Biomass is one of several elements in a comprehensive climate strategy that could cut projected oil use in half by 2030 in the US and phase out the use of coal in the electricity sector [29], and is the largest renewable energy in the US. This paper examines the spatial-temporal patterns of biomass consumption in the US, using the spatial SUR models, for strengthening the link between spatial planning and sustainable biomass systems. Different from other methods used for investigation of biomass consumption which consider merely spatial correlations (e.g. Wang and Wang [18]) or temporal correlations, the spatial SUR model captures the spatial and temporal correlations across observations, making the model more behaviorally convincing and applicable to issues where there is spatial-temporal correlations, like the one of this paper. To the
best of our knowledge, this is the first attempt to apply the spatial SUR model to integrate biomass consumption into spatial planning.The spatial SUR models are estimated by an iterative ML method developed in this paper which takes full advantage of the stationary characteristic of maximum likelihood estimation and can estimate the parameters efficiently. The robust parameters of models can help draw a proper inference for biomass consumption, which is the basis of spatial planning frameworks and is the key for creating evidence-based scientific sustainable biomass policies