Ecological conservation priority requires foresight: coupling multiple scenarios and network perspective.

Carbon footprint of monomer recycling for mixed synthetic textiles - a grave-to-gate analysis.

Land use and land cover change (LUCC) exerts substantial influence on ecosystem service values (ESV). But conventional ESV valuation approaches frequently neglect temporal shifts in crop economic returns. Integrating high-resolution land use data (2000-2020) with the Patch-generating Land Use Simulation (PLUS) model-and augmenting it with spatial econometric analysis-we project 2030 land use configurations under four policy-relevant scenarios: Business-as-Usual (BAU), Economic Development Priority (EDP), Ecological Protection Priority (EPP), and Ecological Economic Balance (EEB). Under EDP, construction land expands by 5.52%, critically low-ESV areas increase by 476.83 km2 (a 2.7-fold surge relative to the 2010-2020 period), and net ESV declines by 1.04%. By contrast, EPP achieves a 10.19% reduction in urban land through targeted ecological restoration, effectively arresting ESV degradation. High-value ESV zones are concentrated in water bodies and forests, which dominate regional regulating services. The three-province in the Middle Reaches of the Yangtze River epitomizes the inherent tension between rapid urbanization and ESV conservation. These findings provide a basis for assessing the social, economic and environmental factors. Furthermore, the results provide a new solution approach for formulating differentiated ecological environment protection policies in the study area and addressing key technical challenges in land use planning for large-scale ecological functional area.

People living in social housing in Australia have higher prevalence of daily smoking and greater exposure to second-hand smoke (SHS), particularly in multi-unit housing where smoke can drift between dwellings. The health impact of this exposure has not been quantified for this population. We developed a Monte-Carlo matrix model of household SHS exposure, including smoke drift between dwellings in multi-unit housing, and linked it to the SHINE Tobacco simulation platform. The model projected health outcomes for the population living in social housing in the state of Victoria, Australia, over 20 years as an open cohort, and over the lifetime of a closed cohort, comparing scenarios that eradicated SHS exposure and/or smoking with a business-as-usual (BAU) scenario of constant smoking rates. Outcomes were health-adjusted life years (HALYs) gained, and premature deaths averted across 31 smoking-attributable and eight SHS-attributable diseases. Eradicating SHS exposure within the social housing population of Victoria in 2025 could result in 5350 HALYs (95% uncertainty interval [UI] 4670-6120) gained, and 600 premature deaths (95% UI 500-700) averted from 2025 to 2044 in the open cohort. In multi-unit housing, about half of the SHS-related health gain was attributable to eliminating smoke drift between units. Overall, SHS eradication accounted for approximately 27% of the total health gain achievable if tobacco smoking were fully eradicated in this setting. Reducing SHS exposure in social housing would deliver substantial health benefits, with a large share resulting from preventing smoke drift in multi-unit housing. Better data on population dynamics and smoke infiltration would strengthen estimates and support policy design.

Circular economy approaches for improving environmental and economic performance of felt-based living walls.

The optimization of new cropland allocation is crucial for promoting the efficient use of cropland resources and safeguarding food security. However, existing studies primarily take suitability as the optimization objective and lack the consideration of stable utilization potential, which may lead to subsequent unstable use. To address this gap, this study quantified the stable utilization potential of new cropland using a machine learning model and integrated it with the ant colony optimization (ACO) model to develop a spatial allocation framework. This framework was validated in Guangdong Province, China, a region characterized by diverse resource endowments and pronounced regional heterogeneity. The results indicated that, under the specified objective-weighting scheme and compared with the business-as-usual (BAU) scenario, the optimized scenario achieved regional cropland quantity balance. It also increased the average stable utilization potential of new cropland and overall utility by 34.84% and 12.74%, respectively, while reducing the mean cost and mean ecological benefit loss per unit area by 10.22% and 41.36%, respectively. Overall, under the specified constraints, the proposed framework offers a promising approach for new cropland planning and provides a basis for governments and land management authorities to improve future allocation practice.

