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This paper mainly explores the relationship between digital inclusive finance and financing constraints of technological-based SMEs, and how digital inclusive finance affects the financing constraints of technology-based SMEs. This paper empirically analyzes the relationship between them through the OLS model, and then further verifies the relationship between them through robust regression and heterogeneity analysis. At the same time, it uses the mechanism test to explore how digital inclusive finance affects the financing constraints of technology-based SMEs. This paper aims to address these issues.

This paper aims to explain the relationship between digital inclusive finance and financing constraints of technological-based SMEs. Technology-based SMEs always face the difficult problem of “financing difficulty” and “financing expensive” in the development process, which hinders the survival and development of enterprises to some extent. Digital inclusive finance development policy vigorously promoted by the state has alleviated the financing constraints of technology-based SMEs and brought opportunities for their development.

The results show that the role of digital inclusive finance in alleviating the financing constraints of technology-based SMEs, and incremental supplement and alleviating information asymmetry are the main reasons for digital inclusive finance to alleviate the financing constraints of technology-based SMEs. In view of the availability of digital inclusive financial data, this paper only uses the data from 2014 to 2019.

The authors’ research clearly found that the development of digital inclusive finance alleviates the financing of technology-based SMEs from the two aspects of “incremental supplement” and alleviating information asymmetry, so as to provide corresponding reference basis for the government to formulate a series of plans to support the development of technology-based SMEs.

The paper attempts to revisit the nexus between economic growth, carbon emissions, trade openness, financial effectiveness and FDI for a sample of seven developed and developing countries using curvilinear relationship as per environmental Kuznets curve (EKC) hypothesis over long term.

The authors determine the unit root properties of variables (using Clemente–Montañés–Reyes unit root test with double mean shifts and AO model and augmented Dickey–Fuller test) for structural breaks at different levels. Autoregressive distributed lag (ARDL) and error correction model (ECM) methodology was used to estimate long- and short-run parameters among the selected variables in sample countries from 1965 to 2016. Vector error correction (VEC) and Granger causality approach was used to determine the direction of causality.

The authors confirmed long-run relationship among the variables and highlighted high economic growth and energy consumption as the main causes of environmental degradation. While in India financial development and FDI inflows depict a negative association with environmental sustainability, however, such relationship was positive in the United Kingdom (UK), which is often considered as a benchmark for policymakers. The authors’ findings were in agreement with existing research insights in reporting FDI and financial development as the major contributors towards (unsustainable) sustainable environment through emissions in case of (developing country like India) developed country like UK. For other sample countries (China, Brazil, Japan, South Africa, United States of America (USA)), the authors’ model failed to capture financial development and FDI as significant contributors of carbon emissions. However, unidirectional causality running from energy to carbon emission was observed leading to the policy adoption of incentivizing alternative energy-based resources to increase energy efficiency across the energy value chain.

Manufacturing with renewable energy, in collaboration with private and foreign players, under an institutional framework is desirable. Policy instruments including mandatory administrative controls, economic incentives and voluntary schemes that promote energy efficiency building blocks need to be established. A sound legal system for implementing technological innovation, financial subsidy incentives, interest-free loan programmes and development of financial sector supports creation and thriving of energy efficient units, often a perquisite for accelerated development.

By undertaking a comparative analysis, the authors address the research gap through revisiting EKC hypothesis with different set of trade policy and financial development framework. To the best of the authors’ knowledge, earlier studies were limited to one-country data analysis and did not consider the comparative data set of developed and developing countries with reference to financial development and FDI components.

Given the increasing importance of engineering insurance, it is still unclear which specific factors can enhance the role of engineering insurance as a risk transfer tool. This study aims to propose a hybrid approach to identify and analyze the key determinants influencing the consumption of engineering insurance in mainland China.

The empirical analysis utilizes provincial data from mainland China from 2008 to 2019. The research framework is a novel amalgamation of the generalized method of moments (GMM) model, the quantile regression (QR) technique and the random forest (RF) algorithm. This innovative hybrid approach provides a comprehensive exploration of the driving factors while also allowing for an examination across different quantiles of insurance consumption.

The study identifies several driving factors that significantly impact engineering insurance consumption. Income, financial development, inflation, price, risk aversion, market structure and the social security system have a positive and significant influence on engineering insurance consumption. However, urbanization exhibits a negative and significant effect on the consumption of engineering insurance. QR techniques reveal variations in the effects of these driving factors across different levels of engineering insurance consumption.

This study extends the research on insurance consumption to the domain of the engineering business, making theoretical and practical contributions. The findings enrich the knowledge of insurance consumption by identifying the driving factors specific to engineering insurance for the first time. The research framework provides a novel and useful tool for examining the determinants of insurance consumption. Furthermore, the study offers insights into the engineering insurance market and its implications for policymakers and market participants.

