Abstract

Climate resilience in urban areas is increasingly critical in the face of climate change, particularly in regions where climate variability poses significant challenges. This study introduces the Climate Resilience Index for Town Sustainability, a novel, multidimensional framework designed to evaluate the resilience of 19 Hungarian cities, including Budapest and county capitals. The framework incorporates 41 parameters across environmental, social, and infrastructural dimensions, addressing significant gaps in existing resilience assessments by providing a region-specific, holistic evaluation. The research employs advanced statistical techniques, including principal component analysis and k-means clustering, to analyze the data sourced from the Hungarian Central Statistical Office and the National Adaptation Geo-Information System. This analysis revealed substantial variability in resilience scores among Hungarian county capitals, with Békéscsaba achieving the highest scores due to its extensive green infrastructure, renewable energy adoption, and lower proportion of vulnerable populations. In contrast, Budapest recorded one of the lowest scores, highlighting challenges such as limited green spaces, high population density, and elevated energy consumption. Clustering analysis grouped the cities into eight distinct categories, emphasizing the role of geographic and climatic factors in shaping urban resilience. The findings demonstrate the critical importance of targeted interventions, such as expanding green infrastructure, improving energy efficiency, and enhancing sustainable practices. By offering actionable insights for policymakers, this index not only advances resilience research but also provides a replicable framework adaptable to other regions. Its innovative approach to integrating multidimensional parameters represents a significant contribution to the understanding and improvement of urban climate resilience in a changing world.

Abstract

This study uses Grey Relational Analysis to investigate corporate resource allocation strategies for carbon reduction. Analysing 214 valid survey responses, it identifies five key measures prioritised by companies: "Resource recovery and reuse" as the most critical, followed by "Energy-saving lighting and equipment." "Enhancement of energy-saving behaviours" and "Optimisation of energy efficiency" are also important, with "Energy-saving policies and management" providing essential support. The research offers a novel perspective on resource allocation strategies and proposes a dynamic knowledge management system framework to evaluate and improve carbon reduction initiatives. Managerial implications include prioritising resource allocation, enhancing employee training, investing in advanced technologies, and reinforcing policy-making support. Recommendations for designing questionnaires tailored to information systems are provided, along with a system interface example to help businesses track carbon reduction progress.

Abstract

This study investigates the asymmetric effects of financial development, energy consumption, and foreign direct investment on carbon dioxide emissions in Vietnam from 1990 to 2021 by employing a nonlinear autoregressive distributed lag. The findings reveal complex and asymmetric relationships in both the short and long term. In the long term, carbon dioxide increases with positive shocks in financial and economic growth, and negative shocks in energy consumption. Conversely, these emissions decrease with negative shocks in financial development and positive shocks in foreign direct investment. Short-term results also show that financial development significantly promotes carbon emissions. Although energy consumption shocks initially reduce these emissions, they later increase them. Notably, positive foreign direct investment shocks were found to reduce carbon dioxide emissions in the short term. These findings emphasise the need for policies that balance economic development, energy use, foreign investment attraction, and environmental protection in a developing economy.

Abstract

Resource oriented management of organic waste streams, such as waste-activated sludge and food waste, presents an opportunity to recover energy and nutrients while mitigating climate change. The characteristics of sludge, including low nutrient and high solid contents, limit its treatment through anaerobic digestion. Anaerobic co-digestion of sludge with an abundant organic-rich substrate such as food waste can overcome the limitations of sludge mono-digestion. The sustainability of the codigestion approach is due to the ability to subsidise the energy demand of the entire wastewater treatment system with the bioenergy produced. Moreover, other resources, namely, struvite, liquid fertiliser, and compost, can be recovered. The present study reviews the anaerobic codigestion of sludge with food waste, focusing on the waste substrates produced in South Africa.

