Environmental Planning Research Articles (Page 1)

Thai Regenerative Design Practice serves as a tool in the built environment design and planning process, specifically tailored to the Thai socio-environmental context. This practice integrates the principles of regenerative design, the Thai Rating of Energy and Environmental Sustainability (TREES), and the backcasting technique to address existing gaps in regenerative design practices, enhancing efficiency in design and planning strategies for comprehensive sustainability. Existing studies indicate that regenerative design principles and TREES share ideological and procedural similarities, guiding genuinely sustainable design. Meanwhile, the backcasting technique is primarily utilised to establish desirable goals within strategic planning processes. Fundamentally, backcasting involves creating a normative vision of the future and then designing pathways back to the present to determine the steps necessary to achieve these goals. This approach ensures that present-day decisions facilitate long-term sustainable change. However, in Thailand, the backcasting technique has not yet been applied within the built environment design and planning process. This study regards it as an opportunity and seeks to evaluate the effectiveness of the backcasting technique as a fundamental component of the Thai Regenerative Design Practice, examining its role in enhancing the credibility of this approach. The research methodology involved a three-day workshop on built environment design for Nong Bua, Chiang Mai, Thailand, with the participation of 17 stakeholders. Data collection was conducted through observation, surveys, and interviews. The results indicate that participants expressed satisfaction with the workshop facilitated by the Thai Regenerative Design Practice, which, according to the empirical evidence, presented as a built environment design and planning initiative for the case study area. Based on a five-point Likert scale, the findings indicate that Thai Regenerative Design Practice and its components received ratings within the "very satisfied" range. Overall, the Thai Regenerative Design Practice scored 4.80, while the regenerative design principle, TREES, and the backcasting technique received scores of 4.46, 4.23, and 4.42, respectively. Additionally, participants highlighted the significance of the backcasting technique in verifying a project's comprehensiveness. They noted that this technique effectively refines discussions, narrowing broad conversations into key focal points, and can be applied in future built environment development charrettes. In conclusion, the findings suggest that the backcasting technique contributes to developing a normative and accurate design planning strategy. However, its role within Thai Regenerative Design Practice requires further validation through repeated applications in future studies to ensure its most comprehensive and effective implementation.

This research aims to provide an implementation of GIS and remote sensing data using SAR Sentinel-1 data to monitor the implementation of spatial policy, especially those related to environmental planning in mitigation zones. This research takes the location in the urban coastal area of Pekalongan, Central Java, Indonesia, which has had high flood events over the last three years. The assessment tools use GIS modeling combined with data extraction from Google Earth Engine, which has accelerated capabilities in obtaining and analyzing flood data using Sentinel-1 SAR remote sensing data. The evaluation results show that GEE can model flood-affected areas with a validation level of 87%. In detail, the evaluation also shows that nine zones are allocated as settlement areas but are located in disaster hazard zones with a flood hazard index of 1 (high), so environmental planning policies need to be strengthened in controlling residential growth in flood disaster-prone areas.

Vibrations caused by construction activities, transportation, and industrial sources can adversely affect buildings, especial ly those constructed from masonry or large prefabricated elements. In order to ensure structural safety and environmental protection, it is necessary to assess the impact of such vibrations in both the design and diagnostic stages. While full dynamic analysis provi des accurate results, it is often impractical for routine assessments due to the complexity of modeling, lack of vibration data, or the urgency of the evaluation process. In response to this need, simplified procedures have been developed in the Polish engineer ing practice in the form of Dynamic Impact Scales SWD - I and SWD - II. The SWD - I and SWD - II scales offer an approximate yet practical method for evaluating vibration - induced structural damage in buildings, based on empirical classification into five vibration zones ranging from "imperceptible" to "structural failure." The classification is derived from the measured peak va lues of ground acceleration and frequency, and the structural characteristics of the building. The SWD - I scale applies to compact, regular - shaped masonry or panel buildings of up to two storeys, while SWD - II is designed for more general low - rise buildings of up to five storeys, with certain limitations on height - to - width ratios. Both scales are based on engineering judgement and are aligned with the framework of the Polish standard PN - B - 02170:2016. This paper presents a detailed description of both scales, including the criteria for their use, methodology for assigning buildings to the correct zone, and proper sensor placement to capture representative vibration input. A comparative example is provided to demonstrate how a real - world vibration scenario (induced by a nearby compressor) can be evaluated using the SWD - II scale. The case illustrates that even a relatively small source can lead to measurable vibrations that may affect building integrity depending on the frequency content and local amplificati on effects. The results confirm that SWD scales can serve as reliable screening tools for preliminary assessment of structural vulnerability to environmental vibrations. Although not a substitute for detailed numerical modeling in complex or high - risk cases, the SWD approach provides a valuable, cost - effective method for engineers, designers, and decision - makers engaged in environmental impact assessments or construction planning. In addition, the scales can be used to support regulatory complian ce and community communication in vibration - sensitive areas. The use of SWD - I and SWD - II scales fills a methodological gap by offering an intermediate level of assessment between qualitative expert judgement and full - scale structural simulations. This contribution aims to bring these effective tools to a broader international audience and encourage their adaptation or furthe r development in the context of local regulatory frameworks and engineering standards.

