Circular Economy and Eco-Innovation Solutions for Low-Carbon Buildings in Cities: The Case of Kayseri

An analysis of eco-innovations solutions for efficient low carbon buildings through circular economy principles (reduce, reuse, recycle), that also consider economic and social indicators has been performed at the national (Turkey) and urban scale (Kayseri). The framework for the city of Kayseri and the implementation of the circular economy for construction chain were determined that the three enabler tools which are policies, funding and awareness and collaboration could help to implement circular city model in Turkey. Reducing energy intensity and understanding the factors that can influence this (such as urbanization and industrialisation) will help mitigate future climate changes, improve local air pollution and health

What drives the viability of waste-to-energy? Modelling techno-economic scenarios of anaerobic digestion and energy generation for the Scottish islands

Anaerobic digestion, a technology which converts biowaste into biogas, can address issues of waste utilisation, energy security and reducing emissions. Co-digestion of waste could improve biogas yields and synergies between sectors but requires transport of waste. To improve on existing biowaste-to-energy models which consider simple transport costs, this work combines a techno-economic model with a capacitated vehicle routing problem (CVRP) solver to consider detailed waste transport costs with actual Open Street Map (OSM) road networks. This addresses whether biowaste-to-energy techno-economic modelling is improved with more specific transport costs and more broadly how factors of resource availability, generation technology and transport costs influence the viability of anaerobic digestion and generation plants. The levelised cost of energy (LCOE) is used to compare scenarios of these aspects. The Scottish islands have been modelled as a case study due to high biowaste potential and varied topographies, which both influence transport costs. Number of waste vehicles required is improved by 42.8% and the unit cost of collection varies from £0.1–1670.0/tonne. Local topographies and waste availability significantly affects the viability of individual facilities, which might not be considered by simpler collection cost metrics. Between 14.0 and 20.6% of the regions electricity demand could be met by biogas. While industrial facilities co-located with demand have the cheapest LCOE, this can in some cases be improved with other waste streams, highlighting the need for further research on and policies supporting co-digestion, as well as improving household and business participation rates. Incentives and avoided costs are crucial to supporting biowaste-to-energy if more isolated regions are to benefit from improved waste utilisation

Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R2 of 0.99. At the hourly level, the MAPE of 6.2% and R2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model

Renewable and Sustainable Energy Transition

For decades, the Greek islands have been facing challenges in terms of quality of power supply, increased carbon dioxide equivalent (CO2eq) emissions, and costs due to their reliance on oil-fired generation subsidised by the Greek state. In light of the recent reforms to decarbonise the islands' region while enhancing their local grids, this study investigates the impact of electromobility considering an autonomous electricity system supported by storage versus an interconnected one. Two Electric Vehicles (EVs) deployment scenarios coupled with several charging strategies have been modelled using the PLEXOS energy systems model. The results highlight that the Vehicle-to-Grid (V2G) scenarios demonstrate the most evident benefits for the islands' electricity systems, performing adequately under both the Autonomous and Interconnection scenarios concerning the economic and environmental impact. Such scenarios have the potential to reduce emissions by 8.5% while dropping costs up to 20% by 2040, when combined with the required renewables expansion plan. From the security of supply perspective, the results demonstrate improvements under the interconnected context accompanied by thermal generation restrictions without however eliminating power shortages recorded already in a non-EV case. The analysis also showcases an escalated impact on power shortages and curtailments during the maximum week, particularly when combined with an ambitious EV deployment. Yet, V2G may increase renewables share up to 7% in 2040. In this context, EVs could mobilise the additional deployment of 600 MW renewables by 2040 if interconnections with the mainland are realised. Assuming islands continue operating as autonomous electricity systems, the additional capacity to accommodate may reach 720 MW