Abstract

Strategic environmental assessment has been considered significant to the achievement of sustainable transformations and many countries have successfully integrated this instrument in their legal frameworks. Nevertheless, its implementation is influenced by several critical factors, and its strategic nature makes its application a challenge in Global South countries. This paper aims to discuss these factors through the case study of strategic environmental assessment integration and its implementation in Nicaragua. To do this, a literature review regarding success factors for implementation was conducted, and a characterization of the state of implementation in Nicaragua was performed. A series of interviews were conducted with environmental experts and stakeholders was executed a) to develop a social cognitive map, that was used to validate the literature-based strategic environmental assessment characterization. And b) to identify the specific factors contributing to the challenges for its implementation. As a result, it was determined that a total of 32 factors were affecting the successful integration of strategic environmental assessment in plans, programmes and policies. Through the social cognitive mapping, the authors identified the most influential factors and challenges to integrate this type of assessment in Nicaragua. This paper provides indicated potential approaches to overcome obstacles to improve strategic environmental assessment for decision-making and environmental planning.

Abstract

Energy efficiency in buildings drives the development of sustainable materials, with Phase Change Materials standing out for their contribution to the construction sector. Phase Change Materials, integrated into materials like cement or concrete, regulate indoor temperatures by absorbing heat during the day and releasing it at night. Accurate thermal property assessment is crucial for optimizing these materials, yet conventional experimental methods are time-consuming, costly, and require specialized labor. While automation and machine learning streamline the process, they do not eliminate the need for expertise but rather shift the focus toward data-driven material innovation, complementing rather than replacing traditional roles. To enhance efficiency, our study integrates deep neural networks. A Generative Adversarial Network first augments the dataset, and a Multilayer Perceptron then predicts the properties of cementitious composites enriched with Phase Change Material and nano-silica aerogel. Using inputs such as mass composition and density, the model outputs compressive strength and thermal conductivity. Training with synthetic data yields high predictive accuracy, highlighting the potential of data augmentation in domains with limited datasets. This approach enhances the precision and efficiency of assessing thermal performance in innovative construction materials while supporting the evolving role of experts in the field.

Abstract

Decarbonization is essential for addressing climate change, but it has wide-ranging implications for sustainable development. This paper examines the spectrum of implications of decarbonization on key areas of sustainable development, such as poverty reduction, job creation, food-water-energy systems, ecosystems, and biodiversity. It highlights both opportunities and challenges, including the need to balance environmental goals with social equity and economic stability. The analysis shows that careful planning and targeted policies are required to address regional disparities, land-use conflicts, and job transitions. By aligning decarbonization efforts with sustainable development goals, this paper argues that the energy transition can support a fairer, more resilient, and prosperous future for all.

Abstract

Renewable Energy Communities are experiencing a period of strong growth throughout Europe. The available data indicate, however, that the growth rate of these entities in the different member states varies greatly depending on the economic policies implemented. Not only the number of contributions made available, but also and above all the modalities and bureaucratic simplification play a crucial role in the long-term sustainability of these bottom-up initiatives. In this article, the case of the new incentive schemes implemented in Italy as a result of the transposition of the European Renewable Energy Directive  II was is analyzed. Decree no. 414 of the Italian Ministry for the Environment and Energy Security published on 23.01.2024 was examined to assess its potential and possible hidden barriers compared to the experimental one. Different scenarios were simulated by varying the variables contained in the formulas determining the incentives available to Renewable Energy Communities in order to understand which configurations could benefit most and which were at risk of being left behind. Results indicate how, as the price of energy sales increases, the new decree may be detrimental to communities, benefiting only investor members, and how, as the plant size varies, southern Italian regions may be disadvantaged by the new incentive scheme compared to those in the north.

