INVESTIGATING THE EFFECTIVENESS OF THE COPENHAGEN ACCORD IN CHINA, INDIA AND INDONESIA: ENERGY SECTOR ANALYSIS1

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COPENHAGEN ACCORD IN CHINA, INDIA AND INDONESIA: ENERGY SECTOR ANALYSIS

¹

Maxensius Tri Sambodo

Economic Research Center, Indonesian Institute of Sciences E-mail: [email protected]

and Tatsuo Oyama

National Graduate Institute for Policy Studies

ABSTRACT

This paper investigates quantitatively the effect of rapid economic growth and total primary energy sup- ply on the carbon dioxide (CO₂) emission profi les in China, India, and Indonesia. This paper also attempts to evaluate NAMA (National Appropriate Mitigation Action) targets with respect to carbon intensity in 2020.

We observe that rapid economic growth is fuelled from high carbon intensity sources. Growth analysis indicates that only in China growth GDP is higher than the growth of CO₂ emissions, and there is also a reduction in CO₂-GDP intensity. Only in China it is possible to achieve the goal for CO₂-GDP intensity by 2020, and for Indonesia we fi nd that CO₂-GDP intensity will continue to grow. However, if India can maintain its economic growth above 6 per cent, it will be able to meet the emissions reduction goal. Currently, in China and India, managing CO₂ emissions from a wide-economy sector will have a bigger effect in mitigating CO₂ emissions, but in Indonesia, measuring CO₂-energy change is more important. China needs to do more to reduce carbon intensity from its primary energy structure. Finally, the model shows that, in the short run, economic growth and energy have a signifi cantly positive effect on CO₂ emissions. In the long run, only in China and India the expected cu- mulation of CO₂ emissions will decrease, but in Indonesia the expected cumulative CO₂ emissions will increase.

China, India, and Indonesia have less than seven years from now to meet the target and there are four sectors that need to be measured seriously and innovatively: power generation, manufacturing, construction and building, and transportation.

Keywords: carbon intensity, economic growth, energy.

JEL Classifi cation: C22, Q40, Q54.

1 This paper has been prepared for the 2nd Congress of the East Asian Association of Environmental and Resource Economics (EAAERE), 2-4 February, 2012, Bandung-Indonesia, Faculty of Economics of Padjadjaran University, under the theme ‘Climate Change’.

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I. INTRODUCTION

The 15th Session of the Conference of the Parties to the United Nations Framework Convention on Climate Change (COP15) held in Copenhagen from 7 to 19 December 2009 produced the Copenhagen Accord, which com- prises 12 agreements between developed and developing countries. Point 5 of the agreement states that non-annex I parties (developing countries) agree to submit their mitigation action plans at the national level on January 31, 2010. Although the mitigation action plan is voluntary, every country needs to provide a report every two years based on clearly defined guidelines.

In general, inventories of anthropogenic greenhouse gases (GHG) can be derived from four sources: energy, agriculture, waste, land use change, and forestry. This paper investigates carbon dioxide (CO₂) emissions from the

energy sector only because, according to International Energy Agency - IEA (2008), energy-related emissions of CO₂ currently account for 61 per cent of total greenhouse-gas emissions, a share that is projected to rise to 68 per cent in 2030. Thus, decarbonising the energy sector will have an important effect on global CO₂ reduction. Figure 1 shows the share of CO₂ emissions from the energy sector. Electricity and heat contribute most of the GHG emissions from the energy sector.

This study focuses on China, India, and Indonesia for four reasons.

First, these countries are members of non-annex I parties and their share of total CO₂ emissions to total non-annex I parties is about 58 per cent or about 28.5 per cent of total CO₂ emissions from fuel combustion in the world (International Energy Agency, 2011). Second, in terms of

Source: Climate Analysis Indicators Tool (CAIT) Version 8.0. (World Resources Institute, 2012).

Figure 1. CO₂ emissions in energy sectors in 2009 in China, India and Indonesia

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energy-related CO₂ emissions, China and India are the world’s biggest emit- ters of energy-related CO₂, with their shares in 2007about 21 per cent and 4 per cent, respectively. Even in 2020, based on the reference scenario, the shares of China and India will increase to about 27 per cent and 6 per cent, respectively.² Further, China accounts for 49 per cent of the global increase in emissions and India about 17 per cent (International Energy Agency, 2008).

Third, according to the Copenhagen Accord, India is to reduce emissions intensity of its GDP by 20 to 25 per cent by 2020 in comparison with 2005;

China is to reduce CO₂ emissions per unit of GDP by 40 to 45 per cent by 2020 compared to 2005; and Indonesia will reduce emissions by 26 per cent by 2020.³ This action, called National Ap-

2 World Energy Outlook 2008 mentioned three scenarios: reference, 550 ppm and 450 ppm.

Based on the reference scenario, the global average temperature will increase up to 6°C by the end of this century, but for 550 ppm and 450 ppm, there will be a global average temperature rise of around 3°C and 2°C, respectively (Inter- national Energy Agency, 2008).

