Fuzzy set qualitative comparative analysis (fsQCA) is a new method to solve complex causal relationship analysis in social science, and data calibration is a core process of fsQCA. Ignoring data calibration will have an impact on the fsQCA consistency analysis, thereby undermining the rigor of the causal mechanism between fsQCA mining conditions and results. This study found that the distribution characteristics of the data did not have a significant impact on the final consistency and the corrected consistency, while the anchor point setting of the crosspoint had a significant impact on the sufficient conditional consistency of fsQCA. When the consistency study is carried out on the crosspoint anchor point setting and calibration transformation, it is found that the effect of crosspoint anchor point setting on consistency is more obvious than that of calibration transformation. The study of the consistency between fuzzy set data calibration and fsQCA can provide useful conclusions and calibration methods for the empirical analysis of fsQCA, reminding researchers that they should avoid using mechanical procedures to perform simple data calibration to obtain misleading results and standardize fuzzy set data. Calibration is beneficial to improve the transparency of fsQCA research and also provides an important reference for fsQCA practitioners to conduct robust analysis of different data-driven methods.

This study investigates the impact of patented innovation on the left-tail risk of stock price, measured as the 1% value at risk (VaR) and expected shortfall (ES). Using the sample of all listed firms in the Chinese A-share market from 2007 to 2020, we find that the quantity and quality of patents significantly decrease left-tail risk. Second, the effect is more pronounced for firms in regions with patent pledge systems or stronger intellectual property rights protection. Short-selling constraints and the engagement of retail investors hinder patented innovation from reducing left-tail risk. Third, we further explore the mechanisms and find that patented innovation enhances financial performance, mitigates financial constraints, and attracts long-term institutional investors, thereby decreasing left-tail risk.

With the transition and upgrading of China's industrial economy, the digital economy has gradually become a pivotal engine enabling the development of manufacturing. According to panel data from China's 30 provinces excluding Tibet from 2011 to 2019, this study analyses the path of the digital economy empowering the manufacturing development level using the moderated mediating effect model. The empirical results show that: Overall, the digital economy can break the time-space constraints of traditional information transmission and positively promote the development of manufacturing; In the viewpoint of the mechanism of action, there are two realisation paths: "the digital economy -alleviating the misallocation of talents-manufacturing development level" and "the digital economy-science and technology driven-manufacturing development level", and the second path is positively regulated by the industrial integration degree.

There is a plethora of current research on economic or financial resources for fostering innovation. These studies lack the micro-analysis and, more importantly, disregard the effect of environmental control. This study will offer a new analytical paradigm by linking financial growth, environmental regulation, and innovation growth throughout the value chain. Using the information on 30 Chinese provinces collected between 1990 and 2020, we develop a dynamic panel data model to examine the interplay between financial effectiveness, ecological regulation, and research and development (R D) innovation. We assess the impact that the efficiency of financial organizations and the stock market have on R D’s ability to influence R D innovation. There are positive spillover effects for stock market efficiency, which boosts the development and conversion of R D innovation; there are positive spillover effects for financial institution efficiency, which hurts the conversion of R D innovation, and there is an adverse effect on environmental regulation efficiency. To what extent environmental rules affect the commercialization of research and development innovation is unclear; human capital is an effective motivator for the advancement of R D innovation, and the volume of FDI may increase the commercialization of R D innovation.

This paper examines imperfect matching between venture capital (VC) firms and entrepreneurial firms in the VC market. We find an anomaly of imperfect matching evidenced by an inflection point of the matching structure in the Chinese VC market. When the market capacity is within a specific critical range, the greater the market capacity, the greater the degree of matching; when the market capacity exceeds a certain critical point, the greater the market capacity, the smaller the degree of matching. We further show that the degree of efficient information exchange in the VC market provides a powerful explanation for this anomaly. Our findings advance the research on matching structures at the market level, explain imperfect matching in the Chinese VC market from a unique angle and provide valuable policy implications for the development of VC markets.

