Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/29011
標題: 評估氣候門檻對人類健康與農業之影響
Essays on the Impacts of Climate Threshold on Human Health and Agriculture
作者: 陳品宇
Chen, Ping-Yu
關鍵字: 經濟-能源-環境關係;Economic-Energy-Environmental Nexus;溫度門檻;生育積溫度數;PSTR模型;Temperature Threshold;Growing Degree Days;Panel Smooth Transition Regression Model
出版社: 應用經濟學系所
引用: Reference in the first part Acaravci, A., Ozturk, I., 2010. On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35, 5412–5420. Adamantiades, A., Kessides, I., 2009. Nuclear power for sustainable development: current status and future prospects. Energy Policy 37, 5149–5166. Agras, J., Chapman, D., 1999. A dynamic approach to the environmental Kuznets curve hypothesis. Ecological Economics 28, 267–277. Akbostanci, E., Turut-Asik, S., Tunc, G.I., 2009. The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy Policy 37, 861–867. Alam, M.J., Begum, I.A., Buysse, J., Huylenbroeck, G.V., 2012. Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis. Energy Policy 45, 217-225. Alam, M.J., Begum, I.A., Buysse, J., Rahman, S., 2011. Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India. Renewable and Sustainable Energy Reviews 15, 3243-3251. Al-Iriani, M.A., 2006. Energy-GDP relationship revisited: an example from GCC countries using panel causality. Energy Policy 34, 3342–3350. Al-mulali, U., Fereidouni, H.G., Lee, J.Y.M., Sab, C.N.B.C., 2013. Exploring the relationship between urbanization, energy consumption, and CO2 emission in MENA countries. Renewable and Sustainable Energy Reviews 23, 107-112. Al-mulali, U., Sab, C.N.B.C., 2012a. The impact of energy consumption and CO2 emission on the economic and financial development in 19 selected countries. Renewable and Sustainable Energy Reviews 16, 4365-4369. Al-mulali, U., Sab, C.N.B.C., 2012b. The impact of energy consumption and CO2 emission on the economic and financial development in the Sub Saharan African countries. Energy 39, 180-186. Ang, J.B., 2007. CO2 emissions, energy consumption, and output in France. Energy Policy 35, 4772–4778. Ang, J.B., 2008. Economic development, pollutant emissions and energy consumption in Malaysia. Journal of Policy Modeling 30, 271–278. Apergis, N., Payne, J.E., 2009. CO2 emissions, energy usage, and output in Central America. Energy Policy 37, 3282-3286. Apergis, N., Payne, J.E., 2010. The emissions, energy consumption, and growth nexus: Evidence from the commonwealth of independent states. Energy Policy 38, 650-655. Arellano, M., Bond, S., 1991. Some test of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297. Arellano, M., Bover, O., 1995. Another look at the instrumental-variable estimation of error components models. Journal of Econometrics 68, 29–52. Bai, J., Kao, C., 2006. On the estimation and inference of panel cointegration model with cross-sectional dependence. In: Baltagi, B. (Ed.), Panel Data Econometrics: Theoretical Contributions and Empirical Applications. In: Contributions to Economic Analysis, vol. 274. Elsevier. Banerjee, A., Carrion-i-Silvestre, J.L., 2006. Cointegration in panel data with breaks and cross-section dependence. In: ECB Working Paper Series, vol. 591. European Central Bank. Bloch, H., Rafiq, S., Salim, R., 2012. Coal consumption, CO2 emission and economic growth in China: empirical evidence and policy responses. Energy Economics 34, 518-528. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. Blundell, R.W., Bond, S.R., Windmeijer, F., 2000. Estimation in dynamic data models: improving on the performance of the standard GMM estimator. In: Baltagi, B. (Ed.), Nonstationary Panels, Panel Cointegration and Dynamic Panels. Advance in Econometrics, vol. 15. JAI Elsevier Science. Bond, S.R., 2002. Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal, 141–162. Chandran, V.G.R., Tang, C.F., 2013. The impacts of transport energy consumption, foreign direct investment and income on CO2 emissions in ASEAN-5 economies. Renewable and Sustainable Energy Reviews 24, 445-453. Chang, C.C., 2010. A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China. Applied Energy 87, 3533–3537. Chang, Y., 2005. Residual based tests for cointegration in dependent panels. Mimeo. Rice University. Chen, P.Y., Chen, S.T., Chen, C.C., 2012. Energy consumption and economic growth - New evidence from meta analysis. Energy Policy 44, 245–255. Coondoo, D., Dinda, S., 2002. Causality between income and emissions: a country group-specific econometric analysis. Ecological Economics 40, 351–367. DeCanio, S.J., 2009. The political economy of global carbon emissions reductions. Ecological Economics 68, 915–924. Dinda, S., Coondoo, D., 2006. Income and emission: a panel data-based cointegration analysis. Ecological Economics 57, 167–181. Doornik, J.A., Arellano, M., Bond, S., 2006. Panel Data Estimation using DPD for Ox. mimeo. Eberhardt, M., 2012. Estimating panel time series models with heterogeneous slopes. The Stata Journal 12, 61-71. Engle, R.F., Granger, C.W.J., 1987. Co-integration and errorcorrection: representation, estimation and testing. Econometrica 55, 251–276. Esteve, V., Tamarit, C., 2012. Threshold cointegration and nonlinear adjustment between CO2 and income: The Environmental Kuznets Curve in Spain, 1857–2007. Energy Economics 34, 2148-2156. Fodhaa, M., Zaghdoud, O., 2010. Economic growth and pollutant emissions in Tunisia: An empirical analysis of the environmental Kuznets curve. Energy Policy 38, 1150-1156. Friedl, B.,Getzner, M., 2003. Determinants of CO2 emissions in a small open economy. Ecological Economics 45, 133–148. Galeotti, M., Lanza, A., 2005. Desperately seeking environmental Kuznets. Environmental Modelling and Software 20, 1379–1388. Gengenbach, C., Palm, F.C., Urbain, J.P., 2010. Panel unit root tests in the presence of cross-sectional dependencies: Comparison and implications for modeling. Econometric Reviews 29, 111-145. Gengenbach, C., Urbainm J.P., Westerlund, J., 2009. Error correction testing in panels with global stochastic trends. Maastricht University: METEOR. Govindaraju, V.G.R.C., Tang, C.F., 2013. The dynamic links between CO2 emissions, economic growth and coal consumption in China and India. Applied Energy 104, 310-318. Groen, J.J.J., Kleibergen, F., 2003. Likelihood-based cointegration analysis in panels of vector error-correction models. Journal of Business & Economic Statistics 21, 295-318. Grossman, G.M., Krueger, A.B., 1991. Environmental impacts of a North American Free Trade Agreement. NBER Working Paper, vol. 3914, Cambridge, MA. Halicioglu, F., 2009. An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37, 1156–1164. Hamit-Haggar, M., 2012. Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective. Energy Economics 34, 358-364. Hatzigeorgiou, E., Polatidis, H., Haralambopoulos, D., 2011. CO2 emissions, GDP and energy intensity: a multivariate cointegration and causality analysis for Greece, 1977–2007. Applied Energy 88, 1377–1385. He, J., Richard, P., 2010. Environmental Kuznets curve for CO2 in Canada. Ecological Economics 69, 1083–1093. He, J., Wang, H., 2012. Economic structure, development policy and environmental quality: An empirical analysis of environmental Kuznets curves with Chinese municipal data. Ecological Economics 76, 49-59. Holly, S., Pesaran, M.H., Yamagata., T, 2010. A spatio-temporal model of house prices in the USA. Journal of Econometrics 158, 160-173. Holtz-Eakin, D., Selden, T.M., 1995. Stokingthefires? CO2 emissions and economic growth. Journal of Public Economics57, 85–101. Hossain, S.M., 2011. Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanisation of newly industrialized countries. Energy Policy 39, 6991-6999. Huang, B.N., Hwang, M.J., Yang, C.W., 2008. Causal relationship between energy consumption and GDP growth revisited: A dynamic panel data approach. Ecological Economics 67, 41-54. Im, K., Pesaran, M.H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. Journal of Econometrics 115, 53-74. Intergovernmental Panel on Climate Change (IPCC), 2007. Climate Change 2007: impacts, adaptation and vulnerability. Cambridge: Cambridge University Press. International Energy Agency (IEA), 2011. World Energy Outlook 2011. OECD/IEA, Paris. Iwata, H., Okada, K., Samreth, S., 2011. Anote on the environmental Kuznets curve for CO2: a pooled mean group approach. Applied Energy 88, 1986–96. Jalil, A., Mahmud, S.F., 2009. Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy 37, 5167–5172. Jayanthakumaran, K., Verma, R., Liu, Y., 2012. CO2 emissions, energy consumption, trade and income: A comparative analysis of China and India. Energy Policy 42, 450-460. Kao, C., 1999. Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90, 1–44. Kapetanios, G., Pesaran, M.H., Yamagata, T., 2011. Panels with nonstationary multifactor error structures. Journal of Econometrics 160, 326-348. Khan, M.A., Khan, M.Z., Zaman, K., Khan, M.M., Zahoor, H., 2013. Causal links between greenhouse gas emissions, economic growth and energy consumption in Pakistan: A fatal disorder of society. Renewable and Sustainable Energy Reviews 25, 166-176. Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. Journal of Energy and Development 3, 401–403. Lean, H.H., Smyth, R., 2010. CO2 emissions, electricity consumption and output in ASEAN. Applied Energy 87, 1858–1864. Lee, C.C., Chang, C.P., 2007. Energy consumption and GDP revisited: a panel analysis of developed and developing countries. Energy Economics 29, 1206–1223. Lee, C.C., Lee, J.D., 2009. Income and CO2 emissions: evidence from panel unit root and cointegration tests. Energy Policy 37, 413–423. Levin, A., Lin, F., Chu, C., 2002. Unit root tests in panel data: asymptotic and finite sample properties. Journal of Econometrics 108, 1-24. Maddala, G.S., Wu, S., 1999. A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics 61, 631-652. Managi, S., Jena, P.R., 2008. Environmental productivity and Kuznets curve in India. Ecological Economics 65, 432-440. Martinez-Zarzoso, I., Bengochea-Morancho, A., 2004. Pooled mean group estimation of an environmental Kuznets curve for CO2. Economics Letters 82, 121–126. Menyah, K., Wolde-Rufael, Y., 2010a. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38, 2911–2915. Menyah, K., Wolde-Rufael, Y., 2010b. Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics 32, 1374–1382. Mostafa, M. M., 2010. A Bayesian approach to analyzing the ecological footprint of 140 nations. Ecological Indicators 10, 808–817. Nelson, M., Ogaki, M., Sul, D., 2005. Dynamic seemingly unrelated cointegrating regressions. The Review of Economic Studies 72, 797-820. Orubu, C.O., Omotor, D.G., 2011. Environmental quality and economic growth: Searching for environmental Kuznets curves for air and water pollutants in Africa. Energy Policy 39, 4178–4188. Oxley, L., Greasley, D., 1998. Vector autoregression, cointegration and causality: testing for cause of the British industrial revolution. Applied Economics 30, 1387–1397. Ozturk and Acaravic, 2010. CO2 emissions, energy consumption and economic growth in Turkey. Renewable and Sustainable Energy Reviews 14, 3220-3225. Pao, H.T., Fu, H.C., Tseng, C.L., 2012. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy 40, 400-409. Pao, H.T., Tsai, C.M., 2010. CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy 38, 7850-7860. Pao, H.T., Tsai, C.M., 2011a. Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy 36, 685-693. Pao, H.T., Tsai, C.M., 2011b. Modelling and forescasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy 36, 2450-2458. Pao, H.T., Yu, H.C., Yang, Y.H., 2011. Modeling the CO2 emissions, energy use, and economic growth in Russia. Energy 36, 5094-5100. Pedroni, P., 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653–669. Pedroni, P., 2004. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20, 597–625. Pedroni, P., Vogelsang, T., 2005. Robust unit root and cointegration rank tests for panels and large systems. Mimeo. Williams College. Pesaran, M.H., 2004. General diagnostic tests for cross section dependence in panels. CESifo Working Papers. No. 1233. Pesaran, M.H., 2006. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967-1012. Pesaran, M.H., 2007. A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics 22, 265-312. Reddy, B.S., Assenza, G.B., 2009. The great climate debate, Energy Policy, 37, 2997–3008. Richmond, A.K., Kaufmann, R.K., 2006. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecological Economics 56, 176-189. Saboori, B., Sulaiman, J., 2013. CO2 emissions, energy consumption and economic growth in Association of Southeast Asian Nations (ASEAN) countries: A cointegration approach. Energy In Press. Selden, T., Song, D., 1994. Environmental quality and development: is there a Kuznets curve for air pollution emissions? Journal of Environmental Economics and Management 27, 147–162. Sephtona, P., Mann, J., 2013. Further evidence of an Environmental Kuznets Curve in Spain. Energy Economics 36, 177-181. Shahbaz, M., Hye, Q.M.A., Tiwari, A.K., Leitao, N.C., Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews 25, 109-121. Song, M.L., Zhang, W., Wang, S.H., 2013. Inflection point of environmental Kuznets curve in Mainland China. Energy Policy 57, 14–20. Soytas, U., Sari, R., 2009. Energy consumption, economic growth, and carbon emissions: challenges faced by an EU candidate member. Ecological Economics 68, 1667–1675. Soytas, U., Sari, R., Ewing, B.T., 2007. Energy consumption, income, and carbon emissions in the United States. Ecological Economics 62, 482–489. Squalli, J., 2007. Electricity consumption and economic growth: Bounds and causality analyses of OPEC countries. Energy Economics 29, 1192–1205. Stern, D.I., 1993. Energy and economic growth in the USA: a multivariate approach. Energy Economics 15, 137–150. Stern, D.J., 2000. Multivariate cointegration analysis of the role of energy in the U.S.macroeconomy. Energy Economics 22, 267–283. Tamazian, A., Chousa, J.P., Vadlamannati, K.C., 2009. Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries. Energy Policy 37, 246–253. The world bank, 2007. Growth and CO2 emissions: how do different countries fare. Environment Department, Washington, DC. Wang, S.S., Zhou, D.Q., Zhou, P., Wnag, Q.W., 2011. CO2 emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy 39, 4870-4875. Westerlund, J., 2005. New simple tests for panel cointegration. Econometric Reviews 24, 297-316. Westerlund, J., 2007. Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics 69, 709-748. Westerlund, J., 2008. Panel cointegration tests of the fisher effect. Journal of Applied Econometrics 23, 193–233. Westerlund, J., Costantini, M., 2009. Panel cointegration and the neutrality of money. Empirical Economics 36, 1–26. Westerlund, J., Edgerton, D., 2007. A panel bootstrap cointegration test. Economics Letters 97, 185–190. Zhang, X.P., Cheng, X.M., 2009. Energy consumption, carbon emissions, and economic growth in China. Ecological Economics 68, 2706–2712. Reference in the second part Alfesio LF, Zanobetti A, Schwartz J. The effect of weather on respiratory and cardiovascular deaths in 12 U.S. Cities. Environ Health Persp 2002;110:859-863. Analitis A, Katsouyanni K, Biggeri A, Baccini M, Forsberg B, Bisanti L, Kirchmayer U, Ballester F, Cadum E, Goodman PG, Hojs A, Sunyer J, Tiittanen P, Michelozzi P. Effects of cold weather on mortality: results from 15 European cities within the PHEWE project. Am J Epidemiol 2008;168:1397-1408. Anderson BG, Bell ML. Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology. 2009;20:205-13. Armstrong B. Models for the relationship between ambient temperature and daily mortality. Epidemiology 2006;17:624-631. Baccini M, Biggeri A, Accetta G, Kosatsky T, Katsouyanni K, Analitis A, Anderson HR, Bisanti L, D''Ippoliti D, Danova J, Forsberg B, Medina S, Paldy A, Rabczenko D, Schindler C, Michelozzi P. Heat Effects on Mortality in 15 European Cities. Epidemiology 2008;19:711-719. Ballester F, Corella D, Perez-Hoyos S, Saez M, Hervas A. Mortality as a function of temperature. A study in Valencia, Spain, 1991-1993. Int J Epidemiol 1997;26:551-61. Basu R, Feng WY, Ostro BD. Characterizing Temperature and Mortality in Nine California Counties. Epidemiology 2008;19:138-145. Basu R, Samet J. The relationship between elevated ambient temperature and mortality: a review of the epidemiological evidence. Epidemiol Rev 2003;24:190–202. Brenner MH, Mooney A. Unemployment and health in the context of economic change. Soc Sci Med 1983;17:1125-1138.Brenner MH. Economic instability, unemployment rates, behavioral risks, and mortality rates in Scotland, 1952-1983. Int J Health Serv 1987;17:475-87. Brenner MH. Mortality and the national economy. A review, and the experience of England and Wales, 1936-76. Lancet 1979;314:568-573. Buckley NJ, Denton FT, Robb AL, Spencer BG. The transition from good to poor health: an econometric study of the older population. J Health Econ 2004;23:1013-34. Chung JY, Honda Y, Hong YC, Pan XC, Guo YL, Kim H. Ambient temperature and mortality: an international study in four capital cities of East Asia. Total Environ 2009;408:390-396. Clark R, Brown S, Murphy, J. Modelling northern hemisphere summer heat extreme changes and their uncertainties using a physics ensemble of climate sensitivity experiments. J Climate 2006;19:4418-4435. Conti S, Meli P, Minelli G, Solimini R, Toccaceli V, Vichi M, Beltrano C, Perini L. Epidemiologic study of mortality during the summer 2003 heat waves in Italy. Environ Res 2005;98:390-399. Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug SL, Patz JA. Temperature and mortality in 11 cities of the Eastern United States. Am J Epidemiol 2002;155:80-87. Diaz J, Garcı’a R, Lo’ pez C, Linares C, Tobı’as A, Prieto L. Mortality impact of extreme winter temperatures. Int J Biometeorol 2005;49:179-183. Diaz J, Garcia R, Velazquez de Castro F, Hernandez E, Lopez C, Otero A. Effects of extremely hot days on people older than 65 years in Seville (Spain) from 1986 to 1997. Int J Biometeorol 2002;46:145-149. Ebi KL, Exuzides KA, Lau E, Kelsh M, Barnston A. Weather changes associated with hospitalizations for cardiovascular disease and stroke in California, 1983-1998. Int J Biometeorol 2004;49:48-58. El-Zein A, Tewtel-Salem M, Nehme G. A time-series analysis of mortality and air temperature in Greater Beirut. Sci Total Environ 2004;330:71-80. Gemmell I, McLoone P, Boddy FA, Dickinson GJ, Watt GC. Seasonal variation in mortality in Scotland. Int J Epidemiol 2000;29:274-279. Gerdtham UG, Johannesson M. Absolute income, relative income, income inequality, and mortality. J Hum Resour 2004;22:228-247. Gouveia N, Hajat S, Armstrong B. Socioeconomic