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Regional disaggregation of China's national carbon intensity reduction target by reduction pathway analysis
Energy for Sustainable Development 23 (2014) 25–31
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Energy for Sustainable Development
Regional disaggregation of China's national carbon intensity reduction target by reduction pathway analysis Li Zhou ⁎, Xiliang Zhang, Tianyu Qi ⁎, Jiankun He, Xiaohu Luo Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
a r t i c l e
i n f o
Article history: Received 2 June 2013 Revised 10 June 2014 Accepted 15 July 2014 Available online xxxx Keywords: Carbon intensity Regional disaggregation Reduction pathway analysis
a b s t r a c t Chinese government announced it is going to reduce carbon intensity in 2020 by 40 to 45% when compared with 2005 levels. One question for how to accomplish this is how to disaggregate national target to provincial level. This study employed a regional disaggregation approach based on a series of principles. Analysis of the carbon intensity revealed three main approaches to reduce CO2 are energy structure adjustment, technical improvement and energy substitution. We proposed a disaggregation model from the view of reduction approaches. Through the data collection and analysis of provinces from 2005 to 2010, a regional disaggregation scheme is carried out in which the national carbon intensity reduction target during the 12th Five-Year Plan was set at 17%. The calculated regional targets were then compared with official published data. The results showed that this method could be useful for disaggregation of the target to the provincial level. © 2014 International Energy Initiative. Published by Elsevier Ltd. All rights reserved.
Introduction The rapid growth of China's economy and energy has caused tremendous environmental cost to the society and made China the largest source of the global green-house-gas emissions (International Energy Agency, 2011). An evaluation from the World Bank reveals that the environmental pollutions cost over 4% of China's GDP each year (The World Bank and China Ministry of Environmental Protection, 2007), threatening the long-term development of the country's economy. Growing intentions to reduce the serious environmental pollutions and control the growing CO2 emission have been signaled in policies in China recently. One important action taken by the government is that in China's Twelfth Five-Year Plan (2-11-2015), a 17% target for national CO2 intensity reduction has been introduced for the first time (The State Council of China, 2011). This will work as a legally binding target in line with the nation's commitment at the Copenhagen conference to reduce its CO2 emission intensity by 40–45% over the period 2005 to 2020 in 2009 (Xinhua, 2009). It is learned from the 11th Five-Year Plan period that decomposing national energy-saving targets to local areas and implementing a local governor target responsibility system are effective energy-saving institutional arrangement means in China. It should be persevered and continuously improved in achieving the carbon dioxide emission
⁎ Corresponding authors. E-mail addresses: [email protected] (L. Zhou), [email protected] (T. Qi).
reduction targets by 2020. Taking the advantage of a centralized political system, China's national emission reduction target is allocated to provincial level to facilitate the achievement of this target. The first allocation trial is implemented during the 11th Five-Year Plan period (2006–2010) for a 20% energy intensity (energy consumption per unit of GDP) reduction. In this trial, a “declaration and negotiation” approach is adopted for the target disaggregation. The provinces first report a volunteer reduction target and the central government negotiates with the provinces to adjust their target to match the national target. One key problem in this aggregation trial is that most of the reduction target raised by provinces is based on a rough evaluation and short of effective and scientific assessment (Haibing, 2011; Xiao, 2012). This shortage is mainly due to the lack of experience and capacity of the impact evaluation. As soon as the provincial target has been set, it works as an administrative instruction and has almost been achieved by all the provinces at the end of the 11th Five-Year Plan period (National Development and Reform Commission of China, 2010; The State Council of China, 2011), as shown in Fig. 1. Although energy efficiency is well improved (Kostka and Hobbs, 2012), some extreme measures such as power rationing have been implemented; and, a significant loss of social welfare is the cost to achieve the absolute and not-well-evaluated target in provinces (steelorbis, 2010). A best sample of emission permit allocation that China can learn from may be the emission allowance allocation in the EU Emissions Trading System (EU-ETS) market. Launched in 2005, the EU-ETS is a cornerstone of the European Union's policy to combat climate change and its key tool for reducing industrial greenhouse gas emissions cost-effectively. The EU-ETS is the largest emission
http://dx.doi.org/10.1016/j.esd.2014.07.003 0973-0826/© 2014 International Energy Initiative. Published by Elsevier Ltd. All rights reserved.
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L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
trading scheme to date, covering around 11,000 power stations and industrial plants in 30 countries (European Union, 2003, 2012). In its first phase (2005–2007), the allocation of allowances in Phase I was determined by the Member States which submitted the so called National Allocation Plans (NAPs) to the Commission for review and approval (The European Commission, 2013; Zhang and Wei, 2010). The NAPs set the overall cap for the country and allocated allowances to every participating installation (Zhang and Wei, 2010). The allocations are determined for each trading period at a time to account for the fact that annual GHG emissions fluctuate depending on the economic conditions (Ellerman and Joskow, 2008). Similarly as in China, due the lack of experience and capacity in emission projection, the allowance in Phase I of the EU ETS is over-allocated causing the permit price to decline considerably (European Environment Agency, 2011). Following the identification of the limitations experienced in Phase I, the commission acquired the authority to impose a formula to assess the allocation plans of Member States and emission projections were objectively based on the verified emissions of 2005. Based on the assessment, the NAPs adjusted its allowance in the second round and were more ambitious. Though due to the unexpected financial crisis, the permit price declined again. This allocation approach delivers a more compelling price signal to the industries. To further control the growing CO2 emissions, China has set up a clear target of 17% CO2 intensity (CO2 emissions per unit of GDP) reduction in its 12th Five-Year Plan over the period 2011 to 2015 (Joana Lewis, 2011; State Council of China, 2011). However, this target will be a great challenge for a developing country. It is urgent and important to determine how to accomplish this challenging task. One aspect that must be addressed is determination of how to disaggregate the national target to the provincial level. A second round of the allocation of this national target to provincial level is ongoing. To avoid repeating the limitations in the first round, it is necessary to establish scientific and reasonable decomposition principles and methodologies to disaggregate target decomposition to the provincial level and implement a target responsibility system. This paper designs a method to allocate the target in the 12th Five Year Period based on a group of decomposition principles and methodologies in the consideration of equity and efficiency. The paper is organized as follows: the Basic assumptions and analysis section describes the principles used in disaggregation, the Regional disaggregation method based on mitigation pathway analysis of carbon intensity section briefly introduces the methodology and formulas, the Results and discussion section presents the results, and the last section gives a conclusion.
