Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments?

Energy Economics 83 (2019) 61–73

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Energy Economics journal homepage: www.elsevier.com/locate/eneeco

Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments? Lianbiao Cui a, Rongjing Li a, Malin Song a,⁎, Lei Zhu b a b

School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China School of Economics & Management, Beihang University, Beijing 100191, China

a r t i c l e

i n f o

Article history: Received 14 December 2018 Received in revised form 2 May 2019 Accepted 22 June 2019 Available online 27 June 2019 JEL classification: C63 O13 O33 O41 Q43 Q54

a b s t r a c t China has proposed carbon intensity targets and energy development targets for 2030. This study investigates the linkages between these targets and assesses if China can achieve its energy development targets by fulfilling its carbon reduction commitments. To this end, it quantitatively evaluates the impact of carbon emission controls on the Chinese economy using a dynamic computable general equilibrium model. The results show that China's carbon abatement pledge cannot guarantee achievement of all energy objectives. China is likely to reach the upper limit of its carbon intensity target in 2020 and the lower limit in 2030 if current abatement efforts are maintained. To achieve the upper limit in 2030, the carbon price will be CNY 83/tCO2. The energy consumption target for 2020 is likely to be realized but the 2030 target is not. A more stringent price constraint on carbon emissions would be helpful to the achievement of the non-fossil energy target in 2030, but would have a limited promoting effect on natural gas development. Our results reveal the linkages between China's energy targets and carbon emission targets, which is valuable to the cost-effective dual control of energy consumption and carbon emission. © 2019 Elsevier B.V. All rights reserved.

Keywords: Carbon targets Energy targets Energy finance Dynamic CGE model

1. Introduction China's industrialization has caused a series of environmental problems while propelling its economic growth. Resource depletion, energy waste, climate change, and smog-related weather problems occur frequently. Insufficient environmental and environmental bearing capacity has been unable to support the extensive development mode and the Chinese economy is facing the dual challenges of energy shortages and environmental degradation (Lin and Du, 2017; Yang et al., 2018). In response to these challenges, China is attempting to introduce more price mechanisms to deal with environmental issues. For example, energy finance instruments including renewable energy subsidies and renewable portfolio standard (RPS) have been introduced to promote low-carbon energy transition. China also signals strong intentions to establish a national carbon market, and the policymakers are expecting to conserve energy and reduce emissions by imposing price restrictions on carbon emissions. With regarding to the medium- and long-term development plans, China has proposed comprehensive targets for energy saving and carbon abatement by 2020 and 2030. These targets have been included in ⁎ Corresponding author. E-mail address: [email protected] (M. Song).

https://doi.org/10.1016/j.eneco.2019.06.016 0140-9883/© 2019 Elsevier B.V. All rights reserved.

national economic and social development as binding indicators. China has promised to reduce its carbon intensity from 2005 levels by 40–45% by 2020 and 60–65% by 2030, and to decrease peak carbon emissions by 2030 (den Elzen et al., 2016; Zhang et al., 2017). Meanwhile, China has also hastened the enactment of its energy transition strategy and proposed higher energy targets to push forward the development of renewable energies. In December 2016, China issued the Energy Production and Consumption Reform Strategy (2016–2030), which clearly sets a course for restraining total energy consumption, energy intensity, and energy mix in the future (NDRC, 2016). For example, energy consumption in 2020 and 2030 should not exceed 5 and 6 billion tons of standard coal equivalent (tce), respectively, while the proportion of non-fossil energy in primary energy consumption should increase to 20% by 2030 (Wang and Zhang, 2017; Duan et al., 2018). To achieve China's energy goals and carbon targets in a cost-effective way, it is necessary to explore the synergies between the two sets of targets. It is currently unclear whether China can achieve its energy development targets by fulfilling its carbon reduction commitments, and the extent to which new efforts will be required if not. The purpose of this study is to explore the linkages between energy targets and carbon targets. A dynamic computable general equilibrium (CGE) model of China was adopted for quantitative evaluation. Three policy scenarios were

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designed considering China's carbon abatement efforts up to 2030. Our study makes three contributions to the literature. First, the promoting function of different carbon abatement efforts on various energy targets (i.e., energy consumption, energy intensity, and energy mix) was comprehensive evaluated by considering key uncertainties, including economic growth and energy elasticity of substitution. Second, in contrast to decomposition analysis and system dynamic (SD) models adopted in previous studies, a dynamic CGE model calibrated using 15 years of historical data was introduced for policy simulation. Based on a detailed description of China's economic system and its linkages with energy and environmental system, the model is expected to provide more accurate results. Third, this research supplements existing studies by exploring the synergy between the energy targets and carbon targets for 2030, which is valuable to the cost-effective dual control of energy consumption and carbon emission. The rest of this paper is organized as follows. Section 2 presents the literature review. Section 3 introduces the model and Section 4 details the data sources and policy scenarios. Section 5 presents the empirical results, while Section 6 outlines the sensitivity analysis. Section 7 presents the conclusion and policy implications. 2. Literature review As China is the world's largest energy consumer and carbon emitter, its policies have significant influence on global energy markets and climate change. A great number of studies have focused on China's energy consumption and carbon emissions using three main methodological approaches. First, many scholars have explored the relationships between China's energy consumption and carbon emissions using methods primarily based on decomposition analyses (Lin and Ahmad, 2017; Jiang and Guan, 2017; Li et al., 2017). For example, based on the Kaya model, Steckel et al. (2011) researched the driving factors of carbon emissions in China from 1971 to 2007 and identified economic growth as the main driver. Using a logarithmic mean divisia index model, Zhang and Da (2015) also found that economic growth was the main reason for the increase in carbon emissions, and that energy intensity and renewable energy development would have a significant suppressing effect on carbon emissions. By applying index decomposition analysis, Yi et al. (2016) found that carbon intensity in 2020 would decrease by 47.8% from 2005 and energy intensity would be the main reason for the decline. While decomposition analysis can reveal the driving factors of carbon and energy intensity changes, it considers only some variables in an economic system while assuming that other variables remain unchanged. This is inconsistent with economic reality. In recent years, some scholars have focused on the complexity and linkages of the economic system and evaluated China's carbon emission reduction target in 2020 by constructing SD models. For example, Liu et al. (2015) used an SD model to forecast China's carbon emissions in 2020, and found a carbon intensity reduction of 52.5% compared with 2005, indicating that China could realize the upper limit of carbon intensity reduction. On the other hand, Xiao et al. (2016) found that China's carbon intensity in 2020 would be reduced by only 22.68% compared with 2005, and that China might not be able to realize its emissions target. Li et al. (2018) indicated that carbon intensity in 2020 would be 74.8% lower than in 2005 but that it would impossible for China's carbon emissions to reach the peak value in 2030 without additional measures. The SD model is a structure dependent model, and the reliability of the simulation results depends on the description of the model on the structure of the object. However, it is difficult to portray the socio-economic system using structural equations. Some recent studies have tried to predict China's energy consumption and carbon emission with the computable general equilibrium (CGE) model. The CGE model is a powerful methodology for understanding and exploring the feedback mechanisms among the energy, economics, and environmental systems (Fan et al., 2016; Babatunde et al., 2017). It provides a consistent framework for analyzing the economic impacts of energy policies and a complete description of the

