IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v250y2019icp1321-1335.html
   My bibliography  Save this article

Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models

Author

Listed:
  • Chen, Guangwu
  • Zhu, Yuhan
  • Wiedmann, Thomas
  • Yao, Lina
  • Xu, Lixiao
  • Wang, Yafei

Abstract

The United Nations Sustainable Development Goals have highlighted the challenges brought about by increasing energy consumption and climate change. Previous studies have concentrated on accounting for urban and rural household energy requirements in China at a macro-scale, which neglects the analysis of individuals and their socio-economic driving factors at the micro-scale. To fill this gap, this study began with an accounting of energy requirements for urban and rural households based on the provincial Multi-Regional Input-Output (MRIO) tables and household survey covering over 25,000 unique samples from 25 provinces in 2012. Multilinear Regression models were employed to estimate the average effect of various demographic and socioeconomic characteristics of samples, and Tree-based models were applied to classify energy requirement groups and identify the key individual characteristics. The results suggest that the energy requirements per capita on average range from 34 to 211 GJ for urban samples and 34 to 149 GJ for rural samples across different provinces, and that the gap between individuals can be over 100 times. Indirect energy requirements representing above 90% of the total is the focus of the study. Changes in lifestyle factors include eating out, drinking and smoking, were all correlated with indirect energy requirements. Furthermore, the one-child family has had a positive effect on indirect energy requirements, while the two or more children family has had a negative effect. In addition, an individual’s mental health plays a role in the level of indirect energy requirements for high-income rural residents, while geographic location plays a key role for urban residents.

