Search Results (99)

Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and 16 of these crises possess independently the capacity to cause human extinction or civilizational collapse. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for over half these crises, simultaneously drive hunger, obesity and chronic diseases. We argue that this PolyCrisis is not the result of isolated failures, but is best understood as the predictable, systemic outcome of Planet A, the prevailing Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: (a) “Together Around Food”, implementing universal, free access to gourmet whole-food, plant-based vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and (b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet. Full article

Decoupling economic growth from CO₂ emissions is a key challenge for developing economies, with limited evidence for small Central American economies. This study evaluates Honduras during 1990–2023 using GDP per capita, CO₂ emissions per capita, energy intensity, and renewable energy share from World Bank Indicators and the Global Carbon Project. The methodology integrates k-means clustering, PELT structural break detection, the Tapio decoupling index, Environmental Kuznets Curve (EKC) modelling, Granger causality, and Random Forest analysis. Since energy-intensity data are available only from 2000, a full GDP-CO₂ series (1990–2023, n = 34) is distinguished from a complete four-variable panel (2000–2021, n = 22). Clustering identifies two structural regimes rather than three (silhouette 0.490 vs. 0.476). EKC results support an inverted-U relationship (β₂ = −3.012, p < 0.001; adjusted R² = 0.766), with an estimated turning point of USD 2388 (95% CI: USD 2156–2644, delta method), near the upper boundary of the estimation sample, which Honduras’ 2023 GDP per capita (USD 2527) marginally exceeds as an out-of-sample extrapolation. However, Granger tests find no significant temporal precedence between GDP, renewable share, and CO₂ emissions (all p > 0.05), and Random Forest shows GDP per capita (%IncMSE = 31.47) vastly outweighs renewable share (%IncMSE = 0.16). Honduras has likely crossed the EKC threshold, but evidence does not support a renewable-driven decoupling. Full article

Against the global backdrop of carbon peaking and carbon neutrality goals, renewable energy subsidy policies worldwide are shifting from universal, tariff-based schemes toward targeted, market-oriented mechanisms. However, existing research remains divided on the incentive effects of subsidies on firm investment and the risk of resource misallocation, and few studies have established a clear micro-macro linkage between firm-level subsidy receipts and regional employment and growth outcomes. Taking China’s 2016 renewable energy subsidy reform—characterized by competitive project bidding and green certificate trading—as a quasi-natural experiment, this study constructs a two-layer panel dataset covering 286 A-share listed renewable energy firms (2010–2023, 2412 firm-year observations) and 30 provincial-level regions in China (2010–2023, 420 region-year observations). Employing difference-in-differences (DID), triple difference-in-differences (DDD), mediation effect models, and threshold regression, combined with instrumental variables and placebo tests to address endogeneity, we empirically examine how subsidy policies transmit from firm behavior to regional employment and economic growth. The results indicate that the 2016 reform significantly boosted regional employment (elasticity = 0.035, p < 0.01) and economic growth (elasticity = 0.031, p < 0.05) in treated provinces. At the firm level, subsidy intensity exhibits an inverted U-shaped relationship with investment efficiency, with an estimated inflection point at 8.3% of operating revenue within the sample. Mechanism analysis shows that easing financing constraints, stimulating technological innovation, and reducing operational risk serve as core transmission channels, with the strongest contribution from financing constraint alleviation. Heterogeneity analysis further finds larger effects for private firms, high-tech enterprises, and coastal regions. This study develops a nonlinear analytical framework of “subsidy intensity–firm behavior–regional outcomes”, identifies context-specific boundaries of subsidy effectiveness, and provides integrated micro-macro empirical evidence for optimizing subsidy policies and advancing the global energy transition. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

