Can China achieve its carbon intensity target by 2020 while sustaining economic growth?

Ecological Economics 119 (2015) 209–216

Contents lists available at ScienceDirect

Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Analysis

Can China achieve its carbon intensity target by 2020 while sustaining economic growth? Bangzhu Zhu a,b,⁎, Kefan Wang c, Julien Chevallier d,e,⁎, Ping Wang b, Yi-Ming Wei f,g,h a

School of Management, Jinan University, Guangzhou, Guangdong 510632, China Institute of Resource, Environment and Sustainable Development Research, Jinan University, Guangzhou, Guangdong 510632, China School of Economics and Management, Wuyi University, Jiangmen, Guangdong 529020, China d IPAG Business School (IPAG Lab), 184 Boulevard Saint-Germain, 75006 Paris, France e Université Paris 8 (LED), 2 rue de la Liberté, 93526 Saint-Denis Cedex, France f Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China g School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China h Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China b c

a r t i c l e

i n f o

Article history: Received 26 November 2014 Received in revised form 23 July 2015 Accepted 22 August 2015 Available online 19 September 2015 Keywords: China Carbon intensity Economic growth Energy structure adjustment Contribution evaluation

a b s t r a c t In 2009, the Chinese government announced its carbon intensity target for 2020. Can China achieve this carbon intensity while sustaining economic growth? To address this essential issue, the contributions of this study lie in three aspects. First, we quantitatively capture the relationship between China's economic growth and energy consumption using the cointegration theory, and predict China's energy consumption by 2020 according to different economic growth targets. Second, we forecast China's energy structure in 2020 using compositional data and ARIMA models under different scenarios. Third, we deduce China's CO2 emission and carbon intensity in 2020. Furthermore, we investigate whether (or not) China can realize the carbon intensity target in premise of ensuring economic growth, and evaluate the contribution of the energy structure's adjustment to meeting this target. Finally, we put forward some relevant policy implications for achieving China's carbon intensity target. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Global climate change is one of the most serious challenges to be faced by mankind for sustainable development during the course of the twenty-first century. To sustain economic growth, economies tend to consume a large amount of fossil energy, leading to dramatic increases in greenhouse gas emissions, especially CO2 emissions. In November 2009, the Chinese government announced the carbon intensity target, namely, that the CO2 emissions per unit of GDP, i.e. carbon intensity, by 2020 should be reduced by 40–45% compared to the levels of 2005. Moreover, this target has been included in the mid- and long-term plan of national economic and social development as a rigid constraint index. China is currently in the rapid development stage of urbanization and industrialization. The main feature of this stage is fast economic growth, rapid and rigid energy consumption, and a coal-dominated energy structure. Using carbon intensity as the emissions reduction target is in line with the characteristics of China's economic development stage (Fan et al., 2007). Indeed, carbon intensity is closely related to GDP and CO2 emissions. The established carbon intensity target can be achieved ⁎ Corresponding authors. E-mail addresses: [email protected] (B. Zhu), [email protected] (J. Chevallier).

http://dx.doi.org/10.1016/j.ecolecon.2015.08.015 0921-8009/© 2015 Elsevier B.V. All rights reserved.

by increasing GDP and/or reducing CO2 emissions. As the largest developing country, economic development is of vital importance for China. Moreover, it is the basis for guaranteeing people's livelihoods, safeguarding national stability, and promoting its international status. However, the rapid growth of China's economy has brought a series of serious environmental and energy problems. Economic development has been excessively dependent on the extensive input of factors including labor, capital and resources, and industrial sectors with high energy consumption. China is subject to severe environmental pollution and energy shortage (Yang et al., 2014; Xie, 2014). Haze has been frequently occurring in urban economic belts such as the Beijing– Tianjin–Hebei region, the Pearl River Delta and the Yangtze River Delta since 2013, which poses increasing threats to the climatic environment, human health and the development of a harmonious society (He et al., 2013). In 2013, China's primary energy consumption accounted for 21.9% of the world's consumption; meanwhile, its CO2 emissions ranked first in the world (BP, 2014). It is imperative that CO2 emissions reduction is implemented as a matter of urgency. Economic growth and carbon intensity targets should both be taken into account. Therefore, with the premise of ensuring economic growth, whether (or not) the carbon intensity target can be achieved is an important issue needing urgent research in the current field of ecological economics (Zhang et al., 2009).

