Impact of corporate motives for sustainable sourcing: key moderating role of regulatory pressure.

Organizational decisions and their motivations are crucial for successfully implementing sustainable sourcing practices (SSP). Still, there is scant research on how SSPs are impacted by corporate motives (CM). To fill this research gap, we formed a three-tiered stakeholder theory (ST) based paradigm that accounts for the moderating impact of regulatory pressure (RP) while examining the relationship between different types of corporate motives (instrumental, relational, and moral) and SSP. Partial least squares structural equation modeling (PLS-SEM) was used to examine data collected from 248 respondents in the Pakistani manufacturing industry. The outputs of SEM disclosed that all CMs affect SSP. RP also confoundedly moderated these targeted relationships. Importance performance map analysis (IPMA) showed that regulatory pressure (0.319) and relational motives (67.38) are more important and perform better than all other exogenous variables. This study sheds light on corporate strategies and decision-making in multi ways. All dimensions of CM greatly enhance SSP directly and through RP, as RP firmly moderates these associations, indicating the relevance of ST. Finally, this empirical investigation ends with a framework of testable assertions and many future research endeavors on environmental sustainability.

What drives the green transformation of enterprises? A case of carbon emissions trading pilot policy in China.

As an important policy instrument to achieve greenhouse gas emission reduction, carbon emissions trading has also promoted the green transformation of enterprises while achieving carbon reduction targets. This study uses the implementation of the Chinese carbon emissions trading pilot policy (CETPP) as a quasi-natural experiment and analyzes the impacts of the CETPP on the green transformation of enterprises with the difference-in-differences (DID) method based on a sample of 297 listed Chinese A-share high-energy-consuming enterprises. The result findings show that CETPP can significantly promote the green transformation of enterprises. The heterogeneity analysis also reveals that CETPP has differential effects on enterprises belonging to different industries, which is caused by the fact that enterprises in different industries differ significantly in their green transformation paths and modes. Moreover, CETPP has a significant facilitating effect on the green transformation of non-state-owned enterprises compared to state-owned enterprises. Finally, marketization and enterprise social responsibility are two major mechanisms for the CETPP to promote the green transformation of enterprises. Our findings reveal that policymakers should further deepen the dynamic management of carbon emission allowances and guide enterprises to actively undertake social responsibility, thus leveraging the market regulation mechanism to promote the green transformation of enterprises.

When will China's industrial carbon emissions peak? Evidence from machine learning.

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP's neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

Does green finance improve carbon emission efficiency? Experimental evidence from China.

As a noteworthy initiative of financial supply-side reform to precisely support the green development system, can green finance (GF) help achieve the dual goals of "carbon peaking" and "carbon neutrality"? Using data from China's provincial panel between 2007 and 2019, this paper measured the green finance index by the entropy method and the carbon emission efficiency (CEE) with carbon emission as the non-desired output by the Super-SBM model. Then, the influence of GF on CEE was empirically investigated by the dynamic panel model and the spatial Durbin model. The findings show that GF can significantly improve CEE and has a positive spillover impact on CEE in provinces with close economic ties; the upgrading of the industrial structure is a key mediator in the transmission of GF to CEE; and regional heterogeneity analysis finds that GF notably improves CEE in eastern, high development levels of economic and GF regions. The research can offer some theoretical and empirical references for green finance to contribute to low-carbon economic growth.

Can new energy demonstration city policy reduce carbon emissions? A quasi-natural experiment from China.

Against achieving carbon peaking by 2030 and carbon neutrality by 2060 context in China, the new energy demonstration city policy (NEDCP) has a crucial function to perform in promoting resource utilization efficiency, building the green development policy system, and facilitating carbon emission reduction. However, existing research has rarely investigated the contribution of NEDCP on carbon reduction. To investigate the policy effect of NEDCP, the differences-in-differences (DID) model is introduced to quantify the influence of NEDCP on carbon reduction, taking a statistical sample of 285 Chinese cities over the period 2005-2017 on the basis of exploring the intrinsic mechanism of NEDCP on carbon emissions. The statistical results reveal that NEDCP significantly inhibits carbon emissions. NEDCP's dampening impact on carbon reduction is more pronounced in the eastern area but not in other areas. City size and resource endowment heterogeneity results suggest that NEDCP significantly inhibits the output of carbon emissions in non-resource-based and large cities but insignificantly in resource-based and small- and medium-sized cities. Finally, we conclude that policy-makers should not only broaden the scope of NEDCP implementation continuously but also design relevant policy combination tools following the basic characteristics of each city to provide institutional guarantees for achieving carbon emission reduction.

Asymmetric effects of fine particulate matter and stringency policy on COVID-19 intensity.

