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Integrating spatial and biomass planning for the United States

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  • Wang, Sicong
  • Wang, Shifeng

Abstract

Biomass is low-carbon energy and has tremendous potential as an alternative to fossil fuels. However, the significant role of biomass in future low-carbon energy portfolio depends heavily on its consumption. The paper presents a first attempt to examine the spatial-temporal patterns of biomass consumption in the United States (US), using a novel method-spatial Seemingly Unrelated Regression (SUR) model, in order to strengthen the link between energy planning and spatial planning. In order to obtain the robust parameters of spatial SUR models and estimate the parameters efficiently, an iterative maximum likelihood method, which takes full advantage of the stationary characteristic of maximum likelihood estimation, has been developed. The robust parameters of models can help draw a proper inference for biomass consumption. Then the spatial-temporal patterns of biomass consumption in the US at the state level are investigated using the spatial SUR models with the estimation method developed and data covering the period of 2000–2012. Results show that there are spatial dependences among biomass consumption. The presence of spatial dependence in biomass consumption has informative implications for making sustainable biomass polices. It suggests new efforts to adding a cross-state dimension to state-level energy policy and coordinating some elements of energy policy across states are still needed. In addition, results consistent with classic economic theory further proves the correctness of applying the spatial SUR models to investigate the spatial-temporal patterns of biomass consumption.

Suggested Citation

  • Wang, Sicong & Wang, Shifeng, 2016. "Integrating spatial and biomass planning for the United States," Energy, Elsevier, vol. 114(C), pages 113-120.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:113-120
    DOI: 10.1016/j.energy.2016.07.142
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    References listed on IDEAS

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    1. Badi H. Baltagi, 2015. "Seemingly Unrelated Regressions," Springer Texts in Business and Economics, in: Solutions Manual for Econometrics, edition 3, chapter 0, pages 233-257, Springer.
    2. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    3. Carley, Sanya, 2009. "State renewable energy electricity policies: An empirical evaluation of effectiveness," Energy Policy, Elsevier, vol. 37(8), pages 3071-3081, August.
    4. Xiaokun Wang & Kara Kockelman, 2007. "Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China," Transportation, Springer, vol. 34(3), pages 281-300, May.
    5. Parke E. Wilde & Paul E. McNamara & Christine K. Ranney, 1999. "The Effect of Income and Food Programs on Dietary Quality: A Seemingly Unrelated Regression Analysis with Error Components," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 959-971.
    6. Sáfián, Fanni, 2014. "Modelling the Hungarian energy system – The first step towards sustainable energy planning," Energy, Elsevier, vol. 69(C), pages 58-66.
    7. Yin, Haitao & Powers, Nicholas, 2010. "Do state renewable portfolio standards promote in-state renewable generation[glottal stop]," Energy Policy, Elsevier, vol. 38(2), pages 1140-1149, February.
    8. Nunes, L.J.R. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Analysis of the use of biomass as an energy alternative for the Portuguese textile dyeing industry," Energy, Elsevier, vol. 84(C), pages 503-508.
    9. World Bank, 2002. "World Development Indicators 2002," World Bank Publications - Books, The World Bank Group, number 13921.
    10. Wang, Sicong & Wang, Shifeng, 2011. "Spatial interaction models for biomass consumption in the United States," Energy, Elsevier, vol. 36(11), pages 6555-6558.
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    Cited by:

    1. Wang, Shifeng & Wang, Sicong & Wang, Hui & Wolstencroft, Peter, 2021. "Growth rate of US state-level biomass consumption," Renewable Energy, Elsevier, vol. 179(C), pages 911-917.

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