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Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China

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  • Guangming Rao

    (Research Centre for Economy of the Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, China
    School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
    School of Business Administration, Xingjiang University of Finance and Economics, XingJiang 830026, China)

  • Bin Su

    (Energy Studies Institute, National University of Singapore, 21 Lower Kent Ridge Rd 119077, Singapore 119077, Singapore)

  • Jinlian Li

    (Research Centre for Economy of the Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, China)

  • Yong Wang

    (School of Economics and Business Administration, Chongqing University, Chongqing 400044, China)

  • Yanhua Zhou

    (School of Business Administration, Xingjiang University of Finance and Economics, XingJiang 830026, China)

  • Zhaolin Wang

    (Research Centre for Economy of the Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, China)

Abstract

To find out whether carbon sequestration is both effective at mitigating climate change and promoting economic growth, in this paper, by adopting a stochastic frontier panel model with translog production function, carbon sequestration is incorporated into endogenous variables to establish estimation model of carbon sequestration total factor productivity (CSTFP) and examine CSTFP growth and its drivers decomposition of the Yangtze River Economic Belt (YREB) of China in three estimations. The result shows that, (1) compared to traditional TFP growth, CSTFP growth in YREB is improved by 26.74 percentages (from −26.55% to 0.20%), contributed by three positive drivers of technical efficiency change (28.59%), technological progress change (18.55%), and scale efficiency change (3.99%); (2) different CSTFP growth exists in three watershed segments of YREB, which firstly is the upper reaches (0.62%), then the lower reaches (0.11%) and the middle reaches (−0.14%). Improved CSTFP growth owes to carbon sequestration’s harmonious symbiosis where natural ecosystems and human activities are naturally blended while insufficient synergies are bottleneck for promotion of CSTFP growth in YREB. Related policy suggestions are provided in the end. The proposed analysis framework is efficient to disclose CSTFP growth in YREB, and can also be applied to similar analysis on CSTFP in regions and extended to multi-country/region analysis.

Suggested Citation

  • Guangming Rao & Bin Su & Jinlian Li & Yong Wang & Yanhua Zhou & Zhaolin Wang, 2019. "Carbon Sequestration Total Factor Productivity Growth and Decomposition: A Case of the Yangtze River Economic Belt of China," Sustainability, MDPI, vol. 11(23), pages 1-28, November.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6809-:d:292720
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    References listed on IDEAS

