Understanding the dynamics of high-intensity rainfall is crucial for assessing hydrological risks and informing climate adaptation strategies, particularly in regions vulnerable to climate change and variability, such as South Australia. This study examines the temporal variations in high-intensity rainfall events across four meteorological stations in Greater Adelaide, South Australia, Australia: Adelaide Airport, Kent Town, Hindmarsh, and Parafield. Daily rainfall data obtained from the Scientific Information for Land-Owners (SILO) database were analysed using the Bivariate Truncated Logistic-Geometric (BTLG) model to examine joint relationships between episode duration and peak intensity. Change-point analysis using the nonparametric Pettitt test was conducted to identify the time at which the effect of climate change on the intensity of daily rainfall became evident. The results indicate noticeable changes in the frequency and rainfall episodes, with key shifts around 1985 toward shorter-duration, generally lower-intensity events at several locations with daily data. The joint probability analysis p (peak > 20 mm, duration > 5 days) indicated a reduction in long-duration extreme events. This study highlights emerging trends of concentrated rainfall and their potential effects on urban runoff, flooding, and infrastructure design.
Furrow irrigation is widely practiced in arid and semi-arid regions but suffers from low efficiency due to infiltration variability and water losses. This systematic review analysed 92 peer-reviewed studies (2017–2024) on short-cycle crops, identified through a reproducible search of five bibliographic databases (Scopus, Web of Science, IEEE Xplore, SpringerLink, ScienceDirect) supplemented by a manually directed search of publisher platforms, following Kitchenham Charters’ protocol. Study quality was assessed narratively, based on validation against field data and comparison with established simulation software (SIRMOD, WinSRFR), rather than through a formal quality-appraisal tool. The narrative synthesis indicates that optimized irrigation techniques achieve efficiency gains ranging from 20
The literature on the underlying factors of environmental sustainability has flourished in recent decades. Despite the increasing attention given to improving environmental quality, few studies have focused on the catalysts for environmental balance. This research aims to explore the spatial spillover effects of financial technology (FINT) on load capacity factor dynamics (LOCAFD) as a proxy for environmental balance dynamics. The present study applied the panel smooth threshold regression (PSTR) approach and the dynamic Spatial Durbin model (SDM) with Lee-Yu transformation in order to identify the determining factors of LOCAFD in the case of 10 emerging market economies. The outcomes of the study reveal that only under the high threshold of natural resources, Fintech innovation positively affects the LOCAFD. The results from both PSTR and SDM indicated the absence of beneficial effects of natural resources on the LOCAFD. Likewise, the results support the positive moderating effect of FinTech on the relationship between financial development and the load factor dynamics. Although FinTech and financial development individually exert significant negative direct effects on the LOCAFD, the results disclose that, using the spatial economic weighting matrix, the interaction term between FinTech (FINT) and financial development (FD) is positive and highly significant for direct, indirect, and total effects. Finally, the findings reveal that the spatial spillovers of Fintech have a beneficial impact on the environmental balance dynamics for economically similar countries, while their effects become negative for geographically closed economies. The integration of an approach combining both spatial dynamics, as well as the non-linear methods, can provide clarification to decision-makers regarding the most appropriate financial technologies and strategic management of natural resources to be implemented to accelerate environmental balance dynamics for the case of emerging market economies.