Abstract The Himalayan region’s complex terrain and ecological fragility make it increasingly vulnerable to vegetation stress and landslides, driven by both natural and human factors. This study focuses on a 2663.92 km² area encompassing Rudraprayag, Ukhimath, Gopeshwar, and Joshimath, aiming to develop a Vegetation Health Index (VHI) and explore its link with landslide susceptibility. Six key parameters such as NDVI, NDMI, soil, rainfall, slope, and road proximity were selected for their relevance and tested for multi-collinearity. All variables showed VIF values below 5, confirming their independence and suitability. Expert weights were applied on six critical parameters using the Analytic Hierarchy Process (AHP) with a Consistency Ratio of 0.093 establishing reliability. The GIS-weighted overlay technique was used to create the spatially varied Vegetation Health Index (VHI) map. Validation using sensitivity analysis, LAI regression (R² = 0.62), and AUC_ROC analysis (0.423) revealed 57.7% occurrence of previous landslides in low VHI areas, establishing a negative relationship between vegetation status and landslide susceptibility. The hybrid geospatial-statistical model is a reliable predictor of ecologically and geologically sensitive regions and an effective tool for land degradation mapping and disaster risk reduction. The results provide vital data for future mountain environmental planning, slope management, and policy formulation.

Environmental Protection Plans (EPPs) are vital for mitigating the socio-ecological impacts of quarry operations, especially in emerging economies like Thailand, where rapid industrialization often intensifies air, water, noise, and land degradation. This study applies the social return on investment (SROI) framework to evaluate the cost-effectiveness of multi-domain EPPs implemented in a quarry. By applying compliance-based assessment and monetization of environmental and health co-benefits, annual economic outcomes were quantified for particulate matter (PM10), total dissolved solids (TDS), noise reduction, and carbon sequestration. The analysis revealed a high SROI ratio of 59.55:1, primarily driven by substantial health benefits from PM10 and noise abatement. This ratio also reflects consideration of investment from an annual operational cost, with a sensitivity analysis of incorporating an estimated capital expenditure, reducing the ratio to moderate value ranges of 5–10:1. A number of limitations, such as exclusion of capital costs, reliance on fixed proxies, and single-year scope, may overstate short-term returns, suggesting the application of stochastic methods for enhanced robustness. Overall, the findings demonstrate that EPPs deliver substantial economic and public health benefits, supporting their role in fostering community resilience and advancing sustainable operations in quarry sectors.

The morphometric analysis of a river basin provides critical insights into its hydrological and geomorphological characteristics, essential for effective watershed management and planning. This study presents a detailed morphometric analysis of the Gostani River Basin using Remote Sensing (RS) and Geographic Information System (GIS) techniques. High-resolution satellite imagery and topographic data, including Digital Elevation Models (DEMs), were utilized to extract drainage networks and basin boundaries. Key linear, areal, and relief morphometric parameters such as stream order, bifurcation ratio, drainage density, stream frequency, elongation ratio, and relief ratio were computed using GIS tools. The results reveal that the Gostani River Basin exhibits dendritic drainage patterns, moderate drainage density, and a sub-mature stage of geomorphic development, indicating semi-permeable sub-surface material and moderate to low relief. The analysis highlights the usefulness of RS and GIS in deriving accurate and comprehensive morphometric parameters, facilitating better understanding of basin dynamics for sustainable water resource management and environmental planning.

