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1 ISSN JES VOL 58 09/2016 미세먼지PARTICULATE MATTER

2 CONTENTS : ( ) 4? ( ) 14 ( / ) 24 BIG DATA - - ( ) ( ) ( ) 47,, -,, ( 32 )

3 ISSN JES VOL 58 09/2016 [이슈] 미세먼지 미세먼지 오염의 현황과 문제점 숨쉬기 안녕하십니까? 미세먼지에 대한 오해와 진실 그리고 접근 방법- 대기환경관리의 관점에서 본 수도권 미세먼지 환경 개선 미세먼지PARTICULATE MATTER BIG DATA 그리고 대기오염물질 산정의 패러다임 변화 -도로부문 미세먼지를 중심으로- 특별한 것이 없는 정부의 미세먼지 특별 대책 - 미세먼지 관리 특별대책 세부이행계획 의 한계를 중심으로- 대기 중 미세먼지와 초미세먼지는 모두가 체감하는 문제로 다가왔다. 대기 중 (초)미세 먼지의 농도는 얼마나 높은가? 이를 측정하는 시기, 장소, 대상 물질은 적절한가? 미세 먼지는 얼마나 위험한가? 미세먼지를 발생시키는 주요 원인은 무엇인가? 어떤 대책이 필요한가? 정부는 무엇을 먼저 급히 실행하고, 무엇을 장기적으로 차근차근 준비해야 하는가? 더 나은 대책을 세우기 위해서는 어떤 자료가 더 필요한가? 시민들이 할 수 있 는 일은 무엇인가? 등등 답을 구해야 할 많은 질문이 산적해있다. 이번 호 e-환경논총에 서는 미세먼지의 현황, 원인, 대책, 정부와 시민사회의 역할에 대해서 대기과학 전문가, 교통 전문가, 그리고 대학원생의 분석과 의견을 싣는다. 표지 설명: 중유 연소 발생 미세먼지 마이크로그래프 이미지를 포토샵으로 편집한 그래픽(표지 디자인: 서예례 편집위원)

4 ( ) I [1] (EPI) [2] /180. (PM2.5) (NO 2 ). OECD [3] PM ,

5 II PM10(Particulate Matter, <10 ) PM ( 1) 65.6%, (PM10) 8.2%, %.,, [4]. PM (2015) (%) 0.02ppm SO ppm ppm ppm NO ppm O 1 0.1ppm ppm ppm CO PM10 8 9ppm ppm /m /m Pb 0.5 /m Benzene 5 /m :, (2014), 2015.

6 PM10 PM2.5 < 2> PM /m /m PM / m 3 50 /m , 5 [5]. PM10 20 < 3>, < 1> , (, ) ( 64 /m 3 ), ( 60 /m 3 )., [4]. PM /m 3, /m /m 3 ( ) PM10 54 /m /m 3.., / 6 2. ( ) PM10 PM ( /m 3 ) (100 /m 3 ) ( /m 3 ) (50 /m 3 ) :, ( ), 2016.

7 3. PM10 ( ) ( : /m 3 ) :, (2014), ( ) ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' PM10

8 4. PM / m 3 2 PM /m 3 (PM10) PM / m 3 2 PM /m 3 PM / m 3 2 PM /m 3 (PM2.5) PM / m 3 2 PM /m 3 : 14, , , , < 4>. III , 2. 1,,, 2,,. 1. [6] PM % 2. 1,.

9 5. PM10 PM2.5 (2013) PM10(ton/yr) PM2.5(ton/yr) 4,524 3,573 1,955 1,226 81,014 41,606 6,249 4,829 12,103 11,135 15,167 13, ,942 17,127 15,663 12, , ,610 9 :, (2013), (CAPSS). < 5> PM10 246,168 /, PM ,610 / PM2.5/PM [7]. < 2> PM10 ( ) ( )..,,..,,.,,,... < 3> PM2.5 PM10. PM10 44% PM2.5

10 15%. PM2.5 40%, PM2.5 22% PM %,, PM10 (2013, ) 3. PM2.5 (2013, )