The coordinated development of real estate industry and ecological environment plays an important role in achieving the "double carbon" goal in China.This study constructs SBM-Undesirable model and GML index model to calculate the ecological efficiency and dynamic productivity of China’s real estate industry respectively,and uses Tobit regression model to explore the influencing factors of ecological efficiency.The results show that:（1） the ecological efficiency of the real estate industry in eastern China is the highest,and the ecological efficiency in central and western China overtakes each other,showing the phenomenon of club convergence at the provincial level;（2） the dynamic productivity of the real estate industry changes from fluctuating to stable,and there are differences in the growth rate of ecological development of the real estate industry at the provincial level;（3） economic development level,enterprise interests,residents’housing consumption capacity,government support and technological innovation degree have a promoting effect on the ecological efficiency of the real estate industry,while industry scale has a inhibiting effect on it.Based on the above conclusions,local governments should pay attention to improving the efficiency of resource allocation in the real estate industry,promoting the circulation of production factors in the real estate industry,and promoting the coordinated and balanced development of the real estate industry.

To accomplish the goals of carbon mitigation, industrial green transformation is an inevitable requirement for achieving high-quality economic development. Based on the data of 30 provinces in China from 2007 to 2017, this paper uses projection pursuit model and entropy method to calculate the industrial green transformation and Chinese fiscal decentralization. It further employs static and dynamic spatial Durbin model to explore the impact of Chinese fiscal decentralization on industrial green transformation by the environmental fiscal policy. The study finds that: 1) China's industrial green transformation presents an unbalanced state with high in the east and low in the west, which has a positive spatial spillover among provinces. 2) Fiscal decentralization is significantly conducive to the industrial green transformation, while the spatial spillover of fiscal decentralization is negative. Moreover, fiscal decentralization affects the industrial green transformation by environmental fiscal policy, in of which environmental fiscal expenditure is the main path. 3) From the perspective of heterogeneity of fiscal decentralization, the impact of fiscal expenditure decentralization in promoting industrial green transformation is significantly greater than that of fiscal revenue decentralization. In terms of heterogeneity of regional location, fiscal decentralization in the eastern and central regions plays a role in accelerating industrial green transformation, while it has an obstacle impact in the western region. In terms of market-based heterogeneity, Fiscal decentralization in high-market areas facilitates the green transformation of industry, while in low-market areas is the opposite.

This paper makes a new attempt to identify the effectiveness of innovation factor allocation with a random forest method. This method avoids the evaluation bias of the relative effectiveness caused by the noneffective selection of production frontier in the nonparametric DEA method. It does not refer to other optimal subjects but shifts the focus to the judgment of its own effectiveness. In addition, it also gets rid of the constraints of the model and variables in the parameter SFA method, ensuring the reliability of the measurement results by resampling thousands of times. The data is collected from 30 provinces in China from 2009 to 2018. The findings show the innovation factor allocation in more than half of the provinces is not fully effective. It indicates that how to make use of innovation factor inputs to achieve the actual innovation output higher than own optimal levels is currently still in a period of exploration in China. To further improve innovation factor allocation efficiency, it deeply analyzes the impacts of innovation factor inputs and finds out the important innovation factor inputs. Furthermore, this study presents the nonlinear characteristics and optimal combination of important innovation factor inputs. According to this, it offers the detailed suggestions about how to adjust current important innovation factor inputs for each province in order to greatly enhance the effectiveness of innovation factor allocation in the future.

Based on the survey data from the China National SME Survey Database in 2019, we analyze the relationship between executive financial literacy and the performance of Chinese small and medium-sized enterprises (SMEs). We find that executive financial literacy helps improve corporate performance, especially for companies with weak external supervision and low competitive pressure. After controlling for the effects of the variables at the individual executive and enterprise levels, addressing possible endogenous problems in the analysis, robustness tests and heterogeneity analyses indicates that our findings remain unchanged. We further show that executive financial literacy improves corporate performance by alleviating corporate financing constraints and improving corporate risk management. Our findings have important policy implications for stimulating the vitality of economy and promoting high-quality economic development.

