[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v239y2019icp991-1002.html
   My bibliography  Save this article

Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?

Author

Listed:
  • Li, Francis G.N.
  • Bataille, Chris
  • Pye, Steve
  • O'Sullivan, Aidan
Abstract
Energy economy models are central to decision making on energy and climate issues in the 21st century, such as informing the design of deep decarbonisation strategies under the Paris Agreement. Designing policies that are aimed at achieving such radical transitions in the energy system will require ever more in-depth modelling of end-use demand, efficiency and fuel switching, as well as an increasing need for regional, sectoral, and agent disaggregation to capture technological, jurisdictional and policy detail. Building and using these models entails complex trade-offs between the level of detail, the size of the system boundary, and the available computing resources. The availability of data to characterise key energy system sectors and interactions is also a key driver of model structure and parameterisation, and there are many blind spots and design compromises that are caused by data scarcity. We may soon, however, live in a world of data abundance, potentially enabling previously impossible levels of resolution and coverage in energy economy models. But while big data concepts and platforms have already begun to be used in a number of selected energy research applications, their potential to improve or even completely revolutionise energy economy modelling has been almost completely overlooked in the existing literature. In this paper, we explore the challenges and possibilities of this emerging frontier. We identify critical gaps and opportunities for the field, as well as developing foundational concepts for guiding the future application of big data to energy economy modelling, with reference to the existing literature on decision making under uncertainty, scenario analysis and the philosophy of science.

Suggested Citation

  • Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
  • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:991-1002
    DOI: 10.1016/j.apenergy.2019.02.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919302922
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.02.002?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. Scarpa, Riccardo & Willis, Ken, 2010. "Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies," Energy Economics, Elsevier, vol. 32(1), pages 129-136, January.
    2. Sandrine Mathy & Patrick Criqui & Katharina Knoop & Manfred Fischedick & Sascha Samadi, 2016. "Uncertainty management and the dynamic adjustment of deep decarbonization pathways," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 47-62, June.
    3. Olivier Sassi & Renaud Crassous & Jean-Charles Hourcade & Vincent Gitz & Henri Waisman & Celine Guivarch, 2010. "IMACLIM-R: a modelling framework to simulate sustainable development pathways," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 10(1/2), pages 5-24.
    4. Michelsen, Carl Christian & Madlener, Reinhard, 2012. "Homeowners' preferences for adopting innovative residential heating systems: A discrete choice analysis for Germany," Energy Economics, Elsevier, vol. 34(5), pages 1271-1283.
    5. Li, Pei-Hao & Pye, Steve, 2018. "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective," Applied Energy, Elsevier, vol. 228(C), pages 965-979.
    6. Sándor Szabó & Magda Moner-Girona & Ioannis Kougias & Rob Bailis & Katalin Bódis, 2016. "Identification of advantageous electricity generation options in sub-Saharan Africa integrating existing resources," Nature Energy, Nature, vol. 1(10), pages 1-8, October.
    7. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    8. Vandyck, Toon & Van Regemorter, Denise, 2014. "Distributional and regional economic impact of energy taxes in Belgium," Energy Policy, Elsevier, vol. 72(C), pages 190-203.
    9. Schuelke-Leech, Beth-Anne & Barry, Betsy & Muratori, Matteo & Yurkovich, B.J., 2015. "Big Data issues and opportunities for electric utilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 937-947.
    10. Nic Rivers & Mark Jaccard, 2005. "Combining Top-Down and Bottom-Up Approaches to Energy-Economy Modeling Using Discrete Choice Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 83-106.
    11. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    12. Deane, J.P. & Chiodi, Alessandro & Gargiulo, Maurizio & Ó Gallachóir, Brian P., 2012. "Soft-linking of a power systems model to an energy systems model," Energy, Elsevier, vol. 42(1), pages 303-312.
    13. 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.
    14. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Institute of Transportation Studies, Working Paper Series qt02n9j6cv, Institute of Transportation Studies, UC Davis.
