Open Access Journal Article

Digital Economy and Energy Intensity: The Light and Dark Side

by Zheng-Yuan Yang a,*  and  Lei Ye a
a
School of Marxism, Fudan University, Shanghai, China
*
Author to whom correspondence should be addressed.
Received: 20 November 2024 / Accepted: 24 December 2024 / Published Online: 21 January 2025

Abstract

The digital economy exerts both positive and negative influences on urban sustainable development, yet there is a notable gap in the existing research concerning its impact on energy intensity and the underlying mechanisms. this study pioneers the investigation of a nonlinear relationship between the digital economy and energy intensity, revealing a significant inverted U-shaped relationship. Specifically, we observe a noteworthy reduction in energy intensity when the digital economy index surpasses 0.286. Our empirical findings indicate that the digital economy not only directly influences energy intensity but also exerts an indirect impact through initiatives such as the promotion of green innovation and the agglomeration of high-tech industries. Importantly, the promotional effects of the digital economy exhibit heterogeneity with respect to geographical location, resource endowment, and urban scale. This paper contributes to the theoretical understanding of information technology in urban green development by analyzing the mechanisms of the digital economy at the urban level and its intricate impact on energy intensity.


Copyright: © 2025 by Yang and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Yang, Z.; Ye, L. Digital Economy and Energy Intensity: The Light and Dark Side. Review of Economic Assessment, 2024, 3, 42. https://doi.org/10.58567/rea03040005
AMA Style
Yang Z, Ye L. Digital Economy and Energy Intensity: The Light and Dark Side. Review of Economic Assessment; 2024, 3(4):42. https://doi.org/10.58567/rea03040005
Chicago/Turabian Style
Yang, Zheng-Yuan; Ye, Lei 2024. "Digital Economy and Energy Intensity: The Light and Dark Side" Review of Economic Assessment 3, no.4:42. https://doi.org/10.58567/rea03040005
APA style
Yang, Z., & Ye, L. (2024). Digital Economy and Energy Intensity: The Light and Dark Side. Review of Economic Assessment, 3(4), 42. https://doi.org/10.58567/rea03040005

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References

  1. Anu, Singh, A. K., Raza, S. A., Nakonieczny, J., and Shahzad, U. (2023). Role of financial inclusion, green innovation, and energy efficiency for environmental performance? Evidence from developed and emerging economies in the lens of sustainable development. Structural Change and Economic Dynamics, 64, 213-224. http://doi.org/10.1016/j.strueco.2022.12.008
  2. Avom, D., Nkengfack, H., Fotio, H. K., and Totouom, A. (2020). ICT and environmental quality in Sub-Saharan Africa: Effects and transmission channels. Technological Forecasting and Social Change, 155, 120028. http://doi.org/10.1016/j.techfore.2020.120028
  3. Bai, J. S. (2013). Fixed-effects dynamic panel models, a factor analytical method. Econometrica, 81(1), 285-314. http://doi.org/10.3982/ECTA9409
  4. Balado-Naves, R., Banos-Pino, J. F., and Mayor, M. (2023). Spatial spillovers and world energy intensity convergence. Energy Economics, 124, 106807. http://doi.org/10.1016/j.eneco.2023.106807
  5. Bashir, M. F., Ma, B. J., Shahbaz, M., Shahzad, U., and Vo, X. V. (2021). Unveiling the heterogeneous impacts of environmental taxes on energy consumption and energy intensity: Empirical evidence from OECD countries. Energy, 226, 120366. http://doi.org/10.1016/j.energy.2021.120366