3 China’s goal is to maintain the share of renewable and nuclear energy to total energy use at 15 per cent by 2020 (Xinhua, 2009, as cited in Stern and Jotzo, 2010). In the case of Indone- sia, on January 19, 2010, the National Council on Climate Change sent a letter to the execu- tive secretary of UNFCCC that stated Indone- sia’s plans to reduce GHG emissions by 26 per cent to 41 per cent of CO2. This means that there would be reduction of around 6 per cent and 24 per cent, respectively, below 2005 emissions levels under a business as usual (BAU) scenario (Ministry of Finance, 2009). Reducing emissions will be concentrated on seven areas;

peat-land, forestry, agriculture, energy, industry, transport and waste. However, a second letter, delivered on January 30, 2010, stated that the

propriate Mitigation Action (NAMA), is measurable, reportable and verifi able (Goldenberg and Prado, 2010). Finally, between 2006 and 2030, China and India will account for just over half of the increase in the world’s primary energy demand (International Energy Agency, 2008). In Indonesia, the share of coal used in the electricity sector has increased from zero in 1984 to 53 per cent in 2004 (Resosudarmo, et al., 2011).

Although we compare the three countries, it is important to note that they are different in terms of the scale of their economic and energy- related CO₂ emissions. In terms of GDP, China contributed about 7.1 per cent of the world’s GDP (see Table 1), and the shares of India and Indonesia were about 2.3 per cent and 0.7 per cent, respectively. Further, in terms of global CO₂ emissions, China emitted about 24 per cent but India and Indonesia were about 6 per cent and 1 per cent, respectively.

Considering further intensity of CO₂ emissions in terms of energy, population and GDP, it is the case that China is above world standards. This situation has consequences for a ‘fair’

responsibility in reducing emissions.

Further, according to International Energy Agency (2008), there are several principles for differentiating responsibilities, such as current or historic emissions rates, cumulative emissions, population, GDP per capita, emissions per unit of GDP,

voluntary mitigation action will be at 26 per cent by 2020.

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Table 1. Economic-energy indicators in 2009

Indicator Name China India Indonesia World

GDP (billion, constant 2005 US$) 3,476 1,128 356 49,098

CO2 emissions (Mt) 7,687 1,979 452 32,042

CO2 intensity (kg per kg of oil equivalent energy use) 3.42 2.93 2.28 2.58

CO2 emissions (metric tons per capita) 5.77 1.66 1.90 4.71

CO2 emissions (kg per 2005 US$ of GDP) 2.21 1.75 1.27 0.65

Source: World Bank (2012)

reduction potential, costs and benefi ts of reduction, and building on existing national groupings.

T h i s p a p e r i nve s t i g a t e s quantitatively the effect of rapid economic growth and energy supply on carbon dioxide (CO₂) emission profi les, comparing China, India and Indonesia. More specifi cally, it attempts to evaluate goals with respect to carbon intensity in 2020. Furthermore, this paper contributes to two groups of studies; fi rst, as an academic exercise, it employs a new approach to measuring the short-run and long-run effects of economic growth and primary energy supply growth on CO₂ emissions.

Second, in terms of a policy exercise, it suggests strategies for China, India and Indonesia in managing CO₂ emissions from the energy sector. This paper is organised as follows: the next section briefly discusses method; following that we investigate historical trends and relations of GDP, energy and CO₂ emissions data. Then, we apply mathematical modelling and time-series analysis to economic, energy and CO₂ emissions data. The conclusion and policy recommendations are in the last section.

II. METHOD

Our data were from the World Devel- opment Indictors (WDI). The WDI defi nes CO₂ emissions as follows:

Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include car bon dioxide produced during consumption of solid, liquid, and gas fuels and gas fl aring.

We applied the following mathematical model for investigating growth.⁴

y = Ket (1) where y is a variable that can de- note GDP, total primary energy supply (TPES) and CO₂ emissions; K and λ are parameters; and t indicates year.⁵ We assumed CO₂ emissions came from two

4 We assume the growth of GDP, TPES and CO2 emission follows exponential growth.

5 Two terminologies are important in analysing the energy sector; total production of energy (TPE) and total primary energy supply (TPES) (OECD, 2011). TPE refers to a country’s en- dowment in producing energy. By defi nition, we can conclude that TPE is a function of the fuels extracted. Thus, for a net energy import- ing country, TPE can be lower than the TPES, because TPES includes energy imports.

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major sources, economic growth and TPES. Managi and Kaneko (2009), says that fossil fuel combustion is respon- sible for 75 per cent of anthropogenic CO₂ emissions in China and changes in energy consumption and production are expected to directly infl uence CO₂ emissions. In this analysis, we proposed an ad hoc model in which CO₂ emissions would depend on lag of CO₂, GDP, and TPES and its lags.

) , , , ,

( 2

2t f CO thGDPt GDPth TPEStTPESth

CO  _ _ _ (2)

We applied a time-series-ARMAX model to investigate factors that affect- ed CO₂ emissions (see equation 2). We estimated short and long-run effects of GDP and TPES on CO₂emissions.

The ARMAX model is an extension of the autoregressive moving-average (ARMA) model. ARMA models use just one variable.

q t q t t p t p y t

t y y y

y₁ ₁₂ ₂...   ₁₁... (3) which can be written as (Verbeek, 2004; Ender, 2010):

t t

t A L y B L

y  ( ) _₁ ( ) (4)

where A(L) and B(L) are polyno- mials in L. If we want to analyse the relations between y and x, we extended the model to create ARMAX because we were interested in analysing the impact multiplier (short-term impact) and long-run multiplier (Verbeek, 2004;

Ender, 2010):

t t

t

t AL y C L x B L

y  ( ) _₁ ( )  ( ) (5)

III. GDP, ENERGY AND CO₂ EMISSIONS ANALYSIS

Deregulation and liberalisation has helped China, India and Indonesia to integrate with global economy and to achieve high economic growth.

