIS-SFA, adaptive network-based-FIS, Takagi–Sugeno adaptive FIS; ANFIS, adaptive network-based FIS; LS-SVM, least squares support vector machine;er perceptron; ARDL, autoregressive distributed lag; GM, grey model; RGM, rolling GM; MAPE, mean absolute per-, normalized root mean square error; R, correlation coefficient; LNG, liquefied natural gas; CBM, coal bed methane; PV,

x: þ86 931 8912481..

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J. Wang et al. / Renewable and Sustainable Energy Reviews 53 (2016) 1149–11671150

4.3. Artificial intelligence models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11564.4. Econometric models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1156

5. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11575.1. The Hubbert model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11575.2. The rolling GM(1,1) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11585.3. Why Hubbert and rolling GM(1,1) models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158

6. Empirical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11596.1. Cycle numbers of the Hubbert model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11596.2. Model evaluation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159

7. Forecasting natural gas production and consumption in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11607.1. Future gas production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11607.2. Future gas consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11617.3. The gap trend between the gas production and consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1162

8. Methods to narrow the gap in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11638.1. Expanding natural gas imports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11638.2. Increasing unconventional natural gas production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11648.3. Energy-saving, emission-reduction and other policies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11658.4. Complementing the gap with other energy resources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165

9. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166

1. Introduction

With the unceasing progress of human civilization, the world isdemanding increasing quantities of energy from natural sources now,including coal, oil, natural gas and other primary energy sources.Worldwide, production and consumption of energy resources isbecoming more and more essential to the prosperous global economy.

Since the start of the 21st century, the continuous development ofthe world economy has resulted in a huge increase in the demand forprimary energy. According to statistics from the British Petroleum(BP) statistical review of world energy [1], from 2003 to 2013, theworld’s total annual primary energy consumption increased sharplyfrom 9943.8 million tons oil equivalent (Mtoe) to 12,730.4 Mtoe withan average annual growth rate of 2.8%.

And currently, oil, natural gas and coal are still three mainenergy resources in the world energy consumption structure,representing 32.9%, 23.7% and 30.0% of the total annual energyconsumption in 2013, respectively. Other fuels such as nuclearenergy, hydroelectricity and other renewable energy are supple-mentary energy resources. With flourishing economic growth, theworld will see a tremendous increasing energy demand in the nearfuture. According to BP energy outlook 2035 the world totalenergy consumption is expected to increase to 17,454.7 Mtoe inthe year of 2035 [2].

With increasing world population and the developing worldeconomy, the demand for energy in the world has increased dra-matically. At the same time, large-scale use of fossil energy all overthe world has also led to a series of environmental problems suchas acid rain, air pollution and global warming [3]. Fossil fuel use isthe primary source of carbon dioxide (CO2). Therefore, to addressthe energy crisis and global climate warming, it is highly impor-tant to develop clean and renewable energy resources. Manycountries are making an effort to balance the growing energydemand while trying to reduce the dependence on fossil fuel andto address environmental safety and sustainability [4].

Natural gas is a type of low-carbon energy, which touches ourlives in countless ways every day, such as heating homes, cookingfoods and fueling cars, and even helping to generate electricity.Especially in recent years, it is of high significance in many nations’energy structure and has received a substantial amount of attentionas an automotive fuel [5]. In particular, it has been reported that theamount of natural gas reserves is much larger than that of oil globally,

so the exploring and exploiting has adequate resources foundation.The preliminary statistics from China’s Ministry of Land and Resour-ces in 2012 indicated that the remaining technologically recoverablereserves of conventional natural gas reach 4.02 trillion cubic meters(tcm), marking the country is relatively rich in total natural gasreserves. Therefore, Substitution of natural gas for coal in China couldsignificantly reduce emissions of carbon dioxide.

China, as the largest developing country as well as the fastest-growing major economy in the world, has risen to the second placeafter the United States in terms of economic aggregate, with aneconomy growth rate of approximately 11% over the past 30 years.Energy consumption has been growing very rapidly since 1953,especially after 1978 when the Chinese economic reform was initi-ated, which can be seen from Fig. 1(a). In 2010, China overtookAmerica for the first time as the world’s largest energy consumer,with total energy consumption of 2339.6 Mtoe, accounting for 19.6%of the world’s total, and in 2013, the figure changed to 2852.4 Mtoe,representing 22.4% of the world’s total. The global shares of oil, nat-ural gas, coal and others in 2013 were approximately 32.9%, 23.7%,30.0% and 13.4%; however, the corresponding shares in China wererespectively 17.8%, 5.1%, 67.5% and 9.6%, which demonstrated thatChina’s energy structure was not well aligned with the global energymix [3] and that the long-term energy consumption structure wasdominated by coal in China, whose usage proportion was nearly 70%.

Coal, as an important strategic material, is still dominated inChina’s energy structure. However, China’s coal-dominated energystructure cannot achieve the harmonious development of socialeconomy. In comparison, natural gas supplies only a small part ofthe primary energy consumption. Natural gas supplied only 5.1% ofthe domestic energy consumption in China, however, the propor-tion in the United States was 29.6%, and naturel gas supplied 23.7%of the total world consumption in 2013. As the case stands, theproportion of natural gas in the energy consumption structure iscurrently still quite low compared with the international stan-dards. Furthermore, China has recently imported more natural gasfrom other countries, which implies the natural gas dependency isgetting higher as demonstrated in Fig. 1(b). Based on an analysis ofCO2 emissions, China has surpassed the United States as the world’scurrent largest emitter of CO2, putting out 9524.3 million tons carbondioxide in comparison with America’s 5931.4 million tonnes. As dis-played in Fig. 1(c), the greenhouse gas emissions in China can also bebroken down by burning different fossil fuels that lead to their

Fig. 1. Energy and emission reduction situation in China. (a) The Five-Year Plans and corresponding natural gas production and consumption from 1990 to 2013. (b) Netimport of natural gas and its dependency of from 2007 to 2012. (c) CO2 emissions from consumption of coal, petroleum and natural gas from 1980 to 2011 in China.(d) Regional shares of global carbon dioxide emissions in 2013 (marked the United States and China separately).

J. Wang et al. / Renewable and Sustainable Energy Reviews 53 (2016) 1149–1167 1151

production, which reveals the amount of greenhouse gas emissionsfrom coal is tremendous. Fig. 1(d) shows the regional shares of CO2emissions in 2013, the United States and China accounted for 16.9%and 27.1%. China is an active participant in the climate change talksand other multilateral environmental negotiations and is activelytaking climate change mitigation measures to reduce CO2 emissions.Therefore, Substitution of natural gas for coal in China could sig-nificantly reduce emissions of carbon dioxide.

There are many studies concerning renewable and alternativeenergy sources that can be used instead of high-carbon fuels [6–10]. In addition, though renewable resources and nuclear resour-ces are developing quickly, many researchers are discussing thegreater use of natural gas to reduce CO2 emissions. They argue thatnatural gas may become a “bridge fuel” that can smooth thetransition of the global energy system from fossil fuels to zerocarbon energy [11]. Therefore, it is necessary to take steps toenhance consumption of natural gas and advisable that Chinaincrease the proportion of natural gas used for primary energy as amain strategy of energy conservation.

The rest of this paper is organized as follows: Section 2 givesthe contribution of our paper. In Section 3, the historical data ofnatural gas from different sources are presented, and then thereserves and determinant factors are analyzed. Section 4 reviewsnatural gas production and consumption forecasting models. TheHubbert model theory and he rolling GM(1,1) (RGM) model areintroduced in Section 5. Then, the empirical analysis is put forwardin Section 6, and the choice of ultimate recoverable reserves (URR)scenarios and cycle numbers and five model evaluation criteria arealso exhibited. Section 7 provides the final forecasting results of

natural gas production and consumption in China obtained bymodels described in Section 5 and effectively evaluates the dif-ference between consumption and production by applying thegrey relationship analysis. Section 8 shows the impacts of thepeaking of natural gas production based on the gap trend analysisin Section 7, such as expanding natural gas imports, increasingnatural gas production, energy saving and emission reduction,other policies and complementing the gap with other energyresources. Finally, Section 9 concludes this paper.

2. Our contributions

Natural gas production and consumption forecasting is alwaysplaying a vital role in national sustainable economic development,and inaccurate prediction may lead to serious blocks of healthy eco-nomic developing or even cracks of environmental policy and energymanagement. On the other hand, forecasting production and con-sumption have proven to be a challenging task because of variousunstable influencing factors. In particular, China is undergoing aperiod of economic transition, which highlights this difficulty.