Background: Buildings and construction account for a substantial share of global energy use, carbon dioxide emissions, and material extraction, making both operational and embodied carbon central to climate-resilient building policy. Methods: This article is framed as a critical scoping review with a structured narrative synthesis. It synthesizes peer-reviewed studies, standards, and policy reports published mainly from 2020 to June 2026, while retaining older foundational standards where they define life-cycle boundaries or verification methods. The counted revision documents a reproducible screened search in OpenAlex plus targeted website and standards searching, with Google Scholar retained only for citation chasing and sensitivity checking; reporting is aligned to PRISMA-ScR and PRISMA-S principles. Results: The evidence shows that operational carbon reduction remains the most immediately measurable pathway through HVAC optimization, envelope improvement, smart energy management systems, and digital measurement, reporting, and verification. However, embodied carbon management through Environmental Product Declarations, material passports, low-carbon procurement, prefabrication, and circularity is necessary to avoid shifting emissions from operation to construction. Contribution: The review develops a four-layer digital life-cycle carbon governance mechanism linking life-cycle boundary setting, data capture, verification and assurance, and policy-market conversion. Singapore pathway: Singapore’s five-phase pathway is repositioned as an operational carbon MRV entry point that must be expanded to whole-life carbon through embodied carbon datasets, EPD-based procurement, and ASEAN-specific localization. The revised pathway identifies implementation risks, including data governance, additionality, double-counting, auditor capacity, SME access, market liquidity, and cross-country transferability. Conclusions: Digital MRV and carbon-market mechanisms can accelerate building decarbonization only when they are coupled with whole-life carbon boundaries, embodied carbon safeguards, transparent review methods, and context-specific financing. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

This paper investigates a discrete diffusion carbon emission-absorption model with periodic boundary conditions derived via the piecewise constant argument scheme. The existence of equilibrium points is established, and sufficient conditions for the local asymptotic stability of the positive equilibrium are derived through eigenvalue analysis. Then, uniform boundedness of positive solutions is proved, and the global asymptotic stability of the interior equilibrium is established by an iterative method and the comparison principle for difference equations. Furthermore, the model is shown to undergo a flip bifurcation when a critical parameter threshold is reached, leading to period-doubling dynamics and chaotic behavior. The influence of spatial diffusion is examined through a Turing instability analysis, yielding conditions for diffusion-driven instability and spatial pattern formation. Finally, Decision Tree and Random Forest classifiers are used as proof-of-concept tools to efficiently approximate the analytically derived stability regions using Monte Carlo-generated data. Both classifiers successfully reproduce the analytical stability structure, while the Random Forest classifier provides higher accuracy and smoother stability boundaries. Numerical simulations support the theoretical results and illustrate the stability and bifurcation phenomena exhibited by the model. These findings indicate that the proposed framework is useful for analyzing carbon emission-absorption dynamics and that machine learning can serve as an efficient computational surrogate for identifying stability regions in nonlinear dynamical systems. Full article

Phosphate waste rock piles (PWRPs) generated by open-pit phosphate mining are highly heterogeneous and difficult to characterize using conventional point sampling alone, which limits representative resource assessment, selective recovery, and rehabilitation planning. This study develops an integrated framework combining PRISMA spaceborne hyperspectral imagery, ground-based mineralogical analyses, and spatially constrained machine learning to map lithological heterogeneity at the Benguerir phosphate mining site, Morocco. A three-stage spectral optimization workflow, including atmospheric band masking, Savitzky–Golay filtering, and analysis of variance (ANOVA)-based feature selection, was applied to identify the most discriminative Short-Wave Infrared (SWIR) bands for lithological classification. After removing redundant observations located within shared PRISMA pixel footprints, 127 spatially independent samples were retained for model development. Five supervised classifiers (Random Forest, Extra Trees, XGBoost, Support Vector Machine, and K-Nearest Neighbors) were evaluated under a spatially constrained cross-validation framework aligned with the 30 m native PRISMA pixel size. Ensemble-based models, especially Extra Trees and Random Forest, provided the most stable performance, with balanced accuracies of 0.56–0.69 and area under the receiver operating characteristic curve (AUC) values exceeding 0.95 for carbonate-dominated lithologies. Lower discrimination between phosphate and siliceous facies reflects intrinsic mineralogical mixing and spectral overlap at the sensor scale. Entropy-based uncertainty and posterior probability mapping revealed spatially structured prediction ambiguity concentrated along lithological boundaries and transitional zones, consistent with petrographic evidence of compositional heterogeneity. These results indicate that moderate but stable accuracies likely represent realistic performance limits for spaceborne hyperspectral mapping of complex mining environments under spatial constraints. The proposed framework provides a transferable and uncertainty-aware basis for lithological mapping, selective recovery assessment, and sustainable phosphate waste management. Full article