210

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

In recent years, scholars have developed related researches on the realization of carbon intensity targets from different angles and by using various methods (Wang, 2011; Yi et al., 2011; Li et al., 2012; Yuan et al., 2012; Jiao et al., 2013; Wang et al., 2013; Zhou et al., 2014; Cui et al., forthcoming). Furthermore, Stern and Jotzo (2010) evaluated the difficulty of meeting the carbon intensity target in China and India by focusing on energy intensity decomposition, but they did not focus on guaranteeing economic growth. Lu et al. (2013) propose the G-cubed – an intertemporal, computable general equilibrium model of the world economy – to investigate the economic implications of China's recent commitments to reduce emissions intensity, and highlight the complexities involved in modeling intensity targets under uncertainty. Wang et al. (forthcoming) developed a hybrid nonlinear grey-prediction and quota allocation model (HNGP-QAM) for supporting the optimal planning of China's carbon intensity reduction at both the departmental and provincial levels in 2020. Their results provide an important reference for this study. However, few other studies have focused on whether (or not) China can meet its carbon intensity target in 2020 while ensuring economic growth. The potential for adjustment of the energy structure has been neglected so far (Zhang, 2009). The aim of this study is to investigate whether the carbon intensity target can be realized in China with the premise of guaranteeing economic growth on the whole in terms of adjusting energy structure. It is found that there are few researches with this purpose in previous literature. Liu et al. (2014) constructed a combined forecasting model with a grey model, an autoregressive integrated moving average (ARIMA) model, and a second order polynomial regression (SOPR) model. The authors improve the forecast accuracy by optimizing three coefficients of the individual aforementioned models with particle swarm optimization (PSO) to examine China's carbon emissions reduction goal in 2020 from the perspective of thermal power development. They set a GDP target, and focused on the CO2 emissions on the basis of the thermal power industry, which means that they investigated the issue of implementing carbon intensity target with respect to an industry. In order to fill this gap, this study mainly contributes to investigating whether (or not) China can achieve its carbon intensity target while sustaining economic growth. First, we quantitatively capture the relationship between China's economic growth and energy consumption using the cointegration theory, and predict China's energy consumption by 2020 according to different economic growth targets. Second, we forecast China's energy structure in 2020 using compositional data and ARIMA models under different scenarios. Third, we deduce China's CO2 emission and carbon intensity in 2020. Furthermore, we investigate whether (or not) China can realize the carbon intensity target with the premise of ensuring economic growth, and evaluate the contribution of the energy structure's adjustment to meeting this target. To presage our results, we cautiously conclude that it appears scientifically plausible for the Chinese government to reach its carbon intensity goal by 2020. Finally, we put forward some relevant policy implications of achieving China's carbon intensity target. As a disclaimer of the research not being undertaken in this paper, energy is usually associated with many aspects of modern economic systems. Economic theory and practical energy–environmental policies can be combined together in computable general equilibrium (CGE) models. Hence, CGE models can represent the complexity of the economy, and quantitatively assess the influence of energy–environmental policies. Moreover, CGE is based on input–output analysis, which means that it investigates the economy–energy–environment nexus from the perspective of industrial development. By contrast, this research explores the relationship between economic growth and energy consumption on the whole. Consequently, CGE models are not applied in this research. Decomposition analysis is also widely applied to examine the influential factors of CO2 emissions. Understanding the driving factors

of CO2 emissions can offer necessary information for governments to effectively formulate climate policies, and use climate policy tools flexibly. On the one hand, decomposition analysis is employed to answer the question: how can China achieve its carbon intensity target? On the other hand, the aim of this study is clearly formulated as investigating whether China's carbon intensity target can be achieved under the premise of guaranteeing economic growth. Therefore, the issue on how to realize the carbon intensity target based on decomposition methods is left for further research. The remainder of the paper is structured as follows. Section 2 details the methodology and the data. Section 3 contains the results. Section 4 concludes. 2. Methodology and Data Carbon intensity is a relative index obtained by the comparison between CO2 emissions and GDP, namely carbon intensity = CO2 emissions / GDP. Thus, there is a need to forecast these two factors, respectively. Inspired by the reference method by the International Panel on Climate Change (IPCC) for estimating CO2 emissions, which is used widely (Wang, 2011; Wang et al., 2011, 2013), in this research we firstly forecast the total energy consumption and energy consumption structure respectively, and then calculate the CO2 emissions in China. According to the goal of China to build a well-off society in an all-round way, we establish the situations for economic growth to predict GDP. Moreover, economic growth is promoted by energy consumption. A cointegration regression equation between economic growth and energy consumption is established to capture the relationship between them, so as to predict the total energy consumption under different situations. Then, the energy consumption structure is predicted using the component data and ARIMA models. Finally, CO2 emissions are counted up to further compute the carbon intensity with different scenarios. 2.1. Energy Consumption Forecasting Using the Cointegration Theory on the Premise of Economic Growth At present, energy consumption prediction methods can be roughly divided into two categories: (i) the factor method, and (ii) the totalizing method. The former method decomposes the total energy consumption into the sum of four factors: coal, petroleum, natural gas and non-fossil energy. With this method, it is necessary to forecast each factor respectively. This method can conform to theoretical explanation in economics. However, errors and uncertainties occur during the forecast of each factor, so that the accumulated effects will induce more errors and uncertainties when these four factors are gathered together as the total energy consumption. As for the totalizing method, total energy consumption is regarded as a whole regardless of its structure. This method can achieve the aim of this paper, which is to explore whether China's carbon intensity target can be fulfilled or not on the whole. Besides, the totalizing method is simple and accurate, and can avoid the error accumulation effect induced by totaling each factor. Thereby, we employ the totalizing method to predict the total energy consumption here. Furthermore, the totalizing method can also be classified into two categories: (i) time series forecasting, and (ii) multi-factor forecasting. The former category predicts the future trend of energy consumption by establishing a mathematical model to extend the trend of the historical change law of energy consumption. It fails to consider the influence of exogenous variables; in other words, when the related influencing factors change little, it can reduce the error produced by the assignment of exogenous variables. However, economic growth, as an exogenous variable, needs to be considered in this research. As indicated in a large number of studies, economic growth and energy consumption are closely related. It is also demonstrated in our research that their correlation coefficient is up to 0.60. As a fundamental input factor of economic growth, energy consumption will inevitably change under

