How does formal credit promote green entrepreneurship? New insights from renewable energy producers in high-, middle-, and low-income countries.

To tackle the growing menace of environmental degradation, the idea of green entrepreneurship has gained popularity, which is the process of creating new goods and technologies to solve environmental problems. Like traditional entrepreneurs, green entrepreneurs also need financial backing from financial institutions. However, no empirical evidence was found regarding the relationship between formal credit and green entrepreneurship. This analysis is an effort to plug this vacuum into the literature by analyzing the impact of formal credit on green entrepreneurship in high, middle, and low-income economies from 2011 to 2021. The study has employed various econometric techniques such as fixed-effects, random-effects, 2SLS, and GMM. The results show that formal credit substantially develops green entrepreneurship in high, middle, low-income, and full samples. Besides formal credit, GDP, environmental pressure, trade openness, technological development, and human capital are crucial in green entrepreneurship development in all samples. Policymakers may collaborate with financial institutions to create and provide specialized financial products and services catering to green entrepreneurs.

Memory representations in a cross-modal matching task: evidence for a verbal component.

In everyday tasks, one often uses touch to find what has been seen. Recent research has identified that when individuals view or touch an object, they may create a verbal memory representation; however, this research involved object naming, which may have prompted the use of verbal strategies. Research has also identified variability in memory representations for objects, which may indicate individual differences. To investigate memory representations and their associations with individual differences in cognitive styles, we measured the cognitive styles of 127 participants and had them complete a non-verbal matching task without distractors, or with verbal or visual distractors. In the task, they viewed an object and then touched an object - or vice versa - and indicated whether the objects were the same or different. On trials where different objects were presented, participants responded consistently more slowly and made more matching errors for similar objects compared to distinct objects. Importantly, higher scores on the verbalizer cognitive style predicted faster reaction times on the matching task across all trial types and distraction conditions. Overall, this indicates that cross-modal object processing in short-term memory may be facilitated by a verbal code.

Green development in BRICS: unraveling the effects of environmental technology, R&D spending, and green investment in the context of COP21.

The BRICS countries are renowned for their rapidly developing economies and growing populations. However, the environmental challenges associated with their economic growth have raised concerns about sustainability and the need for green growth strategies. In recent times, empirics have started to analyze the determinants of green growth, but very few have focused on environmental technological capital, R&D, and green investment. This study examines the impact of environmental technological capital, R&D, and green investment on green growth in BRICS economies from 1995 to 2020 using a panel ARDL model. The study found that green investment, environmental technology, and R&D positively facilitate green growth in the BRICS economies in the long run. The long-run country-wise estimate of green investment is positively significant in Brazil and Russia. On the other side, the estimated coefficient of environmental technology is positive in Brazil, India, and China. However, the estimated coefficient of R&D is positive in China only. In the short run, the results are a mix, whether group-wise or country-wise. Based on the results, the study will recommend policy suggestions for green R&D, development of environmental technology, and green investment that help in enhancing green growth.

Rural areas development and pro-environmental behavior in China: implications for rural development and planning.

This research adds to the body of knowledge by examining the asymmetric link between rural development and pro-environmental behavior in rural China. Rural development is assessed via rural income and digital financial inclusion. We use linear and nonlinear autoregressive distributed lag (ARDL) models to assess short- and long-term effects. The linear analysis suggests that a rise in rural income and digital financial inclusion encourages long-term access to clean fuels and technologies (CFT) in rural areas, while they do not have any significant impact in the short run. On the other side, the nonlinear framework illustrates that a positive shock in rural income and digital financial inclusion encourages long-term access to CFT in rural areas. A negative shock in rural income reduces long-term access to CFT in rural areas, and a negative shock in digital financial inclusion does not significantly impact access to CFT. However, in the short run, only the estimates of rural income are significant, while the estimates attached to digital financial inclusion are insignificant, implying that a rise in rural income increases access to CFT and a fall in rural income reduces access to CFT. Thus, government should encourage collaboration between private sector and civil society organizations to promote sustainable rural development and pro-environmental behavior.

