The quantification of greenhouse gas (GHG) emissions is increasingly important in spatial planning for regions, cities, and areas. The combination of territorial and consumption-based accounting (CBA) approaches can currently be considered best practice for calculating GHG emissions at sub-national levels, in terms of informing local decision-making about the different climate impacts of spatial planning policies, both within the boundaries of a given region and for the inhabitants of that region. This study introduces four European case studies that were conducted using the two quantification approaches to assess the climate impacts of locally relevant planning policies. The case studies represent different scales of spatial planning, different European planning systems, and different situations in terms of data availability. Territorial results are not suitable for inter-regional comparison, but rather for internal monitoring, while CBA allows for comparison and provides a comprehensive picture of the global carbon footprint of residents, however, with indications that are more difficult to link to spatial planning decisions. Assessing impacts, and in particular interpreting results, requires both methodological understanding and knowledge of the local context. The results of the case studies show that setting climate targets and monitoring the success of climate action through a single net emissions figure can give false indications. The study shows that the two approaches to quantifying GHG emissions provide complementary perspectives on GHG emissions at the sub-national level and thus provide a more thorough understanding of the GHG emission patterns associated with spatial planning policies. The identification of the regional differences in GHG emission sources and mitigation potentials are the main functions of sub-national GHG inventories and the impact assessment for spatial planning. Harmonization of the data collection for sub-national GHG inventories and the transparency of underlying assumptions would greatly support the coherence of climate action and the implications to spatial planning.

AI, data science, and other computing domains are fueling an unprecedented increase in the demand for computing power. Satisfying this demand, however, brings significant environmental challenges as computing is energy-intensive and devices improve rapidly making older devices obsolete and resulting in a significant accumulation of e-waste. We contribute by examining the potential of repurposing smartphones classified as e-waste as small-scale data centers with the aim of extending the life-cycle of these devices, thus reducing e-waste accumulation and contributing toward more sustainable computing practices. We demonstrate how hardware features can be bypassed to enable reprogrammability at a cost of approximately 8€ . We use our prototype to implement a custom sensor data collection and analysis solution. We also showcase possible applications, highlighting the range of scenarios where repurposing can be useful, as well as identifying challenges and potential future avenues for research. These include development of better tools to facilitate the repurposing process and development of tools that are agnostic of the underlying hardware characteristics.

Particulate matter (PM) is a major air pollutant that can have adverse effects on human health, especially for vulnerable populations such as children, the elderly, and those with respiratory or cardiovascular conditions. This study presents a method for prediction of particulate matter concentration with aerodynamic diameter smaller then 10 μm (PM₁₀) in an urban environment. Meteorological data and vehicle flow data from an urban road in Curitiba, Brazil, were used. The air quality was analyzed in two monitoring points located 1 km apart, the sampling points are named Politécnico and Perkons, where SDS011 optical sensors were installed. The prediction was based on the machine learning algorithm Random Forest (RF). The baseline concentration was a dataset from historical records of particulate matter measurements from monitoring stations in Curitiba. Several scenarios were tested and it was concluded that the daily time scale presents the best performance in PM₁₀ prediction, with 80.42% accuracy, using the baseline and PM₁₀ Perkons as descriptors. The most important meteorological variables for the prediction were: air temperature (°C), wind speed (m/s), and wind gust (m/s). Throughout the day there were two peaks with large amounts of pollutants in the air, near 8:00 am and 6:00 pm, times when there are the largest flows of vehicles circulating on the road. The Random Forest algorithm proved to be a good estimator of PM concentration, which is a proxy for air pollution.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

Smart plants , sensor-equipped plant containers, are emerging as a promising solution for monitoring indoor environments and human activities. The plant containers are usually strategically placed close to areas where people typically spend time with the aim of enhancing indoor quality, satisfaction, and comfort. We contribute an innovative concept, smart plants on wheels, that integrates smart plant containers with autonomous ground vehicles allowing them to move between different areas within a workspace. By doing so, we aim to foster improved well-being and work productivity. To demonstrate our concept, we have built a proof-of-concept prototype. In addition, we explore applications and research challenges to establish a broader research agenda. In a user study involving $N=24$ participants, we found that our concept significantly enhances productivity in a real working environment. Specifically, we are able to accelerate response times by up to 32% (in complex cases).

Virtual reality (VR) solutions have reached a high level of maturity across various sectors; however, their application in education remains limited. This paper aims to outline the requirements and process for designing and implementing VR as a tool in the educational process. As a part of the design process, we have conducted a pre-study with 20 professionals who have basic knowledge of VR technology and less than three months of practical experience. The pre-study involved a one-day workshop following Frederic Vester's 6-step approach of Interconnected Thinking: identifying and defining the target, defining relevant impact factors, exploring interactions between these factors, evaluating the impact of each factor, developing possible solutions, and planning activities for implementation. The results indicate that individual introductory activities are the main influencing factor, while advanced didactical methods that employ team-based learning support successful introduction. Conversely, motivation is not crucial for successful implementation but has the role of the success measurement, although VR sickness could negatively impact the willingness to use VR in education. The paper also discusses limitations, provides recommendations, and suggests directions for future research, offering additional insights and completing the study.