Taiwan Long-Term Energy Demand Prediction

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1 Taiwan Long-Term Energy Demand Prediction KuanLin Lai 2011/4/20 Energy Economics & Policy Dr. Thomas Rutherford ETH Zurich Contents 1. INTRODUCTION... 3

2 2. TAIWANESE ENERGY PRESENT SITUATION ENERGY TYPE ANALYSIS SOURCES OF ENERGY TYPES ANALYSIS ENERGY SECURITY INDICATORS ENERGY CONSUMPTION ANALYSIS ENERGY ELASTICITY ENERGY PRODUCTIVITY ENERGY INTENSITY APPLIED MODEL AND PARAMETERS EMPIRICAL ANALYSIS IN REGRESSION MODEL SCENARIO ASSUMPTIONS RESULT ENERGY DEMAND FORECAST OPTIMAL ENERGY SUPPLY FORECAST CONCLUSION REFERENCE LIST OF TABLES TABLE 1 ENERGY SUPPLY DISTRIBUTION BY DIFFERENT SOURCES... 4 TABLE 2 ENERGY SUPPLY DISTRIBUTION BY INDIGENOUS AND IMPORTED... 4 TABLE 3 ENERGY SECURITY INDICATORS... 6 TABLE 4 ENERGY CONSUMPTION DISTRIBUTION BY FORMS IN 2009 (103 KLOE)... 7 TABLE 5ENERGY CONSUMPTION DISTRIBUTION BY SECTORS IN 2009 (103 KLOE)... 8 LIST OF FIGURES FIGURE 1 ENERGY SECURITY INDICATORS... 錯誤! 尚未定義書籤 FIGURE 2 DOMESTIC CONSUMPTION GROWTH RATE, REAL GDP AND ENERGY ELASTICITY... 9 FIGURE 3 ENERGY PRODUCTIVITY (CHU, 2010) FIGURE 4 ENERGY PRODUCTIVITY AND ENERGY INTENSITY... 11

3 1. Introduction Energy consumption plays an important role in Taiwan due to rapid development of economics and technology. If energy supply tends to be unbalanced with demand, the impact on the economics and the industries would be tremendous. First, the trend of energy consumptions over the past years needs to be analyzed. This is done by establishing a time series model based on the past 27 years data of Taiwan in 1983 ~ Multivariate is adopted to ensure the validity of the model. The goal is to forecast energy demand and assesses its structure. Through Multiple Regression model and Stepwise Regression procedure, significant factors and the relation between each other can be determined. Energy demand forecast model can be established with the help of the factors and the relationship. The analysis was set under two different scenarios for Taiwanese Government s energy policy and orientation. In addition to Taiwanese long-term energy demand prediction, the model provides best energy supply structure as a reference for Government. 2. Taiwanese Energy Present Situation 2.1. Energy Type Analysis There are many kinds of energy supply in Taiwan including coal, oil, natural gas, nuclear energy, wind energy, hydro energy, solar energy, geothermal energy and biomass energy generation. (Taiwan Bureau of Energy, 2009 Energy Statistical Hand Book, 2010) Among all the sources, coal, oil and gas are non-renewable energy; the others are renewable energy. In 1983, the energy supply in Taiwan (Table4-1) was million KLOE (kilo liter of oil equivalent). (Taiwan Bureau of Energy, 2006 Energy Statistics Hand Book, 2007) Oil held two-thirds share of total energy supply and followed by coal with 18.3 %. Nuclear energy had 12 %, Natural gas accounted for 4.5 %, and Hydro energy accounted for 1.4 %. Since then, Government tried to maintain stability of energy supply with decentralized energy sources. Promotion of energy diversification has been implemented. This gradually increased the proportion of coal. With the Taiwan Power Company from 1978 to1985 completed first three nuclear power plants (Nuclear 1, Nuclear 2 and Nuclear 3) which makes increased proportion of nuclear power generation for a while, but after 1985, without new nuclear power unit constructed, nuclear energy share started to gradually decline. Moreover, taking the fact of environmental issues and energy diversification, starting from 1990 Government imported liquefied natural gas from Indonesia and Malaysia.