Efficient and stable path planning in dynamic and obstacle-dense environments, such as large-scale structure assembly measurement, is essential for improving the practicality and environmental adaptability of mobile robots in measurement and quality inspection tasks. However, traditional reinforcement learning methods often suffer from inefficient use of experience and limited capability to represent policy structures in complex dynamic scenarios. To overcome these limitations, this study proposes a method named DPDQN-TER that integrates Transformer-based sequence modeling with a multi-branch parameter policy network. The proposed method introduces a temporal-aware experience replay mechanism that employs multi-head self-attention to capture causal dependencies within state transition sequences. By dynamically weighting and sampling critical obstacle-avoidance experiences, this mechanism significantly improves learning efficiency and policy performance and stability in dynamic environments. Furthermore, a multi-branch parameter policy structure is designed to decouple continuous parameter generation tasks of different action categories into independent subnetworks, thereby reducing parameter interference and improving deployment-time efficiency. Extensive simulation experiments were conducted in both static and dynamic obstacle environments, as well as cross-environment validation. The results show that DPDQN-TER achieves higher success rates, shorter path lengths, and faster convergence compared with benchmark algorithms including Parameterized Deep Q-Network (PDQN), Multi-Pass Deep Q-Network (MPDQN), and PDQN-TER. Ablation studies further confirm that both the Transformer-enhanced replay mechanism and the multi-branch parameter policy network contribute significantly to these improvements. These findings demonstrate improved overall performance (e.g., success rate, path length, and convergence) and generalization capability of the proposed method, indicating its potential as a practical solution for autonomous navigation of mobile robots in complex industrial measurement scenarios.

GeoPF: Infusing Geometry Into Potential Fields for Reactive Planning in Non-Trivial Environments

HG2P: Hippocampus-inspired high-reward graph and model-free Q-gradient penalty for path planning and motion control.

Multiple unmanned sailboats cooperative coverage: Task allocation and path planning in island environments

Abstract Application EFSA‐GMO‐DE‐2019‐157 (oilseed rape LBFLFK) was submitted to EFSA to be risk assessed as full‐scope, i.e. for import, processing, and food and feed uses, within the EU. Oilseed rape LBFLFK was developed to alter its fatty acid profile and to confer tolerance to imidazolinone herbicides. Oilseed rape LBFLFK contains two inserts, not genetically linked, each consisting of a single copy of the T‐DNA, which contains 13 expression cassettes (coding for 11 unique proteins). The Panel on Genetically Modified Organisms (GMO Panel) was unable to conclude on the safety of oilseed rape LBFLFK when considering the full‐scope of this application. The safety of the elongases and desaturases newly expressed in oilseed rape LBFLFK cannot be established. Similarly, the safety and nutritional adequacy of the oil and full‐fat seeds derived from oilseed rape LBFLFK for its use in terrestrial farmed and companion animals and in aquaculture cannot be established. However, the GMO Panel concludes that the consumption of refined, bleached and deodorised (RBD) oil from oilseed rape LBFLFK is safe and does not represent any nutritional concern in humans under the conditions of use proposed by the applicant. Considering the potential safety concerns, LBFLFK oil should not be used for high‐temperature applications. Post‐market monitoring is appropriate to confirm the expected consumption and the application of conditions of uses in humans. Furthermore, the GMO Panel did not identify any environmental concerns related to increased persistence and invasiveness or to any potential for gene transfer from oilseed rape LBFLFK, and concludes that it poses no risks for terrestrial non‐target organisms (NTOs) or biogeochemical cycles in this environment. The GMO Panel cannot conclude on the safety of oilseed rape LBFLFK to NTOs or biogeochemical cycles in the aquatic environment. Based on this, the GMO Panel is unable to conclude on the adequacy of the post‐market environmental monitoring plan proposed by the applicant.