11 IV < 6> , 2.,., [1] 1 24% 2 29% 11 6.,,, +,, 2 ( / ) ( /m 3 ) ( )

12 ... (MATES- IV) PM 68% [8]. PM (WHO) (IARC) 1 [9].. IV. (PM10) (PM2.5) 2...,..., (filterable PM). (condensable PM)..,. (HAP)... 12

13 < > [1], 2016,. [2] Environmental Performance Index-2016 Report, ( [3] OECD, 2016, The economic consequences of outdoor air pollution. [4], 2015, (2014). [5], 2016, ( ),. [6], 2010,. [7], 2016, [8] South Coast Air Quality District, 2015, Multiple air toxics exposure study in the south coast air basin (MATES-IV). [9] International Agency for Research on Cancer, 2012, Diesel engine exhaust carcinogenic, Press release number

14 ? -? ( ) I., KORUS-AQ(Korea US Air Quality study; ) 1) NASA...., NASA,,,. KORUS-AQ 2). KORUS-AQ NASA KORUS-AQ 5 6,.. 7, 8, 14 1) (NASA, National Aeronautics and Space Administration) (NIER, National Institute of Environmental Research) 5, 6, 2) ,,,.

? - 1. (PM10) ( : [ ],?),,., ( 1 ),, NASA,,??..,,.. II.... 1.? (TSP, Total Suspended Particles, 50 m ) (PM, Particulate Matter).

15 ? - 1. (PM10) ( : [ ],?),,., ( 1 ),, NASA,,??..,,.. II ? (TSP, Total Suspended Particles, 50 m ) (PM, Particulate Matter). PM10( 10 m ) PM2.5( 2.5 m ), ( 2 ). 3) ( :,, ), ( :,, 4) ).,, 15 3) PM2.5 PM1 ( 1 m ),.

16 ? - 2. ( : [ ],?) 16,,.. 2. vs?,.,.,. (Si), (K), (Ca), (Fe),.., 1,, 2, ( 3 ).. 4),.

? - 17 3. (<1 m).,,,,,,. 20-50%. ( : Zhang et al, Geophys. Res. Lett., 34, L13801, 2007) 3.

17 ? (<1 m).,,,,,, %. ( : Zhang et al, Geophys. Res. Lett., 34, L13801, 2007) 3.? (1 ), (2 )..,,,,. < 4> 2. (NO, NO2), (SO2),, 2 ( )., (VOCs) 2 ( ). (OH, O3, NO3 ) ( ). 2 1 (60~80%).

18 ? ( : [ ],?) 4.?.. 30~70%,.,,,, RV,, ( 5 ). ( ) ( : [ ],?)

19 ? - Box 1. KORUS-AQ? KORUS-AQ.,..,,.,, 300m.,.? KORUS-AQ NASA,.,,.,.,,.?......??, ( ). 19

20 ? (PM10, PM2.5) (O3, CO, SO2, NO2), (, or.kr).., ,.,,. NASA KORUS-AQ., KORUS-AQ.... III.?,.,.,... 20

21 ? - 1..,,.,.,...,.,, trend,..,.. 5 KORUS-AQ NASA 9 ( ) 3.,,. 6,..,,.,. 3...,, interboundary transport. 21

22 ? ,,,.,., KORUS-AQ,. NOAA(, National Oceanic and Atmospheric Administration),..,...,. KORUS-AQ?,?.,.,...,.. 22

23 ? ,.,.?,?,?,?..,,,....?,.., KORUS-AQ,.. 23

24 ( / ) I. 12%, 48%,, (, 2009). NASA (KORUS-AQ and MAPS-Seoul).,.,,.,.??. II (PM10), 24

25 (PM2.5) 2.5. PM10 PM2.5 (, 2016), PM10 PM2.5.. (PM10) (50~70 ) 1/5~1/7, PM2.5 1/20~1/30.. (WHO) (PM10) (PM2.5) 1987, 2013 (IARC, International Agency for Research on Cancer) 1 (Group 1) (, 2016). 1m 3 ( 1g ) /m 3, PM10 50 /m 3, PM /m 3.,. (, ),. 2., (1 ) (2 ) PM , PM , ( 2016)