  6. Bilgili, F., Kocak, E., Bulut, U., and Kuloglu, A. (2017). The impact of urbanization on energy intensity: Panel data evidence considering cross-sectional dependence and heterogeneity. Energy, 133, 242-256. http://doi.org/10.1016/j.energy.2017.05.121
  7. Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69(5), 1127-1160. http://doi.org/10.1111/1468-0262.00237
  8. Chan, H. K., Yee, R. W. Y., Dai, J., and Lim, M. K. (2016). The moderating effect of environmental dynamism on green product innovation and performance. International Journal of Production Economics, 181, 384-391. http://doi.org/10.1016/j.ijpe.2015.12.006
  9. Chen, J., Liu, J., Qi, J., Gao, M., Cheng, S., Li, K., and Xu, C. (2022). City- and county-level spatio-temporal energy consumption and efficiency datasets for China from 1997 to 2017. Scientific Data, 9(1), 101. http://doi.org/10.1038/s41597-022-01240-6
  10. Chen, J., Zhou, D., Zhao, Y., Wu, B., and Wu, T. (2020). Life cycle carbon dioxide emissions of bike sharing in China: Production, operation, and recycling. Resources Conservation and Recycling, 162, 105011. http://doi.org/10.1016/j.resconrec.2020.105011
  11. Chen, P. (2022). Is the digital economy driving clean energy development? -New evidence from 276 cities in China. Journal of Cleaner Production, 372, 133783. http://doi.org/10.1016/j.jclepro.2022.133783
  12. Cheng, Y., Zhang, Y., Wang, J., and Jiang, J. (2023). The impact of the urban digital economy on China's carbon intensity: Spatial spillover and mediating effect. Resources Conservation and Recycling, 189, 106762. http://doi.org/10.1016/j.resconrec.2022.106762
  13. Ding, C., Chen, H., Liu, Y., Hu, J., Hu, M. J., Chen, D., and Irfan, M. (2024). Unleashing digital empowerment: Pioneering low-carbon development through the broadband China strategy. Energy, 295, 131034. http://doi.org/10.1016/j.energy.2024.131034
  14. Du, J. T., Shen, Z. Y., Song, M. L., and Zhang, L. D. (2023). Nexus between digital transformation and energy technology innovation: An empirical test of A-share listed enterprises. Energy Economics, 120, 106572. http://doi.org/10.1016/j.eneco.2023.106572
  15. Guo, Q., Wu, Z., Jahanger, A., Ding, C., Guo, B., and Awan, A. (2023). The spatial impact of digital economy on energy intensity in China in the context of double carbon to achieve the sustainable development goals. Environmental Science and Pollution Research, 30(13), 35528-35544. http://doi.org/10.1007/s11356-022-24814-8
  16. Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345-368. http://doi.org/10.1016/S0304-4076(99)00025-1
  17. Hao, Y., Guo, Y. X., and Wu, H. T. (2022). The role of information and communication technology on green total factor energy efficiency: Does environmental regulation work? Business Strategy and the Environment, 31(1), 403-424. http://doi.org/10.1002/bse.2901
  18. Hong, J. J., Shi, F. Y., and Zheng, Y. H. (2023). Does network infrastructure construction reduce energy intensity? Based on the "Broadband China" strategy. Technological Forecasting and Social Change, 190, 122437. http://doi.org/10.1016/j.techfore.2023.122437
  19. Huang, H., Wang, F., Song, M., Balezentis, T., and Streimikiene, D. (2021). Green innovations for sustainable development of China: Analysis based on the nested spatial panel models. Technology in Society, 65, 101593. http://doi.org/10.1016/j.techsoc.2021.101593
  20. Jia, R. N., Shao, S., and Yang, L. L. (2021). High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach. Energy Economics, 99, 105271. http://doi.org/10.1016/j.eneco.2021.105271
  21. Jia, S. H., Guo, N. N., and Liu, Y. K. (2023). Electricity shortage and corporate digital transformation: Evidence from China's listed firms. Finance Research Letters, 57, 104260. http://doi.org/10.1016/j.frl.2023.104260
