Sustainable Planning Research Articles

Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality

The transition towards carbon neutrality and sustainable urban development necessitates innovative strategies for managing multi-energy supply stations (MESS), which integrate oil, gas, electricity, and hydrogen to support diversified energy demands. Existing revenue prediction models for MESS lack interpretability and multi-energy adaptability, hindering actionable insights for sustainable operations. This study proposes a novel Shapley additive explanations (SHAP)-driven machine learning framework for multi-energy supply station revenue forecasting. By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R2 metrics. The RF model achieved an R2 of 0.98, demonstrating superior accuracy in predicting hourly gross transaction values. SHAP analysis further identified consumption volume and transaction frequency as the most influential factors, providing actionable insights for operational optimization. This research not only advances the scientific management of MESS but also contributes to carbon emission reduction by enabling data-driven resource allocation. The proposed framework offers policymakers and industry stakeholders a scalable tool to accelerate urban energy transitions under carbon neutrality goals, bridging the gap between predictive analytics and sustainable infrastructure planning.

Identifying vulnerability factors associated with heatwave mortality: a spatial statistical analysis across Europe

Abstract In recent decades, Europe has experienced severe heatwaves with significant mortality impacts. While hazard and exposure are key factors, vulnerability drivers play a crucial role in shaping these outcomes. However, few studies have examined these drivers at a continental scale. This study presents the first dynamic heat vulnerability assessment for Europe, incorporating spatial and temporal dimensions through Ordinary Least Squares regression. Subnational (NUTS2) heatwave mortality data is used as the dependent variable, while independent variables include high-resolution raster data on heat hazard parameters, estimated population exposure, and socio-economic, demographic, and environmental vulnerability factors at both raster and subnational (NUTS2) scales. Our results (adjusted R^2 = 0.662) identify foreign citizenship and urbanization as the most influential drivers, with a 1% increase in the percentage of foreign citizens and the size of urban areas associated with a 12.1% and 7.3% rise in heatwave-related mortality, respectively. Based on these findings, we construct the European Heat Vulnerability Index (Eu-HVI) for 2000–2019 as a weighted sum of the identified drivers, using the regression coefficients as weights. The results suggest that foreign citizens may face increased heat vulnerability due to intersecting socioeconomic factors. Policy recommendations include promoting inclusive integration measures to address disparities among foreign populations, and prioritizing sustainable urban planning and nature-based solutions to enhance resilience in rapidly urbanizing areas, ensuring equitable access to green spaces.

Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine

Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitoring these dynamic changes is crucial for sustainable urban planning and management. This study leverages machine learning and Google Earth Engine to investigate urban dynamics and its interactions with biophysical conditions in the Kaduna River Basin (KRB), Nigeria. This study utilized a dataset of 192 points, initially extracted from Google Earth Engine, to analyze urban transitions between 1987 and 2020, incorporating biophysical and environmental variables such as population density, precipitation, and surface temperature. The dataset was processed in R using the CARET package, with missing data imputed via K-nearest neighbors (KNN), categorical variables transformed using One-Hot Encoding, and numerical variables rescaled for consistency. A tenfold cross-validation approach was used to train and validate machine learning models, including random forest, support vector machine, KNN, and multivariate adaptive regression splines, ensuring optimal model performance. Model evaluation metrics such as overall accuracy, kappa, F1 score, and area under the curve confirmed the reliability of the models in identifying the biophysical factors influencing urban changes. The findings revealed overall accuracy of 0.80, 0.73, 0.71, and 0.72 and kappa statistics of 0.60, 0.46, 0.42, and 0.45 for random forest (RF), multivariate adaptive regression splines, support vector machine, and KNN, respectively, with RF emerging as the most accurate model (80%) for predicting urban change patterns in KRB. Land cover changes reveal rapid urban expansion (154.81%), declining water bodies (− 95.79%), and vegetation growth (174%). Machine learning models identify population density and water stress index as key urban change drivers, with climate factors like temperature and precipitation playing crucial roles. The results of this study offer valuable insights into the processes driving urban transformation and present a robust methodology for monitoring and predicting future urban development. This study aids in the creation of strategies for sustainable urban growth and the mitigation of adverse environmental impacts.

