Energy-Intensive Adaptation to Particulate Matter Pollution

Transcription

1 The 40 th IAEE International Conference, Singapore Energy-Intensive Adaptation to Particulate Matter Pollution Euikon Jeong, Jaewoong Lee, Jiyong Eom Korea Advanced Institute of Science and Technology (KAIST) College of Business, Graduate School of Green Growth 21. June. 2017

2 Introduction Particulate matter (PM) pollution is now a national security issue: Substance threatening the lives and properties in many countries (Lelieveld et al, 2015) PM 10 classified as Group 1 carcinogenic substance (IARC, 2013) Strong association between PM pollution and health impact (Jo et al., 2017) About 4,000 deaths per day in China due to air pollution (Berkeley Earth, 2015) and many people experiencing decreased life expectancy (Chen et al., 2013) Real-time PM pollution monitored in 322 stations in Korea Intensive use of fossil fuel, the main cause of PM pollution, also damages global climate and national energy security Carbon policy can help address climate risk, air pollution related health impacts, and energy security co-benefits (West et al., 2013; McCollum et all, 2013) Adaptation and mitigation of PM pollution should go hand in hand, taking individual energy behavior into consideration: PM adaptation policy should consider not only the individuals adaptive capacity but also the mitigation potential of the society PM mitigation policy should consider not only the society s mitigative capacity but also the individuals adaptive capacity Understanding individuals adaptation behavior can help develop concrete, integrated policy measures for PM pollution

3 Literature Review People do respond to environmental impacts in some case more noticeably: Sociology literature on individual response to public information and alerts At-risk individuals often reducing exposure to the risk Behavioral adjustment to information about long-term permanent risks such as environmental hazards and terrorism (e.g., Radon cases; Smith & Johnson, 1988) Variations in adaptation behavior to climate change (Anguelovsk et al., 2014) Behavior varying by the age, health status, social class, and the level of knowledge about the risk (e.g., extreme weather events; EPA 2016) Ozone alert reducing outdoor activities particularly for those with children (Neidell, 2009) With Korea s PM pollution level among the top in the world, adaptation behavior may be very pronounced

4 Research Gap Existing economics literature on adaptation behavior to air pollution has several limitations to be used for evidence-based policymaking Responses aggregated over time (e.g., Amato et al., 2005) Not enough to capture instantaneous adaptation behavior in response to acute, sporadic impacts of air pollution (or climate change) Some higher temporal resolution studies on heatwave (Hatvani-Kovacs et al., 2016) and air pollution (Evans et al., 1982; Saberian, 2017) Responses aggregated over people (e.g., Mendelsohn et al., 2000) Not enough to capture the individuals heterogeneous adaptation behavior Target group policies for cost effectiveness requires more detailed understanding Responses (assumed to be) driven by a single impact source Adaptation to air pollution must be understood in conjunction with adaption to climate to give an integrated policy perspective, especially when the adaption is energy-intensive

5 Research Overview An analysis of residential adaptation behavior to PM pollution conducted using real-time smart meter data at the household level A unique natural/quasi experiment setting to uncover the causal relationship between PM pollution and human adaptation (which we find energy-intensive) Local PM pollution varying almost randomly over time and region, affected by local weather conditions and some influx from China i.e., random exposure Behavioral response traced by electricity smart meters installed at 20k households distributed nationwide, which allows for unprecedentedly high temporal (1 second level) and spatial resolution (household level) People aware of local PM pollution level and climate via: Visibility range Media information provided by private weather service providers Occasional alerts from the Ministry of Environment

6 Research Questions 1. Does PM pollution influence household electricity consumption and how? 2. How does PM pollution interact with climate factors if electricity consumption is used as a household adaptation strategy? 3. How do other situational factors affect household adaptation behavior? (e.g., lifestyle, exposure duration, information delivery) The first two questions will be addressed in this talk We demonstrate possible presence of the vicious adaptation cycle, which would require a holistic policy approach: Adaptation to PM pollution > Increased energy demand > Increased fossil fuel combustion > Adaptation to PM pollution

7 Data Real-time smart meter data Encored technologies Inc. 2016/05/01 00:00 ~ 2016/12/31 23:00 (5880 hours) Hourly data for 5,867 households in 17 cities & provinces Weather and PM pollution data Sources: The Ministry of Environment (temperature, humidity), Korea Meteorological Administration (PM 10 ) 322 monitoring stations (observatories) Assignment of weather data to individual households Matched each household with the nearest monitoring station via ellipsoidal Earth projected to a plane formula in google maps geocoding API Panel data