  22. Jiang, H., Han, Y., and Qin, Y. (2024). Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective. Review of Economic Assessment, 3(1), 26. http://doi.org/10.58567/rea03010004
  23. Krause, M. J., and Tolaymat, T. (2018). Quantification of energy and carbon costs for mining cryptocurrencies. Nature Sustainability, 1(11), 711-718. http://doi.org/10.1038/s41893-018-0152-7
  24. Li, D., Xia, Z., and Shi, Y. (2023). The Impact of Executive Academic Experience on Green Innovation in Manufacturing Corporations. Review of Economic Assessment, 2(3), 18. http://doi.org/10.58567/rea02030003
  25. Li, J. L., Chen, L. T., Chen, Y., and He, J. W. (2022). Digital economy, technological innovation, and green economic efficiency-Empirical evidence from 277 cities in China. Managerial and Decision Economics, 43(3), 616-629. http://doi.org/10.1002/mde.3406
  26. Li, L. X. (2022). Digital transformation and sustainable performance: The moderating role of market turbulence. Industrial Marketing Management, 104, 28-37. http://doi.org/10.1016/j.indmarman.2022.04.007
  27. Li, W., Liu, N., and Long, Y. (2023). Assessing carbon reduction benefits of teleworking: A case study of Beijing. Science of the Total Environment, 889, 164262. http://doi.org/10.1016/j.scitotenv.2023.164262
  28. Li, Z. G., and Wang, J. (2022). The Dynamic Impact of Digital Economy on Carbon Emission Reduction: Evidence City-level Empirical Data in China. Journal of Cleaner Production, 351, 131570. http://doi.org/10.1016/j.jclepro.2022.131570
  29. Lin, B. Q., and Zhou, Y. C. (2021). Does the Internet development affect energy and carbon emission performance? Sustainable Production and Consumption, 28, 1-10. http://doi.org/10.1016/j.spc.2021.03.016
  30. Lin, B. Q., and Zhu, J. P. (2021). Impact of China's new-type urbanization on energy intensity: A city-level analysis. Energy Economics, 99, 105292. http://doi.org/10.1016/j.eneco.2021.105292
  31. Lin, B., and Huang, C. (2023). How will promoting the digital economy affect electricity intensity? Energy Policy, 173, 113341. http://doi.org/10.1016/j.enpol.2022.113341
  32. Liu, X. P., and Zhang, X. L. (2021). Industrial agglomeration, technological innovation and carbon productivity: Evidence from China. Resources Conservation and Recycling, 166, 105330. http://doi.org/10.1016/j.resconrec.2020.105330
  33. Liu, X., Cifuentes-Faura, J., Zhao, S., and Wang, L. (2023). Government environmental attention and carbon emissions governance: Firm-level evidence from China. Economic Analysis and Policy, 80, 121-142. http://doi.org/10.1016/j.eap.2023.07.016
  34. Liu, X. Q., Qin, C., Liu, B. L., Ahmed, A. D., Ding, C. J., and Huang, Y. J. (2024). The economic and environmental dividends of the digital development strategy: Evidence from Chinese cities. Journal of Cleaner Production, 440, 140398. http://doi.org/10.1016/j.jclepro.2023.140398
  35. Liu, Y. J., Cui, L. J., Xiong, Y. Y., and Yao, X. G. (2023). Does the development of the Internet improve the allocative efficiency of production factors? Evidence from surveys of Chinese manufacturing firms. Structural Change and Economic Dynamics, 66, 161-174. http://doi.org/10.1016/j.strueco.2023.04.017
  36. Liu, Y., Zhao, X., and Kong, F. (2023). The dynamic impact of digital economy on the green development of traditional manufacturing industry: Evidence from China. Economic Analysis and Policy, 80, 143-160. http://doi.org/10.1016/j.eap.2023.08.005
  37. Liu, Z., Liu, B., Luo, H., and Chen, S. (2024). Digital economy and fiscal decentralization: Drivers of green innovation in China. Heliyon, 10(13), e33870. https://doi.org/10.1016/j.heliyon.2024.e33870
