Analyzing Elasticity Trends for Singapore Household Electricity Demand

1. Analyzing Elasticity Trends for Singapore Household Electricity Demand – Implications for Policy Making and the Rebound Effect Allan Loi, Ng Jia Le 34th United States Association for Energy Economics Conference Hyatt Regency Tulsa Tulsa, Oklahoma, USA 25 October 2016

2. Overview • Introduction - Singapore’s Household Electricity Needs • Motivation and Objectives of Study • Methodology and Specification • Empirical Results • Policy Implications

3. Introduction - Singapore’s Household Electricity Needs • Small City-State. Approximately 760 km2 • Population density: 8000 people/m2 • 1 degree North of equator, temperatures at 25oC during nighttime, 32oC during daytime • 100% urbanized and electrified since 1990 • Fixed electricity prices for residential consumers, set by Singapore Power Services Ltd. • Retail Contestability available for residential consumers in 2018 • Mandatory Energy Labelling Scheme (2008). Mandatory Energy Performance Standards (2011)

4. Introduction - Singapore’s Household Electricity Needs

5. Introduction - Singapore’s Household Electricity Needs • Electricity is a necessity for 1-2 room flats • Condo dwellers face decreasing electricity demand over time. More efficient? • In 2011, Singapore’s temperature dipped by a lot, resulting in large decreases in electricity.

6. Motivation and Objectives of Study • To construct an electricity demand model that accounts for more heterogeneity. (regions and dwelling types) • Better understand the role of price asymmetry in electricity demand for Singapore, which can have implications for rebound effect and demand response. • Provides a starting point to further investigate the rebound phenomenon via disaggregated, individual household data.

7. Motivation and Objectives of Study • There are many cities in the US that may exhibit similar demand characteristics typical of a tropical region. Hawaii, Denver, Tulsa, Miami Beach, Santa Barbara etc… • Also, non studies that analyze within-city electricity demand that take into account heterogeneity across different classifications of household electricity use on a National level are rare (excludes randomized control trials).

8. Methodology and Specification Panel data over 10 years (2005 to 2014) on average household electricity demand, income, prices, and dummies pertaining to recession (2008) and EE policy. All data are either from the National Environment Agency, or from the Singapore Department of Statistics.

9. Methodology and Specification We use a Multivariate Panel Specification, utilizing both one-way cross-sectional fixed effects, and the fully modified least squares estimator (FMOLS). Across Dwelling Types: • ln Ei,t= α0i + α1 ln Pt + α2 ln HSi,t + α3 ln Ii,t + α4Rt + α5 T+ α6 EEt + α7 ln Ei,(t-1) + α8Temp +εi,t Across Districts: • ln Ei,t = boi + b1 ln Pt + b2 Rt + b3 T + b4EEt + b5 ln Ei,(t-1)+ εi,t Across Districts, income, temperature and household size are not used as they are not available across regions for the panel period (only for 2013).

10. Methodology and Specification Price Asymmetry: Decomposition of yearly tariffs based on Gately et al (2002) and Wolfram (1971). Price decomposition methods were first developed to study agricultural commodities, but found use in the energy sector in recent times.

11. Methodology and Specification Price Asymmetry: Decomposition of yearly tariffs based on Gately et al (2002) and Wolfram (1971).

12. Empirical Results Selected Price and Income Estimates across dwellings: (After testing for each series as an I(1) process based on)

13. Empirical Results Selected Price and Income Estimates across Regions (After testing for each series as an I(1) process)

14. Empirical Results • Results show electricity demand inelastic to price (-0.05 to -0.235) • Inelastic response to income, although it plays a bigger role affecting demand (0.17 to 0.21) • Household size most influential for demand usage • Energy Efficiency could have led to 6% reduction in electricity usage as in Model (6) These results are consistent with an earlier work done in Singapore by Loi and Loo (2016) for time series between 1980-2014.

15. Empirical Results • People respond more towards price decreases • Surprising result if you consider individuals to be risk-averse towards costs.

16. Empirical Results • Price responsiveness higher for price decreases than increases. • Insertion temperature data does not change the comparative aspects of price asymmetry. • Estimates range between -0.18 to -0.482.

17. Policy Implications • Likely low responsiveness towards demand response when market opens for retail contestability. – Low price & income elasticities. • More outreach is needed to educate consumers on the benefits of demand response and switching. • Comfort has a greater importance on consumers’ welfare than cost as income level increases. – May not be worthwhile sacrificing thermal pleasures for cost savings. • Price asymmetry exists, and hence the rebound may follow similar trend. --- Magnitude needs to be further estimated with individual household data at higher frequencies.

18. Thank you! Energy Studies Institute 29 Heng Mui Keng Terrace Block A, #10-01 Singapore 119620 Loi Tian Sheng, Allan Tel: (65) 6516 2349 Email: esiltsa@nus.edu.sg

19. Singapore’s Temperature