imported energy sources [20]. In order to strengthen the link between spatial planning and sustainable biomass systems, it is important to understand the spatial-temporal characteristics of biomass consumption, because spatial planning sets frameworks for energy consumption, production and distribution [10]. The spatial SUR models account for both the spatial and temporal characteristics of biomass consumption, and can contribute to integrating spatial and biomass planning. The iterative ML method with BFGS procedure is suggested to estimate the robust parameters of the models which are crucial to draw a proper inference for biomass consumption. The BFGS method demonstrates generally more satisfactory performance than both GD and DFP methods, and would converge to the optimum of a convex function, even when
inexact line searches are used. There is spatial dependence among biomass consumption. Ignoring spatial dependence will lead to invalid results [16,18,21]. Wang and Wang [18] also found the spatial dependence among biomass consumption in the US by postulating different spatial
interaction structures. The presence of spatial dependence among biomass consumption has informative implications for sustainable biomass policy. This will make the full benefits of the policy be underestimated, due to the inability of local authority to gain the benefits accruing to neighboring states, and consequently the statelevel biomass policy tends to be little undertaken by local authorities [22] or has limited effects. Although US states have taken a
leading role in establishing renewable energy policies since the late