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This paper aims to propose an elementary approach toward the identification of assembly defects of a cam curved groove mechanism.

A numerical analysis method for identifying the assembly defects of the cam curved groove mechanism is proposed by resorting to Hertz contact theory. A general mathematical model is established to analyze the kinematic and dynamic characteristics with an interference fit between the main roller and cam curved groove, including the contact points of the external and internal ring.

The analysis method of the contact point characteristics of the cam curved groove mechanism is given in this paper, and the kinematic and dynamic characteristics of the main roller can be analyzed. The numerical examples presented in this paper are implemented in MATLAB, feasibility and validity of the above algorithm are verified by the finite element method.

Regarding the defects of the interference fit, the findings of this paper can serve as a reference for researchers in reducing the defects in the design process of the cam mechanism.

The transformation towards the Green Economy (GE) in Poland is a relatively new topic for researchers, policymakers and business practitioners. A comprehensive picture of the shift towards the GE can help mentioned groups translate theoretical assumptions into practice.

This chapter presents the assessment of Poland's shift towards the GE, measured by the proposed Green Transformation Index (GTI).

The set of GE indicators was elaborated in Structured Literature Review (SLR) variation method. Then, this set of indicators was compared with the Statistics Poland (GUS) secondary data and employed in the taxonometric calculation methods.

In the result, the GTI for the Polish economy was proposed and calculated between 2007 and 2020. The GTI allowed us to present a dynamic analysis of the transformation towards GE in Poland.

The purpose of this paper is to integrate the corporate market and non‐market behaviors (such as social, legal, and political activities) into the research of competitive interaction.

“Structured content analysis” is used in analyzing the longitudinal data of five real estate enterprises from an industrial journal during the last seven years.

Competitive interactions with market and non‐market contents involve the expanded competitive scope, behaviors, and relationships. New explanations beyond previous literatures are provided to explain the expanded competitive behaviors (especially in resource‐oriented and mixed scope) by analyzing the characteristics of actions and responses.

Firms must effectively focus on the interaction of market and non‐market competitive behaviors. Industrial competitors in the market environment can engage in collaborative or cooperative actions with non‐market issues in order to obtain collective benefits.

This paper expands on previous studyies of competitive interaction by integrating the important non‐market content, especially categorizing it in the Chinese transitional environment with typical evidence.

The purpose of this paper is to explore the prediction of competitive response based on the characteristics of market and non‐market actions comprehensively, and develop a four‐stage decision‐making model of firm's competitive action, which is significant for Chinese practicing managers when formulating and implementing the strategies, and further predicting competitors' strategic choices.

The research adopted the method of structured content analysis and carried out the survey in Chinese home appliance industry, mainly covering the largest firms, including TCL, Hisense, Changhong, Konka, Haier, and Skyworth. The method of multiple regression analysis was employed to test the hypotheses.

The results show that in order to comprehensively forecast competitor's responding behaviors, the firms could not only limit their perspective to market field but also pay attention to non‐market. Additionally, in the process of dynamic interaction, the attacking or responding action is not independent and it is related significantly to another three type decisions, which are market and non‐market, strategic and tactic, and collective and individual. Further, the study asserts that, in market field, tactic activity is more likely to trigger competitor's response than strategic one, while in non‐market, the situation is just the opposite. Meanwhile, the study figured out that individual market attack is easier to trigger individual market and non‐market response, as well as collective market response. While for non‐market action, whatever it is individual or collective, both would be easy to provoke competitor's collective response.

The research findings extend the existing competitive interaction theory to non‐market field. When forecasting competitor's choice of the competitive action, the firms could not only limit their perspective to market field but also pay attention to non‐market, attaching importance to certain situation of competitor's taking such non‐market action as corporate philanthropy, etc. to launch an attack or a response for gaining competitive advantage.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

Protecting the planet from unprecedented environmental crises in the presence of economic expansion has become a critical global concern. Hence, the current study aims to analyze the impact of nations’ green behaviors that they adopt to protect the planet while promoting economic expansion. Additionally, the study explores the moderating role of ES in the nexus of GI and green growth.

Data were collected over a period of 1990–2019. Analysis was conducted by employing panel data analysis techniques and various robustness tests, including multicollinearity, serial correlation and spatial correlation, for a sample of 33 nations by categorizing in Organization for Economic Co-operation and Development (OECD) and Brazil, Russia, India, China, and South Africa (BRICS) economies as well as in the pre- and post-financial crisis period.

Analysis of the composite sample reveals a significant positive impact of green investments and green innovations on green growth. Further analysis reveals a significant moderating role of environmental policy stringency in OECD economies, especially in the pre-financial crisis period. The interaction slope shows that the contribution of green innovations to green growth is positive at high and medium levels of ES. However, in the case of BRICS economies, this moderating role is insignificant.

Study findings signify the importance of stakeholder environment and urge governments to prioritize funds for sectors practicing environmentally friendly behaviors to foster green growth and stresses for more effective infrastructure of policy implementation, monitoring and evaluation.

The relationship of green investments, innovations and green growth has been established largely in existing literature; however, there is a dearth of studies to analyze the moderating role of ES. Hence the current study investigates this moderating role in the nexus of nations’ green behavior and green growth in different stakeholder environments and contexts.

Safety efficiency is the key to balance safety and production in construction industry; but the existing safety efficiency evaluation methods have the limitations of overestimating efficiency and ignoring undesirable outputs; therefore, according to the characteristics of safety production in construction industry, this paper innovatively develops a new cross-efficiency data envelopment analysis method to analyze safety efficiency, which can solve the limitations of traditional methods; and then the safety efficiency and its influencing factors of China's construction industry are analyzed, and some useful conclusions are obtained to improve its safety efficiency.

A new cross-efficiency data envelopment analysis method with undesirable outputs is proposed; and the two-stage efficiency analysis framework is designed.

First, the construction industries in different areas have different reasons for affecting their safety efficiency; second, the evaluation results of global safety priority tend to be more acceptable; third, frequent safety accidents and low resource utilization lead to a slow downward trend of the safety efficiency of China's construction industry in the long run; fourth, construction engineering supervision, construction industrial scale, and construction industrial structure have the significant impact on safety efficiency.

Theoretically, a new cross-efficiency data envelopment analysis method with undesirable outputs is proposed for evaluating safety efficiency; practically, the safety efficiency and its influencing factors of China's construction industry are analyzed.

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

Nowadays, growth is the common target of all societies. But rather than growth, it is more important to ensure the sustainability of growth. Worldwide climate changes, damages to natural capital and financial crises necessitate the transition to green growth. The purpose of this paper is to examine the contribution of socio-economic context to green growth, which represents the sum of environmental and resource productivity, natural asset base, the environmental dimension of quality of life and technology.

The paper uses grey relational analysis together with the entropy method to examine the weight of 22 green growth indicators. The green growth indicators based on the compilation of the data from 36 Organization for Economic Co-operation and Development countries in 2015.

The results point out carbon dioxide emissions and environment-related technology are the most essential indicators in achieving green growth across the world.

It provides an objective evaluation of the green growth indicators that creates awareness-raising in green growth, enables the measurement of global developments and the determines opportunities and risks.