As seen in Figure 2, between early 1960s and the 1980s, the GDP gap between China, India and Indonesia was relatively small, but it increased rapidly afterward and China has shown remarkable progress. For example, in the early 1960s, China’s GDP was about 1.1 times of India’s and 3.8 times of Indonesia’s, but in 2010, China’s GDP was about 3.3 times of

Figure 2. GDP and GDP growth in China, India and Indonesia (1960 to 2010)

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India’s and 11.8 times of Indonesia’s.

This is because China has consistently maintained its high economic growth, especially since early 1990s, whereas the economic growth of India and Indonesia is still below the Chinese rate. To make it even worse, in 1998, Indonesia had an economic contraction

of about 13 per cent because of the Asian economic crisis. The average of Indonesia’s economic growth ever since has been still below what it was before the crisis.

Indonesia’s rapid economic growth is supported by dramatic increases in manufacturing value

Figure 3. Manufacturing sector in China, India and Indonesia

Figure 4. Services sector in China, India and Indonesia Source: World Bank (2012)

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added. Figure 3 shows that the share of value added from the manufacturing sector increased from about 9 per cent in 1960 to about 29 per cent in 2001 and afterward the share gradually decreased to about 25.7 per cent in 2010. In contrast, manufacturing value added reached a peak of about 40 per cent in the early 1980s then it gradually decreased and remained stable at around 31 to 33 per cent. In India, manufacturing value added has been constant at about 16 per cent since early 1980s. Although the share of the manufacturing value added to GDP remains constant in China and India, the share of the services sector has increased rapidly in both countries (see Figure 4). It seems that India developed its services sector earlier than China and Indonesia. India has a comparative advantage in software, computer programming, information and communication technology (ICT).

ICT enables services such as business- process outsourcing, back-office outsourcing of business services and call centres (Das, 2006). Development of the services sector in Indonesia is not as good as in China and India, even its share to GDP decreased from about 42 per cent in the mid-1990s to about 35 per cent in 2010. Rapid economic growth in China will increase demand for more services in the future, and we could expect China soon to approach India in terms of the share of the services sector to GDP.

Suffi cient energy is important for sustaining economic growth. Similarly, Narain et al. (2009), cited in Vazhayil

and Balasubramanian (2010), say that

‘no country in history has improved its level of human development without corresponding increases in per capita energy use’. Total primary energy supply (TPES) in China, India and Indo- nesia has shown an upward trend since the 1970s (see Figure 5). And since late 1990s, China’s TPES has even grown more rapidly each year. According to China’s 2008 energy balance, total energy production was about 1,993,306 thousand tonnes of oil equivalent (ktoe) and China had imported 12.6 per cent of its production and exported 3.3 per cent. This indicates that most of its energy production had been allocated for domestic consumption. According to EIA (2007), cited in McKibbin et al.

(2008), China is now the second largest user of energy in the world after the United States, and it will become the largest in 2025. Meanwhile in India, total energy production is about 468,307 thousand tonnes of oil equivalent (ktoe) and it has imported 42.3 per cent of its production and exported about 8.5 per cent, when Indonesia’s energy production was about 346,985 thousand tonnes of oil equivalent and its share of import to total energy production was about 10 per cent. Furthermore, its share of export to total energy production was about 52.4 per cent. Moreover, coal and peat dominate energy production in all three countries; for instance, the share of coal and peat to total energy production for China, India and Indonesia was about 73 per cent, 48.1 per cent and 47.6 per cent, respectively. Thus,

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we might argue that growing energy production was still dominated by high carbon-intensive sources such as coal.

Because the structure of TPES was dominated by fossil fuels such as coal, we forecast that CO₂ intensity (CO₂/ TPES) intensity would also increase (see Figure 5). In the case of China, Bergsten et al. (2009) argued that the dramatic increase in China’s energy consumption is caused by a rise in its heavy industries, for example fl at glass, cement, steel and aluminium. These industries have developed because of several factors: low operating costs, low labour costs, high profi ts, economic incentives from local governments, and the ease of obtaining credit from the banks (Bergsten et al., 2009). We might expect the intensity would increase in

the future while the structure of TPES was still dominated by high-carbon- intensive fuels. As seen in China, the intensity increased from 2236 kt/Mtoe in 1971 to about 3340 kt/Mtoe in 2006; in India from 1310 kt/Mtoe to about 2708 kt/Mtoe; and in Indonesia from 1076 kt/Mtoe to about 2082 kt/

Mtoe.⁶

Energy-GDP intensity measures how much energy is needed to produce one unit of GDP at 2000 USD (see Figure 6).⁷ In the early 1970s, China

6 Stern and Jotzo (2010) say that energy intensity has risen substantially because of a shift of the fuel mix away from biomass towards coal and other fossil fuels.

