Abstract Urban air mobility (UAM), as an emerging transportation mode, represents an effective strategy to alleviate ground traffic congestion and is expected to be promoted globally in the coming years. This study focuses on the data-driven optimization of vertiport location. By integrating travel choice modeling with a three-phase multi-objective integer programming (TP-MOIP) model based on K -means clustering analysis, the study provides decision-making support for scientific vertiport network planning. The study combines revealed preference (RP) and stated preference (SP) surveys, employing binomial logistic regression to analyze the probability of individuals choosing UAM, while introducing time value coefficients to identify potential user groups. Utilizing ride-hailing order data, potential vertiport locations were identified through K -means clustering. The developed TP-MOIP model simultaneously optimizes the objectives of demand coverage maximization and cost minimization across three implementation phases. Using Shenzhen as a case study, the feasibility of the proposed approach was validated. The results show that the average selection probability for UAM is 16.7%, with time-sensitive users being a critical demographic. In addition, the TP-MOIP model achieved a demand coverage rate of 88.21%. Sensitivity analysis indicates that the land cost budget has the most pronounced impact on demand coverage. This research establishes both theoretical foundations and practical methodologies for vertiport location optimization, offering substantial implications for advancing UAM development.

Al-Refaie district in Dhi Qar Governorate has witnessed a remarkable development in land use over the years, as it has been affected by the various economic and social transformations in the region. The study used Landsat 5 TM and Landsat 8 OLI images and Geographic Information Systems (GIS) techniques to analyze changes in Land Use/Land Cover (LULC) and calculate the Normalized Difference Vegetation-Index (NDVI) and the Normalized Difference Water-Index (NDWI) indices for 1990, 2000, 2013, and 2024. Based on the principles of urban planning and sustainable city expansion, this data is examined and its function in planning and designing residential areas and communities in the Al-Refaie district is examined. According to the findings, Al-Refaie's urban areas grew dramatically between 1990 and 2024, growing from roughly 21 km² to 65 km². In contrast, changes in LULC and human activity had a detrimental impact on agricultural land and water resources, causing agricultural lands to decrease from 540 km² in 1990 to 343 km² in 2024 and water to shrink from 304 km² in 1990 to 73 km² in 2024. Water and agricultural regions have decreased in favor of urban areas due to ongoing urban expansion. Land usage in Al-Refaie has been greatly impacted by demographic shifts, as is visible in both urban and residential regions. Decision-makers can effectively examine future patterns of urban expansion in keeping with the anticipated population growth in Iraq in the upcoming years by using long-term urban planning, which is based on land use and cost analysis.

Although access to decent and reasonably priced housing is still a huge global concern, housing is a basic human necessity. Homeownership has become challenging, particularly for low- and middle-class households, due to rising property costs, a lack of social housing, and wage inequality. With its low financing, community involvement, and common ownership, cooperative housing has become a feasible answer. Through systems including member savings, cooperative loans, and government assistance, cooperative housing finance plays a critical role in facilitating housing access while advancing social welfare and financial stability. According to research conducted worldwide, cooperative housing can lower housing prices, improve members' financial security, and fortify community ties. However, obstacles including expensive land or construction costs, governance problems, and policy shortages may restrict their effectiveness In order to provide insights into how cooperatives might be improved to meet the increasing demand for cheap and sustainable housing, this study attempts to assess cooperative housing financing policies and their effects on member welfare and financial stability.

Electric vehicles (EVs) offer a promising solution for reducing environmental pollution. However, the rapid expansion of electric vehicle charging stations (EVCSs) creates significant technical challenges for distribution networks, including increased power losses and voltage instability. Strategic EVCS planning is essential to mitigate these impacts. This paper presents a comprehensive 2-stage framework for optimally siting and sizing EVCSs with photovoltaic (PV) systems and capacitor banks (CBs) in distribution networks. The approach employs the Dandelion Optimizer (DO), a recent metaheuristic algorithm, to solve a multi-objective optimization problem. It is validated on the large-scale IEEE 118-bus distribution system, divided into 3 geographical zones based on land cost. In the first stage, DO determines optimal locations and capacities for EVCSs per zone (an EVCS with 50 kW fast chargers and another one with 100 kW fast chargers) and PV units by minimizing active and reactive power losses, average voltage deviation index, and EVCS land costs while maximizing the voltage stability index. The second stage optimally places and sizes CBs, either centralized per zone or distributed-based mitigation solutions, to ensure voltage regulation, minimizing total reactive compensation instead of land costs. Moreover, the framework considers time-varying loads and PV generation profiles using OpenDSS, incorporates Pareto-based multi-objective optimization to explore trade-offs among competing objectives, and accounts for zone-specific land costs and varying distribution of fast-charging connectors. Comparative analysis demonstrates DO's superior convergence and solution quality with respect to Jaya optimization, particle swarm optimization, marine predators algorithm, artificial gorilla troops optimizer, teaching-learning-based optimization, and ant lion optimizer. Sensitivity analysis further validates the framework's robustness under varying land cost scenarios, providing valuable insights for both distribution network operators and charging station operators.