economy, including the direct and indirect effects of policy changes. CGE models have been used to simulate the economic effects of energy and climate policies in China (Cui et al., 2015; Babatunde et al., 2017; Lin and Jia, 2018). For example, Dai et al. (2011) used a static CGE model to find that carbon intensity in 2020 would be reduced by 30.97% compared with 2005. Chi et al. (2014) used a dynamic CGE model to predict that carbon emissions in 2030 would be 19.06 billion tCO2 (tons of CO2) and energy consumption would be 8.07 billion tce. Zhang et al. (2016) found that carbon emissions would peak at about 12 billion tCO2 from 2035 and 2045 if current efforts for emission reduction were continued. Although the relationship between China's energy consumption and carbon emissions has received significant, existing studies have not reached the same conclusions. Taking the baseline scenario as an example, regarding China's energy consumption, Shan et al. (2012) forecast the national energy consumption of 5.58–5.87 billion tce in 2030, while the results of most other research exceeded 6 billion tce (Ren and Gu, 2016; Yang et al., 2016). Regarding China's energy intensity targets, Dai et al. (2011) and Xiao et al. (2016) argue the 2020 target cannot be achieved with current efforts, but other research suggested that carbon intensity in 2020 would decrease by more than 45% compared with 2005 (Liu et al., 2015; Yi et al., 2016; Li et al., 2018). Regarding China's 2030 carbon emission peak, some research suggests China is likely to reach the emission peak before 2030 (Green and Stern, 2017; Wang and Zhang, 2017). According to other studies, the peak target cannot not be realized without additional efforts (Liu et al., 2015; den Elzen et al., 2016). These discrepancies are the result of many factors, including different models, parameters, and assumptions of future scenarios. Therefore, it is necessary to conduct a more in-depth study of China's energy consumption and carbon emissions, especially evaluating the impacts of the various uncertainties regarding main results. Overall, compared to decomposition analyses and SD models, the CGE model, which depicts all the direct and indirect effects, is more powerful for forecasting energy consumption and carbon emissions since it provides a complete description of the economy. With the introduction of the key uncertainties (e.g., GDP growth rate, energy elasticity of substitution) into the entire economic system, the CGE model is also useful for conducting sensitive analysis. Although several recent studies have adopted the CGE model to forecast China's energy demand and carbon emissions, few focus on the synergy between carbon targets and various energy targets (i.e., energy consumption, energy intensity, and energy mix) for 2030, especially evaluating the impacts of various uncertainties on the synergy. It is currently unclear whether existing abatement efforts are sufficient to realize the 2030 climate targets, and, if not, the extent to which further efforts will be required. Similarly, it is also unclear whether China can achieve all its 2030 energy development targets by fulfilling its national carbon reduction commitments, and, if not, the extent to which further efforts will be required for each energy index. To achieve China's energy and carbon targets in a cost-effective manner, it is essential to explore the linkages between these two kinds of targets. In particular, the uncertainties need to be evaluated to provide robust results. 3. The model The empirical analysis is based on the China dynamic energy computable general equilibrium (CDECGE) model, which was developed by the Center for Energy & Environmental Policy Research of the Chinese Academy of Sciences. The setting of Australia's Monash model was referenced for the modeling architecture and theoretical foundation of the CDECGE model (Song and Cui, 2016). 3.1. Background of the model The CDECGE model is a recursive dynamic CGE model that uses policy simulations to observe variations of different economic variables in a certain year and present variations of a specific variable in different years. The CDECGE model is based on the Chinese economy and depicts

L. Cui et al. / Energy Economics 83 (2019) 61–73

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Fig. 1. Production structure of CDECGE model.

interactions among the energy, environmental, and economic systems through quantity modeling. Similar to the Monash model, the dynamic mechanisms of the CDECGE model include capital accumulation and sticky wage adjustment. The model primarily includes a historical calibration period and a policy simulation period. Main variables are calibrated using historical information in the calibration period to obtain relevant technological progress parameters and shift parameters, while historical trends are extrapolated and future macroeconomic scenarios are reasonably predicted in the policy simulation. More details about the CDECGE model are provided in Appendix A. In recent years, this model has been widely used to evaluate China's energy and environment policies (Cui et al., 2013, 2014; Song and Cui, 2016). 3.2. Main modules 3.2.1. Production module The CDECGE model adopts a nested structure to represent production technology. Cost minimization results in the appropriate input– output demand at each level of the production tree. As shown in Fig. 1, it is assumed that the first nest satisfies the Leontief function, that is, there is no elasticity of substitution among non-energy intermediate inputs, energy-factor composites, and other inputs. Unlike the Monash model, the CDECGE model adjusts the substitution relationships among energy, capital, and labor, assuming there is elasticity of substitution among them that satisfies the constant elasticity of substitution (CES) function from top to bottom as follows: energy-factor composed of energy and factor; factor composed of labor and capital as per CES; energy composed of fossil energy and electric power (mostly power generated by non-fossil energy) as per CES; and fossil energy composed of coal, coke, crude oil, natural gas, refined oil, and fuel gas as per CES. Elasticity of substitution determines the difficulty of mutual substitution among all inputs, and will also affect policy impacts (Guo et al.,

2014; Koesler and Schymura, 2015; Rahman et al., 2019). This study refers to previous studies to set elasticity of substitution. Specifically, the CDECGE model assumes that the elasticity of substitution between capital and labor is 1 (Gerlagh and Van der Zwaan, 2003; Bosetti et al., 2006; Carrara and Marangoni, 2017), elasticity of substitution between energy and value added is 0.5 (Bosetti et al., 2006; Carrara and Marangoni, 2017), elasticity of substitution between fossil energy and electric power is 0.5 (Elliott and Fullerton, 2014; Cui and Song, 2017), and elasticity of substitution among coal, crude oil, natural gas, refined oil, and fuel gas is 1 (Elliott and Fullerton, 2014; Song and Cui, 2016). In Section 6, we change the values of the elasticity parameters to conduct sensitivity analysis. 3.2.2. Carbon tax module To evaluate the economic impacts of carbon abatement policies, the CDECGE model introduces carbon prices for carbon emissions. Since carbon prices can be delivered with a carbon tax or carbon emissions trading scheme, the two approaches will generate the same abatement results under the perfect competition market (Green, 2008). Compared to the emissions trading system, the carbon tax could provide more stable price signals, which is useful for the decision makers to manage risks in the low carbon investment (Cui and Song, 2017). Although China's national carbon market was launched on December 19, 2017, the Climate Division of China's National Development and Reform Commission reported the launch of a study on the introduction of carbon taxes in 2020 (Zhao et al., 2019; Wang et al., 2019). The carbon tax policy was also favored by many Chinese economists and scholars due to its simplicity and transparency (Lin and Jia, 2018; Wang et al., 2009). To facilitate the analysis, a carbon tax was introduced in the CDECGE model for policy simulation. The equations are as follows:
 X P purchase
X c;i ¼ P 0c;i
X c;i
1 þ T c;i þ X c;i;mar
P mar c;i