A compartmental model POSEIDON-G of the transport and fate of radionuclides in the Global Ocean has been developed. The marine environment is a system of 3D compartments (boxes) representing the water column, bottom sediment, and marine organisms forming food chains. Water exchange between boxes was calculated using the monthly circulation data from the ocean general circulation model. Model equations are solved numerically using the high-accuracy matrix exponential method. The efficiency of the numerical algorithm was substantially improved relative to the previous version (POSEIDON-R). We account for all important global sources of 137Cs to reproduce the 137Cs concentration for the period 1945–2030. The simulation agrees well with measurements of 137Cs concentration in the ocean basins. Across the Global Ocean, before 2011 the geometric mean and geometric standard deviation for simulated-to-observed ratios were 1.10 and 1.79, respectively, whereas after 2011, they were 0.97 and 1.73, respectively. The 137Cs inventory in the World Ocean reached its maximum of 491 PBq in 1971, and then decreased to 181 PBq in 2026. Inventory in bottom sediments increased from 1
Methane oxidation biosystems (MOBs) are an effective approach for mitigating fugitive landfill methane (CH₄) emissions, but moisture accumulation near the downslope gas distribution layer (GDL)-methane oxidation layer (MOL) interface can restrict CH₄ entry and concentrate CH₄ migration upslope. Existing analyses have inferred gas occlusion in MOBs from predicted volumetric water content (VWC) at the GDL-MOL interface without explicitly simulating gas redistribution through the MOB. This study used a process-based numerical model that resolves coupled water flow, gas transport, heat transfer, and microbial CH₄ oxidation in MOBs. A dual-porosity hydraulic formulation was incorporated to better represent gravel-amended compost MOLs, and the updated model was validated against CH₄ flux, VWC, and temperature data from a field-scale sloped MOB constructed in Ontario, Canada. Results showed that downslope partial occlusion restricted CH₄ entry locally, but lateral redistribution within the MOL reduced this non-uniformity before CH₄ reached the surface. This finding demonstrates that water-only analyses can overestimate the persistence of gas restriction originating at the GDL-MOL interface and its effect on CH₄ flux distribution. A MOL composition analysis evaluated how gravel amendment of compost altered capillary barrier behaviour, moisture accumulation near the GDL-MOL interface, and gas redistribution. Compost-only MOLs developed near-full saturation at the GDL-MOL interface and produced strongly non-uniform surface CH₄ fluxes, whereas gravel-amended compost MOLs weakened the capillary barrier and preserved more uniform gas distributions. These results show that gravel amendment, previously recommended in literature to improve MOL physical structure, also provides a hydraulic and gas-transport benefit by mitigating downslope gas occlusion.
Accurate quantitative assessment of near-surface wind and turbulence in coastal harbors remains challenging because terrain, vegetation, and built structures interact in a direction-dependent manner and are often treated using lumped or simplified surface representations in numerical models. This study presents a component-resolved Computational Fluid Dynamics (CFD)-based assessment of local wind and turbulence conditions over Mariehamn harbor in the Åland Islands. The objective is to quantify how the inclusion or exclusion of forests and buildings modifies fairway-scale wind and turbulence exposure. Simulations were performed for eight wind directions using three surface configurations: terrain with buildings (TB), terrain with forests (TF), and terrain with both forests and buildings (TFB). The results show that forests had the larger fairway-scale relative contribution to both wind-speed reduction and turbulence generation, with the TF and TFB configurations producing similar broad flow patterns across most wind directions. A deviation-based analysis relative to the complete TFB configuration further quantified the relative contributions of forests and buildings and showed that excluding forests produced substantially larger changes than excluding buildings, particularly in normalized turbulence intensity at higher heights. These findings highlight the larger relative contribution of forest canopies to momentum loss and turbulence modification over the fairway within the lower atmospheric boundary-layer. The resulting CFD dataset and component-resolved assessment approach provide a physically consistent basis for future integration into intelligent fairway and navigation-oriented decision-support frameworks.
Spatial management planning demands integration of strategic multi-criteria prioritization with tactical resource allocation–a challenge often addressed separately in prior work. We propose a framework that couples these levels via Multi-Attribute Utility Theory (MAUT) and Mixed Integer Programming (MIP). MAUT translates stakeholder-informed preferences into utility functions across multiple criteria, while MIP optimizes spatially explicit, cost-effective management plans under technical and operational constraints. A distinctive feature is that management actions are prioritized by the utility change they generate, rather than by zone rankings alone, creating a direct link between stakeholder preferences and tactical allocation. An application to forest fuel management for wildfire risk reduction is presented, showing how the framework prioritizes treatments according to their transformative efficiency. Results demonstrate optimality and computational feasibility for landscape-scale problems, with flexibility across diverse management scenarios. By bridging strategic prioritization and tactical implementation, this framework addresses a critical gap in spatial decision support, offering a replicable approach for integrated planning in conservation and natural resource management.
This paper introduces a novel framework, Optimum Sustainable Value-Added (OSVA), designed to address the shortcomings of existing sustainability measurement approaches. OSVA links sustainability performance to competitive advantage by quantifying environmental and social impacts, optimizing resource allocation, and incorporating context-specific factors to achieve strong sustainability within the Cement Industry. Unlike traditional indexes, OSVA not only measures but also optimizes sustainability contributions, translating them into economic terms that businesses and stakeholders can readily comprehend. This transparency facilitates data-driven decision-making and enhances communication with stakeholders who prioritize sustainability. The OSVA framework incorporates dynamic and importance coefficients in sustainability monetization, enabling adaptability to changing regulations and the varying significance of sustainability indicators. To validate its effectiveness, we conducted a case study in the cement industry. The findings demonstrate OSVA’s ability to significantly improve sustainability measurement and optimization, assisting firms in identifying and prioritizing initiatives that enhance competitive advantage. By integrating social, environmental, and financial aspects, OSVA provides a comprehensive assessment of a company’s sustainability. This study contributes a unique and innovative tool for businesses to adopt more sustainable practices, optimize resource consumption, foster stakeholder collaboration, and achieve superior competitive outcomes.