    15. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    16. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    17. Chris Bataille & Henri Waisman & Michel Colombier & Laura Segafredo & Jim Williams & Frank Jotzo, 2016. "The need for national deep decarbonization pathways for effective climate policy," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 7-26, June.
    18. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Resource and Energy Economics, Elsevier, vol. 31(3), pages 221-238, August.
    19. Lori Bennear & Robert Stavins, 2007. "Second-best theory and the use of multiple policy instruments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(1), pages 111-129, May.
    20. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    21. Henri Waisman & Céline Guivarch & Fabio Grazi & Jean Hourcade, 2012. "The I maclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight," Climatic Change, Springer, vol. 114(1), pages 101-120, September.
    22. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
    23. Doll, Christopher N.H. & Pachauri, Shonali, 2010. "Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery," Energy Policy, Elsevier, vol. 38(10), pages 5661-5670, October.
    24. Lott, Melissa C. & Pye, Steve & Dodds, Paul E., 2017. "Quantifying the co-impacts of energy sector decarbonisation on outdoor air pollution in the United Kingdom," Energy Policy, Elsevier, vol. 101(C), pages 42-51.
    25. Chris Bataille, Mark Jaccard, John Nyboer and Nic Rivers, 2006. "Towards General Equilibrium in a Technology-Rich Model with Empirically Estimated Behavioral Parameters," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 93-112.
    26. Green, Jemma & Newman, Peter, 2017. "Citizen utilities: The emerging power paradigm," Energy Policy, Elsevier, vol. 105(C), pages 283-293.
    27. Andy Stirling, 2010. "Keep it complex," Nature, Nature, vol. 468(7327), pages 1029-1031, December.
    28. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    29. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    30. Daly, Hannah E. & Ramea, Kalai & Chiodi, Alessandro & Yeh, Sonia & Gargiulo, Maurizio & Gallachóir, Brian Ó, 2014. "Incorporating travel behaviour and travel time into TIMES energy system models," Applied Energy, Elsevier, vol. 135(C), pages 429-439.
    31. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    32. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    33. Hausman,Daniel M., 1992. "The Inexact and Separate Science of Economics," Cambridge Books, Cambridge University Press, number 9780521425230, December.
    34. van Alphen, Klaas & van Voorst tot Voorst, Quirine & Hekkert, Marko P. & Smits, Ruud E.H.M., 2007. "Societal acceptance of carbon capture and storage technologies," Energy Policy, Elsevier, vol. 35(8), pages 4368-4380, August.
    35. Di Salvo, André L.A. & Agostinho, Feni & Almeida, Cecília M.V.B. & Giannetti, Biagio F., 2017. "Can cloud computing be labeled as “green”? Insights under an environmental accounting perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 514-526.
    36. Christophe McGlade & Paul Ekins, 2015. "The geographical distribution of fossil fuels unused when limiting global warming to 2 °C," Nature, Nature, vol. 517(7533), pages 187-190, January.
    37. Jean Charles Hourcade & Mark Jaccard & Chris Bataille & Frédéric Ghersi, 2006. "Hybrid Modeling: New Answers to Old Challenges," Post-Print halshs-00471234, HAL.
    38. Brand, Christian & Cluzel, Celine & Anable, Jillian, 2017. "Modeling the uptake of plug-in vehicles in a heterogeneous car market using a consumer segmentation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 121-136.
    39. Rausch, Sebastian & Metcalf, Gilbert E. & Reilly, John M., 2011. "Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households," Energy Economics, Elsevier, vol. 33(S1), pages 20-33.
    40. Geoffrey J. Blanford & James H. Merrick & John E.T. Bistline & David T. Young, 2018. "Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection," The Energy Journal, , vol. 39(3), pages 189-212, May.
    41. Fais, Birgit & Sabio, Nagore & Strachan, Neil, 2016. "The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets," Applied Energy, Elsevier, vol. 162(C), pages 699-712.
    42. Francesco Fuso Nerini & Julia Tomei & Long Seng To & Iwona Bisaga & Priti Parikh & Mairi Black & Aiduan Borrion & Catalina Spataru & Vanesa Castán Broto & Gabrial Anandarajah & Ben Milligan & Yacob Mu, 2018. "Mapping synergies and trade-offs between energy and the Sustainable Development Goals," Nature Energy, Nature, vol. 3(1), pages 10-15, January.