26 PM2.5 92%. RV(SUV), ( )..., PM10 12, PM ,,,, 2 ( 1). 2 2 (PM2.5) 60% (, 2016). III. 1., ( ) 2005, ( 1 )., (RSD),, DPF,,,, PMNOx,, (LEZ), DPF,,,, DPF,,,,,,,,,, (R&D), :, 2013

27 ( ) 2024 ( ) PM10( /m 3 ) PM2.5( /m 3 ) NO 2 (ppb) :, , 2015, ( 2 ). 2, 1., 2., 3., 4, 5. ( 1) %, 7.9%, 9.3%, 1.6% < 2> ( ) PM %, 2) - - ( ), ( ) ( ) 3 PM %, 20%, 15%, PM /m 3. ( - - ( ),,, ), ,,,,, 6 ( 3). < 3> ,000 ( 300 ) PM2.5 WHO (25 /m 3 ) /m ~2017 ( )., 1) 2017 IV. 1. :? 1

28 3. (2013~2017 ) 2017 PM % PM /m 3 1) 2) (2012 1,300 ) - - (600 ) - - ( 4 ) - 3) % - - (20 ) 4) ) (, ) - - ( ) - (,, 1,200 ) (400 ) -,, 28 (,, ) - 7) -, 6) -,, - 9) 6 -, 8) -, 2 10) 3 -, :, 2015

29 NO2 PM10 (PM10 40 /m 3, NO ppm), O3. 2, PM /m 3. PM /m < 4> PM2.5 PM /m 3, 44.8 /m / m 3, 37.9 /m 3 20 /m 3, 30 /m PM /m 3., 50%. < 4> 2., 1),, 2) % (DPF) -, 3) %, 4) 112%,, 5) (9 ), 6) (, 2016).. (, ) 23~32 /m 3,,., ( : /m 3 ) 2024 ( ) PM PM :, 2016

30 ? 2. (1) 2, , 64., (PM10) (PM2.5)., ( 50% ).., DPF -. (2) , 1), 2). 2, ,.,,. 2,, regime,. 2 SO2, NOx, NH3, NMVOC 1,. NMVOC,., NASA KORUS/MAPS NMVOC.,.., 30

31 NO:NO2 95:5 50:50, NOx NO NO2. NO (O3 titration) 2., - -, < 2>. (3) ( ) 2 50%, 80%. 2, (NO3 - ) ( 5 /m 3 ), (SO4 2- ) NOx VOCs (J.-H. Woo, 2013)

32 ( 2 /m 3 ). 2,.,. 2. < 3>,.. (4).,., (co-control) (co-benefit)., PM2.5 (Kim, et al., 2016)

,., (INDC). (CCS). INDC 2030 2024 2.. (5) NASA, (MAPS-Seoul/KORUS-AQ).

33 ,., (INDC). (CCS). INDC (5) NASA, (MAPS-Seoul/KORUS-AQ). MAPS(Megacity Air Pollution Study- Seoul),. KORUS-AQ ( 4). 3, /. (, 2016). 2 R&D 1.1%,.,., ( ) 10 (2005~2014) NO2

34 V.,,. 6 3.,,,,., 2 3 (20 /m ), 10 (, /m /m 3 ) 2.,,,? /m 3,?,,,,,.,,,. -,., DB,,,. 34

35 , ,., 2009,, 25(6)., 2015,., ,,?, , 2., , JTBC. Younha Kim, Jung-Hun Woo, et al., , Future scenario emission inventory for China by various control policy, IUAPPA Conference, Busan. Jung-Hun Woo, 2013, Integrated Management of Climate Change and Air Quality- Building fundamental basis for GAINS- Korea, The 2nd Int. Workshop on Integrated Approach for Climate Change and Air Pollution, Seoul. 35

36 BIG DATA - - BIG DATA 1) ( ) ( ) 36 I.. ( / ) (PM10) 33.2(10.0%), (PM2.5) 30.5(14.5%) (NOx) 919.8(30.8%) [1].. -, /...?.,,.,.? Big Data. 1,500, 90%. 90% 1~2, (1 ) Point-to- 1) (, 2015).