China's economic growth has entered "new normal," and the task of reducing carbon emissions has become more onerous. Hence, this study aimed to explore whether China's carbon emissions trading pilot policy stimulated corporate green innovation capabilities. The data pertained to the green patent data of the listed companies in Shanghai and Shenzhen stock exchanges during 2008-2018. Using a difference-in-difference-in-differences (DDD) method, the study took advantage of the variations across regions, across enterprises, and across years and obtained several novel findings. First, the pilot carbon emissions trading policy significantly stimulated the green innovation capabilities of emission control companies in the pilot areas compared with enterprises in nonpilot areas and the nonemission control list. Second, the effect of the policy on the improvement in corporate green innovation capabilities might be driven by the improvement in corporate input factor allocation efficiency and the additional benefits that could be obtained from the carbon trading market. Third, the positive effect of the policy on the green innovation capabilities of state-owned enterprises was more significant. Therefore, the establishment and promotion of a unified national carbon emissions trading market and supporting mechanisms should be accelerated to achieve the balance of stable economic growth and carbon emission task.

This study uses a network data envelopment analysis (DEA) approach to measure phased innovation efficiency to explore how fiscal technology innovation policy drives the development of regional innovation. A game model is constructed that includes governments, enterprises, universities, and research institutes to explain the influence mechanism. The innovation process is decomposed into the transformation stage of scientific research results and their commercial application. A Tobit model is used to explain the effect of fiscal policy on innovation efficiency. These methods led to novel conclusions: (1) the growth rate of innovation efficiency in the first stage is greater with smaller regional differences, with larger regional differences in innovation efficiency in the second stage; (2) the intensity of fiscal R&D funding in science and technology has a significant positive effect on overall innovation efficiency and phased innovation efficiency; and (3) the positive effect of fiscal R&D funding is greater on the commercial application of scientific achievements. The targeting effect of fiscal innovation policy on industry–university research (IUR) cooperation needs to be improved through resource sharing, joint participation, sharing of achievements, and risk sharing.

The eco-efficiency of real estate development (RED) is an important indicator in evaluating the effectiveness of eco-civilization construction. Thus, analyzing its temporal evolution and spatial spillover effect can help to judge the degree of coordinated development between RED and eco-civilization construction in the Yangtze River Economic Belt (YREB). From an ecology-based angle of RED, the data of 108 cities in YREB from 2006 to 2020 were selected. Then, the Super-SBM model, Moran’s I model, and Markov chain model were used to measure the eco-efficiency value of RED and analyze its spatial–temporal evolutionary characteristics. Research results indicate that the eco-efficiency of RED in YREB increased by 7.3%. Differences were apparent in the regional eco-efficiency of RED, but the gap gradually narrowed, and the range decreased from 0.60 to 0.05. A positive spatial autocorrelation was observed in the eco-efficiency of RED, and the high–high (H-H) cluster areas showed a trend of expansion and transfer. The proportion of H-H cluster cities increased from 11 to 20%, whereas the low–low cluster areas showed a trend of small-scale diffusion. The eco-efficiency of RED exhibited consistently stable and “club convergence” characteristics. When the spatial spillover effect is ignored, the eco-efficiency of RED presents at least 55.1% probability to be maintained in the original state. By contrast, when the spatial spillover effect is considered, the probability can be increased, and the assimilation effect of transferring the eco-efficiency of RED can be enhanced. In the future, the overall eco-efficiency of RED in YREB can be improved by exploring new development technologies, establishing collaborative development mechanisms among cities, and adopting eco-protection-oriented reward and punishment policies.

Establishing a coordinated governance mechanism for regional carbon emissions is an essential way to achieve carbon peak and carbon neutrality, while the study of interprovincial carbon emissions transfer is one of the important foundations of regional carbon emissions coordinated governance research. Based on the multiregional input-output (MRIO) model, this study calculated the carbon emissions from both the producers' perspective and the consumers' perspective and analyzed the interprovincial net carbon emissions transfer decision. Furthermore, the logarithmic mean Divisia index (LMDI) method was adopted to decompose the factors that affect the province's net carbon emissions into technological effect, structural effect, input-output effect, and scale effect. It was revealed that the input-output effect was the primary influencing factor of the net carbon transfer at the provincial level.

