Energy 144 (2018) 232e242

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Energy

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Modeling the point of use EROI and its implications for economic growth in China

* Jingxuan Feng a, Lianyong Feng a, , Jianliang Wang a, Carey W. King b a School of Business Administration, China University of Petroleum (Beijing), Beijing 102249, China b Energy Institute, The University of Texas at Austin, 2304 Whitis Ave, C2400, Austin, TX 78712, USA article info abstract

Article history: Energy return on energy invested (EROI) might be considered a measure of net energy, and most current Received 16 June 2017 studies focus on standard EROI (EROIST). If the energy inputs required to obtain a fuel was extended from Received in revised form a wellhead to the point of use, the energy delivered decreases, and the energy input of delivering the fuel 3 November 2017 increases. These factors combine to reduce the EROI to what is referred to as the point of use EROI Accepted 9 November 2017 ST (EROI ). This study calculates the direct and indirect energy (embodied energy) inputs for energy Available online 11 November 2017 POU production sectors (including extraction, processing and delivery) by means of an Input-Output table to calculate China's EROI and the net energies from 1987 to 2012. Based on calculations in this study, the Keywords: POU Embodied energy EROIPOU of China's energy production sector declined from 11.01:1 to 5.26:1 between 1987 and 2012. In Point of use EROI 1987, the energy production sectors consumed 1 ton standard coal equivalent (TCE) energy inputs for Net energy every 10.01 TCE of produce net energy. However, in 2012, this number declined to 4.25. Additionally, this China'S economic growth study simulates and forecast economic Gross Domestic Product (GDP) trends in China using net energy production function. The results reveal how declining EROIPOU for Chinese fossil fuels influence China's GDP growth. © 2017 Published by Elsevier Ltd.

1. Introduction energy [4]. Economic development is actually supported by net energy, as opposed to gross energy, because energy is used in the Energy production sectors (including energy extraction, production of energy itself. The EROI is significant in both theory processing and delivery) require energy inputs (also known as and reality because net energy supports economic development. energy costs) from themselves for energy outputs because a However, net energy is associated not only with extraction but also significant amount of energy is needed for processing. An approach with the entire energy industry (including the energy extraction, that does not consider the costs of energy (including energy inputs) processing and delivery sectors) that delivers useful energy to a cannot provide a proper assessment of energy consumed at the location at which it is used to generate economic goods and final end (i.e., the point of use). A comprehensive evaluation should services. focus not only on energy resources but also on both the energy In recent years, significant research has been performed on input that is part of the energy production process and the net EROI, but these studies have tended to not be comparable with energy contribution to this process, particularly the impact of its one another because the difference between the boundaries is the size on the economic system [1]. most important and most easily overlooked variable. Most Energy return on investment (EROI) is a concept that originated research on EROI involves calculations based on the extraction of in ecology [2] and represents the ratio of the energy output to the fossil energy; however, net energy is influenced not only by energy input during the process of energy production [3]. This extraction but also by the entire energy industry chain. For metric, which evaluates energy and its related problems from the example, when the EROI of extraction decreases, the EROI of the perspective of the “ratio of net energy”, transforms the gross energy entire energy industry chain may nonetheless be decreasing, sta- by positing that what is important to society is the available net ble or increasing based on the technological development status (the determinants of EROI include a technological component and a physical depletion component [5]). Hall further clarified the * Corresponding author. boundaries used in EROI calculations into the following categories E-mail address: [email protected] (L. Feng). https://doi.org/10.1016/j.energy.2017.11.061 0360-5442/© 2017 Published by Elsevier Ltd. J. Feng et al. / Energy 144 (2018) 232e242 233

