IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1365-d1386520.html
   My bibliography  Save this article

Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors

Author

Listed:
  • Xiao Xiang

    (Research Institute for the Construction of the Chengdu-Chongqing Economic Circle, Chongqing Technology and Business University, Chongqing 400067, China)

  • Qiao Fan

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

Abstract

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods.

Suggested Citation

  • Xiao Xiang & Qiao Fan, 2024. "Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors," Mathematics, MDPI, vol. 12(9), pages 1-33, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1365-:d:1386520
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1365/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/9/1365/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Makiela, Kamil & Ouattara, Bazoumana, 2018. "Foreign direct investment and economic growth: Exploring the transmission channels," Economic Modelling, Elsevier, vol. 72(C), pages 296-305.
    2. Jin, Gang & Shen, Kunrong & Li, Jian, 2020. "Interjurisdiction political competition and green total factor productivity in China: An inverted-U relationship," China Economic Review, Elsevier, vol. 61(C).
    3. Aligui Tientao & Diego Legros & Marie Claude Pichery, 2016. "Technology spillover and TFP growth: A spatial Durbin model," International Economics, CEPII research center, issue 145, pages 21-31.
    4. Chang, Chia-Lin & Robin, Stéphane, 2008. "Public policy, innovation and total factor productivity: An application to Taiwan's manufacturing industry," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 352-367.
    5. Subal Kumbhakar & M. Denny & M. Fuss, 2000. "Estimation and decomposition of productivity change when production is not efficient: a paneldata approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 312-320.
    6. Georganta, Zoe, 1997. "The effect of a free market price mechanism on total factor productivity: The case of the agricultural crop industry in Greece," International Journal of Production Economics, Elsevier, vol. 52(1-2), pages 55-71, October.
    7. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    8. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73-86, March.
    9. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    10. Xia, Fan & Xu, Jintao, 2020. "Green total factor productivity: A re-examination of quality of growth for provinces in China," China Economic Review, Elsevier, vol. 62(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Wei & Qiao Fan & Aijun Guo, 2022. "China’s Industrial TFPs at the Prefectural Level and the Law of Their Spatial–Temporal Evolution," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    2. Jan Kluge & Sarah Lappöhn & Kerstin Plank, 2023. "Predictors of TFP growth in European countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(1), pages 109-140, February.
    3. Tsui-Yueh Cho & Tsai-Yi Wang, 2018. "Estimations of cost metafrontier Malmquist productivity index: using international tourism hotels in Taiwan as an example," Empirical Economics, Springer, vol. 55(4), pages 1661-1694, December.
    4. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    5. Xueqing Wang & Yuan Chen & Bingsheng Liu & Yinghua Shen & Hui Sun, 2013. "A total factor productivity measure for the construction industry and analysis of its spatial difference: a case study in China," Construction Management and Economics, Taylor & Francis Journals, vol. 31(10), pages 1059-1071, October.
    6. Yung-Hsiang LU & Shun-Ching WANG & Chih-Hung YUAN, 2017. "Financial crisis and the relative productivity dynamics of the biotechnology industry: Evidence from the Asia-Pacific countries," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(2), pages 65-79.
    7. Xiaoheng Zhang & Wanglin Ma & Puneet Vatsa & Shijie Jiang, 2023. "Short supply chain, technical efficiency, and technological change: Insights from cucumber production," Agribusiness, John Wiley & Sons, Ltd., vol. 39(2), pages 371-386, March.
    8. Silva, Haroldo José Torres da & Marques, Pedro Valentim, 2021. "Heterogeneity in the productivity of sugar-energy mills in Brazil," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(3), February.
    9. Kao, Chiang & Liu, Shiang-Tai, 2016. "A parallel production frontiers approach for intertemporal efficiency analysis: The case of Taiwanese commercial banks," European Journal of Operational Research, Elsevier, vol. 255(2), pages 411-421.
    10. Suyang Xiao & Susu Wang & Fanhua Zeng & Wei-Chiao Huang, 2022. "Spatial Differences and Influencing Factors of Industrial Green Total Factor Productivity in Chinese Industries," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    11. Bu, Lin-Lan & Kopsakangas-Savolainen, Maria & Xie, Bai-Chen & Li, Hong-Zhou & Liu, Yi-Meng & Yin, Shao-Peng, 2024. "Has benchmarking improved the performance of the Australian electricity distribution utilities? A meta-frontier model," Utilities Policy, Elsevier, vol. 88(C).
    12. Yung-Hsiang Lu & Ku-Hsieh Chen & Jen-Chi Cheng & Chih-Chun Chen & Sian-Yuan Li, 2019. "Analysis of Environmental Productivity on Fossil Fuel Power Plants in the U.S," Sustainability, MDPI, vol. 11(24), pages 1-27, December.
    13. Ancev, Tiho & Azad, Samad Md., 2015. "Environmentally Adjusted Productivity and Efficiency Measurement: A New Direction for the Luenberger Productivity Indicator," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204912, Agricultural and Applied Economics Association.
    14. Zhujia Yin & Yantuan Yu & Jianhuan Huang, 2018. "Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    15. Yang, Jun & Zou, Ran & Cheng, Jixin & Geng, Zhifei & Li, Qi, 2023. "Environmental technical efficiency and its dynamic evolution in China's industry: A resource endowment perspective," Resources Policy, Elsevier, vol. 82(C).
    16. Lei Jiang & Xingyu Chen & Yang Jiang & Bo Zhang, 2023. "Exploring the Direct and Spillover Effects of Aging on Green Total Factor Productivity in China: A Spatial Econometric Approach," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    17. Feng, Rui & Shen, Chen & Dai, Dandan & Xin, Yaru, 2023. "Examining the spatiotemporal evolution, dynamic convergence and drivers of green total factor productivity in China’s urban agglomerations," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 744-764.
    18. Huang, Hongyun & Mo, Renbian & Chen, Xingquan, 2021. "New patterns in China's regional green development: An interval Malmquist–Luenberger productivity analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 161-173.
    19. Barnett, William A. & Erwin Diewert, W. & Zellner, Arnold, 2011. "Introduction to measurement with theory," Journal of Econometrics, Elsevier, vol. 161(1), pages 1-5, March.
    20. Ellis Scharfenaker, Markus P.A. Schneider, 2019. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Paper Series, Department of Economics, University of Utah 2019_08, University of Utah, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1365-:d:1386520. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.