As cities develop new interventions for climate change mitigation, incorporating renewable energy in urban public spaces becomes a common norm to address sustainability objectives. However, current built projects and assessment practices mainly uses a “techno-fixes” approach focusing on strategies that are related to the environmental benefits and neglecting other potential strategies instigating social and economic benefits of renewable energy. The purpose of this study is to present a potential sustainability assessment model introducing new strategies for public space renewable energy use where social and economic benefits of renewables become evident. Supplemented with theories and principles from ecology, the model’s economic strategies refer whether the project considers meaningful part of the produced electricity for generating a local economy; environmental strategy comprises embodied energy, energy storage and self-maintenance; social strategy includes whether the project considers generating active and passive interaction using on-site electricity. Ballast Point Park in Sydney was used as a test bed to examine the model and sustainability of park’s renewable energy use. The findings showed that environmental strategies were evident in the park, social strategies remained average and economic strategies with renewable energy were lacking. Interactions with on-site produced electricity was further claimed to be an imperative feature of any public space. Recommendations were made specific to operational and planning impacts of the integrated model.

Australian cities have experienced a high number of floods and droughts. In Australia, green infrastructure (GI) is increasingly popular in considering natural processes in sustainable water management practices. However, to date, there has been little examination of how the academic literature has addressed the use of GI in Australian landscape planning for water management. To investigate this, we applied a thematic framework and used the PRISMA approach to identify and analyse 98 peer-reviewed papers to better understand whether and how landscape planning perspectives are considered in current water management approaches in Australia. We found a recent increase in Australian-based literature related to GI for water management. However, there is limited literature discussing the significance of landscape connectivity and the multi-functionality of GI. This article concludes with recommendations for future research on the landscape planning principles of multi-functionality, landscape connectivity, and the integration of multiple scales of GI in Australian urban water management.

Integrating renewable energy into public space is becoming more common as a climate change solution. However, this approach is often guided by the environmental pillar of sustainability, with less focus on the economic and social pillars. The purpose of this paper is to examine this issue in the speculative renewable energy propositions for Freshkills Park in New York City submitted for the 2012 Land Art Generator Initiative (LAGI) competition. This paper first proposes an optimal electricity distribution (OED) framework in and around public spaces based on relevant ecology and energy theory (Odum’s fourth and fifth law of thermodynamics). This framework addresses social engagement related to public interaction, and economic engagement related to the estimated quantity of electricity produced, in conjunction with environmental engagement related to the embodied energy required to construct the renewable energy infrastructure. Next, the study uses the OED framework to analyse the top twenty-five projects submitted for the LAGI 2012 competition. The findings reveal an electricity distribution imbalance and suggest a lack of in-depth understanding about sustainable electricity distribution within public space design. The paper concludes with suggestions for future research.

Traditional landslide susceptibility models often rely on statistical methods applied to limited point-based samples, which can limit the spatial continuity of predictions. This study proposes a GIS-integrated, pixel-based machine learning approach that operates directly on raster data to generate continuous landslide susceptibility maps. Implemented in Rwanda, the workflow uses variables derived from a 30 m-resolution digital elevation model (DEM), including slope, aspect, elevation, and curvature, together with auxiliary environmental data such as NDVI, soil type, and distances to hydrological and infrastructural features. Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and ensemble approaches. Using only topographic variables, the Stacked Ensemble achieved the highest performance (F1-score = 0.8515; ROC-AUC = 0.9200). When all predictor variables were included, CatBoost achieved the highest performance (F1-score = 0.8860; ROC-AUC = 0.9564). This scalable approach is particularly suitable for landslide susceptibility assessment in data-scarce regions and can contribute to spatial planning and risk reduction efforts in Rwanda and similar regions.