Cashew cultivation plays a vital role in the horticultural economy of Konkan region of Maharashtra and supports rural livelihoods through production, processing, and trade. This study provides a farm-level economic assessment of the cost and profitability of cashew cultivation in South Konkan region of Maharashtra. A multistage sampling framework was employed to select 120 cashew growers from Ratnagiri and Sindhudurg districts during the agricultural year 2023–24. Primary data were collected through personal interviews using a structured and pre-tested schedule. Cost of cultivation was estimated using Cost-A, Cost-B and Cost-C concept, while economic performance was evaluated through gross returns, net returns and benefit cost ratios. The findings show that the average total cost of cultivation (Cost-C) was ₹98,903 per hectare. Major cost components included the rental value of land, labour expenses and amortization cost associated with orchard establishment. The average yield was 14.76 quintals per hectare, resulting in gross returns of ₹1,76,330 per hectare. The benefit cost ratio over Cost-C was estimated at 1.78, indicating favourable economic returns from cashew cultivation in the region. A district-level comparison suggests that Sindhudurg recorded slightly higher productivity and profitability than Ratnagiri.

Moving vehicle-based sensors (MVSs) have received growing attention for real-time anomaly detection in various applications such as wildfire and oil spill detection. To tackle challenges due to the spatial covariance structure among observations, uncertainties under partial observations, as well as the physical MVS movements, we propose a Bayesian jump model-based pathwise sampling approach to detect abrupt changes in an area of interest in real time using MVSs. Specifically, a jump-model based Bayesian scheme is proposed to exploit spatial correlation and real-time partial observations for status update, and to quantify uncertainties arising from noisy observations, limited observability, and anomaly occurrences. Based on the updated status, a Bayesian upper confidence bound is constructed as the sampling statistic to balance exploration and exploitation under noisy and partial observations. Guided by the sampling statistic, a route optimization model is then formulated to adaptively coordinate the routes of multiple MVSs for quick anomaly detection. We perform theoretical investigations and conduct simulations to confirm the exceptional effectiveness of the proposed method. A case study for early wildfire detection demonstrates that our proposed method outperforms benchmark methods, which contributes to the reduction of the area of affected land and wildfire-related costs.

Aquaculture industry faces challenges such as environmental deterioration, water scarcity, limited land, and high input costs. This study investigated the eight-week growth performance of Nile tilapia Oreochromis niloticus at two fish sizes (2 or 3 fish, 20 g each per tank) and the growth and nutrient removal efficiency of Azolla filiculoides at three Azolla densities (0, 0.04, 0.08 g/L) in aquaponic systems (APS). The experiment used a 2x3x2 factorial design with six treatments. Results showed that FS and AD did not affect tilapia growth performance. However, higher FS and AD increased Azolla weight increment (WI), with treatments containing three fish showing 24.11% higher WI than those with two fish. Nutrient removal efficiency was higher with Azolla, and significant interactions between AD and time (T) on ammonia (NH3), nitrate (NO3-), and total nitrogen implied that the growth of Azolla depends on the nutrients present in (APS).