37 BIG DATA - point ( P2P). P2P Big Data.,.. (,, ),. [7],[8],[9], 30~40%, 300% II. 1. COPERT [6] EPA [5]. 2), (NIER) / (Clean air policy support system, CAPSS), CAPSS COPERT.. 3) ( *km/, Vehicle kilometer travelled, VKT)., Interpolation with Gaussian process regression ( Kriging) 40%, [8],[9]. 2.4% Kriging [3],. 4) CAPSS, ( ) VKT [2],. 5), VKT VKT VKT. CAPSS VKT. NIER. VKT. CAPSS,, 37 2) COPERT(Computer programme to calculate emission from road transport), EPA (EPA, Environmental protection agency) (DOT, Department of transportation). 3) : ( ), ={, }, ={,, },, (, ): (, / )* (, km),, :,, :,, ( ), :, : (%),, ( ): ( ) ( / *km) 4) 225,590,,, 6 5, %. 6, Database. 5) ( ),. -

38 BIG DATA - -.,,. ( ). /,.. (III) (2013), 25~35km/, 30km. 80%.,,,,. /,,.. 2. ). GPS. P2P. P2P. 6). 99%.. VKT ( ) VKT..,. III. : 38 VKT. VKT., In-vehicle (,,,, ( ), P2P. P2P (Digital tacho graph, DTG). 7) 5, < 1>. 1) P2P Profile, P2P.

39 BIG DATA GPS

40 BIG DATA - - [ 1] : / 962, , ,,,. 8) [ 2] : P2P Map matching ( ) 1km/ Profile. Map matching,,. [ 3] : GIS DB.., /. 9) [ 4] : Profile [2],., Profile 1km/,. [ 5] / / / : / / DB, DB. URL( IV. PM10 NOx ( airmiss.nier.go.kr),,. [2] CAPSS Cold start () 0.992, y=x () < 2> 0.985,.. VKT 1%., VKT 40 7), (2,016 GB), 98.2%, 99.9%. (,, ) DTG, GPS. 8) 5, %.. 9), DB GIS Miss matching.

41 BIG DATA VKT ( : *km/ ) (%) 1, , ,. VKT. 2. ( NIER) VKT( *km/ ) 83.5(2012 ), 114.4(2014 ) [3]., 72.6 NIER %, %. < 3> ( PM10) ( NOx) VKT. NIER 26.3%, 29.2%. Cold start 2 NIER 56.9%, 58.4%.,

42 BIG DATA - - NIER.,, VKT., NIER, VKT. NIER.,., VKT ,. 3.,,,. PM10 NOx 42 3.

43 BIG DATA

44 BIG DATA - -.,,.,. VKT 619.0( *km/ ) , km. ( ) 39.8km 9.68km(32.19%).,,,. PM10 NOx (ton/ ) , NIER %., VKT 1.6 NIER VKT CAPSS

45 BIG DATA - - V PM10 NOx (ton/ ) Cold start,. 2 NIER 2013 PM10 NOx < 2>. ( NIER ),, PM10 NOx (ton/ ) , , (%) , PM10 NOx , , %. PM10 5%, NOx 10%., 2015 (km/ ) 39.8, NIER 2014 VKT * / [3] VKT,.,. Cold start,,., GIS. DTG (ton/ ) PM10 NOx ( ) ,988.0 NIER (2013) ( ) (%) ,077.7 ( - + ) , ( ) (%) % ( - +2* ) , (2* ) (%)

46 BIG DATA - - [1] ( [2], 2013, (III). [3], 2015, (II): [4],,, 2009,,, 6(2), [5] California EPA, 1996, Methodology for estimating emissions from on-road moter vehicles. [6] EEA, 1999, Atmospheric emission inventory guidebook 2nd edition. [7] Selby, B. and Kockelman K.M., 2013, Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression. Journal of Transport Geography, 29, [8] Shamo, B., Aza, E., and Membah, J., 2014, Linear spatial interpolation and analysis of annual average daily traffic data. Journal of Computing in Civil Engineering. [9] Zhao, F. and Park, N., 2004, Using geographically weighted regression models to estimate annual average daily traffic. Transportation Research Record, 1879,