Long-term land use projections are essential for food security planning, conservation policy, and sustainable development, yet forecasting frameworks applied to the complete historical record of United States land use remain absent from the literature. Here we present the first comprehensive forecasting analysis of the USDA Economic Research Service (ERS) Major Land Uses (MLU) dataset, which spans 1945 to 2017 across 16 temporal observations for the 48 contiguous states. We develop and compare three forecasting approaches: (1) a Markov chain transition probability model estimated from 15 consecutive period-pairs via constrained least squares; (2) Akaike Information Criterion (AIC)-selected parametric curve fitting among linear, quadratic, logistic, and exponential models; and (3) scenario-modified Markov projections representing business-as-usual (BAU), accelerated urbanization, and conservation pathways. Projections extend 50 years to 2067 with bootstrap-derived uncertainty bounds from 500 iterations. Under BAU, cropland is projected to decline from 20.6% to 17.1% of total land area, forest-use land from 28.0% to 19.3%, while grassland pasture and range increases from 34.8% to 39.8% and special uses from 9.0% to 12.6%. AIC model selection independently identifies logistic saturation curves as the best-fitting model for cropland, forest, and urban land, providing convergent evidence that these major transitions are approaching asymptotic equilibria rather than continuing linearly. Under accelerated urbanization, urban land reaches 6.5% by 2067 with correspondingly greater losses in cropland and forest. State-level Markov models reveal convergence half-lives ranging from 5 to 15 years, demonstrating that the U.S. land use transformation is geographically asynchronous. The proposed framework leverages publicly available census-based tabular data and is designed to complement, rather than replace, remote sensing approaches; it can be readily adapted to any country that maintains periodic census-based or survey-based land use inventories, particularly in contexts where high-frequency national accounting over multi-decadal periods is required.

• We link an ESM to a CGE model to assess the macroeconomic impacts of the energy transition in the Netherlands. • The modelling framework offers new methodological insights by integrating detailed energy technologies within an economy-wide CGE model. • Although the energy transition yields GDP gains in the long term, it leads to unemployment, driven by structural shifts in the economy. This study examines the macroeconomic impacts of the energy transition in the Netherlands. To capture key energy transition dynamics and improve the assessment of alternative fuels adoption, particularly in hard-to-abate sectors such as steel and chemical production, we incorporate hydrogen-related activities into a Computable General Equilibrium (CGE) model and soft-link it to an Energy System Model (ESM). We evaluate two main scenarios: a business-as-usual (BAU) trajectory and an Energy Transition (ET) pathway aligned with a carbon-neutrality target. A variant of the ET scenario with limited capital inflows (ET-CA) is considered to assess the role of financing in the energy transition. Our results show that replacing fossil fuels with renewable alternatives drives GDP growth in the long term, with GDP 1.7% higher in 2050 under the ET scenario compared to BAU. Cumulative GDP over 2025–2050 increases in ET compared to BAU, while it declines by €64 billion in ET-CA. Unemployment peaks around the mid-transition period in ET and declines thereafter, converging to about 0.2% above BAU by 2050. In ET, welfare losses are initially severe but moderate over time, whereas they remain consistently higher under ET-CA. We argue that the negative impacts observed under the ET scenarios should be weighed against the potential climate-related economic damages omitted in BAU, which could otherwise reduce its apparent macroeconomic advantage. Our analysis underscores the necessity of policy frameworks that balance the socio-economic impacts of the energy transition with its environmental benefits, especially under constrained financing conditions.