In our study, multicycle Hubbert models with two cycles areemployed to forecast natural gas production, which uses annualproduction data over the 65-year period from 1949 to 2013 ofChina to accurately calculate future production from 2011 to 2050.To take different situations into consideration, different URR sce-narios are represented using Hubbert models, and then, the peakproduction and decline period are calculated and clearly demon-strated for further analysis. For estimating future consumption,


first, the small-sample effective rolling GM(1,1) model is utilized toforecast the natural gas consumption in China. In addition, dif-ferent GM(1,1) models are established and compared with differ-ent lengths of data sets. Subsequently, grey relationship analysis isused to select the best consumption curves, and then, the supply–demand gap is calculated effectively, and some viable politicalsuggestions are proposed, such as expanding natural gas imports,increasing natural gas production especially unconventional gas,complementing the gap with other energy resources and com-bining energy saving with emission reduction.

Fig. 2. China’s natural gas production and consumption from 1949 to 2013 andspecial view of zooming in the time period from 1970 to 1990.

3. Data sources, reserves and determinant factors

In this following study, natural gas is referred to as only conven-tional gas, excluding the unconventional resources, such as landfillgas, biogas, coal bed methane, and so on [12]. This section gives ananalysis of China’s natural gas reserves and economic factor deter-minants affecting the production and consumption of natural gas.

3.1. Data sources

The following two sources were referenced for historical dataof natural gas production and consumption in China:

Source A: from China Compendium of Statistics 1949–2008 [13].Data published by Department of Comprehensive Statistics ofNational Bureau of Statistics of China.Source B: from BP Statistical Review of World Energy, June 2014[1]. Data provided by Beyond Petroleum, a multinational petro-leum company that is headquartered in London.

BP’s gas production data covered the period from 1970 to 2013,and we added data from 1949 to 1969 from Source A [13]. As forthe historical gas consumption data, data after 1965 can beobtained from the BP Statistical Review of World Energy [1]. Withrespect to gas consumption data from 1949 to 1964, gas produc-tion data can substitute for consumption data in this specificperiod directly. The chief reason for doing this is that for a con-siderably long time, China was self-sufficient in natural gas andscarcely had exports or imports during these years. Even moreimportant, it is in recent years that gas consumption has increasedpersistently, and China has become a net importer of natural gas[14]. Based on the above analysis, we offer a brief summary:

(1) Natural gas production historical data: The data for the periodfrom 1949 to 1969 come from Source A; and the data from theperiod from 1970 to 2013 come from Source B.

(2) Natural gas consumption historical data: The data for theperiod from 1949 to 1964 come from Source A; the data forthe period from 1965 to 2013 comes from Source B.

As demonstrated in Fig. 2, from the general trend and the specialview of zooming in the time period from 1970 to 1990, there was asmall peak around 1980 for both gas production and consumptionand soon afterwards a slight decline from 1980 to 1983, and then thedata started to climb sharply, especially in recent years. The max-imum production and consumption in this time period are approxi-mately 117.05 and 161.61 billion cubic meters (bcm), respectively,which appeared in 2013. In addition, except for the little drop, therehas been a rising tendency toward the amounts of gas production andconsumption. An analysis of Fig. 2 clearly reveals that there has beentremendous rise in production and consumption of natural gas inChina, but the mode of consumption growth varies from that ofproduction. Accordingly, the models employed for the production andconsumption also vary.

3.2. China’s natural gas reserves

Geological resources consist of a part of total identified mineralresources and undiscovered resources. The proved reserves ofnatural gas, namely the remaining technically recoverable reserves(recoverable resources) which is based on all relevant considera-tions, including economic, are a part of total identified mineralresources. The remaining technically recoverable reserves aregenerally taken to be those quantities that geological and engi-neering information indicates with reasonable certainty can berecovered in the future from known reservoirs under existingeconomic and operating conditions. Thus, the remaining techni-cally recoverable reserves are the real item of interest because onlythey can be produced under existing economic and political con-ditions, with existing technology. The URR be calculated by addingthe cumulative historical production and the remaining techni-cally recoverable reserves. The total URR is a very importantparameter in modeling the natural gas production which is usuallydetermined by experts. The cumulative production of historicaldata was approximately 1352.6 billion cubic meters until 2013 [1].

Based on BP’s Statistical Review of World Energy [1], China’sremaining technical recoverable reserves were 3272.2 bcm at theend of 2013, and in this scenario, the gas URR is approximately4624.8 bcm (1352.6þ3272.2). For easy calculation, the URR can beviewed as 4630 bcm in the scenario. This figure is used as thelower scenario.

In the China Statistical Yearbook-2014, the remaining technicalrecoverable reserves of natural gas are approximately 4642.9 bcm,and the URR is 5995.5 (1352.6þ4642.9) bcm. For easy calculation,the URR can be viewed as 6000 bcm in this scenario.

Wang et al. [15] listed an array of the gas resources estimatedby different scholars, who provided a natural gas URR scenario of10.19 tcm. In our opinion, a scenario of 10.19 tcm, which is almostdouble both of the former two figures, seems a little incredible. Wecalculate the average of the above three scenarios and obtain ascenario of 6.94 tcm, which sounds more credible than that of10.19 tcm. Therefore, in the following analysis, four possible URRscenarios are studied, which are 4630 bcm, 6000 bcm, 10,190 bcmand 6940 bcm, respectively.

3.3. Determinant factors

Since 1949, the natural gas production has grown to 117.1 bcmuntil 2013 from an initial amount of less than 100 million cubicmeters. The production growth rate changed from slow to fast, and


it took 28 years (1949–1976) to make a breakthrough of 10 bcm forthe first time and 20 years (1977–1996) for the second time, andonly 5 years (1997–2001) for the third time [16]. China’s naturalgas production has increased rapidly, with an average annualincrease of 6.4 bcm from 2000 to 2013.

Natural gas production has increased quickly, but consumptionhas expanded more rapidly, leading to the current domestic naturalgas in the situation of high demand and short supply. The rapidgrowth of China’s natural gas consumption has being driven byeconomic growth, industrialization and urbanization. Additionally,the country’s low-carbon development strategy, government-controlled gas price, and some other factors also contribute to thesurging gas consumption. Wang and Lin [17] summarized determi-nant factors affecting natural gas consumption including income,price, urbanization and industrialization, change of temperature ofthe climate. In addition to the above several factors, the related pro-ducts and technology progress are also very important factorsaffecting energy production and consumption. The economic factorsuch as GDP (gross domestic product), has serious impacts on thenatural gas production and consumption. Certain events, such as thediscovery of major gas fields, technical progress and a sharp drop ingas prices, may lead to variation of production and consumption. Ourintention, however, is not to focus on the impacts of policy changesand government behaviors, but to concentrate on economic factors,so we take GDP as an example to analyze their correlations betweenthe natural gas production (or consumption) and GDP.

Based on the correlation analysis in Fig. 3, the result shows thatthe grey relational correlation between GDP and natural gas pro-duction is 0.7903, and the value between GDP and natural gasconsumption is 0.8305, which implies that there is close depen-dence between them. Moreover, they change in the same trend. Asfor other determinant economic factors, the impact on them mustbe the similar. Therefore, we take into consideration the historicalproduction data as a time series, in which case, if determinantfactors occurred which would lead to significant changes in thehistorical natural gas production (consumption).

According to the development of China’s economic, the growthrate of the economic from 2010 to 2014 are 10.45%, 9.3%, 7.65%,7.67% and 7.4%, respectively [18]. The general trend of China’s

Fig. 3. The grey relationship between GDP and natural gas p

economic growth rate is declining these years, and at the sametime, GDP and natural gas production and consumption has highcorrelation, which in turn would lead to decelerated growth ofnatural gas production and consumption in China.

However, on the other hand, the government is trying to find themost effective way to get the balance between the economy growthand the climate warming. To achieve this goal, low carbon economy,which is green economy with low energy consumption and lowpollution, is necessary in coping with the climate warming. As well asto the low energy consumption, natural gas with the advantage oflow carbon emission can meet the requirement of economy and cli-mate, thus the government has decreased the usage of fossil fuels andincreased investment on the development of natural gas, andencouraged the consumption of natural gas.

