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Long-run changes in radiative forcing and surface temperature: The effect of human activity over the last five centuries

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  • Dergiades, Theologos
  • Kaufmann, Robert K.
  • Panagiotidis, Theodore

Abstract

We test two hypotheses that are derived from the anthropogenic theory of climate change. The first postulates that a growing population and increasing economic activity increase anthropogenic emissions of radiatively active gases relative to natural sources and sinks, and this alters global biogeochemical cycles in a way that increases the persistence of radiative forcing and temperature. The second postulates that the increase in the persistence of radiative forcing transmits a stochastic trend to the time series for temperature. Results indicate that the persistence of radiative forcing and temperature changes from I(0) to I(1) during the last 500 years and that the I(1) fingerprint in radiative forcing can be detected in a statistically measureable fashion in surface temperature. As such, our results are consistent with the physical mechanisms that underlie the theory of anthropogenic climate change.

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  • Dergiades, Theologos & Kaufmann, Robert K. & Panagiotidis, Theodore, 2016. "Long-run changes in radiative forcing and surface temperature: The effect of human activity over the last five centuries," Journal of Environmental Economics and Management, Elsevier, vol. 76(C), pages 67-85.
  • Handle: RePEc:eee:jeeman:v:76:y:2016:i:c:p:67-85
    DOI: 10.1016/j.jeem.2015.11.005
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    1. Pesaran, M. Hashem & Shin, Yongcheol & Smith, Richard J., 2000. "Structural analysis of vector error correction models with exogenous I(1) variables," Journal of Econometrics, Elsevier, vol. 97(2), pages 293-343, August.
    2. Kim, Kiwhan & Schmidt, Peter, 1990. "Some evidence on the accuracy of Phillips-Perron tests using alternative estimates of nuisance parameters," Economics Letters, Elsevier, vol. 34(4), pages 345-350, December.
    3. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    4. Kaufmann, R. K. & Kauppi, H. & Mann, M. L. & Stock, James H., 2011. "Reconciling anthropogenic climate change with observed temperature 1998–2008," Scholarly Articles 29071926, Harvard University Department of Economics.
    5. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    6. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    7. Robert Kaufmann & Heikki Kauppi & Michael Mann & James Stock, 2013. "Does temperature contain a stochastic trend: linking statistical results to physical mechanisms," Climatic Change, Springer, vol. 118(3), pages 729-743, June.
    8. Harbo, Ingrid, et al, 1998. "Asymptotic Inference on Cointegrating Rank in Partial Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 388-399, October.
    9. Busetti, Fabio & Taylor, A. M. Robert, 2004. "Tests of stationarity against a change in persistence," Journal of Econometrics, Elsevier, vol. 123(1), pages 33-66, November.
    10. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    11. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    12. Anders Rahbek & Rocco Mosconi, 1999. "Cointegration rank inference with stationary regressors in VAR models," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 76-91.
    13. David Stern & Robert Kaufmann, 2014. "Anthropogenic and natural causes of climate change," Climatic Change, Springer, vol. 122(1), pages 257-269, January.
    14. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2006. "Modified tests for a change in persistence," Journal of Econometrics, Elsevier, vol. 134(2), pages 441-469, October.
    15. David F. Hendry & Katarina Juselius, 2001. "Explaining Cointegration Analysis: Part II," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 75-120.
    16. Joachim Elsig & Jochen Schmitt & Daiana Leuenberger & Robert Schneider & Marc Eyer & Markus Leuenberger & Fortunat Joos & Hubertus Fischer & Thomas F. Stocker, 2009. "Stable isotope constraints on Holocene carbon cycle changes from an Antarctic ice core," Nature, Nature, vol. 461(7263), pages 507-510, September.
    17. West, Kenneth D, 1988. "Asymptotic Normality, When Regressors Have a Unit Root," Econometrica, Econometric Society, vol. 56(6), pages 1397-1417, November.
    18. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    19. Joy S. Singarayer & Paul J. Valdes & Pierre Friedlingstein & Sarah Nelson & David J. Beerling, 2011. "Late Holocene methane rise caused by orbitally controlled increase in tropical sources," Nature, Nature, vol. 470(7332), pages 82-85, February.
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    2. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 676-690, September.
    3. Lichun Xiong & Chang Yu & Martin De Jong & Fengting Wang & Baodong Cheng, 2017. "Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    4. Maria Dolores Gadea & Jesus Gonzalo & Andrey Ramos, 2023. "Trends in Temperature Data: Micro-foundations of Their Nature," Papers 2312.06379, arXiv.org.
    5. Lichun Xiong & Martin De Jong & Fengting Wang & Baodong Cheng & Chang Yu, 2018. "Spatial Spillover Effects of Environmental Pollution in China’s Central Plains Urban Agglomeration," Sustainability, MDPI, vol. 10(4), pages 1-15, March.
    6. Bruns, Stephan B. & Csereklyei, Zsuzsanna & Stern, David I., 2020. "A multicointegration model of global climate change," Journal of Econometrics, Elsevier, vol. 214(1), pages 175-197.
    7. Xin Tong & Xuesen Li & Lin Tong & Xuan Jiang, 2018. "Spatial Spillover and the Influencing Factors Relating to Provincial Carbon Emissions in China Based on the Spatial Panel Data Model," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    8. Chang, Yoosoon & Kaufmann, Robert K. & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2020. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Journal of Econometrics, Elsevier, vol. 214(1), pages 274-294.
    9. Luis A. Gil-Alana & Tommaso Trani, 2019. "Time Trends and Persistence in the Global CO2 Emissions Across Europe," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(1), pages 213-228, May.
    10. Elena Antarciuc & Qinghua Zhu & Jaber Almarri & Senlin Zhao & Yunting Feng & Martin Agyemang, 2018. "Sustainable Venture Capital Investments: An Enabler Investigation," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
    11. Udemba, Edmund Ntom & Yalçıntaş, Selin, 2021. "Interacting force of foreign direct invest (FDI), natural resource and economic growth in determining environmental performance: A nonlinear autoregressive distributed lag (NARDL) approach," Resources Policy, Elsevier, vol. 73(C).
    12. Amaryllis Mavragani & Ioannis E. Nikolaou & Konstantinos P. Tsagarakis, 2016. "Open Economy, Institutional Quality, and Environmental Performance: A Macroeconomic Approach," Sustainability, MDPI, vol. 8(7), pages 1-13, June.
    13. Zerbo, Eléazar & Darné, Olivier, 2019. "On the stationarity of CO2 emissions in OECD and BRICS countries: A sequential testing approach," Energy Economics, Elsevier, vol. 83(C), pages 319-332.

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    More about this item

    Keywords

    Global climate change; Radiative forcing; Surface temperature;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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