The energy sector in Pakistan continuously relying on imported fossil fuels, which remain costly, contribute to air pollution, and increase greenhouse gas (GHG) emissions. In this study, the Low Emission Analysis Platform (LEAP) model is used to compare three electricity supply scenarios between 2021 and 2050, including a Business-as-Usual (BAU) scenario, the Alternative and Renewable Energy Policy (AREP 2019) scenario, and a higher target Sustainable Pathway (SP) scenario. The scenarios are compared to evaluate the capabilities of renewable energy policies and interventions in ensuring that energy supply is secured, and climate change is mitigated in the context of Sustainable Development Goals (especially SDG 7 on clean energy and SDG 13 on climate action). The modelling outcomes estimate that by 2050, the electricity demand in Pakistan will be around 1489 TWh, whereas the GHG emissions will increase from 100 MtCO2-e(2025) to 564.7 MtCO2-e annually under BAU. Conversely, the SP scenario, by contrast, where a faster switch to renewables is assumed, would limit 2050 emissions to approximately 34 MtCO2-e, with more than 90% reduction over BAU. Moreover, SP scenario is consistent with cost benchmarks of Pakistan’s IGCEP plan. However, achieving this level assumes significant grid infrastructure upgrades, including advanced transmission and smart distribution systems, which are under ongoing development in Pakistan. These findings highlight Pakistan’s urgent need to speed up the move toward renewable energy. Using the country’s large, unused renewable resources through better policies and investments is essential for improving energy security and protecting the environment from climate change.

• Introduces a unified MILP framework coupling electrical and thermal energy optimization. • Demonstrates synergy of bioenergy and battery storage in hybrid renewable systems. • Identifies key economic and policy levers for accelerating residential decarbonization. • Offers actionable insights for achieving resilient, low-carbon home energy transitions. • Enhances grid resilience and reduces reliance on unstable conventional energy networks. Residential energy analyses often optimize electricity and heat separately, masking their tight operational coupling and the cascading effects of technology choices across both domains. This study addresses that gap with a unified mixed-integer linear programming (MILP) framework that co-optimizes capacity sizing and hourly operation across photovoltaic panels and wind turbines (electric generation), geothermal and biogas systems (thermal generation), an air-source heat pump that couples power-to-heat conversion, and lithium-ion battery storage for a four-person dwelling. The model evaluates three policy scenarios designed to assess progressive decarbonization pathways: business-as-usual (BAU) to establish baseline performance, a 50% natural-gas capacity constraint aligned with European Union emission targets, and dual 70% constraints on gas capacity and CO 2 emissions addressing Iran’s energy challenges and ambitious net-zero commitments. Sensitivity analyses examine electricity price and carbon tax thresholds that drive technology transitions. Under BAU, gas-based technologies dominate, yielding 11.66 kg CO 2 daily emissions. Imposing a 50 % gas constraint electrifies heat via the heat pump, reduces emissions by 54 % to 5.4 kg CO 2 , and increases renewable penetration to 47 %. With dual 70 % constraints, renewables supply 95 % of total energy, grid imports decline by 73 %, and daily emissions fall to 0.93 kg CO 2 as battery cycling intensifies eight-fold. Battery storage mitigates short-term power variability, manages peak grid interactions, and enables load-shifting to periods of higher renewable availability, collectively enabling deeper decarbonization under stringent policy constraints. Economic sensitivity analyses reveal critical thresholds: renewables reach cost parity at €0.18 kWh −1 grid electricity prices, and 57 % renewable penetration occurs at €120 tCO 2 −1 carbon taxation. By optimizing electricity, heat, and storage within a single framework, this study identifies practical policy levers—moderate pricing reforms coupled with storage incentives—for economically viable, net-zero-ready residential energy systems.

This study aims to analyze the sufficient food needs for the national Free Nutritious Meals (MBG) program in all provinces in Indonesia. The method employed is a quantitative approach, by calculating the production results of carbohydrate food in the form of rice in Indonesia. The dynamic system model uses three scenarios: Business as Usual (BAU), Economic Growth, and Sustainable Development to analyze the most effective scenario in meeting the society's carbohydrate needs. The sustainable development scenario is the optimal scenario to effectively achieve the objectives of the National Free Nutrious Meals program with the highest amount of food reserves. Based on these results, this situation must be addressed with several strategies, including both spatial economic and socio-statistical approaches. Optimization to meet food needs can be carried out by optimizing food resources in each province in the form of 1) fulfilling carbohydrate deficits through food diversification and economic cooperation, and 2) optimizing agricultural development through extensification and intensification in food production.