Based on the above analysis, the economic factor and the policywould affect natural gas production and consumption by oppositetrends, therefore, it can be concluded that under the two deter-minant factors, the natural gas production and consumption datawould keep the same trend and grow along the existing trend. Sothey can be employed to establish forecasting models.

4. A review of natural gas forecasting models

In order to effectively and accurately forecast future gas produc-tion, whether there are sufficient resources to balance the supply andthe demand becomes a matter of prime importance. Studying thepeak and lifetime production of natural gas as well as forecasting theconsumption is needed. For this reason, a large number of studieshave been published concerning a variety of models to realize energyproduction and consumption forecasting. For example, in China, theGeneralized Weng model and grey prediction model were applied byMa and Li [19] to forecast China’s future production and consumptiondirectly, and they indicated that there was a supply–demand gapbetween natural gas production and consumption. However, Wanget al. [15] distinguished different definitions of geological resourcesand recoverable resources and then forecasted the future annualproved geological reserves and the future gas production. Finally, theresearch demonstrated that there was a gap between gas production

roduction and consumption in China from 1952 to 2013.


and the soaring demand and the gap would continue to increaserapidly in future, which implied China’s gas security would meet asevere challenge.

Among these various models, there is a type of classic fore-casting model used to estimate the peak and lifetime productionfor fossil fuels, namely the Hubbert model or Hubbert peak. In theHubbert peak theory, a logistic function is usually used to char-acterize peak and ultimate production of oil, coal and natural gas.The research methods of the Hubbert curve have recently drawnsome attention in the field of energy production and consumptionforecasting. In 2007, Tao and Li [20,21] proposed the genericSTELLA model to simulate a Hubbert Peak for oil and raw coalproduction in China. Li et al. [22] employed the data of Americanoil production to test the forecast capability of system dynamicsmodel of Hubbert curve, and then by comparing the sum ofsquares of different models, conclusions were drawn that theforecast results was sensitive to the URR and that the proposedmodel can provide more robust forecast results when the URR isproperly selected. In 2010, Patzek and Croft [23] developed a base-case scenario for global coal production based on the physicalmulticycle Hubbert analysis of historical production data. Lin andLiu [24] predicted the primary energy demand of China through aco-integration model and forecasted the structure of primaryenergy by a stochastic process model named the Markov prob-ability analysis. Then, they applied logistic growth curves andGaussian curves to simulate the historical data of coal productionand forecast the future coal production. In 2011, Brian Gallagher[25] applied a curve-fitting approach, namely a population-growthlogistic function to complete the cumulative production curve,which was then deconstructed into a set of annual oil productiondata producing an idealized Hubbert curve. Wang et al. [26]introduced two typical multicycle models, the Hubbert model andthe Generalized Weng model, and then applied these two modelsto forecast the world’s conventional oil production. Later in 2012,Lin and Wang [14] predicted the primary energy demand of Chinaand the structure of primary energy by the same methods as Linand Liu [24]. They employed logistic growth curves and Gaussiancurves to fit the historical data of natural gas production andpredict gas production in the future.

In addition to the traditional life-cycle models (such as thetypical Hubbert peak model and the generalized Weng Model),many statistical models and artificial models have been developfor natural gas forecasting. Both at home and abroad, a variety ofnatural gas forecasting studies have emerged in recent years, andthe forms of classification are many and varied. The natural gasforecasting models can be classified in several ways such as short-term versus long-term, static versus dynamic, and linear versusnonlinear, with techniques ranging from statistics to artificialintelligence models. The review of natural gas forecasting modelspresented in this paper is categorized under technical methods asthe follows: (i) Physical models, (ii) Statistical models, (iii) Artifi-cial intelligence models, (iv) Econometric models.

4.1. Physical models

System dynamics is an approach to understanding the behaviorof complex systems over time, and it is a powerful methodologyand modeling technique to explore the feedback structure in theentire systems. Currently, it is being used throughout the publicand private sector for policy analysis and design. Natural gasconsumption can be considered a complex system because thereare various factors affecting gas consumption.

To examine the factors influencing the long-term supply anddemand of the UK natural gas industry [27], a system dynamicsmodel was developed for several scenarios, and subsequently theeffectiveness of various policies in UK was analyzed in transition

from self-sufficiency to gas import-dependence in the long term.In China, natural gas consumption was be viewed as a complexnonlinear system affected by many factors, and researchers putforward a new dynamical system model based on previous studiesfor the forecasting of natural gas consumption in the near future[28], which takes into account not only two or three influencingfactors but also various impacting factors of various sectorsaffecting natural gas consumption by different end use. The fore-casting model presented relatively reasonable results throughcomparison analysis, and some policy proposals were proposed forfuture natural gas industry. To diagnose the relationship betweenenergy depletion and climate change during rapid urbanizationand economic growth, a system dynamics model [29] for the mostrepresentative city of Beijing was developed based on the STELLAplatform to investigate the energy consumption and CO2 emissiontendency over the period 2005–2030. The modeling resultsrevealed that Beijing is to be faced with a heavy burden of energysupply and carbon emission, which would confirm the urgentneed for energy savings and emission reductions. To capture theeffect of both past and current energy prices on fuel consumption,an enhanced functional specification using the system dynamics-based model with a vintage representation of capital stock [30]was presented, and a re-calibrated version of this model was usedto confirm the pertinence to represent interfuel substitution atdifferent fuel prices in the industrial sector. After obtaining thesefindings, a dynamic functional specification of the demand func-tion for natural gas was then proposed and calibrated. In thecement industry, which accounts for 15% of total energy con-sumption in the industrial sector The main flow diagram of systemdynamics models is summarized, which is shown in Fig. 4.

4.2. Statistical models

Statistical models describe how one or more influencing factorsare related to natural gas consumption (Table 1). Therefore, thereis a functional relationship between factors and gas consumptionin the form of statistical and mathematical equations. The HubbertPeak model and the generalized Weng model are both statisticalmodels.

In addition, Xu andWang [31] proposed the polynomial curve andmoving average combination projection (PCMACP) model for esti-mation of natural gas consumption in China over 2009–2015. Theresults obtained by the proposed PCMACP model were comparedwith the results of other three models, which indicated that theproposed model had a lower mean absolute percentage error (MAPE)than others. To keep track of natural gas consumption, Aslan [32]attempted to explore, for the first time, the natural gas consumptionfor 50 US states by employing a nonlinear unit root test over theperiod 1960–2008. The results revealed that for 27 US states, naturalgas consumption was a non-stationary process, which means anyshock to natural gas consumption was likely to be permanent,whereas for the other 23 states, natural gas consumption was a sta-tionary process where any shock to natural gas consumption wastransitory. For the long-term forecasting of nonresidential natural gasconsumption in Italy, Bianco et al. [33] proposed a regression modelconnecting nonresidential gas consumption with three explainingvariables, namely average annual minimum temperature, gas priceand GDP per capita. Moreover, a validation of the model was per-formed that demonstrated that it guaranteed a satisfactory level ofaccuracy. For individual residential and small commercial customers,natural gas consumption can also be estimated by a statisticalapproach based on nonlinear regression principles [34]. If only con-sidering natural gas consumption hourly forecast of individual resi-dential and small commercial customers excluding large industrialconsumers, consumption can be regarded as depending mainlyon temperature movement. Mathematical models [35] with FTW

Fig. 4. The main flow diagram of system dynamics models.

Table 1Short summary of statistical models of natural gas forecasting.

