Assessment of Rebound Effects in Response to Improved Energy Efficiency in Taiwan. Abstract

Transcription

1 Assessment of Rebound Effects in Response to Improved Energy Efficiency in Taiwan Szu-Chi Fu 1, Jung-Hua Wu 2, Yun-Hsun Huang 3, Chia-Yon Chen 2, Kuei-Yen Wu 4 1 Master Graduate, 2 Professor, 3 Researcher, Industrial Economics and Knowledge Centre, Industrial Technology Research Institute, 4 Ph.D. Student, Department of Resources Engineering, National Cheng Kung University. Abstract The expectation of energy efficiency only concerns energy savings resulted from improved technology and ignores stimulated consumer behaviors and changed productions which create rebound effects on additional energy usage. This study evaluated direct and indirect rebound effects by supply-driven Input- Output (I-O) model to research the energy saving potential incorporated with rebound effects. In 2011, except for Mining (S3), Other chemical products (S15) and Other metal products (S19), the rebound effect caused by improved energy efficiency were less than 10% in Taiwan. The finding also showed sectors with higher rebound effects had higher forward linkages. On the contrary, sectors with lower rebound effects like Fisher (S2), Motor vehicles (S23) and so on, mainly allocated outputs to final demand. In addition, both rebound effects and energy intensities affect the energy-saving potential. The energy-saving potential may be overestimated if the rebound effect is neglected, then it causes energy policies fail to achieve the effectiveness. Keywords: Rebound effect, Energy efficiency, Supply-driven Input-Output model. 1. Introduction Energy consumption is expected to be reduced as energy efficiency improves. However, considering energy conservation solely through technical 1

2 aspects ignores changes to consumption and production behaviors stimulated by improved energy efficiency, which may indicate a rebound effect. If the rebound effect is not taken into consideration, the energy savings expected to result from the improved energy efficiency may be overestimated. Consequently, the related energy conservation policies are not able to achieve the expected results. The rebound effect has mainly been analyzed from the perspective of an individual industry or the overall economic system. Each sector has its interdependent characteristics and specific linkage effects. In case of focusing on an individual industry may overlook its inter-industry relationships with other industries. In this study, the rebound effect resulted from energy conservation was analyzed using a supply-driven Input Output (abbreviation as I O) model. The impacts of improved energy efficiency within each sector and on other sectors were studied to assess the actual effects of energy conservation. This study adopted Taiwan s I O Table and aggregated original 166 sectors to create a Hybrid (units) Table comprising 33 non-energy sectors and 6 energy ones. The aims were as follows: (i) apply a supply-driven I O model to analyze the direct and indirect rebound effect arising from improved production-side energy efficiencies on various sectors; (ii) investigate the energy intensity of Taiwan s sectors for considering energy saving potentials; and (iii) evaluate energy saving potentials with total (direct and indirect) off-set rebound effects, causing actual energy savings to be lower than expected. 2. Literature Review The concept of the rebound effect can be traced back to Stanley Jevons s 1865 book, The Coal Question. Jevons pointed out that although the use of the 2

3 coal-fired steam engine had greatly improved production efficiency, its widespread use had led to increased coal consumption. Later, Brookes (1978) and Khazzoom (1980), both pioneers in the field of the rebound effect, separately proposed similar views. In 2000, Greening et al. defined the rebound effect in terms of three dimensions, outlined as follows: (1)Direct rebound effect: Assuming ceteris are paribus, improved energy efficiency reduces the energy consumption that is the reduction of the using cost. This causes demand to increase, which in turn increases energy consumption and offsets energy savings arising from improved energy efficiency. (2)Indirect rebound effect: Improved energy efficiency causes variations to the intermediate input and total output sales of other sectors. Thus, reductions in the using cost results in the additional revenue being used for other products and services. (3)Economy-wide rebound effect: Improved energy efficiency drives the productivity of the overall economy and stimulates additional energy demand. In turn, this promotes economic growth and an increase in energy consumption. In addition, empirical results of the study by Greening et al. (2000) showed that for American households in the 1980s 1990s, the rebound effect of air conditioning equipment and transportation vehicles ranged between 0% and 50%. Meanwhile, Berkhout et al. (2000) applied neo-classical theory to evaluate the rebound effect in fuel consumption of the residential, commercial, and transportation sectors in the Netherlands, finding that it ranged between 0% and 30%. In 2005, the British House of Lords noted that despite improved energy efficiency, the country s overall energy consumption and energy intensity did not improve as expected. Hence, Sorrell report published in 2007, a review and 3