ð1Þ

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L. Cui et al. / Energy Economics 83 (2019) 61–73

  X þ X c;i;mar
P mar ð2Þ P purchase
X e;i ¼ P 0e;i
X e;i
1 þ T c;i þ T ctax e;i e;i

4. Data and policy scenarios 4.1. Data sources

Ppurchase c, i

is the purchasing price of the cth intermediate input where (non-energy) of industry i; Xc, i is the amount used; P0c, i is the basic price; Tc, i is the consumption tax; Xc, i, mar denotes the margin services, which are the amount of commodity circulation of the cth product in industry i; Pmar is the circulation price; Ppurchase is the purchasing price of e, i the eth fossil energy used in industry i; and Tctax e, i is the rate of the carbon tax. Following the tax neutrality principle, the CDECGE model assumes that the carbon tax will be returned to residents in the form of income tax redemption. 3.2.3. Dynamic mechanism The dynamic mechanism of the CDECGE model includes two parts: capital accumulation and sticky wage adjustment. With regard to capital accumulation, the model details the relationships among capital stock, expected rate of return on capital, and new investment. Expected rate of return and capital stock are assumed to be negatively correlated. A higher capital stock implies a lower expected rate of return in the next period. The investment and expected rate of return on capital are assumed to be positively correlated. A high expected rate of return on capital stimulates the investment level (Song and Cui, 2016).
 Capital accumulation : K j;t ¼ K j;t−1
1−D j þ I j;t−1

Capital accumulation growth rate : KGR j;t

K j;tþ1 ¼ þ1 K j;t



 Capital supply : KGR j;t ¼ h j Et ROR j;t
 Static expected return on investment : Et ROR j;t 0 1 ð1capÞ P j;t
 1 þ INF t ¼ −1 þ @ ð2Þ þ 1−D j A
1þr P

ð3Þ

ð4Þ

ð5Þ

ð6Þ

j;t

where j refers to the industry; Kj, t is capital stock in period t; Dj is the depreciation rate of capital; Et is the expected value in period t; RORj, t is the rate of return on capital; KGRj, t is the capital growth rate; Pj,(2)t is the cost of capital; P(1cap) is the capital rental rate; r is the interest j, t rate; and INFt is the inflation rate. The CDECGE model has a special setting for sticky wage adjustment. It assumes that labor is mobile and that the transfer of labor among different industries is related to the relative wage rates. In addition, the model assumes that wages are sticky and that wage adjustment does not ensure that labor supply immediately equals labor demand in the case of a policy shock. The labor market is adjusted gradually to achieve the balance of supply and demand over the long term. 

     Wt W t−1 Lt −1 ¼ −1 þ α
−1 þ f t W f ;t W f ;t−1 L f ;t

ð7Þ

where Wt refers to the real wage at period t with policy shock; Wf, t is the real wage without policy simulation in the forecasting period; Lt is employment in the policy scenario; Lf, t is employment in the benchmark; α is the adjustment parameter, which has a positive value; and ft is a shift variable assumed to be endogenous in the historical calibration but exogenous in the policy simulation. Formula (7) shows that the change in current-period wage is related to both last-period and current-period labor change. For more details please refer to Appendix A.

The empirical simulation includes a 15-year historical calibration simulation (2002–2016) and a policy simulation (2017–2030). In the historical calibration period, the model takes the 2002 input–output table as the initial database and obtains key parameters through calibration of historical macroeconomic variables. The calibration data are of three types: annual macroeconomic data, including real GDP, the GDP deflation index, investment, consumption, imports, exports, population, and employment, from the China Statistical Yearbook 2017 (NBSC, 2017a); data of various fossil energies, including production, consumption, imports, and exports, from the China Statistical Yearbook 2017 (NBSC, 2017b); the carbon emission factors of each fossil energy from the IPCC (2006); and China's annual carbon emissions, from BP (2017). In the historical calibration period, macroeconomic variables are set to be exogenous and relevant technological parameters and shift variables are set to be endogenous. On the contrary, in the policy simulation period, the above exogenous and endogenous variables are replaced to explore potential scenarios for China's economic growth under different policy assumptions. Regarding the population before 2030, we refer to the medium birth rate scenario in the World Population Prospects: The 2017 Revision (UN, 2017). 4.2. Policy scenarios Table 1 shows three different policy scenarios–baseline, moderate, and strengthened–based on the main driving forces of carbon emissions and energy demand according to existing planning and mid- and longterm development targets. The baseline scenario assumes that China continues its current efforts toward energy saving and emission reduction, and the values of each macroeconomic variable are obtained from historical trend extrapolation. Relevant research (Chen et al., 2016; Song and Cui, 2016; Green and Stern, 2017) is consulted for assumptions about China's economic growth before 2030, including that China is under increasing pressure due to an economic downturn. Annual average growth of real GDP is assumed to be 6.5% from 2017 to 2020, 5.5% from 2021 to 2025, and 4.5% from 2026 to 2030. In the baseline scenario, the growth rate of real GDP is assumed to be an exogenous variable while technological progress is endogenous. It is noteworthy that despite economic pressure, China will still increase investment and stimulate consumption to promote reasonable economic growth. The sensitivity analysis in Section 6 explores the influences of different economic growth levels on the results. The empirical simulation using the CDECGE model shows that in the baseline scenario, carbon intensity in China in 2020 (2030) would be reduced by 49.88% (61.48%) compared with 2005, exceeding the upper limit target of 45% in 2020 (exceeding the lower limit target of 60% but failing to achieve the upper limit target of 65%). To explore the influence of stricter environmental regulations on the Chinese economy, Scenario 2 assumes that carbon intensity in 2030 decreases by 62.5% from 2005 (moderate scenario) while scenario 3 assumes that carbon intensity in 2030 decreases by 65% from 2005 (strengthened scenario). It is assumed that carbon emissions from fossil energy combustion are punished through pricing, and given that China can realize its carbon Table 1 Policy scenarios. Scenario code

Scenario description

Baseline scenario Moderate scenario Strengthened scenario

Extrapolation as per historical trend Carbon intensity in 2030 decreases by 62.5% compared with 2005; CNY 25/ton of CO2 from 2021 to 2030 Carbon intensity in 2030 decreases by 65% compared with 2005; CNY 83/ton of CO2 from 2021 to 2030

