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This study aims to examine whether and how gender diversity on corporate boards is associated with voluntary nonfinancial disclosures, particularly water disclosures.

This study uses corporate water information disclosure data from Chinese listed firms between 2010 and 2018 to conduct regression analyses to examine the association between female directors and water information disclosure.

Empirical results show that female directors have a significantly positive association with corporate water information disclosure. Additionally, internal industry water sensitivity of firms moderates this significant relationship.

This study determined that female directors can promote not only water disclosure but also positive corporate water performance, reflecting the consistency of words and deeds of female directors in voluntary nonfinancial disclosures.

In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

Due to significant differences between urban and rural areas in terms of geographical environment, building scale, resident culture, social organization and other aspects, the post-disaster recovery and reconstruction models for both exhibit substantial variation. This study identifies critical strategic issues that must be addressed in housing reconstruction in the context of different social structures of urban–rural “integration” and urban–rural “dualization” to achieve the goal of “building back better” in the future.

By taking the experience of the 5.12 Wenchuan earthquake and the Taiwan 9.21 earthquake as a reference, this study provides a thematic analysis and systematic summary of the entire process of post-disaster housing reconstruction.

A successful housing reconstruction process should actively engage disaster-affected populations through participatory institutional design. Providing a diverse housing reconstruction model can coordinate the interests of the government, the market and affected individuals, promoting harmony of residential, productive and ecological functions. However, it can also lead to the division of existing communities.

This research relies on existing literature, government publications, academic studies and news reports, which may carry inherent biases or omissions. Future research can benefit from conducting more extensive and long-term post-reconstruction surveys to assess the sustained impact of recovery efforts while also considering additional data sources to ensure comprehensive and unbiased analyses.

With the support of post-disaster reconstruction policies, diverse changes in land use can lead to urban and rural spatial pattern reform and sustainable regional development, providing a reference for formulating optimal strategies.

This study carries significant societal implications by addressing critical strategic issues in housing reconstruction within varying urban–rural social structures. It highlights the importance of engaging affected populations through participatory design and harmonizing government, market and individual interests. The research introduces strategies for activating rural construction land quotas and creating new funding sources, promoting sustainable regional development. Its findings contribute to post-disaster reconstruction models, offering valuable insights for policymakers and stakeholders, ultimately leading to more effective and inclusive recovery efforts and benefiting disaster-prone areas worldwide.

This research primarily investigates the market circulation patterns of urban and rural land under different social structures, delves into the strategies for sources of housing reconstruction funding, along with an assessment of their effectiveness.