Utilizing Remote Sensing Data to Evaluate the Urban Heat Island: A Case Study of Quetta City, Balochistan

The urban heat island (UHI) effect presents a significant environmental challenge in rapid urbanization. Its mitigating impacts on public health, power consumption, and urban livability require a brief understanding of energy dynamics. MODIS satellite data, has detailed spatial and temporal coverage that serves as an essential tool in analyzing temperature trends, changes in land cover, and other related variables. Therefore, this study leverages MODIS data to analyze the impact of UHI, identifying significant changes in temperature distribution patterns over time. Our results inferred expansion in the high-temperature zone, shrinking in the central temperature zone, and comparatively stable in the low-temperature zone. These trends align with the trajectory of global warming and emphasize a wide-ranged impact on the ecosystem, weather patterns, and human activities. These findings trend a pronounced peak in global warming during the months observed for rainfall, underscoring a clear connection to climate change. However, the analysis highlights the limitations of short-term datasets in capturing long-term UHI trends and calls for more comprehensive temporal data to visualize geographic complexity. urbanization and climate change in the region It provide the basis for creating sustainable urban planning strategies and important step towards achieving climate-friendly urban ecosystems that can mitigate the cascading effects of global warming.

Design of a hybrid renewable energy system and green hydrogen production for smart cities: A carbon emission reduction approach

The rapid growth of population and industrial development worldwide has significantly increased energy demand. With the limitations and environmental impacts of conventional energy resources, renewable energy sources are essential for sustainable development. This study presents a renewable hybrid energy system designed to meet a city's electricity needs while generating green hydrogen for hydrogen-powered vehicles, a rising trend in transportation. The goal is to create a self-sufficient and environmentally friendly smart city. The proposed system integrates photovoltaic panels, wind turbines and a biomass generator, supported by lithium-ion batteries for energy storage and green hydrogen generation. Optimized through numerical analysis of experimental load data, the system is designed to handle an average annual electrical load of 14,946,686.40 kWh and produce 58.5 kg of hydrogen daily. A total of 14,550 simulations were conducted, yielding a levelized cost of electricity (LCOE) of $0.3959/kWh. The system is projected to reduce 9,398 tons of CO2 emissions annually. Additionally, the use of 150 hydrogen-powered vehicles in the smart city is estimated to prevent further emissions of 325,215 tons/year, 295,650 tons/year, and 204,491 tons/year under different scenarios. This research highlights the transformative potential of hybrid renewable energy systems and hydrogen-powered vehicles for urban sustainability. By substantially reducing carbon emissions, it supports the development of greener, smarter cities and opens avenues for future innovations in renewable energy integration and sustainable urban planning.

Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis

The intersection of environmental justice and urban accessibility presents a critical challenge in sustainable city planning. While the “15-minute city” concept has emerged as a prominent framework for promoting walkable neighborhoods, its implications for environmental exposure inequalities remain underexplored. This study introduces an innovative methodology for assessing air pollution exposure disparities within the context of 15-minute activity zones in New York City. By integrating street-level PM2.5 predictions with spatial network analysis, this research evaluates exposure patterns that more accurately reflect residents’ daily mobility experiences. The results reveal significant socioeconomic and racial disparities in air pollution exposure, with lower-income areas and Black communities experiencing consistently higher PM2.5 levels within their 15-minute walking ranges. A borough-level analysis further underscores the influence of localized urban development patterns and demographic distributions on environmental justice outcomes. A comparative analysis demonstrates that traditional census tract-based approaches may underestimate these disparities by failing to account for actual pedestrian mobility patterns. These findings highlight the necessity of integrating high-resolution environmental justice assessments into urban planning initiatives to foster more equitable and sustainable urban development.