Background Z-hypnotics are commonly prescribed for insomnia, but their use in older adults is associated with an increased risk of adverse events Aim We examined the long-term effect of brief intervention (BI) for inappropriate z-hypnotic use, where the control group crossed over to receive the BI 6 months after baseline. Design and setting A before-and-after intervention study was conducted in general practice. Older patients received the BI from trained general practitioners. Method The BI group and the business-as-usual (BAU) group received the intervention with a six-month delay. The primary outcome: proportion of participants without inappropriate z-hypnotic use (≥4 weeks of use, ≥3 times per week). Secondary outcomes: the Global Sleep Assessment Questionnaire (GSAQ), pain visual analogue scale (VAS), Montreal Cognitive Assessment (MOCA), and Hospital Anxiety and Depression Scale (HADS). Patients were assessed at baseline, 6 weeks, 6 months, and 12 months. Results We included 45 patients (31 female, mean age 69.4 years) and 21 GPs in the study. We found a significant reduction in inappropriate z-hypnotic use from baseline (68.9%) to post-treatment (27.78%), OR = 0.16, 95% CI: 0.04, 0.65, p = 0.01. GSAQ—insomnia score stayed low throughout the study. At 6 months, no participant reported insomnia. HADS was significantly reduced from baseline (mean 10.1) to post-intervention (mean 7.3, Cohen’s d = −0.44, p < 0.01), whereas MOCA and VAS pain did not change significantly from baseline to post-intervention. Conclusion The proportion of patients with inappropriate z-hypnotic use decreased after BI without negatively affecting sleep, mood, pain, or cognitive function.. Trial registration clinicaltrials.gov (NCT06032715).

As the world faces the challenge of mitigating climate change, energy- and emissions-intensive industrial processes must be addressed urgently worldwide. The cement production industry accounts for over 8% of global greenhouse gas (GHG) emissions from calcination and fuel use. Mexico, a middle-income economy, has rising cement demand for infrastructure and commercial growth. Thus, this study analysed national cement production, the primary emitting manufacturing industry in the country, under a business-as-usual (BAU) and two alternative scenarios, using a top-down approach to model energy consumption and GHG emissions by 2050. These scenarios follow the projection of national cement production, estimated using socio-economic indicators, which are considered the main drivers of cement demand, reaching 97.3 Mt. A qualitative analysis evaluates the strengths, weaknesses, opportunities, and threats (SWOT) of implementing emission-reduction strategies. The analysis showed that the BAU scenario might reach 66.5 Mt CO2e by 2050, while the most ambitious scenario reduced direct emissions by 80.1% through carbon capture, clinker-to-cement reduction, thermal energy intensity reduction, and the use of municipal solid waste as an alternative fuel. However, incorporating these strategies in Mexico requires a more active role and investment support from key stakeholders.