Accurate cladding segmentation is essential for quantitative quality assessment of stainless-steel/carbon-steel clad wire rods used in bridge cables, yet remains challenging because of weak core–cladding contrast, narrow interfacial transition zones, local cladding-thickness fluctuations, and limited repeatability of manual inspection. This study proposes an improved U-Net framework that integrates residual feature extraction, multi-scale contextual perception, and attention-guided feature refinement for robust cladding identification. A cross-sectional image dataset comprising 18,566 samples was constructed through standardized specimen preparation, chemical color development, image acquisition, pixel-level annotation, and data augmentation. In the proposed model, the original U-Net encoder is replaced with ResNet50 to enhance deep semantic representation, while atrous spatial pyramid pooling and a convolutional block attention module are embedded into the feature-fusion stage to improve boundary discrimination and thin-cladding recognition. On the test set, the model achieved a mean pixel accuracy of 97.29%, cladding intersection over union of 88.82%, and mean intersection over union of 93.72%, outperforming the baseline U-Net by 1.38, 9.19, and 5.17 percentage points, respectively. Ablation and comparative experiments further demonstrate improved boundary continuity, local-detail preservation, and segmentation stability compared with representative CNN-based segmentation models. These findings suggest that the proposed framework provides a practical and reliable vision-based approach for cladding-thickness measurement, eccentricity evaluation, uniformity assessment, and batch quality inspection of clad wire rods. Full article

In this study, 310S austenitic stainless-steel was welded using a laser with varying amounts of Nb to systematically investigate the effect of Nb on solidification cracking susceptibility, mechanical properties, and corrosion resistance of the weld. Under the present experimental conditions, the critical restraint width was higher for the 0.58 wt.% Nb and 1.45 wt.% Nb welds than for the Nb-free and 2.3 wt.% Nb welds, indicating that Nb addition affected the solidification cracking response of the weld. At low-to-moderate Nb contents, Nb can aggravate compositional segregation and increase the presence of low-melting-point liquid films, thereby increasing cracking susceptibility. At higher Nb contents, the reduced cracking susceptibility was accompanied by microstructural refinement and changes in the distribution of Nb-rich constituents during solidification. With increasing Nb content, the number of precipitated phases in the weld increases, mainly distributed at the austenite grain boundaries in granular, elongated, and chain-like forms. The introduction of Nb generally increases the microhardness and tensile strength of the welded joint, attributed to grain refinement strengthening and solid-solution strengthening. The reduction in area first increased and then decreased, suggesting that excessive Nb addition may reduce ductility because of the increased amount of grain-boundary precipitates and local strengthening heterogeneity. With increasing Nb content, the Ir/Ia ratio decreased from 67.6% to 52.2%, suggesting improved intergranular corrosion resistance. This improvement is likely related to the preferential reaction of Nb with carbon, which may suppress the formation of Cr-depleted zones at grain boundaries. Overall, Nb addition improved the corrosion resistance and increased the hardness and tensile strength of the weld; however, its effect on solidification cracking susceptibility was non-monotonic, indicating that careful control of Nb content is required to balance cracking susceptibility, mechanical properties, and corrosion resistance. Full article

The excessive generation and improper disposal of Construction and Demolition Waste (CDW) represent one of the main environmental challenges in the sector. However, its potential for reuse and recycling enables the mitigation of these impacts through sustainable practices. In this context, the present study aimed to estimate reference values for the Federal District, Brazil, regarding the environmental impacts associated both with the transportation stage of CDW—from its point of origin to the processing facility—and with the operations involved in its conversion into recycled aggregates, through the application of a simplified Life Cycle Assessment approach. The analysis focused on quantifying the consumption of electricity, water, and fossil fuels, as well as carbon dioxide emissions and the generation of contaminant residues throughout the analyzed process. The system boundary adopted corresponds to a “cradle-to-gate” scope, with a declared unit of 1 tonne of recycled aggregate. Additionally, a survey of scientific studies providing life cycle inventory data related to aggregate production was conducted, enabling a consistency analysis with the data obtained in this study. Primary data related to the recycled aggregate production process were collected through direct field observations, in situ measurements, and the analysis of operational records from the studied facility. For the year 2024, the environmental indicators obtained showed that the production of 1 tonne of recycled aggregate required 1.23 kWh of electricity, 5.65 L of water, and 2.14 L of diesel, in addition to resulting in emissions of 6.64 kg CO₂ eq and the generation of 2.3 kg of contaminant waste. Full article