Forecasting urban expansion in Delhi-NCR: integrating remote sensing, machine learning, and Markov chain simulation for sustainable urban planning

Forecasting urban expansion in Delhi-NCR: integrating remote sensing, machine learning, and Markov chain simulation for sustainable urban planning

The role of sustainable energy and climate action plans (SECAPs) in urban energy transition.

Holistic energy and climate planning are prominent issues for Sustainable Land Management (SLM). This paper critically evaluates existing data of Sustainable Energy and Climate Action Plans (SECAPs), focusing on medium-sized Spanish cities within the framework of the 1ISECAP project under the H2020 program, as Spain stands out as the EU country with the most energy plans implemented in this range of cities. The study assesses the potential for creating synergies through SECAPs, highlighting challenges and potential contributions to local energy governance. SECAPs were selected based on previous studies, targeting cities with exemplary plans due to their specific implementation, integration with other urban and territorial plans, or success as examples of good practices. The goal is to reconnect land use planning with energy planning, fostering synergies that enhance sustainable energy planning and execution while engaging civil society in the energy transition. Results reveal the interplay between SECAPs and other urban planning tools, emphasizing their role in building synergies across spatial, sectoral, and governance dimensions through a comparative approach. Key success factors include spatial integration, diversification of funding sources, and collaborative frameworks. Overall, SECAPs are essential instruments for local energy governance, with their effectiveness closely tied to the degree of synergy achieved between public and private actors, convergence with other urban plans, and active citizen participation. However, in some cases, SECAPs are used to access external funding without achieving the desired level of coordinated management, limiting their impact.

Improving Ecotourism Inclusion for People with Disabilities : Community-Based Strategies in Ternate, Indonesia

This study examines the effectiveness of a community-based intervention in enhancing public knowledge, attitudes, and behavior toward ecotourism accessibility for people with disabilities in Ternate, Indonesia. Grounded in participatory intervention and behavioral change theories, this research addresses public awareness gaps through educational programs and informational materials, aiming to foster more inclusive tourism experiences. Using a quasi-experimental design with a control group, the study involved 287 participants selected via Cluster Random Sampling from a population of 4,964 residents. The intervention was conducted at three major ecotourism destinations in Ternate: Sulamadaha Beach, Tolire Besar Lake, and Batu Angus Geotourism. Findings indicate a statistically significant improvement in community knowledge (N-Gain = 0.31, p < 0.05) and moderate gains in attitudes and behavior (N-Gain = 0.07, p < 0.05), with notable differences between experimental and control groups. These results underscore the role of participatory approaches in shaping inclusive tourism policies and practices, particularly in ecotourism settings. However, the limited impact on attitudes and behavior suggests the need for extended interventions, reinforcement strategies, and multi-stakeholder collaboration, including policymakers and tourism industry stakeholders. This study contributes to discussions on participatory interventions in tourism development and provides empirical insights for designing policies that integrate accessibility into sustainable ecotourism planning.

Improving Ecotourism Inclusion for People with Disabilities : Community-Based Strategies in Ternate, Indonesia

This study examines the effectiveness of a community-based intervention in enhancing public knowledge, attitudes, and behavior toward ecotourism accessibility for people with disabilities in Ternate, Indonesia. Grounded in participatory intervention and behavioral change theories, this research addresses public awareness gaps through educational programs and informational materials, aiming to foster more inclusive tourism experiences. Using a quasi-experimental design with a control group, the study involved 287 participants selected via Cluster Random Sampling from a population of 4,964 residents. The intervention was conducted at three major ecotourism destinations in Ternate: Sulamadaha Beach, Tolire Besar Lake, and Batu Angus Geotourism. Findings indicate a statistically significant improvement in community knowledge (N-Gain = 0.31, p < 0.05) and moderate gains in attitudes and behavior (N-Gain = 0.07, p < 0.05), with notable differences between experimental and control groups. These results underscore the role of participatory approaches in shaping inclusive tourism policies and practices, particularly in ecotourism settings. However, the limited impact on attitudes and behavior suggests the need for extended interventions, reinforcement strategies, and multi-stakeholder collaboration, including policymakers and tourism industry stakeholders. This study contributes to discussions on participatory interventions in tourism development and provides empirical insights for designing policies that integrate accessibility into sustainable ecotourism planning.