4 In 2009 with total energy supply of 138 million KLOE, the proportion of nuclear energy had fallen to 51.9%, while the coal energy increased to30.45% and liquefied natural gas gradually raised to 8.62%. Table 1 Energy Supply Distribution by Different Sources Energy Sources Year Coal Crude Oil Indigenous Natural Gas Imported Natural Gas Hydro Power Nuclear Power Other Table 2 Energy Supply Distribution by Indigenous and Imported Energy Sources Indigenous Supply Imported Supply Year Coal Crude Oil Natural Gas Hydro Power Coal & Products Crude Oil &Products Liquid Natural Gas Nuclear Power

2.2. Sources of Energy Types Analysis In 1983, total energy supply in Taiwan (Table4-2) was 31.70 million KLOE and gradually increases since then; by 2009, energy supply in Taiwan had reached 142.

5 2.2. Sources of Energy Types Analysis In 1983, total energy supply in Taiwan (Table4-2) was million KLOE and gradually increases since then; by 2009, energy supply in Taiwan had reached million KLOE. The average growth rate is 6%. However, among the total energy supply, indigenous energy supply decreased from 3.66 million KLOE in 1983 to million KLOE in 2009 with an average growth rate of -5%; on the other hand, import energy increased rapidly from million KLOE in 1983 to million KLOE in 2009 with a growth rate of 6.4%. Meanwhile, imported energy accounted for total energy percentage was raised from 88.4% in 1983 to 99.3% Energy Security Indicators Due to limitation of production for indigenous energy, as energy demand increased imported energy played an even more important role in Taiwan (Table3 and Figure 1). The percentage of imported energy to total energy supply increased from 95.29% in 1989 to 99.2f% in Because of energy diversification policy executed back in 1980s; the share of oil in total energy supply decreased from 63.6% (historic highest point) in 1983 to 49.46% in However, almost all the sources of the oil energy relies on imports and dependence on imported oil has been always more than 99%. Figure 1 Energy Security Indicators

6 Table 3 Energy Security Indicators (Taiwan Bureau of Energy, Energy Indicators, 2010) 2.4. Energy Consumption Analysis Final energy consumption in Taiwan can be divided into energy source form and energy usage sector to make discussions. For energy source form, in past 21 years the final energy consumption increased from million (Table 4) KLOE in 1989 to million KLOE in 2009 with an average growth rates of The average growth rates of coal, oil, natural gas, Nuclear were 2.63%, 3.8% 4.66% and 1.98%

respectively; and for energy usage sector, among all the energy consumption, industrial sector has been always the greatest share (). In 2009, industrial energy use had 52.

7 respectively; and for energy usage sector, among all the energy consumption, industrial sector has been always the greatest share (). In 2009, industrial energy use had % while agricultural energy with the least share of 0.89 % (Table 5) Table 4 Energy Consumption Distribution by Forms in 2009 (103 KLOE) (Taiwan Bureau of Energy, Conprehensive Energy, Energy Demand, 2010)

8 Table 5Energy Consumption Distribution by Sectors in 2009 (103 KLOE) (Taiwan Bureau of Energy, Conprehensive Energy, Energy Demand, 2010)

9 2.5. Energy Elasticity Energy elasticity is defined as energy consumption growth rate divided by the rate of economic growth. (Energy elasticity, 2009) In General, the energy elasticity in developed countries is less than 1; on the contrary, the energy elasticity in developing countries is greater than 1. The energy elasticity in Taiwan fluctuates between 1 as economy swings. As the Figure shown below, the economic growth rate in 2001 and the domestic energy consumption growth rate in 2008 were negative; these caused negative values in energy elasticity (see Figure 2). Figure 2 Domestic Consumption Growth Rate, Real GDP and Energy Elasticity (Taiwan Bureau of Energy, Energy Indicators, 2010) 2.6. Energy Productivity Energy productivity is defined as a measure of added value or service per unit of energy. Before 1974, the energy productivity in Taiwan was above 82NT$ per liter oil equivalent. However, during 1979 oil crisis, Taiwan Government continued to promote foundation constructions; and this increased domestic energy consumption and resulted energy productivity decreased. In 1980, it reached historic lowest point with 70.6 NT$ per liter oil equivalent. Since then, as Taiwanese economics rapidly grew up, the energy productivity bounced back. In 1996, it was NT$ per liter oil equivalent. Recently, substantial increases of domestic investment in heavy industry, such as petrochemical and steel, the industrial structure tends to consume more energy than before. This has negative impact on increasing energy productivity and reducing energy intensity (Figure 3). Between 2000 and 2001, global economic recession