Rapid urban expansion and the growing spatial requirements of utility-scale photovoltaic (PV) deployment compete for the same category of land—flat, accessible, and high-insolation terrain—yet the scale, trajectory, and planning-sensitivity of this conflict remain poorly characterised at the regional level. This study quantifies the spatiotemporal competition between urban construction land and PV-suitable land in the Yangtze River Delta (YRD) from 2000 to 2020 and projects its evolution to 2030 under three development scenarios. Built-up areas were extracted for three epochs using a Random Forest (RF) classifier on the Google Earth Engine (GEE) platform, achieving overall accuracies of 87.7–94.5% and Kappa coefficients of 0.718–0.739. PV site suitability was evaluated through a hybrid Multi-Criteria Decision Analysis (MCDA) framework combining Boolean exclusion constraints with an Analytic Hierarchy Process (AHP)-based Weighted Linear Combination model; the weight structure was validated by a Consistency Ratio of 0.006, and a One-At-a-Time sensitivity analysis confirmed spatial robustness across threshold scenarios. Spatial overlay analysis reveals that the cumulative area of PV-suitable land occupied by urban built-up uses grew from 15,862 km2 in 2000 to 23,872 km2 in 2020, representing an incremental loss of 8010 km2 over two decades. Future conflict was projected using the PLUS model, calibrated on 2010–2020 observed expansion and validated against the 2020 classified map (OA = 93.99%, Kappa = 0.91). Under the Business-as-Usual (BAU) scenario, 33,368 km2 of currently open PV-suitable land faces urban encroachment by 2030; the Ecological Conservation Priority (ECP) scenario reduces this figure to approximately 30,767 km2, while the Economic Development (ED) scenario yields a near-identical outcome to BAU, indicating that development velocity alone does not determine the spatial extent of conflict—the allocation of growth does. These findings provide a quantitative basis for designating energy-strategic reserve zones within national spatial planning frameworks and demonstrate that targeted spatial governance, applied at high-pressure locations, can substantially slow the erosion of the region’s solar energy land base.

ABSTRACT Understanding the complex linkages between landscape patterns and ecosystem service multifunctionality (ESMF) and ecosystem service (ES) trade‐offs is crucial for sustainable land management. However, few studies have examined how these relationships vary non‐linearly and spatially under alternative future development pathways. This study developed an integrated framework that combined land‐use simulation, ES assessment, machine learning, and spatial analysis. It was employed to examine how landscape patterns influence ESMF and ES trade‐offs in 2040 under the business‐as‐usual (BAU), cropland protection (CP), and ecological priority (EP) scenarios in the Xiamen–Zhangzhou–Quanzhou metropolitan area. The results show that the EP scenario generally provided the favorable basis for maintaining multifunctionality and avoiding severe trade‐offs. Landscape composition dominated variation in ESMF and ES trade‐off intensity, whereas landscape configuration moderated ES trade‐offs across scenarios and sub‐basins. The combined partial dependence plot (PDP) and geographically weighted regression (GWR) analysis further show that landscape‐ES relationships were governed by scenario‐dependent non‐linear response ranges together with strong local spatial heterogeneity. Forest cover generally promoted ESMF, with the most effective response range occurring at approximately 40%–50%. By contrast, built‐up land consistently suppressed ESMF and shifted several trade‐off relationships toward low‐level equilibrium state. Cropland became more influential under the CP and EP scenario, and the ecological meaning of landscape diversity varied across local landscape contexts. These findings show that similar landscape changes may generate contrasting ES outcomes across scenarios and sub‐basins, and they provide a spatially explicit, scenario‐dependent basis for differentiated land management.

Phosphorus (P) recovery from sewage sludge ash (SSA) represents an interesting solution to P supply concerns. While techno-economic assessments show promising outcomes, the environmental and social performance of these recovery processes remains insufficiently explored, hindering the market uptake of recovered P in bio-based fertilisers. This work investigates the environmental impacts of producing four commercial granular fertilisers representative of the Italian fertilizer market in 2024 (i.e., ENERGEO CV, ENERGEO CV TOP, LITHOZINC and PHEOSCOR). Furthermore, a social hotspot analysis (SHA) was conducted to account for the social implications of raw materials supply (e.g., P, Mg, S, Ca, Cl) in fertiliser manufacturing. Impacts were evaluated for a Business-As-Usual (BAU) scenario, in which fertilisers are based on mineral P (i.e., phosphorite), and a Circular Economy scenario (CE), in which phosphorite is partially replaced with P recovered from SSA via wet chemical extraction and co-precipitated with calcium hydroxide or low-grade magnesium oxide mining by-product (LG-MgO). Raw material supply covered from 56% to 98% of total environmental impacts across all fertilisers, except for ENERGEO CV – BAU and ENERGEO CV TOP – BAU, where core processes were the main contributors to specific sub-categories. LITHOZINC – CE and PHEOSCOR – CE showed similar or enhanced performances compared to the corresponding BAU formulations, indicating potential benefits in employing LG-MgO as precipitant. The supply chains of raw materials exhibited a medium-high social risk in the assessed categories, with Egyptian phosphorite extraction posing the greatest concerns for workers and local communities, and the innovative solution potentially improving the social performance of the fertiliser manufacturing process. • Raw material supply covered 55% – 98% of total environmental impacts. • LG-MgO-based formulations improved environmental performance in CE scenario. • Raw material supply showed medium-high social risk in the assessed categories. • CE solution potentially improves the social performance of fertiliser manufacturing.