    43. Steve Pye & Chris Bataille, 2016. "Improving deep decarbonization modelling capacity for developed and developing country contexts," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 27-46, June.
    44. Bohringer, Christoph & Rutherford, Thomas F., 2008. "Combining bottom-up and top-down," Energy Economics, Elsevier, vol. 30(2), pages 574-596, March.
    45. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    46. Steve Pye & Francis G. N. Li & James Price & Birgit Fais, 2017. "Erratum: Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era," Nature Energy, Nature, vol. 2(6), pages 1-1, June.
    47. Jean-Charles Hourcade, Mark Jaccard, Chris Bataille, and Frederic Ghersi, 2006. "Hybrid Modeling: New Answers to Old Challenges Introduction to the Special Issue of The Energy Journal," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 1-12.
    48. Steve Pye & Francis G. N. Li & James Price & Birgit Fais, 2017. "Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era," Nature Energy, Nature, vol. 2(3), pages 1-7, March.
    49. Elmar Kriegler & Keywan Riah & Nils Petermann & Valentina Bosetti & Pantelis Capros & Detlef van Vuuren & Patrick Criqui & Christian Egenhofer & Panagiotis Fragkos & Nils Johnson & Leonidas Paroussos , 2014. "Assessing Pathways toward Ambitious Climate Targets at the Global and European levels: A Synthesis of Results from the AMPERE Project," Working Papers hal-01866610, HAL.
    50. Mark K. Jaccard & John Nyboer & Crhis Bataille & Bryn Sadownik, 2003. "Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 49-73.
    51. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    52. Sue Wing, Ian, 2008. "The synthesis of bottom-up and top-down approaches to climate policy modeling: Electric power technology detail in a social accounting framework," Energy Economics, Elsevier, vol. 30(2), pages 547-573, March.
    53. Roberts, Simon, 2008. "Demographics, energy and our homes," Energy Policy, Elsevier, vol. 36(12), pages 4630-4632, December.
    54. Hausman,Daniel M., 1992. "The Inexact and Separate Science of Economics," Cambridge Books, Cambridge University Press, number 9780521415019, December.
    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. Bistline, John E.T. & Merrick, James H., 2020. "Parameterizing open-source energy models: Statistical learning to estimate unknown power plant attributes," Applied Energy, Elsevier, vol. 269(C).
    2. Sadeghi, M. & Kalantar, M., 2023. "Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus," Applied Energy, Elsevier, vol. 350(C).
    3. Wang, Zhangyuan & Zhao, Xudong & Han, Zhonghe & Luo, Liang & Xiang, Jinwei & Zheng, Senglin & Liu, Guangming & Yu, Min & Cui, Yu & Shittu, Samson & Hu, Menglong, 2021. "Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study," Applied Energy, Elsevier, vol. 294(C).
    4. Meng, Measrainsey & Sanders, Kelly T., 2019. "A data-driven approach to investigate the impact of air temperature on the efficiencies of coal and natural gas generators," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Jilong Li & Sara Shirowzhan & Gloria Pignatta & Samad M. E. Sepasgozar, 2024. "Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities," Sustainability, MDPI, vol. 16(15), pages 1-26, July.
    6. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
    7. Hofbauer, Leonhard & McDowall, Will & Pye, Steve, 2022. "Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(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. Fox, Jacob & Axsen, Jonn & Jaccard, Mark, 2017. "Picking Winners: Modelling the Costs of Technology-specific Climate Policy in the U.S. Passenger Vehicle Sector," Ecological Economics, Elsevier, vol. 137(C), pages 133-147.
    2. Andersen, Kristoffer S. & Termansen, Lars B. & Gargiulo, Maurizio & Ó Gallachóirc, Brian P., 2019. "Bridging the gap using energy services: Demonstrating a novel framework for soft linking top-down and bottom-up models," Energy, Elsevier, vol. 169(C), pages 277-293.