47 ( ) I. : /. < 1> 4. (PM10 0~30, PM2.5 0~15) 4 21,. (PM10 81~150, PM2.5 51~100) (PM10 151, PM ) 4 (9,10,22,26 ) 2 (23,24 ). 22 (PM10 31~80, PM2.5 16~50), (WHO) (PM10 50, PM ) ), 7 1. / / /. 2),. 47 1), ,,. 2),

48 PM10, PM2.5 PM10( /m 3 ) PM2.5( /m 3 ) 4/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / : (

49 - - II. / 1. /. (PM10) 1/5~1/7 10,,. (PM10) 1/4 (PM2.5). ( ),.,,,,,. 3) (Global Burden Disease) (, ). / (IARC) (Group 1).(< 2> ) 2.,? (PM10) 100µg/m3, 50µg/m3 (PM2.5) 50µg/m3, 25µg/m3.. (WHO) 2. 2~2.5, 2 2.5~10%. < 1> (IARC) 1 (Group 1),, 2A (Group 2A) DDT, 2B (Group 2B), 3 (Group 3), 4 (Group 4) : (2016),.,? 3), ,?, 22~23.

50 WHO PM2.5( /m 3 ) PM10( /m 3 ) % % % : (2014), ,... (PM2.5) ( ) (1 ) 15 /m 3, 12 /m 3, 15 /m 3, 10 /m 3, 8 /m 3 25 /m 3., WHO. 10.,.,.,.( ( :40) 22 /m 3. WHO.)

51 - - III. / 1..,,,,,., (1 ) (2 ) (, 2016) <2 > 1 ( ),, <2013 > CO 58%, NOx 30%, PM10 11%, PM2.5 14%. CO 9%, NOx 16%, SOx 24%., ( 51% ), ),.. 6, µg/m 3. / 2 ( ). 2. : ( ) ? (PM10) m ( ), 80. 5) 2014 (APEC) 2. (PM2.5) 55%. 6) / 51 4), 2015, 2013, 15~17. 5), ,,. 6), APEC 2,.

52 - -. 7) (210µg/m 3 ) /? , , 2010 G , ( : 5/30~31, 6/12~13, 6/24~ ), 22.3%, 5.5%, 2.4%. 97%, 28%, 6%. 8) (CO, NO2, SO2, PM10, O3) 2 3 3~28%, (3 ) 44.9%, %. 2. 9) ) ,. 1, (WHO) ), 2015, 2013, 15~17. 8), 2007., ), ,. 10), &, 173.

53 - - (, ) : (8 : 1 2 / 1 2 / 1 2 / 1 2 ) (2 : 1 2 ). 10 3,345MW , 20 < 4> < 5> 16,100MW, (Daniel Jacob), 1,100. (370 ), (330 ), (150 ), (120 ), (120 ). 11) , ,900. (53 ) ) ( ) (MW) 2 2, , , ,190 GS 2 2, ,680 : (2015),, 11), 2015.,, ), 37~38.

54 ( ) (MW) 2 1, , , , , ,420 : (2015),, 6. PM10 PM2.5 ( : /m 3, %) PM10 PM : (2016), : ( ) PM10 3~21%, PM2.5 4~28%. 13) < 6> ), :.

55 ) ~70km,. 15), ( ) ,. 100% LNG..... IV )..,,.. (, ).., ), ), ,.

56 - - < >, 2016, :., 2014,. _, 2015, 2013., 2016,,., 2005, ,., 2007,, 23 4., 2016,., 2013, : DB., 2015,., 2013, &, 173., 2016,.,? _, 2014, 2 56 < >, ,. ( html?artid= &code=970205#csidxbcb8f8d11ff911dba07f7a852e5a523), ,,. ( html_dir/2016/05/10/ html), APEC 2, ( php?number=9674), ,.

