What is Policy Analytics? An Exploration of 5 Years of Environmental Management Applications.

Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.

Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study.

Various transboundary river basins are facing increased pressure on water resources in near future. However, little is known ab out the future drivers globally, namely, changes in natural local runoff and natural inflows from upstream parts of a basin, as well as local and upstream water consumption. Here we use an ensemble of four global hydrological models forced by five global climate models and the latest greenhouse-gas concentration (RCP) and socioeconomic pathway (SSP) scenarios to assess the impact of these drivers on transboundary water stress in the past and future. Our results show that population under water stress is expected to increase by 50% under a low population growth and emissions scenario (SSP1-RCP2.6) and double under a high population growth and emission scenario (SSP3-RCP6.0), compared to the year 2010. As changes in water availability have a smaller effect when water is not yet scarce, changes in water stress globally are dominated by local water consumption-managing local demand is thus necessary in order to avoid future stress. Focusing then on the role of upstream changes, we identified upstream availability (i.e., less natural runoff or increased water consumption) as the dominant driver of changes in net water availability in most downstream areas. Moreover, an increased number of people will be living in areas dependent on upstream originating water in 2050. International water treaties and management will therefore have an increasingly crucial role in these hot spot regions to ensure fair management of transboundary water resources.

Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015.

An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990-2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015).

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models.

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model.