Environmental Planning Research Articles

Investigating the Effectiveness of Coal-Fired Power Plant Operations: Management, Technical and Air Pollution Aspects

Coal-fired energy has been a major part of Malaysia's power supply, causing environmental pollution and slowing sustainable growth. To address these issues, we evaluated a coal-fired power plant's efficiency using a questionnaire completed by industry experts. This study seeks to find factors affecting coal-fired power generation efficiency and create a statistical model. The questionnaire covered five areas: best management practices, technology efficiency, cost efficiency, fuel efficiency, air pollution control, and the best available technique. Principal Component Analysis (PCA) was used to simplify large data sets. The results showed that 15 principal components were valid, with a KMO value of 0.836 (greater than 0.50) and a Bartlett Test value below 0.05. The results show a strong correlation between the best available technique and various indicators: best management practice (r=0.614, p<0.01), technology efficiency (r=0.719, p<0.01), cost efficiency (r=0.529, p<0.05), fuel efficiency (r=0.662, p<0.01), and air pollution control efficiency (r=-0.752, p<0.01). The model indicates that verifying the standard operating procedure (SOP) is crucial for improving power generation efficiency and reducing human error (R²=0.914). This study pinpoints issues reducing power plant efficiency, particularly regarding emissions, and shows that the regression model is strong (R² = 0.916–0.647). It will assist policymakers and researchers in creating sustainable environmental management plans. Doi: 10.28991/ESJ-2025-09-01-06 Full Text: PDF

Modelling current and future forest fire susceptibility in north-eastern Germany

Abstract. Preventing and fighting forest fires has been a challenge worldwide in recent decades. Forest fires alter forest structure and composition; threaten people's livelihoods; and lead to economic losses, as well as soil erosion and desertification. Climate change and related drought events, paired with anthropogenic activities, have magnified the intensity and frequency of forest fires. Consequently, we analysed forest fire susceptibility (FFS), which can be understood as the likelihood of fire occurrence in a certain area. We applied a random forest (RF) machine learning (ML) algorithm to model current and future FFS in the federal state of Brandenburg (Germany) using topographic, climatic, anthropogenic, soil, and vegetation predictors. FFS was modelled at a spatial resolution of 50 m for current (2014–2022) and future scenarios (2081–2100). Model accuracy ranged between 69 % (RFtest) and 71 % (leave one year out, LOYO), showing a moderately high model reliability for predicting FFS. The model results underscore the importance of anthropogenic parameters and vegetation parameters in modelling FFS on a regional level. This study will allow forest managers and environmental planners to identify areas which are most susceptible to forest fires, enhancing warning systems and prevention measures.

Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system

In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets’ inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes. Our model’s superior performance, demonstrated through rigorous evaluation, exhibits significant improvements in accuracy, precision, recall, and F1 score, even with limited data. The results highlight the potential of ESACN as a reliable tool for enhancing diagnostic accuracy in medical settings. In our case study, the ESACN model was applied to a dataset comprising 659 images across four classes: 178 images of Monkeypox, 171 of Chickenpox, 80 of Measles, and 230 of Normal skin conditions. This case study underscores the model’s effectiveness in real-world applications, providing robust and accurate classification that could greatly aid in early diagnosis and treatment planning in clinical environments.

Based on the New Dynamic Network DEA and Malmquist Index of High-Quality Innovation in the New Energy Vehicle Economy Development Research of the Internet of Things

This paper takes the enterprise economic data of new energy vehicles (NEVs) in 2018–2023 as the research background, combined with the performance evaluation theory of the NEV industry, and systematically discusses how to make the high-quality innovative development of NEVs as the research purpose. We use the new dynamic network data envelope analysis (DEA) model, Marmquist Index and other and the Internet of Things internationally recognized, scientific and effective data models as research methods to make an in-depth analysis of the business performance of five representative enterprises on multi-quantitative input-output issues. The research results show that the NEV industry has a good development prospect and economic foundation on the whole, but it still faces challenges such as limited space for core technology progress, insufficient carbon emission reduction and environmental protection planning. At the end, this paper puts forward innovation suggestions such as accelerating technological innovation, optimizing production process and responding to preferential policies and exploring international market.

