Data privacy and smart home energy appliances: A stated choice experiment.

Data privacy in smart homes is receiving increasing attention due to the growing adoption of smart appliances. Adoption of smart appliances can bring benefits, including energy consumption reduction. This study investigates how people made the trade-offs between sharing privacy-sensitive data and the potential environmental and economic benefits of smart home energy appliances using discrete choice modeling. The findings reveal that the trade-off is mainly affected by four product attributes: the type of data that is processed, the reason why this data is processed, the data sharing frequency, and the financial benefit gained from the smart home appliances. Specifically, individuals tend to share less data daily for their daily routine convenience and demand a (theoretical) financial compensation for the data sharing. The results also show that privacy attitudes are not related to data sharing preferences, while socio-demographics, including gender, age, and income, are. The results emphasize the gap between people's attitudes and behaviors regarding data privacy. This research serves as a foundation for further investigations and can be used by smart appliance retailers, manufacturers, and governments for designing research and development focus and energy reduction incentives, respectively.

Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences.

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository.

The Nature-Based Solutions Case-Based System: A hybrid expert system.

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.

Uptake and implementation of Nature-Based Solutions: An analysis of barriers using Interpretive Structural Modeling.

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.

Assessment of the relation between land use and carbon emission in Eindhoven, the Netherlands.

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.

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations.

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer-generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape-level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class- and patch-level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user-defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.