Energy Output Total Supply EROI ¼ ¼ Energy Input Energy Required to Get That Energy (1) The EROI is defined as the total energy output divided by the sum of all direct and indirect energy inputs, the energy output is the energy returned to society and the economy, and the energy input is the energy required to acquire that energy [6]. The energy input includes direct and indirect energy, i.e., the energy “embodied” in the energy production process or “embodied energy”. “Embodied” indicates the sum of all goods and services (direct and indirect) that are required to produce any goods or services considered as if the goods and services are incorporated or “embodied” in the product itself. Fig. 1. Different boundaries used to calculate the different EROIs. Consider the case of a single fuel in which this fuel is used by the entire economic system. The baseline variables are F, G and C. Variable F is the total primary energy supply by the entire economy. [6,7]: standard EROI (EROI ), point of use EROI (EROI ), ST POU G represents the final energy consumption at the point of use that extended EROI (EROI ), and societal EROI (EROI ). The calcu- EXT SOC also could be defined as the net energy that is used at the final end. lation of the EROI divides the energy output for a project, region ST C is the sum of all energy inputs in each energy production sector, or country by the amount of energy used to generate that output and this energy cannot be used by the non-energy portion of the per year. The EROI is more comprehensive than the EROI POU economic system (see Equation (2)). because the EROIPOU includes the energy consumed in extracting, processing and delivering the energy. The EROIEXT is an expanded analysis that considers the energy required not only to extract but G ¼ F C (2) also to consume the energy at the final end use of a unit of energy.(see Fig. 1). The definition of the EROI includes the energy inputs necessary Hall [8] gives the following description: to invest in producing the net energy. Thus, the EROI can also broadly be described as the ratio of the energy made available to “ A reasonable question is what would be the EROI of a fuel at the society through a certain process to the energy inputs necessary to point where it is used, since there may be very different implement this process [14,15]. fi ef ciencies for different fuels between the source and the point Notably, much research on EROI involves calculating the oil and at which it is used. Unfortunately, such studies are rare, and we gas field extraction processes and does not include final use at the must remember to always start with a source of energy from end, which can lead to a problem, i.e., the EROI and net energy will ” nature. be overestimated. The EROI has primarily been used to estimate changes in the net energy productivities of oil and gas fields A lower EROI value indicates that a smaller ratio of net energy is [16e18]. However, the energy inputs not only include oil and gas provided for the socio-economic system that is defined outside of extraction but also include separation, washing, coking, and the boundary of the EROI analysis. Using a gross output of 100 units . Many studies on EROI find it difficult to calculate the net of energy, if, for example, the EROI is 1.2:1, only 16.7% of the gross energy at the point of use because the input data for fossil fuels are output can be consumed [9]. Lower net energy describes numeri- difficult to collect. cally greater input and thus less net energy remaining to power A metric can be defined in this study that is calculated at society after fossil energy extraction, processing and delivery. different boundaries if “delivered to society” as the total energy Unsurprisingly, lower net energy negatively affects whether the consumption (i.e., the net energy plus what is used in the energy economy continues to grow. Researchers generally study the production sectors) and assume that the energy “used in the connection between economic growth and the gross energy supply energy production sectors” is the sum of the energy inputs of the as opposed to the net energy [10e13]. However, considering the energy extraction, processing and delivery sectors. Based on these long-term structural scarcity in net energy supplies, the energy cost definitions, the ratio of the energy production sectors EROI (point of of obtaining the energy is necessary to calculate the energy con- use EROI) is straints on economic growth. Thus, net energy plays an important role in spurring economic growth. Pn Pn This study aims to build a model to calculate the EROIPOU from Fi Fi embodied energy perspectives in China. Next, using a simulated i¼1 i¼1 EROIPOU ¼ ¼ (3) production function, predicts the effects of EROI changes on Pn Pn Pn POU C F G fl i i i China's economic growth from 2016 to 2030 ( owchart see i¼1 i¼1 i¼1 Fig. 2). where EROIPOU is the point of use EROI ofP all energyP sectors, and the n n net energy from all primary energy is i¼1Gi. i¼1Fi is the total 2. Methods and data energy used by the entire economy (including the energy used in extraction, processing andP delivery). The variable i represents the n 2.1. Modeling point of use EROI with embodied energy analysis different types of energy. i¼1Ci represents all the types of energy inputs in the different processes throughout the entire energy 2.1.1. The relationship between net energy and EROI for fossil fuels industry chain. Therefore, the equation of net energy can be derived The basic equation for EROI is as follows: as follows: 234 J. Feng et al. / Energy 144 (2018) 232e242

Fig. 2. Flowchart for the modeling of the EROIPOU and the evaluation of its impact on economic growth.

an accounting method that allows one to estimate the sum total of Xn Xn 1 the energy necessary for an entire product lifecycle. G ¼ F 1 (4) i i EROI Input-Output tables can be used when calculating the embodied i¼1 i¼1 POU energy used by sectors. An Input-Output table is a matrix that dis- Equations (3) and (4) link the net energy with the EROI. Mearns plays the manner in which industries or sectors interact. The present [19] has discussed this relation and noted there is a negative study also proposes a calculation method to obtain consumption- relationship between gross and net energy. The relation between based intermediate input data based on an Input-Output table. The net and gross energy is called “The Net Energy Cliff”. Input-Output table includes the energy mining or extraction sectors Measuring the net energy is the real application of the EROI. The and the delivery, separating, coking, processing and other energy EROI calculation extends from the energy exploration stage to the production sectors. Therefore, the Input-Output table is used to energy point of use and thus provides a complete estimate of net calculate the energy costs embodied in the energy sectors. energy, which is significant because when the EROI of an energy- The Input-Output (IO) model is useful for analyzing the producing process falls below approximately 10:1, the net energy economic relationships of linkages among the sectors of an delivered to society from that process begins to decline sharply economy and was developed by Leontief [22] in the 1930s. In the [6,19]. basic IO model, X represents the total output of an economy and can be expressed as the sum of intermediate consumption (AX) and final consumption (Y) as follows: 2.1.2. Modeling the point of use EROI for fossil fuels fi The EROIPOU is de ned as a metric of net energy from the AX þ Y ¼ X (5) wellhead to the point of use. The EROIPOU can be difficult to calculate because the complete and accurate energy costs or input where A is the technical coefficient matrix that can be expressed as data are often hard to obtain because the boundary of the EROIPOU in Equation (6). is bigger than that of the EROIST. Thus, a method or perspective to 2 3 obtain these data was needed. a a … a 6 11 12 1n 7 Based on the basic Equation (1), the “energy required to get that 6 a a … a 7 A ¼ 4 21 22 2n 5 (6) energy” includes direct and indirect energy inputs, i.e., the energy ………… “embodied” in the energy production process. The “total primary an1 an2 … ann energy supply” is the final energy consumption. The question is In Equation (6), a is the technical coefficient that represents how the energy “embodied” in the energy production sectors can ij how much sector j must directly consume of sector i's product to be calculated. produce a unit of the product of its sector, which can be calculated Embodied energy analysis is the process of determining the as in Equation (7): energy required directly and indirectly to produce a specific product or service [20] after considering that the energy was x ¼ ij incorporated or “embodied” in the product itself. Embodied energy aij (7) xj primarily refers to the indirect effects described by Bullard and Herendeen [21]. Moreover, calculating embodied energy requires The solution to Equation (5) can be expressed as follows: J. Feng et al. / Energy 144 (2018) 232e242 235