Gaps in the governance of floods, droughts, and heatwaves in the United Kingdom

Disaster risk reduction (DRR) and equitable resilience have cross-cutting challenges relevant to the Sustainable Development Goals (SDGs), Sendai Framework (SF) and Climate Change Adaptation (CCA). The capacity of governments to assess, prevent, prepare, respond, and recover from disasters depends on effective laws, planning, policies, governance instruments, equity indicators, harmonized standards, and a holistic approach to cross-sectoral issues and multi-scalar challenges. The principle of subsidiarity guides the United Kingdom (UK) approach to disaster governance, with decisions taken at lowest level and coordinated at different scales (national, sub-national, local). Cross-scale work needed to address large-scale issues and enable the pooling of resources, happens at a sub-national tier created especially for this purpose. At national level, there is a government lead department for each risk identified in the National Risk Assessment, with Department for Environment, Food and Rural Affairs (DEFRA) serving as the lead for floods and droughts, while the Department of Health and Social Care is the lead for heatwaves. In this paper we present the current state of the art of the governance of floods, droughts, and heatwaves in the UK, with a focus on pre-emergency phases and the shortage of indicators for assessment of the effectiveness of adaptation for all three disasters, which also compromise the realization and monitoring of targets across all three agendas. The governance of floods counts with the most developed legal framework of the three. Droughts are mainly dealt by the water sector, while heatwaves are treated exclusively as a health issue, leaving gaps with regards to the multiple risks these disasters pose to livelihoods and other sectors. Gaps and challenges that remain are related to siloed institutional approaches, lack of adaptation indicators, lack of cross-sectoral resilience standards, and lack of policy instruments and metrics to promote equitable resilience. Commonly, actions have mainly focused on the response and recovery strategies instead of risk reduction and adaptation to address rising vulnerabilities and exposure

Social Media Behaviour Analysis in Disaster-Response Messages of Floods and Heat Waves via Artificial Intelligence

This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform a qualitative analysis via topic inference in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with 7 additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection

Social Media Data Analysis Framework for Disaster Response

This paper presents a social media data analysis framework applied to multiple datasets. The method developed uses machine learning classifiers, where filtering binary classifiers based on deep bidirectional neural networks are trained on benchmark datasets of disaster responses for earthquakes and floods and extreme flood events. The classifiers consist of learning from discrete handcrafted features and fine-tuning approaches using deep bidirectional Transformer neural networks on these disaster response datasets. With the development of the multiclass classification approach, we compare the state-of-the-art results in one of the benchmark datasets containing the largest number of disaster-related categories. The multiclass classification approaches developed in this research with support vector machines provide a precision of 0.83 and 0.79 compared to Bernoulli naïve Bayes, which are 0.59 and 0.76, and multinomial naïve Bayes, which are 0.79 and 0.91, respectively. The binary classification methods based on the MDRM dataset show a higher precision with deep learning methods (DistilBERT) than BoW and TF-IDF, while in the case of UnifiedCEHMET dataset show a high performance for accuracy with the deep learning method in terms of severity, with a precision of 0.92 compared to BoW and TF-IDF method which has a precision of 0.68 and 0.70, respectively

Investigation of high renewable energy penetration in the island of Syros following the interconnection with the national grid system

This paper aims to assess the potential of high renewable energy (wind and solar) integration in the Greek island of Syros, following the scheduled interconnection with the national grid system. Currently, Syros operates an oil fired autonomous power system (APS), emitting large amounts of carbon emissions. Interconnection among a number of islands in the Cyclades and the mainland will eliminate the use of APS, will reinforce islands’ power network and will allow exploitation of high wind and solar potential. It has been concluded that following the interconnection, the installation of 33.5 MW of wind and solar energy is feasible. The assumed capacity will cover the total energy demand by 2030 allowing also electricity exports to the Greek mainland. Transforming Syros into a regional renewable energy hub will contribute to the energy security, providing access to its own energy resources

The Nexus: Estimation of Water Consumption for Hydropower in Brazil

Recent major droughts in Brazil have given rise to discussions about water availability and security in relation to energy production. The relationship of the two resources, the water-energy nexus, is recognised as being of importance in literature and metrics for its estimation and understanding are sought after. One important aspect in understanding the water-energy nexus of hydroelectricity is estimating its water consumption and also its water footprint. In order to do this, this study uses a modified Penman-Monteith method to estimate evaporation from Brazil’s reservoirs for the period 2010-2016 and subsequently calculates the water footprint of hydroelectricity reservoirs. The results show the evaporation variation in space and time in the reservoirs and the differences of water consumed per unit of energy in Brazil. The discussion provides insight as to how the results can be valuable for future management and planning purposes