Basic assumptions and analysis Basic disaggregation principles To disaggregate China's carbon intensity reduction target scientifically and reasonably to the local level, it is necessary to deal with different relationships properly, such as international and domestic, central and local, short-term and long-term, potential and ability, and efficiency and equity. In addition, it is necessary to build scientific and reasonable principles and methodology for disaggregation. Discussions on the principles of reduction commitments allocations for greenhouse gas emissions are drawing increasing attention because of the difficulty in International Climate Change Negotiation. Topics of such discussions primarily include the role of responsibility, capability and efficiency and their balance on the target delegations (Bhatti et al., 2010; Harris and Symons, 2010; Kanie et al., 2010; Zetterberg et al., 2012). Based on these discussions, we developed the principles of target disaggregation from the following aspects. (1) Effect, e.g., whether the disaggregation scheme could ensure or advance realization of the national target. (2) Efficiency, which considers whether the implementation of disaggregation leads to less economic and social cost. (3) Equity, different situations among provinces such as the social and economic development stage, natural resources endowment, energy import and export amount, and energy conservation effort should be considered. (4) Transparency, which specifies that the disaggregation method and supporting data should be transparent and easily repeated. (5) Feasibility, e.g., the approach, ability and potential to realize the carbon intensity reduction target of each province should be considered. (6) Continuity, the efforts made by each province for energy conservation and carbon reduction in the 12th Five Year Plan period should be continuous with the energy conservation goal in the 11th Five Year Plan period, and dramatic increases and decreases should be avoided unless there are special circumstances. (7) Consistency, the results of local disaggregation schemes should be connected with the national target to ensure realization of the target.
Factors affecting the reduction of CO2 emissions per unit GDP The main component of greenhouse gases is CO2, which is mainly produced by fossil fuel combustion. Numerically, the CO2 emissions per GDP (usually referred to as carbon intensity) are equal to the amount of energy consumed per GDP (usually referred to as energy
Fig. 1. Completion of energy consumption per GDP reduction target of provinces during the 11th Five-Year Plan.
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
27
Fig. 2. Reduction pathways and factors affecting carbon intensity reduction.
CO2 =GDP ¼
Energy CO2 : Energy GDP
ð1Þ
The energy intensity relative to the GDP reflects the comprehensive benefits of energy transformation, transportation, distribution and utilization in economic and social activities, which are closely related to efforts such as energy structural adjustment, technical improvements and improved added value of the products. The carbon emission factor of the energy reflects the features of the primary energy consumption structure. Increasing the proportion of non-fossil energy in energy consumption is the main method of reducing the carbon emission factor of the energy. Accordingly, the carbon intensity relative to the GDP reflects not only the comprehensive benefits of energy transformation, transportation, distribution and utilization, but also the reduction in CO2 emissions caused by energy substitution in the energy system. Energy conservation and energy replacement in the 11th Five-Year Plan period led to a reduction in the annual rate of energy intensity of China of 3.2%, an annual increasing rate of non-fossil energy of 1.2%, and an annual decreasing rate of carbon intensity of 4%. According to the non-fossil energy development target proposed in the 11th FiveYear period, by 2020 the proportion of non-fossil energy in China will reach 15% (National Development and Reform Commission of China, 2007). Additionally, if it is set in a way that a target reduction of 45% in carbon intensity in 2020 is compared with that in 2005, it means that an overall reduction in energy intensity should be about 40%.
The following diagram shows the main pathways and factors involved in carbon intensity reduction (Jiankun, 2011). The decrease in GDP carbon intensity shall be achieved by reducing the useful energy demand per GDP and improving the utilization efficiency of energy transformation. The methods for reducing the carbon intensity of the energy include reducing the carbon content rate of energy and increasing carbon sequestration. According to the analysis of reduction pathways shown in Fig. 2, there are three main mitigation pathways for reducing carbon intensity, structural energy conservation, technical energy conservation and energy substitution. (See Fig. 3.) Regional disaggregation method based on mitigation pathway analysis of carbon intensity Based on investigation of the responsibility, potential and ability associated with three energy conservation and carbon emission
1XPEHURISURYLQFHV
intensity) multiplied by the CO2 emissions per energy consumption unit (also referred to as carbon emission factor of the energy). Carbon intensity = energy intensity × carbon emission factor of the energy
!
'LIIHUHQFHRIWKHWDUJHW &DUERQLQWHQVLW\UHGXFLQJWDUJHW
(QHUJ\LQWHQVLW\UHGXFLQJWDUJHW
Fig. 3. Differences in calculated and published targets for carbon intensity and energy intensity reduction.
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L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
reduction approaches in various provinces, autonomous regions and municipalities, a carbon intensity reduction method based on mitigation pathway analysis is proposed. First, the annual decreasing rate of carbon intensity, caused by structural energy conservation, technical energy conservation and energy substitution, is separately predicted for various provinces, autonomous regions and municipalities during the 12th Five-Year Plan period based on the historical data. Next, corrections are made by considering the transfer-in and transfer-out energy of the provinces, energy development forecast during the 12th Five-Year Plan period, and positions of their respective functions. Finally, the energy intensity and carbon intensity disaggregated target for various provinces during the 12th Five-Year Plan period are calculated. The result is composed of two parts: R ¼ R1 þ Δ r 5 R1 ¼ 1− 1− 100 100
ð2Þ
in which, R represents the reducing target, Δ is the correction factor, r is the annual decreasing rate of GDP carbon intensity and R1 represents the reducing target calculated from the annual decreasing rate of indicators. Forecast of annual decreasing rate in carbon intensity in provinces
X
ri;n ¼ ri;s þ ri;t þ ri;a
ð3Þ
n
in which, r is annual carbon intensity decline rate, i represents province i, n represents the kind of indicator, and s, t, and a represent three kinds of indicators, which reflect structural energy conservation, technical energy conservation and energy substitution, respectively. The corresponding annual decreasing rate relating to structural energy conservation, technical energy conservation and energy substitution in various provinces can be determined from the corresponding annual decreasing rate of various indicators and weights using the following formula. The decline rate of single indicator ri,n,j,k is determined by the actual data of the indicator as well as the boundary of indicators in each kind. ri;n ¼
3 X
wi;n; j ri;n; j ; ri;n; j ¼
j¼1
ri;n; j;k ¼
X
wi;n; j;k ri;n; j;k
ð4Þ
k
min
j
j
ð5Þ
In which, w is the weight of indicators, i represents the province i, j represents potential, responsibility or ability, where indicators are divided into these three aspects and k represents the indicator k in one aspect. D represents the actual data of each indicator. L represents the boundary of indicators in one kind, which means the possible largest and smallest decline rates in one kind. The boundary of indicators is determined by formulas (6) and (7); and, max and min represent the maximum value and minimum value, respectively. Thus, the annual decreasing rate of a single indicator must be calculated by interpolation of the upper and lower bounds. Assuming that the maximum value of the calculated decline rate of structural energy conservation in each province is equal to θ times the
k
ð6Þ In which, N means the national data, and θ is equal to the maximum reducing target divided by the minimum reducing target of all provinces. θ is defined by what maximum and minimum reducing targets of all the provinces the government could accept. For example, if the maximum and minimum reducing targets of all the provinces are supposed to be 19% and 15%, then the value of θ is assumed to be 1.3. It is also assumed that the calculated average result of the decreasing rate of structural energy conservation in each province should be equal to the national average decreasing rate, such as the technical energy conservation and energy substitution, i.e., N rn ¼ average ri;n 0 1 0 X X X @ @ ¼ average wi;n; j ri;n; j Þ ¼ average wi;n; j wi;n; j;k ri;n; j;k A: j
k
ð7Þ In one word, according to the above formulas, we can obtain the value of the upper and lower bounds of indicators when certain national carbon reducing target and weights of indicators are given. Then obtain the decline rates of structural energy conservation, technological energy conservation and energy substitution separately in all provinces, as well as the declining target initial value of the corresponding energy intensity and carbon intensity in each province. Annual decreasing rate of national average carbon intensity The national average structural energy conservation, technical energy conservation and energy substitution are calculated as follows. N
r ¼
X
N
N
N
rn ¼ rs þ rt þ ra
N
ð8Þ
n
N
L max −L min n n Di;n; j;k − min Di;n; j;k max Di;n; j;k − min Di;n; j;k þ Ln
N rn θ ¼ max ri;n 0 1 0 1 X X X @ A @ ¼ max wi;n; j ri;n; j ¼ max wi;n; j wi;n; j;k ri;n; j;k A:
j
The annual decline rate of GDP carbon intensity reduction in various provinces during the 12th Five-Year Plan period is composed of three parts: ri ¼
national average decreasing rate; the same is for technical energy conservation and energy substitution. i.e.