2.2. The impact of EROIPOU on economic growth 2 3 B B … B The establishment of the causal links between the EROIPOU and 6 11 12 1n 7 B B … B economic growth is important in this study for interpreting the X ¼ðI AÞ 1Y ¼ BY ¼ 6 21 22 2n 7Y (8) 4 ……1 … 5 impact of net energy on economic growth. The energy-economy Bn1 Bn2 … Bnn link is often omitted from economic growth formulations for which the production function includes multi-factor productivity. The matrix B or ðI AÞ 1 is called the Leontief inverse matrix, Energy is one of important inputs for the development of and it indicates the direct and indirect costs of sector i that are industries and economic growth. The importance of energy as an necessary to produce a unit product of sector j. essential ingredient to life on earth is well established in the Assume that y is the final demand vector of the energy sector. j physical sciences both in terms of biological energy and as a Therefore, the embodied energy of the energy sector can be primary enabler of industrialization [29]. If energy as an input described as follows: factor that includes capital and labor, the multi-factor production function can be expressed as follows:

Xn ¼ Y ¼ f（K; L; E） ¼ AðtÞKkLlEg (11) Embodied Energy of Energy Sectors Ej j¼1 ! Xn Xn where Y is the output in GDP; A(t) is the multi-factor productivity; ¼ $ $ K is the capital; L is the labor; E is the energy; and g, k and l are ei Bij yj (9) j¼1 i¼1 variables with value and the output elasticities of each factor input. apply well to periods with large and fi where ei is the direct energy consumption coef cient that repre- growing energy supplies and relatively low energy extraction costs. sents the total energy consumption of sector i divided by total However, economic theories can no longer regard resources as “free ” e output of sector i. The unit of ei is tons of standard coal equivalent gifts ; indeed, resources are clearly limited [30 32]. Assume gross (TCE) per yuan, and the data are calculated at the producers' prices. fossil fuel extraction does not sufficiently increase due to The Input-Output tables for China that are available for the ten environment constraints or fossil fuel production peaks, the years of 1987, 1990, 1992, 1995, 1997, 2002, 2005, 2007, 2010 and increases in direct and indirect energy inputs in energy production 2012 [23] and that cover a total of 42 sectors. All Input-Output will eventually lead to a reduction in net energy. tables used in this study were obtained from official national The impact of energy consumption on economic growth is institutes, and newer tables are not yet available. Therefore, the significant. Energy has become an essential part of production ac- study period is from 1987 to 2012. tivities. To highlight net energy's restrictions on the growth of the The data associated with domestic energy consumption by economy, Nel and Cooper [29] built the following energy-type sector in China were obtained from the Chinese Energy Statistical production: Yearbook [24]. The total energy production and consumption data X 1 ¼ at ð Þ ð ; Þ were obtained from the IEA data online. The energy data are given Y A0e mi t Eth;i di t Ei (12) as the TCEs. The energy consumption data by sector were extracted i from the Chinese Statistics Year Book [25]. The total energy consumption by each sector can be converted into tons standard where Y represents the economic output as reflected by the GDP, at fi coal equivalent based on the calorific values of the various energy and A0e represents the exponential growth factor. To ful ll the types. need for economic growth, assume that capital and production The Input-Output tables were available for 33 industrial sectors capacity are sufficient; thus, these factors will be resolved as during the following years: 1987, 1990, 1992 and 1995. The tables endogenous variables. a represents the growth index, t represents were available for 40 industrial sectors in 1997 and 42 industrial the time in years, Eth;irepresents the total energy content of the fuel “ ” fi sectors for the years 2002, 2005, 2007, 2010 and 2012. The energy type i , and mi represents the effective factors or fuel ef ciency of “ ” ð ; Þ consumption data were available for 29 sectors. The economic the fuel type i . di Ei t represents the energy inputs of the energy sectors were aggregated into 10 sectors to maintain dataset con- production sectors in t years, which denotes the energy inputs sistency [26]. The similar sectors were merged in this study due to required to obtain theP direct and indirect energy commodities. ð ; Þ n the mismatch between the sectoral classifications in China's IO di Ei t corresponds to i¼1ci in Equation (3). ð Þ tables and the sectoral classifications in energy consumption. In Equation (12), mi t can be represented by a logistic curve or s- Similar classifications of energy consumption sectors have previ- curve to account for long-term (outside the time scales of this ously been for IO-related research in China [27,28]. Table A.1 in the assessment) and past incremental efficiency improvements with appendix presents the different sector classifications and the 10 m0, m1, c and t0 as constants (see Equation (13)). This was necessary aggregated sectors. because efficiency improvement is not only derived from technol- For analysis, sectors B, C and D are the energy production sectors ogy improvement, but also by changing the modes in which fuels and include mining, separating and washing of coal, extraction of are used. fi ð Þ petroleum and natural gas, coking coal and petroleum refining. The On the one hand, ef ciency improvement (mi t ) represents sectors of the economic system are not isolated; for example, the some technologies or programs used on the demand side, e.g., extraction of oil and gas not only requires the consumption of oil demand side management (DSM). DSM programs are designed to and gas but also includes steel consumption from another sector. influence the utility customers' energy utilization for load leveling The embodied energies used by sectors B, C, and D are the direct and optimization of the energy system [33]. DSM does not create and indirect energy inputs of the entire energy industry chain. net energy but rather saves net energy on the demand side. DSM Moreover, the extent to which DSM reduces the intensity of energy consumption is limited because, sometimes, additional energy is 1 http://www.iea.org/statistics/statisticssearch/report/?year¼2014&cou required to replace the existing equipment. For example, the results ntry¼CHINA&product¼Balances. of some studies have predicted a very large rebound effect on 236 J. Feng et al. / Energy 144 (2018) 232e242