This study investigates the impact of renewable energy, nuclear power, and energy efficiency technologies on CO₂ emissions in the United States from 1990 to 2023 within the framework of the Environmental Kuznets Curve (EKC) hypothesis. Employing advanced Fourier-based econometric techniques—including the Fourier Autoregressive Distributed Lag (Fourier-ADL) and Fourier Engle-Granger cointegration tests—the analysis explicitly accounts for structural breaks and nonlinear adjustments in the energy–environment nexus. The empirical findings confirm the EKC hypothesis, showing that economic growth initially exacerbates emissions but subsequently contributes to their reduction after surpassing a critical income threshold. The empirical results indicate that while economic growth and short-run renewable energy advancements actively mitigate CO₂ emissions, the long-run structural impacts of nuclear and energy efficiency R D expenditures remain statistically marginal during the analyzed period, pointing toward delayed market penetration and infrastructure lock-in. Moreover, the results reveal that short-run elasticities are consistently lower than long-run elasticities, underscoring the delayed yet sustained benefits of clean energy innovations. By disaggregating the contributions of specific energy technologies and integrating them into the EKC framework, this study offers novel insights into the mechanisms of decarbonization in advanced economies. The findings emphasize the transformative role of technology-driven energy policies in reconciling economic growth with environmental sustainability and provide actionable guidance for accelerating the United States’ transition toward a low-carbon future.
Elevated terrestrial nutrient loads entering coastal waters in northeastern Australia are adversely affecting the Great Barrier Reef World Heritage Area. ‘Seaweed biofilters’ have been proposed as a mechanism for the bioextraction of elevated nutrient levels from coastal environments, with potential to target diffuse source nutrient discharges while generating biomass with potential applications as fertiliser, soil conditioning agents and agricultural fodder. Here, we use a novel modelling approach to identify optimal candidate locations for seaweed biofilter deployment in northeastern Australia’s coastal waters, prioritising sites for more detailed assessment and on-ground feasibility evaluation. The analysis integrates spatial layers representing regulatory constraints and practical deployment considerations with a model for the seaweed growth potential. Growth potential is determined from outputs of the Environmental Modelling System (EMS), a sophisticated environmental model that simulates coupled physical, chemical, and biological processes and is continuously operated over the Great Barrier Reef region as part of the eReefs project. Based on this analysis, fourteen sites, located near river mouths, satisfied the selection criteria and were identified as potential candidates for seaweed biofilter deployment. Potential sites for seaweed biofilters in the Great Barrier Reef identified using legal and practical criteria. Identified over 800,000 ha of coastal waters as potentially suitable for seaweed cultivation. eReefs biogeochemical simulations used to estimate spatial variability in seaweed growth potential. Fourteen sites shortlisted for further assessment of seaweed mariculture for nutrient biofiltration.
Acid mine drainage (AMD) generated by pyrite oxidation in mine overburden dumps poses a persistent threat to groundwater quality, with contaminant generation and migration governed by soil texture, moisture conditions, and oxygen availability. Despite extensive studies on pyrite oxidation chemistry, the integrated influence of unsaturated flow, soil texture, and water content on the temporal evolution and spatial dispersion of oxidation products remains inadequately understood. This study examines the role of surface soil texture and moisture conditions in controlling coupled unsaturated flow and multicomponent reactive transport of pyrite oxidation products in mine waste deposits. A two-dimensional numerical framework is developed by integrating Richards’ equation for variably saturated flow with multispecies reactive transport equations, incorporating Monod-type pyrite oxidation kinetics and a shrinking-core formulation. The model simulates the generation and migration of ferrous (Fe2⁺), sulfate (SO₄2⁻) and ferric (Fe3⁺) ions, under varying hydrogeological conditions. Simulations are conducted over a 10-year period for three representative soil textures (clay, loam, and sand), porosity values (θ = 0.2, 0.4, and 0.6), and water content scenarios (wc = 0.1, 0.5, and 0.9). Model predictions are validated against published benchmarks and field-scale observations. The results demonstrate strong texture-dependent behavior. Clay restricts oxygen ingress, resulting in reduced oxidation rates and enhanced retention of dissolved species, whereas loam exhibits relatively uniform transport, and sand facilitates rapid advective migration of oxidation products. Ion concentrations decrease from approximately 200 to 40 mol m⁻3 in clay, 300 to 95 mol m⁻3 in loam, and 1 to 0.2 mol m⁻3 in sand. Elevated moisture conditions (wc = 0.9) significantly enhance the ion mobility, increasing the contamination potential. Sensitivity analysis indicates that sulfate exhibits the highest sensitivity in clay under low moisture conditions (wc = 0.2), with SI values up to 4.3 × 104, while ferrous ions show maximum sensitivity in loam under similar conditions (102–103). Overall, clay-rich layers provide effective attenuation of pyrite oxidation products, highlighting their suitability for AMD mitigation.