Authors Models Main equations Data sources

Xu and Wang[30]

PCMACP model Gaŝt ¼ 248:33�31:86tþ4:84t2þμ�0:93εt�2 China over 1995–2008

Aslan [31] Nonlinear unit root testΔrit ¼ δ‘ΔZtþϕSt�1þμt and LMbarNT ¼ 1N �

PNi ¼ 1

LMτi50 US states over 1960–2008

Bianco et al. [32] Linear logarithmic functionregression model

log ðCnres;t Þ ¼ ↓αþβ1 U log ðTminÞþβ2 U log ðPnresÞ↓þβ3 U log ðGDPPC Þþβ4 U log ðCnres;t�1Þ

↓þβ5 U log ðGDPPC;t�1Þ

Nonresidential gas consumption in Italy

Vondracek [33] Nonlinear regression modelĈikðtÞ ¼ μ̂ikϕ̂kðtÞ ¼ ϕ̂kðtÞPNk

j ¼ 1 ϕ̂k ðτj ÞPNkj ¼ 1

CikðτjÞIndividual residential and small commercialcustomers

Sabo et al. [34] Mathematical model y0i ¼ α0þα1T0i þ…þαvT0i�vþ1þεi Natural gas consumption data, temperaturedata and temperature forecast data

Erdogdu [35] ARIMA model E�t ¼ 24:96�0:29E�t�3�0:11E�t�4�0:09E�t�5�0:31E�t�6�0:23E�t�7�0:14E�t�8�0:16E�t�9þ0:02E�t�11þ0:24E�t�12þ0:02E�t�13þ0:08E�t�18�0:11E�t�24þ0:08E�t�35þ0:29E�t�36þμt

Turkish natural gas consumption


estimation (Fermat–Torricelli–Weber), Gompertz model functionsand linear model functions have been established for natural gasconsumption hourly forecasting with respect to the past natural gasconsumption data, temperature data and temperature forecast data.Time series models are the most common of statistical models usingtime series trend analysis for extrapolating the future energy

tendencies. In time series analysis, the autoregressive integratedmoving average (ARIMA) model is widely used. Erdogdu [36] con-sidered natural gas demand in Turkey from an economic perspectiveand then estimated the short- and long-run price and income elas-ticity of natural gas demand with a dynamic partial adjustmentmodel, before employing an ARIMA model to forecast the future


growth of gas demand. The forecasting natural gas consumptionvalues indicated that a relevant increase of gas demand in Turkey wasexpected in coming years.

4.3. Artificial intelligence models

Artificial intelligence refers to machinery and electronic equip-ment used to simulate and substitute some of human intelligence. Ithas attained significant achievements in the field of forecasting recentyears (Table 2). The most widely used methods include geneticalgorithms, artificial neural networks (ANNs), BP neural networks,fuzzy systems and support vector machines [37–39].

In addition, in Iran, Forouzanfar et al. [37] used a logistic basedapproach for the natural gas consumption forecasting of residentialand commercial sectors, in which nonlinear programming and agenetic algorithmwere proposed to calculate the logistic parameters.The overall results indicated that promising improvements in fore-casting accuracy were obtained compared with some older approa-ches. Azadeh et al. [38] proposed an adaptive network-based fuzzyinference system (ANFIS) for the forecasting of short-term natural gasdemand in Iran. Daily natural gas consumption data were used toperform the prediction, and the results indicated that ANFIS achievedcertain improvements in forecasting accuracy compared with artifi-cial neural networks and conventional time series approaches. Toaccurately estimate the daily natural gas consumption demand inTurkey [39], an artificial neural network with multilayer perceptron,ANN with RBF (radial basis function) and multivariate OLS (ordinaryleast square) models were proposed for the short-term forecastingbased on meteorological data such as atmospheric pressure, windspeed and temperature. The proposed algorithm (artificial neuralnetwork with multilayer perceptron) performs well in terms offorecasting short-term natural gas consumption performance andproduces encouraging and meaningful outcomes for future energyinvestment policy. Another adaptive network-based fuzzy inferencesystem-stochastic frontier analysis (ANFIS-SFA) approach [40] waspresented for long-term natural gas consumption forecasting andbehavior analysis. The proposed method, based on annual data of thetime period 1980–2007, was applied to the cases of four MiddleEastern countries, i.e., Bahrain, Saudi Arabia, Syria, and United ArabEmirates, which demonstrates that the approach is capable of dealingwith complexity, uncertainty, and randomness as well as severalother unique features.

For when energy prices experience large increases and toincorporate the impact of price hikes into total natural gas con-sumption forecasting, an integrated Takagi–Sugeno adaptive fuzzyinference system (TS-FIS) mathematical forecasting model [41]was proposed. In their work, linear regressions were applied toconstruct a first-order TS-FIS, and then the adaptive network-

Table 2Artificial intelligence models and econometric models.

Categorizations Authors Models

Artificial intelligence models Forouzanfar et al. [36] A logistic-based model with nAzadeh et al. [37] An adaptive network-based fTaspinar et al. [38] Artificial neural network withAzadeh et al. [39] An adaptive network-based fDalfard et al. [40] An integrated Takagi–SugenoLiu et al. [41] Least squares support vector

Categorizations Authors Study of relationship and coEconometric models Shahbaz et al. [42] The relationship between natu

PakistanHeidari et al. [43] The long-run relationships am

economic growth in IranWadud et al. [44] Investigation and study of th

Bangladesh

based FIS (ANFIS) was used to forecast natural gas consumption, atlast, the proposed model was applied to forecast annual naturalgas consumption in Iran. For scenarios taking temperature intoconsideration, the least squares support vector machine (LS-SVM)algorithm [42] was applied to natural gas load forecasting in Xi’an,China. Daily natural gas load data from 2001 to 2002 were used byLS-SVM and SOFM-MLP (self-organizing feature map and multi-layer perceptron), and the comparison revealed that supportvector machine forecasting methods achieved a better approx-imation of the actual load results.

4.4. Econometric models

Among these various studies examining natural gas production orconsumption forecasting, a group of approaches concerning not onlythe natural gas consumption but also other influencing factors, such aseconomic growth, price, capital, import and export, have been inves-tigated recent years [43–45]. They are econometric models (Table 2).Though some of the studies cannot obtain determine the futureconsumption values, they still play an important role in the field ofenergy policies and for government and policy makers, and thus, theycan help with implementing appropriate and precise policies.

Shahbaz et al. [43] examined the relationship between naturalgas consumption and economic growth. In their works, real capi-tal, labor and real exports were taken into consideration in themodel of multivariate framework in the case of Pakistan. Then, theARDL (autoregressive distributed lag) bounds testing approachwas used to obtain the dynamic causality relationships amongthese variables, which revealed the existence of long-run rela-tionship among the variables. Similarly, in Iran, Heidari et al. [44]attempted to explore the long-run relationships among natural gasconsumption, real GDP, natural gas price, employment and eco-nomic growth. In their studies, a multivariate production modelwas employed to determine the relationship between natural gasconsumption and economic growth, and a demand model basedon ARDL bounds test approach to level relationship was used toinvestigate the impact of natural gas price on both consumptionand economic growth. The results indicated that economic growthin Iran depended on natural gas consumption, whereas, naturalgas conservation policies were likely to have negative effects on itseconomic growth. A dynamic econometric model was developedby Wadud et al. [45] to perform a thorough investigation andstudy of the natural gas demand both at the national level and fortwo sub-sectors in Bangladesh. The demand model indicated largelong-run income elasticity for aggregate demand for natural gas.

onlinear programming and genetic algorithm (GA)uzzy inference system (ANFIS) modelmultilayer perceptron model

uzzy inference system-stochastic frontier analysis (ANFIS-SFA) approachadaptive fuzzy inference system (TS-FIS) mathematical forecasting modelmachine (LS-SVM) model

untriesral gas consumption and economic growth (real capital, labor and real exports) in

ong natural gas consumption, real GDP, natural gas price, employment and

e natural gas demand both at the national level and for two sub-sectors in


5. Methodology

In this section, two forecasting models, including the Hubbertmodel and the rolling grey model, are introduced in detail and arethen employed for gas production and consumption modeling.Fig. 5 shows the flow chart of the whole article.

5.1. The Hubbert model

American geophysicist M. King Hubbert [46,47] first put for-ward a method called the Hubbert peak model to estimate thepeak and lifetime production for oil in 1956, which was based onthe logistic equation and is now known as the Hubbert model. TheHubbert model proved to be a particularly effective method forpredicting the oil peak production of the lower 48 states of theUSA with relative accuracy [46]. Hubbert also mentioned that theproduction of fossil fuels would grow exponentially up to the peakvalue and later would decrease until reserves were completelyused, following a bell-shaped curve described as the Hubbertcurve [46]. Since then, the Hubbert peak theory has been widelyused to predict fossil fuel production, including for oil, coal andnatural gas.

China’s historical natural gas production data are used toestablish Hubbert model, which is then used to determine thepeak production, the peak year and the future production trends.The Hubbert model is usually expressed with the followingequations:

P ¼ 2PM1þ cosh b t�tMð Þ

� �; ð1Þ

NP ¼U

1þe�b t� tMð Þ; ð2Þ

U ¼ 4PMb

: ð3Þ

Fig. 5. Flow chart of the whole article and four

In which P is natural gas production at time t, PM is the peakproduction, NP is the cumulative production, b is a parameterrepresenting the slope of the curve, and tM is the peak year cor-responding to the peak production PM . Thus, PM is the maximumof the curve, and the area under the curve is equal to U ¼ 4PM=b,where U stands for the URR.

