Mapping investment environment by optimizing the forest bioenergy production plant locations.

Abstract

Finland’s long-term climate and energy strategy is to become ‘carbon neutral’ by reducing greenhouse gas emissions (GHG), improving energy efficiency measures and increasing renewable energy production. Forests as a renewable energy resource offers opportunities to boost the bioeconomy, energy security and environmental benefits. This doctoral study aims to analyze the potential expansion of forest biomass based bioenergy production in Finland. Therefore, a spatially explicit techno-economic Mixed Integer Linear Programming (MILP) model was applied to optimize the potential new bioenergy plant locations by minimizing the full costs of the supply chain with respect to forest resources supply, industrial competition, and energy demand. At first, the model was applied at regional level to optimize the methanol and Combined Heat and Power (CHP) production in Eastern Finland (Article I) to replace fossil fuel in transport and district heating supply with local forest and industrial biomass resources. Later in Article II, the model was further extended at national level to optimize the location of Fischer-Tropsch biodiesel production plants to meet the 2020 target of biofuel share in transport. In Article III, the opportunities to increase the share of forest chips through existing and new CHP investments to meet the 2020 target of forest chips consumption in heat and power production were studied. Article IV presents the survey-based approach in Poland to identify key societal parameters (e.g., willingness to biomass supply) that helps to optimize the future production plant locations taking into account of economic, environmental and societal aspects of the bioenergy value chain. The results of this study provide valuable information to the investors with cost-optimal production plant locations (liquid biofuel and CHPs), optimal plant size with respect to economy of scale effects, choice of technology, feedstock resource allocation with import options, minimized cost of supply chain, income from by-product sales and CO2 emission savings. The model results also provide insight on the dynamics of the feedstock flow between end users with respect to market uncertainties. The model parameter sensitivity analysis shown that the investment costs, conversion efficiency and heat price variations were the most plant influential parameters followed by feedstock cost, electricity price, subsidies, and transport cost. The variation of these parameters under uncertain market conditions favoured by unstable policies would cause serious challenges to promote the use of forest biomass in the future biofuel and CHP industries. Survey analysis helped to understand that willingness of feedstock suppliers (farmers or forest owners) would also play a vital role for the future success of biofuel or CHP industries. Therefore, formulation of socially inclusive policies are imperative for the future success of bioenergy industries with long-term market stability.