4 empirical study on the rebound effect of energy conservation were conducted. Subsequently, the European Union became concerned about this issue and commissioned Maxwell et al. (2011) to conduct related researches. Furthermore, the levels of rebound effects can be divided into the macroeconomic level or microeconomic level. At the macroeconomic level, Barker et al. (2007) used the Cambridge multi-sectoral dynamic model (MDM- E3) to estimate and forecast the effectiveness of British energy efficiency policies between 2000 and The total rebound effect was approximately 26%. At the microeconomic level, Hymel and Small (2015) estimated the rebound effect related to vehicle miles of travel and fuel efficiency in the U.S. between 1966 and 2009 by the simultaneous-equations model. In their findings, the long-run and the averaged short-run (one-year) rebound effect over the entire period was close to 30% and about 4.7%, respectively. Both Hymel and Small (2015) and Turner (2013) pointed out although the impact of the rebound effect on the effectiveness of energy policies had been debated in recent years. It lacked a consistent basis in various empirical studies which presented different degrees of the rebound effect. Regarding the research methods used to analyze the rebound effect, the I-O model has been widely utilized. Howells et al. (2010) combined MESSAGE (Model of Energy Supply Strategy Alternatives and their General Environmental Impacts) and multiplier effects of I O models to analyze whether to replace gasfired power plants with nuclear power plants. Freire-González (2011) combined the I O model and the econometric model to estimate the direct and indirect rebound effects in Catalonian households. Thomas and Azevedo (2013) applied an Environmental Extended I O model to analyze the rebound effect of 4

5 improved energy efficiency for home appliances in the United States. Furthermore, Li et al. (2013) used a Hybrid I O Table to study the impact of China s reforms in energy subsidies on the mitigation of the rebound effect. The existing I-O literature mainly applied the demand-driven I O model to analyze the rebound effect. However, the supply situation changes when efficiency is improved in the production process. This leads to the problem of production-side behavioral rigidity when using a demand-driven model. Hence, this study used the supply-driven I O model to simulate producers behavioral responses that arise from improvements in energy efficiency and to estimate the rebound effect and energy saving potentials. The supply-driven I O equations and methodologies are elaborated below. 3. Methodology 3.1 Supply-driven I O model The I-O Table depict the status of product circulation and distribution between various sectors within the economic system. Ghosh brought out supplydriven model in 1958 and stressed two key criteria, monopolistic markets and scarce resources, for applying this model. Under linearity and time-invariance assumptions, the relationship between the various sectors in the I-O Table can be expressed as follows: n X j = Z i=1 ij + V j (Eq. 1) where X j is the total input sales to sector j; Z ij is the amount of input sales from sector i that sector j needs to totally input sales X j for manufacturing products; and V j is the sum of the primary input factors to sector j. 5

6 The supply-driven I O model assumes that the allocation coefficient (b ij ) is constant, b ij = ( Z ij X i ). In addition, it represents the status of the output sales from a certain sector distributed to other sectors based on existing production conditions. The allocation of output sales can be expressed by the allocation coefficients matrix. Hence, the equations for the relationship between output sales of each sector and primary inputs are as follows: X = X B + V (Eq. 2) b 11 b 1n B = [ ] = X 1 Z (Eq. 3) b n1 b nn where B is the allocation coefficient matrix and bij is the output allocation coefficient. The latter refers to the ratio between the amount of sales from sector i used as inputs in sector j and the total output sales in sector i. The fundamental assumption of the supply-driven I O model is related to the allocation coefficient and productive primary inputs; furthermore, this output sales not affected by the price of the products 1. In other words, in the situation where import substitutions are not considered, the industrial structure of the various sectors remains constant, meaning that the allocation coefficient remain stable as well. A transposition conversion of Eq. 2 gives Eq. 4 and Eq. 5: X (I B) = V (Eq. 4) X = V (I B) 1 = V O (Eq. 5) where O is the allocation (output) inverse matrix. Generally, V represents primary input (or named as value added) and contains various factors, such as 1 For b ij = z ij, both the numerator and denominator refer to sales from the same sector. As such, they x i are not affected by the price of the products. 6

