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This study uses a dynamic slacks‐based measure (SBM) model in a data envelopment analysis (DEA) framework with surplus reserve and accounts receivable as desirable and undesirable indicators respectively, based on inter‐connectivity between consecutive periods to estimate the operating efficiency of 110 listed Chinese real‐estate companies in the first step analysis. The results show that 35 listed real‐estate companies in China operated on the efficient frontier by maintaining sustainable growth throughout the entire period. The analysis presents the projection of indicators to suggest a feasible solution to improve the inefficient terms. At the second step analysis, using ordinary least square (OLS) estimation, authors find a curve‐linear relationship between real‐estate company size and efficiency to suggest that operating efficiency increases as size also increases due to economies of scale but only up to an optimum point. Profitability and higher education are also identified as the other significant determinants of operating efficiency. The findings of this research present evidence that average efficiency scores vary over time and generally comove with China's economic growth rate and the average stock returns in the real‐estate sector. By providing a new framework for evaluating input, output and carry‐over indicators of real‐estate companies, the results of this study equip real‐estate companies to have an accurate perspective of their operating position and simultaneously provide investors with an avenue to evaluate real‐estate companies' development before any form of investment.

Asset allocation is a critical concern for any investor in the financial market. This paper aims to prioritize five randomly selected firms from the top ten stocks by market capitalization of the Shanghai Stock Exchange (SSE) by opting for adequate financial procedures and practical criteria under uncertain conditions. Decision makers want not only the ranking order of stocks but also capital proportions to be allocated. Therefore, this study uses a hybrid multi-criteria decision-making (MCDM) approach comprising of an integrated analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) in a grey environment for optimal portfolio selection to provide both ranking and weighting information for decision makers. Results indicate that return, financial ratios, dividends, and risk are causal criteria group, which are the most influential determinants for obtaining high benefits with regards to stock portfolio selection in SSE. The free float of stocks is the least influencing criterion among all identified criteria of stock portfolio selection of SSE. The Industrial and Commercial Bank of China Ltd. stocks have the highest allocated proportion with the highest priority shown by investors and can be described as a suitable alternative. The practical implications of this research are that the approach, when applied, highlights how the grey system theory minimizes the uncertainties in all stages of decision-making of portfolio selection.

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This paper assesses the relationship between carbon emissions, economic growth and, energy consumption, in USA and China from the perspective of Granger causality, in a multivariate framework controlling for financial development, urbanization, and trade openness. Econometric techniques employed include unit root tests, Toda and Yamamoto Granger causality, and generalized impulse response and variance decomposition analysis for the time horizon 1980–2017. Test results indicate that governments of the USA and China cannot implement sturdier strategic energy policies in the long run without inhibiting the growth of the economy because of the bidirectional causative linkage between economic growth and energy use. A causal link does not exist between carbon emissions and financial development for both countries. Nevertheless, in the USA, there exists a unidirectional Granger causality controlling from energy consumption to financial development. In both economies, urbanization Granger causes CO2 emissions and energy use but the reverse does not hold. An upsurge in energy consumption and carbon emissions will lead to a surge in trade openness but not vice versa for China. A noteworthy result is that there is a substantiation of unidirectional causality from energy consumption to carbon emissions in both countries. In the USA, impulse response and variance decomposition analysis disclosed the effect of financial development is projected to have diminutive magnitude whiles in the future, energy use, economic growth, trade openness, and urbanization would influence carbon emissions significantly. The impacts of trade openness and financial development are expected to be of little importance in China. The general findings implied that urbanization, economic growth, and energy consumption influenced CO2 emissions significantly in the USA and China. Understanding these similar and contrasting situations is essential to reaching a global agreement on climate change affecting IMF’s top 2 biggest economies.