PM 10 concentrations in many countries in Europe are above or close to the limit values set out by the European commission for 2030. This includes both the daily mean and annual mean limit values of 45 µg/m 3 and 20 µg/m 3 respectively. In the European Ambient Air Quality Directives (AAQD) from 2008 and the new AAQD for 2030 the possibility exists to discount an exceedance of a daily mean PM 10 concentration if this is caused by the contribution from traction sanding or road salting. To implement this article in the AAQD, some methodology must be applied to determine the contribution of traction sanding to the PM 10 concentrations. The current recommended method is very simple and does not account for the amount of sand applied, the studded tyre share or the traffic volume. This paper outlines the use of mass balance modelling, in combination with observed PM 10 and PM 2.5 concentrations, to determine the contribution of traction sanding to PM 10 non-exhaust emissions. A two-tiered approach is provided. The first tier assumes that all wear sources are deposited onto the road surface, allowing both traction sand and wear particles to undergo the same removal processes, conserving their relative contributions to the emitted PM. The second tier improves this method by including additional information concerning direct emissions and the surface wetness. The two methods are shown to closely follow the more complex NORTRIP road dust emission model and the methods may be applied to deduce traction sanding contributions to PM 10 concentrations with simple spreadsheet calculations. This will enable the eventual negation of exceedances caused by the traction sanding contribution.
Carbon emission trading systems exhibit pronounced nonstationarity and policy-related volatility, which complicates reliable forecasting and risk-oriented monitoring. Many econometric and deep learning models assume near-synchronous effects and therefore struggle to represent time-lagged responses between carbon prices and external drivers such as energy markets and policy uncertainty. We propose a Dynamic Graph-based Carbon Price Network that uses Dynamic Time Warping to construct window-wise interaction graphs capturing lead–lag dependence among drivers, and combines graph convolution with temporal convolution to learn evolving spatiotemporal dynamics. Predictive uncertainty is obtained by Monte Carlo dropout at inference to form prediction intervals. Experiments on China’s pilot markets in Hubei, Shanghai, and Guangdong under time-ordered evaluation show improved accuracy and stable behavior during volatile periods and regime transitions. Perturbation-based attribution and hierarchical feature removal further indicate that policy uncertainty provides informative predictive signals alongside macro-financial indicators, international benchmarks, and energy cost proxies, while lower-ranked variables contribute complementary information under turbulence. These findings support uncertainty-aware monitoring of market stability and time-lagged shock transmission in emerging carbon markets.
Accurately modeling stage‒discharge relationships has become a critical task in flood risk management due to the increasing uncertainty of river systems responses to extreme events under climate change. Despite advancements in real-time water level monitoring and forecasting, traditional methods struggle to accurately forecast flood water levels, particularly during high-flow events. This study addresses this gap by applying Bayesian optimization (BO) to improve the accuracy of the stage‒discharge modeling and enhance efficiency of flood risk management. Various acquisition functions within the BO framework were employed, and their performance was systematically compared across different return periods (2, 5, 10, 25, 50, and 100 years) and flow percentages (1
White rhino (Ceratotherium simum) populations on private land in South Africa have defied the trend in rhino numbers, both globally and in South Africa as a whole, showing an increase over time. Many have argued that this is due to legal trophy hunting of white rhino. But will this trend continue in the future? A bioeconomic model for rhino is developed using data on white rhino populations in South Africa. Under the baseline of trophy hunting with poaching, populations of white rhino on private land continue to increase until around 2040–2045, and then decline to almost zero by around 2050–2055. Even the elimination of poaching does not improve this situation. Profits are higher for ecotourism compared with the trophy hunting option and stocks more sustainable. The argument that trophy hunting is required to pay for high security costs therefore is not supported by the outputs of the present model. Furthermore, the assumption that ecotourism and trophy hunting are complementary is also not supported by the present model. The main driver for the decline in white rhino under the trophy hunting scenario is the high increase in trophy price. In these open access models, price drives species abundance. This is what is known as “trading on extinction”. This has implications for other exploited species that command high prices on markets, and also other iconic species that are threatened. There appears to be sufficient incentive for game farms to switch to ecotourism. The only constraint to this actually occurring could be government’s recent policy on land expropriation without compensation.