Abstract

The use of non-renewable energy in the Tomini Bay region of Gorontalo is high. At the same time, Indonesia's non-renewable energy reserves are dwindling. A breakthrough in energy planning is needed to meet regional energy needs in a sustainable manner. The purpose of this study is to determine energy demand and availability based on regional per capita growth in the present and future. The research method used was the observational method and energy modeling of the Long-Range Energy Alternatives Planning System (LEAP). The results showed that energy demand in Tomini Bay, Gorontalo has increased significantly. In 2050, the energy demand will be 1,425 MWh. The balance between energy demand and availability will be achieved by diversifying energy towards renewable energy and developing energy from natural resources (river energy). The energy value that can be obtained in 2050 is 1489.57 MWh. Good energy management planning can achieve energy balance.

Abstract

In this research work, the proposed Dual Predator Optimization algorithm is inspired by the hybridization of the very well-known Whale Optimization Algorithm and Grey Wolf Optimization. This algorithm integrates with a hybrid micro-grid to optimize the use of renewable resources, reduce reliance on fossil fuel, and increase the cost-effectiveness of using excess energy by adjusting these parameters over time. The Dual Predator Optimization is flexible and more suitable for hybrid energy management. The findings indicate that Dual Predator Optimization effectively manages hybrid systems by substantially lowering electricity expenses and diminishing the likelihood of supply interruptions. It was determined that in comparison to the hybridization algorithms, the Cost of Energy of the proposed Dual Predator Optimization technique is reduced to an average of 20%, however, the Loss of Power Supply Probability rises to an average of 7.5%. it offers zero load shedding within the hybrid system with 100% renewable-satisfied energy production for the microgrid. Moreover, the proposed Dual Predator Optimization outperformed others in terms of producing hydrogen by 21.10% and 17.60% respectively. The findings indicate that Dual Predator Optimization is a superior method for addressing energy reliability and environmental sustainability issues.

Abstract

Water security is a key objective for advancing sustainable development and is becoming increasingly difficult to achieve under the pressures of climate change. Ensuring water security requires equitable access to water services of adequate quantity and quality to meet diverse needs, while also guaranteeing the sustainability of these services in the face of natural and anthropogenic threats. The concept of territorial water vulnerability is introduced as the propensity of a given territory to fail in adequately meeting water needs due to its structural susceptibility to socio-environmental stressors. The Territorial Water Vulnerability framework is applied to assess water security risks related to urban drinking water services in the Gran Valparaíso conurbation in Chile, a territory affected by a decade-long drought, pronounced social inequalities, and fragmented water governance. Using a fuzzy logic approach, an integrative index is developed that combines territorial indicators of sensitivity and response capacity, considering both technical and sociocultural factors driving household vulnerability to water scarcity at a fine spatial resolution. Through cluster analysis, neighbourhood profiles with common characteristics accounting for elevated vulnerability levels are identified. The results show that 4,841 out of 10,042 census blocks exceed a vulnerability threshold of 0.5, primarily due to sensitivity factors such as water supply constraints, coverage of informal settlements, and frequency of unscheduled annual outages, compounded by a consistently low response capacity (median 0.08 on a scale from 0 to 1). This research provides: (1) an analytical framework to assess urban water security from a household-level perspective that incorporates social dimensions; (2) a methodological approach for high-resolution vulnerability analysis that integrates technical and sociocultural systems; and (3) empirical evidence to inform public policy in a region where water security has been underexplored.

Abstract

Traditionally, modelling tools for Multi-energy systems planning and management focus on the minimization of a single monetary objective. Multiple objectives are usually merged via monetization rates. The work presented herein aims to develop modelling frameworks to explore of configurations of Multi-energy systems according to non-comparable objectives and extract trade-off solutions through optimization algorithms. Three different methodologies are presented, integrating the single-objective configuration model CALLIOPE with multi-objective algorithms for exploring the decision space. These are tested on a synthetic case study and evaluated for their input data requirements, computational demands and ability to thoroughly map the solution space. Results show each approach returns optimal system configurations, with considerably different technology mixes depending on objective priorities. The methodologies highlighted here represent a significant step forward in the search for multi-objective models for Multi-energy systems planning and management, to support the search for truly integrated, efficient, and sustainable solutions.

