Empirical validation of an agent-based model of wood markets in Switzerland

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

Formation and Continuation of Thermal Energy Community Systems: An Explorative Agent-Based Model for the Netherlands

Energy communities are key elements in the energy transition at the local level as they aim to generate and distribute energy based on renewable energy technologies locally. The literature on community energy systems is dominated by the study of electricity systems. Yet, thermal energy applications cover 75% of the total energy consumption in households and small businesses. Community-driven initiatives for local generation and distribution of thermal energy, however, remain largely unaddressed in the literature. Since thermal energy communities are relatively new in the energy transition discussions, it is important to have a better understanding of thermal energy community systems and how these systems function. The starting point of this understanding is to study factors that influence the formation and continuation of thermal energy communities. To work towards this aim, an abstract agent-based model has been developed that explores four seemingly trivial factors, namely: neighborhood size, minimum member requirement, satisfaction factor and drop-out factor. Our preliminary modelling results indicate correlations between thermal community formation and the ’formation capability’ (the percentage of households that joined) and with the satisfaction of households. No relation was found with the size of the community (in terms of number of households) or with the ‘drop-out factor’ (individual households that quit after the contract time).

The Application of Time Series Decomposition for the Identification and Analysis of Fluctuations in Timber Supply and Price: A Case Study from Poland

The objective of the study was to verify the applicability and usefulness of time series decomposition in analyzing the variability of timber prices and supply in Poland. The employed multiplicative model was the product of four components: cyclical, seasonal, and irregular fluctuations and the long-term trend. The elements of the time series were determined by means of the Census X11 method, while cyclicality was separated from the trend employing the Hodrick–Prescott filter. Data included quarterly information about the supply (volume) and prices (value) of the timber sold by the State Forests in the years 2005–2018. Analyses were performed for tree species with the greatest economic significance, that is, pine, oak, spruce, beech, birch, and alder, and for their most popular assortments: general purpose large-diameter timber (W0) and medium-diameter timber (S2A). Time series decomposition of quarterly timber production volume and prices revealed irregular, seasonal, and cyclic fluctuations. Within an annual time horizon, irregular fluctuations accounted on average for 6.7% and 28% of overall variability in timber prices and supply, respectively; they exhibited low amplitudes (+5%, −25%, respectively). Cyclical fluctuations were primarily found for prices and were characterized by substantial variations in cycle length (2–4 years) and change amplitude (3–27 Euros). Cyclical fluctuations in timber prices and supply were usually negatively correlated with each other: the upper turning points of price cycles fell near the lower turning points of supply cycles (with a shift of 1 to 3 quarters). The seasonality of prices was also inversely correlated with supply: quarters with low supply exhibited higher prices and vice versa. Seasonal fluctuations were more pronounced for timber supply (36%) as compared to timber prices (20.3%). Different seasonality patterns were found for hardwood and softwood. The lowest supply of softwood was found in the first quarter and the highest in the third quarter (spruce) or fourth quarter (pine). The supply of hardwood was the highest in the first quarter and the lowest in the third quarter.