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

the different situations of economic growth. However, time series forecasting fails to reflect these changes. Thereby, in this study time series forecasting is excluded, and multi-factor forecasting is utilized to predict energy consumption. Multi-factor forecasting can also be classified into two categories: (i) the multiple regression method, and (ii) the cointegration regression method. The former method captures the quantitative variation relationship between energy consumption and its influencing factors by establishing a linear regression equation. More often, this method is applied to measure the elasticity of each influencing factor to energy consumption, rather than to predict energy consumption. Cointegration regression method quantitatively measures the variation relationship between energy consumption by means of the cointegration regression equation between them to forecast the energy consumption. In fact, the changes in energy structure are expected to influence the total energy consumption to some extent. However, the energy structure is hardly regarded as an influencing factor to forecast the total energy consumption in existing researches (Wang, 2011; Wang et al., 2011; Kankal et al., 2011; Geem and Roper, 2009; Yu et al., 2012). The main reason is that there is no better choice, since the total energy consumption is obtained by adding all kinds of energies together, while the energy structure reflects the constitution of total energy consumption, which is a circulation problem as in the “chicken and the egg” situation. The circulation problem of the mutual effect between the energy structure and energy consumption can be avoided by predicting the energy consumption from the perspective of ensuring steady economic growth. Cointegration regression method has a strong ability for predicting and explaining, which suggests that there is a long-term and stable equilibrium relationship between these variables on the basis of econometrics, while the multiple regression method fails. By comprehensively comparing the advantages and disadvantages of these two methods, we finally opt for the cointegration regression method to predict the total energy consumption in accordance with the aim of this research. In theory, when the energy structure changes, the relationship between GDP and energy consumption may be decoupled. But the decoupling theory aims to research the short-term relationship between variables, and fails to judge their long-term relationship. The strategic target in building a well-off society in an all-round way before 2020, proposed in the 16th CPC National Congress in 2002, accelerates China's modernization. In this stage, the Chinese economy aims fundamentally to achieve modernization, speed up the urbanization process and develop an open economy. At present, China is in a phase of rapid urbanization and industrialization, which is characterized by a high rate of economic growth, a rapid increase in energy demand, a rigid demand, and a coal-based energy structure. These periodical characteristics will not change in the short term, which means that the relationship between economic growth and energy consumption can remain stable on the whole. Thus, it is feasible to capture their cointegration relationship by using this historical relationship. In order to forecast the energy consumption while sustaining economic growth, we first adopt the cointegration theory (Engle and Granger, 1987) by building the regression equation of energy consumption and economic growth, and then set several scenarios, including super-high speed, high speed, medium speed and low speed of economic growth, to obtain the total energy consumption in 2020. 2.2. Component Data-based Energy Structure Prediction The energy structure of a country is mainly affected by the level of economic development, resource endowments, the industrial structure, and the energy technology. The energy structure evolves in a specific way with the effects of these factors. Therefore, the historical law of the energy structure can provide a basis for the energy

211

structure prediction (Wang, 2011; Wang et al., 2013). In this study, we use the component data and ARIMA models to predict China's energy structure. The main reasons lie in: (i) the data of energy consumption structure are typical compositional data; (ii) the compositional data forecasting model is able to successfully capture the changing law of each share in compositional data, so as to accurately predict the energy consumption structure; and (iii) the compositional data forecasting model has a high ability for trend analysis and forecasting (Wang et al., 2003). Consider the data of the energy structure:

t

X ¼

(


xt1 ; ⋯; xt4



 ) X 4 t t x i ¼ 1; 0 ≤ xi b 1 ; t ¼ 1; 2; ⋯; n ∈R   4

i¼1

where x1, x2, x3 and x4 are the proportions of coal, oil, natural gas, and non-fossil energy respectively, and t is the year t. The component data forecast modeling steps of China's energy structure is preceded by the following steps (Wang et al., 2003): (1) Conducting the nonlinear transformation on the component data: yti ¼

qffiffiffiffi xti ; i ¼ 1; 2; 3; 4; t ¼ 1; 2; ⋯; n 4

2

Let Y t = (y t1, ⋯ yt4), t = 1, 2, ⋯, n, then kY t k2 ¼ ∑ ðyti Þ ¼ 1. i¼1

(2) Transform Y t from rectangular coordinates into spherical coordinates: For any t, Yt distributes on the 4-dimensional hypersphere in radius of 1. Then Yt = (yt1, ⋯ yt4) is transformed from rectangular coordinates into spherical coordinates (rt, θ t2, θ t3, θ t4) ∈ Θ4. Since (r t)2 = ‖yt‖2 ≡ 1, the mapping relationships of R4 → Θ4− 1 are as follows yt1 ¼ sinθt2 sinθt3 sinθt4 yt2 ¼ cosθt2 sinθt3 sinθt4 yt3 ¼ cosθt3 sinθt4 yt4 ¼ cosθt4 (3) Obtain the angle variable using the recursive algorithm: θ4 ¼ arccos yt4 " θt3 ¼ arccos " θt2 ¼ arccos