This paper reflects on the current state-of-the-art of cumulative effects assessment (CEA), monitoring and management, highlighting recent advances in regional science supportive of CEA practice, the expansion of practice to include non-regulatory regional-scale applications of CEA, and the uptake of CEA across diverse development sectors. We address how the role of CEA is rapidly evolving as an environmental justice tool, as a core component of environmental governance and planning, and as a means to protect Indigenous rights and territories. We also explore how the global shift to renewable energy sources and the related demand for critical minerals is a real and urgent opportunity to get CEA right.

Comprehensive assessment of heavy metal contamination in forested and agricultural soils: Distribution patterns, source apportionment, environmental and human health risks.

Biologically-inspired jumping robots have demonstrated remarkable adaptability in complex environments, making them increasingly valuable across various fields. However, effective path planning in obstacle-dense environments for large-scale jumping robots remains a significant challenge. Inspired by independent decision-making in the efficient collaborative behavior of locust swarms, we propose a two-stage curriculum reinforcement learning (TS-CRL) framework for locust-inspired jumping robots. This framework enables individual robots to autonomously determine actions based on local environmental observations during group crossing tasks. TS-CRL incorporates a population-invariant encoder with an attention mechanism, allowing it to efficiently handle an increased number of training robots. Moreover, it employs an actor-critic network architecture based on Kolmogorov-Arnold networks (KAN) to enhance training performance. To further improve the training efficiency, we divided the policy training process into two stages with gradually increasing environmental complexity. The effectiveness and scalability of TS-CRL were validated through a locust-inspired jumping robot platform in challenging simulation scenarios. Notably, TS-CRL can generate efficient, collision-free paths to guide multiple jumping robots. Compared with typical reinforcement learning algorithms, TS-CRL reduced the average path cost by 13.7% and markedly improved the success rate of robots in reaching the target areas. Finally, we constructed a multi-robot system consisting of locust-inspired jumping robots for experiments in the real world.

Investigation of cadmium adsorption-transport coupling in soil-mineral systems: Integrating batch adsorption and column transport experiments.

Forest fires, intensified by anthropogenic pressure and climate change, pose a significant threat to ecosystems and socioeconomic activities. This study aimed to perform wildfire risk zoning in Teófilo Otoni, Minas Gerais, in order to identify vulnerable areas and support conservation and sustainable management policies. Maps were developed using QGIS software, considering slope aspect, gradient, distance from urban clusters, proximity to road networks, and land use and land cover, based on data from the Digital Elevation Model (MDE), the Spatial Data Infrastructure of the State Environmental System (IDE-SISEMA) and the MapBiomas Project, Collection 10 (2024). The results showed that 65.60% of the municipal territory presents Moderate to Very High risk, particularly in agricultural areas, north-facing slopes, and regions near highways. The consistency of the zoning was confirmed by the 227 fire hotspots recorded between 2020 and 2025, highlighting its relevance for local environmental planning.

Arid and semi-arid ecosystems face a triple environmental threat at the intersection of climate change, biodiversity loss, and desertification. Land use and land cover change (LUCC) in the region is, therefore, a key indicator in monitoring overall ecosystem health. However, persistent challenges hinder LUCC assessment in dry biomes, notably due to the heterogeneity of arid vegetation and its response to high temperature and water availability variations throughout the year. To identify the most important knowledge gaps easily tackled in the near future, this systematic review synthesizes current scientific knowledge on LUCC monitoring in arid and semi-arid ecosystems from the Scopus and Dimensions databases. Study results showed that seasonality (41%) and spatial resolution (28%) were the two main factors limiting LUCC in arid and semi-arid ecosystems. These factors had a significant influence on map accuracy, particularly due to seasonal variations in vegetation and the conditions under which satellite images were taken. Classification accuracy decreased substantially when the studies attempted to describe vegetation classes in more detail. Reported classification accuracies in the literature (accuracy ≥ 80%) reflected overconfidence, as a large proportion of the publications (70%) were limited to general distinctions such as forest and non-forest, without tackling more complex categories. Thus, given the importance of global vegetation maps for ecosystem management, a valuable tool for forest management and environmental planning, wrong estimation can introduce biases, leading to inappropriate management decisions and thus compromising the sustainability of natural resources and the ecosystem services that depend on them.