Table 1 Parameters used in Equation (13).

m0 m1 ctj A0 a

Numerical values 0.1 0.7 0.05 1971 354.66 0.03

energy use in 2040 of 76%, i.e., a 10% improvement in energy efficiency leads to only a 2.4% reduction in energy use [34].To consider the efficiency improvement effect, net energy and eco- nomic growth more generally, the net energy saved by efficiency ð Þ improvement is considered in Equation (13) as mi t . For the pa- ð Þ rameters used in mi t in this study, see Table 1. On the other hand, efficiency improvements are not derived from technology improvements alone but also from changes in the modes in which fuels are used [29]. For example, increasing the percentage of natural gas combined-cycle power plants (which have greater efficiencies than steam cycles and combustion turbines alone) used in electricity generation can increase the productivity of the economy in terms of delivering motive power Fig. 3. EROIPOU for the energy industry in China between 1987 and 2012. and driving electronic equipment [35]. ð ; Þ If di Ei t represents the energy inputs for producing and determined in other studies, how low is this value? A comparison of processing the total energy andEth;i represents the total energy ð Þ ð ; Þ EROI with the study results from Hu et al. (using the standard outputs, then mi t Eth;i di Ei t represents the net energy supply of POU the economic system or the energy return. Based on Equation (1), EROI) and Chen et al. (using the -EROI) [36e38] demon- the net energy can be calculated with the EROIPOU and total energy strated that the EROIPOU in this study was less than the EROIST but consumption, and by combining Equations (1) and (3), the net nearly equal to the emergy-EROI because the emergy-EROI and energy production function incorporates net energy and the EROIPOU have greater borders. King et al. [39] used the ‘energy in- ’ EROIPOU: dustry own use (EIOU) data from the International Energy Agency (IEA) as an estimate of the direct energy consumed by the energy E ¼ :ð Þ¼ at （ þ m1 ） th;i industry to produce energy, e.g., the consumption of oil or gas to Y Eq 12 A0e m0 ctt Eth;i 1 þ e 0 EROIPOU produce the same. These studies do not yet include estimates of (13) indirect energy inputs that represent the embodied energy in capital and labor. Following this idea, data in this study obtain from ð Þ 2 Each mi t (i.e., the effective factor of the fuel) is time related. the IEA to calculate the total primary consumption/EIOU in China With advances in technology and the optimization of the fuel from 1987 to 2012 (Fig. 4). The results illustrated that the EROIPOU in consumption structure, the effective factor follows the trend of a China from 1987 to 2012 exhibited the pattern of change as the logistic curve. EIOU in China. The EROIPOU is also lower than other EROIST esti- The known parameters of the historical data from 1987 to 2012 mates in China. Fig. 4 reveals that, in the long term, the present and used the first-order equation of the residuals to obtain the indicates that both EROIs exhibit downward trends. The question fi unknown coef cients in Equation (13) based on the least squares that remains is what caused the EROIPOU to decline? criterion. The parameters obtain from the simulation are presented Fig. 5 indicates that the embodied energy used by the Chinese in Table 1. energy sectors has been rising rapidly. In 1987, the energy pro- With the above parameters, the historical GDP and project the duction sectors consumed 1 ton of TCE energy inputs for every GDP was estimated using Equation (13).AsFig. 8 shows, the actual 10.01 TCE of produce net energy. However, in 2012, this number GDP and the estimated historical GDP match well. However, declined to 4.25. Although the growth of embodied energy used by compared with other periods, the simulated GDP from 2005 to the energy production sectors caused the EROIPOU to decline, it is 2008 is relatively poor. Since 2005, new policies have been important to understand which sector is the primary cause. The constantly applied, and the country's instability and variability decline of the EROIPOU directly leads to a slowing of the growth in make it difficult to accurately simulate GDP growth. The new pol- net energy. In 2012, China's total primary energy consumption was icies increased subsidies and control over the health care sector, 4.29 billion TCE, and the net energy was only 3.47 billion TCE, which accelerated the urbanization process and adopted a loose monetary means that approximately 0.82 billion TCE could not be utilized by policy. China's economic growth since the reform has been very the economy and society. rapid. China's GDP growth from 1978 to 2013 has been between Fig. 5 illustrates the ratio of indirect energy inputs to direct 9.5% and approximately 11.5% a year. energy inputs. This ratio revealed the degree of change in indirect energy inputs relative to direct energy inputs. If this ratio is suffi- ciently high, the indirect energy inputs require greater percentages 3. Results and discussion of energy inputs. Figs. 5 and 6 illustrate that the rates of embodied energy used by sector D (the processing and supply of energy) are 3.1. Net energy analysis of China's energy production sectors growing far faster than those of the other sectors. The calculation results further proved that the entire energy production chain, 3.1.1. EROIPOU in China from 1987 to 2012 The EROIPOU values for China between 1987 and 2012 can be calculated. Fig. 3 presents the EROIPOU values of the Chinese energy production sectors, which appeared to decline from 1987 to 2012. 2 http://www.iea.org/statistics/statisticssearch/report/?year¼2014&cou The EROIPOU was as low as 5.26:1. However, compared to the values ntry¼CHINA&product¼Balances. J. Feng et al. / Energy 144 (2018) 232e242 237