#

yt3

sin θt4 yt2

#

sin θt4 sin θt3

(4) Constructing the ARIMA prediction models: Using the angle data obtained as fθi t ; t ¼ n þ 1; n þ 2; ⋯; n þ mg; i ¼ 2; 3; 4, three ARIMA models are built: ^θt ¼ f ðt Þ þ εt ; i ¼ 2; 3; 4 i i (5) Predict the angle of year n + l, l = 1, 2, ⋯, m: ^θnþl ¼ f ðn þ lÞ; i ¼ 2; 3; 4 i i

(6) Compute the predicting value of year n + l, l = 1, 2, ⋯, m:   ^nþl ¼ y ^nþl ^nþl ^nþl ^nþl y 1 ; y2 ; y3 ; y4

212

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

(7) Compute the predicted value of the energy structure in year n + l, l = 1, 2, ⋯, m:  2 ^ nþl ¼ ynþl ; i ¼ 1; 2; 3; 4 X i i 4

Obviously, ∑ X nþl ¼ 1. i i¼1

2.3. Calculating the CO2 Emissions The “reference method” recommended by the IPCC is used to calculate China's energy-consumption-based CO2 emissions (IPCC, 2006): CE ¼

4 X i¼1

Yuan. They are calculated by the GDP index with 1978 as the base period, and the GDP data in 1978. The GDP data obtained are all the actual GDP at the constant prices in 1978. Energy consumption (EC) is derived from the Chinese Statistical Yearbook (National Bureau of Statistics of China, 2013) in units of 104 tons of standard coal. We take the logarithms of GDP and EC to reduce the heteroskedasticity, which are denoted as LNGDP and LNEC respectively. Fig. 1 shows the changing trends in energy consumption and real GDP in China during the period 1978–2012. Next, we discuss the results obtained by following the computational steps detailed above. 3. Results and Discussion 3.1. Energy Consumption in 2020

44 ECi  Coei  12

where the EC1, EC2, EC3 and EC4 refer to the consumption of coal, oil, natural gas and non-fossil energy, respectively. Coe1, Coe2, Coe3 and Coe4 represent the carbon emissions factor of coal, oil, natural gas and non-fossil energy, respectively. The carbon emissions coefficients employ the average of the recommended values by China's National Development and Reform Commission, China's State Science and Technology Commission, DOE/EIA, America's Oak Ridge National Laboratory (ORNL), and Japan's Institute of Energy Economics (IEEJ); namely, Coe1 = 0.7304, Coe2 = 0.5630, Coe3 = 0.4190 and Coe4 = 0, with unit of t(C)/t. 2.4. Evaluating the Relative Contribution of Adjusting the Energy Structure to Meet the Carbon Intensity Target By combining the different scenarios of economic growth and energy consumption with the possibility of adjusting the energy structure, various combined scenarios are formed in total. We first calculate the carbon intensity and the corresponding carbon intensity reduction amplitudes under each scenario in 2020. Second, we deduce the contribution of adjusting the energy structure to the realization of the carbon intensity target under each combined scenario compared with 2005. The contribution is defined as the carbon intensity reduction amplitude with respect to the carbon intensity reduction target (in this study, −45% is selected). 2.5. Data GDP data are sourced from the Chinese Statistical Yearbook (National Bureau of Statistics of China, 2013) in units of 108 RMB

Since most economic variables are non-stationary time series, spurious regressions may be run. We use the Augmented Dickey Fuller (ADF) unit root test to identify the stationarity of LNGDP and LNEC sequences. The modeling process employs non-constant terms and the limit regression equation including a time trend. The lag order is determined by the Akaike Information Criterion (AIC). The original sequence, the first-order difference (1st diff) sequence, and the second-order difference (2nd diff) sequence of the LNGDP and LNEC sequences is conducted with the ADF test. The results are shown in Table 1. It can be seen that the LNGDP and LNEC sequences are both non-stationary, whereas the second-order difference sequences of the two variables are stationary, i.e. they are both integrated of order two. Thus, the cointegration test can be further conducted. We adopt the Engle–Granger (EG) two-step method (Engle and Granger, 1987) to test the cointegration relationship between LNGDP and LNEC. First of all, the ordinary least squares method is used to set up the cointegration regression equation of LNGDP and LNEC (without autocorrelation): LNEC ¼ 0:597584LNGDP þ 5:931232 þ ½ARð1Þ ¼ 1:556092; ARð2Þ ¼ −0:678495 þ ε ð16:03485Þ ð15:79518Þ ð11:26706Þ ð−4:863282Þ

R2 ¼ 0:998583; R2 ¼ 0:998436; F−Statistic ¼ 6812:259; Probability ¼ 0; Durbin−Watson statistic ¼ 1:942116:

ð1Þ

The above regression equation shows the favorable fitting degree, and the ideal autocorrelation test value as judged by the Durbin– Watson statistic. Moreover, all variables pass the t test and are strictly significant at the level of 1%. The cointegration equation suggests that, as GDP increases by 1%, energy consumption rises by 0.6% correspondingly.

Fig. 1. Trends of energy consumption and real GDP in China 1978–2012.