  38. Luo, H., Yang, B., Liu, Z., Ding, C. J., and Liu, B. (2024). The bright and dark sides: Unpacking the effect of digital economy on resource curse. Journal of Cleaner Production, 485, 144351. https://doi.org/10.1016/j.jclepro.2024.144351
  39. Luo, K., Liu, Y. B., Chen, P. F., and Zeng, M. L. (2022). Assessing the impact of digital economy on green development efficiency in the Yangtze River Economic Belt. Energy Economics, 112, 106127. http://doi.org/10.1016/j.eneco.2022.106127
  40. Ma, D., and Zhu, Q. (2022). Innovation in emerging economies: Research on the digital economy driving high-quality green development. Journal of Business Research, 145, 801-813. http://doi.org/10.1016/j.jbusres.2022.03.041
  41. Marz, W., and Sen, S. (2022). Does telecommuting reduce commuting emissions? Journal of Environmental Economics and Management, 116, 102746. http://doi.org/10.1016/j.jeem.2022.102746
  42. Matthess, M., Kunkel, S., Dachrodt, M. F., and Beier, G. (2023). The impact of digitalization on energy intensity in manufacturing sectors-A panel data analysis for Europe. Journal of Cleaner Production, 397, 136598. http://doi.org/10.1016/j.jclepro.2023.136598
  43. Meng, Z. Y., Li, W. B., Chen, C. F., and Guan, C. H. (2023). Carbon Emission Reduction Effects of the Digital Economy: Mechanisms and Evidence from 282 Cities in China. Land, 12(4), 773. http://doi.org/10.3390/land12040773
  44. Mu, W. W., Liu, K. F., Tao, Y. Q., and Ye, Y. W. (2023). Digital finance and corporate ESG. Finance Research Letters, 51, 103426. http://doi.org/10.1016/j.frl.2022.103426
  45. Nunn, N., and Qian, N. (2011). The potato's contribution to population and urbanization: evidence from a historical experiment. Quarterly Journal of Economics, 126(2), 593-650. http://doi.org/10.1093/qje/qjr009
  46. Peng, H., Lu, Y. B., and Wang, Q. W. (2023). How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy. Energy, 268 http://doi.org/10.1016/j.energy.2023.126654
  47. Petersen, M. A. (2009). Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. Review of Financial Studies, 22(1), 435-480. http://doi.org/10.1093/rfs/hhn053
  48. Qin, P., Liu, M., Su, L., Fei, Y., and Tan-Soo, J. (2022). Electricity consumption in the digital era: Micro evidence from Chinese households. Resources Conservation and Recycling, 182, 106297. http://doi.org/10.1016/j.resconrec.2022.106297
  49. Ren, S. Y., Hao, Y., Xu, L., Wu, H. T., and Ba, N. (2021). Digitalization and energy: How does internet development affect China's energy consumption? Energy Economics, 98, 105220. http://doi.org/10.1016/j.eneco.2021.105220
  50. Shao, S., Chen, Y., Li, K., and Yang, L. (2019). Market segmentation and urban CO2 emissions in China: Evidence from the Yangtze River Delta region. Journal of Environmental Management, 248, 109324. http://doi.org/10.1016/j.jenvman.2019.109324
  51. Skare, M., de Obesso, M. D., and Ribeiro-Navarrete, S. (2023). Digital transformation and European small and medium enterprises (SMEs): A comparative study using digital economy and society index data. International Journal of Information Management, 68, 102594. http://doi.org/10.1016/j.ijinfomgt.2022.102594
  52. Song, P., Mao, X. Q., Li, Z. Y., and Tan, Z. X. (2023). Study on the optimal policy options for improving energy efficiency and Co-controlling carbon emission and local air pollutants in China. Renewable & Sustainable Energy Reviews, 175, 113167. http://doi.org/10.1016/j.rser.2023.113167
  53. Storch, D., Timme, M., and Schroeder, M. (2021). Incentive-driven transition to high ride-sharing adoption. Nature Communications, 12(1), 3003. http://doi.org/10.1038/s41467-021-23287-6
  54. Sturgeon, T. J. (2021). Upgrading strategies for the digital economy. Global Strategy Journal, 11(1), 34-57. http://doi.org/10.1002/gsj.1364
