Sustainable Design Research Articles

Safety-driven design of carbon capture utilization and storage (CCUS) supply chains: A multi-objective optimization approach

Carbon capture, utilization, and storage supply chains (CCUS) play a pivotal role in achieving sustainability targets but necessitate meticulous risk identification and mitigation measures. Traditional safety assessments often occur post-design, constraining proactive risk management efforts. Hence, there is a pressing need to optimize safety performance during the design stages. To address this challenge, a framework for evaluating and optimizing CCUS supply chain safety performance using inherent safety index system (ISI) is introduced. Recognizing the trade-offs between total cost, environmental impact reduction, and risk mitigation, our approach considers multi-objective optimization to concurrently address these sustainability objectives and generate a Pareto set of solutions. Utilizing the augmented ε-constraint method, we applied this framework to optimize CCUS networks and develop sustainable designs across three key objectives. The method was applied to a CCUS system that includes various CO2 utilization pathways to minimize the total annual cost, CO2 emissions, and safety risks. The resulting Pareto surface illustrates unique network configurations, each representing a distinct trade-off scenario. Through a case study, we optimized a CCUS network to achieve economic, environmental, and safety objectives. The most economically viable design, with a total annual cost of $97 million and a 40 % net carbon reduction, prioritizes CO2 utilization for value-added products, while limiting CO2 sequestration. Conversely, safety-focused designs shift utilization towards safer routes, including CO2 sequestration and algae production. The proposed framework offers a systematic approach to developing sustainable CCUS supply chain designs, balancing economic viability, environmental sustainability, and safety.

Reinterpretation of Passive Cooling Strategies in Hot And Dry Climate Traditional Architecture: Vents in The Building

The building sector is among the most critical factors affecting environmental sustainability due to its impacts, such as energy consumption and greenhouse gas production. The reasons for energy consumption and greenhouse gas production in the building sector may vary according to climatic characteristics. In hot and arid climate regions, a significant portion of energy consumption is caused by indoor cooling, especially since the focus is on reducing the indoor temperature. In the periods when traditional architecture was developed, the lack of technological products that would increase energy consumption was effective in providing indoor air conditioning with passive design strategies. This study aims to identify the building openings for natural ventilation and cooling used in the traditional architecture of hot and dry climates and to analyze and evaluate their modern interpretations. A total of 235 award-winning projects were analyzed, and 21 were included in the study. In this context, it was determined that 16 different strategies used in traditional architecture were re-interpretation, and their modern interpretations were analyzed through award-winning projects. The traditional passive design strategies identified in this context have been adapted to modern architecture in 18 types. Wind orientators come to the forefront. In addition, it was determined that the strategies adapted to modern architecture are generally integrated. This study guides modern interpretations of traditional passive design strategies for sustainable building design.

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

How Socio-Economic and Cultural Factors Shape Privacy in Ibadan's Public Housing Estates

This study investigates the socio-economic and cultural characteristics of residents in selected public housing estates in Ibadan, focusing on the privacy regulatory mechanisms they adopt. By employing a mixed-methods approach, the research involved administering questionnaires to 565 household heads and conducting in-depth interviews with eight key informants from neighbourhood associations. The findings indicate a significant majority (92.00%) of respondents have adopted privacy regulating mechanisms, with personal space and territorial behaviour being the most common. ANOVA results reveal a significant relationship between these mechanisms and the residents' socio-economic and cultural characteristics. Qualitative insights from key informants' interviews provided a nuanced understanding of residents' privacy needs, highlighting emotional and behavioural cues, as well as verbal and non-verbal data. The study concludes that privacy regulation in public housing varies across different estates and is influenced by nine socio-economic and cultural factors, offering guidance for sustainable housing design that considers contemporary urbanization's socio-economic impacts. These findings can inform architects and policymakers in creating housing designs that respect privacy and enhance the quality of life for residents.

N/O Co-doped Porous Carbon with Controllable Porosity Synthesized via an All-in-One Step Method for a High-Rate-Performance Supercapacitor.

A green and economical methodology to fabricate carbon-based materials with suitable pore size distributions is needed to achieve rapid electrolyte diffusion and improve the performance of supercapacitors. Here, a method combining in situ templates with self-activation and self-doping is proposed. By variation of the molar ratio of magnesium folate and potassium folate, the pore size distribution was effectively adjusted. The optimal carbon materials (Kx) have a high specific surface area (1021-1676 m2 g-1) and hierarchical pore structure, which significantly promotes its excellent capacitive properties. Notably, K2 shows an excellent mass specific capacitance of 233 F g-1 at 0.1 A g-1. It still retained 113 F g-1 at 55 A g-1. The assembled symmetric supercapacitor exhibited an outstanding cyclic stability. It maintains 100% capacitance after 100 000 cycles at 10 A g-1. The symmetric supercapacitor demonstrated a maximum power density of 99.8 kW kg-1. This study focuses on the preparation of layered pore structures to provide insights into the sustainable design of carbon materials.

