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This study is carried out to investigate the effects of tourism expenditure, technological development and foreign direct investment on tourism development in the United States of America, a top international tourism destination in the world, from 1995 to 2021.

To this end we use the Autoregressive Distributive Lag method which captures short and long run effects. This method is also fundamental in presenting robust results when time series data with short time periods is used. The FMOLS and DOLS methods are used to ensure the robustness of the findings.

The results of the Autoregressive Distributive Lag indicate that spending on tourism contributes to the growth of the tourist industry in the country. The study reveals that economic growth has a detrimental impact on the development of tourism. Furthermore, carbon emissions are exclusively impeding the long-term progress of tourism development. The country's prioritization of economic growth has led to a rise in carbon emissions, disregarding the desire of tourists to experience a pollution-free and natural environment. Moreover, foreign direct investment exerts a beneficial impact on the advancement of tourism.

Although there has been numerous research on the factors that influence tourism, there is less documentation on the specific factors affecting tourism development. The research examines the effect of carbon emission of tourism development of United States, the World's top tourism destinations. Few studies have attempted to unlock this association in the United States; hence, the research originality.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.