Three-dimensional path planning in emergency logistics is a complex optimization problem, particularly challenging because it requires considering conflicting objectives such as flight time, energy consumption, and obstacle avoidance. Unlike most urban logistics research, this study examines emergency delivery path planning in mountainous environments during natural disasters. One of the most effective approaches to this problem is to employ multi-objective evolutionary algorithms. However, while multi-objective genetic algorithms can handle multiple conflicting objectives, they struggle when dealing with complex constraints. This paper proposes a multi-objective genetic optimization method, Adaptive Crossover-Mutation Multi-Objective Genetic Optimization (ACM-NSGA-II), based on the classic NSGA-II framework. Inspired by the principle of symmetry, this method dynamically adjusts the mutation and crossover rates based on population diversity to maintain a balanced exploration–exploitation trade-off. When population diversity is low, the mutation rate is increased to promote exploration of the solution space; when population diversity is high, the crossover rate is increased to promote better information exchange. The algorithm maintains symmetry by gradually adjusting the step size, balancing adaptability and stability. To address the obstacle avoidance problem, we introduced a dynamic path repair strategy that respects the symmetry of no-fly zone boundaries and terrain features, ensuring the safety and efficiency of Unmanned Aerial Vehicles. This algorithm jointly optimizes three objectives: safety cost, flight time, and energy consumption. The algorithm was tested in a mountainous environment model simulating a remote area. In experiments, ACM-NSGA-II was compared with several mainstream evolutionary algorithms. The Pareto set and hypervolume metrics of each method were recorded and statistically analyzed at a 5% significance level. The results show that ACM-NSGA-II outperforms the baseline algorithms in terms of diversity, convergence, and feasibility. Specifically, compared with the traditional NSGA-II, ACM-NSGA-II improved the average hypervolume metric by 53.39% and reduced the average flight time by 24.26%. ACM-NSGA-II also demonstrated significant advantages over other popular standard algorithms. Experimental results show that it can effectively solve the path planning challenge of emergency logistics Unmanned Aerial Vehicles in mountainous environments.

The armed conflict in Colombia has generated negative environmental impacts that need to be mitigated and compensated for. This article presents a methodological proposal to support the National Army in establishing environmental action plans in each territorial context. The proposal arises from a review of the literature that sought: a territorial diagnostic mechanism, to understand the role of the National Army, to understand the integrity of the territory, and a prioritization mechanism. The methodological proposal begins with a diagnosis of the area, an evaluation of the actions to be taken based on the Sustainable Development Goals and the Environmental Impact Assessment, and prioritization in accordance with the objectives of the Environmental Management Plan to protect the integrity of the territory, contribute to the common good and the progress of the population in harmony with the environment, and promote population and territorial reparation in Colombia.

ABSTRACT Malacca, a UNESCO World Heritage city in Malaysia, is characterized by its rich cultural landscape, diverse land uses spanning urban, agricultural, and manufacturing zones, and a steadily growing population, all of which intensify pressure on its historical assets. As a result, the city's urban heritage is increasingly threatened by land degradation, environmental deterioration, and the intensifying impacts of climate change. Preserving cultural assets while promoting sustainable urban development requires a strategic, data‐driven approach that integrates ecological, social, and technological priorities. This study develops a hybrid multi‐criteria decision‐making framework combining Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Simple Additive Weighting (SAW) to identify key sustainability drivers and prioritize strategic interventions for Malacca. A total of eighteen criteria were analyzed. Fuzzy DEMATEL was used to examine causal relationships among criteria, revealing that green infrastructure, climate resilience measures, early warning systems, and digital monitoring tools act as critical drivers within the urban sustainability system. Subsequently, seven strategic alternatives were evaluated using the SAW method, with digital twin for heritage and environmental monitoring (Vi = 3.779), smart heritage tourism management systems (Vi = 3.768), and integrated green urban planning (Vi = 3.619) ranking as the top‐priority strategies. These findings provide valuable guidance for policymakers and planners in integrating cultural conservation, environmental planning, and smart technologies to strengthen sustainability in climate‐vulnerable heritage cities.