10 influenced Taiwanese energy productivity to decline; and in 2001 it is 98.9 NT$ per liter oil equivalent. There are two possible factors: (1) production and economy usually have time lag. When economy just bounces back, manufacturers might still acquire for orders. Thus, energy demand is affected by production and manufacturing process, adjustment is not as fast as economy. (2) The energy cost is low in total cost. Thus, when there is a global economic recession, it would have negative influence on investment of manufactures in low-energy equipment and decrease energy productivity. Figure 3 Energy Productivity (Chu, 2010) 2.7. Energy Intensity Energy intensity indicates that energy units per unit of product. It often refers as the energy efficiency of a country. (Energy intensity, 2010) Understanding the economic benefit from usage of energy, energy productivity refers as the ratio of GDP to energy input; it is also the inverse of energy intensity. Thus, energy intensity can also indicate the trend of industrial economic benefits and changes in energy consumption. In 2000 and 2001, energy intensity increases due to economic recession. (See Figure 4)

11 Figure 4 Energy Productivity and Energy Intensity (Taiwan Bureau of Energy, Energy Indicators, 2010) 3. Applied Model and Parameters In the model, different sectors of energy demand prediction are assessed through Multiple Regression Analysis. Where i, j, k and t represents energy sector, energy source, influence factor and time respectively, represents the energy demand of source j in i sector at time t. are the explanatory variable which indicates energy consumption of source j in i sector at time t. are the regression coefficients which can be determined by Least Square Method. is the error term. (Draper, N.R. and Smith, H, 1998) For k, there are six main factors including (1) energy sector consumption rate, (2) energy intensity, (3) industrial structure percentage, (4) population growth rate, (5) gross domestic production, and (6) energy price standard. For j, it is corresponding to four different sources: coal, oil, natural gas, and electricity. The data of population and GDP were acquired from website of Directorate-General of Budget, Accounting and Statistics, Ececutive Yuan; and the rest data for influence factor were obtained from website of Taiwan Bureau of Energy,

12 4. Empirical Analysis in 4.1. Regression Model Due to great share of consumption in the industrial sector and in the transport sector, model is explained how it is applied as examples in these two sectors. In these two sectors, outlier was both occurred in both sectors. Thus, the timing of outlier occurs is defined as events occur. The discussion of what causing Taiwan changes in energy demand will be resonated in result section. Figure 5 Oil Consumption in Industrial Sector from 1983 to 2009 (Energy Balanced Table in OECD energy statistics format, Taiwan Bureau of Energy) As shown in the Figure 5, consumption in the industrial sector increase with a steady trend; in the 19 th data (2001) and 25 th data (2007) had a tremendous difference; it will be discussed for the error term of examination in later chapter. For this regression method, parameters are GDP, population, energy prices, energy intensity, sector energy productivity, industrial structure. Through Observation of Stepwise Regression Procedure results (see table6 ). This model can explain 98.7% Oil change in the

13 industrial sector, the overall model pass examination which means the data is significant to this model. For each influence factor, P-values of population and industrial structure are 0. The corresponding standard deviation between prediction values and sample data is Collinearity issues are discussed for further inspection and indicate correlation between population and the industrial structure is.2234 which is a very low correlation. Thus, Collinearity is not an issue for this model. Figure 6 Comparison Between Samples and Predictions Figure 6 illustrates the differences between prediction values and sample data in the model. It shows that prediction has both overestimated and underestimated the value of the real situation. Predictions from 12 th data (1995) to 15 th data (1998) have larger values deviation; and 19 th data (2002) and 26 th (200) rise fast. Noteworthy, the 27 th data (2009) as global economic recession in 2008; the sample value is lower than our forecast values. Observations show the occurrence of the event is to bring the energy sources of variation in demand, enables regression error between prediction values and sample values. However, in this model, event study would not be assessed which leads to a more conservative result.