8 Model for Electricity Demand Base model and interaction model [Model specification] Red terms for interaction model ln y it = α k Hum ikt + β 2 CDH it + β 3 HDH it + β 4 PM it k [1,4ሿ + γ m d m + γ w d w + γ h d h + β 5 CDH it PM it + β 6 HDH it PM it m [5,11ሿ w [1,6ሿ h [1,23ሿ + α 4+j HDH jit Hum jit + α 8+l CDH lit Hum l it + μ i + ε it j [1,4ሿ l [1,4ሿ y it : electricity usage of household i at time-hour t, PM it : the level of PM at time-hour t in the region where household i is located, HDH it : temperature difference (in Celsius) below 18 C; 0 if temperature exceeds 18 C, CDH it : temperature difference (in Celsius) above 24 C; 0 if temperature is below 24 C, Hum ikt : humidity quintiles (20% interval) at time-hour t in the region where household i is located, d m, d w, d h : month (m), weekday (w) and hour (h) dummy variables (with December, Sunday, and 00:00~00:59 am ), μ i : fixed effect term for household i.

9 Results Results support strong positive association between electricity usage and the PM Other climatic variables (temperature + humidity) affect electricity usage in conjunction with PM Impact of PM is less significant in summer and winter periods Fixed Effects Months [May~Dec] (controlled) : Aug > Jul > Dec > Nov > Others Hours [00h~23h] (controlled) : Night > Morning > Afternoon > Dawn Days of Week [Mon~Sun] (controlled) : Sun > Sat > Weekdays HDH CDH PM Coefficients Humidity: 0~20% Humidity: 20~40% Humidity: 40~60% Humidity: 60~80% HDH:PM CDH:PM HDH:Hum(0~20%) HDH:Hum(20~40%) HDH:Hum(40~60%) HDH:Hum(60~80%) CDH:Hum(0~20%) CDH:Hum(20~40%) CDH:Hum(40~60%) CDH:Hum(60~80%) Base Model (OLS SE) 4.57e-04*** (2.21e-05) 1.14e-03*** (7.77e-05) 4.41e-04*** (5.58e-06) -3.61e-03* (1.68e-03) -1.73e-03** (5.43e-04) -4.15e-03*** (3.77e-04) -2.24e-03*** (3.28e-04) Signif. codes: 0 *** ** 0.01 * Base Model (Robust SE) 4.57e-04** (2.21e-04) 1.14e-03 (7.38e-04) 4.41e-04*** (4.20e-05) -3.61e-03 (5.07e-03) -1.73e-03 (2.77e-03) -4.15e-03** (1.99e-03) -2.24e-03* (1.32e-03) R-squared Adj. R-squared Interaction Model (OLS SE) 2.37e-04*** (5.17e-05) 4.99e-03*** (2.91e-04) 4.95e-04*** (7.63e-06) -7.99e-03* (3.36e-03) -4.77e-03*** (8.95e-04) -7.52e-03*** (5.34e-04) -4.87e-03*** (4.45e-04) -6.34e-06*** (7.96e-07) -2.68e-05*** (2.81e-06) 1.66e-03*** (3.65e-04) 8.87e-04*** (8.30e-05) 7.58e-04*** (5.53e-05) 6.09e-04*** (5.09e-05) -5.31e-03*** (9.18e-04) -4.69e-03*** (3.67e-04) -2.63e-03*** (2.89e-04) -2.10e-03*** (2.92e-04) Interaction Model (Robust SE) 2.37E-04 (2.98E-04) 4.99E-03** (1.99E-03) 4.96E-04*** (4.80E-05) -7.99E-03 (7.34E-03) -4.77E-03 (3.88E-03) -7.52E-03*** (2.74E-03) -4.87E-03*** (1.82E-03) -6.34e-06* (3.51e-06) -2.68e-05** (1.08e-05) 1.66e-03** (7.67e-04) 8.87e-04*** (3.33e-04) 7.58e-04*** (2.51e-04) 6.09e-04*** (1.71e-04) -5.31e-03* (2.88e-03) -4.69e-03** (2.03e-03) -2.63e-03 (1.71e-03) -2.10e-03 (1.36e-03)