7 China’s energy intensity is high if market exchange rates are used to compare economies, but less than so if purchasing power parities (PPP) are used (Garnaut et al., 2008). Similarly Figure 5. TPES and CO₂ intensity (CO₂/TPES) intensity in China, India and Indonesia (1971–2007)

Note: intensity = CO₂ emissions/TPES Source: World Bank (2012)

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was energy-inefficient compared to India, and Indonesia but since the late 1990s, the gap in energy intensity (energy-GDP) has narrowed. However, energy intensity in China has decreased rapidly. According to Stern and Jotzo (2010), in general, a good performance in reducing energy intensity needs to

Stern and Jotzo (2010) say that higher exchange rate relative to the PPP level is related to less energy effi ciency. For example, in China and India, their currency revalued over time will lower the effective price of energy, encourage consumption and discourage energy-effi cient investment (Stern and Jotzo, 2010).

be viewed as normal when we take into account their rapid rate of economic growth and initial high degree of inef- fi ciency. Further, according to Vazhayil and Balasubramanian (2010), emissions intensity is determined by energy effi ciency, fuel mix, sectoral composition of GDP, and emissions of greenhouse gases in various sectors.

Further, as shown in Figure 7, between 1971 and 2008, electricity production from coal increased rapidly to about 28 times for China, and 17.5 times for India and for Indonesia, Figure 6. Energy-GDP intensity in China, India and Indonesia (1971–2007)

Figure 7. Electricity production from coal in China, India and Indonesia (1971–2008) Source: World Bank (2012)

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between 1985 and 2008, it increased to 39 times. If we compare China and India in the early 1970s, China produced three times more electricity from coal than did India but, in 2008 it had increased this to about 4.8 times as many. Comparing China and Indo- nesia, in 1985, China produced about 169 times more electricity from coal than did Indonesia, but in 2006 it had decreased to about 44.5 times as many.

Finally, comparing India and Indonesia, in 1985, India produced 74 times more electricity from coal than did Indonesia but in 2008 it fell to about 9.3 times as many. Thus, between 1985 and 2008, we can conclude that Indonesia con- structed coal power plants more rapidly than China and India and China developed coal power plants more rapidly than did India.

China, India, and Indonesia have rapid economic growth, and CO₂ emissions have grown too. Although it seems that China emits more CO₂,

the gap between the three countries has become smaller (see Figure 8).

For instance, in 1960, China emitted 6.5 times more CO₂ than India and 36.5 times more than Indonesia; but in 2007, China emitted 4.1 times more than India and 16.5 times more than Indonesia. Further, in 1960 India emitted 5.6 times more than Indonesia, but in 2007, it was about 4.1 times more. Thus, over the past 50 years, CO₂ emissions in India and Indonesia have shown higher growth than that of China, and the growth of emissions in Indonesia is the highest of the three countries. In terms of CO₂ intensity, Indonesia has shown stable values since early 1980s, that is, below 2 kg per 2000 USD of GDP. India’s emissions intensity is slightly above that of Indonesia, but it has shown a decreasing trend since the mid-1990s.

China has had decreasing emissions intensity since the late 1980s and now is approaching those of India and Indonesia.

Figure 8. CO₂ emissions and CO₂-GDP intensity in China, India and Indonesia (1960–2007)

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Because coal is the main source of CO₂ emissions from the energy sector, we also needed to measure its trend. In the early 1960s, the share of CO₂ emissions from solid fuels was about 96 per cent in China and now it is about 72 per cent (see Figure 9). Although CO₂ emissions from solid fuels have tended to decrease in India, the decrease has not been as much as in China. In In- donesia, the share of CO₂ emissions from solid fuel is the lowest but it has increased rapidly since the mid-1980s and now it is about 38 per cent. With

Table 2. Growth of GDP and GDP per capita (1960–2010)

Indicators China India Indonesia

GDP

GDP growth 0.085 0.051 0.058

Std error 0.001 0.001 0.001

R² 0.989 0.987 0.985

GDP per capita

GDP per capita growth 0.070 0.030 0.040

Std error 0.002 0.001 0.001

R² 0.975 0.949 0.978

such information, we might expect to have negative emission intensity (CO₂- GDP) in China and positive values in India and Indonesia.

IV. MODELLING ANALYSIS 4.1 Growth and intensity analysis Between 1960 and 2010, economic growth in China, India and Indonesia was about 8.45 per cent, 5.1 per cent and 5.8 per cent, respectively (see Table 2). In terms of GDP per capita growth, China has the highest growth.

Figure 9. CO₂emissions from solid fuels in China, India and Indonesia (1960–2007)

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intensity decreases by 2.7 per cent per year, but in India and Indonesia, it increases by about 1 per cent and 0.7 per cent per annum, respectively. Fur- ther, we also calculated CO₂ emissions intensity with respect to TPES.⁸ India’s intensity showed the highest growth, about 2 per cent, but China’s was about 1.13 per cent, while Indonesia’s was slightly lower than that of China, that is, at about 1.06 per cent (see Table 4).

We could argue that the model did not fi t well for Indonesia and slightly better for India, but the R² was still below the expected level; it needed to be approaching 1.00 if we wanted to use this parameter to estimate future events.

Based on information in Table 4, we found that CO₂-GDP intensity in China decreased by 2.7 per cent per year, but in India and Indonesia, they increased by 1 per cent and 0.7 per cent, respectively.⁹ If we assumed this

8 According to Goldenberg and Prado (2010), decarbonisation is measured by the ratio of CO2 emissions to total primary energy supply (TPES). In the period 1990 to 2007, Golden- berg and Prado (2010) show that China is car- bonising its economy at the rate 1.07 per cent per year and India at about 1.09 per cent.