Exploring the use of recycled materials in interior design and their environmental benefits

Sustainable and environmentally friendly interior design practices have grown in popularity in recent years. The field of interior architecture has adopted a fresh perspective in its design process. Consideration of both the user's and the environment's well-being are central to sustainable interior design. Sustainable interior design is discussed in the article, along with the significance and use of certain materials. In addition, examples of completed projects that prioritize environmental friendliness are shown, along with the fundamental principles of sustainable design. As a result of the economy's meteoric rise, "green ecology" has quickly gained widespread acceptance. Investigating and studying the potential uses of biodegradable materials in this setting is worthwhile. The use of biodegradable materials in home decor is the topic of this article. Avoidance, reduction, and reuse are the three main tenets of waste minimization initiatives.

Innovative Sustainable Design Approaches in Urban Architecture: Balancing Aesthetics and Environmental Impact

Innovative sustainable design approaches in urban architecture play a crucial role in addressing the growing challenges of urbanization, environmental degradation, and the need for aesthetically pleasing living spaces. This study explores various sustainable design strategies, such as biophilic design, Net-Zero Energy Buildings (NZEBs), green roofs, vertical gardens, adaptive reuse, and circular economy principles, highlighting their ability to balance environmental impact with aesthetic appeal. Biophilic design integrates natural elements into urban environments, enhancing mental and physical well-being, while NZEBs focus on energy efficiency and reducing carbon footprints through renewable energy sources. Green roofs and vertical gardens provide ecological benefits, including air purification and biodiversity, while also creating visually engaging urban landscapes. Adaptive reuse and the circular economy emphasize the repurposing of existing structures and materials, promoting sustainability while preserving cultural heritage. Through a qualitative literature review, this research identifies the benefits and challenges associated with these design approaches and discusses their potential to transform urban architecture into more sustainable and livable spaces. The findings suggest that a holistic approach, integrating aesthetics and sustainability, is essential for creating urban environments that are resilient, environmentally responsible, and socially inclusive. This study provides valuable insights for architects, urban planners, and policymakers seeking to implement sustainable design principles in contemporary urban settings.

Sustainable Innovative Design of Elderly-Friendly Smart Medical Products: An Integrated Model

Under the promotion of combined medical and elderly care (CMEC) policies, the market demand for elder-friendly smart medical products as convenient intelligent healthcare devices is growing. However, most studies on elderly-friendly smart medical products focus on functional enhancement and cost control, and there is a lack of research on the sustainable innovative design of elder-friendly smart medical products from the perspective of elderly emotional needs. Therefore, this paper proposes an integrated framework based on the fuzzy Kano model, Kansei engineering, and theory of inventive problem solving (TRIZ) for mapping the complex and dynamic emotional needs of the elderly to product design parameters to produce innovative solutions, ensuring the sustainability of the design process and the innovativeness of the design solutions of elder-friendly smart medical devices. We verified the effectiveness and applicability of this integrated framework through a case study involving sustainable innovation design of a smart blood pressure device. The results of this study are of considerable theoretical and practical significance for promoting the development of the market for elder-friendly smart medical products under the policy of CMEC, meeting the needs of the elderly for healthcare devices and improving their quality of life.

Building energy consumption analysis and measures: a case study from an administration building in Chengdu, China

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Thermoelectric Generator Applications in Buildings: A Review

With growing concerns about building energy consumption, thermoelectric generators (TEGs) have attracted significant attention for their potential to generate clean, green, and sustainable power. This comprehensive review explores the applications of thermoelectric generators (TEGs) in building systems, focusing on recent advancements from 2013 to 2024. The study examines TEG integration in building envelopes, including façades, walls, windows, and roofs, as well as non-integrated applications for waste heat recovery and HVAC systems. Key findings highlight the potential of TEGs in energy harvesting and thermal management, with façade-integrated systems generating up to 100.0 mW/m² and hybrid LCPV/T-TEG systems achieving overall efficiencies of 57.03%. The review also identifies critical parameters affecting TEG performance, such as solar intensity, thermoelectric arm length, and PCM melting temperature. Despite promising results, challenges remain in improving overall system efficiency, cost-effectiveness, and scalability. Future research directions include developing more efficient thermoelectric materials, optimizing system designs for various climatic conditions, and exploring integration with smart building management systems. This review provides valuable insights for researchers and practitioners working towards more energy-efficient and sustainable building designs using TEG technology.

