Crude oil, Bitcoin, and carbon dioxide emissions are major issues that are significantly impacting the global economy and environment. These three issues are complexly interlinked, with profound economic and environmental implications. In this study, we explore the correlation among these three issues and attempt to understand the influence of crude oil and Bitcoin on carbon dioxide emissions. We created a novel approach, named quantile mediation analysis, which blends mediation regression with quantile regression, enabling us to explore the influence of Brent crude oil on carbon dioxide emissions by considering the mediating impact of Bitcoin. According to the findings from using our new approach, the impact of Brent crude oil on carbon dioxide emissions is partly mediated by Bitcoin, and the association between Brent crude oil and carbon dioxide emissions involves both direct and indirect effects. Since the carbon dioxide generated by the extraction of crude oil and Bitcoin has a great impact on the environment, accelerating the use of clean energy technologies to reduce our reliance on crude oil should be the direction that the cryptocurrency industry ought to pursue in the future.

During economic depression, improving profits to respond to the current economic crisis is highly crucial for both the regional and national tourism businesses. The operating cost structure of the tourism industry involves high fixed costs and low variable costs. Therefore, cost-based pricing cannot increase the profit of businesses in this industry; alternatively, value-based pricing is required to increase profits. According to the product quality discrimination model implemented in previous studies, artificially degrading the quality or functions of products enables businesses to register maximized profits. This paper examines the benefits of degrading hotel landscape service quality levels in the tourism industry in improving profits. The examination results reveal that when the market size remains unchanged and when the original quality level of high-quality services is maintained, quality discrimination enables businesses to improve their profits.

We provide a computational piecewise linear regression (CPLR) method with endogenous segmentation to construct accurate mathematical representations of data. The method models the unemployment–inflation relationship through a deterministic segmentation algorithm, rather than imposing a predetermined functional form to fit all data for one time. The CPLR method procedure sequentially fits local linear regressions on observations ordered by the explanatory variable and detects structural breakpoints according to a specific rule of breakpoint detection property in the coefficient of determination. A minimum segment size is imposed to ensure numerical stability constraint, and the optimal minimum sample size is determined by minimizing the global root mean squared error (RMSE) across candidate segmentations. We establish a theoretical property of the CPLR method algorithm, based on the fact that the breakpoint detection rule follows directly from the minimum least-squares error principle rather than from heuristic segmentation. The example results, using annual data from 1991 to 2022 as an illustration, reveal clear structural breaks and regime-dependent slope heterogeneity across the G7 economies. The number of segments varies substantially across countries, ranging from a stable two-segment structure in the United States to a six-segment structure in France. Positive slopes appear in specific unemployment ranges for several countries, except in the case of the United States. Comparisons with the results of conventional linear and nonlinear regressions show that in several countries, including Japan, Germany, Italy, and France, the CPLR representation provides a closer local fit to the observed data than conventional global linear or nonlinear specifications. The CPLR method, therefore, provides a deterministic and data-driven algorithm for detecting endogenous structural changes and constructing accurate piecewise linear representations without imposing prior theoretical assumptions or relying on statistical inference.

Based on our empirical estimates, the data did not suggest that federal tax has a larger impact on the retail price of cigarettes than does state tax since the state tax is susceptible to bootlegging bias among adjacent states. The price elasticities of demand for cigarettes are –0.675 and –0.308 in the long term and in the short term, respectively. With current price elasticity stabilizing between –0.3 and –0.5 —a value below profit-maximizing value — there is indeed considerable room for future cigarettes tax hikes to meet the shortfall in the budgets of both federal and state governments.