57 ,, -,, 32,,,, 6.,,,.,, ,,,,. ( ) e (JES) 57. ( ). -

58 ,,,, ( ) (, ) ( ), ( ) ( ) 58 : , 2015 ( ) (1 ). (T), (C), (S), (I).,,.. GNI(Gross National Income) 4 1.,, : ( ).,,, ( ) 70%..., 2011~2012

59 ,,,, : % %., ( ).,,., 1 4. ( ) % OECD 11.5%. ( + ) %, %, % (20.5 ) : KDI, 2015 ( - ).,..,,.., 86,.., , 59

60 ,,,, , , , 60, (2014 ) 27, 30., ( ),, ( ).., ( ), ( ),., OECD %,.,.. :..,. DTI, LTV.. 60 :. 5.5%, 11.5% OECD

61 ,,,, ( ),, ( ),, ( ) ( ) 61 : %,, (,, ),,,,,,,. (CO2) 2/3. 1 C 1986 ~ C. 2 C,. 2 C 2,900 1,900 1, : 2011.,.. 35%,..

62 ,,,, 32 - ( ), 1), 2), 3) (carbon sinks), 4). : C 2 C, 1.5 C. UN,. (no back-sliding) (progressive principle),. 2.7 C 1.5 C %, 33.1%. (CO 2 ) 7 OECD % German Watch 1% (German Watch ).. :, C, cm.,,......, ,

63 ,,,, ha : (UNFC- CC) C. 1997, % (Clean Power Plan), 154 (American Business Act on Climate Pledge) , (INDC) %., UN ,000-10, , ,, : 5, (ajenda). 1), 2), 3),..,.,..,... :... 63

64 ,,,, 32 -,.,.,, (passive house).,.,... :..,,...,.,,.., :. NGO...,. ( 32 )... ( 32 ) 2 64

65 ,,,, 32 -.,..,,. 65

66 ,,,, ( ),,? ( ), : ( ) ( ) 66 :..,,,. ( )..,.. (weak sustainability), (strong sustainability)..... :,, 3.,,. 50

67 ,,,, (Tellico), (Snail Darter) (Endangered Species Act), ,.., KTX, 4,,.,. :...., , (time preference). 1 2.,. :...,,,. :,...,. 67

68 ,,,, 32 -.,.,.,,,..,,..,., 99% :..... :.,, (Fitness),.,.,, 3E(Economy, Environment, Equality),. ( 32 ) (basic needs), ( ).. ( 32 ). 68

69 ,,,, ( 32 ). :., ( ),. ( ) ( ). ( 32 ),. 69 :...

70 ,,,, ( ), (, ), ( ) ( ) 70 : (Eurasia) 40%, 74%, GDP 60%.,.,.,, ( ),, ( ),, ( ). :.. ( : - ) 11,000km 18,000km, 25-30, 36. 7,200-11,000 3,400-5,000...,.,,. :

71 ,,,, : (globalization) 1. Blue Banana,,, BESETO. TGV 1~2 (Metro Nation). 500km 1... : (One Metropolitan Korea) Asian Hub-City Nation (AHN).,. 3,., P. KTX KTX. (One Metropolitan Korea) KTX KTX. (One Metropolitan Korea) KTX. (CBD). AHN 5 : Asian Hub-City Nation (AHN) 5. P KTX, - KTX, KTX, KTX 71

72 ,,,, 32 - KTX Business Park.\ :.... ( 32 ). ( 32 )., ADB, EDCF. KTX : KTX, KTX KTX, KTX., ( 32 ) KTX,... :.. ( 32 ) AHN 5 KTX, KTX. KTX (One Metropolitan Korea),. 72

73 ,,,, 32-12, ( ),, ( ), ( IMC ) ( E&D, ) 73 (trend) :.,. (emerging trend), (megatrend) , IBM,,..., 2005,,, ,. 10 :. 2007

74 ,,,, ), 2), 3), 4), 5), 6), 7), 8), 9), 10)...,.,.,.,. : 10,.....,..,.,., ,...,,.,. :,,..,,,,,.,,,. 74