B. Zhu et al. / Ecological Economics 119 (2015) 209–216 Table 1 ADF test results of GDP and EC. Variable

t-statistic

1% level

LNGDP 3.596767 −2.639210 LNGDP 1st diff. −0.308744 −2.641672 LNGDP 2nd diff. −5.583047 −2.641672 LNEC 2.346297 −2.636901 LNEC 1st diff. −1.270458 −2.636901 nd LNEC 2 diff. −4.557283 −2.641672

5% level

Probability Result

−1.951687 −1.952066 −1.952066 −1.951332 −1.951332 −1.610400

0.9998 0.5662 0.0000 0.9943 0.1837 0.0000

non-stationary stationary stationary non-stationary non-stationary stationary

Secondly, the residuals, ε, are examined with the ADF test. The lag order is also determined by the AIC criterion. Table 2 shows the test results. It can be seen that the residuals sequence is stationary, i.e. there exists a cointegration relationship between LNGDP and LNEC. The two variables are linked in the long-run equilibrium state. According to the report of 18th Communist Party of China's National Congress, China aims at building the comprehensive well-off society by 2020 and increasing the GDP to be twice of that in 2010. To achieve this goal, China needs to maintain an average annual GDP growth rate of 6.9% during the period of 2013–2020. In this study, China's future economic growth is set with four scenarios: super-high speed (scenario I, with an average annual increase rate of 7.9%), high speed (scenario II, with an average annual increase rate of 7.4%), medium speed (scenario III, with an average annual increase rate of 6.9%), and low speed (scenario IV, with an average annual increase rate of 6.4%). On this basis, China's GDP will reach (in billion Yuan): 16,225.46 (scenario I), 16,150.27 (scenario II), 15,011.08 (scenario III), and 14,506.36 (scenario IV) respectively by 2020. According to the cointegration regression equation of energy consumption and economic growth shown as Eq. (1), and the predicted GDP in 2020, it is forecasted that China's energy consumption will reach (in 104 tons of standard coal): 483,878.93 (scenario I), 482,540.44 (scenario II), 461,898.29 (scenario III), and 452,557.45 (scenario IV) respectively by 2020. 3.2. Energy Structure in 2020 Three scenarios of energy consumption structure are established to investigate the issue of achieving the carbon intensity target following the national government planning of China. How to achieve these scenarios specifically can be explored in future research. Moreover, the targets to keep the ratio of petroleum unchanged, and replace coal with other non-fossil energy and natural gas, are set by taking full account of the resource endowment and the strategic planning for energy development in China. The situations of energy structure are set mainly relying on the Chinese national strategy. According to the planning target for the consumption proportion of non-fossil energy and natural gas in China's policies and actions for addressing climate change (2014–2020), published by National Development and Reform Commission (NDRC), these situations can be achieved with the guarantee of the policies and regulations formulated by China. It is proposed to reduce the proportion of coal consumption; to vigorously develop clean energy, including nuclear power, renewable energy sources, etc.; to improve the proportion of natural gas consumption; and to continuously optimize the energy structure

Table 2 ADF test results of the residuals. Equation

t-statistic

1% level

5% level

Prob.

213

in the national energy development strategy action plan (2014–2020). It is known that there is no specific planning target for the proportion of petroleum consumption. Hence, it is reasonable to remain at the same percentage of petroleum and to replace coal with non-fossil energy and natural gas. In other words, the replacement between energy sources is logical, which conforms to the green and low-carbon idea of optimizing the energy structure in China (Wang et al., 2011; Gao et al., 2014; Xie et al., 2015). We propose some policy implications regarding realizing an ideal energy structure as policy recommendations. As the main aim of this study is to discuss whether the carbon intensity target can be achieved or not on the whole, we do not cover how to achieve an ideal energy structure and the specific effect of each strategy, although these questions can be further explored in future research.1 According to the seven computational steps detailed in Section 2.2, and given the component data for 1978–2012, we are able to predict the composition of China's energy structure by 2020 as being: coal (61.42%), oil (18.21%), natural gas (9.99%), and non-fossil energy (10.38%) under the “minor adjustment” scenario.2 “The national plan to address climate changes (2014–2020)”, issued by China's State Council in 2014, points out that non-fossil energy should account for about 15% of primary energy in 2020. In this study, this scenario is called the “medium adjustment”. Recall that the prediction results under the “minor adjustment” scenario suggest that the proportion of non-fossil energy rises to 10.38% by 2020, which has a shortfall from the planning goal of 15%. As a result, the planning goal is taken as a constraint in this study to modify the forecasting results of the component data-based energy structure. The energy structure adjustment target of 15% is mainly achieved by energetically developing non-fossil energies such as hydro energy, nuclear energy, wind energy and solar energy. Assuming that the proportion of oil and natural gas will maintain its historical trends, the proportional increase of nonfossil energy will be achieved by reducing the proportion of coal (Wang, 2011; Wang et al., 2013). Under the “medium adjustment”, China's energy structure will be: coal (56.81%), oil (18.21%), natural gas (9.99%), and non-fossil energy (15%) by 2020. Natural gas is a relatively clean energy, when compared to coal. The continued increase in natural gas consumption can alleviate CO2 emissions — to some extent. To achieve the CO2 emissions reduction target of China, it appears necessary to increase the proportion of natural gas in primary energy as soon as possible. At present, China has invested a lot of manpower, financial and material resources into the infrastructure construction of natural gas. China's National Development and Reform Commission has also formulated a strategic goal of realizing the proportion of natural gas as 10–12% by 2020. It is assumed that the proportion of natural gas will reach 11% by 2020, and that the proportional increase will be achieved by reducing the proportion of coal. Thus, the energy structure with planning restrictions and natural gas target restriction is predictable. This scenario is called the “substantial adjustment”. Accordingly, China's energy structure under the “substantial adjustment” by 2020 is: coal (55.79%), oil (18.21%), natural gas (11%), and non-fossil energy (15%).