L. Cui et al. / Energy Economics 83 (2019) 61–73

intensity reduction target in 2020, the price of carbon is further assumed to be unchanged and effective from 2021 to 2030. Through multiple rounds of simulation using the CDECGE model, the carbon price under the moderate scenario is approximately CNY 25/tCO2 and the carbon price under the strengthened scenario is approximately CNY 83/tCO2. 5. Results 5.1. Overall macroeconomic index Table 2 shows that accumulated energy consumption in China from 2017 to 2030 will be 75.99 billion tce under the baseline scenario versus 74.89 billion tce, or 1.45% lower, under the moderate scenario; accumulated CO2 emissions will be 156.24 billion tons under the baseline scenario versus 152.23 tons, or 2.56% lower, under the moderate scenario. The introduction of carbon pricing will increase the cost of fossil energy use, and thus generate negative impacts on the national economy. Accumulated GDP from 2017 to 2030 will be CNY 980.19 trillion (constant 2002 prices) under the moderate scenario, a decrease of 0.09% from the baseline scenario. With more ambitious targets, China would suffer greater macroeconomic losses, but energy savings and carbon reduction would be more pronounced. As shown in Table 2, reducing carbon intensity by 65% in 2030 compared to 2005 will result in accumulated GDP of CNY 978.21 trillion, a decrease of 0.29% from the baseline; accumulated energy consumption will be 72.38 billion tce, or a decrease of 4.74%; and accumulated carbon emissions will be 143.18 billion tons, or a decrease of 8.35%. 5.2. Real GDP Fig. 2 shows that China's real GDP will keep growing in future, but the growth rate will slow down if stricter carbon abatement measures are introduced. The carbon price will have a large impact on the economy in the early phase of the policy shock. With the continuous adjustment, feedback, and absorption of the economic system, this negative impact will gradually abate over time. Specifically, under the moderate scenario, real GDP in 2021 is CNY 60.63 trillion, a decrease of 0.36% from the baseline scenario. However, by 2030, the value of this index will be CNY 93.93 trillion, a decrease of about 0.01% from the no-tax situation. If China implements stricter carbon reduction targets in the future, that is, carbon intensity decreases 65% in 2030 compared with 2005, there will be a higher macroeconomic cost. The real GDP in 2021 will be about CNY 60.11 trillion, decreasing significantly by 1.22% compared with the baseline scenario. In 2030, the value of this index will be CNY 93.89 trillion, a decrease of 0.01% from the baseline scenario. In sum, the stricter the environmental regulation, the larger the macroeconomic losses suffered. However, such negative impacts mainly occur in the short term and convergence in the long run is certain. 5.3. Energy consumption With economic growth, China's energy consumption will increase rapidly, but the growth rate will be constrained by the strength of environmental regulation. As shown in Fig. 3, China's primary energy consumption in the baseline scenario will increase from 3.38 billion tce in

65

2010 to 4.85 billion tce in 2020 and 6.56 billion tce in 2030. The economic development mode in the baseline scenario is generally extensive and energy utilization efficiency is low. Therefore, energy is in great demand and energy supply pressure is high. China's energy consumption is projected to increase 47.79% from 2010 to 2030, compared with 47.29% estimated by Zhang et al. (2016). A more stringent price constraint on carbon emissions is helpful in restricting the everincreasing tendency of energy consumption. Specifically, in the moderate scenario, China's energy consumption will increase to 4.84 billion tce in 2021 and to 6.47 billion tce in 2030, or by 2.68% and 1.38% less than in the baseline scenario, respectively. Under the strengthened scenario, national energy consumption will increase to 4.55 billion tce in 2021 and to 6.26 billion tce in 2030, or 8.64% and 4.70% less than in the baseline scenario, respectively, and 6.12% and 3.37% less than in the moderate scenario, respectively. Similar to the change in real GDP, the energysaving effect in the early phase of the policy shock will be significant. With low-carbon transformation of the energy mix, the influence of a carbon policy on energy consumption might decline gradually. Fig. 4 shows China's energy intensity changes under different scenarios. Since energy consumption grows faster than the economy, energy intensity increased from 1.27 tce/CNY 10,000 in 2002 to 1.51 tce/ CNY 10,000 in 2006, or at a rate of 19.06%, owing to rapid industrial development and a sharp increase in energy exports (Guan et al., 2014). Thereafter, energy intensity was in a continuously declining trend at a rate of 22.21% during the 11th Five-year Plan (2006–2010) and 14.74% during the 12th Five-year Plan (2011–2015). The study indicates that the decline in energy intensity from 2016 to 2020 is expected to be 15.95%. Energy intensity will decrease further when stricter carbon abatement measures are adopted. As shown in Fig. 4, in the baseline scenario, energy intensity will decrease from 0.82 tce/CNY 10,000 in 2021 to 0.70 tce/CNY 10,000 in 2030, a rate of 14.6%. In the moderate scenario, it will decrease by 13.75%, and in the strengthened scenario, it will decrease by 11.95%. Compared with the baseline scenario, China's energy intensity in 2030 will decrease by 1.36% and 4.65% in the moderate and strengthened scenarios, respectively. Overall, regardless of the strength of environmental regulation, China's energy intensity will decrease more than 50% by 2030 compared with 2005. This finding implies that China's energy efficiency is expected to greatly improve in the future. 5.4. Energy mix China's carbon abatement efforts will have complex and profound influences on the energy mix. It will raise the consumption price of fossil energy, stimulating the low-carbon transformation. High-carbon energies, such as coal, will be constrained while low and no-carbon energies, such as natural gas and non-fossil energy, will be favored. Fig. 5 shows the variations in China's energy mix under different abatement efforts. Fig. 5(a) details the corresponding changes in the baseline scenario, in which different energy indicators vary significantly. Since coal dominates China's energy mix, its consumption is expected to increase before 2030 due to national economic expansion, although the proportion will decrease from 57.64% in 2020 to 47.78% in 2030. Inspired by a series of promoting policies of low-carbon energy, China's natural gas and nonfossil energies may increase in the future, and the shares of these two kinds of energy will increase from 6.50% and 14.73% in 2020 to 11.75%

Table 2 Accumulated variations of main macroeconomic indexes. Real GDP

Baseline scenario Moderate scenario Strengthened scenario

Energy consumption

Carbon emissions

2017–2030 (CNY trillion)

Change from baseline (%)

2017–2030 (billion tce)

Change from baseline (%)

2017–2030 (billion tCO2)

Change from baseline (%)

981.04 980.19 978.21

– −0.09 −0.29

75.99 74.89 72.38

– −1.44 −4.74

156.24 152.23 143.18

– −2.56 −8.35

100

1.50

80

1.20

60

0.90

40

0.60

20

0.30

0

0.00 -20 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 -0.30 -40

-0.60

-60

-0.90

-80

-1.20

-100

Variation of the real GDP (%)

L. Cui et al. / Energy Economics 83 (2019) 61–73

The real GDP˄CNY Trillion˅

66

-1.50

Baseline scenario Moderate scenario compared to baseline scenario Strengthened scenario Moderate scenario compared to baseline scenario Strengthened scenario compared to baseline scenario Fig. 2. Variation trends of real GDP under different scenarios.

percentage points, respectively. Overall, regardless of the strength of carbon emission control, coal remains dominant in China's energy consumption, although its share continues to decline until 2030. 5.5. Carbon emissions China's carbon emissions are expected to increase before 2030 and there will be severe pressure to reduce emissions. As shown in Fig. 6, China's carbon emissions in the baseline scenario will increase from 8.12 billion tCO2 in 2010 to 10.26 billion tCO2 in 2020, an average annual growth rate of 2.37%. Since China is still in the process of industrialization and urbanization, carbon emissions after 2020 are likely to increase continuously, but the growth rate will slow slightly. Carbon emissions in 2030 are expected to reach 12.85 billion tCO2, an increase of 27.7% from 2020 and an average annual growth rate of 2.27%. The estimation results are in line with those of Grubb et al. (2015). As shown in Fig. 6, the price mechanism can be an effective tool for reducing carbon emissions, but it is difficult for China to achieve the target of peaking CO2 emissions before 2030. In particular, in the moderate scenario, national carbon emissions will increase from 10.02 billion tons in 2021 to 12.49 billion tons in 2030 at an average annual growth rate of 2.48%. Compared with the baseline scenario, carbon emissions in 2021 and 2030 will be lower by 4.36% and 2.75%, respectively. Moreover, in the strengthened scenario, China's carbon emissions will increase

