What are the merits of endogenising land-use change dynamics into model-based climate adaptation planning?

Integrated assessment models often treat land-use change as an external driving force. In reality, land-use is influenced by environmental conditions. This paper explores the merits of endogenising land-use change, i.e. making the land-use change a dynamic internal process, in models used for supporting climate adaptation planning. For this purpose, we extend the Waas model, a hypothetical case study used before for testing new model-based climate adaptation approaches. We use a utility-based land-use change model for endogenising the land-use dynamics, evaluate its implications, and identify the conditions under which it becomes important. We find that endogenising land-use dynamics changes the performance of the policies, allows for assessing policies that affect land-use, and widens the outcomes of interest that can be considered. The relevancy of endogenising land-use dynamics depends on (i) the expected severity of future climate change, (ii) the society’s sensitivity to climate events, and (iii) the types of policy options that decision makers want to evaluate. Ignoring the interaction between the environment and the society (in this case land-use) can result in both under- and overestimation of the impacts of adaptation and might limit the adaptation options that are considered

Dimensions of data sparseness and their effect on supply chain visibility

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility

Knowledge co-production for decision-making in human-natural systems under uncertainty

Decision-making under uncertainty is important for managing human-natural systems in a changing world. A major source of uncertainty is linked to the multi-actor settings of decisions with poorly understood values, complex relationships, and conflicting management approaches. Despite general agreement across disciplines on co-producing knowledge for viable and inclusive outcomes in a multi-actor context, there is still limited conceptual clarity and no systematic understanding on what co-production means in decision-making under uncertainty and how it can be approached. Here, we use content analysis and clustering to systematically analyse 50 decision-making cases with multiple time and spatial scales across 26 countries and in 9 different sectors in the last decade to serve two aims. The first is to synthesise the key recurring strategies that underpin high quality decision co-production across many cases of diverse features. The second is to identify important deficits and opportunities to leverage existing strategies towards flourishing co-production in support of decision-making. We find that four general strategies emerge centred around: promoting innovation for robust and equitable decisions; broadening the span of co-production across interacting systems; fostering social learning and inclusive participation; and improving pathways to impact. Additionally, five key areas that should be addressed to improve decision co-production are identified in relation to: participation diversity; collaborative action; power relationships; governance inclusivity; and transformative change. Characterising the emergent strategies and their key areas for improvement can help guide future works towards more pluralistic and integrated science and practice

Smoothness metrics for reaching performance after stroke:Part 1: which one to choose?

BACKGROUND: Smoothness is commonly used for measuring movement quality of the upper paretic limb during reaching tasks after stroke. Many different smoothness metrics have been used in stroke research, but a ‘valid’ metric has not been identified. A systematic review and subsequent rigorous analysis of smoothness metrics used in stroke research, in terms of their mathematical definitions and response to simulated perturbations, is needed to conclude whether they are valid for measuring smoothness. Our objective was to provide a recommendation for metrics that reflect smoothness after stroke based on: (1) a systematic review of smoothness metrics for reaching used in stroke research, (2) the mathematical description of the metrics, and (3) the response of metrics to simulated changes associated with smoothness deficits in the reaching profile. METHODS: The systematic review was performed by screening electronic databases using combined keyword groups Stroke, Reaching and Smoothness. Subsequently, each metric identified was assessed with mathematical criteria regarding smoothness: (a) being dimensionless, (b) being reproducible, (c) being based on rate of change of position, and (d) not being a linear transform of other smoothness metrics. The resulting metrics were tested for their response to simulated changes in reaching using models of velocity profiles with varying reaching distances and durations, harmonic disturbances, noise, and sub-movements. Two reaching tasks were simulated; reach-to-point and reach-to-grasp. The metrics that responded as expected in all simulation analyses were considered to be valid. RESULTS: The systematic review identified 32 different smoothness metrics, 17 of which were excluded based on mathematical criteria, and 13 more as they did not respond as expected in all simulation analyses. Eventually, we found that, for reach-to-point and reach-to-grasp movements, only Spectral Arc Length (SPARC) was found to be a valid metric. CONCLUSIONS: Based on this systematic review and simulation analyses, we recommend the use of SPARC as a valid smoothness metric in both reach-to-point and reach-to-grasp tasks of the upper limb after stroke. However, further research is needed to understand the time course of smoothness measured with SPARC for the upper limb early post stroke, preferably in longitudinal studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-021-00949-6