Suggested Citation

  • Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1321-1335
    DOI: 10.1016/j.apenergy.2019.04.170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191930830X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.04.170?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Owen, Anne & Brockway, Paul & Brand-Correa, Lina & Bunse, Lukas & Sakai, Marco & Barrett, John, 2017. "Energy consumption-based accounts: A comparison of results using different energy extension vectors," Applied Energy, Elsevier, vol. 190(C), pages 464-473.
    2. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "The impact of household consumption on energy use and CO2 emissions in China," Energy, Elsevier, vol. 36(1), pages 656-670.
    3. Leahy, Eimear & Lyons, Sean, 2010. "Energy use and appliance ownership in Ireland," Energy Policy, Elsevier, vol. 38(8), pages 4265-4279, August.
    4. Wiedenhofer, Dominik & Lenzen, Manfred & Steinberger, Julia K., 2013. "Energy requirements of consumption: Urban form, climatic and socio-economic factors, rebounds and their policy implications," Energy Policy, Elsevier, vol. 63(C), pages 696-707.
    5. Tso, Geoffrey K.F. & Guan, Jingjing, 2014. "A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption," Energy, Elsevier, vol. 66(C), pages 722-731.
    6. Sozen, Adnan & Arcaklioglu, Erol, 2007. "Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey," Energy Policy, Elsevier, vol. 35(10), pages 4981-4992, October.
    7. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    8. Nair, Gireesh & Gustavsson, Leif & Mahapatra, Krushna, 2010. "Factors influencing energy efficiency investments in existing Swedish residential buildings," Energy Policy, Elsevier, vol. 38(6), pages 2956-2963, June.
    9. Chen, Shaoqing & Chen, Bin, 2015. "Urban energy consumption: Different insights from energy flow analysis, input–output analysis and ecological network analysis," Applied Energy, Elsevier, vol. 138(C), pages 99-107.
    10. Jiansheng Qu & Tek Maraseni & Lina Liu & Zhiqiang Zhang & Talal Yusaf, 2015. "A Comparison of Household Carbon Emission Patterns of Urban and Rural China over the 17 Year Period (1995–2011)," Energies, MDPI, vol. 8(9), pages 1-21, September.
    11. Huo, Hong & Zhang, Qiang & He, Kebin & Yao, Zhiliang & Wang, Michael, 2012. "Vehicle-use intensity in China: Current status and future trend," Energy Policy, Elsevier, vol. 43(C), pages 6-16.
    12. Bin, Shui & Dowlatabadi, Hadi, 2005. "Consumer lifestyle approach to US energy use and the related CO2 emissions," Energy Policy, Elsevier, vol. 33(2), pages 197-208, January.
    13. Liu, Hong-Tao & Guo, Ju-E & Qian, Dong & Xi, You-Min, 2009. "Comprehensive evaluation of household indirect energy consumption and impacts of alternative energy policies in China by input-output analysis," Energy Policy, Elsevier, vol. 37(8), pages 3194-3204, August.
    14. Ding, Qun & Cai, Wenjia & Wang, Can & Sanwal, Mukul, 2017. "The relationships between household consumption activities and energy consumption in china— An input-output analysis from the lifestyle perspective," Applied Energy, Elsevier, vol. 207(C), pages 520-532.
    15. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    16. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    17. Zhou, Kaile & Yang, Shanlin, 2016. "Understanding household energy consumption behavior: The contribution of energy big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 810-819.
    18. Elisha R. Frederiks & Karen Stenner & Elizabeth V. Hobman, 2015. "The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review," Energies, MDPI, vol. 8(1), pages 1-37, January.
    19. Mansouri, Iman & Newborough, Marcus & Probert, Douglas, 1996. "Energy consumption in UK households: Impact of domestic electrical appliances," Applied Energy, Elsevier, vol. 54(3), pages 211-285, July.
    20. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    21. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    22. Bin, Shui & Dowlatabadi, Hadi, 2005. "Corrigendum to "Consumer lifestyles approach to US energy use and the related CO2 emissions": [Energy Policy 33 (2005) 197-208]," Energy Policy, Elsevier, vol. 33(10), pages 1362-1363, July.
    23. Nie, Hongguang & Kemp, René, 2014. "Index decomposition analysis of residential energy consumption in China: 2002–2010," Applied Energy, Elsevier, vol. 121(C), pages 10-19.
    24. Chen, Shaoqing & Chen, Bin, 2017. "Coupling of carbon and energy flows in cities: A meta-analysis and nexus modelling," Applied Energy, Elsevier, vol. 194(C), pages 774-783.
    25. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    26. Krey, Volker & O'Neill, Brian C. & van Ruijven, Bas & Chaturvedi, Vaibhav & Daioglou, Vassilis & Eom, Jiyong & Jiang, Leiwen & Nagai, Yu & Pachauri, Shonali & Ren, Xiaolin, 2012. "Urban and rural energy use and carbon dioxide emissions in Asia," Energy Economics, Elsevier, vol. 34(S3), pages 272-283.
    27. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    28. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    29. Lenzen, Manfred, 1998. "Energy and greenhouse gas cost of living for Australia during 1993/94," Energy, Elsevier, vol. 23(6), pages 497-516.
    30. Henri C. Moll & Klaas Jan Noorman & Rixt Kok & Rebecka Engström & Harald Throne‐Holst & Charlotte Clark, 2005. "Pursuing More Sustainable Consumption by Analyzing Household Metabolism in European Countries and Cities," Journal of Industrial Ecology, Yale University, vol. 9(1‐2), pages 259-275, January.
    31. Zhang, Dahai & Wang, Jiaqi & Lin, Yonggang & Si, Yulin & Huang, Can & Yang, Jing & Huang, Bin & Li, Wei, 2017. "Present situation and future prospect of renewable energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 865-871.