7 6 5 4 3 2 1 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Pimary energy consumption (Billion tce)

and 17.52% in 2030. These results can be compared with other studies; for example, the coal consumption share predicted for 2020 is consistent with Guan et al. (2018). Fig. 5(b) presents China's energy mix in the moderate scenario. To reduce its carbon intensity by 62.5% relative to 2005 levels by 2030, China should adopt additional carbon abatement measures, which would further reduce fossil energy consumption. However, coal is still dominant in China's energy mix. Specifically, in 2030, the shares of coal, petroleum, natural gas, and non-fossil energy will be 46.81%, 23.19%, 11.83%, and 18.17%, respectively. Compared with the baseline, the share of coal will decrease by 0.97 percentage points while the shares of non-fossil energy, petroleum, and gas will increase 0.65, 0.24, and 0.08 percentage points, respectively. Overall, carbon pricing will be useful for China's low-carbon transformation, but not all fossil energies will be reduced. As coal is high-carbon energy, the carbon price policy will suppress coal consumption more significantly. In contrast, the proportions of petroleum and natural gas will increase. Fig. 5(c) displays China's energy mix in the strengthened scenario. It shows that China's low carbon energy transition will be accelerated if more stringent measures are adopted. In 2030, the energy mix comprises coal (44.38%), petroleum (23.78%), natural gas (12.04%), and non-fossil energy (19.81%). Compared with the baseline, the share of coal will be reduced 3.4 percentage points and the shares of non-fossil energy, petroleum, and natural gas will increase 2.29, 0.82, and 0.29

Baseline scenario

Moderate scenario

Strengthened scenario

Fig. 3. Variation trends of primary energy consumption in China under different scenarios.

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1.60 1.40 1.20 1.00 0.80 0.60 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Energy Intensity (tce/CNY 10000)

L. Cui et al. / Energy Economics 83 (2019) 61–73

Baseline scenario

Moderate scenario

Strengthened scenario

Fig. 4. Variation trend of energy intensity of China under different scenarios.

from 9.03 billion tons in 2021 to 11.66 billion tons in 2030, or an average annual growth rate of 2.88%. Compared with the baseline scenario, carbon emissions in 2021 and 2030 will be lower by 13.81% and 9.24%, respectively. Fig. 7 illustrates China's carbon intensity changes under different scenarios. Similar to energy intensity, China's carbon intensity will continuously decrease with technology progress and adoption of energy-saving and emission reduction measures. Specifically, with current abatement efforts, China's carbon intensities will be 1.78 tCO2/CNY 10,000 (a decrease of 49.88% from 2005) and 1.37 tCO2/CNY 10,000 (a decrease of 61.45% from 2005) in 2020 and 2030, respectively. In particular, with slowing economic growth during the 13th Five-year Plan, it is expected that carbon intensity will decrease by 18.89%, realizing the national target of 18%. In the strengthened scenario, the decreasing amplitude of carbon intensity will increase and then shrink gradually over time. China's carbon intensity will decrease from 1.50 tCO2/CNY 10,000 in 2021 to 1.24 tCO2/CNY 10,000 in 2030; compared with the baseline scenario, carbon intensity will decrease 12.75% and 9.19%, respectively. 5.6. Linkages between energy and carbon targets This subsection explores the linkages between China's energy targets and carbon targets and discusses each energy index change when China delivers on its carbon reduction commitments. Regarding the energy targets, three official documents are referenced: Energy Production and Consumption Reform Strategy (2016–2030) (NDRC, 2016); Work Program for Greenhouse Gas Emission Control of the 13th Five-year Plan (SCC, 2016); and About Accelerating Natural Gas Utilization (NDRC, 2017). Energy targets primarily involve total energy consumption, energy mix, and energy intensity. China proposed restricting total energy consumption to within 5 billion tce in 2020 and 6 billion tons in 2030. The shares of non-fossil energy and natural gas in primary energy consumption should be 15% and 10%, respectively, in 2020 and 20% and 15%, respectively, in 2030. China proposed that energy intensity should be reduced by 15% during the 13th Five-year Plan period, but did not set a target for 2030. Table 3 details the linkages between China's energy targets and carbon targets for 2020 and 2030. It also reveals the potential coupling effects between these two kinds of targets over time. In particular, with more and more stringent carbon abatement targets, the easier to achieve the total energy and the non-fossil energy targets, and is less difficult to achieve the natural gas target. As shown, regarding carbon intensity, the national carbon intensity will decrease by 18.89% from 2015 to 2020, exceeding the target of 18%. Meanwhile, carbon intensity

will decrease by 49.88%, exceeding the upper target of 45%. The national carbon intensity in 2030 will decrease by 61.48% from 2005, which is higher than 60% but lower than 65%. These results indicate that if current abatement efforts are maintained, China is likely to realize its carbon intensity reduction commitments, but fail to realize the upper target of 65% in 2030. This conclusion is consistent with that of Yang et al. (2016). Regarding energy consumption, China's economic downturn will restrain energy consumption. The national energy demand in 2020 is projected to be 4.85 billion tce, which is smaller than the target of 5.00 billion tce. Since China is still in the process of industrialization and urbanization, the national energy demand will continue to increase before 2030, and total energy consumption in 2030 is expected to vary from 6.26 to 6.56 billion tce, indicating that even if low-carbon measures are strengthened, China faces difficulties in restricting total energy consumption to less than 6 billion tce. Regarding energy mix, since China has vigorously supported and guided the utilization of new energies in recent years, the proportion of non-fossil energy in 2020 will be 14.74%, which is close to the target of 15%. However, the 2030 target requires strengthened low-carbon measures. For example, the share of non-fossil energy in the strengthened scenario will be 19.81%. Similarly, Yang et al. (2016) found that the share of non-fossil energy consumption will increase from 16% in 2020 to 18% in 2030 in the baseline scenario, and increase from 17% to 23% in the strengthened scenario. Our simulation results fall squarely in these intervals. As shown in Table 3, China faces difficulties in realizing the natural gas target in 2030, and the share of natural gas in 2030 will be only 12.04% even in the strengthened scenario, which is lower than 15%. Overall, China's carbon abatement pledge cannot guarantee the achievement of all energy objectives. With its current abatement efforts, China may face difficulties in restricting total energy consumption to less than 6 billion tce and increasing the shares of non-fossil energy and natural gas to 20% and 15%, respectively, in 2030. More stringent carbon abatement measures (i.e., carbon intensity in 2030 decreases by 65% compared with 2005) will reduce the difficulty of reaching these energy targets, although the natural gas target will remain challenging. 6. Sensitivity analysis Sensitivity analysis is undertaken from two perspectives—economic growth and key parameters of elasticity of substitution—to discuss the impacts of parameter value changes on China's energy consumption and carbon emissions beyond 2030.