75 ,,,, (,,,, ).,,.,.,., ( ), ( ),,, ( ) ( ). :,,,.., 21..,,.,.,,,,,.,,,,,, ,,,,..,,,,,,,. :,.,., - -,,.,,..., 7 75

76 ,,,, :. ( 32 ),. ( 32 ).,...,. (how) (what)...,.,. ( 32 ) 10-20,.,.,. :...,....,... 76

77 ,,,, ( ), ( ),, ( ) ( ) 77 : ,,, , ,.,,,,. 21, , 20,,. 20,. 21 :,,,,.,,, 1,000

78 ,,,, 32 -,,,,. 21..,,.,.. (Garden by the bay),,.,.,,. (Brooklyn Bridge Park) - (Millennium Park). :..,....,,.,,,.. ( ) :., ( ),.,...,,...,., ( ).,. 78

79 ,,,, 32 - : ,,,,,,,,.,,,.,.. :...,.. 1,..,,,.,,,.. :... 1,700,... 79

80 ,,,, 32 -,. : ( 32 ).,,. (social mix),.... ( 32 ) 30%.. ( 32 ). -. ( 32 ). :.., 100..,,,,.,,,,. ( 32 ). 18., 80

81 ,,,, 32 -.,. 81

82 ,,,, 32-14, ( ) ( ), 21 (, ) 82 : ,,,,.,, ,.,,..... (John Rawls),,. :. OECD, IMF,. OECD 10 ( 10% 10% ) , , OECD.

83 ,,,, IMF. Piketty 21.,,,.. :,.,., OECD IMF...,. OECD 20% 20%.,..,,... IMF OECD % % % 2.5% %.,., 81%.,... Piketty,, IMF Lipton. :,,

84 ,,,, 32 -, % 3 2,,. Piketty,.. ( 32 ) 50%. (22 ).,. ( 32 ). SH ( 32 ) /.,.,. ( 32 ).,.,... *,.. *, (gses. snu.ac.kr- - ). 84

85 2016 8

86 (. )....,,,.,..,.,., 62.4%,.,,..,.,, 87.7%,.,.,,.,..,,. 86

87 (. )., ), 2), 3).,,,,.,,,.,,,...,.,..., ;, ;,.,,,...,. 87

88 (. ) 2000.,..,..,.,..,.,.,,. 1.01, 3,600 /.,., 1.58m. 95% 1.51m 1.65m, 99% 1.49m 1.67m m, 2.08m.,.,. 88

89 (. ),. (,, ), (OLS) (Spatial Lag Model) (Spatial Error Model), (General Spatial Model) (Geographically Weighted Regression). GWR.., (evidence-based).,, -,. 30km/h 10% 3.3%, 60km/h 10% 10% km/h (%) (%). 65,.,. 60km/h, 30km/h, OECD(2016). IT. 89

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91 (. ) -,.,..,.., (,,, ),.,.,,.,.,. Moran s I LISA,,,,,.,.,.,., 1/3,.,,,.,,,.,,,.,,.,. -.,,.,,. 91