The purpose of this study is to validate the impact of economic and financial development along with energy consumption on environmental degradation using dynamic panel data models for the period 1980-2010. The study uses three sub-panels constructed on the basis of income level to make panel data analysis more meaningful.

Larsson et al. panel cointegration technique, fully modified ordinary least squares and vector error correction model causality analysis are applied for empirical estimation.

Main empirical findings demonstrate that financial development reduces environmental degradation in the high-income panel and increases environmental degradation in the middle- and low-income panels. Hypothesis of the environmental Kuznets curve is accepted in all income panels. Granger causality results show the evidence of bidirectional causality between financial development and CO₂ emission in the high-income panel, and unidirectional causality from financial development to CO₂ emission in the middle- and low-income panels.

In empirical literature, only a few studies explain the effect of financial development on environment. The present study is an effort to fill this gap by exploring the effect of economic and financial development on environmental degradation.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

The purpose of this paper is to establish a group of grey prediction models and relative degree model to study the characteristics and trend of the logistics industry development in Henan province scientifically. The study results can provide references for the development policy of the logistics industry in Henan province.

The paper constructs grey prediction models and grey buffer operator models which are related to the distribution of logistics industry in Henan province, and selects prediction models by comparing model accuracy, and use them to forecast the development trend of logistics industry in future ten years of Henan province. Using the grey relative models, the paper analyses development dynamic and prospect which support the development of logistics industry, and provide some references for transferring the pattern of economic growth of Henan province, forming new economic growth point and formulating relevant policies. High prediction accuracy models are selected to forecast the future development trend of logistics industry in the next ten years.

Results show that the modern logistics industry in Henan province has been a steady growth in overall, the main growth points of the logistics industry development in Henan province are roadway miles (km), roadway (100 million tonnes/km), freight turnover (100 million tonnes/km) and waterway (100 million tonnes), the growth points for the future development of logistics industry in Henan province are the roadway freight volume, roadway passenger volume and waterway freight volume.

Regional economic competition has become an important index for measuring a country's economic development level. Logistics industry plays an important role in the regional economic development, such as promoting coordinated development of regional economy and upgrading industrial optimization, and playing a major role in industrial transfer. Hence, logistics industry, which is urgently needed to solve by the government, has become important forces for promoting the growth of economy and a basic pillar industries of regional economy.

The paper presents the systematic results of development prediction of modern logistics industry in Henan province and its dynamic analysis by using grey systems theory, not only to predict the trend of the development of the logistics industry, also to analyse the future development of logistics industry in the leading power factors.

The purpose of this paper is to investigate the role of institutions in the financial development-output growth volatility nexus. It provides new channels through which financial development can dampen the output growth volatilities of the countries under investigation.

A comprehensive data set for 71 countries covering the period from 1996 to 2012 and the System GMM approach were used. The choice of the methodology is to deal with endogeneity issues such as measurement errors, reverse causality among other issues.

A number of findings were emanated from the empirical analysis. First, the estimates provided evidence of the volatility-reducing effect of financial development. Second, institutions do not have the same reducing influence on output growth volatility. Third, the interaction of financial development and institutions showed that the output volatility reduction arising from financial development is enhanced in the presence of improved institutions.

The policy implications derived from this study are in twofolds: first, it is important for policymakers to formulate policies that would ensure and enhance the development of the financial sectors, since its importance in minimizing output volatility has been established. Second, institutional quality should be developed so as to further enhance the growth volatility-reducing influence of financial development. Particularly, institutions should be improved along the multiple dimensions captured in the analysis.

To the best knowledge, the novelty of this study to the literature is the introduction of institutions, which is hypothesized to increase the dampening effects of financial development in output growth volatility.

In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey system theory to analyze the circular economy of the e-commerce market.

Construct a Grey system theory model, analyze the big data of e-commerce and circular economy of the e-commerce market and predict the development potential of China's e-commerce market.

The results show that the Grey system theory model can play an important role in the data analysis of circular economy of the e-commerce market.

Use Grey model to analyze e-commerce data, discover e-commerce market rules and problems and then optimize e-commerce market.

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

A new two-stage multi-level equipment state classification system was proposed to forecast equipment operation status. The first stage involves predicting the equipment's normal state, and the second stage involves forecasting the equipment's abnormal status. Meanwhile, the equipment state classification is done according to the manufacturing company's internal specifications to define various equipment statuses. Then, the trouble state and waiting state were predicted by grey state prediction model.

A new two-stage multi-level equipment status classification system and a new approach for equipment states prediction has been proposed in this paper.

The application on a real-world case shown that the model is very effective for predicting equipment state. The equipment's major failure risk can be reduced significantly.

The proposed approach can help improve the effective prediction of the equipment's various operation states and reduce the equipment's major failure risk and thus maintenance costs.