L. Cui et al. / Energy Economics 83 (2019) 61–73

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Energy mix

68

Coal

Crude oil

Natural gas

Non-fossil Energy

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Energy mix

(a) Baseline scenario

Coal

Crude oil

Natural gas

Non-fossil Energy

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Energy mix

(b) Moderate scenario

Coal

Crude oil

Natural gas

Non-fossil Energy

(c) Strengthened scenario Fig. 5. Variations in energy mix in China under different scenarios.

6.1. Economic growth rate When using the CDECGE model for empirical analysis, future macroeconomic trends are important input variables because the economic growth rate will not only affect energy demand and energy intensity, but also change variation trends in carbon emission and carbon intensity, eventually affecting the realization of China's energy and carbon targets in future. In this subsection, sensitivity analysis of the economic growth rate is undertaken by developing two scenarios. In the first, the

average annual growth rate of real GDP from 2017 to 2030 is assumed to increase by 1 percentage point from the base (the high growth scenario) and in the second it is assumed to decrease by the same amount (the low growth scenario). Table 4 shows the achievement or non-achievement of China's energy targets and carbon targets under different economic growth assumptions. Regarding the total energy consumption, China is likely to realize the 2020 target, but the 2030 target can only be achieved with a low economic growth rate and additional carbon abatement efforts.

Carbon emission (100 million tCO2)

L. Cui et al. / Energy Economics 83 (2019) 61–73

69

140 120 100 80 60 40 20

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

0

Baseline scenario

Moderate scenario

Strengthened scenario

Fig. 6. Variation of carbon emissions in China under different scenarios.

This is inconsistent with the conclusion in Table 3 that China is unable to restrict total energy consumption to 6 billion tce by 2030. Since an economic downturn will restrain energy consumption, it will also decrease the difficulty of realizing the 2030 energy target. Regarding the energy mix, China is likely to increase the share of non-fossil energy to approximately 15% in 2020, but the 2030 target cannot be achieved with low economic growth. Even so, the differences will shrink gradually with the enhancement of environmental regulation. As shown in Table 4, a higher economic growth rate implies a larger share of non-fossil energy, indicating that China's economic growth is conducive to the national low carbon transformation. In both economic growth scenarios, China is unlikely to achieve the natural gas development goals of 2020 and 2030, and this is consistent with the conclusion in Table 3. 6.2. Elasticity of substitution

7. Conclusions and policy implications China faces multiple challenges, including energy shortages, environmental pollution, and a declining rate of economic growth. The Chinese government has not only enacted low carbon development planning for 2020 and 2030, but also placed constraints on total energy consumption, energy intensity, and energy mix in the future. Exploring

4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Carbon intensity (tCO2/CNY 10000˅

Elasticity of substitution in the production function greatly influences empirical results. In this section, sensitivity analysis of elasticity of substitution is therefore undertaken by changing these values. Three main types of elasticity of substitution are involved: that between capital and labor; that between the energy set and factor set; and that among fossil energies. Specifically, two scenarios with high and low elasticity of substitution are developed by adding and subtracting 10% from the base, respectively.

Table 5 shows the achievement or non-achievement of China's energy targets and carbon targets under different assumptions of elasticity of substitution. Regarding the total energy consumption, China is likely to realize the 2020 target, but the 2030 target cannot be achieved, and this is consistent with the conclusion in Table 3. Regarding the energy mix, the development of non-fossil energy is sensitive to the elasticity parameters. China will be able to realize its development targets in 2020 and 2030 under high elasticity of substitution, but will achieve neither under low elasticity of substitution. This is because elasticity of substitution determines the difficulty of substitution between fossil and non-fossil energies. Under high elasticity of substitution, with an increase in the consumption price of fossil energy, enterprises will use more non-fossil energy to offset cost increase, leading to increased fossil energy consumption. The natural gas target is a significant challenge for China, and whatever the elasticity of substitution, it is unlikely the share of natural gas will increase to 10% in 2020 and 15% in 2030, which is consistent with the conclusion in Table 3.

Baseline scenario

Moderate scenario

Strengthened scenario

Fig. 7. Variation in carbon intensity in China under different scenarios.

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L. Cui et al. / Energy Economics 83 (2019) 61–73

Table 3 Linkages between China's energy targets and carbon targets under different scenarios. Control targets

Energy consumption (billion tce) Non-fossil energy (%) Natural gas (%) Carbon intensity (%) Energy intensity (%)

Baseline scenario

Moderate scenario

Strengthened scenario

2020

2030

2020

2030

2020

2030

2020

2030

5.00 15.00 10.00 18.00a 15.00c

6.00 20.00 15.00 60.00–65.00b –

4.85 14.74 6.50 18.89a 15.95c

6.56 17.52 11.75 61.48b –

4.85 14.74 6.50 18.89a 15.95c

6.47 18.17 11.83 62.50b –

4.85 14.74 6.50 18.89a 15.95c

6.26 19.81 12.04 65.00b –

There is temporarily no control target for 2030. a Decreasing amplitude of carbon emission intensity in 2020 compared with 2015. b Index of carbon emission intensity in 2030 relative to 2005. c Decreasing amplitude of energy intensity in 2020 compared with 2015.

the linkages between energy targets and carbon targets is of vital importance to the cost-effective realization of these objectives. The purpose of this study is to investigate whether China's various energy targets for 2020 and 2030 could be simultaneously achieved if the country's carbon intensity target for 2030 were achieved. By introducing the CDECGE model, this study simulates the variation trends in China's economic growth, energy consumption, and carbon emission under different policy scenarios. The promoting function of carbon abatement on each energy objective was also evaluated. This study finally provides a sensitivity analysis in terms of economic growth and substitution elasticity. The following interesting findings are drawn from the empirical analysis. 1) With current emission reduction efforts, China is likely to realize the upper limit of the carbon intensity target in 2020 and the lower limit in 2030; however, the decreasing amplitude of carbon intensity in 2030 cannot be controlled to within 65%. 2) Affected by slowing economic growth, China is likely to realize the energy consumption target in 2020, but will have difficulty restricting total energy consumption in 2030 to 6 billion tce. 3) Introducing a price constraint on carbon emissions has significant energy-saving and emission reduction effects, but also generates negative effects on economic growth. In the strengthened scenario, accumulated GDP from 2017 to 2030 will drop 0.29%, accumulated energy consumption will drop 4.74%, and accumulated carbon emissions will drop 8.35% compared with the baseline scenario. 4) China is likely to realize the non-fossil energy target in 2020, but it will be difficult to raise the non-fossil share to 20% by 2030, although adoption of strengthened abatement measures would shrink the gap. 5) The natural gas targets in 2020 and 2030 are a significant challenge for China, indicating that development of natural gas in the future will require additional policy support. This research suggests that the adoption of carbon abatement measures help China restrict energy consumption, reduce energy intensity, stimulate non-fossil energy development, and accelerate low-carbon transformation of its energy mix, that is, carbon emission reduction has a multidimensional synergy effect with energy development. Although carbon emission control cannot guarantee the realization of all

