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Averting Behavior Among Singaporeans During Indonesian Forest Fires

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Abstract

We estimate averting behavior in the form of increased electricity usage in Singapore during the haze caused by Indonesian forest fires. Our results indicate that increases in fire radiative power in Indonesia result in statistically significant increases in one- and two-day ahead electricity demand. Further results show that the Indonesian fires accounted for 0.5% of Singaporean electricity demand between February 2012 and August 2017 at a total cost of over $270 million. In addition, we find that the residential electricity share increases and the industrial share decreases during fire episodes, suggesting the increase in demand may be due to Singaporeans staying home and/or increasing their air conditioning use during these times. This averting behavior is persistent, not diminishing, during periods of frequent poor air quality.

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Fig. 1

Note: This figure plots the daily fire radiative power (FRP) from all fires in Indonesia from November 2000 through August 2017. The fires are somewhat seasonal, with more fires occurring in late summer and early fall. The data come from NASA’s Fire Information for Resource Management System. Our full estimation sample starts in February of 2012 when electricity data becomes available. Prior years are included in figure to show historical trends in Indonesian fires

Fig. 2

Note: This figure plots total monthly rainfall and mean monthly pollution standards index (PSI) in Singapore and total fire radiative power (FRP) in Indonesia from June 2015 to March 2016. This figure shows the typical seasonal patterns in the region, with a negative correlation between rainfall and FRP and a positive correlation between FRP and PSI

Fig. 3

Note: This figure plots average daily pollution standards index (PSI) in Singapore along with daily fire radiative power (FRP) of Indonesian fires from February of 2012 through August of 2017. PSI appears to be positively correlated with FRP, which exhibits a seasonal pattern as fires tend to be worse during the late summer and early fall. PSI data come from Singapore’s National Environmental Agency and fire data from NASA’s Fire Information for Resource Management System

Fig. 4

Note: This figure displays prevailing wind direction in Singapore across month and time of day. Source: http://www.weather.gov.sg/climate-climate-of-singapore/

Fig. 5

Note: This figure plots monthly cumulative Indonesian fire radiative power (FRP) and three measures of Singaporean port activity: monthly total cargo, number of ships on Singapore’s Registry of Ships, and bunker fuel sales, from November 2000 to August 2017. The fire data come from NASA’s Fire Information for Resource Management System and the port activity data from the Marine and Port Authority of Singapore. Note these data include dates prior to the estimation sample, which begins in February of 2012, since they are available and increase the sample size of this monthly data set

Fig. 6

Note: This figure shows monthly Indonesian fire radiative power (FRP) and the share of aggregate Singaporean electricity demanded by the Commercial, Industrial, and Residential sectors from January 2005 to December 2016. The fire data come from NASA’s Fire Information for Resource Management System and the electricity demand data by sector from the Energy Market Authority of Singapore

Fig. 7

Note: This figure shows the amount of aggregate Singaporean electricity demand resulting from Indonesian fires from February 2012 to August 2017. It is calculated as the cumulative difference in electricity demand predicted using estimation results from Column 3 of Table 4 assuming actual FRP versus assuming no fires

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Notes

  1. The Republic of Singapore is a small island nation with a land area of approximately 277.6 square miles and a 2015 population of approximately 5,535,000; Singapore’s mainland measures approximately 31 miles from east to west and 16 miles from north to south (Department of Statistics Singapore 2017).

  2. In a recent working paper, Agarwal et al. (2017) find an increase in utility use in Singapore when PSI is high. In contrast to our paper, they use monthly (rather than daily) electricity data, include only the residential sector, and regress utility usage directly on PSI, which may be endogenous to utility use as both are likely impacted by covariates such as local economic trends as detailed in Sect. 4. We use Indonesian fires as an exogenous source of air pollution.

