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The impact of the urban morphology on greenhouse gas emission is one of the key issues on global climate change. Since the urban form is directly related to the spatial distribution of urban land use, it is necessary to investigate the relation between carbon emission and different land use categories. In this paper, the city of Eindhoven (230,000 inhabitants) was used as a case study. According to the main road network, the entire city is divided into 6754 irregular patterns. Agglomerative cluster analysis was conducted to classify the patterns into 14 valid land use categories based on their land use function and land cover composition namely: agriculture, transport, retail trade, green space (with 3 sub-categories), residential (with 7 sub-categories), and others. The random forest algorithm was applied to select the significant features and to measure the relation between land use and carbon emission. The results have shown the importance of various landscape metrics on the carbon emission in each land use category. The most significant landscape metric is selected to describe the impact of spatial attributes on carbon emission. The outcomes show the carbon emission distribution of each land use category in the city. The retail trade and residential land use categories contribute a large proportion of carbon emission, terrace houses produce more carbon emission than other residential building categories. The combination of mid-rise buildings and low-rise buildings has a higher probability to produce more carbon emission. The assessment results can provide important support for the low carbon city spatial planning. • Statistics of landscape metrics shows high reliability on land use classification. • Random forest and hierarchical clustering improve classification performance. • CO 2 emission in the residential area is significantly influenced by building layout. • Knowledge of the relation of land use and CO 2 emission supports fine scale plan. [ABSTRACT FROM AUTHOR]

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process. • Learning from experience is essential for enhancing the uptake of NBS. • A case-based system (NBS-CBS) is developed and tested in a Dutch city. • NBS-CBS integrates an artificial neural network with a case-based reasoning model. • NBS-CBS supports the extraction of knowledge from an NBS repository. • NBS-CBS recommends NBS measures and accordingly finds relevant past experiences. [ABSTRACT FROM AUTHOR]

Cities increasingly have to find innovative ways to address challenges arising from climate change and urbanization. Nature-based solutions (NBS) have been gaining attention as multifunctional solutions that may help cities to address these challenges. However, the adoption and implementation of these solutions have been limited due to various barriers. This study aims to identify a taxonomy of dominant barriers to the uptake and implementation of NBS and their relationships. Fifteen barriers are identified from the literature and expert interviews and then ranked through a questionnaire. Interpretive Structural Modeling (ISM) serves to identify the mutual interdependencies among these barriers, which results in a structural model of six levels. Subsequently, Cross-impact matrix multiplication applied to classification (MICMAC analysis) is used to classify the barriers into four categories. The results suggest that political, institutional and knowledge-related barriers are the most dominant barriers to NBS. Cities that intend to apply NBS can draw on these findings, especially by more effectively prioritizing and managing their actions. • Barriers to the uptake and implementation of NBS are highly interdependent. • Identifying the interdependencies is essential for addressing barriers strategically. • The six-level ISM model illustrates the interdependencies among barriers. • Political, institutional and knowledge barriers are the most influential ones. • To spur the adoption of NBS, more engagement of policymakers is needed. [ABSTRACT FROM AUTHOR]