Study on A-Star Algorithm-Based 3D Path Optimization Method Considering Density of Obstacles

Collision avoidance and path planning are essential for ensuring safe and efficient UAV operations, particularly in applications like drone delivery and Advanced Air Mobility (AAM). This study introduces an improved algorithm for three-dimensional path planning in obstacle-rich environments, such as urban and industrial areas. The proposed approach integrates the A* search algorithm with a customized heuristic function which incorporates local obstacle density. This modification not only guides the search towards more efficient paths but also minimizes altitude variations and steers the UAV away from high-density obstacle regions. To achieve this, the A* algorithm was adapted to output obstacle density information at each path node, enabling a subsequent refinement process. The path refinement applies a truncation algorithm that considers both path angles and obstacle density, and the refined waypoints serve as control points for Non-Uniform Rational B-Splines (NURBS) interpolation. This process ensures smooth and dynamically feasible trajectories. Numerical simulations were performed using a quadrotor model with integrated PID controllers in environments with varying obstacle densities. The results demonstrate the algorithm’s ability to effectively balance path efficiency and feasibility. Compared to traditional methods, the proposed approach exhibits superior performance in high-obstacle-density environments, validating its effectiveness and practical applicability.

How public libraries reach out to adolescents: a study of adolescents’ perceptions of teen spaces1) in Korean public libraries

ABSTRACT Adolescence is a crucial period for developing abilities necessary for future success. This study focuses on Teen Spaces in public libraries as an environment where adolescents can develop diverse future visions tailored to their personalities. We aim to identify environmental planning factors that influence adolescents’ preference for Teen Spaces, to create dedicated spaces supporting healthy growth. A survey of 234 adolescents using public libraries was conducted to derive key environmental planning factors. Linear regression analysis revealed that “creativity support”, “amenity support”, and “learning support” significantly influenced adolescents’ preference for Teen Spaces. Interestingly, adolescents with prior experience of youth spaces were additionally influenced by “universal design support”, while those without experience were affected by “safety support”, “leisure support”, and “openness support”. The findings suggst that improving Teen Spaces in public libraries, considering these factors and the segmented characteristics of adolescents, can positively impact their growth and future development. This research contributes to ongoing efforts to create supportive environments for adolescents in public libraries, emphasizing the need for spaces that foster diverse future visions and support individual growth trajectories.

Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds

Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response.

A motion planning method for a car based on environmental feature-adaptive polynomial fitting and intelligent obstacle avoidance strategies

This paper proposes an improved method for car motion planning aimed at addressing the limitations of traditional path planning and obstacle avoidance algorithms in complex environments. The study utilizes Bi-RRT* and polynomial fitting for path planning, incorporating an environment-adaptive polynomial fitting technique based on obstacle density to enhance path precision in areas with high obstacle density. In the local planning phase, intelligent switching of the car’s obstacle avoidance strategies is implemented, allowing the car to use reverse motion or lateral avoidance in high-density regions to prevent stalling. Furthermore, problem decomposition and approximation methods are applied to large-scale quadratic programming (QP) problems in path tracking, improving the efficiency of the MPC algorithm. Experimental results demonstrate that the proposed method significantly enhances the car’s motion performance and stability in complex environments.

Parallel Parking Path Planning and Trajectory Tracking in Narrow Environments for Autonomous Unmanned Vehicles

Parallel Parking Path Planning and Trajectory Tracking in Narrow Environments for Autonomous Unmanned Vehicles

Impact of Soil Use On Winter Greenhouse Gas Emissions: The Agricultural Sector in Alagoas Through An Emissions Inventory

