The Journal of Fisheries Business Administration (수산경영론집)

The Korean Society of Fisheries Business Administration (한국수산경영학회)
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A Comparison of Predictive Power among SSP Scenarios of Oyster Aquaculture Production

SSP 시나리오별 굴 양식 생산량 예측력 비교

  • Min-Gyeong Jeong (Department of Resource and Environmental Economics, Graduate School, Pukyong National University) ;
  • Jong-Oh Nam (Division of Marine & Fisheries Business and Economics, College of Fisheries Sciences, Pukyong National University)
  • 정민경 (부경대학교 일반대학원 자원환경경제학과) ;
  • 남종오 (부경대학교 수산과학대학 해양수산경영경제학부)
  • Received : 2023.02.27
  • Accepted : 2023.03.28
  • Published : 2023.03.31
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Abstract

Climate change is a major global problem. Oysters, one of the most representative farmed fish in Korea, are attracting attention as candidates for blue carbon, an alternative to carbon neutrality. This study is analyzed by the SSP scenarios to determine the impact of oyster aquaculture production according to climate change. Based on the analysis, future productions of oysters are predicted by the SSP scenario. Significant differences by the SSP scenario are confirmed through predictive power tests among scenarios. Regression analysis was conducted from January 2001 to December 2014. As a result of the analysis, water temperature, water temperature quadratic term, salinity, salinity quadratic term, and month × water temperature cross term were estimated as significant variables. Oyster production which is predicted by the SSP scenario based on the significant variables from 2015 to 2022 was compared with actual production. The model with the highest predictive power was selected by RMSE and MAPE criteria. The predictive power was compared with the MDM test to determine which model was superior. As a result, based on RMSE and MAPE, the SSP1-2.6 scenario was selected as the best model and the SSP1-2.6, SSP2-4.5, and SSP3-7.0 scenarios all showed the same predictive power based on the MDM test. In conculusion, this study predicted oyster aquaculture production by 2030, not the distant future, due to the short duration of the analytical model. This study was found that oyster aquaculture production increased in all scenarios and there was no significant difference in predictive power by the SSP scenario.

Keywords

References

  1. 국립수산과학원(2016), "참굴 양식 표준 매뉴얼".
  2. 국립수산과학원(2019), "수산분야 기후변화 평가 백서".
  3. 국립수산과학원(2022), "2022 수산분야 기후변화 영향 및 연구 보고서".
  4. 기상청(2021), "우리나라 109년 기후변화 분석 보고서".
  5. 김봉태.엄기혁.이준수.박혜진.육근형(2015), "공간패널모형을 이용한 연안어업 생산량과 기후변화 요소의 관계에 대한 연구", 수산경영론집, 45(3), 63-72.
  6. 김봉태.이준수.서영상(2016), "RCP 시나리오에 따른 남해안 어업 및 어종의 기후변화 노출분석: 수온변동을 대상으로", 수산경영론집, 47(4), 31-44.
  7. 김봉태.이준수.서영상(2018), "RCP 시나리오를 적용한 한국 연근해어업의 기후변화 취약성 평가 남해안지역을 대상으로", Ocean and Polar Research, 40(1), 37-48. https://doi.org/10.4217/OPR.2018.40.1.037
  8. 김태형.신종암.최상덕(2021), "기후변화가 마로해의 김 양식에 미치는 영향 및 대응방안", 수산경영론집, 52(2), 55-67.
  9. 남준우.이한식.허인(2002), 계량경제학, 홍문사.
  10. 박영진(2020), "기후변화가 양식어류에 미치는 경제적 영향분석", 박사학위논문, 건국대학교.
  11. 심성보.김지선.성현민.이재희.권상훈.선민아.하종철.변영화.김연희(2021), "SSP 시나리오에 따른 동아시아 극한기후 미래전망", 한국기후변화학회지, 12(2), 143-162.
  12. 안송희(2022), "SSP 시나리오를 이용한 국내 미래 가뭄 특성 분석", 석사학위논문, 인천대학교.
  13. 엄기혁.김홍식.한인성.김도훈(2015), "기후변화에 따른 한국 연근해 어업생산량 변화 분석", 수산경영론집, 46(2), 31-41.
  14. 한국농촌경제연구원(1988), "수산재해에 따른 양식공제시험 사업 설계: 굴 수하식양식을 중심으로".
  15. 국가통계포털(2022), "어업생산동향조사", 접속: 2022.09.14.(http://kosis.kr).
  16. 국가통계포털(2022), "천해양식어업권통계", 접속: 2022.09.14.(http://kosis.kr).
  17. 기상청 기후정보포털(2022), "기후변화 시나리오", 접속: 2022.09.13.(http://www.climate.go.kr).
  18. 현대해양(2021), "블루카본의 보고, 우리 바다를 사수하라!", 접속: 2023.02.07.(http://www.hdhy.co.kr/news/articleView.html?idxno=15688).
  19. 한국해양수산개발원 수산업관측센터(2023), "굴 품목정보", 접속: 2023.02.10.(www.foc.re.kr).
  20. Diebold, F. X. and Mariano, R. S. (1995), "Comparing predictive accuracy", Journal of Business and Economic Statistics, 13(3), 253-265. https://doi.org/10.1080/07350015.1995.10524599
  21. Diebold, F. X. (1997), Elements of Forecasting, South Westren.
  22. FAO (2021), "Fishery and Aquaculture Statistics".
  23. Harvey, D., Leybourne, S. and Newbold, P. (1997), "Testing the equality of prediction mean squared errors", International Journal of Forecasting, 13(2), 281-291. https://doi.org/10.1016/S0169-2070(96)00719-4
  24. IPCC (2021), "Climate Change 2021:The Scientific Basis", Contribution if Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 3948.
  25. O'Neill, B. C. et al. (2014), "A new scenario framework for climate change research: the concept of shared socioeconomic pathways", Climatic Change, 122(3), 387-400. https://doi.org/10.1007/s10584-013-0905-2