9 Vazhayil and Balasubramanian (2010) say that

‘the problem with a linear intensity target is that its stringency depends on the economic growth rate of a country. If actual GDP growth rate exceeds projected GDP growth rate substantially, the target will become meaningless as its achievement is ensured by GDP growth with little additional effort on emissions reduction.

This is the genesis of the “hot air” problem.

On the other hand, if the actual GDP growth rate is proportionally lower than projected, the target would become relatively more stringent on emission reduction front leading to non-

We estimated population growth by subtracting GDP from GDP per capita growth and it seemed that population growth in China was about 1.46 per cent and in India and Indonesia were about 2.03 per cent and 1.86 per cent, respectively. Further, as seen in Table 2, the growth of CO₂ emissions from the burning of fossil fuels and the manufacture of cement in China, In- dia and Indonesia was about 5.57 per cent, 5.69 per cent and 6.59 per cent, respectively. Thus, we fi gured that CO₂ emissions would increase in the future and it was a matter of concern that needed to be addressed by Indonesia because Indonesia shows the highest growth. Table 3 showed that Indone- sia had the highest growth in TPES between 1971 and 2008 of about 4.8 per cent, whereas in China and India, TPES grew by 4 per cent and 3.8 per cent, respectively. Further, in terms of TPES/GDP intensity growth, all three countries show decreasing growth: in China, it decreases by 5 per cent, and in India and Indonesia, it decreases by 1.5 per cent and 0.8 per cent, respectively.

If we compared economic growth and CO₂ emissions growth, in the case of India and Indonesia, the growth of CO₂ emissions was greater than the economic growth, but in China it was the other way around. We found a negative parameter of CO₂ emissions intensity (kg per 2000 USD of GDP) in China only (see Table 4). This indicated that China was experiencing a reduction in emissions intensity, but in India and Indonesia they were still increasing. In China, CO₂ emissions

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parameter remained constant until year 2020, referring to the direct approach, we expected in 2020 that CO₂-GDP intensity in China would decrease by 34 per cent, but in India and Indone- sia, th increase by 16.8 per cent and

compliance risk’.

10.5 per cent, respectively (Table 5)¹⁰. However, using the indirect approach, we calculated emissions intensity and economic growth separately (see Table 6). Data in 2005 are actual figures, but data in 2020 are estimated after we utilised growth information from

10 Direct approach indicates that we use directly estimated parameter from the growth estimate.

Table 3. Growth of TPES (1971–2008)

TPES (Mtoe)

TPES growth 0.0399 0.0376 0.04837

Std error 0.00117 0.0003 0.001

R² 0.9701 0.998 0.9845

TPES-GDP intensity (Mtoe per 2000 USD million) TPES-GDP

intensity -0.0504 -0.0146 -0.00794

Std error 0.0015 0.00096 0.00094

R² 0.968 0.8645 0.6644

Table 4. Growth of CO₂and CO₂ intensity (1960–2007)

CO₂emissions (kt)

CO₂emissions 0.056 0.057 0.066

Std error 0.002 0.001 0.002

R² 0.957 0.994 0.973

CO₂emissions intensity (kg per 2000 USD of GDP)

CO₂ intensity -0.027 0.010 0.007

Std error 0.002 0.001 0.001

R² 0.786 0.665^† 0.485^†

CO₂emissions intensity (kt per Mtoe)

CO₂ intensity 0.01129 0.0212 0.01059

Std error 0.0006 0.0008 0.00153

R² 0.90625 0.9553 0.5788^†

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Tables 2 and 4. In the case of China, if we compared Tables 5 and 6, emissions intensity will decrease between 35 per cent and 38 per cent. However, for India and Indonesia, we did not obtain consistent results. Thus, we needed to explain clearly the method used to calculate emissions. For India, the direct approach suggested that emissions would increase by 16.8 per cent because India’s annual emissions intensity is increasing. However, the indirect approach showed that between 2005 and 2020, emissions intensity would decrease by about 5 per cent.

In the case of Indonesia, using results from direct and indirect approaches, emissions intensity would increase by between 9.8 per cent and 15 per cent.

Further, we also evaluated the sensitivity analysis using the indirect approach. We developed two scenarios:

fi rst, we assumed growth of emissions intensity was unchanged while we al- lowed economic growth to be more flexible. If we assumed economic growth in China was about 10 per cent, emissions intensity in 2020 would decrease by 44.25 per cent compared to 2005 (see Figure 10). Similarly, in India, with 7 per cent economic growth, emissions intensity could decrease by 20.85 per cent. For Indonesia, although emissions intensity showed increasing growth, if economic growth could be maintained at 7 per cent, the emissions would grow by 2.89 per cent only, but if economic growth was 6 per cent, Table 5. Evaluation on emission intensity reduction I (direct approach)

CO₂ emissions (kg per 2000 USD of GDP)–2005 2.968 2.188 1.639

Annual intensity reducti on (%) -2.7 1 0.7

CO₂ emissions (kg per 2000 USD of GDP)–2020 1.936 2.556 1.800

Percentage change (2005–2020) -34.77 16.84 9.80

Table 6. Evaluation on emission intensity reduction II (indirect approach)

CO₂ emissions (kt)–2005 ^a 5,609,478 1,409,973 340,814

GDP (constant 2000 USD, in millions)–2005 ^b 1,908,795 644,500 207,892

Emissions intensity–2005 (a : b) 2.939 2.188 1.639

CO₂ emissions (kt)¹–2020 ^x 13,228,797 3,309,085 910,382

GDP (constant 2000 USD, in millions)²–2020 ^y 7,298,804 1,590,000 483,259

Emissions intensity–2020 (x : y) 1.812 2.081 1.884

Change in emissions intensity 2005–2020 (%) -38.34 -4.88 14.91 1 We assume annual growth of CO2 is similar to results from Table 2.