40

35

30

25

20

15

EROI From From Present EROI Study 10

5

0 4 3 9 5 88 9 9 97 0 11 99 05 0 9 9 9 0 0 0 1 1993 1996 1 1987 1989 1990 1991 1992 19 19 1 1998 2 2010 2000 2001 2006 2 2002 2004 2 2007 2008 2012 20

EIOU (Total primary energy supply / Energy industry own use)

Emergy-EROI for Daqing oilfield in China by Chen et al. 2017

EROIst of Daqing oilfield in China by Chen et al. 2017

EROIst of Oil and NG in China by Hu et al 2011

EROIst of Coal in China by Hu et al 2011

EROIpou

Fig. 4. Comparison of the EROIPOU with the present EROI study for fossil fuels in China.

500000 140000

450000 120000 400000 CE T 350000 100000

300000 80000 250000 60000 200000 Unit: 10,000 TCE

150000 40000

100000 Embodied Energy, Unit: 10,000 Total Primary Energy and Net Energy 20000 50000

0 0 1988 1989 1991 1992 1994 1995 1997 1999 2000 2001 2003 2005 2006 2007 2009 2011 1987 1990 1993 1998 2004 2010 2012 1996 2002 2008

Indirect Energy Embodied in Extrac on of Petroleum and Natural Gas Direct Energy Embodied in Extrac on of Petroleum and Natural Gas Indirect Energy Embodied in Mining and Washing of Coal Direct Energy Embodied in Mining and Washing of Coal Indirect Energy Embodied in Processing and Supply of Energy Direct Energy Embodied in Processing and Supply of Energy Total Primary Energy Supply Net Energy Supply

Fig. 5. The gaps between the net energy and total primary energy consumption in China from 1987 to 2012 (unit: 10,000 TCE). 238 J. Feng et al. / Energy 144 (2018) 232e242

2.5 Mining and Washing of Coal Extrac on of Petroleum and Natural Gas 2.210 Processing and Supply of of Energy 2

1.727 1.607 1.581 1.616 1.5 Direct Energy Inputs

/ 1.290 1.229 1.218 1.234

1 0.977

0.5 0.455 0.359

Indirect Energy Inputs 0.327 0.260 0.264 0.254 0.167 0.197 0.171 0.189 0.119 0.1210.150 0.141 0.0830.088 0.069 0.087 0.093 0.110 0 1987 1990 1992 1995 1997 2002 2005 2007 2010 2012