rs ¼ rt
N
ð9Þ
Learning from other research, the impact of structural energy conservation contributes around 50% for energy intensity decreasing, which means that structural energy conservation and technical energy conservation contribute half separately for energy intensity decreasing. Then, the national decline rate of structural energy conservation and technical energy conservation could be calculated as follows: N
N
rs ¼ rt ¼
N
1 N 1 ΔEI ¼ r 1− 1− 100 2 EI 2
N
N
¼
ΔCI 1− 1− 100
N
!ð1=5Þ !
r ¼ rCI −rEI !ð1=5Þ !a
100
N
ΔEIN 100− 1− 1− 100
ð10Þ
!ð1=5Þ ! 100
ð11Þ
where CI represents the carbon intensity, EI represents the energy intensity, ΔCIN represents the national carbon intensity decline target during the 12th Five-Year Plan period, and ΔEIN is the national energy intensity decline target during the same period.
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
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The national carbon intensity in 2015 and 2010 was determined using formula (15), in which N still presents national data.
Indicators and weights Based on the principle of local decomposition as well as decomposition pathways of carbon intensity decline, we selected the following index system and provided the corresponding reasonable weights according to their economic meaning and impact based on experts' opinions and experiences. Relative data were mainly obtained from the national yearbook, (National Bureau of Statistics of China, 2006, 2007, 2008, 2009a,b) and the local yearbooks of each province, (Department of Development Planning in Ministry of science and technology of China, 2009; National Bureau of Statistics of China, 2010) as shown in Appendix A. Correction factor
30 X
CIN ¼
CO2i ð15Þ GDPi
i¼1
The relationship between national and provincial carbon intensities in 2015 can be obtained from the above two equations:
2015
CIN
To correct the above calculation data according to historical data indicators, the three indicators should be considered as correction factors from experts' experiences. As shown in formula (12), Δ is the correction factor and m is the kind of correction factor.
CO2N ¼ i¼1 30 GDPN X
¼
" # 30 X CO2010 2015 2i ð1−ΔCIi Þ GDPi GDP2010 i i¼1 30 X
Δ¼
Δm
ð12Þ
m¼1
ð16Þ
i¼1
Then, according to the above equations, we can get the national carbon intensity decreasing rate during the 12th Five-Year period: 2015
3 X
:
2015
GDPi
2010
ΔCIN ¼ 1−CIN =CIN# 0 " 1 0 2010 30 30 X X CO2i 2015 2010 B B ð1−ΔCIi Þ GDPi CO2i C B C B GDP2010 B C B i ¼ 1−B i¼1 Þ B i¼1 C: 30 30 B C B X X 2015 2010 @ A @ GDP GDP i
The first is set according to the proportion of net input or output energy in total energy consumption of each province. If the net input proportion is higher than 50%, the correction factor is 1, that is, the decline target in carbon intensity will increase by one percentage point. If the net input ratio is in the range of 0 to 50%, the correction factor is 0. If the net input is in the range of −100 to 0%, the correction factor is −1. If the net input ratio is less than 100%, then the correction factor is 2. The second is set according to the difference between the energy intensity decline target during the 11th Five-Year Plan period and the predicted decline target in energy intensity in various provinces during the 12th Five-Year Plan period. When the difference is greater than 5, it is less difficult to meet the target, and the correction factor is 1. If the difference is between 3 and 5, it is moderately difficult to meet the target, and the correction factor is 0. If the difference is between 0 and 3, there is a greater difficulty in meeting the target; therefore, the correction factor is − 1. If the difference is less than 1, meeting the target is very difficult and so the correction factor is − 2. The third is to consider other factors in each province without changing the national target. Method for estimating national emission reducing target based on targets of each province In this study, the carbon intensity reduction targets in each province were used to generate a simplified algorithm to estimate the decline in national carbon intensity. Carbon intensity reduction targets in each province, which is shown as ΔCIi, were calculated by formulas (13) and (14), in which 2010 and 2015 mean the years 2010 and 2015, and CO2 means carbon dioxide.
CIi ¼
CO2i GDPi
ΔCIi ¼
CI2010 −CI2015 i i 100% CI2010 i
ð13Þ
ð14Þ
i¼1
ð17Þ
i
i¼1
It is assumed that the GDP growing speed during the 12th Five-Year period in each province is the same as the growing speed of the whole country. We can obtain the simplified formula based on formula (17): ΔCIN ¼
30 X i¼1
ΔCIi
! CO2010 2i : CO2010 2N
ð18Þ
The decreasing rate in national carbon intensity is equal to the summation of the carbon intensity reduction target in each province multiplied by the proportion of its carbon dioxide emissions in 2010. The decreasing rate in national energy intensity can be estimated similarly; specifically, it is equal to the summation of energy intensity decline target in each province multiplied by its energy consumption proportion in 2010. Results and discussion Our research was in close coordination with the national 973 emergency project for addressing climate change, which is named as “disaggregation and implementation of GDP carbon dioxide emissions intensity target of 40%–45%”. The main target of the project is to disaggregate the GDP carbon intensity (carbon intensity) target by region and by sector. We finally selected a scenario in which energy consumption per GDP (energy intensity) decreases by 16% and carbon dioxide emissions per GDP (carbon intensity) decreases by 17% in the 12th Five-Year period. In this scenario, a program for national carbon intensity reduction target disaggregation in the 12th Five-Year period is proposed. The calculated carbon intensity target by region in the 12th Five-Year period is compared with the official published data (State Council of China, 2011) in Table 2. Qinghai, Hainan and Xinjiang showed larger differences between the calculated results and the decline targets of energy intensity and carbon intensity. In the calculation process, data from various provinces obtained using this method on the basis of the differential accounting method had continuity. As shown by the actual disaggregated data provided by the National Development and Reform Commission, three provinces have decline targets that are significantly
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L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
lower than those of the other provinces. The underlying reasons for these differences may be that these three provinces have a significantly larger gap than other provinces between the development stage and was actually able to implement their plans. If you ignore the differential data of these three provinces, and calculate the average of the absolute difference values between the calculated decline targets and published targets for other provinces, then the average differences of energy intensity and carbon intensity are both smaller than 1. Considering the actual work needs associated with decomposition of the carbon intensity target, one percentage point difference is generally acceptable, and the targets can also be fine-tuned through later negotiation. Therefore, this method has reference significance for target setting in most provinces. For individual provinces, it is necessary to adjust and consider the correction factor in each province to reduce the accounting error associated with the method. In short, despite the approach also having some limitations in considering the specificity of individual regions, it provides a more comprehensive index method and design system, enjoys better operability, and provides accounting data showing good significance when used as a reference. Therefore, this method can be used as an effective policy tool in the decomposition of carbon intensity target. (See Table 1.) Conclusion In 2009, the Chinese government officially announced that the country is going to reduce the carbon dioxide emissions per unit of GDP (carbon intensity) in 2020 by 40 to 45% when compared with 2005 levels, which has become an important national macro-target. According to this, the China 12th Five-Year Plan recently established a restrictive index in which the carbon dioxide emissions per unit of GDP in 2015 should be reduced by 17% when compared with those of 2010. Based on the realization of energy saving targets during the 11th Five-Year period, breaking the national target down to the provincial level and applying a local governor target responsibility system were effective institutional arrangement means. Thus, to achieve an effective carbon intensity reduction in 2015, it is necessary to break the national target down to provincial level so that regional restrictive indexes can be formed. However, China has little experience in this, and there is almost no methodological research pertaining to the provincial or regional disaggregation of national carbon intensity targets in China available. Although China has given each province energy consumption per GDP (energy intensity) reduction target according to the national target, the disaggregation approach is based on one universal standard followed by negotiation between national and regional governments, which lacks effective and scientific support.