differentials in the temperature-mortality relationship in Sao Paulo, Brazil. Int J Epidemiol 2003;32:390-397. Guest CS, Wilson K, Woodward A, Hennessy K, Kalkstein LS, Skinner C, McMichael AJ. Climate and mortality in Australia: retrospective study, 1979 – 1990, and predicted impacts in five major cities in 2030. Climate Res 1999;13:1-15. Ha J, Shin Y, Kim H. Distributed lag effects in the relationship between temperature and mortality in three major cities in South Korea. Sci Total Environ 2011;409:3274-3280.Hajat S, Kovats RS, Atkinson RW, Haines A. Impact of hot temperatures on deaths in London: a time series approach. J Epidemiol Commun H 2002;56:367-372. Hajat S, Kovats RS, Lachowycz K. Heat-related and cold-related deaths in England and Wales: who is at risk? Occup Environ Med 2007;64:93-100. Hansen BE. Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 1996;64: 413–30. Hansen BE. Sample splitting and threshold estimation. Econometrica 2000;68:575-604. Hansen BE. Threshold effects in non-dynamic panels: estimation, testing and inference. J Econometrics 1999;93:345–68. Healy JD. Excess winter mortality in Europe: a cross country analysis identifying key risk factors. J Epidemiol Commun H 2003;57:784-789. Huynen MMTE, Martens P, Schram D, Weijenberg MP, Kunst AE. The impact of heat waves and cold spells on mortality rates in the Dutch population. Environ Health Persp 2001;109:463-470. Im KS, Lee J, Tieslau M. Panel LM unit root tests with level shift. Oxford B Econ Stat 2005;67:393-419. IPCC (Intergovernmental Panel on Climate Change): Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z and others (eds). Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 2007. Johnson H, Kovats RS, McGregor GR, Stedman JR, Gibbs M, Walton H, Cook L, Black E. The impact of the 2003 heat wave on mortality and hospital admissions in England. Health Stat Q 2005;25:6-12. Kalkstein LS, Davis RE. Weather and human mortality: an evaluation of demographic and interregional responses in the United States. Ann Assoc Am Geogr 1989;79:44-64. Keatinge WR, Donaldson GC, Bucher K, Jendritsky G, Cordioli E, Martinelli M, Dardanoni L, Katsouyanni K, Kunst AE, Mackenbach JP, McDonald C, Nayha S, Vuori I. Cold exposure and winter mortality from ischaemic heart disease, cerebrovascular disease, respiratory disease, and all causes in warm and cold regions of Europe. Lancet 1997;349:1341-1346. Keatinge WR, Donaldson GC. The impact of global warming on health and mortality. Southern Med J 2004;97:1093-1099. Kim Y, Joh S. A vulnerability study of the low-income elderly in the context of high temperature and mortality in Seoul, Korea. Sci Total Environ 2006;371:82-88. Laaidi M, Laaidi K, Besancenot JP. Temperature-related mortality in France, a comparison between regions with different climates from the perspective of global warming. Int J Biometeorol 2006;51:145-153. Loughnan M, Nicholls N, Tapper N. Mortality-temperature thresholds for ten major population centres in rural Victoria, Australia. Health Place 2010;16:1287-90. Mcleod CB, Lavis JN, Mustard CA, Stoddart GL. Income inequality, household income, and health status in Canada: a prospective cohort study. Am J Public Health 2003;93:1287-93. McMichael AJ, Wilkinson P, Kovats RS, Pattenden S, Hajat S, Armstrong B, Vajanapoom N, Niciu EM, Mahomed H, Kingkeow C, Kosnik M, O''Neill MS, Romieu I, Ramirez-Aguilar M, Barreto ML, Gouveia N, Nikiforov B. International study of temperature, heat and urban mortality: the ''ISOTHURM'' project. Int J Epidemiol 2008;37:1121-1131. Montero JC, Miron IJ, Criado JJ, Linares C, Diaz J. Comparison between 2 methods of defining heat waves: a retrospective study in Castile-La Mancha (Spain). Sci Total Environ 2010;408:1544–1550. Montero JC, Miron IJ, Criado JJ, Linares C, Diaz J. Comparison between two methods of defining heat waves: a retrospective study in Castile-La Mancha (Spain). Sci Total Environ 2010;408:1544-1550. Morabito M, Crisci A, Moriondo M, Profili F, Francesconi P, Trombi G, Bindi M, Gensini GF, Orlandini S. Air temperature-related human health outcomes: Current impact and estimations of future risks in Central Italy. Total Environ 2012;441:28-40. Nastos PT, Matzarakis AP. Variability of tropical days over Greece within the second half of the twentieth century. Theor Appl Climatol 2008;93:75–89. Nicholls N, Skinner C, Loughnan ME, Tapper N. A simple heat alert for Melbourne, Australia. Int J Biometeorol 2008;52:375-384. Pan WH, Li LA, Tsai MJ. Temperature extremes and mortality from coronary heart disease and cerebral ingraction in elderly Chinese. Lancet 1995;345:353-355. Rey G, Fouillet A, Bessemoulin P, Frayssinet P, Dufour