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Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial-temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial-temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective.

A multiscale discrete-continuum mosaic (MDCM) method for nonlinear mechanical behaviors of periodic micro/nano-scale structures is presented in this paper. The representative cell including nonlinearity and chirality of a micro/nano-scale structure can be combined with the corresponding virtual continuum counterpart for establishing the multiscale “bone-flesh” model. The moving least-square (MLS) approximation is utilized for bridging the macroscale and microscale deformation fields. Furthermore, a MDCM contact model is developed with the real discrete particles as the force points of their non-bonded van der Waals (vdW) interactions. The discreteness of the non-bonded interactions can be taken into account naturally. Based on the proposed MDCM method, a variationally consistent meshless computational scheme is constructed for simulating nonlinear mechanical responses of micro/nano-scale components. Benefiting from the order-N nature, this method can deal with large scale problems with less computational efforts.

In order to develop an efficient and accurate quasi-continuum approach for contact problems of low-dimensional nanoscale carbon materials, a van der Waals contact-bond model is proposed in this study. This method can involve the important information of nano- and micro-structures, such as the bonded carbon atom interactions and the long-range van der Waals effect, which is usually homogenized and neglected in continuum methods. The degree of freedom of the atomic systems can be reduced dramatically; therefore, the model is beneficial for rapid simulations and large-scale computations of carbon nano-components. The so-called higher-order Cauchy–Born rule is used to establish the geometrically consistent constitutive model, and a meshless local Petrov–Galerkin-based computational framework is constructed for the mechanical responses of carbon nanoscale systems. The stiffness matrix is derived analytically, and the incremental governing equation is solved with the Newton–Raphson iteration method. Consequently, this method is much faster than order-N2 approaches such as molecular dynamic simulation.

Background In September 2020, China put forward the goal of carbon neutrality by 2060, which is of great and far-reaching significance for coping with climate change, achieving sustainable development and promoting the transformation of energy structure. In this context, the carbon market has become an important policy tool to realize China's vision of carbon neutralization. As can be seen from the stock market, the carbon neutralization industrial chain is evolving, and green and low-carbon has become the main investment trend. Therefore, in the context of carbon neutrality, whether there is a connection between China's carbon market and stock market, whether there is spillover effect between them, and what will be based on investors' emotional behavior are the main problems to be solved in this study. Subjects and Methods This paper takes China's carbon market, carbon neutralization concept stock market and thermal power stock market as the main research objects; This paper uses the price index and VaR BEKK GARCH model from January 1, 2019 to December 31, 2020 to study the interaction and spillover effect between the above markets. It also reveals the reasons from the perspective of investment psychology. In order to better study the changes of investors' emotional perception and behavior, this study uses the self rating Depression Scale (SDS) system William W K. Zung compiled the self rating scale in 1965 to measure the severity of depression and its changes in treatment. In 1972, Zung added the corresponding examiner's book and changed the self-evaluation to other evaluation, which is called Depression Status Inventory (DSI). The assessment time span is the latest week. Content: SDS and DSI are composed of 20 declarative sentences and corresponding question entries respectively. Each item is equivalent to a relevant symptom, which is scored on a scale of 1-4. 20 items reflect depression, and four groups of specific symptoms: 1 Psychotic affective symptoms, including depression and crying; 2. Somatic disorders, including eight items: daytime differences in mood, sleep disorders, anorexia, decreased libido, weight loss, constipation, tachycardia and fatigue; 3. Psychomotor disorders, including psychomotor retardation and agitation; 4. Psychological disorders of depression include eight items: confusion of thinking, hopelessness, irritability, indecision, self depreciation, emptiness, repeated thinking, suicide and dissatisfaction. From the scoring method: each item is scored according to four levels: 1, 2, 3 and 4. Ask the subject to read each statement carefully, or the examiner to ask questions one by one, and circle 1 (from none or occasionally), or 2 (sometimes), or 3 (often), or 4 (always) according to the time and frequency most suitable for the subject's situation. Among the 20 items, 10 items (items 2, 5, 6, 11, 12, 14, 16, 17, 18 and 20) are stated with positive words, which are scored in reverse order, and the remaining 10 items are stated with negative words, which are scored in the order of 1-4 above. The depression severity index assessed by SDS and DSI is calculated according to the following formula: depression severity index - cumulative score of each item / 80 (maximum total score). The index range is 0.25-1.0. The higher the index, the heavier the degree of depression. Results 2019 coronavirus disease has a significant impact on the relationship between carbon market and stock market. (1) Affected by 2019 coronavirus disease, CTI first affects cni300 and then TPI; Investors' expectations of earnings are distorted by the extension of the current trend. (2) The two-way average spillover effect between TPI and Guoxin 300 in 2019 has become the one-way average spillover effect in 2020. Investors' psychological bias and thinking shortcuts make them choose to invest in familiar fields. These areas cannot spread investment risks, but will damage their long-term wealth. (3) The amplitudes of cni300 and TPI will change dramatically with historical fluctuations, which confirms that most people's judgment on the profit and loss mentioned in the prospect theory is usually determined according to the reference point. The results showed that impulsive investment was negatively correlated with economic support ability (P < 0.01), and its internal consistency was satisfactory: odd and even items split half correlation: 0.73 (1973) and 0.92. There was a high and moderate correlation between the scores of “d” subscale of MMPI. Zung et al. Also compared the scores of SDS and dis with CG1 score and proposed that those with SDS and dis score index below 0.5 were non depressed; 0.50-0.59 is mild to mild depression; 0.60-0.69 is moderate to severe depression; more than 0.70 is severe depression. Conclusion This shows the confidence of Chinese investors in the national sustainable development initiative. Carbon market performance has become an important reference for investing in carbon neutral concept stocks. Therefore, the government should constantly promote the innovation of carbon trading system while stabilizing the expected return on green and low-carbon investment. In addition, the government should take multiple measures to send sufficient and accurate signals on specific measures to promote carbon neutralization, eliminate unreasonable investment to the greatest extent, and guide the industrial layout through the capital market. Acknowledgements This manuscript was supported by the National Natural Science Foundation of China (71473230) (71803180), and the Philosophy and Social Sciences Planning Project of the Ministry of Education in China (18YJCZH140) (17YJCZH048).

In the present paper, general analytical formulas for calculating the Young's modulus and Poisson's ratio of single-walled carbon nanocones (SWCNCs) at finite temperatures are derived based on the proposed temperature-related multiscale quasi-continuum (QC) model. To this end, a temperature-related higher-order Cauchy-Born (THCB) rule is employed to establish the hyper-elastic constitutive model of SWCNCs. With use of the proposed approach, the influences of the temperature, apex angle, rotation angle of cutting lines and top end radius on the Young's moduli and Poisson's ratios of SWCNCs are investigated systematically.

In this study, the buckling behaviors of single-walled carbon nanocones (SWCNCs) under bending at finite temperatures are predicted using a proposed multiscale quasi-continuum approach based on the temperature-dependent higher order Cauchy–Born (THCB) rule. The hyper-elastic constitutive model is derived exactly in the context of the higher order gradient theory that incorporates the details of the interatomic interaction. The numerical simulations for the deformation of SWCNCs are implemented using the developed meshless computational framework based on moving least-squares interpolation, which can precisely reproduce the deformation process and buckling patterns of SWCNCs under bending. The underlying correlations of the critical bending angle with respect to the geometry of SWCNCs and temperature are revealed by the numerical results. Furthermore, our simulation captures the transformation from the local to the global buckling process of SWCNCs, accompanied with an average strain energy jump. Our results correspond with previous studies.