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PurposeThis study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.Design/methodology/approachA two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.FindingsOn average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.Originality/valueThis study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

<abstract> <p>The macroeconomic forecast is of great significance to the government macroeconomic policy formulation and micro-agent operational decisions. The individual systemic risk measurement has a certain scope of application and application conditions and, therefore, it is difficult for the individual indicator to reflect the systemic risk comprehensively. In this paper, the systemic risk is divided into four types: institution-specific risk, comovement and contagion, financial vulnerability, liquidity and credit. Next, the optimal combination is selected from multiple individual systemic risk indicators through dominance analysis to forecast the macroeconomic performance. The macroeconomic performance selects consumer price index (CPI), producer price index (PPI), industrial growth value (IVA), growth rate of broad money supply (M2) and gross domestic product (GDP) as proxies to compare the forecast effect of systemic risk, with the period considered spans from 2003M4 to 2022M7. The results of immediate forecasts of different macroeconomic performance proxies demonstrate the individual indicator cannot cover all the information of systemic risk, can only reflect the specific aspect of macroeconomic performance, or is only highly relevant in a given period. The contribution of systemic risk to the forecast of different macroeconomic performance proxies in different terms is diverse, and show various types of results. This paper uses the optimal combination of systemic risk to forecast the macroeconomic performance, which provides a valuable reference for improving the macro prudential supervision mechanism.</p> </abstract>

The China government focuses on changes in carbon emissions efficiency with establishing carbon emissions trade exchange (CETE). It is meaningful to study whether the pilot CETEs can facilitate the betterment of carbon emissions efficiency. Using the data of 30 provinces in China within 2000–2017, this article gauges the carbon emissions efficiency with the SBM-DEA model. This paper analyzes the impact of China's pilot CETEs, which was gradually launched from 2013 to 2014, on carbon emissions efficiency through the Time-Varying difference-in-difference (DID) model. Finally, the mediating effect model is further used to analyze the impact mechanism of the pilot CETEs on carbon emission efficiency from the perspectives of innovation investment and pollution control investment. The results reveal that the carbon emissions efficiency of each province from 2000 to 2017 is not very ideal. All provinces have some room to facilitate the carbon emissions efficiency. The pilot CETEs have increased the carbon emissions efficiency and reduced carbon dioxide emissions. The policy affects the innovation investment and pollution control investment, further influences the carbon emissions efficiency.

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