  55. Su, Y. Q., Tian, G. G., Li, H. C., and Ding, C. J. (2024). Climate risk and corporate energy strategies: Unveiling the Inverted-N relationship. Energy, 310, 132968. http://doi.org/10.1016/j.energy.2024.132968
  56. Sun, H. P., Edziah, B. K., Sun, C. W., and Kporsu, A. K. (2022). Institutional quality and its spatial spillover effects on energy efficiency. Socio-Economic Planning Sciences, 83, 101023. http://doi.org/10.1016/j.seps.2021.101023
  57. Sun, Y. P., and Razzaq, A. (2022). Composite fiscal decentralisation and green innovation: Imperative strategy for institutional reforms and sustainable development in OECD countries. Sustainable Development, 30(5), 944-957. http://doi.org/10.1002/sd.2292
  58. Tanaka, K., and Managi, S. (2021). Industrial agglomeration effect for energy efficiency in Japanese production plants. Energy Policy, 156, 112442. http://doi.org/10.1016/j.enpol.2021.112442
  59. Tang, C., Xu, Y. Y., Hao, Y., Wu, H. T., and Xue, Y. (2021). What is the role of telecommunications infrastructure construction in green technology innovation? A firm-level analysis for China. Energy Economics, 103, 105576. http://doi.org/10.1016/j.eneco.2021.105576
  60. Wang, E. Z., Lee, C. C., and Li, Y. Y. (2022). Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Economics, 105, 105748. http://doi.org/10.1016/j.eneco.2021.105748
  61. Wang, H., and Zhou, P. (2018). Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach. Energy Economics, 74, 310-320. http://doi.org/10.1016/j.eneco.2018.05.038
  62. Wang, L., Yang, D., and Luo, D. D. (2022). Policy Uncertainty, Official Social Capital, and the Effective Corporate Tax Rate-Evidence From Chinese Firms. Frontiers in Psychology, 13, 899021. http://doi.org/10.3389/fpsyg.2022.899021
  63. Wang, J., Dong, K., Dong, X., and Taghizadeh-Hesary, F. (2022). Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Economics, 113, 106198. http://doi.org/10.1016/j.eneco.2022.106198
  64. Wang, J., Sun, F. R., Lv, K. J., and Wang, L. S. (2022). Industrial agglomeration and firm energy intensity: How important is spatial proximity? Energy Economics, 112, 106155. http://doi.org/10.1016/j.eneco.2022.106155
  65. Wang, L., Liu, X. Q., Liu, R. W., and Pan, J. Y. (2024). Structural tax reform and local government non-tax revenues - evidence from rural tax reform of China. Applied Economics, 1-16. http://doi.org/10.1080/00036846.2024.2331024
  66. Wang, L., and Shao, J. (2023). Digital economy, entrepreneurship and energy efficiency. Energy, 269, 126801. http://doi.org/10.1016/j.energy.2023.126801
  67. Wei, T. Y., Zhou, J. J., and Zhang, H. X. (2019). Rebound effect of energy intensity reduction on energy consumption. Resources Conservation and Recycling, 144, 233-239. http://doi.org/10.1016/j.resconrec.2019.01.012
  68. Wu, H. T., Hao, Y., and Ren, S. Y. (2020). How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Economics, 91, 104880. http://doi.org/10.1016/j.eneco.2020.104880
  69. Wurlod, J., and Noailly, J. (2018). The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries. Energy Economics, 71, 47-61. http://doi.org/10.1016/j.eneco.2017.12.012
  70. Yang, G. Q., Nie, Y. M., Li, H. G., and Wang, H. S. (2023). Digital transformation and low-carbon technology innovation in manufacturing firms: The mediating role of dynamic capabilities. International Journal of Production Economics, 263, 108969. http://doi.org/10.1016/j.ijpe.2023.108969
  71. Yang, Q. C., Zheng, M. B., and Chang, C. P. (2022). Energy policy and green innovation: A quantile investigation into renewable energy. Renewable Energy, 189, 1166-1175. http://doi.org/10.1016/j.renene.2022.03.046
