  Gaussian Process (GP) emulators are widely used to approximate complex
computer model behaviour across the input space. Motivated by the problem of
coupling computer models, recently progress has been made in the theory of the
analysis of networks of connected GP emulators. In this paper, we combine these
recent methodological advances with classical state-space models to construct a
Bayesian decision support system. This approach gives a coherent probability
model that produces predictions with the measure of uncertainty in terms of two
first moments and enables the propagation of uncertainty from individual
decision components.
  This methodology is used to produce a decision support tool for a UK county
council considering low carbon technologies to transform its infrastructure to
reach a net-zero carbon target. In particular, we demonstrate how to couple
information from an energy model, a heating demand model, and gas and
electricity price time-series to quantitatively assess the impact on
operational costs of various policy choices and changes in the energy market.
