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This study aims to fabricate a cool phthalocyanine green/TiO₂ composite pigment (PGT) with high near-infrared (NIR) reflectance, good color performance and good heat-shielding performance under sunlight and infrared irradiation.

With the help of anionic and cationic polyelectrolytes, the PGT composite pigment was prepared using a layer-by-layer assembly method under wet ball milling. Based on the light reflectance properties and color performance tested by ultraviolet-visible-NIR spectrophotometer and colorimeter, the preparation conditions were optimized and the properties of PGT pigment with different assembly layers (PGT-1, PGT-3, PGT-5 and PGT-7) were compared. In addition, their heat-shielding performance was evaluated and compared by temperature rise value for their coating under sunlight and infrared irradiation.

The PGT pigment had a core/shell structure, and the PG thickness increased with the self-assembly layers, which made the PGT-3 and PGT-7 pigment show higher color purity and saturation than PGT-1 pigment. In addition, the PGT-3 and PGT-7 pigment showed 11%–16% lower light reflectance in the visible region. However, their light reflectance in the NIR region was similar. Under infrared irradiation the PGT-5 and PGT-7 pigment coating showed 1.1°C–3.4°C and 1.3°C–4.7°C lower temperature rise value than PGT-1 pigment coating and physical mixture pigment coating, respectively. And under sunlight the PGT-3 pigment coating showed 1.5–2.6°C lower temperature rise value than the physical mixture pigment coating.

The layer-by-layer assembling makes the core/shell PGT composite pigment possess low visible light reflectance, high NIR reflectance and good heat-shielding performance.

For coastal bridges, the ability to recover traffic functions after the earthquake has crucial implications for post-disaster reconstruction, which makes resilience become a significant index to evaluate the seismic behavior. However, the deterioration of the material is particularly prominent in coastal bridge, which causes the degradation of the seismic behavior. As far, the research studies on resilience of coastal bridges considering multiple degradation factors and different disaster prevention capability are scarce. For further evaluating the seismic behavior of coastal bridge in the long-term context, the seismic resilience is conducted in this paper with considering multiple durability damage.

The fuzzy theory and time-varying fragility analysis are combined in this paper to obtain the life-cycle resilience of coastal bridges.

The results show that durability damage has a remarkable impact on the resilience. After 100 years of service, the seismic resilience of bridge with poor disaster-prevention capability has greatest reduction, about 18%. In addition, the improvement of the disaster prevention capability can stabilize the resilience of the bridge at a higher level.

In this paper, the time-varying fragility analysis of case bridge are evaluated with considering chloride ion erosion and concrete carbonization, firstly. Then, combining fuzzy theory and fragility analysis, the triangular fuzzy values of resilience parameters under different service period are obtained. Finally, the life-cycle resilience of bridge in different disaster prevention capability is analyzed.

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

This paper aims to propose the five-phase sphere equivalent model of recycled concrete, which can be used to deduce the theoretical formulas for the Poisson’s ratio and effective elastic modulus.

At a mesoscopic level, the equivalent model converts the interfacial layer, which consists of the new interfacial transition zone (ITZ), the old mortar and the old (ITZ), into a uniform equivalent medium. This paper deduces a strength expression for the interfacial transition zone at the microscopic level using the equivalent model and elastic theory. In addition, a new finite element method called the base force element method was used in this research.

Through numerical simulation, it was found that the mechanical property results from the five-phase sphere equivalent model were in good agreement with those of the random aggregate model. Furthermore, the proposed model agree on quite well with the available experimental data.

The equivalent model can eliminate the influence of the interfacial layer on the macroscopic mechanical properties, thereby improving the calculation accuracy and computational efficiency. The proposed model can also provide a suitable model for multi-scale calculations.

Although officially ended in July 2020, China’s dispute about its non-market economy (NME) status at the World Trade Organization (WTO) is far from being resolved. The NME status enables China’s counterparts to disregard Chinese prices in antidumping proceedings and instead use the so-called surrogate country methodology. This paper aims to structure and analyze the complex debate, which emerged with the disputes China has filed against the European Union and the USA at the WTO, and therefore provide a point of reference for future analysis of and debates about China’s NME status.

The analysis is based on the existing academic literature on the topic and on the legal WTO-related documents (e.g. multilateral agreements, China’s Accession Protocol, legal findings of the WTO dispute panels).

Four different interpretations of the respective legal documents about China’s NME status are discussed and strong and weak aspects of these interpretations are pointed out. Also, several misunderstandings and mistakes appearing in the debate are clarified.

As the question of China’s position at the WTO and its NME status has not been resolved yet and some authors believe that China will pursue its case again once the WTO Appellate Body revives its functionality, the analysis of the debate can serve as a point of reference for the academic debate and the future research on this topic. Moreover, it offers an introduction to China’s NME position at the WTO for the newcomers to this topic.

Although China’s NME status has been much discussed, there is no literature review that would structure the debate and point out some of the (dis)advantages of the respective arguments and interpretations. Rather than adding to the large corpus of literature about the NME status, this study takes this corpus as the object of its analysis.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.