    32. Longhi, Simonetta, 2015. "Residential energy expenditures and the relevance of changes in household circumstances," Energy Economics, Elsevier, vol. 49(C), pages 440-450.
    33. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    34. Selin Atalay & Margaret G. Meloy, 2011. "Retail therapy: A strategic effort to improve mood," Post-Print hal-00596836, HAL.
    35. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    36. Chen, Shaoqing & Chen, Bin, 2016. "Urban energy–water nexus: A network perspective," Applied Energy, Elsevier, vol. 184(C), pages 905-914.
    37. Wei, Yi-Ming & Liu, Lan-Cui & Fan, Ying & Wu, Gang, 2007. "The impact of lifestyle on energy use and CO2 emission: An empirical analysis of China's residents," Energy Policy, Elsevier, vol. 35(1), pages 247-257, January.
    38. Vassileva, Iana & Wallin, Fredrik & Dahlquist, Erik, 2012. "Analytical comparison between electricity consumption and behavioral characteristics of Swedish households in rented apartments," Applied Energy, Elsevier, vol. 90(1), pages 182-188.
    39. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    40. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    41. Long, Yin & Yoshida, Yoshikuni & Fang, Kai & Zhang, Haoran & Dhondt, Maya, 2019. "City-level household carbon footprint from purchaser point of view by a modified input-output model," Applied Energy, Elsevier, vol. 236(C), pages 379-387.
    42. Edgar Hertwich, 2011. "The Life Cycle Environmental Impacts Of Consumption," Economic Systems Research, Taylor & Francis Journals, vol. 23(1), pages 27-47.
    43. Kok, Rixt & Benders, Rene M.J. & Moll, Henri C., 2006. "Measuring the environmental load of household consumption using some methods based on input-output energy analysis: A comparison of methods and a discussion of results," Energy Policy, Elsevier, vol. 34(17), pages 2744-2761, November.
    44. Lenzen, Manfred & Wier, Mette & Cohen, Claude & Hayami, Hitoshi & Pachauri, Shonali & Schaeffer, Roberto, 2006. "A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan," Energy, Elsevier, vol. 31(2), pages 181-207.
    45. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    46. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    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. Han, Jiashi & Hou, Xiaochao & Zhang, Lei, 2022. "Policy implications of China's rural household coal governance from the perspective of the spillover effect," Energy, Elsevier, vol. 242(C).
    2. Yawale, Satish Kumar & Hanaoka, Tatsuya & Kapshe, Manmohan & Pandey, Rahul, 2023. "End-use energy projections: Future regional disparity and energy poverty at the household level in rural and urban areas of India," Energy Policy, Elsevier, vol. 182(C).
    3. Lei, Mingyu & Cai, Wenjia & Liu, Wenling & Wang, Can, 2022. "The heterogeneity in energy consumption patterns and home appliance purchasing preferences across urban households in China," Energy, Elsevier, vol. 253(C).
    4. Huang, Rui & Tian, Lixin, 2021. "CO2 emissions inequality through the lens of developing countries," Applied Energy, Elsevier, vol. 281(C).
    5. Ren, Zhiyuan & Zhu, Yuhan & Jin, Canyang & Xu, Aiting, 2023. "Social capital and energy poverty: Empirical evidence from China," Energy, Elsevier, vol. 267(C).
    6. Han, Jiashi & Zhang, Lei & Li, Yang, 2022. "Spatiotemporal analysis of rural energy transition and upgrading in developing countries: The case of China," Applied Energy, Elsevier, vol. 307(C).
    7. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    8. Lei, Yang & Wang, Dan & Jia, Hongjie & Li, Jiaxi & Chen, Jingcheng & Li, Jingru & Yang, Zhihong, 2021. "Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties," Applied Energy, Elsevier, vol. 300(C).
    9. Cuihui Xia & Tandong Yao & Weicai Wang & Wentao Hu, 2022. "Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach," Energies, MDPI, vol. 15(9), pages 1-20, May.
    10. Wang, Shubin & Sun, Shaolong & Zhao, Erlong & Wang, Shouyang, 2021. "Urban and rural differences with regional assessment of household energy consumption in China," Energy, Elsevier, vol. 232(C).
    11. Andante Hadi Pandyaswargo & Mengyi Ruan & Eiei Htwe & Motoshi Hiratsuka & Alan Dwi Wibowo & Yuji Nagai & Hiroshi Onoda, 2020. "Estimating the Energy Demand and Growth in Off-Grid Villages: Case Studies from Myanmar, Indonesia, and Laos," Energies, MDPI, vol. 13(20), pages 1-22, October.
    12. Jingjing Chen & Yangyang Lin & Xiaojun Wang & Bingjing Mao & Lihong Peng, 2022. "Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis," Energies, MDPI, vol. 15(14), pages 1-22, July.
    13. Yawale, Satish Kumar & Hanaoka, Tatsuya & Kapshe, Manmohan, 2021. "Development of energy balance table for rural and urban households and evaluation of energy consumption in Indian states," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    14. Yi Liang & Aixi Han & Li Chai & Hong Zhi, 2020. "Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet," IJERPH, MDPI, vol. 17(19), pages 1-17, October.
    15. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    16. Yi Chen & Yinrong Chen & Kun Chen & Min Liu, 2023. "Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software," IJERPH, MDPI, vol. 20(3), pages 1-19, January.
    17. Chen, Lei & Xu, Linyu & Velasco-Fernández, Raúl & Giampietro, Mario & Yang, Zhifeng, 2021. "Residential energy metabolic patterns in China: A study of the urbanization process," Energy, Elsevier, vol. 215(PA).
    18. Yang, Chengying & Li, Mingming & Zhou, Dianyi, 2024. "Energy assessment in rural regions of China with various scenarios: Historical–to–futuristic," Energy, Elsevier, vol. 302(C).