This paper aims to investigate the persistence of the load capacity factor (LCF), a comprehensive indicator of sustainable development, defined as biocapacity (BC) divided by ecological footprint (EF). Our contribution is methodological. We use long-memory processes for LCF as an alternative to combining short-memory or unit root processes with structural breaks, as in the literature. We use data for 31 OECD (Organization for Economic Cooperation and Development) countries from 1961 to 2024. We investigate the nature of sustainable development shocks associated with the LCF. A persistent LCF suggests that shocks may have long-run effects, potentially influencing the time horizon considered in environmental and sustainable development policy decisions. We empirically motivate long memory in LCF time series by testing against a short-memory process with structural breaks. We use several semiparametric estimators of the degree of fractional integration parameter from the literature. These involve alternative specifications of the (i) local Whittle estimator, (ii) exact local Whittle estimator, and (iii) two-step exact local Whittle estimator. Our results suggest predominant evidence of long memory in load capacity time series for most OECD countries. We discuss these results in terms of country-specific characteristics and environmental policies. One implication is that long-term initiatives concerning BC and EF are needed to pursue sustainable development in OECD countries.
Urban energy consumption shows a spatial association. This paper aims to investigate the spatial patterns of electricity consumption in Poland and determine its environmental, economic, demographic and infrastructural predictors. Spatial autocorrelation, Local Indicators of Spatial Association (LISA) and spatially lagged regression model were applied to selected predictors. Analysis was performed at the poviat (municipalities) scale for the year 2022. Per capita electricity (PCE) consumption showed a significant spatial autocorrelation and spatial distribution among the districts of Poland. Local clusters of similar consumption patterns were identified. Urbanisation rate and particulate matter showed negative total spatial impacts, while per capita municipal waste and petrol cars showed positive total impacts on PCE in a Spatial Autoregressive Combined (SAC) model. Creating strategies for cities with similar energy consumption can lead to local and regional renewable and sustainable energy transition. Exploration of these clusters is important for an organic understanding of spatial planning.
In this study, a system dynamics model with a particular focus on the impact of energy policies on Turkey’s energy self-sufficiency was developed, incorporating various variables that interact dynamically with each other. The set of targets for renewable energy capacity building and the commissioning of nuclear power plants, which are key priorities for the Turkish government, were examined through three different scenarios. Turkey’s annual energy import is expected to reach almost 50 TWh if no energy capacity investments are made in the future (Scenario-1). Although this gap could be closed with renewable energy capacity expansion within the scope of the 1st set of development targets, model simulation outcomes show that current capacity will fall short of meeting the national energy demand in the long run (Scenario-2). In a scenario where the capacity installations of solar and wind power, coupled with nuclear energy investment in the scope of the 2nd set of development targets, Turkey is expected to meet its national energy demand and to export 3.5
Accurate and precise wood volume estimates are vital for sustainable forest management and are typically obtained using sample-based estimators. This study explores cluster sampling for estimating wood volume in an Amazonian forest, aiming to: (1) measure the loss of precision and deviation in volume estimates by comparing full samples to reduced samples and (2) analyze how the distance between cluster subunits affects the volume estimates. Data were collected from 22 clusters (model used in the Brazilian National Forest Inventory) installed in the Bom Futuro National Forest, Brazil. Our analyses consisted of examining the effect of reducing the (i) cluster size, in two directions (inward and outward), and (ii) sample size, by reducing the number of sample units from 22 to 4 clusters. We found that reducing the cluster size to 0.56 ha still yielded timber volume estimates as accurate and precise as the original 0.80 ha clusters, a useful innovation for forest inventories in the Amazon. Precision loss from reduced sampling ranged from 1.2 to 1.5 times, while deviation loss ranged from 7.2 to 19 times. We concluded that the precision was more influenced by the cluster size reduction than the sample size reduction. The sampled area required to stabilize precision of the volume estimate is smaller than that required to stabilize the mean volume estimate. The effect of the distance between subunits on deviation and precision was attenuated as the cluster size increased.
This study examines the impact of water scarcity on agro-social welfare in the Iranian saffron market. To capture interregional market interactions, a multi-objective spatial equilibrium model is developed in which the Total Water Footprint (TWF) serves as the constraint representing water availability. The framework allows for the evaluation of environmental limitations on economic outcomes and distinguishes welfare effects for saffron producers and consumers across provinces. Scenario analysis simulate gradual reductions in TWF ranging from 10