This study demonstrated that a regional disaggregation approach should be based on a series of principles, including effective disaggregation, guaranteed efficiency and justice, implementation feasibility and continuity of target, method transparency and consistency between the regional and state indexes. Additionally, content analysis of the carbon intensity revealed that the three main approaches that must be taken to realize carbon emission reduction are energy structure adjustment, technical improvement and application of alternative energy. Accordingly, a disaggregation model that consists of several assessment indicators, weights, and relevant experience correction factors was proposed from the view of the three reduction approaches. All indicators were selected based on consideration of important differences among regions with respect to economic development stage and energy consumption structure. These differences include GDP per person, urbanization ratio, proportion of eight high energy intensity sectors in GDP, proportion of industry, growth rate of the industry added value during the 11th Five-Year period, ratio of industry energy intensity to the national average level, and natural endowment of new energy. In this way, a complete method of regional disaggregation of the national carbon intensity reduction target was established. Through collection and analysis of provincial economic and energy data from 2005 to 2010 in China, a regional disaggregation scheme in which the national carbon intensity reduction target during the 12th Five-Year period was set to be 17% was developed. The calculated carbon intensity target by region in the 12th Five-Year period was then compared with the official published data. Results showed that the differences between the calculated and official targets were acceptable. This method provides a more comprehensive index method and design system, enjoys better operability, and provides calculated data as a useful reference. Therefore, a detailed provincial target of carbon intensity reduction in the 12th Five-Year Plan can be provided in a scientific way. Acknowledgments We appreciate the national 973 emergency project for addressing climate change and providing funding and support for this study. The authors acknowledge other colleagues in the Institute of Energy, Environment and Economy for conducting data collection. The comments of other anonymous referees are also highly appreciated. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.esd.2014.07.003.
Table 1 Index and weight of regional disaggregation method. Weight wi,n,j
Indicator ri,n,j,k
Weight wi,n,j,k
Potential
0.4
Responsibility
0.4
Ability
0.2
Potential Responsibility
0.4 0.4
Ability
0.2
Potential Responsibility Ability
0.5 0.3 0.2
Tertiary industries pulling rate during the “Eleventh Five-Year” period Added value share of eight high-energy consumption in local GDP Per capita consumption expenditure GDP per capita Urbanization rate GDP growth rate during the “Eleventh Five-Year Plan” Regional industrial added value/national industrial added value High-tech industries index Provincial energy consumption per unit of industrial added value/national average value The proportion of industrial added value in provinces The growth rate of industrial added value Environmental index of scientific and technological advances Scientific and technological activities input index The proportion of non-fossil energy in the “12th Five-Year Plan” period in China Provincial carbon intensity/national average carbon intensity Industrial investment in urban energy in each region Fiscal expenditure per capita
0.25 0.5 0.25 0.5 0.25 0.25 0.5 0.5 1 0.5 0.5 0.5 0.5 1 1 0.5 0.5
Decline pathways and aspect Structural energy conservation
Technique conservation
Energy replacement
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
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Table 2 Calculated and published carbon intensity and energy intensity reducing target by region in the 12th Five-Year Period. Region
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
Energy intensity reducing target %
Carbon intensity reducing target %
Calculated
Published
Difference
Calculated
Published
Difference
17 17 17 16 16 18 17 14 18 18 17 16 16 16 17 15 17 16 16 15 13 16 16 15 14 14 16 14 17 15
17 18 17 16 15 17 16 16 18 18 18 16 16 16 17 16 16 16 18 15 10 16 16 15 15 16 15 10 15 10
0 −1 0 0 1 1 1 −2 0 0 −1 0 0 0 0 −1 1 0 −2 0 3 0 0 0 −1 −2 1 4 2 5
18 18 18 17 18 19 18 15 19 19 18 17 17 16 19 16 18 17 17 16 14 17 17 16 15 14 17 15 18 16
18 19 18 17 16 18 17 16 19 19 19 17 17.5 17 18 17 17 17 19.5 16 11 17 17.5 16 16.5 17 16 10 16 11
0 −1 0 0 2 1 1 −1 0 0 −1 0 −1 −1 1 −1 1 0 −3 0 3 0 −1 0 −2 −3 1 5 2 5
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National Bureau of Statistics of China. China statistical year book 2007. Beijing: China Statistics Press; 2007. National Bureau of Statistics of China, editor. China statistical yearbook 2008; 2008. National Bureau of Statistics of China. China industrial economy statistical yearbook. Beijing: China Statistics Press; 2009a. National Bureau of Statistics of China. China statistical yearbook 2009. Beijing: China Statistics Press; 2009b. National Bureau of Statistics of China. Bulletin of energy consumption per GDP of provinces, autonomous regions, municipalities in 2009 (Chinese). Retrieved from http://www.stats.gov.cn/tjgb/qttjgb/qgqttjgb/t20100715_402657560.htm, 2010. National Development and Reform Commission of China. Medium and long-term development plan for renewable energy in China; 2007. National Development and Reform Commission of China. The provinces, autonomous regions and municipalities in 2009 completion of energy-saving targets. Retrieved from http://www.ndrc.gov.cn/zcfb/zcfbgg/2010gg/t20100624_356266.htm, 2010. State Council of China. Notice of the state council on issuing the work plan for greenhouse gas emission control during the 12th five-year plan period. Retrieved from http:// www.gov.cn/zwgk/2012-01/13/content_2043645.htm, 2011. steelorbis. MIIT: power rationing measures should not be centralized. Retrieved, 2013, from http://www.steelorbis.com/steel-news/latest-news/miit-power-rationingmeasures-should-not-be-centralized-554038.htm, 2010. The European Commission. National allocation plans. Retrieved from http://ec.europa.eu/ clima/policies/ets/pre2013/nap/index_en.htm, 2013. The State Council of China. 12th five-year plan for energy saving and emission reduction. Retrieved from http://www.gov.cn/zwgk/2011-09/07/content_1941731.htm, 2011. The World Bank, China Ministry of Environmental Protection. Cost of pollution in china: economic estimates of physical damages; 2007. Xiao C. Problems and solutions of the provincial distribution methods on China's carbon emission-controlling target. Modern Economic Information (Chinese), 9; 2012. p. 23–6. Xinhua. China announces targets on carbon emission cuts. Retrieved from http://news. xinhuanet.com/english/2009-11/26/content_12544181.htm, 2009. Zetterberg L, Wrake M, Sterner T, Fischer C, Burtraw D. Short-run allocation of emissions allowances and long-term goals for climate policy. Ambio 2012;41:23–32. http://dx. doi.org/10.1007/s13280-011-0238-1. Zhang Y, Wei Y. An overview of current research on EU ETS: evidence from its operating mechanism and economic effect. Appl Energy 2010;87(6):1804–14. http://dx.doi. org/10.1016/j.apenergy.2009.12.019.