14 Table 6 Stepwise Regression Procedure Result Factor Coefficient Std. error P-value GDP Population Energy Price Energy Intensity Sector Productivity Industrial Structure Scenario Assumptions As time goes, accuracy of prediction for future energy demand in Taiwan would decrease. Thus a scenario with assumption (see Table) has to be made to simulate future situation in Taiwan. As the development of future energy demand, Government s policy and Taiwanese economy are the most relevant assumptions in this model. Energy Economics,wrote by Chien-Chiang Lee, Chun-Ping Chang in 2005 also indicated that policy of saving energy will have negative impact on economic growth at some certain level. Thus, the most important issue for Taiwan Government is to minimize negative imapct on economy from green energy policies and reduce energy demand in an efficient way. Table 7 Scenario Assumption List Scenario Description Population According to Government policy, energy demand is predicted under a reducing energy demand without compromising economics growth million at 2020 (Council For Economic Planning and

15 Development, Gross Growth rate of 3 % Domestic Product Energy Annual growth rate of -2% (Ministry of Economic Affairs,R.O.C) Intensity Energy Price Coal: annual growth rate of 1.18% Oil: annual growth rate of 1.13% Natural Gas: annual growth rate of 1.08 % Industrial Adapt to high added value industry with low energy input Policy Environment Policy Energy Demand Renewable According to green energy policy, by 2025, CO2 emission reduce back to level of 2000 (214 million tons) Coal: 7733 KLOE, Oil: KLOE, Natural Gas: KLOE, Electricity: KLOE, Total: KLOE Nuclear: KLOE, Wind: 300, Hydro: 250 Energy Sector Productivity Agriculture: annual growth rate of 0.3% Industry: annual growth rate of -0.68% Transport: annual growth rate of 4.88% Service: annual growth rate of 0.05% 5. Result 5.1. Energy Demand Forecast The forecast of energy demand was assessed for There is a main reason to take forecast in The model can not only assess energy demand, but also compare energy demand forecast made by Ministry of Economic Affairs (Table 9). Energy demand forecast in 2020 through the model is shown in Table 8.

16 Energy Source Table 8 Enegy Deamnd Forecast Total Domestic Consumption (KLOE) Total Internal Energy Use Energy Use Sub-total Industry Transport Agriculture Service Resdential Coal & Products Crude Oil & Products Natural Gas Electricity Total 9, , , , , , , , , , , , , , , , , , , , , , , , , , , Table9 is the energy forecast made by Bureau of Energy, Ministry of Economic Affairs in It predict domestic energy demand is million KLOE whereas the model predict million KLOE with a difference of 3.1 million KLOE (2.05 %) The energy forecast by Ministry of Economic Affairs indicates by 2020, the energy demands of Coal, Oil, Natural Gas, Electricity, and Renewable are 15.9 million KLOE, 36.8 million KLOE, 3.3 million KLOE, 79.5 million KLOE, and 10.7 million KLOE respectively. In this model, the energy demands of Coal, Oil, Natural Gas, and Electricity are 9.7 million KLOE, 50.6 million KLOE, 2.8 million KLOE and 80.2 million KLOE respectively. As comparison, predictions of oil and coal estimated differently. This can be explained by an assumption of not taking renewable energy into account for the model; therefore renewable energy of consumption will shift to other energy. Due to uncertainty, technology factor, development of renewable energy is difficult to forecast.

17 Table 9 Energy Forecast from Ministry of Economic Affairs ~2020 Item GLOE % GLOE % GLOE % Growth rate Domestic Consumption Coal Petroleum Natural Gas Electricity Renewable Energy Optimal Energy Supply Forecast 2025 is chosen for optimizing energy supply forecast. Due to fluctuation of petrochemical energy price, in the future it may development an alternative energy which can substitute for petrochemical energy. On the other hand, Taiwan Government currently has more comprehensive goal with its parameters which mapped until Therefore it is set in 2025 as a study of forecast. For Taiwan's energy supply future structure, this model can support with an optimal energy supply structure and develop the most appropriate industrial and environment policy. The target of reduce CO2 emissions has been set and tied with other industrial and environment policy with the help of regulations. To achieve the goal, cost of 3.15 trillion NT$ is calculated by the Central Government. (Directorate-General of Budget, Accounting and Statistics, Ececutive Yuan ) In the model, 214 million tons of CO2 equivalents were set for the goal to achieve CO2 reduction. Substituting parameter into model leads following results.