7 compensations of employee, operating surpluses, taxes by governments, and depreciation. After transposing the matrix in equation 5, equations are as follows: X = O V (Eq. 6) X = O V (Eq.7) 3.2 Hybrid (units) I-O Table The Hybrid I-O Table can be applied on energy-related issues to better understand the energy consumption and linkage effect. According to the basis of I-O Tables, sectors were classified as being either energy or non-energy related. The output allocation of the energy sector was then converted to physical units (e.g., BTU, kcal, or oil equivalents). Furthermore, data from the Energy Balances for the current year was substituted into the corresponding row of the specific energy sector in the original I-O Table. The equation for the relationship is as follows: n g r = e rj + q r (Eq. 8) j=1 where g r is the total demand for type-r energy; e rj is the amount of type-r energy that sector j needs as input to manufacture X j amount of sales; and q r is the energy consumption of type-r energy used directly by the final demand sector. Notice that energy can be classified as primary and secondary energy. When calculating the total energy intensity of a sector, a double counting occurs if the primary and secondary energy intensities are simply added. As such, equation 9 must be used as a modifier when calculating the total primary energy intensity of a sector: 7

8 r α j = α ij + f α η ej (Eq. 9) i=1 r where α j is the total primary energy intensity of sector j; i=1 α ij is the sum of the other primary energy intensities of sector j, except for electricity; α ej is the electrical energy intensity of sector j; f is the ratio between the power generated by the primary energy sector (including hydraulic and nuclear powers) and the total amount of power generated; and η is the net plant efficiency of thermal power plants. 3.3 Rebound effect model The rebound effect is defined as follows: although expected energy savings are resulted from improved energy efficiency, the proportion of energy consumption increases instead, as shown in equation 10. The difference between expected and actual energy savings is the additional amount of energy consumption ( G). RE = (expected energy savings actual energy savings)/expected energy savings = H ( H G) H = G H (Eq.10) where RE is the rebound effect; G is additional energy consumption; and H is expected energy savings. It was assumed that improved energy efficiency exists in the production process of sector k and that λ is the amount of improvement. Because less energy input is required to maintain the same level of output to meet the final demand, the energy input ratio of sector k becomes the original (1 λ). This means that the input coefficient (A k) of energy sectors corresponding to sector k has changed. Expected energy savings ( H ) arising from the improvement in energy efficiency are shown as equation 12. 8

9 a 11 a 1k a 1n A k = [ a r1 (1 λ k )a rk a ] (Eq. 11) rn a n1 (1 λ k )a nk a nn n m n m H = e rj e rj j=1 r=1 j=1 r=1 = (I A) 1 X (I A k ) 1 X (Eq. 12) where λ k is the value of improvements to the energy efficiency of sector k; e rj is the amount of type-r energy to be inputted by sector j to output X j sales before its energy efficiency has improved; and e rj is the amount of type-r energy to be inputted by sector j to output X j sales after its energy efficiency has improved. With improved energy efficiency, the cost of energy input by the sector is reduced and becomes an operating surplus (one of the primary input factors). This study assumes that 100% of all operating surpluses are channeled back as primary inputs to the production process, with no savings behavior. The resultant change in the primary input (or called as an added value) affects the other sectors through the inter-industry relationships, which in turn changes the output sales and amounts of energy used by the various sectors within the economic system. Additional energy consumption (ΔG) is shown in equation 15. b 11 b 1k b 1n b k = [ ] b r1 (1 λ k )b rk b rn b n1 (1 λ k )b nk b nn (Eq. 13) V k = λ k G k (Eq. 14) G = (I B k ) 1 V k (Eq. 15) 9

10 where b k is the allocation coefficient after the energy efficiency of sector k has improved; V k is the change to the added value of sector k; G k is the amount of energy input of sector k; and B k is the allocation matrix after the energy efficiency of sector k has improved. In summary, this study used the supply-driven I O method for analysis. Only the direct and indirect rebound effects were considered; however, neither the economy-wide effect (i.e., the overall growth of the economic system arising from improved energy efficiency) nor the demand-driven rebound effect caused by structural changes in output distribution (i.e., changes in final demand) were included. 3.4 Energy saving potential The study assumes that the improvement of energy efficiency of sector k accompanies with rebound effects, the calculation of actual energy saving potentials is shown in equation 16. ES k = F(X ) 1 B k (Eq. 16) B k = (1 (1 RE k )A k) 1 (Eq. 17) where ES k is the energy saving of sector k; B k is B matrix considering the rebound effect of sector k; and RE k is the rebound effect arising from energy efficiency improved of sector k. 4. Data collection and processing Taiwan s Directorate-General of Budget, Accounting, and Statistics (DGBAS) follows the international practice of conducting an industry, commerce and service census and editing an industrial Input-Output Table once every 5 years. The up-to-date I-O Table of the year of 2011 for Taiwan was 10