    3. Bhardwaj, Chandan & Axsen, Jonn & McCollum, David, 2022. "Which “second-best” climate policies are best? Simulating cost-effective policy mixes for passenger vehicles," Resource and Energy Economics, Elsevier, vol. 70(C).
    4. Willenbockel, Dirk, 2017. "Macroeconomic Effects of a Low-Carbon Electricity Transition in Kenya and Ghana: An Exploratory Dynamic General Equilibrium Analysis," MPRA Paper 78070, University Library of Munich, Germany.
    5. Lee, Hwarang & Kang, Sung Won & Koo, Yoonmo, 2020. "A hybrid energy system model to evaluate the impact of climate policy on the manufacturing sector: Adoption of energy-efficient technologies and rebound effects," Energy, Elsevier, vol. 212(C).
    6. Xavier Labandeira, Pedro Linares and Miguel Rodriguez, 2009. "An Integrated Approach to Simulate the impacts of Carbon Emissions Trading Schemes," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    7. Giraudet, Louis-Gaëtan & Guivarch, Céline & Quirion, Philippe, 2012. "Exploring the potential for energy conservation in French households through hybrid modeling," Energy Economics, Elsevier, vol. 34(2), pages 426-445.
    8. Daniel Scamman & Baltazar Solano-Rodríguez & Steve Pye & Lai Fong Chiu & Andrew Z. P. Smith & Tiziano Gallo Cassarino & Mark Barrett & Robert Lowe, 2020. "Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective," Energies, MDPI, vol. 13(8), pages 1-28, April.
    9. Steve Pye & Chris Bataille, 2016. "Improving deep decarbonization modelling capacity for developed and developing country contexts," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 27-46, June.
    10. Felder, F.A. & Kumar, P., 2021. "A review of existing deep decarbonization models and their potential in policymaking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    11. Rhodes, Ekaterina & Hoyle, Aaron & McPherson, Madeleine & Craig, Kira, 2022. "Understanding climate policy projections: A scoping review of energy-economy models in Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    12. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    13. Sebastian Rausch & Valerie J. Karplus, 2014. "Markets versus Regulation: The Efficiency and Distributional Impacts of U.S. Climate Policy Proposals," The Energy Journal, , vol. 35(1_suppl), pages 199-228, June.
    14. Julien Lefevre, 2018. "Modeling the Socioeconomic Impacts of the Adoption of a Carbon Pricing Instrument – Literature review," CIRED Working Papers hal-03128619, HAL.
    15. Mark Jaccard and Suzanne Goldberg, 2014. "Technology Assumptions and Climate Policy: The Interrelated Effects of U.S. Electricity and Transport Policy," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    16. Anandarajah, Gabrial & Strachan, Neil, 2010. "Interactions and implications of renewable and climate change policy on UK energy scenarios," Energy Policy, Elsevier, vol. 38(11), pages 6724-6735, November.
    17. Chyong, Chi Kong & Newbery, David, 2022. "A unit commitment and economic dispatch model of the GB electricity market – Formulation and application to hydro pumped storage," Energy Policy, Elsevier, vol. 170(C).
    18. Fortes, Patrícia & Pereira, Rui & Pereira, Alfredo & Seixas, Júlia, 2014. "Integrated technological-economic modeling platform for energy and climate policy analysis," Energy, Elsevier, vol. 73(C), pages 716-730.
    19. Charlotte Senkpiel & Audrey Dobbins & Christina Kockel & Jan Steinbach & Ulrich Fahl & Farina Wille & Joachim Globisch & Sandra Wassermann & Bert Droste-Franke & Wolfgang Hauser & Claudia Hofer & Lars, 2020. "Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches," Energies, MDPI, vol. 13(18), pages 1-30, September.
    20. Chris Bataille & Henri Waisman & Michel Colombier & Laura Segafredo & Jim Williams & Frank Jotzo, 2016. "The need for national deep decarbonization pathways for effective climate policy," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 7-26, June.

    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:239:y:2019:i:c:p:991-1002. 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.