2 We assume annual growth of GDP is similar to results from Table 1.

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emissions intensity would increase by 13 per cent. Second, if we assumed economic growth could be maintained at a particular rate, there was a possibility of reducing emissions intensity in the future.¹¹ If countries could reduce

11 Such as 8.5 per cent in China, 5.1 per cent in India and 5.8 per cent in Indonesia.

their growth of CO₂ emissions by 7 per cent, then by 2020, emissions intensity would decrease by 38.55 per cent in China and 5.23 per cent in India (see Figure 11). In the case of Indonesia, emissions intensity increased with slower rates of growth. However, according to Jotzo and Pezzey (2007) and Fischer, Carolyn and Springborn Note: we assume economic growth constant.

Figure 11. Sensitivity of emissions intensity II

Note: we assume growth of CO₂ emissions to be constant.

Figure 10. Sensitivity of emissions intensity I

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(2011), cited in Stern and Jotzo (2010), intensity targeting can mean a contin- ued increase in absolute emissions, but there are some risks towards managing economic uncertainty, and setting goals for structural and technological change. Thus, setting intensity goals for emissions reduction also has implica- tions for the management of wider economic risks rather than for GDP growth.

In conclusion, emissions intensity reduction in China will range between 35 per cent and 38 per cent between 2005 and 2020. Continuing high economic growth will help China to achieve higher reduction goal. Stern and Jotzo (2010) fi nd that the change in emissions intensity would be about 24 per cent. Similarly, McKibbin et al.

(2008) and Garnaut et al. (2008a), cited in Stern and Jotzo (2010), state that projections of GDP growth, energy use and carbon intensity imply a 21 per cent reduction in the business-as- usual (BAU) scenario. McKibbin et al.

(2008), cited in Stern and Jotzo (2010), find that China’s Copenhagen commitments will be amount to a 22 per cent reduction based on BAU in 2020.

In the case of India, Stern and Jotzo (2010) fi nd that India can reach its goal of a 20 to 25 per cent cut in emissions intensity if the recent rate of progress in energy effi ciency can be maintained.

Vazhayil and Balasubramanian (2010) also measure stringency factors; for India, it is about 40 per cent and for China about 90 per cent. This means more effort is needed by China than by India to achieve their goals.

We argued that differences in CO₂ intensity reduction between our estimate and previous studies was because of a different method in obtaining parameters, such as energy effi ciency, growth projections, projections on energy share, consumption of fuel projections, technological change, and projections on industrial structure.

Similarly, Vazhayil and Balasubrama- nian (2010) said that the stringency of achievement of targets depends on the nature of intensity target as well as on the historical relations between GDP growth rate and emissions growth rate, which is captured by the GDP elasticity of emissions.

4.2 ARMAX model

The short-run effect (impact multiplier) of GDP on CO₂ emissions is β₂ and the effect of TPES on CO₂ emissions is β₄ (see Table 7). Thus, if economic growth was 1 per cent, CO₂ emissions would increase by 0.4076 per cent (ceteris paribus). Similarly, if TPES increased by 1 per cent, CO₂emissions would increase by 1.14 per cent (ceteris paribus).¹² Thus, a rise in TPES had a bigger effect on CO₂ emissions than the GDP. It was interesting to analyse the long-run effect of economic growth and TPES on CO₂ emissions.¹³ The long-run effect (long-run multi-

12 We check for serial correlation test by apply- ing Q-stat correlogram analysis and Breusch- Godfrey serial correlation LM test; and to test heteroscedasticity we apply the ARCH test.

13 Please refer to Appendix 1 for the derivation of the long-run effects for China, India and Indonesia.

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Table 7. ARMAX results for China Dependent variable: LOG(CHINACO2) Method: Least squares

Sample (adjusted): 1977 2007

Included observati ons: 31 aft er adjustments Convergence achieved aft er 10 iterati ons

White heteroscedasti city-consistent standard errors and covariance

Variable Coeﬃ cient Std Error t-Stati sti c Prob

C 1.306981 0.193176 6.765740 0.0000

YEAR 0.006563 0.001717 3.822240 0.0011

LOG(CHINAGDP) 0.407585 0.062387 6.533160 0.0000

LOG(CHINAGDP(-1)) -0.436766 0.050682 -8.617821 0.0000

LOG(CHINATPES) 1.140421 0.029861 38.19152 0.0000

LOG(CHINATPES(-1)) -1.232954 0.061630 -20.00569 0.0000

LOG(CHINACO2(-1)) 0.967849 0.030353 31.88662 0.0000

AR(1) -1.053732 0.254214 -4.145065 0.0006

AR(2) -1.219292 0.296850 -4.107432 0.0006

AR(3) -1.164419 0.360923 -3.226228 0.0044

AR(4) -0.797502 0.296871 -2.686361 0.0146

AR(5) -0.623023 0.221290 -2.815411 0.0110

R-squared 0.999215 Mean dependent var 14.80523

Adjusted R-squared 0.998760 S.D. dependent var 0.448745

SE of regression 0.015802 Akaike info criterion -5.172775

Sum squared resid 0.004744 Schwarz criterion -4.617683

Log likelihood 92.17801 Hannan-Quinn criter. -4.991829

F-stati sti c 2197.799 Durbin-Watson stat 1.813273

Prob (F-stati sti c) 0.000000

Inverted AR Roots

.34-.85i .34+.85i -.40+.80i -.40-.80i -.93

plier or equilibrium multiplier) if GDP increased by one unit was that the expected cumulative increase in CO₂ emissions would be about -0.91 per cent; that is, less than the short-run effect. If the increase in GDP was permanent, the long-run multiplier also helped to explain the expected long-run permanent increase in GDP.