This study aims to examine the influence of environmental performance (PM2.5) on COVID-19 intensity . For this purpose, we employ the newly introduced Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag model. Results indicate the asymmetric linkages between PM2.5 and COVID-19 intensity, as the positive shock in PM2.5 raises the COVID-19 intensity by 21%, whereas the negative shock in PM2.5 decreases COVID-19 intensity by 12% in long-run. On the contrary, the positive shock in stringency measures decreases COVID-19 intensity by 42.8%, while the negative shock in stringency policy increases COVID-19 intensity by 66.7%. These findings imply that higher pollution increases the COVID-19 severity while higher stringency measures slow down people's movement and reduce COVID-19 intensity. However, a sudden negative shock in lockdown increases people's interaction, leading to a higher spread of the virus. These results suggest that governments should adopt stringent action plans to contain the transmissibility of COVID-19.

Influencing factors of consumers' buying intention of solar energy: a structural equation modeling approach.

Due to China's massive usage of fossil fuels, climate change concerns have become serious challenges to the country's sustainable development. Despite the fact that China has effectively employed solar technology to address these problems, there is a paucity of research examining consumers' intention to adopt solar energy in the rural region of China. This study intends to fill this gap in the literature by studying consumers' buying intentions for solar energy in rural China for household purposes. Additionally, the study added to the theory of planned behavior by adding three new variables, namely, environmental knowledge, environmental concern, and beliefs about the benefits of solar energy. Primary data were collected from 847 respondents in Hebei Province using a comprehensive questionnaire survey. Structural equation modeling was employed to examine the data. Empirical results revealed that attitude, environmental knowledge, subjective norm, perceived behavioral control, and beliefs about the benefits of solar energy positively influence buying intention of solar energy. On the contrary, environmental concern had no significant effect on buying intention for solar energy. Study outcomes emphasize the critical significance of changing societal norms, boosting consumer awareness, redesigning regulatory mechanisms, and stressing the benefits provided by solar power through coherent and persistent efforts while simultaneously enhancing environmental sustainability practices.

Modeling factors of biogas technology adoption: a roadmap towards environmental sustainability and green revolution.

In a developing country such as Pakistan, adopting biogas technology is a complicated process. The government has taken several steps to address energy issues by increasing biogas facilities. This research seeks to identify the major barriers to the deployment of biogas plants. Respondents were selected using the snowball sampling method. As a result, 79 adopters of biogas plants participated. Utilizing a structured questionnaire, primary data were collected. Hypotheses were evaluated using partial least squares structural equation modeling (PLS-SEM). Study results demonstrate that all influencing factors are favorably associated with implementing biogas technology, minimizing energy crises, and achieving cost-cutting objectives. In addition, the findings show that properly reducing economic and governmental barriers, encourage farmers to use biogas plants productively and substantially. To build biogas facilities, the government should adopt an economic strategy, owner training, day-to-day operations, and professional technical assistance.

Integration of renewable energy and technological innovation in realizing environmental sustainability: the role of human capital in EKC framework.

For the purpose of this study, the role of technological innovation is examined. Few studies have examined empirically and theoretically the relationship between technological innovation and ecological footprint in conjunction with other factors, such as the human capital index and renewable energy sources, such as biofuels and nuclear power. This study examines the impact of technological innovation on G-7 countries' ecological footprints from 1990 to 2020. A cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model is used in the study. The results of the study show that technological innovation minimizes the ecological footprint. A lower ecological footprint is also associated with increased usage of human capital and renewable energy. Depletion of the natural environment is a short-term and long-term consequence of increased GDP growth. Our results confirm that ecologically sustainable technology enhances the quality of the environment. Consistent panel causality results were achieved. In the context of the G-7 countries, our study's results could support the idea that there are new policy ideas that could help achieve the Sustainable Development Goals (SDG 3, 4, 7, 8, 9, and 13).

The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications.

Small- and medium-sized enterprises (SMEs) in China have been hit hard by the coronavirus (COVID-19) outbreak, which has jeopardized their going out of business altogether. As a result, this research will shed light on the long-term impacts of COVID-19 lockdown on small businesses worldwide. The information was gathered through a survey questionnaire that 313 people completed. Analyzing the model was accomplished through the use of SEM in this investigation. Management and staff at SMEs worldwide provided the study's data sources. Research shows that COVID-19 has a significantly bad influence on profitability, operational, economic, and access to finance. In the study's findings, outside funding aids have played an important role in SMEs' skill to persist and succeed through technological novelty than in their real output. SME businesses, administrations, and policymakers need to understand the implications of this study's results.

Assessing the Role of Green Finance and Education as New Determinants to Mitigate Energy Poverty.

