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This study aimed to develop the scoring functions for the recently developed value assessment framework (VAF) for China, which comprises 12 attributes. We implemented a factorial survey among Chinese healthcare stakeholders from July to September 2022. A total of 240 hypothetical drug value profiles described by the VAF were grouped into 60 blocks and randomly assigned to respondents. Each respondent was assigned with 1 block, each presented in 3 disease scenarios of different levels of severity. For each profile, respondents were asked to assess the drug's value on a scale from 0 (lowest) to 10 (highest) and make 1 of the 3 insurance recommendations: cover, to be negotiated for coverage, or reject. Linear and logistic mixed-effects models were used to develop scoring functions for aggregating the value attributes. A total of 365 respondents participated in the survey. 3968 responses from 331 respondents were included in the analysis. Most of the included respondents were under 45 (n = 256, 77.3%), females (n = 208, 62.8%), living in urban areas (n = 296, 89.4%), and with a bachelor's degree or higher (n = 303, 91.5%). Health benefits and safety carried more weights than other attributes in the scoring functions across disease scenarios. The value and probability of entering negotiation or receiving insurance coverage for the attribute profiles for severe/critical disease were higher than for mild/moderate disease. The scoring functions of the VAF can be used to assess the value of a drug and its probability of entering negotiation or receiving insurance coverage in China. • Through qualitative interviews and document analysis, 12 attributes grouped in 8 categories were identified and included in a value assessment framework for China. The value assessment framework comprises severity of disease, health benefits, safety, economic impact, innovation, organizational impact, health equity, and quality of evidence. • We conducted a factorial survey in which 240 value profiles with 10 attributes experimentally varying in their levels were randomly assigned to 365 healthcare stakeholders in China to assess the value of hypothetical drugs and make insurance coverage recommendations. • The scoring functions can be used to estimate the value of a drug and the probability of its entering negotiation or receiving coverage in China in different disease scenarios. In the scoring functions for severe/critical disease or mild/moderate disease, health benefits and safety carried higher weights than other attributes. The value and probability of entering negotiation or receiving insurance coverage are higher for attribute profiles for severe/critical disease than for mild/moderate disease. [ABSTRACT FROM AUTHOR]

China has pledged to peak its carbon emissions before 2030 and achieve the net-zero ambition in the 2060s. Reducing the national carbon emission intensity can help achieve these ambitions effectively. To explore the tradeoff between emission reduction and system health, a factorial hypothetical extraction method has been proposed. It was applied to identify key carbon emission sectors, and further help formulate countermeasures on reducing the national emission intensity. A seven-factor factorial analysis was developed to evaluate the effects of factors (i.e., 7 countermeasures) and the combinations (i.e., 128 scenarios) on responsive variables (i.e., system health). Main effects and interactions for response variables were also detected between these factors. Results show that the most effective combination, i.e., simultaneously enlarging the production scales of agriculture and other services, and lessening those of metallurgy sectors, can help reduce emission intensity by −19.2%. Lessening the production scales of electricity-generation/metallurgy, and enlarging those of wholesale and retailing sectors, can help reduce national emission intensity, while these factors negatively impacted system sustainability and robustness. The mitigation effects of these countermeasures will be weakened if these countermeasures are implemented simultaneously. Enlarging the production scales of leasing and commercial services and other services sectors can help achieve a win-win outcome. China has pledged to reduce the national carbon emission intensity (NEI) by 60%–65% in 2030 compared to the level in 2015. However, emission reduction policies may come at the expense of system health. In this research, key carbon emission sectors are extracted and seven countermeasures targeting on these sectors are proposed. Then, there are 128 combinations of these countermeasures. Under all the combinations, the changing rates of NEI are evaluated under all the combinations. In addition, the contribution effects of all the combinations on system health (i.e., robustness and sustainability) are also assessed. Accordingly, enlarging the production scales of leasing and commercial services, and other service sectors can positively impact system health, as well as NEI. Lessening the production scales of electricity-generation/metallurgy, and enlarging those of wholesale and retailing sectors, can help reduce NEI, while these factors negatively impacted system sustainability and robustness. [ABSTRACT FROM AUTHOR]