Contents lists available at ScienceDirect
Energy for Sustainable Development
Regional disaggregation of China's national carbon intensity reduction target by reduction pathway analysis Li Zhou ⁎, Xiliang Zhang, Tianyu Qi ⁎, Jiankun He, Xiaohu Luo Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
a r t i c l e
i n f o
Article history: Received 2 June 2013 Revised 10 June 2014 Accepted 15 July 2014 Available online xxxx Keywords: Carbon intensity Regional disaggregation Reduction pathway analysis
a b s t r a c t Chinese government announced it is going to reduce carbon intensity in 2020 by 40 to 45% when compared with 2005 levels. One question for how to accomplish this is how to disaggregate national target to provincial level. This study employed a regional disaggregation approach based on a series of principles. Analysis of the carbon intensity revealed three main approaches to reduce CO2 are energy structure adjustment, technical improvement and energy substitution. We proposed a disaggregation model from the view of reduction approaches. Through the data collection and analysis of provinces from 2005 to 2010, a regional disaggregation scheme is carried out in which the national carbon intensity reduction target during the 12th Five-Year Plan was set at 17%. The calculated regional targets were then compared with official published data. The results showed that this method could be useful for disaggregation of the target to the provincial level. © 2014 International Energy Initiative. Published by Elsevier Ltd. All rights reserved.
Introduction The rapid growth of China's economy and energy has caused tremendous environmental cost to the society and made China the largest source of the global green-house-gas emissions (International Energy Agency, 2011). An evaluation from the World Bank reveals that the environmental pollutions cost over 4% of China's GDP each year (The World Bank and China Ministry of Environmental Protection, 2007), threatening the long-term development of the country's economy. Growing intentions to reduce the serious environmental pollutions and control the growing CO2 emission have been signaled in policies in China recently. One important action taken by the government is that in China's Twelfth Five-Year Plan (2-11-2015), a 17% target for national CO2 intensity reduction has been introduced for the first time (The State Council of China, 2011). This will work as a legally binding target in line with the nation's commitment at the Copenhagen conference to reduce its CO2 emission intensity by 40–45% over the period 2005 to 2020 in 2009 (Xinhua, 2009). It is learned from the 11th Five-Year Plan period that decomposing national energy-saving targets to local areas and implementing a local governor target responsibility system are effective energy-saving institutional arrangement means in China. It should be persevered and continuously improved in achieving the carbon dioxide emission
⁎ Corresponding authors. E-mail addresses: [email protected] (L. Zhou), [email protected] (T. Qi).
reduction targets by 2020. Taking the advantage of a centralized political system, China's national emission reduction target is allocated to provincial level to facilitate the achievement of this target. The first allocation trial is implemented during the 11th Five-Year Plan period (2006–2010) for a 20% energy intensity (energy consumption per unit of GDP) reduction. In this trial, a “declaration and negotiation” approach is adopted for the target disaggregation. The provinces first report a volunteer reduction target and the central government negotiates with the provinces to adjust their target to match the national target. One key problem in this aggregation trial is that most of the reduction target raised by provinces is based on a rough evaluation and short of effective and scientific assessment (Haibing, 2011; Xiao, 2012). This shortage is mainly due to the lack of experience and capacity of the impact evaluation. As soon as the provincial target has been set, it works as an administrative instruction and has almost been achieved by all the provinces at the end of the 11th Five-Year Plan period (National Development and Reform Commission of China, 2010; The State Council of China, 2011), as shown in Fig. 1. Although energy efficiency is well improved (Kostka and Hobbs, 2012), some extreme measures such as power rationing have been implemented; and, a significant loss of social welfare is the cost to achieve the absolute and not-well-evaluated target in provinces (steelorbis, 2010). A best sample of emission permit allocation that China can learn from may be the emission allowance allocation in the EU Emissions Trading System (EU-ETS) market. Launched in 2005, the EU-ETS is a cornerstone of the European Union's policy to combat climate change and its key tool for reducing industrial greenhouse gas emissions cost-effectively. The EU-ETS is the largest emission
http://dx.doi.org/10.1016/j.esd.2014.07.003 0973-0826/© 2014 International Energy Initiative. Published by Elsevier Ltd. All rights reserved.
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L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
trading scheme to date, covering around 11,000 power stations and industrial plants in 30 countries (European Union, 2003, 2012). In its first phase (2005–2007), the allocation of allowances in Phase I was determined by the Member States which submitted the so called National Allocation Plans (NAPs) to the Commission for review and approval (The European Commission, 2013; Zhang and Wei, 2010). The NAPs set the overall cap for the country and allocated allowances to every participating installation (Zhang and Wei, 2010). The allocations are determined for each trading period at a time to account for the fact that annual GHG emissions fluctuate depending on the economic conditions (Ellerman and Joskow, 2008). Similarly as in China, due the lack of experience and capacity in emission projection, the allowance in Phase I of the EU ETS is over-allocated causing the permit price to decline considerably (European Environment Agency, 2011). Following the identification of the limitations experienced in Phase I, the commission acquired the authority to impose a formula to assess the allocation plans of Member States and emission projections were objectively based on the verified emissions of 2005. Based on the assessment, the NAPs adjusted its allowance in the second round and were more ambitious. Though due to the unexpected financial crisis, the permit price declined again. This allocation approach delivers a more compelling price signal to the industries. To further control the growing CO2 emissions, China has set up a clear target of 17% CO2 intensity (CO2 emissions per unit of GDP) reduction in its 12th Five-Year Plan over the period 2011 to 2015 (Joana Lewis, 2011; State Council of China, 2011). However, this target will be a great challenge for a developing country. It is urgent and important to determine how to accomplish this challenging task. One aspect that must be addressed is determination of how to disaggregate the national target to the provincial level. A second round of the allocation of this national target to provincial level is ongoing. To avoid repeating the limitations in the first round, it is necessary to establish scientific and reasonable decomposition principles and methodologies to disaggregate target decomposition to the provincial level and implement a target responsibility system. This paper designs a method to allocate the target in the 12th Five Year Period based on a group of decomposition principles and methodologies in the consideration of equity and efficiency. The paper is organized as follows: the Basic assumptions and analysis section describes the principles used in disaggregation, the Regional disaggregation method based on mitigation pathway analysis of carbon intensity section briefly introduces the methodology and formulas, the Results and discussion section presents the results, and the last section gives a conclusion.