Fig. 6. Ratio of indirect energy inputs to direct energy inputs for energy production sectors in China. especially sectors D, cannot be ignored in the EROI calculation 1987 and 2012, a large proportion of the energy flow was from when calculating net energy in the energy industry. sector G to D. Furthermore, sector G consumed a large amount of energy from sectors B, C, and D, which means that sector D 3.1.2. Embodied energies among the energy production sectors consumed a large amount of indirect energy. Simultaneously, from fi Table 2 and Fig. 5 present the direct and indirect embodied 1987 to 2012, all the energy production sectors exhibited signi - energies used by 3 energy production sectors. The largest and cant increases in embodied energy consumption. fastest-growing consumer of embodied energy in China is sector D, which is the processing and supply of energy. 3.2. Forecast of China's GDP growth considering the declining Fig. 7 illustrates the energy flows among the sectors of energy EROI production in China. According to the widths of the flows, the POU amount of flow between sectors can be discerned. The directions of 3.2.1. Setting up the future scenarios the flows indicate the roles of the sectors in the energy network. If the energy EROI continues to decline, energy inputs in the Links of variable thickness can represent the extent or magnitude of POU energy production sectors will continue to increase, which will the relationships between the elements. The data for the links with result in the energy industry being able to provide a net energy of variable thicknesses come from an aggregated Input-Output table. zero. Eventually, net energy limitations will be reflected in the From the directions of the flows, in 2012, sector D was dependent weakening of the economy and in the depletion of resources. on sector G. Moreover, sectors B and D were relatively self-energy- Therefore, the foundations of economic theories each have their sufficient in 2012 (e.g., they used their own inputs), but in 1987, their self-energy-sufficiency was almost negligible. By comparing the flows of different sectors, the result shows that sector D is the sector that consumed the largest quantity of embodied energy and played an important role in the energy production chain; moreover, this trend is still strengthening. In

Table 2 Embodied energy used by the Chinese energy sectors (10,000 TCE).

Year Mining and Extraction of Processing and Washing of Coal Petroleum and Supply of Energy (sector code: B) Natural Gas (sector (sector code: D) code: C)

Direct Indirect Direct Indirect Direct Indirect

1987 3433.0 302.9 1643.0 136.6 2138.0 2626.8 1990 4159.0 495.6 1930.0 133.0 2508.0 4030.6 1992 4427.2 664.7 2106.5 255.8 3188.1 3882.4 1995 5499.8 475.8 2812.6 397.2 5908.6 5775.3 1997 5791.2 966.5 3564.9 331.4 7815.6 9644.5 2002 5132.8 1009.0 4680.6 515.6 8612.8 13615.7 2005 6711.7 1744.5 3763.4 643.8 12507.4 16138.8 2007 8269.9 2179.7 3651.3 1195.3 13449.3 21738.1 2010 10574.4 4809.6 4057.6 1030.2 16694.8 28834.7 2012 12339.1 4430.9 3807.9 718.1 18809.1 41571.6 Fig. 7. Energy flows among the energy production and other sectors. J. Feng et al. / Energy 144 (2018) 232e242 239

140000 95% Confidence Band of GDP Growth 120000 Actual GDP (constant LCU)

100000 Simulated GDP with decline EROIpou

80000

60000 CNY Constant LCU

40000 Billion

20000

0

Fig. 8. Results of the forecast of China's GDP growth (Local Current Unit, LCU) with its confidence band. appropriate time of applicability and periodicity. If we neglect the Comparisons of the results of this study with those of other background of the net energy theory, the neoclassic economic studies reveal that the GDP growth rates forecast in this study from theory that promotes the growth of the economy may impede 2016 to 2030 are lower than the predictions of the World Bank subsequent growth [40]. Global Economic Prospects (6.3% in 2019) [42] and the IMF World The first step to use a net energy production function (see Economic Outlook Database (5.9% in 2020, 5.7% in 2022) [43], Equation (4)) to forecast the future GDP is to forecast the TPES. The which includes the U.S. Energy Information Administration (EIA) projections of the TPES from the International Energy Agency (IEA) IEO-2016 (5.3% in 2020,4.2% in 2030, reference case) [44] and the were used in this study. International Energy Agency WEO-2016 (5.8% in 2021) [41]. The new policy scenario of the World Energy Outlook 2016 Because fossil fuel constraints and the energy inputs of growth in (WEO2016) was published by the IEA and incorporates existing energy production, GDP forecast for 2016 to 2030 in this study is energy policies and an assessment of the results that are likely to slower than the results predicted by other researchers. One of the stem from the implementation of announced intentions, including primary reasons for these differences is that the World Bank, IMF, those in the climate pledges submitted to the United Nations EIA and IEA have not considered the decline of the EROIPOU for fossil Conference in 2015. The climate pledges, known as fuels in China. Nationally Determined Contributions (NDCs), are the building However, for the entire energy industry, the EROIPOU can be blocks of the Paris Agreement and provide a rich and authoritative increased by adding a high EROI energy ratio because a high EROI source of guidance for this scenario. The NDCs have been carefully energy uses fewer inputs to obtain the identical (or additional) and individually assessed in this edition of the WEO2016. Where output. Increasing the proportion of gas used in electricity gener- policies exist to support the NDCs, the implementation measures ation thereby increases its productivity in the economy in terms of are clearly defined, and the effects are reflected in the new policies delivering power and driving electronic equipment [45]. scenario. Additionally, this scenario considers some of the policies However, the effects of fossil energy growth inputs on economic that are being implemented in further efforts by the Chinese gov- growth cannot be ignored. Inevitably, rising energy inputs will ernment to reform the economic and energy structure, for example, reduce the net energy supply if energy continues to be dominated efforts to achieve peak CO2 emissions by approximately 2030 and to by fossil energy, which will affect economic growth. Additionally, as expand the use of natural gas and renewable energy [41]. the energy EROI weakens, the net energy supply will be further Additionally, the EROIPOU trends from 1987 to 2030 presented in reduced such that the decline in the EROI will substantially impact Fig. 3 were used to support the decline of the EROIPOU in China. The economic growth. function and parameters of the curve-fitting model are also pre- sented in Fig. 3. 4. Conclusion