3.3. CO2 Emissions in 2020 Using the “reference method” recommended by the IPCC, as well as the energy consumption and energy structure by 2020, China's CO2 emissions by 2020 can be calculated. Table 3 shows the predicted values of China's CO2 emissions under different scenarios (in units of 104 tons).

Result

Intercept −5.349452 −3.653730 −2.957110 0.0001 stationary Trend and intercept −5.260689 −4.273277 −3.557759 0.0009 stationary None −5.438615 −2.639210 −1.951687 0.0000 stationary

1

We are very grateful to an anonymous reviewer for this piece of advice. The future trend is merely forecasted based on the evolution law of the historical energy structure (instead of the adjustment of national policies). 2

214

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

Table 3 The predicted values of China's CO2 emissions under different scenarios in 2020. Scenario

Super-high speed

High speed

Medium speed

Low speed

Minor adjustment Medium adjustment Substantial adjustment

1,052,074.23 992,248.46 986,663.66

1,049,164.02 989,503.73 983,934.38

1,004,282.82 947,174.68 941,843.58

983,973.49 928,020.23 922,796.94

Table 5 Amplitude of reduction and relative contribution of adjusting the energy structure in meeting the carbon intensity target. Scenario

Super-high speed

High speed

Medium speed

Low speed

Minor adjustment

−48.30 (107.34) −51.24 (113.87) −51.52 (114.48)

−48.20 (107.12) −51.15 (113.67) −51.42 (114.28)

−46.66 (103.68) −49.69 (110.42) −49.97 (111.05)

−45.92 (102.04) −48.99 (108.87) −49.28 (109.51)

Medium adjustment Substantial adjustment

3.4. Carbon Intensity in 2020 According to the predicted values of GDP and CO2 emissions under various scenarios, it is possible to deduce the carbon intensity in 2020. Table 4 lists the predicted values of China's carbon intensity under different scenarios in 2020 (with units of ton/104 Yuan). In 2005, China's CO2 emissions were equal to 5.53 billion tons and the constant-price GDP was 4,412.09 billion Yuan. Thus, the carbon intensity was equal to 12.54 tons/104 Yuan in 2005. According to China's targets for the year 2020, China's carbon intensity should be 40–45% lower than in 2005. In this study, the reduction of 45% is taken as the carbon intensity target. Consequently, the carbon intensity will be reduced from 12.54 tons/104 Yuan in 2005 to 6.90 tons/104 Yuan in 2020. 3.5. Contribution of Adjusting the Energy Structure to Meet the Carbon Intensity Target By combining the above scenarios of economic growth and energy consumption with the adjustment of the energy structure, twelve combined scenarios are formed in total. We calculate the amplitude of the carbon intensity reduction, as well as the relative contribution of adjusting the energy structure adjustment to meeting the carbon intensity target under each combined scenario (compared with 2005). The specific results are shown in Table 5 (in %). Under the scenario of “slow economic growth” and “minor energy structure adjustment”, the carbon intensity in 2020 will decline by 45.92% as compared to 2005. The contribution of the energy structure adjustment to the carbon intensity target would be 102.04%. Under the scenario of “super-high-speed economic growth” and “substantial energy structure adjustment”, the carbon intensity in 2020 will decrease by 51.52% as compared to 2005. The contribution of the energy structure adjustment to the carbon intensity target would be 114.48%. Even under the relatively conservative scenario, namely, the “medium-speed economic growth” and “minor energy structure adjustment”, the carbon intensity in 2020 will decrease by 46.66% as compared to 2005. The contribution of the energy structure adjustment to the carbon intensity target would be 103.68%. For a given level of economic growth, the larger the energy structure adjustment, the larger its “reduction amplitude” and “contribution” to meeting the carbon intensity target. For a given energy structure adjustment, the higher the economic growth, the larger its “reduction amplitude” and “contribution” to meeting the carbon intensity target. Under all scenarios, the contribution to the carbon intensity target will exceed 100%, that is, 45% decline of carbon intensity can be realized. We may cautiously conclude that it appears scientifically plausible for the Chinese government to reach its carbon intensity goal by 2020.

Table 4 The prediction results of China's carbon intensity under different scenarios in 2020. Scenario