Although the Hubbert model has been used the most ofworldwide by many researchers and scholars, derivations of thelogistic equation for the Hubbert model have rarely been studiedin articles, with only explanations by Laherrere [48]. Once giventhe URR, the equations in the Hubbert model can be figured out.

By changing Eq. (2), we obtain the following:

U�NPNP

¼ e�b t� tMð Þ: ð4Þ

By taking logarithms on both sides of Eq. (4), it gives the fol-lowing:

lnU�NPNP

� �¼ btM�bt: ð5Þ

Then, providing A¼ btM and B¼ �b, Eq. (5) can be rewrittenas:

lnU�NPNP

� �¼ AþBt: ð6Þ

According to Eq. (6), the value of intercept A and slope B can beobtained. Accordingly, parameters b and tM can be calculated.Moreover, peak production PM can be calculated by Eq. (3), and theforecasting production P at each year can be obtained by sub-stituting the above calculated parametersPM , b and tM into Eq. (1).

For regions with several obvious cycles in historical production,the multicycle Hubbert model [26] is built to forecast gas pro-duction. Using the multicycle Hubbert approach, Eq. (1) may begeneralized further as follows:

P ¼XKi ¼ 1

2PMi1þ cosh �bi t�tMið Þ

� �: ð7Þ

URR scenarios used in the Hubbert model.


where K is the number of cycles. Analogously, Eqs. (2) and (3) maybe written for each cycle by substituting the parameters PM , k, Uand b with specific values that correspond to each cycle.

5.2. The rolling GM(1,1) model

Grey system theory was initiated by Deng in 1982 [49,50] andfocuses on model uncertainty and information insufficiency inanalysis and making best use of previous data. Generally, the greymodel (GM) is written as GM (m, n), in which m is the order of thedifferential equations, and n is the number of variables of themodel. Thus, GM(1,1) is short for “grey model first order onevariable,” which is the dominant model [51] of the grey forecastingtheory in grey systems theory.

GM(1,1) has been widely used in forecasting studies because of itsadvantages, low requirement for data items to build forecastingmodels and higher forecasting accuracy as compared with otherforecasting methods [52]. Especially in recent years, grey systemstheory has been successfully applied in agriculture, geography,meteorology, environment, management, economy and many otherfields [53]. As mentioned before, China’s natural gas demand has beentremendously rising, especially in recent years along with someuncertainty. In this sense, the GM(1,1) model can be establishedbecause that the sequence of raw natural gas consumption data areanalogous to an exponential distribution. Some studies have revealedthat though simple, the GM(1,1) model has high simulation accuracywhen it is applied to small sample time series data [52], which makesGM(1,1) suitable and effective for forecasting natural gas consumption.

The original GM(1,1) model is a time series forecasting modelincluding a set of time-varying differential equations adapted forparameter variance. Thus, to improve the forecasting accuracy ofthe original GM(1,1) model, there are many types of optimized GM(1,1) models for parameter optimization that have been developed[54–58]. Only a few data (as few as 4) are needed to build a GMmodel [49], so it is not necessary to employ all the original data inthe GM(1,1) differential equations. In the natural gas consumptionanalysis, we employ the rolling GM(1,1) to construct the fore-casting model, and the construction process is expressed asfollows:

Step 1: The non-negative raw sequence Xð0Þ that denotes thenatural gas production in China is expressed as

X 0ð Þ ¼ x 0ð Þ 1ð Þ; x 0ð Þ 2ð Þ;…; x 0ð Þ nð Þ� �: ð8ÞStep 2: Obtain 1-AGO (one-time accumulating generation opera-tion) sequenceXð1Þ, which is monotonically increasing andexpressed as

X 1ð Þ ¼ x 1ð Þ 1ð Þ; x 1ð Þ 2ð Þ;…; x 1ð Þ nð Þ� �; ð9Þwhere x 1ð Þ kð Þ ¼ Pki ¼ 1 x 0ð Þ ið Þ, k¼ 1;2;…;n.Step 3: The basic form of GM(1,1) is described by the followingequation:

x 0ð Þ kð ÞþaUz 1ð Þ kð Þ ¼ b; k¼ 2;3;…;n: ð10Þwhere a and b are the coefficients, and z 1ð Þ kð Þ ¼ 0:5x 1ð Þ kð Þþ0:5x 1ð Þ k�1ð Þ, k¼ 2;3;…;n.

Step 4: According to the least squares method, we havea

b

� ¼ BTB

��1

BTY ,

where B¼

�z 1ð Þ 2ð Þ 1�z 1ð Þ 3ð Þ 1

⋮ ⋮�z 1ð Þ nð Þ 1

266664

377775, and Y ¼

x 0ð Þ 2ð Þx 0ð Þ 3ð Þ

⋮x 0ð Þ nð Þ

266664

377775.

Step 5: According to Eq. (7), the solution of X 1ð Þ at time k is

x̂ 1ð Þ kð Þ ¼ x 0ð Þ 1ð Þ�ba

� �Ue�a k�1ð Þ þb

a; k¼ 1;2;…;n: ð11Þ

Step 6: To obtain the predicted value X̂0ð Þ, IAGO (inverse accu-

mulated generating operation) is used to establish the followinggrey model:

x̂ 0ð Þ 1ð Þ ¼ x 0ð Þ 1ð Þx̂ 0ð Þ kð Þ ¼ x̂ 1ð Þ kð Þ� x̂ 1ð Þ k�1ð Þ k¼ 2;3;…;n

:

(ð12Þ

That is

x kð Þ ¼ x 0ð Þ 1ð Þ�ba

� �Ue�a k�1ð Þ U 1�ea� �; k¼ 2;3;…;n: ð13Þ

Step 7: Obtain the rolling GM(1,1) model.

Typically, in the original GM(1,1) model, the picked “wholedata” set is used for prediction. However, it is recommended to useonly the latest data to increase forecasting accuracy in futureprediction [59]. The rolling GM(1,1) model is based on the pre-dicted value of the next point. For example, in the first step,using x 0ð Þ 1ð Þ; x 0ð Þ 2ð Þ;…; x 0ð Þ hð Þ� � as the initial sequence to establishmodel, the value of the next point x̂ 0ð Þ hþ1ð Þ is forecasted byapplying the original GM(1,1) model. In the next step, the fore-casting procedure is repeated, but the newly predicted data x̂ð0Þðhþ1Þ is added to the end of sequence, and the first point is alwaysshifted to the second. This means that the oldest data x 0ð Þ 1ð Þ isremoved, and x 0ð Þ 2ð Þ; x 0ð Þ 3ð Þ;…; x 0ð Þ hð Þ; x̂ 0ð Þ hþ1ð Þ

�are used as the

initial sequence in the second step to forecast the next point x̂ 0ð Þ

hþ2ð Þ [59, 60]. That is, the model is reestablished when the newdata becomes available to the forecasting model. For each step, thelatest forecasted data are considered as the forecasting value, andthe last simulated value is added to the end of the sequence, ofwhich the first value then is removed to keep the length of theinput data sets unchanging.

It is common to all data sets that GM(1,1) can be establishedwith a minimal amount of data (as few as four observations), andits computation is simple [57]. As long as the amount of data isgreater than 4, the rolling GM(1,1) forecasting model can forecastthe future gas production. We analyzed the rolling GM(1,1) modelusing different lengths of data sets.

5.3. Why Hubbert and rolling GM(1,1) models

The Hubbert model, as a traditional oil model, has been widelyused to forecast fossil fuel production, including oil, coal andnatural gas, from the day it was first introduced. Hubbert model isa model of growth over time, and it can forecast the production,peak production and peak year in oil and gas fields by simplederivation. Thus the Hubbert model is employed to forecast thenatural gas production for the simplicity and its good accuracy.

The rolling GM(1,1) has been widely used in forecasting studiesbecause of its advantage of low requirement for data in buildingforecasting models and higher forecasting accuracy as comparedwith other forecasting methods. Thus, we take the natural gas


consumption as a time series and apply rolling GM(1,1) to forecastthe future value.

The current widely used artificial intelligence models, such asANNs and RBF models, need a large amount of data to conduct thesimulation experiment, in which case, the artificial intelligencemodels could show good performance [61]. However, the naturalgas production and consumption data are small sampled, which isfar less than that the artificial intelligence model needs. So theartificial intelligence model is not a good choice for our study.Econometric models, such as ARMA and ARIMA models, also needa large amount of data, which is not suitable to our study with asmall sample data [62].

