Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
The last decades, the number of dairy cows per farm has increased and the time spent on individual cows by the farmer has been reduced. To help the farmer detect changes in activity of cows associated with health issues, activity monitoring systems are being developed. The systems can help with daily farm management decisions, thus increasing farm profitability. Besides this economic benefit there is a social benefit: farmers highly value the herd being under continuous surveillance. Nedap Livestock Management (the Netherlands) introduced a leg activity meter and a neck activity collar: Smarttag Leg and Smarttag Neck. By registering activity of individual cows, the Smarttags help the farmer to detect oestrus and give alerts when activity deviates from normal patterns. The objective of this study was to validate results from the Smarttag Leg and Smarttag Neck, by comparing them with results from live observations and video recordings. Eight lactating dairy cows were observed for 22.5 hours each and video recordings were made of six dry dairy cows for 5.5 hours each. Lying, standing, walking, eating, standing up and ruminating were recorded. Data were compared with results from the Smarttag Leg and Smarttag Neck by calculating Cohen’s Kappa, univariate linear regression analysis, Pearson’s correlation and concordance correlation coefficient. Visual observations and video observations show correlation coefficients of >0.85 with the results of the Smarttags for all behaviours except walking. Correlations between visual and video observations and Smarttag results for walking were 0.45 and 0.50 respectively, possibly due to low incidence and difficulties in observing this behaviour. These results provide strong evidence that the Smarttag Leg and Smarttag Neck can reliably be used to monitor specific behaviours. With this system, the farmer can monitor behaviour and detect behavioural changes.
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