The net energy from fossil fuels is the real contribution to the 3.2.2. The effect of the decline of the EROIPOU of fossil fuels on economic growth social economy that is related to the GDP. However, the declining Based on the new policies scenario from the WEO2016 and the declining EROIPOU scenarios presented in this study, trends for the Table 3 GDP from 2016 to 2030 as presented in Fig. 8 for net energies. The Forecast of the rate of change of the actual GDP in China from 2016 to 2030. EROIPOU trend line in Fig. 3 was used to create basic forecasts. 2016e2020 2021e2025 2025e2030 Although the actual GDP generally trends upward, its growth rate over five-year periods is in a downward trend, as we demonstrate EROIPOU 4.88 4.55 4.27 GDP growth 4.51% 4.33% 3.99% in Table 3. 240 J. Feng et al. / Energy 144 (2018) 232e242

EROIPOU will reduce the net energy according to the proportion of Acknowledgements total fossil fuel consumption. This study aimed to build a model to calculate the EROIPOU in China from embodied energy perspectives. This study was supported by the National Natural Science Then, simulated net energy production function was used to fore- Foundation of China (501100001809) (grant No. 71373285/ cast the effects of the declining EROIPOU on China's economic 71503264/71303258), the National Social Science Foundation of growth from 2016 to 2030. China (grant No. 11&ZD164/13&ZD159) and the Humanities and The EROI method focuses on physical energy and reveals the Social Sciences Foundation of the Ministry of Education of China importance of net energy in calculating the outputs and inputs. This (501100002338) (Grant No. 15YJC630121). study calculated the indirect and direct energies (including the Additionally, we thank Carey King for his many helpful embodied energies) used by the energy production sectors with an comments. Input-Output table. The mathematical relationships among the EROIPOU, net energy and total primary energy consumption is core in this study. Then simulated and predicted economic GDP trends in Nomenclature/list of symbols China using a net energy production function. The results indicate the following: CNY China Yuan DSM Demand Side Management 1) Fossil fuels has long processing and supply chain convert pri- EIA Energy Information Administration mary energy source into secondary energy. Excessive embodied EIOU Energy Industry's Own Use energy consumption by the fuel processing and supply energy EROI Energy Return on Investment sector causes the EROIPOU to decline. The embodied energies EROIPOU Point of use EROI from the processing and supply of energy are larger than those EROIST Standard EROI of the fossil fuel extraction and mining stages. Clearly, the in- EROIEXT Extended EROI direct energy cost of energy processing (after extraction) cannot FFC Full Fuel Cycle be ignored. GDP Gross Domestic Product 2) The slowing economic growth enlightens our sense of the GHG Greenhouse Gas considerable importance of energy inputs or the costs in the IEA International Energy Agency energy production sectors. The impact of net energy on China's IEO International Energy Outlook economy is significant, as determined by the ratio of energy cost LCU Local Current Unit in production to energy produced. Over the long term, TCE Tons Standard Coal Equivalent decreasing and increasing energy costs have NDC Nationally Determined Contributions significant and increasing influences on economic growth. TPES Total Primary Energy Supply China's energy-related policy-makers needs strengthen the USD United States Dollar deep policy analysis regarding the relationship between GDP WEO World Energy Outlook and net energy. For example, a policy that aims to decrease the A technical coefficient matrix [dimensionless] embodied energy in equipment and buildings is necessary to A(t) multi-factor productivity [dimensionless] control energy costs. B Leontief inverse matrix [dimensionless] C sum of all energy inputs in each energy production sector Net energy also concerns human welfare and a sustainable [10,000 TCE] environment, which should be part of a nation's policy emphasis. c constants in logistic curve of efficiency improvements Present-day human welfare cannot simply be measured by GDP [dimensionless] alone [46]. With the declining EROI values of traditional major fossil di (Ei,t) energy inputs of the energy production sectors in t years fuel energy sources in China, China's fossil fuels industry may be [dimensionless] moving toward the “net energy cliff” [5]. Alternatively, decreasing E energy input [10,000 TCE] the net energy will increase the capital and technological costs, and ei direct energy consumption coefficient of sector I China will experience less growth. Consequently, the negative [dimensionless] impact on welfare improvement in developing countries such as Ei embodied energy of sector i [10,000 TCE] China will be greater than that in developed countries. Eth,i total energy content of the fuel type “i” [10,000 TCE] With rapid economic growth, human welfare and quality of life F total primary energy supply [10,00 TCE] have been prioritized by the Chinese government. A previous study G final energy consumption at the point of use [10,000 TCE] [5] found that societal well-being and the per capita net energy i different types of energy available to society appear to be linked to quality of life based on K capital input empirical analyses relating societal EROI to indices of quality of life. k output elasticities of capital [dimensionless] Therefore, it is necessary to review our work from a Chinese L labor input perspective that is informed by other analyses, including those of l output elasticities of labor [dimensionless] Chinese societal well-being, net energy availability and EROI, to m0 constants in logistic curve of efficiency improvements develop China's welfare and quality of life with rational net energy [dimensionless] use and distribution. m1 constants in logistic curve of efficiency improvements Therefore, China should consider energy efficiency policies and [dimensionless] industrial restructuring policies, among others, to eliminate or mi(t) efficiency improvements [dimensionless] mitigate excessive natural resource exploitation in the environ- X total output of an economy matrix [dimensionless] ment. In the future, we expect more developed technology to ease Y final consumption matrix [dimensionless] fossil fuel depletion and the development of alternative energies. a constants in logistic curve of efficiency improvements Moreover, for substitutes, a new evaluation method to compre- [dimensionless] hensively consider energy, the economy, and the environment aij technical coefficient j consumed i [dimensionless] must urgently be developed. g output elasticities of energy [dimensionless] J. Feng et al. / Energy 144 (2018) 232e242 241