92 Social-ecological Memory in Korea s Traditional Village Landscapes: Ethnographic and Spatial Approaches (. ) In nurturing resilience insocial-ecological systems (SESs), memories of ecosystem stewardship practices that are retained by actors of SESs referred to as social-ecological memories (SEMs) play vital roles, particularly relevant in the face of change. My dissertation investigates the ways in which SEM is created, mobilized, and manifested to cope with disturbances and changes by employing various social and ecological resources while maintaining the system s identity, also referred to as resilience. It proposes SEM as a person-practice-place complex with crucial individual components. In other words, SEM that nurtures socialecological resilience involves (1) memory carriers as the primary agents of SEM (person); (2) ecosystem stewardship practices based on local observations and experiential knowledge that has undergone a learningby-doing process (practice); and (3) physical sites in which the person has experienced and learned through practice about ecosystem management, complex systems thinking, and the link between nature and humans. In this regard, my research explores the characteristics of each indicator of SEM with individual cases concerning Korea s traditional village landscape (KTVL) and highlights their implications in the context of socialecological resilience. Landscape here is understood as a unit of SES that is significant for its adaptive qualities. This adaptation is a feedback loop comprising the potential of the land and the ways in which humans make a living from it based on their knowledge systems and cosmologies. Additionally, I focus on traditional ecological knowledge as a type of SEM that has undergone vigorous trial-and-error over time, because in certain circumstances there is a reluctance to innovate and adapt in the face of change within an SES. In studying SES concerning KTVL, I use both autobiographical and historical memories as sources for analyzing the SEM. For instance, in Chapter Three, I use Park Wan-suh snovel Who AteUp All the Shinga? as an example of autobiographical memory to analyze aspects of ecoliteracy and place attachmentas reflected in SEM. Ecoliteracy is defined as ecological knowledge with regard to the names of living and physical components, practices of the resource management system, and landscape management systems. Worldviews and cosmologies that are closed related with person-place attachment are also delineated. These observations exemplify how memories of person-practice and person-place interactions are manifested in forms of ecoliteracy and place attachment. The study also shows how SES in relation to KTVL is highly influenced by village landscape management practices within a watershed. In Chapter Four, I explore the role of SEM in fostering the adaptive capacity of a community through its synergy with other sources of resilience such as leadership, and with cross-scale and cross-level interactions. The result of ethnographic study conduced in a rural area in South Korea indicates that SEM concerning village landscape configuration is reinforced through land use changes and scale-related issues brought about by top-down policy processes. Although the evidence used here focuses on villagers attempts to cope with flood damages, it demonstrates the importance of SEM in allowing for community-based resilience practices. In Chapter Five, I draw on historical records as types of historical memory to define the social-ecological identity of KTVL with emphasis on Korea s traditional village grove and to assess the current spatial identity of the landscape. With the analyzed spatial identity, I was able to locate potential traditional village grove sites in KTVLs that are not in the current governmental data. Although cognitive dimensions of SEM highlight the place-based values of physical environments, based on an SES framework, this dissertation claims that person-practice-place dynamics are also manifested through the spatial characteristics and spatial resilience of a place. It concludes that person-practice-place interactions are central to SEM, which plays a critical role in allowing for ecosystem stewardship in various regions. Institutions to support SEM-based stewardship activities and conservation strategies to protect physical sites in which SEM is accumulated and stored are needed for the maintenance, transmission, and mobilization of sources of resilience. 92

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97 Solar Developers Losses due to Sunshine Duration Shortfall and Marketability of Sunshine Insurance: a Loss Distribution Approach : TheEffect of Land Transaction Amount with Foreign Investors on Housing Price -Focus on Chinese Investors in Jeju Island- : - - The Policy Mobility of the Korean U-City model: The case of Songdo as the benchmark for the Clark Green City Project in the PhilippinesPhilippines Characteristics of Kampung Upgrading Programs in Indonesia - 97

98 : : 1 : : 1 :,, People, plants, place & process: a landscape ethnography through the gardens of an urban village, Kaemi-Maul, Hongjae-dong, Seoudaemun-gu, Seoul. Kore 98

99 58 [Vol. 58]

232 도시행정학보 제25집 제4호 I. 서 론 1. 연구의 배경 및 목적 사회가 다원화될수록 다양성과 복합성의 요소는 증가하게 된다. 도시의 발달은 사회의 다원 화와 밀접하게 관련되어 있기 때문에 현대화된 도시는 경제, 사회, 정치 등이 복합적으로 연 계되어 있어 특

232 도시행정학보 제25집 제4호 I. 서 론 1. 연구의 배경 및 목적 사회가 다원화될수록 다양성과 복합성의 요소는 증가하게 된다. 도시의 발달은 사회의 다원 화와 밀접하게 관련되어 있기 때문에 현대화된 도시는 경제, 사회, 정치 등이 복합적으로 연 계되어 있어 특 한국도시행정학회 도시행정학보 제25집 제4호 2012. 12 : pp.231~251 생활지향형 요소의 근린주거공간 분포특성 연구: 경기도 시 군을 중심으로* Spatial Distribution of Daily Life-Oriented Features in the Neighborhood: Focused on Municipalities of Gyeonggi Province