Alternative, 2701.59 Coal, 25989.26 Nuclear, 33473.27 Oil, 53576.61 Natural Gas, 33898.32 Figure 7 Optimal Energy Supply Structure (MLOE) Alternative, 1479908.485 Coal, 4600335.679 Hydro, 1198840.

18 Alternative, Coal, Nuclear, Oil, Natural Gas, Figure 7 Optimal Energy Supply Structure (MLOE) Alternative, Coal, Hydro, Natural Gas, Nuclear, Figure 8 Optimal Electricy Power Plant Structure (million KWH) 6. Conclusion This study established the model of Taiwan energy demand forecast in a time series. Although its effectiveness is less than to overall economic energy prediction models, it has advantage of capturing the trend at certain time with a low cost to achieve the purpose of prediction. Through forecast future development of energy demand, it makes decision makers in industry planning and economic development to have a balanced policy and makes industrial structure tends to be less energy intensive. Moreover, the model might even adapt into another model for source distribution

19 which contains more parameters such as energy conversion efficiency, and pollution degree, and economic benefits and various available energy and so on. In the end, the study use existing sources and limit data to look for the optimal energy structure which meets resource conditions. Empirical analysis of results display, regardless of model simulation or report from Ministry of Economic Affairs, both indicates electricity will have a largest growth in consumption energy; and for energy structure, in the future electricity will also be accounted for the most energy consumption. For energy supply aspect, nuclear energy power will dominate in the future and followed by natural gas. The result forecast it will be 80 million KLOE in 2020 and petroleum and electricity remain as Taiwan two major uses of energy for the structure of the future energy. Due to not considering renewable energy in the model, share of the renewable energy has shifted to other energy demand. Thus, structure of energy source should be different. According to literature and experimental results of the study, effective adjustment of industrial structure will be a key factor in reducing energy consumption, furthermore in addition to saving energy and improving energy efficiency and development of green energy technology policy, effective energy price strategy forcing a reduced use of energy will be the important subject. Due to the cost of petrochemical energy will be higher than the cost of alternative energy. CO2 emissions are much lower in alternative energy than petrochemical energy. Therefore, reducing share of petrochemical energy can not only reduce cost but also reduce CO2 emissions. Moreover, this can also reduce dependency on imported oil. However, uncertainties take place in the future; sudden occurrences are hardly expected. The goal is to forecast the trend of future development and help the policy-maker to make the right decisions.

20 Reference Ministry of Economic Affairs: Energy elasticity. ( ). wikipedia: Energy intensity. ( ). Wikipedi: Bureau of EnergyEconomic AffairsMinistry. Seasonal Indicator Report. Chien-Chiang Lee, Chun-Ping Chang. (2005). Structural Breaks,Energy Consumption, and Economic Growth Revisited: Evidence from Taiwan. 於 Energy Economics ( ). ChuRobert. (2010). 長期能源需求預測與替代能源可行性研究. Taipei: National Tsing Hua University. Directorate-General of Budget, Accounting and Statistics, Ececutive Yuan. Directorate-General of Budget, Accounting and Statistics, Ececutive Yuan: Draper, N.R. and Smith, H. (1998). Applied Regression Analysis. 於 Wiley Series in Probability and Statistics. Taiwan Bureau of Energy, M. o. (2010). Conprehensive Energy, Energy Demand Energy Statistical Hand Book, pp Taiwan Bureau of Energy, M. o. (2010). Energy Indicators Energy Statistical Hand Book., p. 18. Taiwan Bureau of Energyof Economic AffairsMinistry. (2007) Energy Statistics Hand Book. Taipei. Taiwan Bureau of Energyof Economic AffairsMinistry. (2010) Energy Statistical Hand Book. Taipei.

CHINA 2050 HIGH RENEWABLE ENERGY PENETRATION SCENARIO AND ROADMAP STUDY. Energy Research Institute National Development and Reform Commission

CHINA 2050 HIGH RENEWABLE ENERGY PENETRATION SCENARIO AND ROADMAP STUDY Energy Research Institute National Development and Reform Commission ENERGY RESEARCH INSTITUTE NATIONAL DEVELOPMENT AND REFORM COMMISSION