Basic assumptions and analysis Basic disaggregation principles To disaggregate China's carbon intensity reduction target scientifically and reasonably to the local level, it is necessary to deal with different relationships properly, such as international and domestic, central and local, short-term and long-term, potential and ability, and efficiency and equity. In addition, it is necessary to build scientific and reasonable principles and methodology for disaggregation. Discussions on the principles of reduction commitments allocations for greenhouse gas emissions are drawing increasing attention because of the difficulty in International Climate Change Negotiation. Topics of such discussions primarily include the role of responsibility, capability and efficiency and their balance on the target delegations (Bhatti et al., 2010; Harris and Symons, 2010; Kanie et al., 2010; Zetterberg et al., 2012). Based on these discussions, we developed the principles of target disaggregation from the following aspects. (1) Effect, e.g., whether the disaggregation scheme could ensure or advance realization of the national target. (2) Efficiency, which considers whether the implementation of disaggregation leads to less economic and social cost. (3) Equity, different situations among provinces such as the social and economic development stage, natural resources endowment, energy import and export amount, and energy conservation effort should be considered. (4) Transparency, which specifies that the disaggregation method and supporting data should be transparent and easily repeated. (5) Feasibility, e.g., the approach, ability and potential to realize the carbon intensity reduction target of each province should be considered. (6) Continuity, the efforts made by each province for energy conservation and carbon reduction in the 12th Five Year Plan period should be continuous with the energy conservation goal in the 11th Five Year Plan period, and dramatic increases and decreases should be avoided unless there are special circumstances. (7) Consistency, the results of local disaggregation schemes should be connected with the national target to ensure realization of the target.
Factors affecting the reduction of CO2 emissions per unit GDP The main component of greenhouse gases is CO2, which is mainly produced by fossil fuel combustion. Numerically, the CO2 emissions per GDP (usually referred to as carbon intensity) are equal to the amount of energy consumed per GDP (usually referred to as energy
Fig. 1. Completion of energy consumption per GDP reduction target of provinces during the 11th Five-Year Plan.
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
27
Fig. 2. Reduction pathways and factors affecting carbon intensity reduction.
CO2 =GDP ¼
Energy CO2 : Energy GDP
ð1Þ
The energy intensity relative to the GDP reflects the comprehensive benefits of energy transformation, transportation, distribution and utilization in economic and social activities, which are closely related to efforts such as energy structural adjustment, technical improvements and improved added value of the products. The carbon emission factor of the energy reflects the features of the primary energy consumption structure. Increasing the proportion of non-fossil energy in energy consumption is the main method of reducing the carbon emission factor of the energy. Accordingly, the carbon intensity relative to the GDP reflects not only the comprehensive benefits of energy transformation, transportation, distribution and utilization, but also the reduction in CO2 emissions caused by energy substitution in the energy system. Energy conservation and energy replacement in the 11th Five-Year Plan period led to a reduction in the annual rate of energy intensity of China of 3.2%, an annual increasing rate of non-fossil energy of 1.2%, and an annual decreasing rate of carbon intensity of 4%. According to the non-fossil energy development target proposed in the 11th FiveYear period, by 2020 the proportion of non-fossil energy in China will reach 15% (National Development and Reform Commission of China, 2007). Additionally, if it is set in a way that a target reduction of 45% in carbon intensity in 2020 is compared with that in 2005, it means that an overall reduction in energy intensity should be about 40%.
The following diagram shows the main pathways and factors involved in carbon intensity reduction (Jiankun, 2011). The decrease in GDP carbon intensity shall be achieved by reducing the useful energy demand per GDP and improving the utilization efficiency of energy transformation. The methods for reducing the carbon intensity of the energy include reducing the carbon content rate of energy and increasing carbon sequestration. According to the analysis of reduction pathways shown in Fig. 2, there are three main mitigation pathways for reducing carbon intensity, structural energy conservation, technical energy conservation and energy substitution. (See Fig. 3.) Regional disaggregation method based on mitigation pathway analysis of carbon intensity Based on investigation of the responsibility, potential and ability associated with three energy conservation and carbon emission
1XPEHURISURYLQFHV
intensity) multiplied by the CO2 emissions per energy consumption unit (also referred to as carbon emission factor of the energy). Carbon intensity = energy intensity × carbon emission factor of the energy
!
'LIIHUHQFHRIWKHWDUJHW &DUERQLQWHQVLW\UHGXFLQJWDUJHW
(QHUJ\LQWHQVLW\UHGXFLQJWDUJHW
Fig. 3. Differences in calculated and published targets for carbon intensity and energy intensity reduction.