    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. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2019. "Interactions in Swiss households’ energy demand: A holistic approach," Energy Policy, Elsevier, vol. 128(C), pages 136-149.
    2. Rui Huang & Shaohui Zhang & Changxin Liu, 2018. "Comparing Urban and Rural Household CO 2 Emissions—Case from China’s Four Megacities: Beijing, Tianjin, Shanghai, and Chongqing," Energies, MDPI, vol. 11(5), pages 1-17, May.
    3. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shahtahmassebi, Golnaz & Cumberbatch, Miranda & Shrahily, Raid, 2017. "The impact of the UK household life-cycle transitions on the electricity and gas usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 505-518.
    4. Liu, Lan-Cui & Wu, Gang, 2013. "Relating five bounded environmental problems to China's household consumption in 2011–2015," Energy, Elsevier, vol. 57(C), pages 427-433.
    5. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    6. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    7. Age Poom & Rein Ahas, 2016. "How Does the Environmental Load of Household Consumption Depend on Residential Location?," Sustainability, MDPI, vol. 8(9), pages 1-18, August.
    8. Wiedenhofer, Dominik & Lenzen, Manfred & Steinberger, Julia K., 2013. "Energy requirements of consumption: Urban form, climatic and socio-economic factors, rebounds and their policy implications," Energy Policy, Elsevier, vol. 63(C), pages 696-707.
    9. Tao Lin & Yunjun Yu & Xuemei Bai & Ling Feng & Jin Wang, 2013. "Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    10. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    11. Xin Li & Xiaoqiong He & Xiyu Luo & Xiandan Cui & Minxi Wang, 2020. "Exploring the characteristics and drivers of indirect energy consumption of urban and rural households from a sectoral perspective," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(5), pages 907-924, October.
    12. Yu, Biying & Zhang, Junyi & Fujiwara, Akimasa, 2011. "Representing in-home and out-of-home energy consumption behavior in Beijing," Energy Policy, Elsevier, vol. 39(7), pages 4168-4177, July.
    13. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    14. Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
    15. Wenwen Wang & Ming Zhang, 2015. "Direct and indirect energy consumption of rural households in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1693-1705, December.
    16. Aleksandra Matuszewska-Janica & Dorota Żebrowska-Suchodolska & Agnieszka Mazur-Dudzińska, 2021. "The Situation of Households on the Energy Market in the European Union: Consumption, Prices, and Renewable Energy," Energies, MDPI, vol. 14(19), pages 1-21, October.
    17. Ramachandra, T.V. & Bajpai, Vishnu & Kulkarni, Gouri & Aithal, Bharath H. & Han, Sun Sheng, 2017. "Economic disparity and CO2 emissions: The domestic energy sector in Greater Bangalore, India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1331-1344.
    18. Yarbaşı, İkram Yusuf & Çelik, Ali Kemal, 2023. "The determinants of household electricity demand in Turkey: An implementation of the Heckman Sample Selection model," Energy, Elsevier, vol. 283(C).
    19. Heidi Bruderer Enzler & Andreas Diekmann, 2015. "Environmental Impact and Pro-Environmental Behavior: Correlations to Income and Environmental Concern," ETH Zurich Sociology Working Papers 9, ETH Zurich, Chair of Sociology.
    20. Kerkhof, Annemarie C. & Benders, Ren M.J. & Moll, Henri C., 2009. "Determinants of variation in household CO2 emissions between and within countries," Energy Policy, Elsevier, vol. 37(4), pages 1509-1517, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:250:y:2019:i:c:p:1321-1335. 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.