Super-high speed

High speed

Medium speed

Low speed

Minor adjustment Medium adjustment Substantial adjustment

6.48 6.12 6.08

6.50 6.13 6.09

6.69 6.31 6.27

6.78 6.40 6.36

4. Conclusions and Policy Implications Using real data during 1978–2012 and the multidisciplinary methods of cointegration theory, scenario analysis, component data and ARIMA models, we calculate in this paper China's GDP, energy consumption, energy structure, CO2 emissions, and carbon intensity in 2020. Moreover, we evaluate the relative contribution of adjusting the energy structure to the realization of carbon intensity target under twelve combined scenarios. From this, we make the following conclusions: (1) Energy consumption has a long-term equilibrium relationship with economic growth. As GDP increases by 1%, energy consumption increases by 0.6% correspondingly. The variation rate of energy consumption is lower than that of GDP. (2) The adjustment of the energy structure stands out as an effective measure for driving the reduction of carbon intensity. For a given level of economic growth, the larger the energy structure adjustment, the larger the “reduction amplitude” of the carbon intensity. For a given energy structure adjustment, the higher the economic growth, the larger the “reduction amplitude” of the carbon intensity. (3) Under the ideal scenario (i.e. “super-high-speed economic growth” and “substantial energy structure adjustment”), the energy structure adjustment contributes most to the realization of the carbon intensity goal, with a contribution of 114.48%. The carbon intensity would reduce by 51.52% in 2020 as compared to 2005. Under the conservative scenario (i.e., “medium-speed economic growth” and “minor energy structure adjustment”), the contribution of the energy structure adjustment to meeting the carbon intensity goal will also reach 103.68%. At the same time, the carbon intensity in 2020 will decrease by 46.66% as compared to 2005. (4) The reduction by 45% of the carbon intensity goal can be achieved under all the combined scenarios. The higher the economic growth, the greater the CO2 emissions. In absolute value, CO2 emissions under “super-high economic growth” will be about 0.65 billion tons higher than under the scenario “low-speed economic growth”.

Based on the above conclusions, we may formulate the following policy recommendations: (i) Improve the quality of economic growth. China is moving decisively to a “new normal”. The central feature of the new model focuses on: still-strong but lower economic growth; continuous optimization of the economic structure; and transformation of an investment-based growth engine into one based on innovation. Under China's new development model, the rate of economic growth needs a certain decline, and then a slowdown in the energy demand. A shift in the balance of growth is needed away from heavy-industrial investment and toward domestic consumption, particularly of services and high value-added

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

industries, eliminating excess production capacity (e.g. steel, cement, electrolytic aluminum and photovoltaic industries), and transforming the economic growth mode to being low-carbon and green. Also, energy efficiency must be continuously improved, and the implementation promoted of mandatory energy efficiency standards for key fields such as buildings, appliances and vehicles. Cities must be planned along the lines of the compact, high-density, public-transport-linked models, with the gradual electrification of the transport system with electricity supplied from an increasingly de-carbonized electricity generation system, and vigorously promoting development and use of electric, hybrid and fuel cell vehicles. (ii) Adjust the energy structure. Total coal consumption should be controlled in key regions and major fields, such as Beijing– Tianjin–Hebei–Shandong, Yangtze River Delta, and Pearl River Delta. Coal-fired power stations should be gradually phased out (unless equipped with carbon capture and storage, CCS), strictly limiting approvals for and investments in new coal plants, and limiting additional coal-based energy and industrial developments. Meanwhile, the technology for developing and utilizing coal cleanly is expected to be developed quickly to improve the clean utilization of coal. The development of the gas industry should be accelerated, promoting its application widely in households, industry and transport, along with significant development of gas for electricity production. Low carbonization of the power generation system should be realized: the proportion of coal-fired power generation should be reduced, while vigorously developing non-coal power including gas, nuclear, hydropower, wind, and solar, especially nuclear. Considering the restriction of domestic resource endowment and the pressure of large-scale petroleum imports to energy supply security, petroleum must be used to meet basic domestic demands rather than to be an alternative energy to coal. (iii) Control total energy consumption. Taking full account of the economic development level, the industrial structure, the endowment of resources and the environment for each province, the control target of total energy consumption should be assigned to each province in accordance with different principles. Then, the target for energy conservation and emissions reduction should be decomposed into each industry and key energy-consuming units by combining the energy-using characteristics of each industry. Meanwhile, the assessment method for the target of energy conservation and emission reduction should be perfected, and the legislation strengthened to define the specific responsibilities of local governments. Besides, governments and enterprises should be monitored step by step to supervise local governments and enterprises to accomplish the established targets of energy conservation and emission reduction. (iv) Improve the management level of low carbon services. The energy pricing mechanism should be reformed, with timely levying of resource taxes, particularly on coal and carbon. The carbon market pilot program should be expanded nationwide, and a unified national carbon trading market system be established as soon as possible. Policy and financial support for clean innovations to be strengthened, especially the technologies with a high potential for emissions reductions and cost reductions. A green financial system should be aggressively developed to invest in infrastructures which are low-carbon, resource-efficient and environmentally friendly. The development of the energy service industry should be promoted to provide energy-saving technical service and policy support for enterprises and governments. The smart grid system and distributed power grid dynamic monitoring system should be developed to ensure stability, economy

