IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2536-d783321.html
   My bibliography  Save this article

Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance

Author

Listed:
  • Juanjuan Tian

    (College of Energy Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Research Center for Energy Economy and Management, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Xiaoqian Song

    (China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China
    China SJTU-UNIDO Joint Institute of Inclusive and Sustainable Industrial Development, Shanghai Jiao Tong University, Shanghai 200030, China
    School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China
    These authors contributed equally to this work.)

  • Jinsuo Zhang

    (Research Center for Energy Economy and Management, Xi’an University of Science and Technology, Xi’an 710054, China
    School of Management, Yan’an University, Yan’an 716000, China)

Abstract

The improvement in city-level carbon efficiency ( CE ) is crucial for China to achieve its CO 2 emission targets. Based on the panel data from 2003 to 2017, total factor CE values of 283 prefecture-level cities were measured using the super-efficiency SBM model. Through the exploratory spatial data analysis (ESDA), we found that the average city-level CE from 2003 to 2017 showed a “W”-type growth trend. Additionally, there are significant spatial heterogeneity and spatial dependency characteristics of city-level CE . The results of local spatial correlation analysis showed that the Low–Low clusters are distributed in all cities of Shanxi and Northern Shaanxi, and gradually expand to Inner Mongolia, Gansu, Ningxia, and Hebei over time, and the High–High clusters are mainly located in the southeast coastal cities and central and eastern Sichuan. High–Low clusters are generally scattered in cities with relatively superior political–economic status in Northeast China, North China, and Northwest China, and gradually concentrated in North China during 2003–2017. Additionally, the dynamic spatial econometric model was employed to investigate the influencing factors of CE , and we found that the city-level CE has the characteristic of path dependence on time. Factors such as industrial structure upgrading and environmental regulation have significant improvement effects on city-level CE , while technological progress, financial development, energy intensity, and government intervention can significantly inhibit city-level CE . Compared with short-term effects, the long-term effects are insignificant with higher absolute values, indicating the long-term persistence and gradual strengthening characteristics of driving factors on city-level CE ; however, the acting long-term mechanism has not been formed. Additionally, the regional spillover effect of driving factors on CE is more significant in the short term. Based on the empirical results, some policy implications for cities to improve CE are proposed.