11 released in November As such, the time frame of this study included the years of 1996, 2001, 2006, and The study assessed the rebound effect of improved energy efficiency in various sectors in Taiwan s economic system. The study also used data in Energy Balances and I-O Tables of years of 1996, 2001, 2006, and 2011 published by the Bureau of Energy, Ministry of Economic Affairs to construct a table with hybrid units. In the Hybrid I-O Table, economic system in Taiwan were classified into seven main industries and 39 sectors, of which 33 were non-energy and 6 were energy-related sectors, which were used as the basis for analysis, shown as Table 1. In the Energy Balance, the energy sector s outputs became the inputs for the non-energy sector. In addition to being used as energy for consumption, the former are also raw materials (i.e., energy products not used for kinetic combustion). These are referred to as non-energy consumption inputs in the Energy Balance Table, and this type of energy use was not included in the scope of this study. With regards to the assuming value of improved energy efficiency, many countries have relied on energy conservation and carbon reduction as the main focuses of their energy policies in response to global warming and depletion of traditional energy resources. In Taiwan, the Energy Conservation and GHG Emission Reduction Promotion Council was established, ratifying the National Action Plan on Energy Conservation and GHG Emission Reduction in In terms of energy conservation, the goal was to improve energy efficiency by more than 2% within the next 8 years such that the energy intensity in 2015 would be reduced by more than 20% compared to Besides, a reduction of more than 50% (compared to 2005) is planned by 2025 through technological 11

12 breakthroughs and supporting measures. In other words, the values of annual reductions that must be reached in order to achieve the ultimate goal for 2025 are as follows: 2% before 2015 and approximately 4% for Hence, the amount of energy efficiency improvements for this study was assumed to be 2% and 4%. When calculating energy intensity in the Hybrid I-O Table, it was necessary to consider the conversion of secondary energy sources to primary ones. This was to avoid double counting for total energy inputs. Hence, in terms of the numerical values for the calculation of power, likes equation 12, ratios (f) between the power generated by primary energy sources (hydraulic and nuclear powers) and the total amount of power generated was set at 0.336, 0.249, 0.218, and between 1996 and 2011 (time interval at 5 years). Net plant efficiencies (η) of thermal power plants were set at 0.338, 0.343, as well as in the time frame of this study. Table 1. Sectoral classification of this study in Sectors in this study Energy Balance I-O Table in 166 sectors Classification ID Sector Name No I-O No. Agriculture, forestry, livestock, fishery and Mining Agriculture, forestry, S livestock S2 Fisheries S3 Mining Manufacturing (MFG) S4 Food, beverage and tobacco

13 S5 Textile and leather products 38, S6 Timber and wooden products , S7 Pulp and papers S8 Printing and publishing S9 Basic industrial chemicals S10 Petrochemical basic products S11 Fertilizers S12 Synthetic fibers S13 Plastic products 49,53 54,64 S14 Rubber products 52 55,63 S15 Other chemical products 44, S16 S17 Other non-metallic mineral products Cement and cement products , S18 Iron and steel S19 Other metal products 61, S20 Metal products S21 Electronics and electric and optical products S22 Machinery and equipment S23 Motor vehicles

14 S24 Other manufacturing products 68, Utility S25 Water supply Construction S26 Construction Transportation S27 Railway and highway 75, S28 Aviation and shipping 74, S29 Wholesale and retail trade S30 Transportation service and warehousing 86, Services S31 Telecommunication and postal services ,136 S32 Finance and insurance S33 Public administration, defence and other services 85, , , S34 Coal mining 23 14(01420) S35 Crude petroleum and natural gas Energy supply S36 Petroleum refinery products S37 Coal products 24,25 50 S38 Electricity supply S39 Gas supply Sources:Input-Output Table in 2011 (DGBAS, 2014); Energy Balances in 2011 (BoE, 2012), this study sorting. 14