Sustainable Development Planning of the Tribal Villages in the Kalsubai-Harishchandragad Wildlife Sanctuary (MS), India

Sustainable Development Planning of the Tribal Villages in the Kalsubai-Harishchandragad Wildlife Sanctuary (MS), India

Study on the enhancement of ecosystem service function and sustainability planning of landscape gardening based on biomechanics

With the acceleration of urbanization and the impact of climate change, landscape gardening plays an increasingly important role in improving urban ecological environments, enhancing biodiversity and improving ecological service functions. Based on the principle of biomechanics, this paper discusses the strategy of enhancing the ecosystem service function of landscape gardening and proposes to optimize the sustainability of landscape gardening by means of multi-scale planning frameworks, ecological corridor construction, and microclimate regulation techniques. Through the implementation of biomechanical optimization practices in the study area, in this study, multiple technical methods were used for data collection to ensure the accuracy and reliability of the data. For monitoring plant growth, a regular sampling method was adopted to measure growth parameters (such as plant height, crown width, root depth, etc.) of plants in the study area on a monthly basis. In addition, high-precision meteorological monitoring equipment (such as temperature and humidity meters, anemometers, etc.) was used for continuous monitoring of microclimate conditions, with a data collection time span of one year, covering seasonal changes. Soil stability assessment quantifies soil stability and water management capacity through regular sampling of different soil types, using soil shear tests and water retention capacity tests. All data were processed using statistical analysis methods and combined with model simulations to validate the effectiveness of optimization strategies, ensuring the scientific and reproducible nature of research results. It was found that the optimization of vegetation configuration, soil structure and water flow management could significantly improve soil and water conservation capacity, wind speed regulation and microclimate regulation. The high density of vegetation not only enhanced the soil water retention capacity but also effectively reduced the wind speed and improved the local climate environment. In addition, the construction of ecological corridors and reasonable vegetation layout enhances the ecological connectivity and stability of the landscape. By evaluating the sustainability under different seasons, this paper further discusses how to dynamically optimize the landscape according to seasonal changes to ensure the stability of its long-term ecological service function.

An optimization approach for resilient and sustainable planning of intermodal transport

An optimization approach for resilient and sustainable planning of intermodal transport

Landscape Visual Affordance Evaluation at a Regional Scale in National Parks: A Case Study of the Changhong Area in Qianjiangyuan National Park

National parks play a vital role in safeguarding natural scenery, maintaining ecological integrity, and preserving cultural heritage, while simultaneously offering valuable opportunities for recreation and education. Among the diverse resources provided by national parks, visual landscape resources hold particular significance due to their capacity to inspire, educate, and enhance aesthetic appreciation. However, assessing and managing these resources remain challenging, as they span both the physical attributes of the landscape and the human visual perception process. This study aims to develop a theoretical and practical framework for evaluating the “landscape visual affordance” of national parks. Grounded in ecological psychology’s affordance theory, the proposed approach integrates physical affordance and sensory affordance, encompassing both the objective physical attributes of the landscape and the subjective processes of human perception. Drawing on a multi-dimensional set of indicators, the research quantifies physical features—such as topography, land use, vegetation cover, and landscape structure—as well as sensory dimensions, including visibility, visual prominence, and viewing frequency. These elements are synthesized into a landscape visibility assessment model built upon the affordance theory framework. The results demonstrate that landscape visual affordance effectively identifies landscape patches with varying degrees of visual quality and importance within national parks and other protected areas. By providing robust support for management decisions—such as zoned protection, optimizing recreational facilities, and evaluating visitor carrying capacity—this model offers new insights and practical guidance for the sustainable planning and management of landscapes in national parks and other ecologically critical regions.