Algae Windows: A Novel Approach Towards Sustainable Building Design and Energy Conservation

Algae Windows: A Novel Approach Towards Sustainable Building Design and Energy Conservation

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Numerical and experimental analyses of hydraulic rams

This study explores hydraulic rams, a technology that exploits the effect of water hammer to pump water without the need for external artificial energy. Although first developed in the 18th century, a comprehensive numerical model elucidating the dynamics of each element, including valve manoeuvres, remains absent. Few authors have contributed specific models, often relying on simplistic approaches like rigid water column theory, or the method of characteristics (MOC) for water hammer equations with either instantaneous valve manoeuvre boundary conditions, or depicting the hydraulic ram as a single-node boundary condition. Some use sophisticated computational fluid dyamics (CFD) based models, primarily for optimizing individual components like the waste valve. Addressing this gap, this work introduces a novel comprehensive numerical model detailing full system dynamic – pipes, junctions, and valve behaviour. Analytical validation and experimental verification, using original laboratory-scale data, affirm the model's reliability. This advance improves understanding of hydraulic ram performance and encourages innovative, environmentally sustainable designs.

Analysis of home thermal energy conservation and green environment design based on intelligent devices and optical image monitoring

Global climate change and energy crisis are becoming increasingly serious, and household energy consumption occupies a certain proportion in the overall energy consumption. The purpose of this study is to analyze thermal energy use in home environment through intelligent devices and optical image monitoring technology, so as to achieve effective energy conservation and environmental optimization design. In this paper, intelligent sensors and optical image monitoring equipment are used to monitor and collect the thermal energy consumption of different households in real time. At the same time, data analysis technology is used to deeply analyze the heat flow, heat loss and equipment use efficiency in the home. The study compares the effect of traditional energy-saving methods and intelligent monitoring technology. After implementing targeted optimization design through intelligent monitoring equipment, the overall thermal energy saving rate is higher, and the comfort level and green index of the home environment are significantly improved. The results show that intelligent equipment and optical image monitoring technology show a good application prospect in home thermal energy saving. Through accurate monitoring and data analysis, heat energy waste can be effectively identified, so as to take corresponding measures to achieve energy conservation and emission reduction, and promote the landing of green environmental design.

Multi-objective optimization for a green forward-reverse meat supply chain network design under uncertainty: Utilizing waste and by-products

The essential requirement for food stands as a pivotal human need, exerting significant ecological impact from production to consumption. Meat, a key dietary component, offers essential nutrients vital for human health. This paper presents a bi-objective two-stage stochastic optimization model for a green forward-reverse meat supply chain network design, addressing both economic and environmental concerns throughout the chain. In the forward flow, the supply chain manages various meat products consist of fresh, processed, and frozen meat products, ensuring their eco-efficient production and distribution. Meanwhile, in the reverse flow, waste and by-products generated during the production process are repurposed and reused. The study promotes environmental sustainability by repurposing waste, utilizing by-products, and minimizing carbon emissions. The proposed model is solved using popular exact ε-constraint method for smaller instances and Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Strength Pareto Evolutionary Algorithm 2 (SPEA2) meta-heuristic algorithms are employed for larger instances. SPEA2 outperforms both MOPSO and NASG-II, demonstrating less average gap that is 0.38% and 0.76%, respectively. Additionally, the findings reveals that an average 2.5% reduction in environmental impacts associated with an average 5% decrease in profit. The noteworthy outcomes of the research empower managers to navigate the implications of fluctuating demand effectively. Moreover, it is crucial to underscore the effects of conversion rates, particularly those associated with the manufacturing process. An excessively high conversion rate can negatively impact profitability and worsen environmental issues. Conversely, lowering the conversion rate can enhance profitability and mitigate environmental impacts.