215

and low-carbon electricity. (v) Expand and implement Carbon Capture and Storage (CCS) on a large scale. Special funds and compensation mechanisms should be set up for CCS as soon as possible. CCS demonstration projects to be expanded and CCS cost reduced, and the security of CCS to be improved through technology research and development. Meanwhile, new commercial utilizations of captured and stored CO2 should be explored. Acknowledgments We express our gratitude to the National Natural Science Foundation of China (71473180, 71201010 and 71303174), the Natural Science Foundation for Distinguished Young Talents of Guangdong (2014A030306031), the Natural Science Foundation of Guangdong (S2011010001591) and the Department of Education of Guangdong ([2013]246, [2014]145) for funding supports. References BP, 2014. BP Statistical Review of World Energy. British Petroleum, London, UK (http:// www.bp.com/zhcn/china/reports-and-publications/bp_2014.html). Cui, L.B., Fan, Y., Zhu, L., Bi, Q.H., 2014. How will the emissions trading scheme save cost for achieving China's 2020 carbon intensity reduction target. Appl. Energy http:// dx.doi.org/10.1016/j.apenergy.2014.05.021 (forthcoming). Engle, R., Granger, C., 1987. Cointegration and error correction: representation, estimation, and testing. Econometrica 55, 251–276. Fan, Y., Liu, L.C., Wu, G., Tsai, H.T., Wei, Y.M., 2007. Changes in carbon intensity in China: empirical findings from 1980–2003. Ecol. Econ. 62, 683–691. Gao, C.X., Sun, M., Shen, B., Li, R.R., Tian, L.X., 2014. Optimization of China's energy structure based on portfolio theory. Energy 77, 890–897. Geem, Z.W., Roper, W.E., 2009. Energy demand estimation of South Korea using artificial neural network. Energy Policy 37, 4049–4054. He, H., Wang, X.M., Wang, Y.S., Wang, Z.F., Liu, J.G., Chen, Y.F., 2013. The tracking and control of atmospheric haze. Bull. Chin. Acad. Sci. 7, 344–352. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change, Bonn, Germany (http://www.ipcc-nggip.iges.or.jp/ pub-lic/2006gl/chinese/index.htm). Jiao, J.L., Qi, Y.Y., Qun, C., Liu, L.C., Liang, Q.M., 2013. China's targets for reducing the intensity of CO2 emissions by 2020. Energy Strateg. Rev. 2, 176–181. Kankal, M., AkpInar, A., Komurcu, M.I., Ozsahin, T.S., 2011. Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables. Appl. Energy 88, 1927–1939. Li, H.Q., Wang, L.M., Shen, L., Chen, F.N., 2012. Study of the potential of low carbon energy development and its contribution to realize the reduction target of carbon intensity in China. Energy Policy 41, 393–401. Liu, L.W., Zong, H.J., Zhao, E.D., Chen, C.X., Wang, J.Z., 2014. Can China realize its carbon emission reduction goal in 2020: from the perspective of thermal power development. Appl. Energy 124, 199–212. Lu, Y.Y., Stegman, A., Cai, Y.Y., 2013. Emissions intensity targeting: from China's 12th five year plan to its Copenhagen commitment. Energy Policy 61, 1164–1177. National Bureau of Statistics of China, 2013. China Statistical Yearbook. China Statistics Press, Beijing, China. Stern, D., Jotzo, F., 2010. How ambitious are China and India's emissions intensity targets? Energy Policy 38, 6776–6783. Wang, F., 2011. Driving Factors for China's Carbon Emissions Growth and Evaluation of Carbon Reduction Policies. Economic Science Press, Beijing, China. Wang, H.W., Huang, W., Liu, Q., 2003. Forecast analysis for Beijing's industry structure. Syst. Eng. Theory Pract. 6, 123–127. Wang, R., Liu, W.J., Xiao, L.S., Liu, J., Kao, W., 2011. Path towards achieving of China's 2020 carbon emission reduction target—a discussion of low-carbon energy policies at province level. Energy Policy 39, 2740–2747. Wang, P., Liu, Z.X., Zhu, B.Z., Li, J., Xiao, J.H., 2013. Potential assessment of optimizing energy structure to carbon intensity target in Guangdong province. China Popul. Resour. Environ. 4, 49–54. Wang, X.W., Cai, Y.P., Xu, Y., Zhao, H.Z., Chen, J.J., 2014. Optimal strategies for carbon reduction at dual levels in China based on a hybrid nonlinear grey-prediction and quota-allocation model. J. Clean. Prod. http://dx.doi.org/10.1016/j.jclepro.2014.07. 015 (forthcoming). Xie, S.C., 2014. The driving forces of China's energy use from 1992 to 2010: an empirical study of input–output and structural decomposition analysis. Energy Policy 73, 401–415. Xie, N.M., Yuan, C.Q., Yang, Y.J., 2015. Forecasting China's energy demand and selfsufficiency rate by grey forecasting model and Markov model. Int. J. Electr. Power Energy Syst. 66, 1–8. Yang, C.J., Zhou, Y., Jackson, R.B., 2014. China's fuel gas sector: history, current status, and future prospects. Util. Policy 28, 12–21. Yi, W.J., Zou, L.L., Guo, J., Wang, K., Wei, Y.M., 2011. How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development. Energy Policy 39, 2407–2415.

216

B. Zhu et al. / Ecological Economics 119 (2015) 209–216

Yu, S.W., Wei, Y.M., Wang, K., 2012. A PSO-GA optimal model to estimate primary energy demand of China. Energy Policy 42, 329–340. Yuan, J.H., Hou, Y., Xu, M., 2012. China's 2020 carbon intensity target: consistency, implementations, and policy imp1ications. Renew. Sust. Energ. Rev. 16, 4970–4981. Zhang, Y., 2009. Structural decomposition analysis of sources of decarbonizing economic development in China; 1992–2006. Ecol. Econ. 68, 2399–2405.

Zhang, M., Mu, H., Ning, Y., Song, Y., 2009. Decomposition of energy-related CO2 emission over 1991–2006 in China. Ecol. Econ. 68, 2122–2128. Zhou, L., Zhang, X.L., Qi, T.Y., He, J.K., Luo, X.H., 2014. Regional disaggregation of China's national carbon intensity reduction target by reduction pathway analysis. Energy Sustain. Dev. 23, 25–31.