energy targets, it will reduce the difficulty of accomplishing these targets in China. This finding is significant to the cost-effective realization of the dual control of energy and carbon emissions. This study reveals the gaps in each energy index with regard to its expected target under different abatement scenarios, specifying the direction and degree of efforts for future energy-saving and emission reduction. More specifically, with current abatement efforts (the baseline scenario), China still faces difficulties in achieving its national energy targets by 2030. Further efforts should therefore be devoted to reducing the total energy consumption by 0.56 billion tce and increase non-fossil energy share and natural gas share by 2.48 percentage points and 3.25 percentage points, respectively. If China were to adopt stringent carbon abatement measures (the strengthened scenario), the non-fossil energy target could be achieved naturally, but China needs to undertake additional efforts to reduce energy consumption and promote the development of natural gas. In particular, the total energy consumption should be reduced by 0.26 billion tce, and the natural gas share should be increased by 3 percentage points to reach its expected level by 2030. This study shows that China's natural gas target is radical, which is consistent with previous research. Chi et al. (2014) pointed out that the share of natural gas consumption in 2030 would be only 7.03% in the baseline scenario. Yang et al. (2016) found that the share of natural gas consumption would increase from 7% in 2020 to 10% in 2030 in the strengthened scenario. Therefore, based on current emission reduction efforts, it will be difficult for China to raise the share of natural gas consumption to 15% by 2030. The reasons are as follows. China's resource endowment is defined by “much coal, little gas.” The increase in natural gas consumption share is not significant under the condition of rapid demand for total primary energy consumption, even though natural gas consumption has increased rapidly in recent years. To achieve the natural gas goal by 2030, China needs to take more efforts. From the supply side, China needs to continue to deepen natural gas cooperation with Russian and Central Asian countries, further strengthen the construction of natural gas transportation pipelines, and improve natural gas supply and transportation capacity. In terms of demand, China should actively cultivate the urban natural gas market, promote the

Table 4 Sensitivity analysis of economic growth rate. Baseline scenario

Energy consumption (billion tce) Non-fossil energy (%) Natural gas (%) Carbon intensity (%) Energy intensity (%) a b c

Moderate scenario

Strengthened scenario

High growth

Low growth

High growth

Low growth

High growth

Low growth

2020

2030

2020

2030

2020

2030

2020

2030

2020

2030

2020

2030

4.96 15.04 6.46 18.85a 15.63c

7.07 18.35 11.25 61.56b –

4.74 14.43 6.55 18.83a 16.27c

6.09 16.62 12.30 61.40b –

4.96 15.04 6.46 18.85a 15.63c

6.95 19.11 11.33 62.61b –

4.74 14.43 6.55 18.83a 16.27c

6.00 17.33 12.40 62.72b –

4.96 15.04 6.46 18.85a 15.63c

6.73 20.72 11.52 65.00b –

4.74 14.43 6.55 18.83a 16.27c

5.81 18.83 12.61 65.04b –

Decreasing amplitude of carbon intensity in 2020 compared with 2015. Decreasing amplitude of carbon intensity in 2030 compared with 2005. Decreasing amplitude of energy intensity in 2020 compared with 2015.

L. Cui et al. / Energy Economics 83 (2019) 61–73

71

Table 5 Sensitivity analysis of elasticity of substitution. Baseline scenario

Energy consumption (billion tce) Non-fossil energy share (%) Natural gas share (%) Carbon intensity (%) Energy intensity (%) a b c

Moderate scenario

Strengthened scenario

High elasticity of substitution

Low elasticity of substitution

High elasticity of substitution

Low elasticity of substitution

High elasticity of substitution

Low elasticity of substitution

2020

2030

2020

2030

2020

2030

2020

2030

2020

2030

2020

2030

4.84 16.09 6.37 19.96a 16.16c

6.44 20.55 11.06 63.21b –

4.85 13.58 6.66 17.95a 15.81c

6.64 15.03 12.25 60.05b –

4.84 16.09 6.37 19.96a 16.16c

6.34 21.33 11.17 64.40b –

4.85 13.58 6.66 17.95a 15.81c

6.54 15.71 12.34 61.23b –

4.84 16.09 6.37 19.96a 16.16c

6.14 23.01 11.38 66.76b –

4.85 13.58 6.66 17.95a 15.81c

6.33 17.14 12.54 63.60b –

Decreasing amplitude of carbon intensity in 2020 compared with 2015. Decreasing amplitude of carbon intensity in 2030 compared with 2005. Decreasing amplitude of energy intensity in 2020 compared with 2015.

replacement of coal and fuel gas in urban areas. In addition, decisionmakers should also vigorously promote the use of natural gas in the field of transportation, and promote the use of natural gas vehicles. This study has two main limitations. First, although it discussed linkages between energy and carbon targets, it failed to provide the optimal cost of coordinated control between them. This is because the energy targets involve many indexes, and each index corresponds to different technology options and policy measures that cannot all be included in the model. Moreover, interactions among energy indexes, the uncertainty of technology progress, and the lack of availability of key data made it difficult to estimate the economic cost of all the energy targets. Second, for simplicity, this study considered non-fossil energies as a whole without differentiating the generation technologies of different non-fossil energies. Therefore, the influences of technological progress and technological substitution of nuclear power, wind power, solar power, and hydropower cannot be simulated. The next step is to expand and improve the original model to simulate the influences of policies with respect to different non-fossil energies on the realization of China's energy-saving and emission reduction targets in 2030. Acknowledgments This work was supported by the National Natural Science Foundation of China (Nos. 71503001 and 71471001) and supported by Provincial Natural Science Research Project of Colleges and Universities in Anhui Province (No. KJ2019A0649). We would like to thank Editage [www.editage.cn] for English language editing.

illustrate the production functions, consumption module, and imports and exports. A.1. Production module Similar to other CGE models, CDECGE applies a nested structure to represent production technology. Cost minimization results in the appropriate input–output demand in each level of the production tree. At first, the Leontief form is satisfied among non-energy commodities, energy-factor composites, and other costs; that is, each input satisfies the fixed proportion assumption in the total production cost. The specific function is Min

X

PQ j
X j;i þ PKLEi
KLEi

j

S:t: Z i ¼ Min



X 1;i X ne;i KLEi ;… ; α 1;i α ne;i αKLEi

ðA1Þ



where PQj is the price of the jth intermediate input; PKLEi is the demand price of production sector i to value-added energy composite; Zi is the output level of sector i; Xj, i is non-energy commodity j used to produce product i; KLEi is the factor-energy input composite required to produce the ith commodity; and α1, i, αne, i and αKLEi are technological variables. Under given constraints of technological conditions, producers choose the proper input composite to minimize enterprise production costs. Second, the primary factors of the production set and fossil energy set are composed to form the production factor-energy set. Assume this layer satisfies the following CES function.

