IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v307y2022ics0306261921015117.html
   My bibliography  Save this article

Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017

Author

Listed:
  • Gao, Kang
  • Yuan, Yijun

Abstract

City-level accounting of industrial green productivity (IGP) is necessary to identify the different circumstances of industrial green transformation and development across Chinese cities. However, the lack of energy consumption data in the urban industrial sector leads to the assessment of China’s IGP at the macro provincial level, which is not conducive to further formulating systematic measures to improve IGP. Hence, based on the multi-source nighttime light (NTL) data fitted and corrected by DSMP/OLS and NPP/VIIRS images, this study simulates the total energy consumption of urban industrial sector and establishes an IGP analysis database combined with socio-economic data. Subsequently, the spatiotemporal pattern and drivers of IGP are systematically investigated by means of the Dagum Gini coefficient, spatial autocorrelation model, Markov transition probability matrix, and spatial econometric model. Four important findings arise from the analyses. Stylized facts show that an overall increase in IGP is accompanied by the exacerbation in spatial inequality, the year 2006 is a turning point from frequent fluctuations to steady growth. Specifically, interregional disparities have gradually become the main source of overall disparities in IGP across eight comprehensive economic zones, hypervariable density is the main source of overall disparities across three major urban agglomerations. Spatially, Global Moran’s I of IGP uninterruptedly increases from 0.043 in 2008 to 0.155 in 2017, showing a positive spatial correlation of high-high agglomeration and low-low agglomeration. Additionally, IGP has a strong spatial locking effect with the unchanged probability of 78.81%, 72.18%, 75.94%, 83.67%, and there is the coexistence of win–win and beggar-thy-neighbor phenomenon to a large extent. Results further reveal that IGP has a positively spatial spillover effect, economic development level, human capital, industrial agglomeration, and environmental regulations all work for driving IGP growth in local and neighboring areas. Based on the findings, policy recommendations for the improvement of IGP are provided.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921015117
    DOI: 10.1016/j.apenergy.2021.118248
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921015117
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118248?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Allen Blackman & Arne Kildegaard, 2010. "Clean technological change in developing-country industrial clusters: Mexican leather tanning," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 12(3), pages 115-132, September.
    3. William L. Weber & Bruce Domazlicky, 2001. "Productivity Growth and Pollution in State Manufacturing," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 195-199, February.
    4. Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    5. Michael Storper & Anthony J. Venables, 2004. "Buzz: face-to-face contact and the urban economy," Journal of Economic Geography, Oxford University Press, vol. 4(4), pages 351-370, August.
    6. Borghesi, Simone & Cainelli, Giulio & Mazzanti, Massimiliano, 2015. "Linking emission trading to environmental innovation: Evidence from the Italian manufacturing industry," Research Policy, Elsevier, vol. 44(3), pages 669-683.
    7. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    8. Rolf Färe & Shawna Grosskopf & Carl A Pasurka, Jr., 2001. "Accounting for Air Pollution Emissions in Measures of State Manufacturing Productivity Growth," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 381-409, August.
    9. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    10. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    11. 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.
    12. Yang, Zhenbing & Shao, Shuai & Yang, Lili & Liu, Jianghua, 2017. "Differentiated effects of diversified technological sources on energy-saving technological progress: Empirical evidence from China's industrial sectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1379-1388.
    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. Wang, Kai-Hua & Umar, Muhammad & Akram, Rabia & Caglar, Ersin, 2021. "Is technological innovation making world "Greener"? An evidence from changing growth story of China," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    15. 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.
    16. Federico Belotti & Gordon Hughes & Andrea Piano Mortari, 2017. "Spatial panel-data models using Stata," Stata Journal, StataCorp LP, vol. 17(1), pages 139-180, March.
    17. He, Feng & Zhang, Qingzhi & Lei, Jiasu & Fu, Weihui & Xu, Xiaoning, 2013. "Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs," Energy Policy, Elsevier, vol. 54(C), pages 204-213.
    18. Eberle, Jonathan & Böing, Philipp, 2019. "Effects of R&D subsidies on regional economic dynamics: Evidence from Chinese provinces," ZEW Discussion Papers 19-038, ZEW - Leibniz Centre for European Economic Research.
    19. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    20. 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.
    21. Arocena, Pablo & Waddams Price, Catherine, 2002. "Generating efficiency: economic and environmental regulation of public and private electricity generators in Spain," International Journal of Industrial Organization, Elsevier, vol. 20(1), pages 41-69, January.
    22. Tao, Feng & Zhang, Huiqin & Hu, Jun & Xia, X.H., 2017. "Dynamics of green productivity growth for major Chinese urban agglomerations," Applied Energy, Elsevier, vol. 196(C), pages 170-179.
    23. Shuai Shao, Zhenbing Yang, Lili Yang, and Shuang Ma, 2019. "Can China's Energy Intensity Constraint Policy Promote Total Factor Energy Efficiency? Evidence from the Industrial Sector," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    24. Gersbach, Hans & Muller, Philippe & Tejada, Oriol, 2019. "Costs of change and political polarization," European Journal of Political Economy, Elsevier, vol. 60(C).
    25. 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.
    26. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    27. Wei, Zixiang & Han, Botang & Pan, Xiuzhen & Shahbaz, Muhammad & Zafar, Muhammad Wasif, 2020. "Effects of diversified openness channels on the total-factor energy efficiency in China's manufacturing sub-sectors: Evidence from trade and FDI spillovers," Energy Economics, Elsevier, vol. 90(C).
    28. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    29. 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).
    30. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    31. Zhu, Xuehong & Chen, Ying & Feng, Chao, 2018. "Green total factor productivity of China's mining and quarrying industry: A global data envelopment analysis," Resources Policy, Elsevier, vol. 57(C), pages 1-9.
    32. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    33. Zhu, Xuehong & Zuo, Xuguang & Li, Hailing, 2021. "The dual effects of heterogeneous environmental regulation on the technological innovation of Chinese steel enterprises—Based on a high-dimensional fixed effects model," Ecological Economics, Elsevier, vol. 188(C).
    34. Michael E. Porter & Claas van der Linde, 1995. "Toward a New Conception of the Environment-Competitiveness Relationship," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 97-118, Fall.
    35. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    36. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    37. Guan, JianCheng & Yam, Richard C.M., 2015. "Effects of government financial incentives on firms’ innovation performance in China: Evidences from Beijing in the 1990s," Research Policy, Elsevier, vol. 44(1), pages 273-282.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Limei Ma & Qianying Wang & Dan Shi & Qinglong Shao, 2023. "Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Long Liu & Zhichao Li & Xinyi Fu & Xuan Liu & Zehao Li & Wenfeng Zheng, 2022. "Impact of Power on Uneven Development: Evaluating Built-Up Area Changes in Chengdu Based on NPP-VIIRS Images (2015–2019)," Land, MDPI, vol. 11(4), pages 1-21, March.
    3. Ran, Qiying & Yang, Xiaodong & Yan, Hongchuan & Xu, Yang & Cao, Jianhong, 2023. "Natural resource consumption and industrial green transformation: Does the digital economy matter?," Resources Policy, Elsevier, vol. 81(C).
    4. Yu, Binbin, 2022. "The Impact of the Internet on Industrial Green Productivity: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    5. Peng, Hui & Lu, Yaobin & Wang, Qunwei, 2023. "How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy," Energy, Elsevier, vol. 268(C).
    6. Jiao Hou & Xinhai Lu & Shiman Wu & Shangan Ke & Jia Li, 2022. "Analysis of the Dynamic Relationship between Green Economy Efficiency and Urban Land Development Intensity in China," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    7. Chen, Hao, 2022. "Industrial production evaluation with the consideration of technology accumulation," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 72-84.
    8. Chuan Li & Liangrong Song, 2022. "Regional Differences and Spatial Convergence of Green Development in China," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    9. Zi Ye & Chen Zou & Yongchun Huang, 2022. "Impact of Heterogeneous Spatial Structure on Regional Innovation—From the Perspectives of Efficiency and Gap," Sustainability, MDPI, vol. 14(19), pages 1-22, September.