Expectancy-value theory mostly focuses on how expectancy and value beliefs are associated with academic achievement. Both expectancy and value beliefs are multidimensional and can be further decomposed into general and specific factors. However, few studies have explored the associations of these general and specific factors with academic achievement and learning strategy use. This study aimed to investigate the bifactor structure of expectancy and value beliefs and investigate the linkages between general and specific expectancy-value factors and mathematics achievement and learning strategy use. Based on the expectancy and value dimensions measured by the Motivated Strategies for Learning Questionnaire (MSLQ), bifactor models were used to analyse the data collected from 563 senior secondary school students in China. Three key findings emerged: (1) both expectancy and value beliefs could be disentangled into general and specific factors; (2) general factors were significantly associated with mathematics achievement and learning strategy use; and (3) the specific factors were positively associated with mathematics learning strategy use but not mathematics achievement. Methodological and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Organizational Career Growth scale (OCG scale) is a measure of employee perceptions of their chances of development and advancement within an organization. Despite the growing interest of scholars in this field, research using the OCG scale is in its early infancy. The current study aims to contribute to the validation of the OCG scale by testing its dimensionality and measurement invariance across Italian (N = 882) and Chinese (N = 256) samples. Structural Equation Modeling with Confirmatory Factorial Analysis, Multi-Group Confirmatory Factorial Analysis and latent means analysis were used. The four-factor model of the OCG scale prevailed both in the Italian and Chinese samples. Moreover, full metric and partial scalar invariance were achieved. Overall, the results supported the factorial validity and cross-cultural equivalence of the OCG scale. Latent means examination showed evidence of significantly higher latent means in the Chinese sample for three dimensions of the OCG scale (career goal progress, professional ability development, promotion speed) than in the Italian sample. Limitations, suggestions for future research and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Energy and water are rapidly consumed as the most basic strategic resources of various nations. It is of vital importance to systematically explore the environmental and economic impacts of energy-water co-management policies. This study is to develop a multiperspective-driven factorial metabolic network analysis framework (MPDF) to (a) investigate the direct/indirect/total resource consumption response mechanisms induced by changes in production and consumption; (b) explore the factor interactions of different policies in diverse energy and water metabolic networks by initiating factorial analysis; (c) quantify the economic effects of co-management policies by proposing multiple vulnerability indicators. A typical energy-dependent region, Shanxi Province, China was selected as a case study. The results indicated that the production- and consumption-oriented policies have various guidelines for reducing direct and indirect energy–water consumption. Significant interactions in simulation results suggest synergistic effects across sectors. Considering that Shanxi's energy-water nexus economic vulnerability is as high as 2.22%, it is recommended to prioritize the allocation of resources to sectors with significant factor effects to avoid economic losses. Implementing corresponding resource conservation policies for light industry, machinery manufacturing, construction can reduce water consumption by 18.8%. The findings are expected to provide a solid scientific basis for formulating co-management strategies to alleviate resource scarcities. [Display omitted] • A multiperspective-driven factorial metabolic network framework is developed for resource-scarcity assessment. • A policy simulation response mechanism is revealed from production- and consumption-oriented perspectives. • Factorial effects and interactions are quantified to identify key sectors. • Energy–water nexus economic vulnerability is explored at sectoral and metabolic network scales. [ABSTRACT FROM AUTHOR]

In this study, a two-phase factorial input-output model (TFIOM) is developed for carbon dioxide (CO 2) emission mitigation, through integrating input-output model (IOM) and two-phase factorial analysis (TFA) into a general framework. TFIOM can identify the main sectors of CO 2 emission, quantify the individual and interactive effects of the main factors (i.e. various CO 2 management strategies implemented on different sectors) on reduction target, as well as seek CO 2 -emission reduction pathways and strategies from multiple perspectives (i.e. energy structure adjustment strategy (ESAS), technology upgrade strategy (TUS), and industrial structure adjustment strategy (ISAS)). The developed TFIOM is applied to Fujian province (in China), and results reveal that industries of chemical, non-metallic, metal smelting (MSI), and electric heating (EGW) are the main CO 2 -emission sectors (i.e. occupying 57.6% of direct emission and 56.4% of indirect emission). The joint application of ESAS (applied to EGW) and TUS (applied to MSI) has the best effect in reducing carbon emission intensity (decreasing by 24.5%). Among all scenarios, Scenario 115 shows the most desirable emission-reduction effect (i.e. carbon emission intensity, energy intensity, total direct emission, and total indirect emission would respectively decrease by 22.3%, 7.6%, 21.3% and 5.1%, and the cost of reducing CO 2 per ton would be 182 RMB¥). [Display omitted] • A two-phase factorial input-output model (TFIOM) is developed. • Input-output model is employed to identify the main emission sectors. • Definitive screening design is adopted to screen out the main factors of main sectors. • The proportion of electricity consumption should be maintained at more than 40%. • Application of energy-technology strategies would reduce energy intensity by 9.8%. [ABSTRACT FROM AUTHOR]

Climate change is a huge threat to our globe. As the largest CO 2 emitter and the biggest developing country in the world, China has pledged to cut its national carbon emission intensity (NCEI). The objective of this study is to develop a multi-perspective factorial hypothetical simulation model to facilitate the policy development for robust NCEI reduction. Based on multi-regional input-output model and hypothetical extraction method, the individual- and the system-based effects of 900 sectors on NCEI are investigated. Then, targeted countermeasures against key sectors are developed. After that, the effects and the complex interactions of the countermeasures on the system robustness and the system sustainability are examined by factorial analysis. The results reveal that it is feasible to cut NCEI of China through the proper adjustments of a few sectors, while some of them may at the cost of the system health. For example, controlling the production-based CO 2 emission of Electricity and hot water production and supply in Shandong and shrinking the production scale of Metallurgy in Hebei could efficiently cut China's NCEI. However, the two countermeasures would deteriorate the system health through their main effects and interaction. These findings can provide new information for achieving China's NCEI goal. • A multi-perspective factorial hypothetical simulation model is developed. • A study of China is conducted on National Carbon Emission Intensity (NCEI). • Individual- and system-based effects of sectors on NCEI are examined. • Effects and interactions of policies on the system performances are investigated. [ABSTRACT FROM AUTHOR]

In this study, a factorial Bayesian copula (FBC) method is proposed to quantify parameter uncertainties in copula-based models and then reveal their impacts on hydrologic risk inferences within a multivariate context. In detail, Bayesian inference and factorial analysis are integrated into copula-based multivariate risk models to (1) quantify parameter uncertainties, (ii) reveal their individual and interactive effects, and (iii) identify their detailed contributions to uncertain risk inferences. Streamflow observations at Xiangxi and Wei River basins is China are used to illustrate the applicability of FBC. The results indicate that imprecise parameters in marginal distributions and the dependence structure would lead to extensive uncertainties in predictive joint return periods and failure probabilities. Also, individual and interactive effects of parameters are well revealed through multilevel factorial analysis, and the detailed contributions of one parameter to different failure probabilities under different service time scenarios are identified. • FBC is developed for uncertainty quantification and partition in risk inference. • Parameter uncertainties are quantified through a Markov Chain Monte Carlo method. • Parameter effects and contributions are revealed by multilevel factorial analysis. • Results indicate FBC can track the major uncertainty sources in risk inference. [ABSTRACT FROM AUTHOR]