Email: okagawa.azusa@nies.go.jp Econometric analysis of key factors contributing to energy intensity changes Azusa OKAGAWA National Institute for Environmental Studies Email: okagawa.azusa@nies.go.jp Good afternoon, everyone. I am Azusa Okagawa from NIES, the National Institute for Environmental Studies in Japan. Today I’d like to talk about our econometric analysis of the reason for energy intensity changes of Japan.

Outline Background Purpose of the study Econometric model Result Conclusion This is the outline of my talk. First, I will brabrabrabra…

Background Japan’s mid-term target is a reduction in GHG of 15% from 2005 level by 2020. Prime Minister Taro ASO (10 June, 2009) “The target will be achieved through energy conservation measures and not carbon emission credits.” The key factor will be technical innovation and diffusion. Last year Japanese government committed Japan to reduce GHG of 60-80% by 2050. Last week Prime Minister Aso announced that Japan’s mid-term target is a reduction in GHG of 15% from 2005 level by 2020. This target will be achieved through energy conservation measures and not carbon emission credit. So, the key issue is technical innovation and diffusion.

Energy intensity improvement Three channels Inputs substitution Innovation Autonomous energy efficiency improvement (AEEI) Induced technical change (ITC) Change in the mix of industries

Energy intensity and factor prices This figure shows the relationship between energy intensity and energy price of a whole economy. Energy intensity improved by energy price increasing in oil crisis, but recently it is getting worse besides energy price compared to labor and capital price were increasing.

Energy intensity and capital Energy can be substituted by capital in a economic model, but is it true? I thought I should distinguish capitals because one might be energy using, but others are energy-saving, it might depends on industries.

Purpose of our study To investigate key factors contributing to energy efficiency change of Japanese economy Three factors: Substitution between factors Technological innovation Autonomous energy efficiency improvement Induced by energy cost increase Character of assets

Econometric model The model to be estimated is simultaneous equation like this. These equations are derived from producers’ cost minimization problem. I assumed short-run restricted variable cost function with dynamic adjustment of quasi-fixed assets, using a quadratic approximation. And then, by Shepherd’s Lemma, I derived the optimal conditional short-run input demand functions for energy, materials, services and labor. Using alpha eei and …., short- and long-term elasticities can be calculated. E: energy Y: output M: materials S: services L: labor t: time trend p: input price normalized by wage x: quasi-fixed assets normalized by output

Knowledge capital Accumulation of energy price increase : Decay rate : Diffusion rate

Econometric model Short-run elasticity Long-run elasticity : Input substitution : AEEI : Induced tech change of knowledge capital Long-run elasticity where Short-term elasticity and long-term elasticity can be calculated using estimate results. Epsilon evi indicates how much increase/decrease energy intensity when variable price increases. Epsilon Eti is change of energy intensity when time goes by, that is exogenous technical progress as a time trend. Mu Eti is elasticity for ITC of knowledge capital caused by contemporaneous energy price shocks. Using equilibrium condition, optimal quasi-fixed assets are expressed by estimated parameters and original data. This xi is the difference between short-run and long-run elasticities, which might make the production process more flexible, that is, input substitution easier. The last is average long-term effect through the change of knowledge and other capital accumulation. where

Data EU-KLEMS EU committee Time series data of 71 industries Factor inputs and its prices Outputs and its prices Capital stocks Aggregated into 28 industries European countries, US, Korea and Japan 1973-2005 Quasi fixed assets ICT, transport equipment, other machinery and other consumption I used the EU-KLEMS dataset formed by the EU committee. This is the time series data of 71 industries for OECD countries. I estimated 28 industries which covers Japanese economy. I used capital stock data of ICT, transport eq, other machinery and other consumption as quasi-fixed assets.

Knowledge capital accumulation Decay and diffusion rate Using the value Sue Wing (2008) estimated This is a graph of knowledge capital data for total industries. I used the value of delay and diffusion rate in Sue Wing (2008) for each industry.

Estimation results This is the estimation results. I picked up 8 of 28 industries. We can find significant relation between energy intensity and factor prices. I expected negative elasticities of energy prices for energy intensive industries, but in chemical, basic metals and electricity, gas and water industries, I could not get significant relations.

Long- and short-run elasticity Higher intensiveness of ICT brings higher intensiveness of energy in a half of industries. Transport eq. brings energy efficiency improvement in more than a half of industries. Other consumption capital are energy-using in many industries. Long-run elasticities of factor prices are smaller than short-run elasticities in many industries. Knowledge capital is energy-using … This is the using estimation results. Capitals have the variety of impacts between industries and we cannot say which capital is energy- saving or using.

Median and summary of results Carbon pricing improved energy efficiency for these 35 years. Materials and energy were substitutable in a short-run, but it decreased energy intensity in a long-term. AEEI was positive and did not help energy efficiency improvement. Except for some industries, we might not able to expect tech progress so much in short- and long-term.

Conclusion The factor of energy intensity improvement Energy cost increasing Effective to improve energy intensity Production plants and equipments Variety Transport eq. improved energy efficiency in many industries. AEEI Averagely positive Knowledge capital

Future work Estimation of delta 1 and delta 2 Patent data as a proxy variable of knowledge stock おまけ（見せない）

Summary of result (1) Positive Negative EE CKREFOIL, MANUF, HOTELRES, FIN, EDU (5) AGR, MIN, FOOD, WOOD, PULP, RUBBER, O_MIN, TRANS, POSTEL, PUB, HEALTH (11) EM AGR, CONST, O_MIN, BMET, MANUF, EGW, HOTELRES, POSTEL, HEALTH, PSERV (10) WOOD, PULP, CKREFOIL, RUBBER, MACH, ELEQ. TREQ, BSERV (8) ES CKREFOIL, MANUF, EGW, TRADE, EDU (5) AGR, WOOD, BMET, POSTEL, PUBSERV (5) ET AGR, TXT, BMET, TRANS, POSTEL, ESTATE, EDU, HEALTH, PSERV (9) FOOD, PULP, CHEM, MANUF (4) If energy price increases, energy intensity will decrease in energy intensive industries.

Summary of result (2) Positive Negative E, ICT FOOD, PULP, CHEMI, BMET, MACH, EGW, HTLRES, BSERV, EDU (9) MIN, TXT, ELEQ, TRANS, POSTEL, FIN, PUBSERV (7) E, Transport Equipment AGR, RUBBER, MACH, TRADE, HEALTH, PSERV (6) CONST, FOOD, TRANS, HTLRES, POSTEL, ESTATE, BSERV, PUBSERV, PSERV (9) E, Other Machinery MIN, FOOD, CKREFOIL, MACH, MANUF, TRANS, POSTEL, FIN, ESTATE, BSERV, EDU, HEALTH (12) PULP, RUBBER, OMIN, PUBSERV (4) E, Other Consumption FOOD, PULP, RUBBER, OMIN, BMET, MACH, ESTATE, BSERV, HEALTH (9) MIN, CONST, MANUF, TRANS, FIN, BSERV, EDU (7) E, Other TXT, RUBBER, OMIN, MACH, MANUF, TRANS, TRADE, POSTEL, FIN, PUBSERV (10) FOOD, PULP, CKREFOIL, BMET, ESTATE, HEALTH (6) E, Knowledge CONST, CKREFOIL, MACH, MANUF, TRANS, FIN (6) PULP, OMIN,TREQ, EGW, ESTATE (5)