Network science and explainable AI-based life cycle management of sustainability models.

Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning techniques can provide data-driven solutions throughout the modeling life cycle. In a changing environment, existing models must be continuously reviewed and developed for effective decision support. Thus, we propose to use the Machine Learning Operations (MLOps) life cycle framework. A novel approach for model identification and development is introduced, which involves utilizing the Shapley value to determine the individual direct and indirect contributions of each variable towards the output, as well as network analysis to identify key drivers and support the identification and validation of possible policy intervention points. The applicability of the methods is demonstrated through a case study of the Hungarian water model developed by the Global Green Growth Institute. Based on the model exploration of the case of water efficiency and water stress (in the examined period for the SDG 6.4.1 & 6.4.2) SDG indicators, water reuse and water circularity offer a more effective intervention option than pricing and the use of internal or external renewable water resources.

Bringing the Nature Futures Framework to life: creating a set of illustrative narratives of nature futures.

To halt further destruction of the biosphere, most people and societies around the globe need to transform their relationships with nature. The internationally agreed vision under the Convention of Biological Diversity-Living in harmony with nature-is that "By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people". In this context, there are a variety of debates between alternative perspectives on how to achieve this vision. Yet, scenarios and models that are able to explore these debates in the context of "living in harmony with nature" have not been widely developed. To address this gap, the Nature Futures Framework has been developed to catalyse the development of new scenarios and models that embrace a plurality of perspectives on desirable futures for nature and people. In this paper, members of the IPBES task force on scenarios and models provide an example of how the Nature Futures Framework can be implemented for the development of illustrative narratives representing a diversity of desirable nature futures: information that can be used to assess and develop scenarios and models whilst acknowledging the underpinning value perspectives on nature. Here, the term illustrative reflects the multiple ways in which desired nature futures can be captured by these narratives. In addition, to explore the interdependence between narratives, and therefore their potential to be translated into scenarios and models, the six narratives developed here were assessed around three areas of the transformative change debate, specifically, (1) land sparing vs. land sharing, (2) Half Earth vs. Whole Earth conservation, and (3) green growth vs. post-growth economic development. The paper concludes with an assessment of how the Nature Futures Framework could be used to assist in developing and articulating transformative pathways towards desirable nature futures. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-023-01316-1.

Effort for money? Farmers' rationale for participation in agri-environment measures with different implementation complexity.

European agri-environment programmes are based on the common principle that farmers deliver environmental services for which society pays. Due to the voluntary nature of agri-environment measures (AEM), the issue of farmers' motives or reasons for participation has been an important topic of investigation in past years. The present paper examines farmers' rationale for participation in AEM against the backdrop of continued debate over whether to develop relatively simple measures that can be readily applied by many farmers or give greater priority to measures that are more targeted - i.e. to the specific management requirement of particular habitats or species - but are often more complex. The paper draws on empirical material from a case study in the Dyle valley, Belgium, including in-depth interviews, expert consultations and a mail survey. It was sought not only to identify and quantify the importance of separate reasons for participation, but also to reveal how these reasons and other elements of relevance were logically interrelated in the explanation that farmers themselves give for their participation. As a result, six modes or styles of participation were identified: opportunistic, calculative, compensatory, optimising, catalysing and engaged. The analyses suggest that there were notable differences in that both separate reasons for and modes of participation do vary with the complexity of the measures' requirements. Overall, the study demonstrates that participation in AEM is not simply a matter of weighing the money against the effort for adoption. Whereas money is an important driver for participation (in particular, for those adopting complex AEM) it plays widely differing roles depending on the level of farmers' reasoning (farm enterprise, single practice or landscape feature) and the importance they give to other considerations (environmental effect, production potential of land, goodness of fit, etc.). Practical implications are drawn for both policy makers and programme managers who develop and make available tailor-made support.