This article summarizes our research on integrated timber investment returns, wood stumpage costs, and forest carbon production costs in 2023 for a representative selection of 15 countries, across 44 planted and 1 natural species/management regimes, using capital budgeting criteria, at a real discount rate of 8%, without land costs. Despite a large amount of disparate research, few, if any, studies have provided integrated estimates of investments, wood fiber costs, and forest carbon costs for CO2 offsets using fundamental primary data and production economics approaches. We expanded our prior research that estimated present values and internal rates of return for timber investments, using planted forest growth and yield, input management costs, and timber products prices by selected countries and species. We extended the production economics and forest investment calculations to estimate average stumpage costs for wood fiber per cubic meter per rotation. In addition, we calculated the costs to produce forest carbon for offsets in terms of carbon dioxide equivalent. Timber investment returns by country and species were the largest in tropical Asia and smallest in the Northern Hemisphere (including the United States and Europe), and South America had calculated returns between the two regions. Based on prior research, more developed countries had less investment risk, and had land markets that were more open for foreign investments. Wood costs and forest carbon production cost outcomes by country and species differed substantially from timber investment returns. The discounted wood costs per m3 were generally cheapest if the establishment costs and management inputs were minimized, or for long rotation planted stands. Forest carbon costs had similar rankings to wood costs, and even similar numbers, but were using a different metric—dollars per tCO2e. The calculated forest carbon costs are certainly among the cheapest of all types of typical carbon offsets. They were equal to or much less than the six primary developed country compliance market prices. However, they were higher than the current depressed public voluntary market prices. These results can be used for private, government, or nongovernment investments and for public policy intervention considerations.

This article critically examines the transformations of retailwithin the context of planetary urbanization and contemporary capitalist restructuring. Drawing on the concept of retail-less cities, it argues that traditional retail has lost its structuring role in the city, displaced by new logistical logics. The paper identifies five key dimensions that explain this process: the logistics revolution, the rising cost of urban land, changes in consumption habits, increasing inequalities, and the fragmentation of urban planning. Through an urban political economy approach, it demonstrates how these dynamics generate uneven urban landscapes and erode the local commercial fabric. The article concludes by advocating for multiscalar urban planning, the recovery of retail as a form of social infrastructure, and the development of new analytical categories to better understand —and ultimately challenge— the exclusionary logics of the dominant commercial model.

Scaling up solar photovoltaics (PV) is essential for global decarbonization, particularly in China-the world's largest greenhouse gas (GHG) emitter. Despite leading in PV installations, China has yet to widely adopt the more efficient tracking technologies for capturing solar radiation (12% adoption rate), in stark contrast to the United States (90%). To examine the rationale behind this divergence and its consequences, we develop a spatially explicit, integrated model to evaluate and compare tracking and fixed-tilt systems in China-comparing power generation, land use, cost, sustainability, and policy resilience. We find that although single-axis tracking provides electricity gains and appears technically more cost-effective, rising land prices in China could offset its benefits. Land costs increase the levelized cost of electricity by 20% for tracking systems, compared to 8% for fixed-tilt, making the latter cheaper in real-world conditions. Consequently, land-efficient fixed-tilt systems are favored, despite requiring 18 to 26% more panels for the same output-intensifying material demands. Under a 6 PWh target in 2060, current land policies would drive 59% of electricity toward fixed-tilt. Reducing soft land costs could increase the adoption of tracking systems to 63% and reduce installed capacity by up to 8% (219 GW) under the same electricity output, compared with an increasing costs scenario, but would expand land use by 35% or 12.9 thousand km2. Our findings underscore how land economics and policy shape renewable technology deployment. They highlight critical trade-offs between energy yield, land use, and material demand, offering insights for designing more balanced and resilient decarbonization strategies.

Purpose Sustainable housing encompasses residential development that balances environmental responsibility, economic efficiency and social well-being. In practice, sustainable housing goes beyond reducing the environmental footprint of buildings to address human welfare and community cohesion. Despite its significance, sustainable housing faces multiple challenges that hinder its widespread adoption, particularly in developing countries. Thus, this study therefore seeks to explore the barriers that constrain the provision of sustainable housing for low-income communities in Nigeria and to propose viable strategies for overcoming them. Design/methodology/approach A quantitative research design was employed, with data gathered through structured, closed-ended questionnaires distributed to housing professionals in Nigeria, and subsequently analysed using both descriptive and inferential statistics, including Kruskal–Wallis test and Kendall's coefficient of concordance. Findings The results from the study identified six top most barriers to sustainable housing provision in low-income communities in Nigeria to include weak economic conditions, poor urban planning and land-use policies, rising urban population and overcrowding, high land costs, limited access to housing finance options, and poor enforcement of housing policies and regulations. Originality/value The study's findings contributed to more effective sustainable housing studies by highlighting barriers to sustainable housing provision in low-income communities. This study advances both theory and practice by articulating actionable pathways for inclusive urban development, emphasising the integration of affordability, sustainability and equity as core principles for housing-sector stakeholders. By bridging conceptual insights with practical strategies, it provides a framework that can guide policymakers, practitioners and researchers in fostering more equitable and sustainable housing systems.