    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. Chaofan Chen & Qingxin Lan & Ming Gao & Yawen Sun, 2018. "Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy," Sustainability, MDPI, vol. 10(4), pages 1-25, April.
    2. Juan Aparicio & Javier Barbero & Magdalena Kapelko & Jesus T. Pastor & Jose L. Zofio, 2016. "Environmental Productivity Change in World Air Emissions: A new Malmquist-Luenberger Index Approach," JRC Research Reports JRC104083, Joint Research Centre.
    3. Xi Qin & Xiaoling Wang & Yusen Xu & Yawen Wei, 2019. "Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry," Sustainability, MDPI, vol. 11(4), pages 1-11, February.
    4. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).
    5. 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.
    6. 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.
    7. 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.
    8. Meiling Wang & Silu Pang & Ikram Hmani & Ilham Hmani & Cunfang Li & Zhengxia He, 2021. "Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 217-227, January.
    9. Chen, Xiang & Chen, Yong & Huang, Wenli & Zhang, Xuping, 2023. "A new Malmquist-type green total factor productivity measure: An application to China," Energy Economics, Elsevier, vol. 117(C).
    10. Wang, Miao & Feng, Chao, 2021. "The win-win ability of environmental protection and economic development during China's transition," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    12. Joshi, Shruti & Nath, Siddhartha & Ranjan, Abhishek, 2023. "Green Total Factor Productivity for India: Some Recent Estimates and Policy Directions," MPRA Paper 117717, University Library of Munich, Germany.
    13. Zhang, Ning & Zhou, Peng & Kung, Chih-Chun, 2015. "Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 584-593.
    14. Xi Zhang & Rui Li & Jinglei Zhang, 2022. "Understanding the Green Total Factor Productivity of Manufacturing Industry in China: Analysis Based on the Super-SBM Model with Undesirable Outputs," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    15. Li, Kai & Yan, Yaxue & Zhang, Xiaoling, 2021. "Carbon-abatement policies, investment preferences, and directed technological change: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    16. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    17. Manello, Alessandro, 2017. "Productivity growth, environmental regulation and win–win opportunities: The case of chemical industry in Italy and Germany," European Journal of Operational Research, Elsevier, vol. 262(2), pages 733-743.
    18. Ren, Shenggang & Hu, Yucai & Zheng, Jingjing & Wang, Yangjie, 2020. "Emissions trading and firm innovation: Evidence from a natural experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    19. Zhang, Yijun & Li, Xiaoping & Song, Yi & Jiang, Feitao, 2021. "Can green industrial policy improve total factor productivity? Firm-level evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 51-62.
    20. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2020. "An Efficiency Perspective on Carbon Emissions and Financial Performance," Ecological Economics, Elsevier, vol. 175(C).

    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:eee:appene:v:307:y:2022:i:c:s0306261921015117. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.