6. Empirical analysis

In the above discussion, natural gas production in China can beforecasted with no difficulty by using the multicycle Hubbertmodel. However, in regard to forecasting the gas consumption, insome ways, it is not that easy. We regard the historical natural gasconsumption as a time-series, and then, the gas consumptionforecasting can be performed using the rolling GM(1,1) model.

In this paper, four URR scenarios are employed for Hubbertmodeling using annual production data provided by NationalBureau of Statistics of China and Beyond Petroleum over the 65-year period from 1949 to 2013. Rolling GM(1,1) forecasting modelscan be established to forecast the future gas consumption withdifferent data set lengths. To select the gas consumption curveswith the best correlation, grey relationship analysis is employed.Then, four consumption curves are obtained in correspondence tothe four respective Hubbert production models. In addition, a gapis observed between the forecasting production and predictedconsumption from year 2011 to 2050, which is analyzed andcompared. Therefore, it can provide basis for decision making.

6.1. Cycle numbers of the Hubbert model

Obviously, the multicycle Hubbert model is more complex thana model based on a single cycle. Thus, the key questions emerge:How much of the URR is of each cycle? How many cycles have tobe considered?

Firstly, in the Hubbert model, the total URR is a very importantparameter which is discussed in Section 2.

Secondly, the number of production cycles should be deter-mined. As discussed in Section 3.1, according to the 65 values ofnatural gas production from the year of 1949 to 2013, there was asmall peak around 1980 for gas production and soon afterwards aslight decline from 1980 to 1982, and then the data started toclimb sharply, especially in recent years. Therefore, the Hubbertmodel can be divided into two cycles. Moreover, the Hubbertmodel is symmetric. Thus, the first Hubbert cycle covers timeperiod from 1975 to 1982 in the model with the peak occurredaround 1980. As we all know that the remaining technicallyrecoverable reserves are changing year by year, so the URR of firstHubbert cycle can be calculated by doubling the cumulative his-torical production instead of adding the cumulative productionand the remaining technically recoverable reserves. Statisticshowed that the cumulative production from 1975 to 1982 wasapproximately 100 bcm. The URR1 of the first Hubbert cycle isfixed at 200 bcm in accordance with the Hubbert theory. There-fore, URR2 of the second Hubbert cycle is determined as the dif-ference between URR and URR1 (URR2¼URR–URR1) (see Fig. 5).

In this study, we consider historical data on China’s natural gasproduction (1949–2013, 65 values) as the sum of two cycles. Then,

Eq. (7) becomes the following:

P ¼ 2PM11þ cosh �b1 t�tM1ð Þ

� �þ 2PM21þ cosh �b2 t�tM2ð Þ

� �: ð14Þ

6.2. Model evaluation criteria

To estimate the forecasting model in different scenarios and fordifferent data sets, three forecasting error measures are employedfor model evaluation and model comparison: the mean absolutepercentage error (MAPE), the root mean square error (RMSE) andthe normalized root mean square error (NRMSE). The MAPE andthe RMSE are used for evaluation the rolling GM(1,1) model underdifferent consumption data sets, and the NRMSE is for comparisonof the multicycle Hubbert models in natural gas production fore-casting under different scenarios. Moreover, the correlation coef-ficient (R) is adopted for evaluating the correlation of the rawhistorical production data and the corresponding predicted data ofthe multicycle Hubbert model. These analysis criteria can beobtained from the calculations listed below.

Mean Absolute Percentage Error (MAPE): an average of theabsolute percentage errors between the actual historical con-sumption data and the forecasting data, which is given by thefollowing:

MAPE¼ 1n

Xnk ¼ 1

x 0ð Þ kð Þ� x̂ 0ð Þ kð Þx 0ð Þ kð Þ

��: ð15Þ

where x 0ð Þ kð Þ indicates the actual gas consumption data at time k,and x̂ 0ð Þ kð Þ is the forecasting data at time k, and n is the number offorecasting values.

Root Mean Square Error (RMSE):

RMSE¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n

Xnk ¼ 1

x 0ð Þ kð Þ� x̂ 0ð Þ kð Þ

�2vuut : ð16Þ

The RMSE is used for measure of the rolling GM(1,1) modelunder different consumption data sets.

Normalized Root Mean Square Error (NRMSE):

NRMSE¼ RMSEQmax

: ð17Þ

In which Qmax is the peak production of the multicycle model.It can be observed that, the lower values for MAPE, RMS andNRMSE are, the better results obtained.

Correlation Coefficient (R):

R¼P

x 0ð Þ kð ÞU x̂ 0ð Þ kð Þ

�

�1nP

x 0ð Þ kð ÞU P x̂ 0ð Þ kð ÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPx 0ð Þ kð Þ� �2�1n P x 0ð Þ kð Þ� �2h iU P x̂ 0ð Þ kð Þ �2�1n P x̂ 0ð Þ kð Þ �2

� s :

ð18ÞThe maximum of R is 1. The higher value of R is, the better

result obtained.Posterior-variance-test: it is a commonly used testing method

based on the probability and statistics between the actual valueand the predicted value obtained from the rolling GM(1,1). Cri-terions of posterior-variance-test include the indicators, posteriorvariance ratio C and small error probability P.

The posterior variance ratio is defined as follows:

C ¼ SeSx

¼ffiffiffiffiffiffiffiSe

2

Sx2

s¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n

Pnk ¼ 1

ε kð Þ�εð Þ2

1n

Pnk ¼ 1

x 0ð Þ kð Þ�x� �2

vuuuuuut ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPnk ¼ 1

ε kð Þ�εð Þ2

Pnk ¼ 1

x 0ð Þ kð Þ�x� �2

vuuuuuut : ð19Þ

where Se2 ¼ 1=nU Pnk ¼ 1 ε kð Þ�εð Þ2, Sx2 ¼ 1=nU Pnk ¼ 1 x 0ð Þ kð Þ�x� �2,


and ε kð Þ represents the residual between predicted value x̂ 0ð Þ kð Þand the actual value x 0ð Þ kð Þ at time k, ε kð Þ ¼ x 0ð Þ kð Þ� x̂ 0ð Þ kð Þ; k¼ 1;2;…;n. Then ε¼ 1=nU Pnk ¼ 1 ε kð Þ and x¼ 1=nU Pnk ¼ 1 x 0ð Þ kð Þ.

The small error probability P is expressed as P ¼P ε kð Þ�ε

�� o0:6745Sx� �. The range of indicator values C and P issuch that: C40 and 0rPr1. To better evaluate the performance ofthe rolling GM(1,1) model, P and C are calculated respectively [63].

7. Forecasting natural gas production and consumptionin China

The future gas production is acquired by the multicycle Hub-bert model. Accordingly, the future gas consumption trend isobtained with the rolling GM(1,1) model. Section 7.1 addresses thenatural gas production, whereas Section 7.2 discusses natural gasconsumption.

7.1. Future gas production

Fig. 6 shows detailed forecasting processes and the results ofChina’s natural gas production based on scenario 1. The results of

Fig. 6. Natural gas production forecasting results by two-cycle Hubbert model forURR2¼4430 bcm.

the multicycle Hubbert model in different scenarios are clearlydisplayed in Fig. 7.

It can be observed that there are two cycles in Fig. 6 for all thecases examined.

The Hubbert model has two cycles, which can be described asthe first Hubbert1 and the second Hubbert2. And the final Hubbertmodel can be expressed as Hubbert¼Hubbert1þHubbert2. Thetime range of the first cycle lasts only approximately 40 years, so itis a small cycle. The first small peak appears at the year of 1979with URR1 fixed at 200 bcm, which begins in 1960s and completesat the end of 2000s. Therefore, peak production and peak year ofgas production coincide almost precisely with those in the secondHubbert cycle (cycle 2 on Fig. 6). The data used to establish theHubbert2 model covers the time period from 1990 to 2013, whilethe gas production values in Hubbert2 from 1980 to 1990 are notzeros, so we can say that the second Hubbert cycle beginsapproximately in 1980s and fades out in near 2050 or after andthat the peak appears in approximately in the 2020s.