ABSTRACT Gigaton‐scale carbon removal demands geologic permanence at low land, water, and energy cost. Ocean pathways are promising, but many electrochemical routes require large pH swings, membranes/sorbents, and suffer from fouling. We report the self‐biased electro‐mineralization as a practical route to ocean carbon removal. Porous core‐shell electrodes program interfacial fields that direct Ca 2+ /CO 3 2− transport and trigger in‐pore crystallization in simulated seawater, without membrane stacks or large bulk pH swings. Field strength is tunable via core/shell ratio, polymer chemistry, and fixed‐charge density, enabling the architecture to deliver long‐duration, fouling‐resistant operation (>2000 h), ∼25% DIC conversion under flow. A 400 cm 2 cell and a simple 100‐liter stirred reactor show that the microscale, uniform field both preserves performance under geometry area scale‐up and enables low‐overhead capacity expansion. Techno‐economic analysis projects an energy consumption of 44 kJ mol −1 CO 2 and a cost of $139 t −1 CO 2 . Extending beyond CaCO 3 , we precipitate additional sparingly soluble phases (CaF 2 , BaSO 4 , PbSO 4 ) from complex brines, establishing a platform also supporting resource recovery. These results shift ocean mineralization from bulk‐solution manipulation to programmable reaction‐environment design, advancing a scalable, cost‐effective pathway to climate relevant carbon removal.

Gigaton-scale carbon removal demands geologic permanence at low land, water, and energy cost. Ocean pathways are promising, but many electrochemical routes require large pH swings, membranes/sorbents, and suffer from fouling. We report the self-biased electro-mineralization as a practical route to ocean carbon removal. Porous core-shell electrodes program interfacial fields that direct Ca2+/CO3 2- transport and trigger in-pore crystallization in simulated seawater, without membrane stacks or large bulk pH swings. Field strength is tunable via core/shell ratio, polymer chemistry, and fixed-charge density, enabling the architecture to deliver long-duration, fouling-resistant operation (>2000h), ∼25% DIC conversion under flow. A 400 cm2 cell and a simple 100-liter stirred reactor show that the microscale, uniform field both preserves performance under geometry area scale-up and enables low-overhead capacity expansion. Techno-economic analysis projects an energy consumption of 44kJ mol-1 CO2 and a cost of $139 t-1 CO2. Extending beyond CaCO3, we precipitate additional sparingly soluble phases (CaF2, BaSO4, PbSO4) from complex brines, establishing a platform also supporting resource recovery. These results shift ocean mineralization from bulk-solution manipulation to programmable reaction-environment design, advancing a scalable, cost-effective pathway to climate relevant carbon removal.

The population growth in Indonesia is leading to new challenges in housing. Due to the scarcity and high cost of land in Jakarta, inhabitants are actively exploring alternate housing options in neighboring regions such as Bogor. The rise in rivalry among housing development enterprises, including the XYZ Residence project, has been a direct result of this trend. Nevertheless, it is common for these organizations to exhibit a deficiency in doing thorough risk analysis. The objective of this study is to analyze, evaluate, prioritize, and suggest strategies to manage construction-related hazards in the XYZ Residence Area project by utilizing the Severity Index. The study revealed a total of 51 pertinent risk factors. Using a Probability Impact matrix, 28 hazards had a considerably low probability of affecting the project timeline, 14 had a low probability, 2 had a moderate probability, 3 had a high probability, and 4 had an extremely high probability. Similarly, 36 risks had a significantly low probability of affecting project expenses, compared to 5 low, 5 moderate, 1 high, and 4 extremely high. Risk responses were formulated for risks with high and extremely high probabilities, based on these findings.

India’s fruit productivity remains significantly lower compared to many horticulturally advanced nations, largely due to senile orchards, inferior planting materials, inadequate orchard management, and limited adoption of modern technologies. With rising land and labour costs and increasing consumer demand, High-Density Planting (HDP) systems have gained momentum as an efficient and economically viable approach to fruit cultivation. HDP allows maximum utilization of horizontal and vertical space by accommodating more plants per unit area, resulting in reduced juvenile period, enhanced productivity, uniform canopy structure, improved fruit quality, and higher profitability. The success of HDP depends on precise control of vegetative growth, efficient canopy management, and optimal light penetration. This review compiles up-to-date information on suitable cultivars, rootstocks, planting densities, pruning techniques, growth regulators, and other management practices essential for establishing and sustaining high-density orchards across major fruit crops.

