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We develop an economic model to derive the conditions under which individuals will invest in human capital and move on to adopt sustainable technology instead of natural resource-intensive technology. For this purpose, we extend the overlapping generation model developed by Ikefuji & Horii as our analytical framework. Unlike Ikefuji & Horii who developed an overlapping generation model (OLG) in the context of local pollution, the authors adopted it in the context of renewable natural resources. To do this, we have introduced the production sector that relies on natural resource-intensive technology. This research extends beyond the Ikefuji & Horii model by assuming that an individual derives utility by investing in his child’s education apart from utility derived from consumption when young and adult. Human capital accumulation enables individuals to participate in human capital-intensive production, which produces output through sustainable production technology. As the main result of our theoretical analysis, we find that more educated individual is less dependent on the natural resource endowment for earning their income. We also find that sustainable consumption growth requires that individuals assign a certain positive weight to investment in their child’s education. A long-run steady-state equilibrium level of human capital accumulation is higher and higher than the weight assigned by the parents to the child’s education. In this overlapping generation’s economy, sustainable consumption growth requires that individuals assign a certain weight or give some importance to human capital accumulation. This follows from the fact that the long-run steady-state value of the income earned by an individual depends positively on the expenditure on education.

This paper studies an overlapping generations model with multiple securities and heterogeneously informed agents. The model produces multiple equilibria, including highly volatile equilibria that can exhibit strong or weak correlations between asset returns-even when asset supplies and future dividends are uncorrelated across assets. Less informed agents rationally behave like trend-followers, while better informed agents follow contrarian strategies. Trading volume has a hump-shaped relation with information precision and is positively correlated with absolute price changes. Finally, accurate information increases the volatility and correlation of stock returns in the highly volatile, strongly correlated equilibrium.

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators.

This paper reexamines the Serendipity Theorem of Samuelson (1975) from the stability viewpoint, and shows that, for the Cobb–Douglas preference and CES technology, the most-golden golden-rule lifetime state being stable depends on parameter values. In some situations, the Serendipity Theorem fails to hold despite the fact that steady-state welfare is maximized at the population growth rate, since the steady state is unstable. Through numerical simulations, a more general case of CES preference and CES technology is also examined, and we discuss the realistic relevance of our results. We present the policy implication of our result, that is, in some cases, the steady state with the highest utility is unstable, and thus a policy that aims to achieve the social optima by manipulating the population growth rate may lead to worse outcomes.

This paper develops an overlapping generations model where consumption is the source of polluting emissions. Pollution stock accumulates with emissions but is partially assimilated by nature at each period. The assimilation capacity of nature is limited and vanishes beyond a critical level of pollution. We first show that multiple equilibria exist. More importantly, some exhibit irreversible pollution levels although an abatement activity is operative. Thus, the simple engagement of maintenance does not necessarily suffice to protect an economy against convergence toward a steady state having the properties of an ecological and economic poverty trap. In contrast with earlier related studies, the emergence of the environmental Kuznets curve is no longer the rule. Instead, we detect a sort of degenerated environmental Kuznets curve that corresponds to the equilibrium trajectory leading to the irreversible solution.

To improve the control precision and stability of the drying process, this work investigates a drying process control model based on a Back Propagation Neural Network (BPNN). It constructs a data generation model to address the issue of insufficient sample space for process parameters in drying machines. This model includes a complete data generation model structure, integrating the discriminator and generator network structures of the BPNN, with optimized loss functions. The model's performance is validated through experiments, including fit analysis of the generated results and the model’s reliability analysis. The results show that the composite R² value of the data generation model reaches 0.93915 in the fit analysis. This consistency validates the model's ability to accurately fit the global data distribution, reflecting its generalization capability. Additionally, significance analysis reveals that the H values of the process parameters in the datasets generated by the data generation model and the original datasets are all 0, with p-values greater than 0.05. This indicates no significant statistical difference between the two, and confirms the reliability of the data generation model in filling the insufficient sample space. It suggests that the model can effectively enhance the completeness of the dataset without affecting the data distribution characteristics. The findings of this work provide theoretical and practical guidance for optimizing control in the drying process, contributing to improved control precision and stability in industrial drying operations.

Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping‐based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow‐related hydrological signatures. Key Points A classification‐based training strategy is introduced for the regional long short‐term memory (LSTM) model The influence of static attributes on the performance of the regional LSTM model in ungauged basins is investigated There is a high level of consistency in the enhancement achieved by the two training strategies, either incorporated with static catchment attributes or based on classification

Day-ahead residential load forecasting is important for power system demand response. Considering the fluctuation of the residential electricity load and the small accumulation of electricity consumption data in some households, the prediction accuracy of the residential electricity consumption load is significantly challenging. In this study, a scenario prediction scheme for residential electricity consumption load using a transferable flow-based generation model was proposed. First, to make full use of the source domain data, different source domain families were selected to form multi-source domain families according to the association index of the source and target domains by introducing grey correlation analysis. Thereafter, the method of model transfer was adopted, and the pretraining model was established using multi-source household electrical load data. The network parameters of part of the step of flow structure were frozen in the pretraining model, the structural parameters of the unfrozen step of flow structure were fine-tuned and trained by household electrical load data in the target domain, and the day-ahead electricity load prediction model under a small sample was constructed. The experimental results show that the algorithm combined with model transfer performs well in solving the residential load-forecasting effect for small samples.