IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223028098.html
   My bibliography  Save this article

Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization

Author

Listed:
  • Zeng, Qingshun
  • Shi, Changfeng
  • Zhu, Wenjun
  • Zhi, Jiaqi
  • Na, Xiaohong

Abstract

In this paper, 41 prefecture level cities in the Yangtze River Delta (YRD) are selected as the research objects. Based on the semantic mining model, the policy data information from three dimensions namely national, provincial and municipal levels is integrated to build data-driven multiple scenarios. Then the long short-term memory neural network optimized by particle swarm optimization (PSO-LSTM) is further used to predict the peaking time of the YRD urban agglomeration under multiple scenarios. The results show that the YRD urban agglomeration as a whole can achieve carbon peaking on time under all three simulation paths, but the peaking situation of specific cities within the region will be significantly different due to differentiated policy orientations. Under the business as usual scenario, some cities in the north of the region cannot achieve carbon peaking by 2030. Under the stable growth scenario, more cities cannot achieve carbon peaking on time. However, under the low carbon scenario, all cities within the region can achieve carbon peaking by 2030. Therefore, when exploring effective carbon peaking paths, adopting more stringent policies and measures to limit carbon emissions can help achieve the regional carbon peaking goal earlier, ensure that all cities within the region can achieve carbon peaking on time, and can also narrow the gap between cities within the region in terms of peaking time. This paper explores a new scenario analysis parameter setting and carbon emission prediction method, which overcomes the subjectivity of current scenario analysis research in parameter setting to a certain extent, and also provides a scientific reference for predicting the diversified development trend of carbon emission.