  3. Numerous studies have utilized FRP as a proxy for biomass consumption (e.g., Ellicott et al. 2009; Vermote et al. 2009; Kaiser et al. 2012). Freeborn et al. (2014) compare near-simultaneous FRP satellite scans on a per pixel (250–1000 square meters) basis, finding the measurements are normally distributed with a large standard deviation (26%), concluding that while per pixel FRP measurements should not be compared across space and time, the aggregation of pixels drastically reduces the standard deviation to less than 5% when 50 pixels are aggregated. They recommend aggregating to coarser spatiotemporal resolutions when utilizing FRP as a proxy for fire intensity. Since we aggregate many instantaneous per pixel FRP measurements to the day-country level, the FRP uncertainty should be very small.

  4. Specifically, from February 6, 2012 to September 4, 2017.

  5. We follow the current methodology of PSI construction found at http://www.haze.gov.sg/docs/default-source/faq/computation-of-the-pollutant-standards-index-(psi).pdf and follow http://aqicn.org/faq/2013-06-25/singapore-psi-and-pm25-aqi-why-is-there-a-difference-between-the-two-readings/ to convert PM2.5 to an air quality index.

  6. For 157 days in the roughly 5-year sample when the Newton weather station was down, we instead use data from the Marina Barrage station, which is also centrally located.

  7. Specifically, these stations include 486,870 Singapore/Tengah (central), 486,920 Singapore/Seletar, 486,940 Paya Lebar, 486,980 Singapore Changi Intl, 486,990 Seletar.

  8. The year and month fixed effects should also account for trends in population, economic activity, and shipping traffic.

  9. See http://www.mpa.gov.sg/web/portal/home/port-of-singapore/port-statistics.

  10. https://comtrade.un.org/data/.

  11. Out of the 18 major oil palm plantation groups in Indonesia, only one is owned by a Singaporean company (Gelder and Willem 2004).

  12. We consider the North to be 315–45 degrees, East to be 45–135 degrees, South to be 135–225 degrees, and West to be 225–315 degrees.

  13. We use PSI rather than FRP because the PSI has a well-defined threshold for poor air quality (at a level of 100, it goes from moderate to unhealthy). There are almost always some fires burning in Indonesia, so it is not meaningful to distinguish between days with and without fires, and it would be arbitrary to define an FRP threshold.

  14. See http://www.haze.gov.sg/faq\#chapterF.

  15. See http://www.mom.gov.sg/haze/guidelines-on-protecting-employees-from-haze.

  16. We use these estimates rather than those from Column 5 of Table 3 because the predictive power of this specification is slightly better. Results are very similar across these two specifications.

  17. Overall, the model estimated in Column 3 of Table 4 provides reasonably good in-sample predictions of aggregate electricity demand, with an r-squared value of 0.85. This holds for days with few or no fires, since the weather, year, month, and day of week fixed effects are all also highly significant. For the whole sample, the mean difference between actual and predicted demand is 1.57%. This number is 1.61% for days with FRP less than the 25th percentile and 1.46% for days with FRP more than the 75th percentile, suggesting that while fires improve the predictions, predictions are nearly as good for low fire days.

  18. We calculate average annual electricity prices for each year in our sample based on monthly electricity prices from Singapore's Energy Market Authority (see https://www.ema.gov.sg/cmsmedia/Publications\_and\_Statistics/Statistics/46RSU.pdf). These range from S$63.2 in 2016 to S$222.6 per MWh in 2012.

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Acknowledgements

We would like to thank Meredith Fowlie, Gautam Gowrisankaran, Daniel Hicks, Ashley Langer, Jamie Mullins, Stanley Reynolds, Alberto Salvo, Catherine Wolfram and sponsors and participants in the 2017 AEA Annual Conference CSWEP Session on Environment and Health for helpful comments.

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  1. University of South Carolina, 1014 Greene St., Columbia, SC, 29208, USA

    Tamara L. Sheldon

  2. Boston College, 387 Maloney Hall, Chestnut Hill, MA, 02467, USA

    Chandini Sankaran

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  1. Tamara L. Sheldon
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Correspondence to Tamara L. Sheldon.

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Sheldon, T.L., Sankaran, C. Averting Behavior Among Singaporeans During Indonesian Forest Fires. Environ Resource Econ 74, 159–180 (2019). https://doi.org/10.1007/s10640-018-00313-8

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