Potato (Solanum tuberosum L.) is a strategic horticultural commodity in West Java, making micro-level empirical evidence essential to understand productivity variation at the farm level. This study aims to (1) describe the characteristics of potato farmers and potato farming in West Java; (2) describe land area, production, and potato productivity at the farmer level; and (3) analyze the effects of farmer and farm characteristics on potato productivity. The study employed a quantitative survey approach involving 210 potato farmers in West Java selected through simple random sampling. Data were analyzed using descriptive statistics and multiple linear regression (OLS). Descriptive results show an average landholding of 1.25 ha, production of 26.86 tons per cropping season, and productivity of 21.44 tons/ha; approximately 66.19% of farmers achieved productivity ≥20 tons/ha. Regression results show that age and seed use per hectare significantly and positively influence productivity, while experience and equity percentage significantly but negatively influence it. Other variables, including education, percentage of irrigated land, labor, fertilizer, pesticides, and other costs per hectare, have no significant impact. These findings highlight the importance of strengthening the management of key inputs (especially seed), improving access to productive financing, and providing technical extension support to enhance potato productivity at the farm level in West Java.

ABSTRACT The stability of housing prices is crucial to both macroeconomic stability and people's livelihoods. In order to explore the root causes of the decline in China's real estate prices and formulate targeted policies for stabilizing housing prices, this study adopts a configuration perspective and employs the fuzzy set Qualitative Comparative Analysis (fsQCA) method. Taking the housing price changes of 267 prefecture‐level cities in China from 2021 to 2023 as the research object, this paper identifies the key combined paths influencing the downward trend of housing prices. The research findings reveal four core configuration paths for housing price decline: cost relief type, cost reduction adaptation type, supply‐demand contraction type, and supply‐demand recession type. All four paths include a high decline rate of land prices, indicating that the decrease in land prices serves as a critical foundational condition for the decline in housing prices. The interaction between the supply and demand sides determines the market mechanism underlying the housing price decline: when demand is strong, the supply side proactively adjusts its structure and reduces costs by leveraging the decrease in land costs to achieve active price adjustment; when demand is weak, the supply side is forced into passive price discounts due to inventory backlogs and capital chain pressures. Financial support, functioning as a lubricant, amplifies elasticity on the demand side and mitigates risks on the supply side, yet it cannot reverse the fundamental trends of demand recession and supply clearance.

The article discusses the issues of specifics and the state of rental and property relations in the territory of Domodedovo, Moscow region. The activities of the Property Management Committee of the Domodedovo City District Administration are presented for the period 2023 and 2024. The main areas of activity in the field of disposal of real estate objects are presented in terms of the formation of land plots from municipal ownership, registration on state cadastral registration and provision of land plots, municipal control over their use, analysis of land tax receipts and rent for the use of land and real estate, analysis of factors affecting the effectiveness of management and disposal of land resources. The Domodedovo municipality is a unit of the administrative structure of the Moscow region, which is located in close proximity to the capital of the Russian Federation, which is the dominant factor in the development and feature of land and property relations related to the demand and cost of land and the location of real estate.

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction.

Amidst the surge of high-density urban development and the growing demand for high-quality spaces, the Usable Area Coefficient (UAC) has emerged as a pivotal metric in the architectural planning. The rational calibration of the UAC for primary school buildings is key to balancing intensive land use, educational demands, and the well-being of children. Taking primary schools in a district of Qingdao as the research subject, this research rationally optimizes the range of UAC by constructing an evolutionary game model, based on quantitatively analyzing the divergent perspectives and requirements of three stakeholders: the government, school administrators, and students. After further identifying the key factors that influence the ultimate decision, the study yields the following insights: (1) The incremental comprehensive benefit emerges as the linchpin influencing the UAC. (2) The government’s risk compensation to schools and the benefit-sharing coefficient between schools and students exert significant impacts on system evolution. (3) Effective control of construction and land costs, coupled with enhanced availability of open activity spaces, paves the way for consensus on low UAC. This research not only furnishes a theoretical framework and practical guidance for harmonizing land use efficiency with educational excellence but also steers the design of salubrious primary school environments and informs pertinent policy-making.