Sustainable development through corporate social responsibility adoption and its drivers: evidence from corporate industrial sector.

Corporate social responsibility (CSR) is a vital strategy for promoting firms' sustainable development goals, encompassing social, environmental, and economic factors. In emerging economies, manufacturing firms face mounting challenges in ensuring their sustainability. This study aims to identify the key drivers of CSR and examine their impact on CSR adoption in the manufacturing industry of Pakistan. The study employs the survey method to collect data, utilizing a one-to-one interview approach with a matrix-style questionnaire administered to a panel of experts selected through purposive sampling technique. Interpretive structural modeling (ISM) combined with Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) and structural equation model (SEM) are used for data assessment, modeling, and analysis. The findings indicate that compliance with government regulations and stakeholders' pressure are the essential drivers of CSR adoption and positively influence CSR adoption. Consequently, firms should consider adopting CSR strategies to enhance their sustainability based on the outcomes of this analysis.

Evaluating policy-driven capital for renewable energy investments in the presence of cross-sectional dependence: perspectives from financial institutions and markets.

Access to financing is crucial for renewable energy projects due to their high initial capital costs and long payback times. In this regard, well-performing financial institutions and markets can provide the necessary capital for investments in renewable energy sources. This study emphasizes the impact of financial institutions and markets on renewable energy investment using data from various regions (Asia, Africa, Europe, and America). The CS-ARDL model is employed for analysis during 1998-2021. The findings of the CS-ARDL highlight that financial institutions, financial markets, greenhouse gas emissions, and GDP help promote investments in renewable energy in the long run for all samples, including global, Asian, American, African, and European. However, the estimates of ICT and Trade have insignificant effects on investments in renewable energy. In the short run, most of the factors do not show any significant effect on renewable energy investments. Therefore, policymakers should try to increase the role of financial institutions and markets in promoting renewable by inducing them to ease their policies for grant of loans to invest in renewable energy sources.

Country risk and its impact on renewable energy investment in highly polluted economies.

The country's risk may significantly impact every sector of the economy, and the energy sector is no exception. However, no past study has empirically tested the relationship between country risk and renewable energy investment. Therefore, this study is an effort to investigate the relationship between country risk and renewable energy investment in highly polluted economies. We have employed different econometric techniques to analyze the relationship between renewable energy investment and country risk, including the OLS, 2SLS, GMM, and panel quantile regressions. The estimate of country risk influence renewable energy investment negatively in OLS, 2SLS, and GMM models. Similarly, the country's risk negatively impacts the renewable energy investment from the 10th to 60th quantiles in the panel quantile regression model. Moreover, the GDP, CO2 emissions, and technological development help promote renewable energy investment in OLS, 2SLS, and GMM models, while the human capital and financial development do not significantly impact the renewable energy investment. Furthermore, in the panel quantile regression model, the GDP and CO2 emission estimates are positively significant almost at all quantiles, and the estimate of technological development and human capital are positively significant at higher quantiles only. Therefore, the authorities in highly polluted economies should consider the respective countries' risk considerations while formulating rules about renewable energy.

A moderated mediation model to predict the adoption intention of renewable wind energy in developing countries.

Developing countries are deprived of resources, and their economies are dwindling. Energy shortfall is one of the leading issues in developing countries that not only shatter economies but is the leading cause of depletion of natural resources and environmental pollution. There is an urgent need to shift to renewable energy sources to save economies and natural resources and to save our ecological system. Aiming for this, we have collected a cross-sectional data set to study the household intentions of shifting to wind energy and analysed the moderated mediation interactions of the variable to better understand socio-economic and personal factors. 840 responses were analysed using smart-PLS 4.0; results revealed that cost value and social influence directly relate to renewable energy adoption. Environmental knowledge directly influences attitude towards the environment, and health consciousness influences perceived behavioural control. Results also revealed that social influence strengthens the indirect relationship between awareness of renewable energy and its adoption, while it weakens the indirect relationship between health consciousness and renewable energy adoption.

Examining the potential role of ICT diffusion on green growth: does financial development matter in BRICS economies?

Green growth refers to the economic growth strategy, which is less detrimental to natural assets, predominantly the environment. Therefore, the main motive of investment and innovation activities should be to attain economic growth while sustainably utilizing natural assets. In this regard, we aim to explore whether ICT diffusion and financial development matter for attaining green economic growth in BRICS economies. The long-run autoregressive distributed lag (ARDL) results show that internet development exerts a positive influence on green growth in four BRICS economies excluding India. However, the long-run estimates of mobile cellular subscriptions are positive only in Russia and China. On the other side, the increase in bank credit and insurance premium also contributes to the long-run green economic growth in almost all BRICS economies. Policymakers should focus on the increased use of ICT in the economy that would replace the physical resources in the economy with information resources. Besides, financial services should be provided to individuals and small and medium enterprises involved in green consumption and production activities.

Determinants of China's renewable energy industry development: do eco-innovation and financial inclusion matter?

Fossil fuels are causing irreparable damage to the environment and lead to the depletion of reservoirs of coal, oil, and gas, which may give rise to the issue of energy scarcity and security. Therefore, policymakers and empirics have looked for alternative sources of energy that are affordable, reliable, and clean sources of energy. Consistent with this view, we have tried to examine the impact of eco-innovation and financial inclusion on renewable energy development in China. In order to empirically investigate, we have applied the autoregressive distributive lag model. The long-run estimates of eco-innovations are statistically significant and positive models, confirming that environmental-related innovations help increase the production of different renewable energy. Similarly, the long-run estimates of financial inclusion are positively significant, implying that an increase in financial inclusiveness intensifies the production of solar, biomass, and renewable energy in China. Generally, our findings imply that both eco-innovations and financial inclusion help increase renewable energy production in China in the long run.

A PLS-SEM approach to determine farmers' awareness about climate change mitigation and adaptation strategies: pathway toward sustainable environment and agricultural productivity.

This research was conducted in a significant agricultural region to gauge farmers' knowledge of climate change adaption strategies. We employed a semi-structured questionnaire based on the literature; it was broken up into sections, and used certain statistical techniques (PLS-SEM) to examine the results. Farmers who had sufficient assets and resources thought they were safer and could withstand the adverse effects of climate change. A total of 900 completed questionnaires were gathered to investigate the link between the control, moderator, and DV variables in the future. As a consequence, the PLS-SEM path analysis findings showed that our model is fit. PLS-SEM direct path analysis revealed AM > FACC, UA- > FACC, SA- > FACC, FS- > FACC, PR- > FACC, and SI- > FACC are significant. The established hypotheses H1-H6 are strengthened by these findings. We also examined the respondents' ages and genders to use as controls; whereas gender showed no correlation with FACC, there was a strong link between age and the dependent variable. There is no statistically significant correlation between gender and climate change awareness, but older people tend to have a broader understanding of the topic and its consequences. Education significantly moderates the relationship of farmer's awareness (climate change) associated with AM, UA, SA, FS, PR, and SI. depicts the moderation role of education on the relationship between AM*Education- > FACC, UA*Education- > FACC, SA*Education- > FACC, FS*Education- > FACC, PR*Education- > FACC, and SI*Education- > FACC. H2a and H5a in this study showed significant correlations with education as a moderator; however, H1a, H3a, H4, and H6a did not demonstrate any moderator relationships. There is a medium to strong correlation between various factors, and the correlation values of a few chosen variables are significant when compared to all other variables in the current study. Highly significant correlations were found between PR, SA, SI, and UA with FACC. Governmental policies and effective monitoring systems will be developed as a result of the research to enable integrated and sustainable water development.

Analysis of the factors influencing the college students' employment willingness under the strategy of "strengthening the provincial capital".

University graduates can inject strong impetus into the development of cities. However, China's "Strengthening the Provincial Capital" strategy attracts many college graduates, resulting in many limitations in attracting college students to employment in non-provincial capital cities. This paper systematically studies college students' work and entrepreneurial intention factors. We conduct a social survey of employment and entrepreneurial intentions among university graduates in Xiangtan City, Hunan Province, and obtain 12,897 questionnaires. Then, we construct an index analysis system (IAS) and analyze the 12897 questionnaires systematically based on IAS. The results show that urban characteristics and personal orientation most directly affect college graduates' employment and entrepreneurial willingness. Based on the statistical conclusions, we put forward corresponding policy recommendations. Colleges should build a "government-enterprise-school" cooperation mechanism, implement strategically focused attraction policies following local conditions and local conditions, and promote the employment of college students to promote employment and entrepreneurship in non-provincial capitals.

An SEM-ANN approach to evaluate public awareness about COVID, A pathway toward adaptation effective strategies for sustainable development.

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.

Public perceptions About Foreign Investment, A PLS-SEM Analysis toward republic sustainable infrastructure.

Over the years, Pakistan and China have developed strong bilateral trade and economic linkages. China Pakistan economic corridors (CPEC) is a multidimensional development plan, encircling several diverse projects and the Chinese government is going to invest a huge amount in China-Pakistan Economic Corridor on several different projects. CPEC is an approach toward economic stability for both countries in the globalized world and is known as a game changer in this region. In the present study China-Pakistan economics door has been discussed, to check the CPEC implementations situation, its restraints, challenges, and benefits. An empirical process was adopted to check public opinion about it with aimed public perceptions About Foreign Investment, A PLS-SEM Analysis toward republic sustainable infrastructure. PLS-SEM direct path analysis revealed that there is direct relationship among IV and DV, these results offer support to hypotheses H1-H6 accepted. We have also looked at the levels of education and gender of the respondents as control variables, gender and education showed insignificant relationships with CPEC. It means there is no direct relationship between gender and education to DV. Age significantly moderates the relationship of CPEC associated with IV, depicts the moderation role of age on the relationship, under this research H2a and H3a have significant relationships with age as a moderator while other moderators did not show any relationship. In the current study, there is a significant relationship among all parameters, and there is a medium to strong correlation between diverse variables. The study showed that CPEC will have positive and strong impacts on the economic development of both countries and public of Pakistan consider it can open many doors for them for education, jobs and tourism. This study can help policy makers to make policy to make more effective policy in this are for sustainable development.

Environmental concern in the era of digital fiscal inclusion: The evolving role of human capital and ICT in China.

To achieve environmental sustainability, the role of human capital and financial inclusion has been debated in limited empirical studies. Employing a reliable ARDL model approach, this study examines the dynamic link between human capital and ICT, financial inclusion, and CO2 emissions using the China economy dataset over the period 1998-2020. The vivacious side of human capital shows that literacy rate and average year of schooling curb CO2 emissions in long run. The results of human capital are also based on facts in magnitude as well as in direction. Also, empirics unfold that digital financial inclusion significantly increases CO2 emissions. Based on these novel findings, a wide set of economic policies are repaired for environmental quality. Environmental education should be considered at early levels of education. The authorities and policymakers should fix energy-related issues through education. The China government should stimulate the educational sector to conduct a clean and green revolution that acts as a mechanism for a green and clean economy. This study's finding is more effective than the previous unlike empirical studies for policy-making because of the advanced econometric method.

Examining the effects of information and communications technology on green growth and environmental performance, socio-economic and environmental cost of technology generation: A pathway toward environment sustainability.

Human capital and ICT have a significant role in determining human development. The impacts of ICT and human capital on green growth and environmental sustainability should be explored for sustainable economic development. This research contributes to the literature on the role of ICTs and human capital in the determination of green growth and environmental performance. Based on time-series data 1990-2019, the study intends to investigate the impact of ICTs and human capital on environmental and green growth performance for China. The study reports that ICTs tend to reduce CO2 emissions and improve green growth in the long-run. However, education reduces CO2 emissions in the long-run but does not produce any significant impact on green growth in the long-run. It is suggested that government should invest in environmental efficiency and environmental technologies simultaneously with human capital that could significantly contribute to pollution reduction. Lastly, policies to increase human capital should be implemented simultaneously with policies to promote ICTs contribution in order to confirm green growth and environmental protection.

A PLS-SEM Analysis to Check Public Willingness to Use Water from Filtration Plants Installed by Public Own Organizations; a Study of Psychological Behavior Toward Sustainable Development.

Introduction: Water is the basic need of human beings and there is no substitute for water. This research was conducted to analyze public willingness to use water from filtration plants installed by public own Organizations and which psychological factors may help them to decide to use safe water. Methods: There ought to be some nudges in the right direction to encourage people to use water filters. A questionnaire was adopted from the literature for this research. PLS-SEM is used to evaluate data to check the connection among variables since it is the most recommended way for assessing and predicting explained variables to account for the largest latent variance, and PLS-SEM is one of the most effective approaches for predicting outcomes. Results: PLS-SEM direct path analysis revealed that hypotheses H1-H6 were accepted and the gender and levels of education of the respondents as control variables, gender and education showed insignificant relationships with WUFWFP. Age showed a substantial relationship as a moderator variable between WQK*Age, and HC*Age with a willingness to use filter water from filtration plants=WUFWFP, while the rest of the variables showed an insignificant relationship. Discussion: As per our results, H1a and H3a have a significant relationship as moderator variables between IV and DV, while H2a, H4a, H5a, and H6a did not show any relationship as moderator variables between IV and DV. Hence H1a and H3a were accepted while the rest of the moderator was rejected under this research. This study would be helpful for the decision-makers and planners to take proper sustainable measures to address the repercussions of urbanization and land use changes to protect the ecosystem.

Analysis of factors influencing public employees' work cognition under a public health crisis: A survey of China's response to COVID-19.

The work cognition of public employees lays importance on tackling an escalating health crisis situation. At the micro-level and macro-level, different factors contribute to different degrees of public employees' work cognition. However, there are limited studies examining the work cognition of public employees and its influencing factors, particularly in situations such as a public health crisis. Our research takes China's response to COVID-19 as an example. The data have been taken from six Chinese provinces, Hunan, Hubei, Jiangsu, Shanxi, Henan, and Shandong, through a total of 738 questionnaires and telephonic interviews. Furthermore, this study used a logistic multiple regression model to analyze the factors that influenced the public employees' work cognition when working under a public health crisis. The results of our study showed that at a micro-level, the educational background, attitudes, and actions (initiative, responsibility, administrative capacity, and timeliness of feedback), and their level of concern with work influenced the work cognition of the public employees. At a macro-level, we found that it was the anti-epidemic measures that most influenced public employees' work cognition. Our findings provide important policy implications for emergency preparedness and handling of major emergencies, and have important reference value for the management of public employees and the improvement of national governance capabilities under similar major challenges in the future.

A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods; A way forward to sustainable agriculture.

The present study was conducted in one of the major agriculture areas to check farmers indigenous knowledge about the impacts of floods on their farming lives, food security, sustainable development, and risk assessment. In the current study, primary data was used to analyze the situation. A semi-structured questionnaire was distributed among farmers. We have collected a cross-sectional dataset and applied the PLS-SEM dual-stage hybrid model to test the proposed hypotheses and rank the social, economic, and technological factors according to their normalized importance. Results revealed that farmers' knowledge associated with adaption strategies, food security, risk assessment, and livelihood assets are the most significant predictors. Farmers need to have sufficient knowledge about floods, and it can help them to adopt proper measurements. A PLS-SEM dual-stage hybrid model was used to check the relationship among all variables, which showed a significant relationship among DV, IV, and control variables. PLS-SEM direct path analysis revealed that AS (b = -0.155; p 0.001), FS (b = 0.343; p 0.001), LA (b = 0.273; p 0.001), RA (b = 0.147; p 0.006), and for FKF have statistically significant values of beta, while SD (b = -0.079NS) is not significant. These results offer support to hypotheses H1 through H4 and H5 being rejected. On the other hand, age does not have any relationship with farmers' knowledge of floods. Our study results have important policy suggestions for governments and other stakeholders to consider in order to make useful policies for the ecosystem. The study will aid in the implementation of effective monitoring and public policies to promote integrated and sustainable development, as well as how to minimize the impacts of floods on farmers' lives and save the ecosystem and food.