As a central issue in macro-finance studies, the spanning hypothesis has always been the focus of research. Previous studies have focused on whether this hypothesis holds true in developed markets, while paying little attention to that in emerging markets. Because of their unique monetary systems, governments in most emerging markets play a key role in bond returns. This study identifies macroeconomic factors for forecasting excess returns in emerging government bond markets under spanning hypothesis. We find that in previous research, government intervention factors employed in excess returns forecasting have no additional predictive ability, as they are already incorporated in current yields. Using dynamic factor analysis, we find that macroeconomic information, including pure macroeconomic activities and financial factors, has robust incremental predictive power for in-sample and out-of-sample bond excess returns.

研究生教育对于培养高素质创新创业人才和构建创新型国家具有重要的促进作用.该文提出"四驱三位一体"的"双创"教育模式,用来增强研究生创新实践能力,该体系以《计量经济学》课程改革为依托,构建高校、政府、社会三位一体的创新创业教育系统,以专业驱动、项目驱动、平台驱动、机制驱动为核心,完善教育实践方案,将创新创业教育与教师科研攻关和成果转化相结合,与专业教育、职业规划、社会实践和毕业实习相结合,以适应经济结构转型升级的需要.

Worldwide, there has been an ongoing debate about whether corporate social responsibility (CSR) can lead to better financial market performance, or whether corporations can do well by doing good. Working with a sample of all listed companies in China from 2010 to 2017, this study examines the impacts of three dimensions of CSR on stock price crash risk. We find that CSR, especially firms' responsibility to the environment and stakeholders, significantly reduces stock price crash risk, while social contributions such as charitable donations have no significant effect on stock crash risk. Attracting long-term institutional investors is the primary mechanism through which CSR can curb crash risk. Mitigating earnings management is also a channel through which overall CSR and stakeholder responsibility contribute to a lower stock crash risk. Finally, we find that stakeholder responsibility and environmental responsibility can help improve stock market performance.

With the implementation of the "Going Out" policy and the "Belt and Road" initiative, Chinese outward foreign direct investment (OFDI) in the countries along the "Belt and Road" increased substantially in the past decade. This paper analyzes the impact of institutional distance on Chinese OFDI and whether Chinese OFDI exhibits institutional risk preferences, using data on Chinese OFDI in 41 countries along the "Belt and Road" for the period from 2003 to 2018. We find that political institutional distance and economic institutional distance are both positively related to China's OFDI scale, while cultural distance has a negative impact on the investment scale. We also find that institutional distance has an asymmetric effect on China's OFDI. In particular, the worse the host country's political environment, the larger the Chinese OFDI, indicating that Chinese OFDI exhibits the political institutional risk preference. On the other hand, Chinese multinational enterprises are more willing to invest in host countries with high economic freedom. Culture institution environment of the host country has a positive but insignificant impact on the Chinese OFDI scale, indicating that Chinese OFDI shows the characteristics of cultural distance proximity.

随着国际投资活动的不断深入,我国越来越多的企业逐渐突破传统的市场拓展和资源依赖目标,将提升企业自身技术创新能力作为海外投资战略的主要动机.对企业自身来说,通过对外直接投资方式进一步实现企业发展战略和目标,带动产能、装备、技术、品牌和标准"走出去",在投资合作中提升企业自身技术水平和管理能力是大势所趋.自主研发投入仍然是提高我国自主创新能力的主要方式,但通过对外直接投资活动获得的逆向技术溢出也已经成为提高我国自主创新能力的重要途径;对外直接投资活动的逆向技术溢出效应对东、中、西三大区域的 自主创新能力的提升均有正向作用,其中,东部地区最为显著,中部地区和西部地区则不显著.

China declared a long-term commitment at the United Nations General Assembly (UNGA) in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas (hereafter referred to as quotas) is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model (BP) to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China.