Declaration of Competing Interest No potential conflict of interest was reported by the authors. Appendix A. Description of the CDECGE model

Min PEnergyi
Energyi þ PVAi
VAi

1  
 ρEVA;i ρEVA;i ρ EVA;i S:t:KLEi ¼ AKLE;i
α energy;i
Energyi þ 1−α energy;i
VAi ðA2Þ

The CDECGE model has three features. First, it stresses that technological change in production factors should reflect the influences of technological progress of factor use on production and department capital formation; furthermore, it assumes there are preference parameters for residents' consumption and government consumption for the convenience of using historical data to calibrate the model. Second, when carrying out dynamic design, the higher the capital stock, the lower the expected rate of return on capital; however, this rate can also affect current-period newly added investment, which can change capital stock in the next period. The dynamic nature of the model is driven by the dynamic accumulation of capital. Finally, employment has special treatment in the CDECGE model. The flow of the labor force among industries relates not only to the relative wage rate but also to the relative ability of different sectors to absorb employment. Under the impact of external policies, wages gradually adjust the labor market to a new equilibrium (Bi et al., 2013; Song and Cui, 2016). This section provides additional discussion of the CDECGE model. More specifically, we

where PVAi is the composite price of value added of production sector i; PEnergyi is the energy composite price of production sector i; Energyi is the level of energy composite used to produce i; VAi is the level of factor input; AKLE, i is the technology variable; αenergy, i is the share parameter; ρEVA, i is the substitution parameter between energy and value added; and the elasticity of substitution is σEVA, i = 1/(1 − ρEVA, i). Third, based on the CES function assumption, the primary production factor set is composed of labor force and capital; the energy set is composed of electric power and fossil energy, where electric power mainly refers to electricity generated by non-fossil energies, including nuclear energy, hydropower, wind power, and solar power. Min W i
Li þ Ri
K i

1  
 ρVA;i ρVA;i ρ VA;i S:t:VAi ¼ AVA;i
α L;i
Li þ 1−α L;i
K i

ðA3Þ

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L. Cui et al. / Energy Economics 83 (2019) 61–73

Min Pfossili
Fossili þ PQ elec
X elec;i

1

 
 ρ ρen;i ρ en;i S:t:Energyi ¼ Aenergy;i
α fossil;i
Fossili en;i þ 1−α fossil;i
X elec;i ðA4Þ where Wi is the wage rate of sector i; Ri is the capital price of sector i; Li is the labor input quantity needed to produce product i; Ki is the required capital input; Fossili is fossil energy input; Xelec, i is the primary electric power input; AVA, i, Aenergy, i are variables of technology; αL, i, αfossil, i are share parameters; ρVA, i is the substitution parameter among value added, and the elasticity of substitution is σVA, i = 1/(1 − ρVA, i); and ρen, i is the substitution parameter among fossil energies, and the elasticity of substitution is σEN, i = 1/(1 − ρEN, i). Finally, the fossil energy set is composed of coal, crude oil, natural gas, refined oil, coke, and fuel gas as per the CES and specific function. Min

X

PQ f
X f ;i

f

S:t:Fossili ¼ AFossil;i


∑ α f ;i
f

ρ X f fo;i ;i

! 1 ρVA;i

ðA5Þ

where PQf is the price of six kinds of fossil energy intermediate inputs; Xf, i is the demand level of sector i for fossil energy; AFossil, i is a technology variable; αf, i is a share parameter; ρfo, i is the parameter substitution among fossil energies; and the elasticity of substitution is σfo, i = 1/(1 − ρfo, i).

Xluxi
PSi ¼ Sluxi
Vluxi

ðA10Þ

Xsubi ¼ Q
Asubi

ðA11Þ

As shown in Eq. (A9), the Xluxi is luxury usages, or the difference between the subsistence quantities and total demand. Eq. (A10) states that luxury expenditure follows the marginal budget share Sluxi. Together, Eqs. (A9) and (A10) are equivalent to Eq. (A7). Eq. (A11) is necessary because our demand system applies to the aggregate instead of to individual households. It states that total subsistence demand for each good i is proportional to the number of households, Q, and to the individual subsistence demands, Asubi. A.3. Imports and exports With regard to imports, similar to many CGE models, the CDECGE model assumes that imported products and domestic products are imperfectly substituted, that is, the Armington assumption is satisfied and the two are composed as per CES. Commodities after composition mainly follow three directions: government consumption, residents' consumption, and enterprise intermediate inputs. The CDECGE model adopts a small-country assumption with regard to imports, that is, the average global price of imported products is constant and import demands are decided simultaneously by domestic demand and trade conditions (Song and Cui, 2016). Import tariffs are levied on landed imported commodities and then circulated in the domestic market. PMi ¼ PW i
ð1 þ mtaxi Þ
phi

ðA12Þ

A.2. Consumption module The CDECGE model assumes that government consumption satisfies the Cobb–Douglas function, that is, the consumption share of the government for every commodity is constant. As for residents' consumption, it assumes residents are price receivers and choose which kind of commodity to consume, given their disposable income, to maximize their utility function (Song and Cui, 2016). Residents' disposable income is the result of total residents' income minus savings and the proportion of savings in total income is assumed constant. Total residents' income primarily comes from wages, capital gains, and transfer payments from the government. Residents' consumption satisfies the Klein– Rubin function: 1 Y UP ¼ ðXsi −Xsubi ÞSluxi Q i

ðA6Þ

where UP is utility per household, Q is the number of households, Xsi and Xsubi are behavioral parameters, and the Sluxi must sum to unity. The demand equations that arise from this utility function are Xsi ¼ Xsubi þ Sluxi
Vluxi =PSi

ðA7Þ

Vluxi ¼ Vtoti −∑i Xsubi
PSi

ðA8Þ

where PWi is the average global price of the ith commodity; PMi is the price of commodity i after entering the domestic market; mtaxi is the import tariff; and phi is the exchange rate level. With regard to exports, the CDECGE model assumes that export demand is depicted by a downward-sloping curve of fixed price elasticity, in which the fixed elasticity of export demand of commodities that China provides to the global market in small proportion can be set as infinite, that is, variation of exports from China will not affect global supply or change the global market (Song and Cui, 2016). For commodities exported in large amounts from China, the fixed elasticity of export demand will be limited, yielding   ER −γi Ei ¼ FQ i
PEi
FP i

ðA13Þ

where PEi is the export price; Ei is the export volume; γi is the fixed price elasticity of export demand; ER is the nominal exchange rate; and FQi and FPi represent the transfer variables of export quantity calculated in US dollars and CNY, respectively, to reflect changes in export demand resulting from these two factors. Thus, the price level of exported products is decided simultaneously by domestic production cost, export policy, and the exchange rate. Appendix B. Supplementary data

The name of the linear expenditure system is derived from the property that expenditure on each good i is a linear function of price (PSi) and expenditure (Vtoti). The form of the demand equations gives rise to the following interpretation. The Xsubi are said to be the “subsistence” requirements of each good i, where the quantities are purchased regardless of price. Vluxi is what remains of the consumer budget after subsistence expenditures are deducted, and we call this “luxury” or “supernumerary” expenditure. The Sluxi is the share of this remnant allocated to good i (the marginal budget share). The following formulas can be obtained by previous analysis. Xsi ¼ Xsubi þ Xluxi

ðA9Þ

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