Next, the long-run effect of TPES intensity was about -2.878 per cent.

This indicated that if TPES increased by one unit, CO² emissions would de-

crease about 2.9%. This means in the long run, share of less carbon intensity sources, such as natural gas and renewable energy, would increase.

In the case of India, GDP and the lag of GDP were signifi cant at 10 per cent critical level (see Table 8). We interpreted the growth parameter as follows: if the economy increased by 1 per cent, CO₂ emissions would increase by 0.385 per cent (ceteris paribus). Simi- larly, if TPES increased by 1 per cent,

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Table 8. ARMAX results for India Dependent variable: LOG(INDIACO2) Method: Least squares

White heteroscedasti city-consistent standard errors and covariance

C - β₀ 1.962236 1.767975 1.109878 0.2768

YEAR – β₁ -0.001312 0.006098 -0.215135 0.8313

LOG(INDIAGDP) - β₂ 0.384894 0.203492 1.891450 0.0693

LOG(INDIAGDP(-1)) – β₃ -0.590706 0.230256 -2.565427 0.0162

LOG(INDIATPES) - β₄ 0.854363 0.339036 2.519976 0.0180

LOG(INDIATPES(-1)) - β₅ -0.211555 0.622118 -0.340056 0.7364 LOG(INDIACO2(-1)) - β₆ 0.772262 0.205834 3.751873 0.0009

AR(1) -0.335157 0.206959 -1.619435 0.1170

S.E. of regression 0.023905 Akaike info criterion -4.431793

Prob(F-stati sti c) 0.000000

Inverted AR Roots -.34

CO₂ emissions would increase by 0.85 per cent (ceteris paribus). The expected value of CO₂ emissions in the long run, after a change in GDP, was about -0.9 per cent. Further more, the long- run effect of TPES on CO₂ emissions was about 2.82 per cent or would be about 3.75 per cent if insignificant values did not count.

In the case of Indonesia, GDP and TPES were signifi cant at 5 per cent critical level, except for lagged TPES (see Table 9). In the short run, if economic growth increased by 1 per cent, CO₂emissions would increase by 1.12 per cent (ceteris paribus), and if TPES

increased 1 per cent, CO₂ emissions would increase by 0.82 per cent (ceteris paribus). In the long run, the effect of economic growth on CO₂ emissions is about 0.423 per cent; that is, lower than the short-run effect, and the effect of TPES on CO₂ emissions was about 1.019 per cent.

The ARMAX model confirmed that it was only in China and India that the expected cumulative CO₂ emissions would decrease in the long run;

in Indonesia the expected cumulative CO₂ emissions would increase. This was mainly because of the negative value of lagged GDP growth for all the

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country, but in Indonesia the absolute lag value was lower than the current value. We argued that this was because Indonesia had the highest growth of CO₂ emissions compared to China and India which had relatively similar growth.

The long-term effect of TPES on the expected cumulative CO₂ emissions would increase in India and Indonesia, but decreased in China with a relatively low margin. The result for China was quite surprising because, in terms CO₂- TPES energy intensity, all the three

countries still had positive growth. We argued that in the case of China, there was a huge opportunity to change its energy composition toward low carbon intensive since the share of CO₂ emissions from solid fuel consumption has tended to decrease (see Figure 9). Meanwhile, in Indonesia it was increasing and in India it was stable.

Such tendency was because between 2000 and 2007 the average elasticity of CO₂-TPES in China was the lowest for the entire period and lower than in India and Indonesia.

S.E. of regression 0.047608 Akaike info criterion -3.058506

Prob (F-stati sti c) 0.000000

Inverted MA roots -.96

Table 9. ARMAX results for Indonesia Dependent variable: LOG(INDONESIACO2) Method: Least squares

White heteroscedasti city-consistent standard errors and covariance MA Backcast: 1971

C – β₀ 3.687778 1.896889 1.944119 0.0620

YEAR – β₁ 0.003579 0.009530 0.375609 0.7100

LOG(INDONESIAGDP) – β₂ 1.122895 0.205253 5.470796 0.0000 LOG(INDONESIAGDP(-1)) – β₃ -1.085417 0.227771 -4.765389 0.0001 LOG(INDONESIATPES) – β₄ 0.818615 0.280203 2.921503 0.0068 LOG(INDONESIATPES(-1)) - β₅ -0.230358 0.209720 -1.098410 0.2814 LOG(INDONESIACO2(-1)) - β₆ 0.423047 0.137658 3.073161 0.0047

MA(1) 0.959303 0.023020 41.67235 0.0000

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V. CONCLUSIONS AND POLI- CY RECOMMENDATIONS China, India and Indonesia have shown strong commitments to voluntary mitigation action to reduce CO₂ emissions intensity by 2020, based on the benchmark year of 2005. The energy sectors need to set this as the top prior- ity out of the most focused-on-issues list because of the high dependence of energy consumption from fossil fuels, especially coal. In addition, in the case of China, CO₂ emissions as a function of population, GDP and energy consumption are the highest in the world although in India and Indonesia are still below the world average.