Suggested Citation

  • Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028098
    DOI: 10.1016/j.energy.2023.129415
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223028098
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129415?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. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Echeverría, Lucía & Giménez-Nadal, J. Ignacio & Alberto Molina, José, 2022. "Who uses green mobility? Exploring profiles in developed countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 247-265.
    3. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas, 2021. "Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition," Applied Energy, Elsevier, vol. 283(C).
    4. Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
    5. Wang, Saige & Fath, Brian & Chen, Bin, 2019. "Energy–water nexus under energy mix scenarios using input–output and ecological network analyses," Applied Energy, Elsevier, vol. 233, pages 827-839.
    6. Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021. "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, vol. 96(C).
    7. Li, Li & Shan, Yuli & Lei, Yalin & Wu, Sanmang & Yu, Xiang & Lin, Xiyan & Chen, Yupei, 2019. "Decoupling of economic growth and emissions in China’s cities: A case study of the Central Plains urban agglomeration," Applied Energy, Elsevier, vol. 244(C), pages 36-45.
    8. Shi, Anqing, 2003. "The impact of population pressure on global carbon dioxide emissions, 1975-1996: evidence from pooled cross-country data," Ecological Economics, Elsevier, vol. 44(1), pages 29-42, February.
    9. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    10. Li, Gang, 2015. "Energy and exergy performance assessments for latent heat thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 926-954.
    11. Yin, Yanhong & Mizokami, Shoshi & Aikawa, Kohei, 2015. "Compact development and energy consumption: Scenario analysis of urban structures based on behavior simulation," Applied Energy, Elsevier, vol. 159(C), pages 449-457.
    12. Lu, I.J. & Lewis, Charles & Lin, Sue J., 2009. "The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector," Energy Policy, Elsevier, vol. 37(8), pages 2952-2961, August.
    13. Trutnevyte, Evelina & McDowall, Will & Tomei, Julia & Keppo, Ilkka, 2016. "Energy scenario choices: Insights from a retrospective review of UK energy futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 326-337.
    14. Cai, Bofeng & Liu, Helin & Zhang, Xiaoling & Pan, Haozhi & Zhao, Mengxue & Zheng, Tianming & Nie, Jingxin & Du, Mengbing & Dhakal, Shobhakar, 2022. "High-resolution accounting of urban emissions in China," Applied Energy, Elsevier, vol. 325(C).
    15. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    16. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    17. Zhu Liu & Dabo Guan & Scott Moore & Henry Lee & Jun Su & Qiang Zhang, 2015. "Climate policy: Steps to China's carbon peak," Nature, Nature, vol. 522(7556), pages 279-281, June.
    18. Li, Gang & Zheng, Xuefei, 2016. "Thermal energy storage system integration forms for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 736-757.
    19. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    20. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    21. Li, Gang, 2016. "Sensible heat thermal storage energy and exergy performance evaluations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 897-923.
    22. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    23. Udemba, Edmund Ntom & Emir, Firat & Philip, Lucy Davou, 2022. "Mitigating poor environmental quality with technology, renewable and entrepreneur policies: A symmetric and asymmetric approaches," Renewable Energy, Elsevier, vol. 189(C), pages 997-1006.
    24. Di Leo, Senatro & Pietrapertosa, Filomena & Salvia, Monica & Cosmi, Carmelina, 2021. "Contribution of the Basilicata region to decarbonisation of the energy system: results of a scenario analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    25. Gambhir, Ajay & Schulz, Niels & Napp, Tamaryn & Tong, Danlu & Munuera, Luis & Faist, Mark & Riahi, Keywan, 2013. "A hybrid modelling approach to develop scenarios for China's carbon dioxide emissions to 2050," Energy Policy, Elsevier, vol. 59(C), pages 614-632.
    26. Wang, Jianda & Dong, Xiucheng & Dong, Kangyin, 2022. "How does ICT agglomeration affect carbon emissions? The case of Yangtze River Delta urban agglomeration in China," Energy Economics, Elsevier, vol. 111(C).
    27. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    28. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    29. Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
    30. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    31. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    32. Wang, Shaojian & Xie, Zihan & Wu, Rong & Feng, Kuishang, 2022. "How does urbanization affect the carbon intensity of human well-being? A global assessment," Applied Energy, Elsevier, vol. 312(C).
    33. Huo, Tengfei & Cong, Xiaobo & Cheng, Cong & Cai, Weiguang & Zuo, Jian, 2023. "What is the driving mechanism for the carbon emissions in the building sector? An integrated DEMATEL-ISM model," Energy, Elsevier, vol. 274(C).
    34. Li, Wei & Gao, Shubin, 2018. "Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China's cement industry," Energy, Elsevier, vol. 165(PB), pages 33-54.
    35. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).
    36. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    37. Wang, Yanan & Yin, Shiwen & Fang, Xiaoli & Chen, Wei, 2022. "Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China," Energy, Elsevier, vol. 241(C).
    38. Ma, Guangcheng & Qin, Jiahong & Zhang, Yumeng, 2023. "Does the carbon emissions trading system reduce carbon emissions by promoting two-way FDI in developing countries? Evidence from Chinese listed companies and cities," Energy Economics, Elsevier, vol. 120(C).
    39. Li, Gang, 2016. "Organic Rankine cycle performance evaluation and thermoeconomic assessment with various applications part I: Energy and exergy performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 477-499.
    40. Li, Gang, 2016. "Organic Rankine cycle performance evaluation and thermoeconomic assessment with various applications part II: Economic assessment aspect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 490-505.
    41. Knapp, Tom & Mookerjee, Rajen, 1996. "Population growth and global CO2 emissions : A secular perspective," Energy Policy, Elsevier, vol. 24(1), pages 31-37, January.
    42. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
    43. Li, Kunming & Fang, Liting & He, Lerong, 2019. "How population and energy price affect China's environmental pollution?," Energy Policy, Elsevier, vol. 129(C), pages 386-396.
    44. Kumar, Sandeep & Muhuri, Pranab K., 2019. "A novel GDP prediction technique based on transfer learning using CO2 emission dataset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    45. Xie, Pinjie & Gong, Ningyu & Sun, Feihu & Li, Pin & Pan, Xianyou, 2023. "What factors contribute to the extent of decoupling economic growth and energy carbon emissions in China?," Energy Policy, Elsevier, vol. 173(C).
    46. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    47. Awaworyi Churchill, Sefa & Inekwe, John & Smyth, Russell & Zhang, Xibin, 2019. "R&D intensity and carbon emissions in the G7: 1870–2014," Energy Economics, Elsevier, vol. 80(C), pages 30-37.
    48. Huang, Yuan & Yu, Qiang & Wang, Ruirui, 2021. "Driving factors and decoupling effect of carbon footprint pressure in China: Based on net primary production," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    49. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    50. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
    51. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
    52. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
    53. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
    54. Kacprzyk, Andrzej & Kuchta, Zbigniew, 2020. "Shining a new light on the environmental Kuznets curve for CO2 emissions," Energy Economics, Elsevier, vol. 87(C).
    55. Yu, Miao & Meng, Bo & Li, Rong, 2022. "Analysis of China's urban household indirect carbon emissions drivers under the background of population aging," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 114-125.
    56. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    57. Holland, Robert A. & Beaumont, Nicola & Hooper, Tara & Austen, Melanie & Gross, Robert J.K. & Heptonstall, Philip J. & Ketsopoulou, Ioanna & Winskel, Mark & Watson, Jim & Taylor, Gail, 2018. "Incorporating ecosystem services into the design of future energy systems," Applied Energy, Elsevier, vol. 222(C), pages 812-822.
    58. Alsaleh, Mohd & Yang, Zhengyong & Chen, Tinggui & Wang, Xiaohui & Abdul-Rahim, Abdul Samad & Mahmood, Haider, 2023. "Moving toward environmental sustainability: Assessing the influence of geothermal power on carbon dioxide emissions," Renewable Energy, Elsevier, vol. 202(C), pages 880-893.
    Full references (including those not matched with items on IDEAS)