Appendix A

Table A1 Sector Classifications of the Chinese Economic Sectors.

Sector Sector Sectoral Sector Name Code Code

A Agriculture, Hunting, Forestry and Fishing 01 Agriculture, Hunting, Forestry and Fishing B Mining and Washing of Coal 02 Mining and Washing of Coal C Extraction of Petroleum and Natural Gas 03 Extraction of Petroleum and Natural Gas D Processing and Supply of Energy 04 Processing of Petroleum, Coking and Processing of Nuclear Fuel 05 Production and Distribution of Gas E Production and Supply of Electric Power and Heat Power 06 Production and Supply of Electric Power and Heat Power F Other 07 Mining of Metal Ores 08 Mining and Processing of Nonmetal Ores and Other Ores 09 Food and Tobacco 10 Manufacture of Textiles 11 Textiles, Apparel, Footwear, Caps, Fur, Leather 12 Processing of Timber and Manufacture of Furniture 13 Study making, Printing and Manufacture of Articles for Culture, Education and Sports 14 Chemicals Industry 15 Nonmetallic Mineral Products 16 Smelting and Rolling of Metals 17 Metal Products 18 General Machinery Purpose and Special Purpose Machinery 19 Transport Equipment 20 Electrical Machinery and Equipment 21 Communication, Computer and Other Electronic Equipment 22 Measuring Instrument and Machinery for Cultural Activity and Office Work 23 Other Manufacture 24 Scrap and Waste 25 Production and Distribution of Water G Construction 26 Construction H Transport, Storage and Post 27 Transport, Storage 28 Post I Wholesale and Retail Trade, Hotels and Catering Services 30 Wholesale and Retail Trade, 31 Hotels and Catering Services J Other Service Industries 29 Information Transmission, Computer Service and Software 32 Financial Intermediation 33 Real Estate 34 Leasing and Business Services 35 Research and Experimental Development 36 Comprehensive Technical Services 37 Water Conservancy, Environment and Public Facilities 38 Services to Households and Other Services 39 Education 40 Health, Social Security and Social Welfare 41 Culture, Sports and Entertainment 42 Public Management and Social Organization

Table A2 Energy Consumption by Chinese Economic Sectors (10000 TCE).

Years Agriculture, Mining Extraction of Processing Production and Other Construction Transport, Wholesale and Retail Other Total Hunting, and Petroleum and and Supply Supply of Electric Manufacturing Storage Trade, Hotels and Service Forestry and Washing Natural Gas of Energy Power and Heat and Post Catering Services Industries Fishing of Coal Power

1987 4471.0 3433.0 1643.0 2138.0 2969.0 48609.0 1260.0 4126.0 907.0 17076.0 86632.0 1990 4852.0 4159.0 1930.0 2508.0 3867.0 55113.0 1213.0 4541.0 1247.0 19273.0 98703.0 1992 4982.6 4427.2 2106.5 3188.1 4504.1 59074.0 1237.3 4805.4 1401.2 19471.2 105197.6 1995 5505.1 5499.8 2812.6 5908.6 7052.7 74918.2 1334.5 5862.9 2017.8 20263.8 131176.0 1997 5905.4 5791.2 3564.9 7815.6 10076.4 72832.3 1179.0 7543.1 2394.4 21070.8 137799.0 2002 6612.5 4591.2 4550.3 15321.7 11807.0 80160.0 2544.0 11171.0 3520.0 24407.0 164684.7 2005 7978.4 6711.7 3763.4 12507.4 15826.8 119990.3 3411.1 16629.2 5031.1 32832.7 224682.0 2007 8244.6 7170.8 3677.5 13792.9 18474.6 146249.8 4031.4 20643.4 5962.1 37335.8 265582.9 2010 6477.3 10574.4 4057.6 16694.8 22584.1 177137.6 5309.3 26068.5 6826.8 49208.8 324939.1 2012 6784.4 12339.1 3807.9 18809.1 23809.2 192582.2 6167.4 31524.7 8545.9 57362.2 361732.0 242 J. Feng et al. / Energy 144 (2018) 232e242

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