15 5. Results According to the I-O Tables and the Hybrid I-O Table for the 39 sectors constructed in this study, we applied supply-driven I O model to analyze the rebound effect, while costs of energy savings created the operating surpluses which worked as the changes of primary inputs. Moreover, for considering the energy saving potential, this study also analyzed the energy intensity. 5.1 Analysis of the rebound effect When a sector improves its energy efficiency and ceteris paribus, its energy consumption at the level of output is reduced. The energy costs that were saved would cause changes to the primary input, which then affects the outputs of the entire economic system because of the inter-industry linkage effect. Ultimately, additional energy consumption was created and over expected energy savings. Under energy efficiency improvements of 2% and 4%, the resultant trend for the rebound effect was similar. This indicates that the magnitude of the rebound effect is dependent on the characteristics of each sector rather than on the improvement-to-energy efficiency ratio (Figure 1). The rebound effect for most sectors in 2011 was less than 10%, except for Mining (S3), Other chemical products (S15), and Other metal products (S19) at 19.81%, 11.51%, and 10.62%, respectively. Meanwhile, sectors with low rebound effects include Fisheries (S2), Motor vehicles (S23), manufacturing of Synthetic fibers (S12), Electrical and electronic products (S21), and Textile and leather products (S5) at 0.13%, 0.81%, 0.81%, 0.92%, and 0.96%, respectively. Using the mining sector as an example, when energy efficiency improved by 2% and ceteris paribus, the reduction in energy consumption at the same level of output was originally expected to be NTD million. However, the inter- 15

16 industry linkage effect reduced the actual energy savings to NTD million, with additional energy consumption amounting to NTD million. The rebound effect of approximately 19.81% was the highest among all sectors. In the I-O Table, the mining sector encompassed the economic activities of three sectors: (i) crude petroleum and natural gas products (was classified under S35 in this study); (ii) ore, stone, and clay; and (iii) other minerals (coal mining was classified under S34) and quarrying (DGBAS, 2014). In this study, coal mining (originally under other minerals ) and crude oil and natural gas were both categorized under the energy sector (S34 and S35, respectively). As such, output sales from the mining sector mainly were resulted from the economic activities of the following: (i) ore, stone, and clay used for construction and engineering and (ii) raw materials for other mineral products (e.g., iron ore and industrial salt). When energy efficiency improved, the direct and indirect effects induced an increase in the input of raw materials. This led to additional energy consumption by the sector, resulting in a high rebound effect for the mining sector. This study found that for 2011, the trend between the rebound effect and the industrial forward linkage exhibited a similar phenomenon (Figure 2). Furthermore, an analysis of the relationship between the rebound effect and the sectors output allocation structure revealed that the outputs of those sectors with high rebound effects were mainly allocated as production inputs by the manufacturing industry (i.e., intermediate products). When sectors with high forward linkages improved energy efficiencies, they caused more influence on downstream manufacturing sectors (Figure 3). 16

17 On the other hand, sectors with lower rebound effects were mostly supplied for final demands, like households, private investments, government spending, and export, rather than as intermediate products for other sectors (Figure 4). Because the forward linkage of such industries was low, even when their energy efficiency improved, the impact on the indirect rebound effect to industries was not significant. Figure 1. The rebound effect after energy efficiency improved by 2% and 4% in various sectors in

18 Figure 2. Trends of the rebound effect and the industry linkage in Figure 3. Output allocation structures of sectors with high rebound effects in

19 Figure 4. Output allocation structures of sectors with low rebound effects in Analysis of energy intensity Prior to researching the energy saving potential, we analyzed the energy intensity of various sectors because sectors with higher energy intensities should have greater energy saving potentials, assuming that improvements in energy efficiency were similar. Energy intensity is defined as the sum of various energy types that a sector must input to produce per unit of output sales. In this study, the total energy intensity included both direct and indirect energy intensities. The indirect energy consumption was inputted at the intermediate products. From 1996 to 2011 in Taiwan, sectors with high total energy intensities were Railway and highway sector (S27) and manufacturing sectors, such as Other chemical products (S15), Cement and cement products (S17), and Synthetic fibers (S12), shown as Table 2. A gap between direct and total energy 19

20 intensities was related to the type of sector. Using 2011 as an example, most sectors with a large gap were manufacturing sectors, specifically for Printing and publishing (S8), Synthetic fibers (S12), and Metal products (S20), as well as Construction (S27), in which intermediate inputs and linkage effects contributed to indirect energy intensities. Most with a small gap were the agriculture (S1- S3), service (S29-S33), and utility sector (S25), shown as Figure 5. Inputs for them are raw materials, laboring forces, or water resources. In addition, in Table 2 it can be seen that the ranking of fertilizer manufacturing (S11) and basic industrial chemicals (S9) showed a downward trend within the study period. The root causes were an industrial downsizing (factory close), equipment upgrade and development of high value-added products. Figure 5. Direct, indirect, and total energy intensities for the various sectors in (Unit: kiloliter oil equivalent/million New Taiwan Dollars) 20