    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. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Li, Jian & Liu, Qiang & Ge, Zhong & Duan, Yuanyuan & Yang, Zhen & Di, Jiawei, 2017. "Optimized liquid-separated thermodynamic states for working fluids of organic Rankine cycles with liquid-separated condensation," Energy, Elsevier, vol. 141(C), pages 652-660.
    3. Wang, Zhenfeng & Xu, Guangyin & Lin, Ruojue & Wang, Heng & Ren, Jingzheng, 2019. "Energy performance contracting, risk factors, and policy implications: Identification and analysis of risks based on the best-worst network method," Energy, Elsevier, vol. 170(C), pages 1-13.
    4. Pantano, Fabio & Capata, Roberto, 2017. "Expander selection for an on board ORC energy recovery system," Energy, Elsevier, vol. 141(C), pages 1084-1096.
    5. An Cheng & Xinru Han & Guogang Jiang, 2023. "Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    6. Xiao, Huijuan & Duan, Zhiyuan & Zhou, Ya & Zhang, Ning & Shan, Yuli & Lin, Xiyan & Liu, Guosheng, 2019. "CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. Huang, Junwei & Xiao, Qingtai & Liu, Jingjing & Wang, Hua, 2019. "Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD," Energy, Elsevier, vol. 173(C), pages 306-316.
    9. Ding, Song & Hu, Jiaqi & Lin, Qianqian, 2023. "Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model," Energy Economics, Elsevier, vol. 126(C).
    10. Bennici, Simona & Dutournié, Patrick & Cathalan, Jérémy & Zbair, Mohamed & Nguyen, Minh Hoang & Scuiller, Elliot & Vaulot, Cyril, 2022. "Heat storage: Hydration investigation of MgSO4/active carbon composites, from material development to domestic applications scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    12. Yang, Yi & Qin, Huan, 2024. "The uncertainties of the carbon peak and the temporal and regional heterogeneity of its driving factors in China," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    13. Liu, Sijia & Winter, Michaela & Lewerenz, Meinert & Becker, Jan & Sauer, Dirk Uwe & Ma, Zeyu & Jiang, Jiuchun, 2019. "Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature," Energy, Elsevier, vol. 173(C), pages 1041-1053.
    14. Peng, Hui & Lu, Yaobin & Wang, Qunwei, 2023. "How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy," Energy, Elsevier, vol. 268(C).
    15. Ceylin Şirin & Fatih Selimefendigil & Hakan Fehmi Öztop, 2023. "Performance Analysis and Identification of an Indirect Photovoltaic Thermal Dryer with Aluminum Oxide Nano-Embedded Thermal Energy Storage Modification," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
    16. Hussain, Jafar & Lee, Chien-Chiang & Chen, Yongxiu, 2022. "Optimal green technology investment and emission reduction in emissions generating companies under the support of green bond and subsidy," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    17. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    18. Miao, Ankang & Yuan, Yue & Wu, Han & Ma, Xin & Shao, Chenyu & Xiang, Sheng, 2024. "Pathway for China's provincial carbon emission peak: A case study of the Jiangsu Province," Energy, Elsevier, vol. 298(C).
    19. Shu, Yunxia & Deng, Nanxin & Wu, Yuming & Bao, Shuming & Bie, Ao, 2023. "Urban governance and sustainable development: The effect of smart city on carbon emission in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    20. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028098. 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.journals.elsevier.com/energy .

    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.