28
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
reduction approaches in various provinces, autonomous regions and municipalities, a carbon intensity reduction method based on mitigation pathway analysis is proposed. First, the annual decreasing rate of carbon intensity, caused by structural energy conservation, technical energy conservation and energy substitution, is separately predicted for various provinces, autonomous regions and municipalities during the 12th Five-Year Plan period based on the historical data. Next, corrections are made by considering the transfer-in and transfer-out energy of the provinces, energy development forecast during the 12th Five-Year Plan period, and positions of their respective functions. Finally, the energy intensity and carbon intensity disaggregated target for various provinces during the 12th Five-Year Plan period are calculated. The result is composed of two parts: R ¼ R1 þ Δ r 5 R1 ¼ 1− 1− 100 100
ð2Þ
in which, R represents the reducing target, Δ is the correction factor, r is the annual decreasing rate of GDP carbon intensity and R1 represents the reducing target calculated from the annual decreasing rate of indicators. Forecast of annual decreasing rate in carbon intensity in provinces
X
ri;n ¼ ri;s þ ri;t þ ri;a
ð3Þ
n
in which, r is annual carbon intensity decline rate, i represents province i, n represents the kind of indicator, and s, t, and a represent three kinds of indicators, which reflect structural energy conservation, technical energy conservation and energy substitution, respectively. The corresponding annual decreasing rate relating to structural energy conservation, technical energy conservation and energy substitution in various provinces can be determined from the corresponding annual decreasing rate of various indicators and weights using the following formula. The decline rate of single indicator ri,n,j,k is determined by the actual data of the indicator as well as the boundary of indicators in each kind. ri;n ¼
3 X
wi;n; j ri;n; j ; ri;n; j ¼
j¼1
ri;n; j;k ¼
X
wi;n; j;k ri;n; j;k
ð4Þ
k
min
j
j
ð5Þ
In which, w is the weight of indicators, i represents the province i, j represents potential, responsibility or ability, where indicators are divided into these three aspects and k represents the indicator k in one aspect. D represents the actual data of each indicator. L represents the boundary of indicators in one kind, which means the possible largest and smallest decline rates in one kind. The boundary of indicators is determined by formulas (6) and (7); and, max and min represent the maximum value and minimum value, respectively. Thus, the annual decreasing rate of a single indicator must be calculated by interpolation of the upper and lower bounds. Assuming that the maximum value of the calculated decline rate of structural energy conservation in each province is equal to θ times the
k
ð6Þ In which, N means the national data, and θ is equal to the maximum reducing target divided by the minimum reducing target of all provinces. θ is defined by what maximum and minimum reducing targets of all the provinces the government could accept. For example, if the maximum and minimum reducing targets of all the provinces are supposed to be 19% and 15%, then the value of θ is assumed to be 1.3. It is also assumed that the calculated average result of the decreasing rate of structural energy conservation in each province should be equal to the national average decreasing rate, such as the technical energy conservation and energy substitution, i.e., N rn ¼ average ri;n 0 1 0 X X X @ @ ¼ average wi;n; j ri;n; j Þ ¼ average wi;n; j wi;n; j;k ri;n; j;k A: j
k
ð7Þ In one word, according to the above formulas, we can obtain the value of the upper and lower bounds of indicators when certain national carbon reducing target and weights of indicators are given. Then obtain the decline rates of structural energy conservation, technological energy conservation and energy substitution separately in all provinces, as well as the declining target initial value of the corresponding energy intensity and carbon intensity in each province. Annual decreasing rate of national average carbon intensity The national average structural energy conservation, technical energy conservation and energy substitution are calculated as follows. N
r ¼
X
N
N
N
rn ¼ rs þ rt þ ra
N
ð8Þ
n
N
L max −L min n n Di;n; j;k − min Di;n; j;k max Di;n; j;k − min Di;n; j;k þ Ln
N rn θ ¼ max ri;n 0 1 0 1 X X X @ A @ ¼ max wi;n; j ri;n; j ¼ max wi;n; j wi;n; j;k ri;n; j;k A:
j
The annual decline rate of GDP carbon intensity reduction in various provinces during the 12th Five-Year Plan period is composed of three parts: ri ¼
national average decreasing rate; the same is for technical energy conservation and energy substitution. i.e.
rs ¼ rt
N
ð9Þ
Learning from other research, the impact of structural energy conservation contributes around 50% for energy intensity decreasing, which means that structural energy conservation and technical energy conservation contribute half separately for energy intensity decreasing. Then, the national decline rate of structural energy conservation and technical energy conservation could be calculated as follows: N
N
rs ¼ rt ¼
N
1 N 1 ΔEI ¼ r 1− 1− 100 2 EI 2
N
N
¼
ΔCI 1− 1− 100
N
!ð1=5Þ !
r ¼ rCI −rEI !ð1=5Þ !a
100
N
ΔEIN 100− 1− 1− 100
ð10Þ
!ð1=5Þ ! 100
ð11Þ
where CI represents the carbon intensity, EI represents the energy intensity, ΔCIN represents the national carbon intensity decline target during the 12th Five-Year Plan period, and ΔEIN is the national energy intensity decline target during the same period.
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
29
The national carbon intensity in 2015 and 2010 was determined using formula (15), in which N still presents national data.
Indicators and weights Based on the principle of local decomposition as well as decomposition pathways of carbon intensity decline, we selected the following index system and provided the corresponding reasonable weights according to their economic meaning and impact based on experts' opinions and experiences. Relative data were mainly obtained from the national yearbook, (National Bureau of Statistics of China, 2006, 2007, 2008, 2009a,b) and the local yearbooks of each province, (Department of Development Planning in Ministry of science and technology of China, 2009; National Bureau of Statistics of China, 2010) as shown in Appendix A. Correction factor
30 X
CIN ¼
CO2i ð15Þ GDPi
i¼1
The relationship between national and provincial carbon intensities in 2015 can be obtained from the above two equations:
2015
CIN
To correct the above calculation data according to historical data indicators, the three indicators should be considered as correction factors from experts' experiences. As shown in formula (12), Δ is the correction factor and m is the kind of correction factor.
CO2N ¼ i¼1 30 GDPN X
¼
" # 30 X CO2010 2015 2i ð1−ΔCIi Þ GDPi GDP2010 i i¼1 30 X
Δ¼
Δm
ð12Þ
m¼1
ð16Þ
i¼1
Then, according to the above equations, we can get the national carbon intensity decreasing rate during the 12th Five-Year period: 2015
3 X
:
2015
GDPi
2010
ΔCIN ¼ 1−CIN =CIN# 0 " 1 0 2010 30 30 X X CO2i 2015 2010 B B ð1−ΔCIi Þ GDPi CO2i C B C B GDP2010 B C B i ¼ 1−B i¼1 Þ B i¼1 C: 30 30 B C B X X 2015 2010 @ A @ GDP GDP i
The first is set according to the proportion of net input or output energy in total energy consumption of each province. If the net input proportion is higher than 50%, the correction factor is 1, that is, the decline target in carbon intensity will increase by one percentage point. If the net input ratio is in the range of 0 to 50%, the correction factor is 0. If the net input is in the range of −100 to 0%, the correction factor is −1. If the net input ratio is less than 100%, then the correction factor is 2. The second is set according to the difference between the energy intensity decline target during the 11th Five-Year Plan period and the predicted decline target in energy intensity in various provinces during the 12th Five-Year Plan period. When the difference is greater than 5, it is less difficult to meet the target, and the correction factor is 1. If the difference is between 3 and 5, it is moderately difficult to meet the target, and the correction factor is 0. If the difference is between 0 and 3, there is a greater difficulty in meeting the target; therefore, the correction factor is − 1. If the difference is less than 1, meeting the target is very difficult and so the correction factor is − 2. The third is to consider other factors in each province without changing the national target. Method for estimating national emission reducing target based on targets of each province In this study, the carbon intensity reduction targets in each province were used to generate a simplified algorithm to estimate the decline in national carbon intensity. Carbon intensity reduction targets in each province, which is shown as ΔCIi, were calculated by formulas (13) and (14), in which 2010 and 2015 mean the years 2010 and 2015, and CO2 means carbon dioxide.