  72. Yang, S. B., Jahanger, A., and Hossain, M. R. (2023). Does China's low-carbon city pilot intervention limit electricity consumption? An analysis of industrial energy efficiency using time-varying DID model. Energy Economics, 121, 106636. http://doi.org/10.1016/j.eneco.2023.106636
  73. Yang, X. D., Wu, H. T., Ren, S. Y., Ran, Q. Y., and Zhang, J. N. (2021). Does the development of the internet contribute to air pollution control in China? Mechanism discussion and empirical test. Structural Change and Economic Dynamics, 56, 207-224. http://doi.org/10.1016/j.strueco.2020.12.001
  74. Yang, Z., and Wei, X. (2019). The measurement and influences of China's urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. Journal of Cleaner Production, 209, 439-450. http://doi.org/10.1016/j.jclepro.2018.10.271
  75. Zhang, D., Li, J., and Ji, Q. (2020). Does better access to credit help reduce energy intensity in China? Evidence from manufacturing firms. Energy Policy, 145, 111710. http://doi.org/10.1016/j.enpol.2020.111710
  76. Zhang, J. N., Lyu, Y. W., Li, Y. T., and Geng, Y. (2022). Digital economy: An innovation driving factor for low-carbon development. Environmental Impact Assessment Review, 96, 106821. http://doi.org/10.1016/j.eiar.2022.106821
  77. Zhang, W., Liu, X. M., Wang, D., and Zhou, J. P. (2022). Digital economy and carbon emission performance: Evidence at China's city level. Energy Policy, 165, 112927. http://doi.org/10.1016/j.enpol.2022.112927
  78. Zhang, X., Cai, Z., Song, W., and Yang, D. (2023). Mapping the spatial-temporal changes in energy consumption-related carbon emissions in the Beijing-Tianjin-Hebei region via nighttime light data. Sustainable Cities and Society, 94, 104476. http://doi.org/10.1016/j.scs.2023.104476
  79. Zhang, Y. R., and Ran, C. J. (2023). Effect of digital economy on air pollution in China? New evidence from the "National Big Data Comprehensive Pilot Area"policy. Economic Analysis and Policy, 79, 986-1004. http://doi.org/10.1016/j.eap.2023.07.007
  80. Zhang, Z. (2022). Research on the Impact of Digital Finance on China's Urban-Rural Income Gap. Review of Economic Assessment, 1(1), 5. http://doi.org/10.58567/rea01010005
  81. Zhao, J., Ji, G., Yue, Y., Lai, Z., Chen, Y., Yang, D., Yang, X., and Wang, Z. (2019). Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets. Applied Energy, 235, 612-624. http://doi.org/10.1016/j.apenergy.2018.09.180
  82. Zheng, H., and He, Y. (2022). How does industrial co-agglomeration affect high-quality economic development? Evidence from Chengdu-Chongqing Economic Circle in China. Journal of Cleaner Production, 371, 133485. http://doi.org/10.1016/j.jclepro.2022.133485
  83. Zhou, X. Y., Zhou, D. Q., Zhao, Z. Y., and Wang, Q. W. (2022). A framework to analyze carbon impacts of digital economy: The case of China. Sustainable Production and Consumption, 31, 357-369. http://doi.org/10.1016/j.spc.2022.03.002
  84. Zhou, Y., Chen, M., Tang, Z., and Zhao, Y. (2022). City-level carbon emissions accounting and differentiation integrated nighttime light and city attributes. Resources Conservation and Recycling, 182, 106337. http://doi.org/10.1016/j.resconrec.2022.106337
  85. Zhu, C. (2019). Big Data as a Governance Mechanism. Review of Financial Studies, 32(5), 2021-2061. http://doi.org/10.1093/rfs/hhy081
  86. Zhu, J., Fan, Y., Deng, X., and Xue, L. (2019). Low-carbon innovation induced by emissions trading in China. Nature Communications, 10(1), 4088.
  87. Zhu, X. H., Zou, J. W., and Feng, C. (2017). Analysis of industrial energy-related CO2 emissions and the reduction potential of cities in the Yangtze River Delta region. Journal of Cleaner Production, 168, 791-802. http://doi.org/10.1016/j.jclepro.2017.09.014