Suggested Citation

  • Juanjuan Tian & Xiaoqian Song & Jinsuo Zhang, 2022. "Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance," Energies, MDPI, vol. 15(7), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2536-:d:783321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2536/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2536/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Yaqin & Zhao, Guohao & Zhao, Yushan, 2016. "An analysis of Chinese provincial carbon dioxide emission efficiencies based on energy consumption structure," Energy Policy, Elsevier, vol. 96(C), pages 524-533.
    2. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency: Some clarifications," European Journal of Operational Research, Elsevier, vol. 206(3), pages 702-702, November.
    3. Li, Ke & Zhang, Ning & Liu, Yanchu, 2016. "The energy rebound effects across China’s industrial sectors: An output distance function approach," Applied Energy, Elsevier, vol. 184(C), pages 1165-1175.
    4. Chen, Jiandong & Gao, Ming & Shahbaz, Muhammad & Cheng, Shulei & Song, Malin, 2021. "An improved decomposition approach toward energy rebound effects in China: Review since 1992," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Paloma Almodóvar & Javier Saiz-Briones & Brian Silverman, 2014. "Learning through foreign market participation: the relative benefits of exporting, importing, and foreign direct investment," The Journal of Technology Transfer, Springer, vol. 39(6), pages 915-944, December.
    6. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    7. Werner Antweiler & Brian R. Copeland & M. Scott Taylor, 2001. "Is Free Trade Good for the Environment?," American Economic Review, American Economic Association, vol. 91(4), pages 877-908, September.
    8. Smulders, Sjak & Tsur, Yacov & Zemel, Amos, 2012. "Announcing climate policy: Can a green paradox arise without scarcity?," Journal of Environmental Economics and Management, Elsevier, vol. 64(3), pages 364-376.
    9. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    10. Liangen Zeng & Haiyan Lu & Yenping Liu & Yang Zhou & Haoyu Hu, 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015," Energies, MDPI, vol. 12(16), pages 1-21, August.
    11. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    12. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    13. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
    14. Tamazian, Artur & Bhaskara Rao, B., 2010. "Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies," Energy Economics, Elsevier, vol. 32(1), pages 137-145, January.
    15. Paul Lanoie & Jérémy Laurent‐Lucchetti & Nick Johnstone & Stefan Ambec, 2011. "Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 20(3), pages 803-842, September.
    16. Duan, Yuwan & Yan, Bingqian, 2019. "Economic gains and environmental losses from international trade: A decomposition of pollution intensity in China's value-added trade," Energy Economics, Elsevier, vol. 83(C), pages 540-554.
    17. Marbuah, George & Amuakwa-Mensah, Franklin, 2017. "Spatial analysis of emissions in Sweden," Energy Economics, Elsevier, vol. 68(C), pages 383-394.
    18. Xiaoxiao Chu & Hong Geng & Wen Guo, 2019. "How Does Energy Misallocation Affect Carbon Emission Efficiency in China? An Empirical Study Based on the Spatial Econometric Model," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    19. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    20. Humaira Yasmeen & Qingmei Tan & Hashim Zameer & Junlan Tan & Kishwar Nawaz, 2020. "Exploring the impact of technological innovation, environmental regulations and urbanization on ecological efficiency of China in the context of COP21," Post-Print hal-03558085, HAL.
    21. Sheng, Pengfei & Li, Jun & Zhai, Mengxin & Huang, Shoujun, 2020. "Coupling of economic growth and reduction in carbon emissions at the efficiency level: Evidence from China," Energy, Elsevier, vol. 213(C).
    22. Borck, Rainald & Schrauth, Philipp, 2021. "Population density and urban air quality," Regional Science and Urban Economics, Elsevier, vol. 86(C).
    23. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    24. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    25. Wang, Guofeng & Deng, Xiangzheng & Wang, Jingyu & Zhang, Fan & Liang, Shiqi, 2019. "Carbon emission efficiency in China: A spatial panel data analysis," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    26. 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.
    27. Han, Yonghui & Zhang, Fan & Huang, Liangxiong & Peng, Keming & Wang, Xianbin, 2021. "Does industrial upgrading promote eco-efficiency? ─A panel space estimation based on Chinese evidence," Energy Policy, Elsevier, vol. 154(C).
    28. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    29. Zhang, Lulu & Xiong, Lichun & Cheng, Baodong & Yu, Chang, 2018. "How does foreign trade influence China’s carbon productivity? Based on panel spatial lag model analysis," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 171-179.
    30. van der Werf, Edwin & Di Maria, Corrado, 2012. "Imperfect Environmental Policy and Polluting Emissions: The Green Paradox and Beyond," International Review of Environmental and Resource Economics, now publishers, vol. 6(2), pages 153-194, March.
    31. Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    32. Cynthia Rosenzweig & William Solecki & Stephen A. Hammer & Shagun Mehrotra, 2010. "Cities lead the way in climate–change action," Nature, Nature, vol. 467(7318), pages 909-911, October.
    33. Shao, Shuai & Yang, Lili & Yu, Mingbo & Yu, Mingliang, 2011. "Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994-2009," Energy Policy, Elsevier, vol. 39(10), pages 6476-6494, October.
    34. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    35. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    36. Zagorchev, Andrey & Vasconcellos, Geraldo & Bae, Youngsoo, 2011. "Financial development, technology, growth and performance: Evidence from the accession to the EU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(5), pages 743-759.
    37. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    38. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    39. Fan, Wei & Li, Li & Wang, Feiran & Li, Ding, 2020. "Driving factors of CO2 emission inequality in China: The role of government expenditure," China Economic Review, Elsevier, vol. 64(C).
    40. Clark, Thomas A., 2013. "Metropolitan density, energy efficiency and carbon emissions: Multi-attribute tradeoffs and their policy implications," Energy Policy, Elsevier, vol. 53(C), pages 413-428.
    41. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    42. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    43. Lu, Yingying & Liu, Yu & Zhou, Meifang, 2017. "Rebound effect of improved energy efficiency for different energy types: A general equilibrium analysis for China," Energy Economics, Elsevier, vol. 62(C), pages 248-256.
    44. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    45. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    46. Su, Bin & Ang, B.W., 2020. "Demand contributors and driving factors of Singapore’s aggregate carbon intensities," Energy Policy, Elsevier, vol. 146(C).
    47. Hans-Werner Sinn, 2008. "Public policies against global warming: a supply side approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 15(4), pages 360-394, August.
    48. Sadorsky, Perry, 2011. "Financial development and energy consumption in Central and Eastern European frontier economies," Energy Policy, Elsevier, vol. 39(2), pages 999-1006, February.
    49. R. Ramanathan, 2002. "Combining indicators of energy consumption and CO 2 emissions: a cross-country comparison," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 214-227.
    50. Liu, Nan & Ma, Zujun & Kang, Jidong, 2015. "Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis," Energy Policy, Elsevier, vol. 87(C), pages 28-38.
    51. Acheampong, Alex O. & Amponsah, Mary & Boateng, Elliot, 2020. "Does financial development mitigate carbon emissions? Evidence from heterogeneous financial economies," Energy Economics, Elsevier, vol. 88(C).
    52. Chen, Shimin & Sun, Zheng & Tang, Song & Wu, Donghui, 2011. "Government intervention and investment efficiency: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 17(2), pages 259-271, April.
    53. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    54. Duan, Yuwan & Jiang, Xuemei, 2021. "Pollution haven or pollution halo? A Re-evaluation on the role of multinational enterprises in global CO2 emissions," Energy Economics, Elsevier, vol. 97(C).
    55. Floros, Nikolaos & Vlachou, Andriana, 2005. "Energy demand and energy-related CO2 emissions in Greek manufacturing: Assessing the impact of a carbon tax," Energy Economics, Elsevier, vol. 27(3), pages 387-413, May.
    56. 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.
    57. Cheng, Shulei & Fan, Wei & Chen, Jiandong & Meng, Fanxin & Liu, Gengyuan & Song, Malin & Yang, Zhifeng, 2020. "The impact of fiscal decentralization on CO2 emissions in China," Energy, Elsevier, vol. 192(C).
    58. Acheampong, Alex O., 2018. "Economic growth, CO2 emissions and energy consumption: What causes what and where?," Energy Economics, Elsevier, vol. 74(C), pages 677-692.
    59. Binswanger, Mathias, 2001. "Technological progress and sustainable development: what about the rebound effect?," Ecological Economics, Elsevier, vol. 36(1), pages 119-132, January.
    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. Chiqun Hu & Xiaoyu Ma, 2023. "Regional Differences, Dynamic Evolution and Convergence of Carbon Emissions from Rural Residents’ Living Consumption: Evidence from China," Energies, MDPI, vol. 16(16), pages 1-30, August.
    2. Xu Chu & Yiying Jin & Xuan Wang & Xiankun Wang & Xiaoqian Song, 2022. "The Evolution of the Spatial-Temporal Differences of Municipal Solid Waste Carbon Emission Efficiency in China," Energies, MDPI, vol. 15(11), pages 1-23, May.