A, Jougla E, Hemon D. Heat exposure and socio-economic vulnerability as synergistic factors in heat-wave-related mortality. Eur J Epidemiol 2009;24:495-502. Schar C, Vidale PL, Luthi D, Frei C, Haberli C, Liniger MA, Appenzeller C. The role of increasing temperature variability in European summer heatwaves. Nature 2004;427:332-336. Schwartz J. The distributed lag between air pollution and daily deaths. Epidemiology 2000;11:320-326. Schwartz J. How sensitive is the association between ozone and daily deaths to control for temperature? Am J Respir Crit Care Med 2005a;171:627–631. Schwartz J. Who is sensitive to extremes of temperature? Epidemiology 2005b;16:67-72. Smith JP. Healthy bodies and thick wallets: the dual relation between health and economic status. J Econ Perspec 1999;13:144-66. Tobias A, De Olalla P, Linares C, Bleda M, Cayla J, Diaz J. Short-term effects of extreme hot summer temperatures on total daily mortality in Barcelona, Spain. International Journal of Biometeorology 2010, 54: 115–117. Vandentorren S, Bretin P, Zeghnoun A, Mandereau-Bruno L, Croisier A, Cochet C, Riberon J, Siberan I, Declercq B, Ledrans M. August heat wave in France: risk factors for death of elderly people living at home. Eur J Public Health 2006;16:583–591. Vandentorren S, Suzan F, Medina S, Pascal M, Maulpoix A, Cohen JC, Ledrans M. Mortality in 13 French cities during the August 2003 heat wave. Am J Public Health 2004;94:1518–1520. Vaneckova P, Hart MA, Beggs PJ, De Dear RJ. Synoptic analysis of heat- related mortality in Sydney, Australia, 1993–2001. Int J Biometeorol 2008;52:439–451. Wenbiao H, Kerrie M, Anthony M, Shilu T. Temperature, air pollution and total mortality during summers in Sydney, 1994–2004. Int J Biometeorol 2008;52:689–696. Williams S, Nitschke M, Sullivan T, Tucker GR, Weinstein P, Pisaniello DL, Parton KA, Bi P. Heat and health in Adelaide, South Australia: assessment of heat thresholds and temperature relationships. Total Environ 2012;414:126-133. Yip FY, Flanders WD, Wolkin A, Engelthaler D, Humble W, Neri A, Lewis L, Backer L, Rubin C. The impact of excess heat events in Maricopa County, Arizona: 2000-2005. Int J Biometeorol 2008;52:765-772. Yu W, Guo Y, Ye X, Wang X, Huang C, Pan X, Tong S. The effect of various temperature indicators on different mortality categories in a subtropical city of Brisbane, Australia. Total Environ 2011;409:3431-3437. Yu W, Vaneckova P, Mengersen K, Pan X, Tong S. Is the association between temperature and mortality modified by age, gender and socio-economic status? Sci Total Environ 2010;408:3513-3518. Reference in the third part Ainsworth, E.A., Ort, D.R., 2010. How Do We Improve Crop Production in a Warming World? Plant Physiology, 154, 526-530. Allan, R.P., Soden, B.J., 2007. Large discrepancy between observed and simulated precipitation trends in the ascending and descending branches of the tropical circulation. Geophysical Research Letters 34, L18705. Almaraz, J.J., Mabood, F., Zhou, X., Gregorich, E.G., Smith, D.L., 2008. Climate change, weather variability and corn yield at a higher latitude locale: South-western Quebec. Climatic Change, 88, 187–197. Asseng, S., Foster, I., Turner, N.C., 2011. The impact of temperature variability on wheat yields. Global Change Biology, 17, 997-1012. Auffhammer, M., Ramanathan, V., Vincent, J.R., 2012. Climate change, the monsoon, and rice yield in India. Climatic Change, 111, 411-424. Barnett, D.N., Brown, S.J., Murphy, J.M., Sexton, D.M.H., Webb, M.J., 2006. Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations. Climate Dynamics, 26, 489-511. Barnwal, P., Kotani, K., 2013. Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India. Ecological Economics, 87, 95-109. Basak, J.K., Ali, M.A., Islam, M.N., Rashid, M.A., 2010. Assessment of the effect of climate change on Boro rice production in Bangladesh using DSSAT model. Journal of Civil Engineering, 38, 95-108. Battisti, D.S., Naylor, R.L., 2009. Historical warnings of future food insecurity with unprecedented seasonal heat. Science, 323, 240-244. Bauer, A., Frank, A.B., Black, A.L., 1984. Estimation of spring wheat leaf growth rates and an thesis from air temperature. Agronomy Journal, 76, 829-835. Bauer, A., Frank, A.B., Black, A.L., 1985. Estimation of spring wheat grain dry matter assimilation from air temperature. Agronomy Journal, 77, 743-752. Bereau, S., Villavicencio, A.L., Mignon, V., 2010. Nonlinear adjustment of the real exchange rate towards its equilibrium value: a panel smooth transition error correction modelling. Economic Modelling 27, 404–416. Betts, R.A., Collins, M., Hemming, D.L., Jones, C.D., Lowe, J.A., Sanderson, M.G., 2011. When could global warming reach 4℃? Philosophical Transactions of The Royal Society A, 369, 67-84. Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F.X., Huard, F., 2010. Why are wheat yields stagnating in europe? A comprehensive data analysis for France. Field Crops Research, 119, 201-212. Butler, E.E., Huybers, P., 2013. Adaptation of US maize to temperature variations. Nature Climate change, 3, 68-72. Cabas, J., Weersink, A., Olale, E., 2010. Crop yield response to economic, site and climatic variables. Climatic Change, 101, 599-616. Carew, R., Smith, E.G., Grant, C., 2009. Factors influencing wheat yield and variability: evidence from Manitoba Canada. Journal of Agricultural and Applied Economics, 41, 625-639. Cerrato, M., de Peretti, C., Larsson, R., Sarantis, N., 2011. A nonlinear panel unit root test under cross section dependence. SIRE Discussion Papers 2011-30, Scottish Institute for Research in Economics (SIRE). Chang, T., Chiang, G., 2011. Regime-switching effects of debt on real GDP per capita the case of Latin American and Caribbean countries. Economic Modelling, 28, 2404–2408. Chang, T., Kang, S., Chiang, G., 2010. Exploring an efficient investment regime: the case of SP100 companies. International Review of Financial Analysis, 19, , 134–139. Chen, C.C., McCarl, B.A., Schimmelpfennig, D.E., 2004. Yield variability as influenced by climate: A statistical investigation. Climatic Change, 66, 239-261. Coelho, D.T., Dale, R.F., 1980. An energy-crop growth variable and temperature function for predicting corn growth and development: planting to silking. Agronomy Journal, 72, 503-510. Colletaz, G., Hurlin, C., 2006. Threshold effects of the public capital productivity: an international panel smooth transition approach, Working Paper, Document de Recherche LEO 2006-04, Universite’ d’Orleans, France. Daughtry, C.S.T., Cochran, J.C., Hollinger, S.E., 1984. Estimating silking and maturity dates of corn for large areas. Agronomy Journal, 76, 415-420. Easterling, W.E., Aggarwal, P.K., Batima, P., Brander, K.M., Erda, L., Howden, S.M., Kirilenko, A., Morton, J., Soussana, J.-F., Schmidhuber, J., Tubiello, F.N., 2007. Food, fibre and forest products. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 273-313. Fouquau, J., Hurlin, C., Rabaud, I., 2008. The Feldstein-Horioka puzzle: a panel smooth transition regression approach. Economic Modelling, 25, 284-99. Food and Agriculture Organization of the United Nations (FAO), 2009. Global agriculture towards 2050. High level expert forum - how to feed world in 2050. Rome, Italy. At: http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF20
摘要: 
近年來,不論是已開發或開發中國家其溫室氣體排放量隨著工業發展增加幅度劇烈,尤其是占溫室氣體排放量超過一半的二氧化碳排放量是造成氣候變遷主要原因。政府間氣候變遷小組(Intergovernmental Panel on Climate Change,簡稱IPCC)2007年報告中指出溫室氣體排放量相較於2000年的排放水準將會增加25%至90%,而與能源有關的二氧化碳排放量則會增加40%至110%。因此,如何面對因氣候變遷所帶來的威脅是全球首要的任務。由於二氧化碳排放量對人類的影響是全球性而非地域性,首先,利用Panel Cointegration與Vector Error-Correction Model討論全球(188個國家)1993年到2010年期間經濟-能源-環境三者間的動態關係。接著,考量到不同的經濟發展程度探討經濟-能源-環境之間的關係。實證結果指出全球,已開發或開發中國家的國內生產毛額(Gross Domestic Product,簡稱GDP),能源消費,與二氧化碳排放量三者間均存在長期均衡關係,能源消費增加則造成二氧化碳排放量增加,GDP與二氧化碳排放量兩者的關係則存在環境顧志耐曲線(Environmental Kuznets Curve,簡稱EKC),即GDP與二氧化碳排放量之間為倒U字型的關係。就短期因果關係實證結果,已開發國家呈現能源消費對二氧化碳排放量與GDO對能源消費的單一因果關係,而GDP與二氧化碳排放量則互為因果。然而,開發中國家呈現能源消費對二氧化碳排放量的單一因果關係,而二氧化碳與能源消費則分別與GDP互為因果。
IPCC 2007年報告同時也認為全球溫度上升導致熱浪發生頻率增加,並預測未來極端氣候事件發生的頻率與強度會越來越嚴重,因此找出氣候門檻對人類社會的影響刻不容緩,以幫助我們面對極端氣候事件的影響之不確定性。一開始利用Panel Threshold Model去檢定22個OECD國家的78個主要城市在1990年至2008年期間溫度與死亡率之間是否存在門檻關係,實證發現,溫度與死亡率間存在三個門檻效果,即不同的溫度門檻(-9.33℃,8.32℃,以及30.85℃)對死亡率有不同的影響。當溫度超過30.85℃,高溫則會造成死亡率的增加。當溫度在30.85℃與8.32℃之間,溫度對死亡率則無顯著的影響。當溫度在8.32℃與-9.33℃之間與小於-9.33℃時,溫度降低對人類生命健康產生危害。根據溫度對死亡率的彈性以及未來可能的溫度情境預測未來氣候變遷在不同的緯度區(低於30°,31°-40°,41°-50°,51°-60°,61°-70°)於2021-2040,2041-2060,2061-2100期間內對死亡率的影響。發現在41°-50°與51°-60°的緯度區內夏季死亡率增加的速度遠大於其他緯度區,而冬季死亡率相較於其他緯度區也呈現明顯下滑的趨勢。
接著,探討氣候門檻對農業之影響,由於農業產品對氣候條件極為敏感,未來會因全球氣候變遷將會面對更多的挑戰,因此尋找一合適的氣候-作物模型則為重要的工作,傳統的作物生產函數利用生育度數法(Growing Degree Days,GDD),外生給定一適合作物生產的溫度門檻值,調查溫度與作物產量間的關係。然而,作物生長狀況與種類,會隨著不同的生長地帶而不盡相同,作物生長會隨著生產地的不同而有不同的溫度容忍度,可能會造成高估或低估作物生產量的問題,因此在此部分利用Panel Smooth Transition Regression model內生以2002年-2009年台灣不同地區(北部、中部、南部、東部)稻米產出為例找出一適合台灣稻米生產的溫度門檻值,並且利用此內生溫度門檻值重新計算GDD,估計作物生產函數。實證結果顯示溫度與台灣稻米產量間呈現非線性的關係,影響稻米生產的溫度門檻會隨著不同地區與耕種期間而不同。同時也發現利用內生的溫度門檻所計算出來的GDD對稻米產量的估計表現較傳統方法(利用外生的溫度門檻計算GDD)佳。

The greenhouse gas (hereafter GHG) emissions have sharply increased with the industrial development both in developed and developing countries. In particular, carbon dioxide (hereafter CO2) emissions account for over half of GHG emissions which is likely related to climate change. The Intergovernmental Panel on Climate Change (hereafter IPCC) report in 2007 indicated that the GHG emissions in 2030 will increase by 25-90% as compared with year 2000, and energy-related CO2 emissions in 2030 will increase by 40%-110%. Hence, how to face the threat of climate change has become the world-wide primary task. This part employs a panel cointegration and vector error-correction model to discuss the dynamic economy-energy-environment nexus for 188 countries from time period of 1993 to 2010. Moreover, considering the different level of economic development might induce divergent results. The empirical results indicate that there exist the long-run relationships between GDP, energy consumption, and CO2 emissions in developed, developing countries, and all over the world. The energy consumption has positively influenced CO2 emissions while the relationship between GDP and CO2 emissions are fitting the Environmental Kuznets Curve (EKC) hypothesis. For the short-run causality results, developed countries present that the unidirectional causality from energy consumption to CO2 emissions and GDP to energy consumption, and bidirectional causality between GDP and CO2 emissions. However, the unidirectional causality from energy consumption to CO2 emissions, and bidirectional causality between CO2 emissions and energy consumption with GDP exist in developing countries.
Simultaneously, IPCC report in 2007 considered that the phenomenon of the sustained increase in global surface temperatures causes a higher frequency of heat waves, and predicted that extreme weather events will become more serious and frequent in the future. Hence, an amendment to conform the actual climatic thresholds for Human society is urgently required to help us cope with the uncertainty in our comprehension for the risks of the impacts of extreme weather events.
First, we use the multiple panel threshold model to test whether there are threshold effects between temperature and mortality, using a panel of 78 major cities in 22 OECD countries for 1990-2008. From the empirical analysis, we find that the relationship between temperature and mortality has three threshold effects, namely 15.21℉ (-9.33℃), 46.97℉ (8.32℃), and 87.53℉ (30.85℃). If the temperature is below 15.21℉ (-9.33℃), the magnitude of the temperature effect below 15.21℉ (-9.33℃) is greater than the effect between 15.21℉ (-9.33℃) and 46.97℉ (8.32℃). When the temperature exceeds 87.53℉ (30.85℃), higher temperature leads to higher mortality rate. Based on the estimated coefficients of mean temperatures in four regimes, we separate 78 cities into five areas with latitudes below 30°, 31°-40°, 41°-50°, 51°-60°, and 61°-70°, and predict the impacts of future climate change on mortality for 2021-2040, 2041-2060, and 2061-2100. In summer, climate is predicted to increase mortality rates for 2021-2040, 2041-2060, and 2061-2100. For latitudes 41°-50° and 51°-60°, the rate of increases in mortality rates in summer induced by future climate change is much larger than for other latitudes mainly due to increases in mean temperature. In winter, the magnitude of mortality rate induced by future climate change is found to become smaller mainly due to increasing mean temperature and decreasing temperature variance.
Next, this part discusses the impacts of climate threshold on crop production. Agricultural productions are sensitive to climate change and will encounter multifaceted challenges due to global climate change. Applying the appropriate climate-crop model on this issue is an imperative job. Traditional crop yield function using growing degree days method assumes exogenously temperature threshold to investigate the relationship between temperature and crop yield. However, such exogenously temperature threshold may be altered by climate change and will cause inconsistent outcomes if such approach is applied. This study uses a new approach, Panel Smooth Transition Regression Model, to estimate the endogenously temperature threshold and then applying this threshold back to the crop yield function. Rice yields for four regions in Taiwan during the period of 2002 to 2009 are applied. The empirical results show that that there exists the nonlinear temperature effects on rice crop and the related temperature thresholds for rice yield are also estimated. Identifying temperature thresholds could help us to quantify the impacts of different temperature regimes on rice yield. We discover that using the endogenously identified temperature thresholds for rice growth has better performance than the traditional approach.
URI: http://hdl.handle.net/11455/29011
其他識別: U0005-2907201314250000
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