    as
    1. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2018. "Analysis of green total-factor productivity in China's regional metal industry: A meta-frontier approach," Resources Policy, Elsevier, vol. 58(C), pages 219-229.
    2. Wang, Qunwei & Zhang, Cheng & Cai, Wanhuan, 2017. "Factor substitution and energy productivity fluctuation in China: A parametric decomposition analysis," Energy Policy, Elsevier, vol. 109(C), pages 181-190.
    3. Mahlberg, Bernhard & Luptacik, Mikulas & Sahoo, Biresh K., 2011. "Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries," Ecological Economics, Elsevier, vol. 72(C), pages 60-69.
    4. Grimaud, André & Rouge, Luc, 2014. "Carbon sequestration, economic policies and growth," Resource and Energy Economics, Elsevier, vol. 36(2), pages 307-331.
    5. Yang, Wei & Shi, Jinfeng & Qiao, Han & Shao, Yanmin & Wang, Shouyang, 2017. "Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 14-24.
    6. Bai, Caiquan & Du, Kerui & Yu, Ying & Feng, Chen, 2019. "Understanding the trend of total factor carbon productivity in the world: Insights from convergence analysis," Energy Economics, Elsevier, vol. 81(C), pages 698-708.
    7. Li, Ke & Lin, Boqiang, 2015. "Measuring green productivity growth of Chinese industrial sectors during 1998–2011," China Economic Review, Elsevier, vol. 36(C), pages 279-295.
    8. Boyd, Gale A. & Lee, Jonathan M., 2019. "Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis," Energy Economics, Elsevier, vol. 81(C), pages 159-174.
    9. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    10. Martin Heimann & Markus Reichstein, 2008. "Terrestrial ecosystem carbon dynamics and climate feedbacks," Nature, Nature, vol. 451(7176), pages 289-292, January.
    11. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    12. Su, Bin & Ang, B.W., 2017. "Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities," Energy Economics, Elsevier, vol. 65(C), pages 137-147.
    13. Tian, Peng & Lin, Boqiang, 2017. "Promoting green productivity growth for China's industrial exports: Evidence from a hybrid input-output model," Energy Policy, Elsevier, vol. 111(C), pages 394-402.
    14. Li, Hong-Zhou & Tian, Xian-Liang & Zou, Tao, 2015. "Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function," Applied Energy, Elsevier, vol. 151(C), pages 296-305.
    15. Shen, Zhiyang & Boussemart, Jean-Philippe & Leleu, Hervé, 2017. "Aggregate green productivity growth in OECD’s countries," International Journal of Production Economics, Elsevier, vol. 189(C), pages 30-39.
    16. Balliauw, Matteo & Meersman, Hilde & Onghena, Evy & Van de Voorde, Eddy, 2018. "US all-cargo carriers’ cost structure and efficiency: A stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 112(C), pages 29-45.
    17. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    18. Kumbhakar, Subal C., 1987. "The specification of technical and allocative inefficiency in stochastic production and profit frontiers," Journal of Econometrics, Elsevier, vol. 34(3), pages 335-348, March.
    19. Yang, Zhenbing & Fan, Meiting & Shao, Shuai & Yang, Lili, 2017. "Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis," Energy Economics, Elsevier, vol. 68(C), pages 271-282.
    20. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas & Sun, Chuanwang, 2019. "Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator," Energy Policy, Elsevier, vol. 132(C), pages 665-677.
    21. Battese, George E., 1992. "Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics," Agricultural Economics, Blackwell, vol. 7(3-4), pages 185-208, October.
    22. Song, Malin & Peng, Jun & Wang, Jianlin & Zhao, Jiajia, 2018. "Environmental efficiency and economic growth of China: A Ray slack-based model analysis," European Journal of Operational Research, Elsevier, vol. 269(1), pages 51-63.
    23. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    24. Bauer, Paul W., 1990. "Recent developments in the econometric estimation of frontiers," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 39-56.
    25. Najm, Sarah, 2019. "The green paradox and budgetary institutions," Energy Policy, Elsevier, vol. 133(C).
    26. Arazmuradov, Annageldy & Martini, Gianmaria & Scotti, Davide, 2014. "Determinants of total factor productivity in former Soviet Union economies: A stochastic frontier approach," Economic Systems, Elsevier, vol. 38(1), pages 115-135.
    27. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    28. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    29. Sonia I. Seneviratne & Markus G. Donat & Brigitte Mueller & Lisa V. Alexander, 2014. "No pause in the increase of hot temperature extremes," Nature Climate Change, Nature, vol. 4(3), pages 161-163, March.
    30. Pieri, Fabio & Vecchi, Michela & Venturini, Francesco, 2018. "Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach," Research Policy, Elsevier, vol. 47(9), pages 1842-1852.
    31. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    32. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    33. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    34. Boussemart, Jean Philippe & Leleu, Hervé & Shen, Zhiyang, 2015. "Environmental growth convergence among Chinese regions," China Economic Review, Elsevier, vol. 34(C), pages 1-18.
    35. Kay, Sonja & Rega, Carlo & Moreno, Gerardo & den Herder, Michael & Palma, João H.N. & Borek, Robert & Crous-Duran, Josep & Freese, Dirk & Giannitsopoulos, Michail & Graves, Anil & Jäger, Mareike & Lam, 2019. "Agroforestry creates carbon sinks whilst enhancing the environment in agricultural landscapes in Europe," Land Use Policy, Elsevier, vol. 83(C), pages 581-593.
    36. Forsund, Finn R. & Lovell, C. A. Knox & Schmidt, Peter, 1980. "A survey of frontier production functions and of their relationship to efficiency measurement," Journal of Econometrics, Elsevier, vol. 13(1), pages 5-25, May.
    37. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    38. Ondrich, Jan & Ruggiero, John, 2001. "Efficiency measurement in the stochastic frontier model," European Journal of Operational Research, Elsevier, vol. 129(2), pages 434-442, March.
    39. Coelli, T. J., 1992. "A computer program for frontier production function estimation : Frontier version 2.0," Economics Letters, Elsevier, vol. 39(1), pages 29-32, May.
    40. Zhang, Chunhong & Liu, Haiying & Bressers, Hans Th.A. & Buchanan, Karen S., 2011. "Productivity growth and environmental regulations - accounting for undesirable outputs: Analysis of China's thirty provincial regions using the Malmquist–Luenberger index," Ecological Economics, Elsevier, vol. 70(12), pages 2369-2379.
    41. Diewert, W. Erwin & Fox, Kevin J., 2017. "Decomposing productivity indexes into explanatory factors," European Journal of Operational Research, Elsevier, vol. 256(1), pages 275-291.
    42. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    43. Fabio Pieri & Michela Vecchi & Francesco Venturini, 2017. "Modelling the joint impact of R and D and ICT on productivity: A frontier analysis approach," DEM Working Papers 2017/13, Department of Economics and Management.
    44. Nishimizu, Mieko & Page, John M, Jr, 1982. "Total Factor Productivity Growth, Technological Progress and Technical Efficiency Change: Dimensions of Productivity Change in Yugoslavia, 1965-78," Economic Journal, Royal Economic Society, vol. 92(368), pages 920-936, December.
    45. Zhang, Zibin & Ye, Jianliang, 2015. "Decomposition of environmental total factor productivity growth using hyperbolic distance functions: A panel data analysis for China," Energy Economics, Elsevier, vol. 47(C), pages 87-97.
    46. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    2. Luis Antonio Galiano Bastarrica & Eva M. Buitrago Esquinas & María Ángeles Caraballo Pou & Rocío Yñiguez Ovando, 2023. "Environmental adjustment of the EU27 GDP: an econometric quantitative model," Environment Systems and Decisions, Springer, vol. 43(1), pages 115-128, March.
    3. Lucia Domaracká & Marcela Taušová & Katarína Čulková & Peter Tauš & Peter Gomboš, 2023. "Development of Greenhouse Gas Emission and Evaluation of Carbon Resource Use in Chosen EU Countries," Energies, MDPI, vol. 16(3), pages 1-17, January.

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