Fig. 6 refers to the most pessimistic scenario (URR¼4630 bcm)among those considered, in which the peak gas production is173.55 bcm and will occur in 2019. The results of the other sce-narios have similar trends and are shown in Fig. 7(c). When theURR is 6000 bcm, the peak gas production is approximately

scenario 1 when total URR¼4630 bcm. For cycle 1, URR1¼200 bcm; cycle 2,

Fig. 7. Peak forecasting results of China’s natural gas production: (a) Fitting results for different scenarios. (b) Natural gas production forecasting results by two-cycleHubbert model for four scenarios. (c) Forecast of China’s natural gas peak in different scenarios.


223.34 bcm and will occur five years later (2024) compared withthe pessimistic scenario. As seen from the results in Fig. 7, the gasproduction is forecasted to reach the peak in 2025 with a max-imum production of 257.59 bcm when the URR is 6940 bcm. Theresults of the most optimistic scenario (URR¼ 10,190 bcm) arepresented that indicate that gas production will peak in 2028 at376.24 bcm.

Fig. 7 shows the peak forecasting results of China’s natural gasproduction. The first part in Fig. 7 demonstrates all the values ofthe fitting parameters calculated by the Hubbert model in fourdifferent scenarios. It can be observed that the whole peak valueand peak time are in line with the values from the second cycle,which is in agreement with previous analysis. In addition, thevalue of parameter b2 drops from 0.157 to 0.151 when the URRincreases from 4630 bcm to 10,190 bcm, moreover, the peak timewill come later with a larger URR. Accordingly, the peak produc-tion will be larger. Then we can conclude that if more remainingtechnically recoverable reserves are discovered, the URR will belarger, and the peak tome of the natural gas will be later.

As to the forecasting errors of the four models, MAPE increasesfrom 0.52% to 0.572%, which indicates that the forecasting valuesand the observed production value are almost the same. The RMSEand the NRMSE are increasing when the URR increases. Never-theless, the correlation coefficient R declines from 0.994 to 0.989,the minimum of which is even very high. Thus, it can be concludedthat all the four forecasting models based on different scenariosperform very good with only a slight difference between them.

According to the forecasting production curves of natural gas(Fig. 7), which are well shaped by Hubbert peak theory, no matterthe scenario, the multicycle models provide similar results. Thepeak time and peak production under different scenarios areshown in Fig. 7(a), which implies that China’s gas production willreach a peak between 2019 and 2028, and peak production willrange from 173.55 bcm to 376.24 bcm. Furthermore, it can beobserved that the different postulation of URR will result in dif-ferent production curves. The second and the third part in Fig. 7display a comparison of the results obtained by the Hubbert model

with two cycles. It can be observed how the peak value, peak timeand the shape of the production curve change as the value of URRchanges between 4630 bcm and 10,190 bcm.

7.2. Future gas consumption

Figs. 2 shows that the natural gas consumption has an overallrising tendency superficially, but a close review clearly reveals thatthe consumption of natural gas remained almost stagnant during1950s to 1970s and even decreased from 1979 to 1984. The GM(1,1) model tends to give a large amount of errors for such fluc-tuating consumption data as the grey model supports only anexponential type of growth [64]. In addition, because of thecharacteristics of grey model, only with a small amount ofincomplete information, the grey model can make a prediction forthe long-term development of things. Once a small amount of datais obtained, usually more than four data sets, the grey model canmake a forecast. In this study, historical data (1949–2013, 65 data)of natural gas consumption were gathered. It is usually unwise toadopt all data to establish the rolling GM(1,1) model. Therefore,based on the above two reasons, the grey model can be establishedas long as the number of data sets is larger than four. Accordingly,it is feasible that we just select the recent natural gas consumptiondata with exponential growth trend to establish a rolling GMmodel and change the length and scope of the data to create dif-ferent models. The practical application of the rolling GM(1,1)model is achievable because it has an inherent capacity to capturesuch changes and can prove exceedingly promising for gas con-sumption forecasting. In regard to consumption forecasting, aslong as the amount of consumption data is greater than 4, therolling GM(1,1) model can make a prediction. In the rolling GM(1,1) forecasting, with the different lengths of data sets, the per-formance of the forecasting models, forecasting trends and results(Fig. 8) are different.

According to the definition of URR, it is a time-dependentparameter that is always determined by experts in the Hubbertmodels. Accordingly, given different URRs, we obtain different

Fig. 8. Final forecasting results of China’s natural gas consumption under different data sets: (a) Consumption forecasting results of rolling GM(1,1) model under differentdata sets and gas peak forecasting results in different scenarios. (b) Accuracy evaluation criteria of the rolling grey model. (c) Model evaluation criteria under different lengthof data sets.


production trends, namely different Hubbert curves. When theURR is fixed at 10,190 bcm, the production curve is the red one,when 4630 bcm, the green one (Fig. 8).

Fig. 8 shows detailed forecasting processes, and the results ofChina’s natural gas consumption. The final results display differentdata sets and the corresponding results of the rolling GM(1,1)model, in which the line color, the line type, MAPE, RMSE, NRMSE,R and other accuracy evaluation criteria of the RGM can be found.

The forecasting results of China’s natural gas consumptionbased on RGM under different data sets can be seen in the firstpart of Fig. 8, which demonstrates in the coordinate scale range of900 bcm and 450 bcm, respectively. The left includes the first fourdata sets, and the right contains the last four data sets. It can beobserved that when the amount of the data sets is reduced, theforecasting consumption curve moves from the green one to theorange one. And meanwhile, the MAPE decreases from 0.049% to0.032%, and the same trending to R which decreases from 0.9978to 0.9964. However, there is an opposite trend to RMSE andNRMSE which increase quickly from 3.139 bcm to 3.456 bcm, form0.0181% to 0.206. From the evaluation criteria of RGM, all thesemodels are performing excellent (Fig. 8(b)).

7.3. The gap trend between the gas production and consumption

According to the above analysis, the information shows thatthere are four natural gas production forecasting curves and eightconsumption forecasting curves, respectively. In regard to choos-ing the concordant gas consumption curve under different sce-narios, to a certain extent, it is not particularly easy. Thus, weemployed grey relational analysis to solve this difficult problem inthis study. Grey relational analysis refers to quantitative

description and comparative method of an evolving system; thebasic idea is that by comparing the degree of geometric similaritybetween the reference and comparative sequence to determinewhether their relation is close, which reflects the degree of asso-ciation between curves [65]. And in our study, the larger of thegrey relational coefficient, the better of the forecasting model.After that, the gas consumption forecasting curves can be selectedby applying the grey relational analysis. To facilitate the followingstudy, we select the first four curves with best correlation tocontinue our research, which affects the accuracy but theoreticallydoes not affect the trend of the results. The results of grey rela-tional analysis are shown in Table 3. The gap trends between thegas production and consumption from year 2011 to 2050 areshown on Table 4.

The results shown in Table 3 refer to the grey relational coef-ficient obtained by the grey relational analysis. As seen in Table 3and Fig. 8, it is evident that when less data is used in modeling, thegrey relational coefficient is smaller. From scenario 1 to scenario 2,the four curves from Xf4 to Xf7 have the best correlation. In scenario3, Xf1, Xf2, Xf3 and Xf8 have larger grey relational coefficient thanthe other four curves, and in scenario 4, curves from Xf3 to Xf6 havethe best correlation.

The results of the difference from year 2011 to 2050 are pre-sented in Table 4, which is obtained through the gas productionminus the consumption. As can be observed, the differencebetween production and consumption is continuously increasinglyyearly from 2011 to 2025, and the following data are demonstratedevery 5 years until 2050. Part of the data reveals the gap increasingfrom 2011 to 2015, whereas in some cases, the gap decreasesslightly to from 2016 to 2025, but then the gap expands drama-tically. From this chart, except in scenario 1, the gap displays the

Table 3The grey relational coefficients of the forecasting natural gas production and forecasting consumption.

Grey relationalcoefficient

Xf1 Xf2 Xf3 Xf4 Xf5 Xf6 Xf7 Xf8

Scenario 1 0.600 0.604 0.609 0.619 0.634 0.652 0.676 0.600Scenario 2 0.629 0.638 0.650 0.668 0.694 0.715 0.699 0.629Scenario 3 0.702 0.723 0.705 0.680 0.686 0.669 0.645 0.702Scenario 4 0.641 0.652 0.668 0.691 0.712 0.692 0.664 0.641

Table 4The difference between the forecasting gas production and consumption from year 2011 to 2050 (unit: bcm).