Deregulation and liberalisation are important in boosting economic growth, but they also have consequences for any rapid growth in primary energy supply. Graphical analysis shows that the transformation of an economy from agriculture to manufacturing and services changes the phase of energy-GDP-CO₂ emissions relations. We observed a general pattern that, when CO₂-TPES intensity tends to increase, energy-GDP intensity tends to decrease. This indicates that rapid economic growth is fuelled by high-carbon-intensive energy sources;

for example, electricity production from coal is still dominant in total electricity production. However, the share of CO₂emissions from solid fuels is decreasing in China and India, but not in Indonesia. We also discovered that CO₂-GDP intensity has decreased rapidly in China and is approaching that of India and Indonesia.

Growth analysis indicates that in only China is GDP growth higher than the growth of CO₂ emissions and there is also a reduction in CO₂-GDP intensity. However, in terms of CO₂- energy intensity, all three countries show growth. By utilising the growth parameter estimates, we observe that only in China is the objective for CO₂- GDP intensity possible to achieve with extra effort, whereas in the case of Indonesia we found that CO₂-GDP intensity will still grow. However, if the country can maintain economic growth within more than 6 per cent and reduce its emissions intensity, India will be able to reach its emissions reduction goal. Further, an objective on economic growth and policy to reduce emissions intensity positively contributes to emissions reduction.

Finally, the ARMAX model shows similar results to previous analyses, that in the short run economic growth and TPES have signifi cant positive effects on CO₂ emissions. In the long run, in China and India only will the expected cumulative CO₂ emissions decrease, but in Indonesia, the expected cumulative CO₂ emissions will increase.

China, India, and Indonesia have fewer than seven years from now to achieve their emissions intensity goals.

A study of current energy policies, obstacles, and suggested policies shows that there are many internal weak- nesses that need to be resolved, such as institutional capacity, coordination, implementation, and enforcement among the agents, nationwide and locally. Further, based on analysis of

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past data, we show that there are huge opportunities for China and India to reduce their expected cumulation of CO₂ emissions; even for Indonesia, there is a possibility of slowing the emissions. However, Olson (1996) says that ‘The problem is that the really big sums cannot be picked up through uncoordinated individual actions. They can only be obtained through the effi - cient cooperation of many millions of specialised workers and other inputs’.

Further, Olson (1996) also asserts the importance of the structure of incentives. We believed that lack of incentives to encourage and reward energy effi ciency, conservation, and diversifi - cation will lead to higher rates of CO₂ emissions.¹⁴ Similarly with International Energy Agency (2005) which said ‘[C]

limate change is a global issue, which depends on how we heat or cool our homes and offi ces, how we travel, what technologies we develop, and what industries we set up. It requires global action, and it requires the action of all’.

Within a short period, countries need to promote those sectors of the economy that can contribute signifi - cantly to CO₂ reductions. Key principles are scientifi cally sound, economi- cally rational and politically pragmatic (Aldy and Stavins, 2009). There are four sectors that need to be assessed seriously: power generation, manufac-

14 A clear distinction between energy effi ciency and energy conservation is that the former refers to adoption of a specifi c technology that reduce overall energy consumption without changing the relevant behaviour, while the latter implies merely a change in consumers’ behaviour (Oikonumou et al., 2009)

turing, construction and building, and transport. In the power generation sector, the share of electricity production from renewable and relatively clean sources not only needs to be declared, but also a national standard of which needs to be set on kilograms of CO₂ per kWh of electricity produced.

According to International Energy Agency (2007), it has been proven that, based on current technology, manufacturing can improve its energy effi ciency by 18 to 26 per cent, and at the same time reduce that sector’s CO₂ emissions by 19 to 32 per cent. Helping the industrial sectors to upgrade their energy supply systems can contribute signifi cantly to energy and carbon saving. Demand management can be im- plemented in the construction, building or household sectors. Governments can also provide more energy subsidies for implementing energy conversion, such as replacing kerosene with LPG for household use and replacing inefficient light bulbs with those more energy-effi cient. For example, in 2011, the Indonesian government planned to provide an electricity subsidy as much as about USD6.64 billion. If the government were to spend about 10 per cent of the electricity subsidy to promote LED lighting, more than 37 million light bulbs could be bought.¹⁵ This policy would not only help fami- lies to reduce their electricity bills but

15 Assume that USD1 = IDR10,000, based on au- thors discussion with the consumer, the LED 3rd generating cost is about USD17.5, life span is about 50,000 hours and 9 watt is equivalent with 20 watt conventional lamp saving energy.

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would also reduce the likelihood of unexpected blackouts, especially during peak hours, and it would also help reduce fossil fuel consumption in the production of electricity. Finally, for the transport sector, the government needs to develop effi cient, rapid mass transport and to lift the standards on car exhaust emissions.

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Before period t* the value of GDP_t was GDP*, so that the expected value of y_t was

Deriving long-run effects of TPES in China APPENDIX 1

DERIVING LONG-RUN EFFECTS OF GDP ON CO2- TPES FOR CHINA, INDIA AND INDONESIA

(suppose  < 1) stability condition, we have econometric model as follow: