Green governance and institutional resilience: strengthening environmental policies for a low-carbon economy in mangrove ecosystems

New Dimensions of Vulnerability to Energy and Transport Poverty

Achieving the Paris Agreement 1.5 C target requires a reversal of the growing atmospheric concentrations of methane, which is about 80 times more potent than CO 2 on a 20-year timescale. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report stated that methane is underregulated, but little is known about the effectiveness of existing methane policies. In this review, we systematically examine existing methane policies across the energy, waste, and agriculture sectors. We find that currently only about 13% of methane emissions are covered by methane mitigation policies. Moreover, the effectiveness of these policies is far from clear, mainly because methane emissions are largely calculated using potentially unrepresentative estimates instead of direct measurements. Coverage and stringency are two major blind spots in global methane policies. These findings suggest that significant and underexplored mitigation opportunities exist, but unlocking them requires policymakers to identify a consistent approach for accurate quantification of methane emission sources alongside greater policy stringency. ll

: Green Governance is an essential framework for integrating environmental sustainability into policy and decision-making processes at various levels. This chapter explores the theoretical foundations, policy instruments, and institutional roles that define Green Governance. It discusses key concepts such as sustainable development, ecological modernization, and the precautionary principle, and examines the diverse policy tools, including regulatory measures, economic incentives, and collaborative initiatives, used to promote sustainability. The chapter also highlights the roles of governments, international organizations, NGOs, and the corporate sector in shaping and implementing green policies. It addresses the challenges and barriers to effective Green Governance, such as political resistance, economic constraints, technological gaps, and cultural factors. Additionally, it explores emerging trends and innovations, including green digitalization, circular economy practices, and sustainable finance. The chapter concludes with a reflection on the importance of collective responsibility and collaboration in achieving a sustainable and equitable future, urging all stakeholders to engage actively in Green Governance initiatives. Keywords : Green Governance,Sustainable Development,Ecological Modernization,Precautionary Principle,Environmental Policy,Regulatory Approaches,Economic Instruments,Circular Economy,Green Digitalization,Sustainable Finance,Non-Governmental Organizations (NGOs),Corporate Social Responsibility (CSR),Technological Innovation,Public Engagement,Environmental Sustainability,Climate Change,International Cooperation and Environmental Stewardship.

Circular economy and green governance are gaining momentum and traction with industry and policymakers. The circular economy is viewed as a restoration and regeneration system in which resources, energy usage, and greenhouse gases are minimized. At the same time, the Green Governance structure is a good transition to a circular economy and helps enterprises to move toward sustainable development. This study’s objective is to explore the hidden possible relationship that exists between the Green Governance of companies and the much-anticipated Circular Economy for low waste and carbon society. Furthermore, an extensive literature review was undertaken to create the first green governance circular economy framework (GGCE) for businesses to integrate the proposed model into their operational activities. This GGCE framework will be developed by exploring the similarities between green governance and circular economy. This study has three expected findings; firstly, the proposed framework will help firms to change their business approach to addressing climate change. Secondly, the GGCE framework will help policymakers to develop policies for circular economy governance. Lastly, it will be the point of reference to the academician for further extension of the GGCE framework.
 Keywords: green governance, circular economy, GGCE framework, Nigeria oil and gas sector

The aim of present investigation was to examine mediation effect of human capital among Strategic alignment, strategic foresight as well as competitive advantage in Ethiopian medium and large manufacturing firms. The target population for this study comprised of medium and large manufacturing firms operating in Sidama Regional State, Ethiopia. 950 employees working in 20 medium and large manufacturing firms were included in this investigation. Structured questionnaires were used to ensure consistency in data collection from a sample of 282 respondents. A quantitative research approach was used to establish relationships between multiple constructs. Using AMOS software, the investigators used techniques like exploratory factor analysis (EFA) to find hidden connections between variables, the Kaiser–Meyer–Olkin (KMO) measure to check if the sample size was good for factor analysis and structural equation modeling (SEM) to investigate both direct and indirect effects. The results showed that competitive advantage and strategic alignment, as well as strategic foresight and competitive advantage, are significantly correlated. Human capital was also found to be an important mediator, improving the efficiency of both strategic alignment and strategic foresight in reaching competitive results. The findings emphasize how crucial it is to incorporate human capital development into strategic management procedures and how medium and large manufacturing firms must cultivate an aligned and forward-thinking culture. For managers and policymakers looking to improve medium as well as large manufacturing firms' strategic competencies in a changing landscape of business, this research adds to the understanding of how these constructs interact to affect competitive advantage. To gain the competitive advantage, managers of medium and large manufacturing firms of Ethiopia should focus on building human capital and improving strategic alignment and foresight.

Purpose In confronting the challenge of climate change and progressing towards dual carbon goals, China is actively implementing low-carbon city pilot policy. This paper aims to focus on the potential impact of this policy on enterprise green governance, aiming to promote the reduction and balance of carbon emissions. Design/methodology/approach Based on the panel data of China's large-scale industrial enterprises from 2007 to 2013, this paper uses the Difference-in-differences (DID) method to study the impact and path mechanism of the implementation of low-carbon city pilot policy on enterprise green governance. Heterogeneity analysis is used to compare the effects of low-carbon city pilot policy in different regions, different enterprises and different industries. Findings The low-carbon pilot can indeed effectively enhance corporate green governance, a conclusion that still holds after a series of robustness tests. The low-carbon city pilot policy mainly enhances enterprise green governance through two paths: an industrial structure upgrade and enterprise energy consumption, and it improves green governance by reducing enterprise energy consumption through industrial structure upgrade. The impact of low-carbon city pilot policy on enterprise green governance shows significant differences across different regions, different enterprises and different industries. Research limitations/implications This paper examines the impact of low-carbon city pilot policy on enterprise green governance. However, due to availability of data, there are still some limitations to be further tackled. The parallel trend test in this paper shows that the pilot policy has a significant positive effect on the green governance of enterprises. However, due to serious lack of data in some years, the authors only selected the enterprise data of a shorter period as our experimental data, which leads the results to still have certain deficiencies. For the verification of the impact mechanism, the conclusions obtained in this paper are relatively limited. Although all the mechanism tests are passed, the reliability of the results still needs to be further tested through future data samples. In addition, as the pilot policy of low-carbon cities is still in progress, the policy can be tracked and analysed in the future as more data are disclosed, and further research can be carried out through dimensional expansion. Practical implications Low-carbon city pilot policy plays an important role in inducing the green governance of enterprises. Therefore, policy makers can continue to strengthen the construction of low-carbon city pilots by refining pilot experience, building typical cases, actively promoting pilot policy experience, expanding pilot scope and enhancing the implementation efficiency of pilot policy nationwide, which will contribute to the optimization and upgrading of the regional industrial structure at the urban level and will provide experience and reference for the synergistic implementation plan of pollution reduction and carbon reduction. Social implications The impact of the low-carbon city pilot policy on enterprise green governance not only exists in two separate paths of urban industrial upgrading and enterprise energy consumption but also exists in a chain transmission path from macro to micro. The authors find that the effect value of each influence path is different, and there is an obvious leading influence path for the role of enterprise green governance. Therefore, in the process of implementing a low-carbon city pilot policy, policies should be designed specifically for different mechanisms. Moreover, complementing and coordinating several paths should be advocated to give full play to the green governance effect of enterprises brought by different paths and to further expand the scope of industries and enterprises where policies play a role. Originality/value To the best of the authors’ knowledge, for the first time, this paper connects macro mechanisms with micro mechanisms, discovering a macro-to-micro transmission mechanism in the process of low-carbon city pilot policy affecting enterprise green governance. That is, the low-carbon city pilot policy can facilitate industrial structure upgrading, resulting in reduced enterprise energy consumption, ultimately enhancing enterprise green governance.

What drives the green and low-carbon energy transition in China?: An empirical analysis based on a novel framework

The chapter demonstrates an application of structural equation modeling (SEM) and path analysis (PA) in tourism research, and the associated statistics are described. First, we discuss the basic concepts of SEM, followed by an explanation of the key statistics and terms associated with this procedure. Then we describe the procedure for conducting SEM, including second-order confirmatory factor analysis (CFA). Finally, we describe the related technique of path analysis. In doing so, this chapter provides an example of structural equation modeling with a path model, of which path analysis assumes that all variables are measured without error so that it has a more restrictive set of assumptions than general structural equation models. The study examines antecedents of corporate commitment to sustainable tourism and corporate environmental responsibility in a country in Asia. The study employs factor analysis (i.e., exploratory factor analysis, confirmatory factor analysis, and internal consistency reliability tests) and structural equation modeling analysis and path analysis (i.e., the analysis of moment structures and regression analysis) using 386 samples collected from tourism employees in South Korea.

Background/Objectives: In Saudi Arabia, most organizations are rushing to adopt new technologies of digital transformation to achieve Saudi Vision 2030. Shaqra University has, therefore, adopted a new system, the Makken system, which allows users to conduct transactions such as printing salary details and helping managers to manage their employees and other faculty members. This study aimed to assess the factors that impact on users\' satisfaction with the Makken system in Shaqra University. Method: A questionnaire was distributed among administrative employees and faculty members in Shaqra University to assess users\' satisfaction with the Makken system based on the following factors: Information quality (IQ), System quality (SQ) and Service quality (SV). In this study, the snowballing selection technique is used among 122 staff members (academic and administrative) who are working in the Shaqra University from both the male and female sections. The structural Equation Model (SEM) was employed to analyse the data via maximum Likelihood operation. This was done by calculating each pathway for significance and also by estimating the strength of each path in terms of the obtained Beta value (β). Findings: Structural equation model (SEM) analysis was used in this study and the results revealed that Information quality, System quality and Service quality had positive impacts on user satisfaction with the Makken system. It was also found that most users were satisfied, but not completely satisfied, with the Makken system in Shaqra University. Keywords: Makken system; Shaqra University; SEM; User Satisfaction; Information System

This study offers a comprehensive investigation of mediation analysis in Structural Equation Modelling, highlighting its theoretical basics, statistical practices, and real-world applications. It differentiates mediation from moderation, explaining how mediation helps in understanding indirect relationships between latent variables. Various proposed mediation models, including simple mediation, multiple mediation, and moderated mediation, are discussed in detail. The study also analyses statistical methods such as the Causal Steps Approach (Baron & Kenny, 1986), the Product-of-Coefficients Method (Sobel Test), Bootstrapping, the Bayesian Estimation Method, and Monte Carlo Simulation, each with its respective advantages and limitations. Additionally, advanced Structural Equation Modelling techniques, such as multigroup mediation, longitudinal mediation, and latent variable mediation, are examined to address complex research scenarios. Employing a literature review-based methodology, the study synthesizes existing knowledge on best practices for estimating mediation effects using Structural Equation Modelling. Software tools like AMOS, Mplus, LISREL, and SmartPLS are discussed in the context of model specification, estimation, and evaluation. Real-world applications in business, psychology, human resource management, and marketing are illustrated, including customer trust mediating the relationship between service quality and purchase intention, employee engagement mediating the effect of transformational leadership on job performance, and social media engagement mediating brand trust and purchase intention. Key findings highlight bootstrapping as a better method for estimating indirect effects due to its non-reliance on normality of the data assumptions and Bayesian SEM as a robust substitute for handling small sample sizes and incorporating preceding knowledge. The study also discusses crucial challenges such as measurement error, model misspecification, the need for longitudinal data to establish causal inference, and comparisons between Structural Equation Modelling-based mediation and regression-based mediation using the PROCESS macro. By presenting a structured framework for mediation analysis in Structural Equation Modelling, this current study contributes to advancing causal modelling methods across various disciplines and provides directions for future research.

A growing number of nursing studies have used structural equation modelling (SEM) analysis. However, there is little research assessing the use of SEM analysis in nursing research. To present a systematic review of nursing research that uses SEM. The review revealed poor reporting of information about the determination of sample size, missing data, normality and outliers. Most studies neither computed composite reliability nor assessed convergent and discriminant validity. There was a lack of consistency in performing the analysis. Some of the studies conducted exploratory factor analysis before performing confirmatory factor analysis, without discussing its necessity. Although most studies declared the estimation method and software used, there were many that did not. Little information about the different steps of conducting SEM analysis was provided in the studies. Weaknesses and areas of improvement for future empirical SEM studies were identified. When conducting SEM, there are many issues that should be addressed. Overlooking these issues may invalidate findings. The results of this review provide nurse researchers with best practice guidelines for conducting SEM and pave the way for researchers to adopt this method in their studies.

This study aims to examine and analyze the effect of transformational leadership and non-physical work environment on turnover intention through job satisfaction as an intervening variable at PT Saba Pratama. The method used in this research is descriptive analysis method with a quantitative approach, namely research with quantitative data which is then processed and analyzed for conclusions. The number of samples in this study was 115 employees. This research uses saturated sample technique. Data collection methods in this study were carried out in several ways including questionnaire method, observation and interview methods. Data analysis techniques are in accordance with the research pattern and the variables to be studied. In this study used structural Equation Modeling (SEM) analysis which is a multivariate analysis. SEM is a statistical model that seeks to explain the relationship between many variables. To do SEM analysis, the tools or software used in this study are used SmartPLS. The results show that transformational leadership has a negative and significant influence on turnover intention. Non-physical work environment has a negative and significant influence on turnover intention. Transformational leadership has a positive and significant influence on job satisfaction. Non-physical work environment has a positive and significant influence on job satisfaction. Job satisfaction has a negative and significant influence on turnover intention

transfer, create conditions for the establishment of farmers' behavioral psychological contracts in the process of agricultural land transfers, and guide farmers to establish relationship psychological contracts. The second is to improve the market system, properly cultivate and develop agricultural land transfer intermediaries, reduce transaction costs, and reduce the probability of farmers' psychological contracts being broken. The third is to guide farmers to establish a positive agricultural land transfer psychology based on their resource endowments such as labor force quality and cultural quality, and encourage farmers to make agricultural land transfer decisions such as subcontracting, leasing, reselling, and interchanging.

Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p < 0.01). Conclusions: Transfer, which was not heavily considered in the previous prediction formula, was found in causal relationships with prediction error. It is suggested to intervene to transfer together with positive factors to recovery for achieving the prediction goal.

본 연구는 R을 이용하여 구조방정식모형에서 이중매개효과 분석을 수행하는 방안을 제시하고 있다. 이를 위해 본 연구에서는 매개효과 분석을 위한 다양한 기법들에 대한 문헌연구를 통해 부트스트랩 기법을 이중매개 효과분석을 수행하는데 있어서 가장 적합한 기법으로 선정하고, PLS 경로분석 수행을 지원하는 R패키지인 plspm을 이용하여 구조방정식모델링에서 부트스트랩 기반의 이중매개효과를 분석하는 방안을 개발했다. 결과물로 본 연구에서는 예시모형을 대상으로 한 이중매개효과 분석기법과 관련 R스크립트들이 제시되었다. 본 연구는 대부분의 SEM 패키지들에서 지원하지 못하는 구조방정식모델링에서의 이중매개효과 분석을 포함하는 연구들에서 유용하게 활용될 것이며, 또한 연구자들에게 R을 이용한 새로운 매개효과 분석기법 제시를 통하여 심도 있는 연구를 위한 기반 지식을 제공할 것이다. This study provides an approach to perform the double mediation analysis in structural equation models using the R. For this purpose, the study reviews a variety of techniques for mediation analysis, selects the bootstrapping technique as the most suitable way for performing the double mediation analysis and develops an approach for the double mediation analysis in structural equating models with the bootstrapping using the plspm which is the R package for the performing PLS path analysis. This study will be useful for the studies including the double mediation analysis in structural equation modeling, which is not supported by most of SEM packages, also will provide the knowledge base for in-depth analysis through suggesting the new mediation analysis technique using R for the researchers.