Abstract

The widespread adoption of Autonomous Vehicles made it crucial for researchers to understand how Autonomous Vehicles affect transportation networks. When studying the impact of Autonomous Vehicles on transportation networks, it is essential to consider traffic flow, infrastructure readiness, and overall performance. Furthermore, the implementation of Autonomous Vehicles raises concerns regarding safety due to various reasons including, but not limited to public acceptance and regulatory frameworks. Thus, analyzing Autonomous Vehicles interaction with existing transportation systems provides valuable insights regarding the benefits and challenges related to the implementation of Autonomous Vehicles. This underscores the need to examine these interactions to better understand the influence of Autonomous Vehicles on travel patterns, congestion levels, and network performance. The aim of this paper is to provide a summary of the impacts of Autonomous Vehicles’ integration into existing transportation system, which aids in formulating strategies and frameworks for the future that affirms optimal traffic flow and safety. The methodology used in this paper is a systematic literature review in accordance with PRISMA guidelines. Various literature sources were retrieved from Google and Google Scholar search engines, Scopus and Web of Science databases, and the AUS e-library. This paper highlights research gaps in this area that serve as a reference for future studies and provides perspectives on the impact of adopting Autonomous Vehicles.

Abstract

Decarbonisation is a difficult process and a core scepticism lies in the potentially higher cost of energy due, for instance, to the Emission Trading System. It is possible to analyse the energy transition process and provide guidance for policy actions thanks to integrated planning tools. This research explores the impact of carbon prices on the optimal design of the Italian energy system in order to understand their influence on the uptake of renewable energy sources as well as on consumers’ energy cost. The research employs the H2RES software under different carbon pricing scenarios and understand its impact on the optimal energy mix, cost of electricity and hydrogen. The outcomes show how different carbon price trends lead to different energy costs until 90% renewable penetration, point at which the energy mix is mostly detached from carbon pricing mechanisms and energy costs become stable and independent from external price signals.

Abstract

The recycling of construction and demolition waste is one of the solutions for reducing the environmental impacts, where residues can be used in permeable paving, produced with large recycled aggregates. The novelty herein is to conduct an experimental testing stage for producing permeable concrete slabs, using coarse Recycled aggregates from Construction and Demolition waste, as a future application in permeable paving. The methodology consists of evaluating the effects of Construction and Demolition waste aggregates on the properties of permeable concrete in the fresh state and the hardened state. Permeable concrete slabs were produced based on five types of concrete mixtures, which can be applied as a final coating on soils with permeable profiles. A sustainable environment contributes to the improvement of knowledge and development on one of the applications of permeable concrete elements by recycling the waste generated by Civil Construction activities, mainly from non-renewable resources.

Abstract

Real-world insights to conserve water in constructions without overshooting embodied energy meet sustainable development goal 12, specifically in the current thirsty world. The study aims to outline conclusive strategies to conserve embodied water and embodied energy, together with regulatory insights. Following the International Organization for Standardization’s 14046 and 14044 frameworks, the experiment accounts for cradle-to-gate lifecycle assessment, taking Jammu’s conventional houses in India as cases. Observing top-impacting embodied water materials differ from embodied energy ones, the experiment delves into the appraisal of ‘threats’ and ‘opportunities’ in locals’ preferences using the scenario manager technique. Not only was the embodied water and embodied energy offsetting by almost 30% achieved, but also, the flexible scenarios suiting economically diverse users have significant pragmatic worth. While the recommendations base is the embodied water-energy nexus and retains societal interests; indeed, the methodology and study’s implications are global and replicable. The experiment meets the three pillars of sustainability and thereby remarkably boosts the sustainable building practice.