Objective: To prepare a greenhouse gas inventory within the state of Alagoas for 2019, using data and factors from livestock and agriculture. Theoretical framework: Preparing an inventory is an important tool for understanding climate change that is directly related to greenhouse gas (GHG) emissions, mostly resulting from anthropogenic activities. In Brazil, agriculture stands out as one of the main sources of emissions, driven by the production of grains and animals for domestic and foreign consumption. Due to the difficulty of direct measurement, emissions are often assessed through inventories. The GHG emissions inventory is one of the basic tools that provides relevant data to direct certain public policies, or civil society actions, to reduce GHG emissions into the atmosphere, given that the state of Alagoas has few scientific productions on GHG sources and emissions. Methodology: Emissions were calculated by multiplying the emission coefficient following the methodology proposed by the IPCC, GHG Protocol, using the multiplication of the emission coefficient also indicated by the IPCC, MCTIC, EMBRAPA by the total number of heads or by the number of hectares of each crop in 2019 to calculate emissions. The results were then transformed into figures to facilitate understanding and analysis. Results and Discussions: N2O emissions from temporary and permanent crops, and N2O and CH4 emissions from flooded rice crops; as well as CH4 and N2O emissions from the livestock sector, totaling 10.53 MtCO2e of emissions in 2019. Comparing with other results, differences were noted in the total emissions due to the adoption of different methodologies and factors, as well as in what to measure, so this article presents advances and limitations, contributing to a greater depth of understanding of emissions. Research Implications: This research contributes to the scientific literature, public policy actions and environmental planning, as well as understanding and analyzing the impacts on the agricultural sector, and methodological and scientific advances, and the promotion of new research and data updates that can guide new research and updates. Originality/value: This study provides a regional focus on Alagoas, with consistent applications of international methodologies (IPCC, GHG Protocol) in local contexts. It serves as a basis for local public policies, environmental and climate impacts, expanding knowledge about the state's emissions, and encouraging innovation in local methods, as well as awareness and social mobilization.

Spatial Modeling of Forest Fire Risks in A Watershed Using Remote Sensing

Objective: This study investigates remote sensing techniques for monitoring and environmental planning in areas with a higher risk of wildfires in the São Nicolau watershed, located in the municipality of São João Evangelista-MG, Brazil. Method: The research employed a quantitative approach, analyzing a 30-year historical series and incorporating nine significant variables influencing wildfire risk. The data were processed and analyzed using advanced geotechnologies, enabling the mapping of risk areas. Results and Discussion: The results indicate that the study area comprises 3.02 ha of low risk (0.13%), 3,835.66 ha of moderate risk (16.35%), 17,540.28 ha of high risk (74.79%), 2,073.87 ha of very high risk (8.84%), and 0.18 ha of extremely high risk (0.00%). These findings reveal the predominance of high-risk areas, highlighting the urgent need for mitigation measures. Wildfire risk maps represent the areas most prone to fire on a base map, integrating factors that influence fire ignition and spread. Based on this information, various measures can be implemented, such as increased surveillance in high-risk areas, restricted access, construction of preventive firebreaks, and reorganization of management practices (Ferraz & Vettorazzi, 1998). Research Implications: The findings provide valuable input for strategic planning of preventive actions against wildfires, contributing to public policies for environmental conservation and sustainable management in critical areas.

Freshwater life-cycle timing of Pacific salmon and steelhead (Oncorhynchus spp.) in Canada

Information on species’ phenology and distribution is essential for assessing and mitigating exposure to pressures that vary over space and time, such as development projects or climate changes. Pacific salmon (Oncorhynchus spp.) have complex life cycles that span freshwater and marine environments and unfold over several years and many thousands of kilometers. Currently, there is no central repository of life-cycle timing for salmon and steelhead populations throughout their Canadian range, hindering conservation and recovery efforts. To fill this gap, we have compiled timing data for fry migration from incubation to rearing grounds, juvenile migration from rearing grounds to the ocean, adult entry into rivers, and spawning for salmon and steelhead from British Columbia and the Yukon, Canada. For each species, we analysed the compiled data using linear models to describe patterns in population-specific timing across latitude, distance from the ocean, river gradient, and life-history types. Although we found significant data gaps in remote regions, our compiled dataset represents the best-available information on life-history timing that can inform environmental planning and salmon conservation actions

Visualization microclimate scenarios of Classical Chinese Garden in Suzhou, China