    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. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    2. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    3. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    4. Minyoung Yang & Jinsoo Kim, 2022. "A Critical Review of the Definition and Estimation of Carbon Efficiency," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    5. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Zhang, Wei & Liu, Xuemeng & Zhao, Shikuan & Tang, Tian, 2024. "Does green finance agglomeration improve carbon emission performance in China? A perspective of spatial spillover," Applied Energy, Elsevier, vol. 358(C).
    7. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    8. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    9. Zhang, Ning & Wang, Bing & Chen, Zhongfei, 2016. "Carbon emissions reductions and technology gaps in the world's factory, 1990–2012," Energy Policy, Elsevier, vol. 91(C), pages 28-37.
    10. 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.
    11. Zhang, Yijun & Song, Yi, 2020. "Unified efficiency of coal mining enterprises in China: An analysis based on meta-frontier non-radial directional distance functions," Resources Policy, Elsevier, vol. 65(C).
    12. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    13. Feng Dong & Chang Qin & Xiaoyun Zhang & Xu Zhao & Yuling Pan & Yujin Gao & Jiao Zhu & Yangfan Li, 2021. "Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency," IJERPH, MDPI, vol. 18(24), pages 1-23, December.
    14. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    15. Fan, Meiting & Li, Mengxu & Liu, Jianghua & Shao, Shuai, 2022. "Is high natural resource dependence doomed to low carbon emission efficiency? Evidence from 283 cities in China," Energy Economics, Elsevier, vol. 115(C).
    16. Jiao Wang & Zhenliang Liao & Hui Sun, 2023. "Analysis of Carbon Emission Efficiency in the Yellow River Basin in China: Spatiotemporal Differences and Influencing Factors," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    17. Li-Ming Xue & Zhi-Xue Zheng & Shuo Meng & Mingjun Li & Huaqing Li & Ji-Ming Chen, 2022. "Carbon emission efficiency and spatio-temporal dynamic evolution of the cities in Beijing-Tianjin-Hebei Region, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 7640-7664, June.
    18. Wancheng Xie & Andrew Chapman & Taihua Yan, 2023. "Do Environmental Regulations Facilitate a Low-Carbon Transformation in China’s Resource-Based Cities?," IJERPH, MDPI, vol. 20(5), pages 1-23, March.
    19. Acheampong, Alex O., 2019. "Modelling for insight: Does financial development improve environmental quality?," Energy Economics, Elsevier, vol. 83(C), pages 156-179.
    20. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(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:gam:jeners:v:15:y:2022:i:7:p:2536-:d:783321. 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.