Kumar Ujjwal, Jain V.K. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy. 2010;35,(4):1709–1716. Akyürek Zuhal. LMDI decomposition analysis of energy consumption of Turkish manufacturing industry: 2005–2014. Energy Efficiency. 2020;13(4):649–663. Although information theoretic tools were being used to analyze and interpret the data in these studies we should note that what was actually being detected by the experimental procedures was not information per se but the organization of energetic activity or processing in the brain. Energetic processing – the processes by which the brain regulates the flow of energy through its structures – is routinely detected at varying degrees of spatial and temporal resolution, either directly or indirectly, by neuroimaging techniques such as positron emission tomography (PET), functional magnetic resonance image (fMRI), and EEG ( Niedermeyer and Lopes da Silva, 1987; Bailey et al., 2005; Shulman, 2013). Referring again to the study by Casali et al. (2013), the perturbations from which the PCI was calculated were generated by a pulse of magnetic energy from the TMS and were imaged with EEG that measures electrical voltage differences, that is, fluctuations in energetic potentials between clusters of neurons in the cortex ( Niedermeyer and Lopes da Silva, 1987; Hu et al., 2009; Koponen et al., 2015). The PCI and wSMI can therefore be interpreted as measures of the complexity or organization of energetic processing in the brain during the experimental procedures. If we are to naturalize consciousness, then we must reconcile energy and the mind. I have outlined a principle that may help to explain consciousness as a physical process. It entails re-examining the modern scientific concept of energy in the light of Aristotle’s energeia and its Heraclitean roots. Accordingly, we arrive at a view of physical processes in nature as actualized differences of motion and tension. Sherrington understood that “Energy acts, i.e., is motion.” But he went on “…of a mind a difficulty is to know whether it is motion.” Treating the brain as a difference engine that works to actualize and organize differences of motion and tension to serve the interests of the organism is, I submit, a natural approach to understanding consciousness as a physical process. Conclusion

Forest biomass can also be mixed with other biomass to enhance the overall properties of the mixture for pellet production ( de Souza et al., 2020). For instance, the water content of biomass pellets could affect their durability, a property that could be adjusted by mixing different types of forest biomass. More specifically, when the moisture content of forest biomass is reduced to 1–5%, the average durability reaches 95%, which is convenient for the storage and transportation of the product ( Pradhan et al., 2018). Treating brains as neural information processors does not help us to understand consciousness as a physical process because information, according to the commonly accepted definitions, is not a physical property of brains at the neural level; there is no information in a neuron 15. It is useful, however, to apply information-theoretical methods to study the organization of physical systems, such as brains. Wiener (1948) stated: “…the amount of information in a system is a measure of its degree of organization…” As exemplified in several studies and theories cited here, we can measure and model the way the organization of energetic processes in the brain contributes to the presence of consciousness in a person 16. But the abstract difference between 0 and 1 is not equivalent to the actualized difference between a neuron at rest and firing. The Brain as a ‘Difference Engine’Voigt Annette, Wurster Daniel. Does diversity matter? The experience of urban nature’s diversity: Case study and cultural concept. Ecosystem Services. 2015;12:200–208. v)The energy consumption structure diversity in Shaanxi is positively correlated with carbon emissions. Shaanxi H index is positively correlated with Shaanxi carbon emissions, and for every 1% increase in Shaanxi H index, Shaanxi carbon emissions increase by 7.175%, indicating that with the improvement of the diversity index of energy consumption structure in Shaanxi, carbon emissions will gradually increase. Although the energy consumption structure diversity index in Shaanxi is improving, while it is mainly due to the internal substitution of fossil energy, and the proportion of primary electricity and other energy sources is still relatively low, which leads to a higher output level of carbon emissions. The proportion of coal is positively correlated with carbon emissions in Shaanxi, and for every 1% increase in the proportion of coal, carbon emissions in Shaanxi increase by 11.087%, indicating that with the increase in the proportion of coal consumption in Shaanxi’s total energy consumption, carbon emissions are also increasing. Coal consumption is one of the main factors causing the increase in carbon emissions, excessive reliance on coal energy will lead to increase carbon emissions. Therefore, it is necessary to gradually reduce the proportion of coal consumption and pay more attention to reduce the carbon emissions of primary electricity and other energy sources in the process of optimizing the energy consumption structure. Liao Hua, Wei Yi-Ming. China’s energy consumption: A perspective from Divisia aggregation approach. Energy. 2010;35(1):28–34. In the manufacturing process of forest biomass pellets, the biomass needs to be dehydrated in advance ( Civitarese et al., 2018). A rotary dryer could be used to remove the moisture in poplar wood chips, with a moisture removal rate of about 17%. In comparison, the moisture removal rate for Robinia pseudoacacia sawdust stands at a higher rate of 31%. These differences are ascribed to the differences in the density of various types of forest biomass ( Prokkola et al., 2014; Del Giudice et al., 2019). Notably, if the rotary dryer cannot remove the moisture effectively, the pneumatic dryer would be a good choice, also increasing the drying rate by 22% ( Frodeson et al., 2013).

Among them, l t represents the number of employees at the end of the year, yr t represents the average years of education of the population over the age of 15, the number of employees is from the Shaanxi Statistical Yearbook, and the average number of years of education of the population over the age of 15 in 2010 and 2020 comes from the Sixth and Seventh National Census data, the average years of education of the population over the age of 15 in 2000 is estimated using the years of education method, and different years are set according to different levels of education, 0 years for illiteracy, 3 years for literacy classes, 6 years for primary schools, 9 years of junior high school, 12 years of high school and technical secondary school, 16 years of junior college, undergraduate and postgraduate. It is estimated that the average number of years of education of people over the age of 15 in Shaanxi was 8.04 years in 2000. Arieh Ben-Naim sets out in some detail how Shannon information is a probabilistic measure rather than a physical property ( Ben-Naim, 2015 ). Note that the act of measurement presupposes a conscious mind capable of carrying out the measurement procedure and interpreting the result.Abid Mehdi. Impact of economic, financial, and institutional factors on CO2 emissions: Evidence from Sub-Saharan Africa economies. Utilities Policy. 2016; 41: 85–94. Reference to the concept of species diversity, the paper defines the energy consumption structure diversity as: within a certain period, the degree of heterogeneity and equilibrium of various energy sources in energy consumption, including the richness of energy consumption and the degree of equilibrium of energy consumption. The richness of energy consumption refers to the number of various energy types in the process of energy consumption, and the equilibrium degree of energy consumption refers to the proximity of various energy consumption in the process of energy consumption. The energy consumption structure diversity index is: The most widely cited technical definition of information is that given by Shannon (1948) as part of his mathematical theory of communication. For Shannon, information does not refer to meaning or semantics, as it does colloquially. The information is the amount of uncertainty in a message (a sequence of data) measured through probabilistic analysis of its elements. Information theory has developed into an exceptionally powerful mathematical tool that can be used, among many other things, to measure the complexity of physical systems. But a quantity of Shannon information is a measure of what can be known about a system as distinct from the system itself. The information lies with the measurer rather than the measured 13. Caspersen John P., Pacala Stephen W. Successional diversity and forest ecosystem function. Ecological Research. 2001;16(5):895–903.