A DECADE TEMPORAL EVOLUTION OF RICE FIELD AREA CHANGES IN KESIMAN VILLAGE, BALI-INDONESIA

Aim: This research focuses on understanding the extent of rice field loss and the corresponding increase in non-agricultural land to promote sustainable land management and planning. It examines the significant land-use changes in the Jepun Pipil Region, Kesiman Village, Bali, from 2013 to 2024, with a focus on rice fields and non-rice fields. Methodology and results: The study utilizes remote sensing technology and Landsat satellite imagery, applying four vegetation indices (NDVI, EVI, SAVI, and LAI) to analyze temporal changes in land cover. Among these, NDVI proved to be the most effective index for monitoring the transformations. The analysis shows a notable reduction in rice fields, particularly between 2023 and 2024, with a 43.48% decrease due to urban expansion and infrastructure development. Conversely, non-rice fields expanded by 130.43% during the same period, driven by the increasing demand for residential and commercial areas. Conclusion, significance, and impact study: The study highlights the importance of sustainable land-use planning and urges policymakers to strike a balance between development and the conservation of agricultural lands. The findings provide critical insights for local governments and stakeholders in land management and urban planning, contributing to wider efforts in monitoring agricultural land use and shaping effective policy strategies.

Empowering Communities Through Gamified Urban Design Solutions

The rapid urbanization of recent decades has intensified climate change challenges, demanding sophisticated solutions to build resilient and sustainable cities. A key aspect of sustainable urban planning is decentralizing and democratizing its processes, which requires citizen involvement from the early design stages. While current solutions such as digital tools, participatory workshops, gamification, and social media can enhance participation, they often exclude non-experts or those lacking digital skills. To address these limitations, this manuscript proposes a VR/AR gamified solution using open-source software and open GIS data. Specifically, it investigates the euPOLIS game as an innovative participatory tool offering an alternative to traditional approaches. This game decentralizes urban planning by shifting technical tasks to experts while citizens engage interactively, focusing solely on proposing solutions. To explore the potential of the proposed methodology, the euPOLIS game was demonstrated as a workshop activity in TNOC 2024 Festival, where 30 individuals from different academic background (i.e., citizens, architects, planners, etc.) voluntarily engaged and provided their impressions and feedback. The findings suggest that gamified solutions such as serious/simulation AR/VR games can effectively promote co-design, co-participation, and co-creation in urban planning in an inclusive and engaging manner.

Impact of Urban Block Morphology on Solar Availability in Severe Cold High-Density Cities: A Case Study of Residential Blocks in Harbin

Improving solar availability in urban blocks is vital to promoting energy conservation and emissions reduction. However, there are very few studies on the impact of block morphology on solar energy availability in high-density cities based on the particularities of climate and solar energy resources in severe cold regions at higher latitudes. This study took 434 block models generated through seven orientation conditions of 62 residential blocks in Harbin, China, as its research object. Through numerical simulations and statistical analysis, it revealed the quantitative relationship between block morphology and the availability of active photovoltaic and solar thermal collector technologies and passive thermal heating technologies. The results show that active solar technology has the highest availability in multi-story enclosed residential blocks, and passive thermal heating has the highest availability in the multi-high-level mixed-row type. The south façade of the building has the greatest active and passive solar availability. The overall active solar availability of the residential block is significantly negatively correlated with the mean building height, floor area ratio, and volume area ratio; it is significantly positively correlated with site coverage and the standard deviation of the building height. Controlling the block’s orientation between 15° south by west and 15° south by east can increase the active solar availability of the façade. This study provides a reference and evaluation basis for the sustainable planning and design of high-density cities in severely cold regions.