This study systematically investigated the alkali activation behavior of construction waste micro-powder (CWM) to develop a low-carbon, high-performance cementitious material. The activator formulation was optimized, the hydration thermodynamics were analyzed, and a kinetics model was constructed to reveal the reaction mechanism. The composite activator (sodium silicate and Portland cement) exhibited a significant synergistic effect, outperforming single activators. The optimal ratio was determined: 40% CWM, 60% Portland cement, and 8% water glass (modulus 1.0), which balances the system’s alkalinity and silicate modulus. Thermogravimetric analysis revealed a notable net weight gain at 3 days, indicating an ongoing secondary hydration reaction. By 7 days, the main hydration was complete, accompanied by microstructural densification, which confirmed the efficiency of the composite activator. A key contribution was the successful application of the Krstulović–Dabić (KD) model to quantify the hydration mechanism. The hydration process evolved sequentially through nucleation and growth (NG, dominant before 0.05~0.15 h), phase boundary reaction (I), and diffusion (D). The period of 0.21–50 h was governed by both I and D, after which D became the sole rate-limiting step. The model yielded the rate constants (K_NG, K_I, K_D), Avrami exponent (n), and transition points (α1, α2), providing a kinetic explanation for the ‘early strength and rapid hardening’ characteristic. In conclusion, this work establishes a material design framework guided by activator optimization, supported by thermodynamics, and explained by kinetics. The KD model proves to be a powerful tool for deciphering the hydration behavior of alkali-activated CWM, offering theoretical guidance for developing sustainable cementitious materials with controllable performance. Full article

The buckling and post-buckling responses of carbon fibre-reinforced polymer (CFRP) structures are strongly affected by geometric imperfections, boundary conditions, and material nonlinearities, making their reliable numerical prediction challenging. This work presents an integrated experimental–numerical investigation of a stiffened CFRP panel subjected to compressive loading, with the aim of improving model validation in instability regimes. The experimental campaign combines full-field measurements obtained through digital image correlation with local strain data from strain gauges, adopting a back-to-back configuration to capture the strain reversal associated with global buckling. The experimental results are compared with nonlinear finite element simulations incorporating intralaminar damage based on Hashin’s failure criteria. A good agreement between the numerical and experimental results is observed in the pre-buckling and early post-buckling regimes. However, increasing discrepancies arise at higher load levels, mainly due to manufacturing imperfections and uncertainties in boundary conditions, which influence the onset and evolution of localized deformation. Statistical indicators are employed to quantitatively assess the correlation between the experimental and numerical responses. The analysis focuses on the key response parameters, including the load–displacement behaviour, out-of-plane displacements, strain evolution, and damage initiation, enabling a comprehensive comparison of experimental and numerical results. The results demonstrate the effectiveness of combining full-field and point-wise measurements for validating numerical models of composite structures. Furthermore, the study highlights the limitations of idealized modelling assumptions and provides insights into the sensitivity of CFRP structures to imperfections in post-buckling and failure regimes. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Recent years have witnessed growing interest in CO₂ and in the possibility of injecting it into the Earth’s crust for multiple purposes. In addition to the fact that pure CO₂ is already present in some geological formations, the most debated is Carbon Capture and Storage (CCS), which aims to capture and trap CO₂ through water-assisted reactions that promote its precipitation; moreover, proposed technological improvements to geothermal plants foresee the use of pure CO₂ as a working fluid and energy carrier for electricity generation in terms of MWh. These applications require detailed knowledge and a deep understanding of CO₂ behaviour under non-standard conditions. Upon entering the Earth’s crust, CO₂ is subjected to progressively increasing temperature and pressure. The resulting effects are not limited to a reduction in intermolecular distance; they also include changes in molecular geometry, as well as in chemical and thermodynamic behaviour. For instance, a dipole moment may arise even in the gaseous phase as intermolecular distances decrease. Moreover, CO₂ typically reaches supercritical conditions at depths of approximately 700 m. It is therefore necessary to account for both phase transitions and variations in molecular structure, as these can significantly influence the surrounding environment and the stoichiometric relationships with other substances. In this work, a steady-state column was simulated, representing CO₂ injection down to a depth of 5 km, assuming an average geothermal gradient of 30 °C/km and nine different initial pressures, so nine different steady state columns. The results highlight the presence of a wedge-shaped region acting as a barrier for stepwise-equilibrated CO₂: the computed CO₂ column profiles avoid this region. This wedge includes part of the liquid–gas boundary under subcritical conditions, as well as the Widom lines above the critical point. It effectively separates two supercritical regimes, namely gas-like and liquid-like domains. In this context, the present work provides insights into the Widom region—possibly extending into subcritical conditions—and into these two distinct regimes. This may have implications for the solvent capacity of CO₂ for ionic species. Ultimately, the initial pressure appears to determine the behaviour of CO₂ at depth. Full article