Abstract

Food waste contributes to social inequalities and sustainability issues by worsening resource overuse and environmental harm. The United Nations Sustainable Development Goal 17 highlights the importance of reducing food waste to address hunger and promote a sustainable, economically viable global food system. This paper examines the geographic differences in food waste levels among European Union member nations and analyses the associations between food waste and diverse environmental, geographic, social and economic indicators, including Sustainable Development Goals and other sustainability metrics. Using dimensionality reduction methods, nontrivial multivariate connections between food waste and these parameters were identified, allowing for the characterisation of countries based on a few significant factors. Principal Component Analysis (PCA), applied to food waste data across European Union countries, uncovered three distinct groups: (1) those with elevated food waste in primary production, manufacturing and distribution stages; (2) those with lower waste in these domains yet elevated waste in restaurants and households; and (3) those with all of their food waste components smaller than or equal to the average. The multivariate linear correlation between the PCA factors and socio-economic parameters is nonsignificant, but a few (nonlinear) regularities could be identified: five of the six countries of the first group above are characterised by the population settled mainly on flatland and an above-average supply of meat or fish. Another pattern observed is that former Eastern Bloc countries belong to the third group. The research findings offer valuable insights that can inform the efforts of environmental experts, professionals and policymakers working in the circular economy and waste management domains. This knowledge can facilitate the development of more effective strategies aimed at mitigating food waste and promoting sustainability.

Abstract

The study aim was to determine the technical parameters of Solid Recovered Fuel (SRF) fuel inlet into the Air Through precalciner (AT-type) while maintaining process stability and the quality characteristics of the produced cement clinker. The simulation was done for a real-scale plant using numerical Computer Fluid Dynamics (CFD) modelling with the use of own developed calculation method. The method enables consideration of the primary effects of combustion, identification of process changes trends, and incorporation of particulate matter from SRF fuel and raw meal into process stability. The model was based on Eulerian–Lagrange two phase flow approach. The processes occurring in the decarbonization zone are modelled using a simplified one-dimensional approach. Based on the conducted research, the optimal location for the Solid Recovered Fuel fuel inlet was determined with a fuel inlet diameter of 150 mm, a fuel flow rate of 1000 kg/h, and a transport air flow rate of 1000 Nm³/h.

Abstract

Heating and cooling sectors are pivotal in the European Union’s pursuit of climate neutrality by 2050. District heating and cooling networks, in particular the 5th generation, offer a valuable solution for reducing primary energy use and carbon dioxide emissions. This paper delves into the control optimisation of 5^th generation district heating and cooling networks using white-box model predictive control techniques. The aim is to develop a nonlinear model predictive control approach for a 5^th generation district heating and cooling network characterised by a directional medium flow and compare it to a non-directional medium flow network also controlled by nonlinear model predictive control. Physics-based models of building envelopes and hydraulics, developed in Modelica, are used. A model predictive control simulation is carried out to investigate the system’s operation and make a comparison of both model predictive controlled 5^th generation district heating and cooling networks. Despite being less flexible, the directional configuration achieves lower energy use and good thermal comfort by leveraging fluctuating network temperatures.

Abstract

Natural dyes offer a sustainable alternative to synthetic dyes, addressing environmental concerns in textile production. Currently, the dyeing and finishing stages typically consume large amounts of water, energy, and chemicals. A microwave-assisted dyeing process using chitosan and banana floral stem extract has been developed for cotton fabrics to enhance sustainability. This process was optimised using Response Surface Methodology and BoxBehnken Design to minimise lightness and maximise colour difference. The optimal conditions include a radiant power of 554.12 W, radiation time of 7.10 min, and chitosan concentration of 0.80%. These conditions result in a lightness value of 62.04, a colour difference of 35.36, and a desirability coefficient of 0.856. Cotton samples treated under such conditions exhibited superior colourfastness compared to conventional methods, with ratings of 4 (good) for washing, 45 (good to excellent) for lightfastness, and 45 (good to excellent) for perspiration. The colourfastness scale ranges from 1 (very poor) to 5 (excellent), indicating the level of resistance to fading or colour change. The study demonstrates the ecofriendly and efficient potential of microwave-assisted dyeing with chitosan, promoting sustainable textile practices.