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This article uses the concept of embodied exergy as metrics in designing incentive policy instruments to tackle the inefficiency of energy operations. Based on the second law of thermodynamics and energy's economic properties as both a private commodity and a public good, it maintains that energy can be measured by separating the useful exergy embodied in a manufactured product from its waste exergy (anergy) as emissions and sunk wastes in a production process. It is rational to benchmark the content of useful exergy embodied in products for any incentive policy design to encourage green production. This article uses trade data between China, Japan and the EU countries to compare the embodied exergy and waste exergy embodied in traded manufactured products. It proposes using a negative value-added tax as an incentive instrument instead of full-scale carbon tariffs to encourage green production and to fence against carbon evasion behaviour.

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In clinical practice, diseases with a prolonged course and disease characteristics at the time of diagnosis are often classified into specific stages. The precision of disease staging significantly impacts the therapeutic and curative outcomes for patients, and the diagnosis of multi-clinical-stage diseases based on electronic medical records is a problem that needs further research. Gout is a multi-stage disease. This paper focuses on the research of gout and proposes a staging diagnosis method for gout based on deep reinforcement learning. This method firstly uses the candidate binary classification model library for accurate diagnosis of gout, and then corrects the results of the binary classification through the set medical rules for diagnosis of gout, and then uses the machine learning model to diagnose different stages of corrected accurate data. In the course of the experiment, deep reinforcement learning was introduced to solve the hyperparameter tuning problem of the staging model. Through experiments conducted on 24,872 electronic medical records, the accuracy rate of gout diagnosis was found to be 90.03%, while the accuracy rate for diagnosing different stages of gout disease reached 86.85%. These findings serve as a valuable tool in assisting clinicians with accurate staging and diagnosis of gout. The application of deep reinforcement learning in gout staging diagnosis demonstrates a significant enhancement in diagnostic accuracy, thereby validating the effectiveness and feasibility of this method.