To evaluate a 7 day multidisciplinary allied health assistant (mAHA) model of care in an acute general medical setting as a strategy to support service sustainability in the context of hospital bed block. Regional and rural hospitals face challenges delivering timely allied health (AH) care due to workforce shortages, limited weekend coverage and increasing patient complexity. mAHAs may support service sustainability; however, evidence for acute 7-day models is limited. Analysis of 1615 delegations (pilot n = 594; BAU n = 1021) compared a 5 day business-as-usual (BAU) service (1/10/2021-28/2/2022) with a pilot 7-day model (1/10/2022-28/2/2023) servicing 56 acute beds. Outcomes assessed demand, utilisation and care efficiency. A quasi-experimental evaluation of an organisational intervention. Regional hospital. Outcomes included measures of demand (delegations, occasions of service (OOS), therapy time), utilisation (by profession, day of week), and care efficiency (OOS/delegations per rostered day, time/OOS per delegation). The 7-day model delivered increased delegations (81.9%, p < 0.0001), occasions of service (61.2%, p < 0.0001), and therapy time (127.4%, p < 0.0001), primarily for physiotherapy. The 7-day service model improved load-levelling, reducing early-week demand. Care efficiency improved, with more OOS and delegations per rostered day (p < 0.0001), shorter time per OOS (p < 0.0001) and greater therapy time per admission (p < 0.0001). These improvements occurred without additional AH productive FTE. Utilisation remained physiotherapy-led. In regional hospitals experiencing hospital bed block, a 7-day mAHA model improved access to and efficiency of AH services without the need for additional AH staffing. Limited uptake by non-physiotherapy disciplines constrained full multidisciplinary benefit and warrants further investigation.

The contributions of refined anthropogenic sources to PM2.5 air quality and health impacts in China from 2020 to 2030, and associated policy benefits.

Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input–output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for Türkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for Türkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20–30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.

Accurate time-series simulation of carbon emissions for both the whole society and the electricity industry is pivotal for realizing China’s “Dual Carbon” goals. This research constructs a hybrid simulation architecture integrating factor decomposition with deep learning to quantify emission trajectories for both the whole society and the electricity industry in Anhui Province. First, the extended Kaya identity and Logarithmic Mean Divisia Index (LMDI) are employed to analyze socioeconomic drivers. The decomposition analysis indicates that per capita income is the primary driver of carbon emissions, whereas energy intensity exerts the strongest inhibitory effect. Subsequently, Variational Mode Decomposition (VMD) is applied to the nonstationary emission series to produce multi-scale sub-signals, which are then fed into a predictive model comprising a Bayesian-optimized (BO) Transformer coupled with Long Short-Term Memory (LSTM) networks. The study establishes three distinct evolution scenarios: Moderate Sustainability (MS), Business as Usual (BAU), and Strong Economic Growth (SEG). Simulation results indicate that under MS, carbon emissions from the whole society and the electricity industry peak in 2029 at 435.2 Mt and 2030 at 281.2 Mt, respectively. Conversely, the SEG scenario delays the peak of the whole society to 2034, while the electricity industry fails to peak before 2035. These findings reveal significant risks of temporal asynchrony between the whole society and the electricity industry peaks, providing robust methodological support for regional decarbonization planning.