The intensification of urbanization worldwide, particularly in China, has led to significant challenges in maintaining sustainable urban environments, primarily due to the Urban Heat Island (UHI) effect. This effect exacerbates urban thermal stress, leading to increased energy consumption, poor air quality, and heightened health risks. In response, urban green spaces are recognized for their role in ameliorating urban heat and enhancing environmental resilience. This paper has studied the microclimate regulation effects of three representative classical gardens in Suzhou—the Humble Administrator’s Garden, the Lingering Garden and the Canglang Pavilion. It aims to explore the specific impacts of water bodies, vegetation and architectural features on the air temperature and relative humidity within the gardens. With the help of Geographic Information System (GIS) technology and the Inverse Distance Weighted (IDW) spatial interpolation method, this study has analyzed the microclimate regulation mechanisms in the designs of these traditional gardens. The results show that water bodies and lush vegetation have significant effects on reducing temperature and increasing humidity, while the architectural structures and rocks have affected the distribution and retention of heat to some extent. These findings not only enrich our understanding of the role of the design principles of classical gardens in climate adaptability but also provide important theoretical basis and practical guidance for the design of modern urban parks and the planning of sustainable urban environments. In addition, the study highlights GIS-based spatial interpolation as a valuable tool for visualizing and optimizing thermal comfort in urban landscapes, providing insights for developing resilient urban green spaces.

Evaluation of Environmental Management at Pulang Pisau Regional General Hospital Based on Environmental Impact Assessment Studies and Regional Development

This study aims to evaluate environmental management at Pulang Pisau Regional General Hospital (RSUD) within the framework of participatory development planning. The research employs a qualitative approach with a descriptive method, collecting data through interviews, observations, and documentation. Data analysis follows the Miles and Huberman model, with validation through methodological and source triangulation. The findings indicate that environmental planning and management at RSUD Pulang Pisau remain technocratic, with limited public involvement. The lack of public dialogue spaces and multi-stakeholder consultations results in low community participation in sustainable development. Implementing medical and liquid waste management policies follows national regulations but still faces challenges in resource efficiency and environmental impact monitoring. This study recommends strengthening inclusive environmental governance by establishing multi-stakeholder forums and public consultation mechanisms and enhancing community education and empowerment in environmental monitoring. Policy evaluation based on George C. Edward III's implementation theory highlights the importance of clear communication, resource availability, stakeholder commitment, and an effective bureaucratic structure in successfully implementing environmental policies. This research emphasizes that active public participation in hospital environmental management is not merely a technical aspect but also an integral part of an inclusive and sustainable development process.

Construction and Application of Urban Green Space Ecosystem Service Assessment Indicator System and Assessment Method: A Case Study of Chifeng Central Urban Area, China

The assessment of urban green space ecosystem services is an important basis for urban ecosystem management and decision-making. To further enhance the accuracy of this evaluation, this paper refers to the main research results of international urban ecosystem service assessment and puts forward the main principles for the construction of an urban green space ecosystem service assessment index system. Based on this principle, the framework of the assessment indicator system was carried out by adopting the expert consultation method and frequency analysis method. This system includes the functions of leisure and recreation and the landscape premium in cultural services, air purification, noise abatement, carbon sequestration, oxygen release, and precipitation storage in regulatory services, and biodiversity conservation in supply services. The assessment methods for each functional category are chosen for their appropriateness, or the inadequacies of the existing methods are refined and improved. Then, we take the urban center of Chifeng City, China, as an example to carry out an empirical study on the urban green space ecosystem service assessment index system and assessment methods proposed in this paper. This study shows that the total value of green space ecosystem services in the downtown area of Chifeng City is CNY 229.91 million (Chinese Yuan). Among them, cultural services, regulating services, and supporting services accounted for 89.62%, 9.06%, and 1.32% of the total value, respectively. This study has important reference significance for the comprehensive quantitative assessment of the ecological service function value of urban green spaces, revealing the socio-economic value of urban green space resources while providing insights and references for the environmental assessment, planning, construction, and management decision-making of urban green spaces.