Spatial Coupling Characteristics Between Tourism Point of Interest (POI) and Nighttime Light Data of the Changsha–Zhuzhou–Xiangtan Metropolitan Area, China

Metropolitan areas, as pivotal hubs for global tourism and economic growth, necessitate sustainable spatial planning to balance development with ecological preservation. As critical geospatial datasets, nighttime light (NTL) and point of interest (POI) data enable the robust analysis of urban structural patterns. Building upon coupling coordination theory and polycentric spatial frameworks, this study investigates the spatial interdependencies between tourism POI and NTL data in China’s Changsha–Zhuzhou–Xiangtan Metropolitan Area (CZTMA). Key findings reveal high spatial coupling homogeneity, with three urban cores exhibiting radial value attenuation from city centers toward the tri-city intersection; concentric zonation patterns where NTL-dominant rings encircle high-coupling nuclei, contrasting with sporadic POI-intensive clusters in peri-urban towns; and sector-specific luminosity responses, where sightseeing infrastructure demonstrates the strongest localized NTL impacts through multiscale geographically weighted regression (MGWR). These findings establish a novel “data fusion-spatial coupling-governance” analytical framework and provide actionable insights for policymakers to harmonize tourism-driven urbanization with ecological resilience, contributing to United Nations Sustainable Development Goal (SDG) 11 (Sustainable Cities).

Mapping Groundwater Potential in Arid Regions: A Geographic Information System and Remote Sensing Approach for Sustainable Resource Management in Khamis Mushayt, Saudi Arabia

Groundwater is a critical resource in arid regions such as Khamis Mushayt, located in southwestern Saudi Arabia, where surface water availability is limited. This study integrates various geospatial and environmental datasets to delineate groundwater potential zones (GWPZs) using Geographic Information Systems (GISs) and remote sensing (RS) techniques. Key parameters considered include lithology, slope, drainage density, precipitation, soil type, and vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous studies and field data. The results reveal three distinct groundwater potential zones: poor, moderate, and good. Areas with good groundwater potential account for 8.2% of the study area (16.3 km2) and are predominantly located in the eastern and central parts of the study area, in valleys and low-lying regions with permeable geological formations such as alluvial deposits, supported by higher drainage density and favorable precipitation. Conversely, poor-potential zones represent 27.6% (54.50 km2), corresponding to areas with steep slopes and impermeable rock formations. Moderate-potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and account for 64.2% (127.0 km2). Validation using existing well data demonstrates strong agreement between the identified potential zones and actual groundwater availability. These findings provide a strong framework for sustainable water resource management, urban planning, and agricultural development in Khamis Mushayt and similar arid regions.

Impact of planned urban development on urban heat island effect: resilient cities for a sustainable future.

This comprehensive study delved into the urban dynamics of Bangalore and Hyderabad, focusing on land cover changes, temperature variations, and their implications for sustainable urban development. Analyzing land cover trends from 2001 to 2021, the study revealed substantial urban expansion in both cities, with notable shifts in built-up, vegetation, bare soil, and water bodies. The analysis indicated intensified urbanization, particularly in Hyderabad, raising environmental concerns. The study employed a random forest model to predict contrasting urbanization patterns for 2031, emphasizing the need for resilient and sustainable strategies. The model successfully predicted land surface temperature (LST) and land cover changes, offering insights for informed urban planning. Correlation analyses unveil the influence of factors like proximity to bare soil and built-up areas on LST. The model's performance is robust, outperforming other models, and is employed for 2021 land cover (LC) and LST predictions. Validation demonstrates the model's accuracy in predicting LC distribution and LST across diverse land cover types, urban and rural areas. Furthermore, the model is applied to forecast 2031 LC and LST, revealing contrasting urbanization trends. The study incorporates a hot spot analysis, categorizing thermal zones and highlighting the urban heat island (UHI) effect. The model showcases a capacity to project long-term temperature dynamics, making it a valuable tool for urban planning and climate resilience. The findings underscore the need for sustainable land use planning, emphasizing environmental preservation amid rapid urban growth.