10 Results Results support strong positive association between electricity usage and the PM Other climatic variables (temperature + humidity) affect electricity usage in conjunction with PM Impact of PM is less significant in summer and winter periods Fixed Effects Months [May~Dec] (controlled) : Aug > Jul > Dec > Nov > Others Hours [00h~23h] (controlled) : Night > Morning > Afternoon > Dawn Days of Week [Mon~Sun] (controlled) : Sun > Sat > Weekdays HDH CDH PM Coefficients Humidity: 0~20% Humidity: 20~40% Humidity: 40~60% Humidity: 60~80% HDH:PM CDH:PM HDH:Hum(0~20%) HDH:Hum(20~40%) HDH:Hum(40~60%) HDH:Hum(60~80%) CDH:Hum(0~20%) CDH:Hum(20~40%) CDH:Hum(40~60%) CDH:Hum(60~80%) Base Model (OLS SE) 4.57e-04*** (2.21e-05) 1.14e-03*** (7.77e-05) 4.41e-04*** (5.58e-06) -3.61e-03* (1.68e-03) -1.73e-03** (5.43e-04) -4.15e-03*** (3.77e-04) -2.24e-03*** (3.28e-04) Signif. codes: 0 *** ** 0.01 * Base Model (Robust SE) 4.57e-04** (2.21e-04) 1.14e-03 (7.38e-04) 4.41e-04*** (4.20e-05) -3.61e-03 (5.07e-03) -1.73e-03 (2.77e-03) -4.15e-03** (1.99e-03) -2.24e-03* (1.32e-03) R-squared Adj. R-squared Interaction Model (OLS SE) 2.37e-04*** (5.17e-05) 4.99e-03*** (2.91e-04) 4.95e-04*** (7.63e-06) -7.99e-03* (3.36e-03) -4.77e-03*** (8.95e-04) -7.52e-03*** (5.34e-04) -4.87e-03*** (4.45e-04) -6.34e-06*** (7.96e-07) -2.68e-05*** (2.81e-06) 1.66e-03*** (3.65e-04) 8.87e-04*** (8.30e-05) 7.58e-04*** (5.53e-05) 6.09e-04*** (5.09e-05) -5.31e-03*** (9.18e-04) -4.69e-03*** (3.67e-04) -2.63e-03*** (2.89e-04) -2.10e-03*** (2.92e-04) Interaction Model (Robust SE) 2.37E-04 (2.98E-04) 4.99E-03** (1.99E-03) 4.96E-04*** (4.80E-05) -7.99E-03 (7.34E-03) -4.77E-03 (3.88E-03) -7.52E-03*** (2.74E-03) -4.87E-03*** (1.82E-03) -6.34e-06* (3.51e-06) -2.68e-05** (1.08e-05) 1.66e-03** (7.67e-04) 8.87e-04*** (3.33e-04) 7.58e-04*** (2.51e-04) 6.09e-04*** (1.71e-04) -5.31e-03* (2.88e-03) -4.69e-03** (2.03e-03) -2.63e-03 (1.71e-03) -2.10e-03 (1.36e-03)

11 Summary An analysis of residential adaptation behavior to PM pollution conducted using real-time smart meter data at the household level Individuals (Korean residences) showed avoidance/adaptation behavior in response to the risk of PM pollution Strong positive association between electricity usage and the PM 5% increase in PM concentration is equivalent to 1 C increase in summer on electricity consumption The degree of adaptive behavior varied with climatic factors such as humidity and temperature The impact of PM on electricity usage were more significant in spring and autumn, when cooling and heating are not required In summer, residences were more energy-intensive on the days of high humidity level In winter, residences were more energy-intensive on the days of low humidity level Presence of potential vicious adaptation cycle Adaptation to PM pollution > Increased energy demand > Increased fossil fuel combustion > Adaptation to PM pollution

12 Limitations and Future Work Robustness check Time lagged effect of CDH and HDH Other situational factors that affect household adaptation behavior (e.g., lifestyle, exposure duration, information delivery)

13 Acknowledgment Special thanks to Encored Technologies Inc Dr HyoSeop Lee HyeSoo Lee National Research Foundation in Korea Ministry of the Environment in Korea

14 Thank you. Eui Kon Jeong Korea Advanced Institute of Science and Technology (KAIST) Graduate School of Green Growth