Climate change projections in temperature and precipitation using cmip6 in Central Mexico

Knowing the spatiotemporal variability of temperature and precipitation, as well as their future projections, is essential to assess environmental risks and plan long-term mitigation and adaptation. In this study, the General Circulation Models (GCMs) HadGEM3-GC31-LL, MRI-ESM2-0 and ACCESS-ESM1-5 of the Coupled Model Intercomparison Project (CMIP6) have been used, under two Shared Socioeconomic Pathways (SPP2-4.5 and SSP5-8.5), to project the minimum and maximum air temperature and the mean annual precipitation in Atlacomulco Rural Development District (ARDD). 30 meteorological stations were used to perform regionalized climate change projections by downscaling using LARS-WG 7. The expected changes were evaluated for three time horizons: near horizon (2021–2040), mid horizon (2041–2060) and far horizon (2061–2080); compared to the historical period (1985–2017). Based on the results, the annual minimum temperature towards the far horizon is projected to increase by 2.0 ºC with SSP2-4.5, and 3.1 ºC with SSP5-8.5; while the annual maximum temperature towards the far horizon is projected to increase between 2.5 ºC with SSP2-4.5 and 3.8 ºC using SSP5-8.5. Precipitation shows an increase for SSP2-4.5 in the near and middle horizons with 15.7 and 42.5 mm.year−1, respectively, while the largest decrease is projected towards the middle horizon of SSP5-8.5 with − 26.3 mm.year−1. The findings of this study provide detailed information on climate change projections in the ARDD and can serve as a useful guide for the management of different ecosystems.

Assessing Urban Heat Islands Using GIS and Remote Sensing for Sustainable Urban Planning in the Philippines

Abstract- Growing urbanization and a tropical climate intensify the known environmental challenges of urban heat islands (UHIs) in the Philippines. UHIs are formed by human activity, infrastructure, and decreased vegetation, resulting in elevated temperatures in urban areas compared to their rural surroundings. This tendency exacerbates energy consumption, air pollution, and health concerns, posing significant issues for urban planners and policymakers. Geographic Information Systems (GIS) and remote sensing technologies provide efficient methods for evaluating and reducing UHIs. GIS enables the merging and examination of geographical data, providing a comprehensive understanding of urban settings. Remote sensing uses thermal data from satellites to provide a comprehensive understanding of land surface temperatures and heat distribution patterns. In the Philippines, these technologies are essential for detecting areas with high temperatures, assessing the elements that contribute to this, and creating specific policies for sustainable urban development. By utilizing GIS and remote sensing, urban planners can strategically create geographically accurate green spaces, optimize construction materials, and apply cooling measures, ultimately fostering a healthier and more resilient urban environment. This paper shows the significant utilization of these technologies to facilitate data-driven decision-making, which is crucial for mitigating the detrimental impacts of UHIs. This research can guide environmental planners on how to utilize GIS and remote sensing for a more scientific approach as urban areas continue to grow and science-based sustainable urban planning becomes more and more apparent. Evaluating UHIs using GIS and remote sensing is an essential step in the development of habitable, sustainable, and climate-resilient cities in the Philippines.

Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning.

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.

Assessment of environmental degradation and conservation in the Maracanã River Basin, eastern amazon

Coastal basins stand out for their continent-estuary interface and connection as corridors of mangrove forests. The Maracanã River Basin (MRB) represents this environment, holding various ecosystem services for the component municipalities, protected areas with highly sensitive environments and water demand, and potential for multiple water uses. The proposed aim was to identify areas of degradation and environmental conservation in the MRB using the Blueprint model as support for water resource management. The methodology involved the application of the Blueprint model in the MRB, based on land use and cover information, rainfall, and characterization of the ecological units. The results showed that the MRB basin predominantly exhibits the degradation and restoration/connectivity classes in the Annual Blueprint (ABP) and Dry Blueprint (DBP), respectively. On the other hand, the Rainy Blueprint (RBP) predominantly exhibits Environmental Integrity. Statistical tests showed significant differences between ABP-RBP and DBP-RBP, which can be explained by the fact that on an annual scale of analysis, Blueprint classes are more heterogeneous, with a tendency toward environmental integrity, and intermediate classes in the rainy period; in the dry period, restoration and connectivity and degradation classes predominate. The correlation analysis indicates that natural vegetation cover shows a significant correlation with annual precipitation, rainy and dry quarters. These results provide significant insight into understanding the dynamics of degradation and conservation areas, assisting decision-makers in the environmental planning of the basin. In addition, the climatic component showed a differential response on annual and seasonal scales, acting as a modulating agent of the indicators.

Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm

Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method.