Scenarios Year

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 2035 2040 2045 2050

Scenario 1 Xf4 �26 �36 �50 �67 �76 �84 �92 �101 �111 �121 �134 �148 �163 �179 �198 �297 �384 �446 �486 �512Xf5 �26 �36 �50 �68 �74 �80 �86 �92 �99 �108 �119 �129 �141 �156 �172 �258 �332 �383 �414 �432Xf6 �25 �36 �49 �67 �71 �75 �79 �83 �88 �95 �101 �109 �120 �132 �146 �220 �285 �328 �352 �365Xf7 �25 �35 �48 �65 �68 �69 �71 �74 �77 �80 �84 �91 �99 �110 �121 �186 �244 �281 �301 �312


Scenario 3 Xf1 �22 �29 �37 �48 �54 �58 �60 �62 �63 �63 �63 �64 �66 �71 �79 �173 �346 �537 �694 �807Xf2 �24 �30 �39 �50 �54 �57 �58 �57 �55 �52 �49 �47 �47 �48 �52 �126 �279 �448 �583 �676Xf3 �24 �31 �40 �52 �54 �55 �53 �50 �45 �40 �34 �30 �26 �24 �24 �81 �216 �367 �485 �563Xf8 �24 �30 �38 �49 �41 �31 �20 �6 12 30 47 63 77 91 102 102 17 �92 �175 �225


n The negative sign indicates the gas production is lower than the gas consumption.


above up-down-up trend, and the rest of the difference increasesyearly.

8. Methods to narrow the gap in China

In China, the major source of energy still depends heavily oncoal and oil at present, and as the world’s biggest developingcountry and largest energy consumer, the demand for energy isconstantly increasing. China became a net oil importer in 1993, anet importer of crude oil in 1996, and the foreign dependency roserapidly. To ease the imbalance between supply and demand of oil,we should increase the consumption of natural gas as a greenalternative energy source, which is an effective effort to promoteenergy conservation and reduction of pollutant emissions. Datafrom National Bureau of Statistics of China indicate that China’snatural gas consumption was 24.5 bcm in 2000 and grew over six-fold reaching 161.6 bcm, accounting for 4.8% of the world’s total in2013, which suggests that consumption grew with strongmomentum over the past decade. The production of natural gaswas 117.1 bcm, whereas the consumption was 161.6 bcm in 2013,which far exceeded the production [66]. China will make efforts toincrease natural gas exploration and development and promotethe rapid growing of natural gas production as well as the devel-opment of clean energy.

In context of these reasons mentioned above, there are fouressentially different methods to tackle the contradiction betweenthe growing natural gas demand and supply. In Section 8.1, weconsider expanding natural gas imports. In Section 8.2, we think

about developing and utilizing unconventional gas as well asconventional gas from the viewpoint of increasing production. InSection 8.3, we combine our study with the present nationalpolicies together such as energy saving and emission reduction. InSection 8.4, complementing the gap with new energy sources istaken into consideration.

8.1. Expanding natural gas imports

To reduce this growing gap, China has imported liquefied nat-ural gas and pipeline gas from Turkmenistan, Australia, Qatar,Indonesia, Malaysia and Russia [1]. According to predictions, by2020, China will import natural gas approximately 60 bcm fromTurkmenistan, Russia, and other countries by pipeline and willimport liquefied natural gas approximately 40 million tons(equivalent to 54 bcm) from the Middle East, Australia, Indonesiaand other regions [16]. The total gas imports will reach 114 bcm by2020, and based on our forecasting, the maximum gap will be144 bcm, which can be complement by imports, implying aremaining 30 bcm gap. However, these existing import plans stillcannot close the predicted gap. China’s natural gas imports fromdifferent countries via LNG and pipeline transfer from 2009 to2012 are shown in Fig. 9.

The National Development and Reform Commission stated thatto alleviate the “gas shortage” and to solve the problem of insuf-ficient domestic supply of natural gas, China will seek to importmore liquefied natural gas (LNG) [67]. The current sources ofimported liquefied natural gas display diversification. China stillneeds to sign more long-term liquefied natural gas sales and

Fig. 9. China’s natural gas imports from different countries via LNG and pipeline transfer from 2009 to 2012.


purchase agreements and pipeline gas import contracts with othercountries, including Australia, Indonesia, and Malaysia, Qatar andPapua New Guinea.

Moreover, for a long time, there has been a serious inversionphenomenon among the domestic gas price and internationalmarket price, leading domestic enterprises importing natural gasnot enthusiastic. At the same time, the Domestic and InternationalOil and Gas Industry Development Report in 2012 issued by CNPCEconomics and Technology Research Institute indicates that Chi-na’s foreign dependence on gas continue to increase in 2012, andthe future challenges to the gas supply remain grim [68]. Thereport estimated that China imported 42.8 bcm of natural gas in2012. Since 2006, when natural gas was first imported, China’snatural gas import dependency has risen to 29% with just sevenyears [68]. The report predicts that in 2013, China’s gas demandwill continue to rise. Natural gas consumption will continue tomaintain double-digit growth, and external dependency will reach32% [69].

8.2. Increasing unconventional natural gas production

So-called unconventional natural gas refers to gas that is dif-ficult to extract by conventional techniques because of its complexreservoir conditions. Coal bed methane (CBM), deep basin gas,tight gas, shale gas and gas hydrates (combustible ice) areunconventional gases. China is rich in unconventional gasresources. These known unconventional gas resources currentlytotal approximately 79.8 tcm, which is 3.7 times the total amountof conventional natural gas resources. According to a new nationalCBM resource evaluation [70], only the CBM reserves amount to36.81 tcm, and Chinese onshore conventional natural gas resour-ces (38 tcm) are approximately equal, thus accounting for 13% ofthe total global coal bed methane resources, which is globallyranked third. After years of effort, China has progressed rapidly inunconventional gas development and formed theoretical knowl-edge and a series of key technologies. Unconventional gas

production was approximately 30 bcm (CBM 9 bcm, tight gas20 bcm) in 2010, which accounted for 25% of the national totalnatural gas production [71]. However, in 2012, China’s tight gasproduction exceeded 30 bcm, which accounted for approximatelyone-third of the country’s total gas output and is expected toincrease to 100 billion cubic meters by 2030. Regarding China’sshale gas resources, the reserves are approximately 26 (�31)trillion cubic meters. However, the exploitation of shale gas ismore difficult because it is much deeper than reserves of theconventional natural gas [72]. Because of a late start regardingexploration and development, China has not yet achieved indus-trialization. China’s shale gas and coal bed methane reserves areabundant and widely distributed, and commercial exploitation hasbeen performed in some areas, but slow advancements in miningtechnology have impeded progress. With reasonable guidelines,shale gas and coal bed methane can succeed current conventionaloil and gas and fundamentally ease the supply shortage of China’sconventional natural gas [73].

According to the current economic conditions, the key techni-ques, development trends, prediction theory and foreign experi-ence in the exploration of the unconventional gas, experts predictthat the exploitation of such unconventional gas as coal bedmethane and shale gas will increase considerably and play a majorrole in national hydrocarbon resources until the next ten or twentyyears in China [73].

If various types of natural gas resources can be developed andused, they will positively participate in securing a natural gassupply. Driven by market demand and technological progress,developing an unconventional gas program on a large scale can beachieved. China is now increasing its own natural gas develop-ment to increase domestic natural gas production. Unconventionalgas prospects are promising, but exploration and exploitation aredifficult. The development of unconventional gas may be con-strained by an absence of geological theory research, explorationand development technology and supporting policies. AlthoughChina’s natural gas industry has developed rapidly in recent years,


which shows optimistic expectations regarding the future, there isa large gap compared with the large, growing demand.

8.3. Energy-saving, emission-reduction and other policies

Less CO2 is released when natural gas (2.35 t CO2 emissions pertonne of oil equivalent) is burned than coal (3.96 t CO2) or oil (3.07t CO2) when burning one tonne of oil equivalent. With high-speedeconomic development, China’s CO2 emissions are quickly grow-ing. China emitted approximately 9.2 billion tonnes carbon dioxidein 2012, which accounted for 26.7% of the global total, and becamethe biggest global CO2 emitter [1]. Currently, climate change is auniversal problem, and China is facing increasing pressure toreduce carbon emissions. For this large, responsible country,energy-saving and emission-reducing actions have made a sub-stantial contribution to mitigating climate change in recent years.In particular, China is facing a more urg

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