CIi ¼
CO2i GDPi
ΔCIi ¼
CI2010 −CI2015 i i 100% CI2010 i
ð13Þ
ð14Þ
i¼1
ð17Þ
i
i¼1
It is assumed that the GDP growing speed during the 12th Five-Year period in each province is the same as the growing speed of the whole country. We can obtain the simplified formula based on formula (17): ΔCIN ¼
30 X i¼1
ΔCIi
! CO2010 2i : CO2010 2N
ð18Þ
The decreasing rate in national carbon intensity is equal to the summation of the carbon intensity reduction target in each province multiplied by the proportion of its carbon dioxide emissions in 2010. The decreasing rate in national energy intensity can be estimated similarly; specifically, it is equal to the summation of energy intensity decline target in each province multiplied by its energy consumption proportion in 2010. Results and discussion Our research was in close coordination with the national 973 emergency project for addressing climate change, which is named as “disaggregation and implementation of GDP carbon dioxide emissions intensity target of 40%–45%”. The main target of the project is to disaggregate the GDP carbon intensity (carbon intensity) target by region and by sector. We finally selected a scenario in which energy consumption per GDP (energy intensity) decreases by 16% and carbon dioxide emissions per GDP (carbon intensity) decreases by 17% in the 12th Five-Year period. In this scenario, a program for national carbon intensity reduction target disaggregation in the 12th Five-Year period is proposed. The calculated carbon intensity target by region in the 12th Five-Year period is compared with the official published data (State Council of China, 2011) in Table 2. Qinghai, Hainan and Xinjiang showed larger differences between the calculated results and the decline targets of energy intensity and carbon intensity. In the calculation process, data from various provinces obtained using this method on the basis of the differential accounting method had continuity. As shown by the actual disaggregated data provided by the National Development and Reform Commission, three provinces have decline targets that are significantly
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L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
lower than those of the other provinces. The underlying reasons for these differences may be that these three provinces have a significantly larger gap than other provinces between the development stage and was actually able to implement their plans. If you ignore the differential data of these three provinces, and calculate the average of the absolute difference values between the calculated decline targets and published targets for other provinces, then the average differences of energy intensity and carbon intensity are both smaller than 1. Considering the actual work needs associated with decomposition of the carbon intensity target, one percentage point difference is generally acceptable, and the targets can also be fine-tuned through later negotiation. Therefore, this method has reference significance for target setting in most provinces. For individual provinces, it is necessary to adjust and consider the correction factor in each province to reduce the accounting error associated with the method. In short, despite the approach also having some limitations in considering the specificity of individual regions, it provides a more comprehensive index method and design system, enjoys better operability, and provides accounting data showing good significance when used as a reference. Therefore, this method can be used as an effective policy tool in the decomposition of carbon intensity target. (See Table 1.) Conclusion In 2009, the Chinese government officially announced that the country is going to reduce the carbon dioxide emissions per unit of GDP (carbon intensity) in 2020 by 40 to 45% when compared with 2005 levels, which has become an important national macro-target. According to this, the China 12th Five-Year Plan recently established a restrictive index in which the carbon dioxide emissions per unit of GDP in 2015 should be reduced by 17% when compared with those of 2010. Based on the realization of energy saving targets during the 11th Five-Year period, breaking the national target down to the provincial level and applying a local governor target responsibility system were effective institutional arrangement means. Thus, to achieve an effective carbon intensity reduction in 2015, it is necessary to break the national target down to provincial level so that regional restrictive indexes can be formed. However, China has little experience in this, and there is almost no methodological research pertaining to the provincial or regional disaggregation of national carbon intensity targets in China available. Although China has given each province energy consumption per GDP (energy intensity) reduction target according to the national target, the disaggregation approach is based on one universal standard followed by negotiation between national and regional governments, which lacks effective and scientific support.
This study demonstrated that a regional disaggregation approach should be based on a series of principles, including effective disaggregation, guaranteed efficiency and justice, implementation feasibility and continuity of target, method transparency and consistency between the regional and state indexes. Additionally, content analysis of the carbon intensity revealed that the three main approaches that must be taken to realize carbon emission reduction are energy structure adjustment, technical improvement and application of alternative energy. Accordingly, a disaggregation model that consists of several assessment indicators, weights, and relevant experience correction factors was proposed from the view of the three reduction approaches. All indicators were selected based on consideration of important differences among regions with respect to economic development stage and energy consumption structure. These differences include GDP per person, urbanization ratio, proportion of eight high energy intensity sectors in GDP, proportion of industry, growth rate of the industry added value during the 11th Five-Year period, ratio of industry energy intensity to the national average level, and natural endowment of new energy. In this way, a complete method of regional disaggregation of the national carbon intensity reduction target was established. Through collection and analysis of provincial economic and energy data from 2005 to 2010 in China, a regional disaggregation scheme in which the national carbon intensity reduction target during the 12th Five-Year period was set to be 17% was developed. The calculated carbon intensity target by region in the 12th Five-Year period was then compared with the official published data. Results showed that the differences between the calculated and official targets were acceptable. This method provides a more comprehensive index method and design system, enjoys better operability, and provides calculated data as a useful reference. Therefore, a detailed provincial target of carbon intensity reduction in the 12th Five-Year Plan can be provided in a scientific way. Acknowledgments We appreciate the national 973 emergency project for addressing climate change and providing funding and support for this study. The authors acknowledge other colleagues in the Institute of Energy, Environment and Economy for conducting data collection. The comments of other anonymous referees are also highly appreciated. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.esd.2014.07.003.
Table 1 Index and weight of regional disaggregation method. Weight wi,n,j
Indicator ri,n,j,k
Weight wi,n,j,k
Potential
0.4
Responsibility
0.4
Ability
0.2
Potential Responsibility
0.4 0.4
Ability
0.2
Potential Responsibility Ability
0.5 0.3 0.2
Tertiary industries pulling rate during the “Eleventh Five-Year” period Added value share of eight high-energy consumption in local GDP Per capita consumption expenditure GDP per capita Urbanization rate GDP growth rate during the “Eleventh Five-Year Plan” Regional industrial added value/national industrial added value High-tech industries index Provincial energy consumption per unit of industrial added value/national average value The proportion of industrial added value in provinces The growth rate of industrial added value Environmental index of scientific and technological advances Scientific and technological activities input index The proportion of non-fossil energy in the “12th Five-Year Plan” period in China Provincial carbon intensity/national average carbon intensity Industrial investment in urban energy in each region Fiscal expenditure per capita
0.25 0.5 0.25 0.5 0.25 0.25 0.5 0.5 1 0.5 0.5 0.5 0.5 1 1 0.5 0.5
Decline pathways and aspect Structural energy conservation
Technique conservation
Energy replacement
L. Zhou et al. / Energy for Sustainable Development 23 (2014) 25–31
31
Table 2 Calculated and published carbon intensity and energy intensity reducing target by region in the 12th Five-Year Period. Region
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
Energy intensity reducing target %
Carbon intensity reducing target %
Calculated
Published
Difference
Calculated
Published
Difference
17 17 17 16 16 18 17 14 18 18 17 16 16 16 17 15 17 16 16 15 13 16 16 15 14 14 16 14 17 15
17 18 17 16 15 17 16 16 18 18 18 16 16 16 17 16 16 16 18 15 10 16 16 15 15 16 15 10 15 10
0 −1 0 0 1 1 1 −2 0 0 −1 0 0 0 0 −1 1 0 −2 0 3 0 0 0 −1 −2 1 4 2 5
18 18 18 17 18 19 18 15 19 19 18 17 17 16 19 16 18 17 17 16 14 17 17 16 15 14 17 15 18 16
18 19 18 17 16 18 17 16 19 19 19 17 17.5 17 18 17 17 17 19.5 16 11 17 17.5 16 16.5 17 16 10 16 11
0 −1 0 0 2 1 1 −1 0 0 −1 0 −1 −1 1 −1 1 0 −3 0 3 0 −1 0 −2 −3 1 5 2 5
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