21 Table 2. Top 10 Taiwanese sectors in terms of energy intensities for KLOE/ A ranking pattern of total energy intensity million NTD Rank 1 Basic industrial Railway and Railway and Railway and chemicals(s9) highway(s27) highway(s27) highway(s27) Rank 2 Railway and Other chemical Other chemical Other chemical highway(s27) products(s15) products(s15) products(s15) Rank 3 Other chemical Basic industrial Cement and cement Synthetic fibers(s12) products(s15) chemicals(s9) products(s17) Rank 4 Synthetic fibers(s12) Synthetic fibers(s12) Cement and cement products(s17) Synthetic fibers(s12) Rank 5 Other non-metallic Cement and cement Cement and cement Textile and leather mineral products(s17) products(s17) products(s5) products(s16) 7.64 Rank 6 Fertilizers(S11) Fisheries(S2) Rank 7 Other non-metallic mineral products(s16) Rank 8 Fisheries(S2) Other non-metallic mineral products(s16) Textile and leather products(s5) Basic industrial chemicals(s9) Textile and leather products(s5) Other non-metallic mineral products(s16) Pulp and papers(s07) Plastic products(s13) 9.42 Plastic products(s13) 7.19 Rank 9 Pulp and papers(s07) Plastic products(s13) Pulp and papers(s07) 9.23 Rank 10 Textile and leather products(s5) Petrochemical basic products(s10) Printing and publishing(s8) 7.98 Basic industrial chemicals(s9) Printing and publishing(s8) Analysis of energy saving potentials (ESk) Due to part of energy savings was offset by the rebound effect, causing actual energy savings to be lower than expected. The energy saving potential evaluated the actual energy saving arising from improved energy efficiency accompany with the rebound effect considered. Using the 2% improvement in energy efficiency in 2011 as an example, the rebound effect caused different degrees of offset to the energy saving potential, depending on the sector. The actual energy saving potential of various sectors are shown in Figure 6. For Mining sector (S3), when energy efficiency improved by 2%, the rebound effect was 19.81%. This indicates that with improved energy efficiency (λ=2%), the actual energy savings achieved was only 80.19% of that expected. 21

22 The results for energy saving potentials for 2011 indicate that the following sectors have high energy saving potentials: railway and highway (S27), other chemical products (S15), cement and cement products (S17), and manufacture of synthetic fibers (S12) (Figure 7). They are transportation and manufactures of intermediate products with high proportion of direct energy inputs during production processes. Furthermore, the outputs of such manufacturing sectors are often input as intermediate products of other industries. As such, the interindustry relationships can also indirectly reduce the energy intensity of downstream sectors. Hence, better results can be achieved if such sectors are proposed and persuaded to improve their energy efficiency. On the other hand, sectors with low potential energy savings, such as Finance and insurance (S32), Wholesale and retail trade (S29), as well as Telecommunications and postal services (S31), were service sectors. Hence, improvements to their energy efficiency often do not result in substantial energy savings. That means their energy saving potentials were also not significant. Such sectors should be avoided and cautioned that even with the input of large amounts of resources, no obvious effect would be seen. 22

23 Figure 6. Actual energy saving potentials by the various sectors in 2011 (λ = 0.02). Figure 7. Energy saving potentials (λ = 0.02), energy intensities, and the difference between the two indicators of various sectors in

24 6. Conclusion Most existing literature applied the demand-driven I O model to analyze the rebound effect. Due to the supply situation changes leads to the problem of production-side behavioral rigidity when using a demand-driven model. Hence, this study used the supply-driven I O model to simulate producers behavioral responses that arise from improvements in energy efficiency and to estimate the rebound effect and energy saving potentials. The findings were summarized as follows: (1)Industries with high rebound effects included Mining (S3) and upstream manufacturing sectors for intermediate products. Their outputs were mainly allocated as intermediate inputs to other industries; as such, those sectors had strong the industrial forward linkage. When their energy efficiency improved, the high rebound effect was resulted from the linkage effect and the accumulation of additional energy consumption. (2)From 1996 to 2011, sectors with high total energy intensities were Railway and highway (S27), Other chemical products (S15), Cement and cement products (S17), and Synthetic fibers (S12). In addition, the high indirect energy intensity came from intermediate inputs and the inter-industry relationships. (3) When considering the rebound effect, a sector with high energy intensity still has high energy saving potentials. However, high rebound effects offset energy saving potentials in some sectors. In other words, when formulating energy policies, the focus should be on sectors with high energy saving potentials, low rebound effects and high energy intensities. Therefore, the 24

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