JAPANESE ENERGY SAVING AND CO 2 EMISSION REDUCTION POTENTIALS IN 2030 IN THE HOUSEHOLD SECTOR

Transcription

1 JAPANESE ENERGY SAVING AND CO 2 EMISSION REDUCTION POTENTIALS IN 2030 IN THE HOUSEHOLD SECTOR TAKAKO WAKIYAMA CLIMATE AND ENERGY AREA/GREEN ECONOMY AREA, INSTITUTE FOR GLOBAL ENVIRONMENTAL STRATEGIES 5TH IAEE ASIAN CONFERENCE, UNIVERSITY OF WESTERN AUSTRALIA, PERTH, WESTERN AUSTRALIA

2 OUTLINE Research Questions Electricity demand and CO2 emissions target in 2030 Methodology Scenario settings and results (electricity demand in 2030) Scenario settings and results (CO2 emissions in 2030) Conclusion

3 RESEARCH QUESTIONS Whether Japan can reduce CO 2 emissions more than the level the INDC indicate in electricity demands of household sector? How much electricity can be reduced with efforts made in a household? How much the reduction of energy use can contribute to CO 2 emission reductions?

4 ELECTRICITY DEMAND AND CO2 EMISSION TARGETS IN INDC: CO2 emissions by sectors 2 Long-term energy demand and supply outlook: Electricity demand 3 Electricity companies target in 2030: 0.37MtCO2/KWh (emission intensity of electricity) Electricity demand in households: 139 TWh CO2 emission intesntisy from electricity in Source: household: METI MtCO2/kWh: 80 MtCO2 Electricity demand in households: 141 TWh CO2 emissions from electricity in households: 52 MtCO2

5 METHODOLOGY 1. Scenario settings (For scenario 1-3) 2. Examine a trend of electricity use by 2015 Time-series regression model: ARMA (Autoregressive moving-average) model 3. Forecast a trend of electricity use to 2030

6 SCENARIO SETTINGS (ELECTRICITY DEMANDS) Name of Scenario Scenario description Electricity scenario 1 (ES1) Use a trend of electricity use from 2005 to 2015 Electricity scenario 2 (ES2) Use a trend of electricity use after Fukushima to 2015 Electricity ES3-1 ES2 and other exogenous shock in 2017 (Expected scenario 3 consumption tax hike in 2017) (ES3) ES3-2 Same as ES3-1 with additional exogenous shock in 2020 (Electricity market reform in 2020) ES3-2 GDP Same as ES3-2, but use lower GDP assumption Electricity scenario 4 (ES4) 30% of households reduces 14% of electricity use from 2014 level by installing energy efficient home appliances and the rest of households follows the pattern of ES 1 Electricity scenario 5 (ES5) Same as ES 4 with 30% of households reduce additional 15% reduction of electricity use by HEMS Electricity scenario BAU (ES BAU) 50% of households maintain electricity use at the level of 2014 and 50% follows ES 1

7 ANALYTICAL PROCESS Input Data Electricity monthly data Temperature data GDP Number of households Dummy variables Analysis 1. Date transformation to log 2. Model identification 3. Model test/ diagnostic check 4. Model determination Forecasting Forecasted input data: GDP (1.7% growth rate: INDC)/ Population decrease/ temperature: degree increase from 2016 to 2030 (IPCC)/ dummy variables Apply these variables to ARMA model

8 SCENARIO ANALYSIS (ELECTRICITY DEMAND)

9 RESULT OF ARMA MODEL: ELECTRICITY SCENARIO 1 (ES1) Parameter Estimate Standard error Z-ratio First-order autoregressive (AR 1) First-order moving average (MA 1) Seasonal autoregressive (AR 1) Intercept Temperature GDP Fukushima (onset, step function) Electricity price in Electricity price in Electricity price & consumption tax in Notes: Estimates computed in R Residual variance= AIC= log-likelihood= , Ljung-Box text for autocorrelation= , (p = )

10 ELECTRICITY SCENARIO 1 (ES1) Estimate a trend of electricity use by 2015 Electricity company Date of changes 2012 Electricity price hike: Tokyo 2013 Electricity price hike: Kyushu, Tohoku, Shikoku, Hokkaido 2014 Electricity price hike: Chubu Consumption tax hike

11 ELECTRICITY SCENARIO 2 (ES2) Parameter Estimate Standard error Z-ratio First-order autoregressive (AR 1) First-order moving average (MA 1) Seasonal autoregressive (AR 1) Intercept Temperature GDP Electricity price in Electricity price in Electricity price & consumption tax in 2014 Notes: Estimates computed in R Residual variance= AIC= loglikelihood= 65.31, Ljung-Box text for autocorrelation= , (p = )

12 ELECTRICITY SCENARIO 3 (ES3) Parameter Estimate Standard error Z-ratio First-order autoregressive (AR 1) First-order moving average (MA 1) Seasonal autoregressive (AR 1) Intercept Temperature Electricity price in Electricity price in Electricity price in GDP Electricity price in 2015 (assumption) Electricity price in 2017 (assumption: impact of consumption tax) ES3-1 Electricity price in 2017 (assumption: impact of electricity market reform) ES

13 Electricity scenario 5 (ES5) Equipment Technology improvement by top runner program average age of service (years) as of 2014 reduction amount (kwh/year per household) air conditioner shift from 2003 to fridge shift from 2002 to lighting shift to LED n/a 84 TV shift from 2007 to Total electricity use per household 5,566 Average electricity reduction per household 782 Average electricity use per household with energy saving 4,784 Reduction rate per year 14.0%

14 FORECASTING Temperature: 0.5 degree increase from 2016 to 2030 (IPCC)

15 RESULTS Changes of electricity from 2013 to 2030 (TWh) ES ES ES ES ES GDP ES ES ES BAU

16 SCENARIO ANALYSIS (CO2 EMISSIONS)

17 SCENARIO SETTING (CO2 EMISSIONS) Name of Scenario Scenario description CO 2 scenario 1 (CO 2 S1) Electricity Scenario: ES BAU scenario CO 2 emission intensity: as of 2013 CO 2 scenario 2 (CO 2 S2) CO 2 scenario 3 (CO 2 S3) Electricity Scenario: ES BAU scenario CO 2 emission intensity: as of 2030 estimated for INDC Electricity Scenario: Electricity Scenario 3 CO 2 emission intensity: as of 2030 estimated for INDC

18 RESULTS Electricity scenario Emission intensity Changes of CO 2 (Mt CO 2 ) from 2013 CO 2 S1 ES BAU CO 2 S2 ES BAU INDC target CO 2 S3 ES 3-1 INDC target ES 3-2 INDC target

19 CONCLUSION Electricity Demands in 2030 in household sector Although from INDC electricity demand is assumed to increase 2 TWh from 2013, scenario analysis indicates the reduction potential in Scenario 2, 3-1, 3-2 and 5. CO2 emissions Although electricity demand is assumed to reduce 20MtCO2 from 2013 with energy mix the INDC, scenario analysis indicates further reduction potential in Scenario 2, 3-1, 3-2 However, improvement of CO2 emission intensity is necessary