Optimizing the thermal performance of phase change materials in building applications using deep reinforcement learning and Bayesian optimization

This research presents a novel methodology for Deep Reinforcement Learning (DRL) and Bayesian Optimisation of the thermal performance of PCMs in building operations. The developed models utilise a unique and large dataset comprising 1500 building thermal profiles, simulated for various climates and building setups obtained from our industry partners and as open data. PCM-based systems are deployed for thermal insulation of building envelopes to regulate indoor temperature conditions and reduce the need for heating and cooling systems, resulting in enhanced energy efficiency. Through the real-time management of the thermal efficacy of PCMs using the DRL method trained on the large dataset and fine-tuning of the underlying model parameters using Bayesian Optimisation, the optimised system achieves energy saving in heating and cooling load of up to 45 percent, along with the induced reduction in CO2 emission. At the same time, DRL contributes to decreasing the thermal fluctuation in the indoor temperature and keeps it in the narrow range of 1.2 °C in case of high thermal variability scenarios. Currently, the best performance is reported in the literature. This research exemplifies the potential of DRL and Bayesian optimisation in sustainable building. It depicts the applications of advanced intelligent computing algorithms with big building energy data as a novel, robust and superior approach for optimising real-world building energy management systems. The methodology and the improvements in energy savings in thermal and energy management of buildings highlight the novelty and potential benefit of the implemented research as a new intellectual property towards sustainable building design.

60 Sustainable impact of an outdoor exercise program on health outcomes of older adults: a quasi-randomized controlled trial with a 3-month follow-up

Abstract Older people benefit in several health-promoting outcomes from needs-oriented exercise programs in their immediate living environment. For a low-threshold, and thus sustainable and cost-effective design of exercise programs a transfer to self-organisation is favourable. The aim of our study is to examine the health-promoting impacts of an outdoor exercise program developed for older persons, as well as its sustainability after 3 months. Ninety-five persons aged 60+ were randomized according to their residence (IG: n = 40, mean age = 72.35 years, 24 female; KG: n = 55, mean age = 75.05 years, 34 female). An age-adjusted exercise program was carried out over 12 weeks (2x/week á 45-60min; adherence: 90.9%) by professional exercise instructors in urban parks. During this instructor-led exercise phase, seven participants were trained as mentors, and thus, enabled to continue the exercise program for further 12 weeks (1-2x/week á 45-60min; adherence: 2-3x/month = 11.5%, 1-2x/week = 88.5%). Before (T1) and after the instructor-led exercise phase (T2) as well as after the mentor-led exercise phase (T3), health outcomes (e.g., walking distance/6MWT, neurocognitive performance/DSST) of IG and CG (no intervention) were assessed and analysed using paired t-Tests. 75.1% of IG participants completed both phases of the exercise program. Neurocognitive performance improved significantly for IG between T1 and T2 (+2.32 correct number-symbol matches; p = .003; d = -.509) and remained stable between T2 and T3 (+0.15 correct matches; p = .863; d = -.028) while for CG the opposite development was observed (T1-T2: -0.6 correct matches; p = .451; d = .102; T2-T3: +2.18 correct matches; p = .004; d = -.423). IG improved their walking distance for T1-T2 by 21.39m (p = .172; d = -.223), which could be maintained for T2-T3 (+5,1m; p = .648; d = -.074). Improvements in CG for walking distance were smaller (T1-T2: +2.8m; p = .690; d = -.055; T2-T3: +14.26m, p = .315; d = -.147). The mentoring of the exercise groups proved to be very successful, as evidenced by the maintained effects in the mentor-led phase, ensuring sustainability of health-promoting interventions. (Funded by the Free State of Saxony, “Demographics” #100535625)

Improved Variable Scale Optimisation Algorithm in Water Saving Landscape Design and Its Application

Abstract With the acceleration of urbanisation, the problem of water resource shortage is becoming increasingly serious. Currently, the water use situation in Chinese cities is severe and the contradiction between the supply and demand of water resources is prominent, which has become one of the main factors restricting sustainable development of cities. Traditional research on water-saving landscape design focuses mainly on the analysis of the efficiency of water resource utilisation within a single area or city. The scope of the research is limited and the research methods are individual, which cannot fully meet the current needs of sustainable urban water-saving landscape design. The improved variable scale optimisation algorithm has become an indispensable tool in modern landscape design and would play a more important role in landscape design and ecological environment protection in the future. In response to the shortcomings of traditional variable-scale optimisation algorithms in solving the optimisation problem of water-saving landscape design for ecological sustainable development, this article would use an improved variable-scale optimisation algorithm to study the water-saving landscape design for ecological sustainable development in Community X, City B, Province A. The research results indicated that there were three experts who rated the effect of the water saving landscape design of the first group of designers as good or above, accounting for 37.5 %. The number of people who rated the effectiveness of water-saving landscape design in the second group as good or higher was 8, representing 100 %. The improved variable-scale optimisation algorithm could effectively improve the application of water-saving landscape design in ecological sustainable development.