Energy poverty (EP) is a problem that affects developed and developing economies, and its mitigation is of great significance to social welfare. EP affects Latin American countries, and policymakers have recently attempted to address this issue, particularly in the aftermath of the recent economic crisis. It is essential to measure and evaluate EP to implement strategies and policies effectively. Using a panel quantile regression approach, we investigate the heterogeneous impact of green finance, renewable energy (RE), and energy efficiency (EE) on EP for 33 Latin American countries from 2000 to 2018. Furthermore, certain associated control variables are incorporated into our model to avoid an omitted variable bias. According to empirical results, the impact of independent variables on EP is heterogeneous. Specifically, green finance is an essential source of alleviating EP, and it has a significant positive effect across all quantiles, but it is especially strong in the middle quantiles. RE and EE significantly mitigate EP, with the strongest effects occurring at higher quantiles. By including green finance, RE, and EE as the main explanatory determinants of EP, the findings urge policymakers in Latin American countries to design a comprehensive energy conservation policy to minimize the effects of massive EP.

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach.

The study investigates the asymmetric effect of temperature, exchange rate, metals (rare metals and electrical conductors), and investor sentiments on solar stock price performance in China. The novel econometric techniques, i.e., QARDL (quantile autoregressive distributive lag) approach and Granger causality-in-quantiles to analyze the results. In both short- and long-run estimations, the findings suggest that rare metals (cadmium, germanium, indium, and selenium) and electrical conductors (silver, aluminum, and copper) have significant and positive linkage with solar energy stocks at different quantiles based on bullish, bearish, and normal market conditions. On the other hand, negative effects are found for temperature, RMB exchange rate, and investor sentiments in both the short- and long-run. In the short run, the effect of exchange rate varies across different quantiles but it confines to only lower quantiles (bearish market condition) in the longer run. Solar stocks are more prone to investor sentiments under higher quantiles (bullish market conditions). Lastly, we find that temperature is not merely a behavioral anomaly for the solar energy market as it spreads across middle quantiles (normal market conditions) in the longer run. The findings of Granger causality in quantiles further confirm the results of QARDL.

Firm characteristics, governance mechanisms, and ESG disclosure: how caring about sustainable concerns?

Environmental, social, and governance (ESG) pillars help determine the business organizations' sustainable business practices. Considering the same, this research examines the association between firm characteristics, governance mechanisms, and ESG for a sample of 564 firms from fifteen developed economies. For empirical analysis, ordinary least square, fixed effect, and random effect estimations techniques were applied using annual data from 2010 to 2019. The overall findings reveal that the governance structure of firms (board size, board independence, and cross-listing) play a significant role in ESG disclosure. Moreover, low corruption perception reflects higher ESG disclosure among the targeted firms. Additionally, firm characteristics; such as liquidity position shows a better reporting of ESG during the study period. When accounting for the ESG disclosure individually, the findings confirm the productive role of board size, current ratio, and low corruption towards environmental exposure. Lastly, the results demonstrate that board size and ESG disclosure promote better financial performance. These results offer valuable policy recommendations.

Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach.

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

Pathways to decarbonization in India: the role of environmentally friendly tourism development.

The paradigm of sustainable tourism policy implications aims to prioritize the decoupling association between tourism development and environmental deterioration. The study revisits the dynamic associations among carbon dioxide emissions, economic growth, international tourism, education, renewable energy consumption, and gross capital formation for the case of India through the lens of the environmental Kuznets curve hypothesis framework. The long-run dynamics among the variables confirm the inverted U-shaped environmental Kuznets curve hypothesis for India. The regression findings affirm that higher international tourist arrivals, renewable energy use, and gross capital formation curb emissions in the long run. Besides, the coefficient of the interaction term between tourist arrivals and capital formation is evidenced to be positive implying capital formation has not been conducive in the pathway of sustainable tourism practices. On the other hand, the negative coefficient of the interaction term between education index and renewable energy consumption unveils the importance of educational advancement in the pathway of renewable energy penetration to thrive environmental sustainability. This study concludes with some policy suggestions to be incorporated within the existing ecological and energy approaches that may aid India in practicing the smooth functioning of low-carbon tourism models.

Asymmetric and time-varying linkages between carbon emissions, globalization, natural resources and financial development in China.

In the real world, economic covariates follow asymmetric and time-varying patterns. Therefore, it is imperative to integrate these effects while estimating environmental and economic relationships. Although prevailing literature reveals various emissions-deriving and eliminating factors, however, there is a dearth of empirical evidence that estimates the asymmetric and time-varying effect of globalization, natural resources, and financial development from a multidimensional perspective in China. In doing so, we employ the nonlinear autoregressive distributed lag (NARDL) and cross-wavelet modeling framework to explore the long- and short-run nonlinear and time-variant association between globalization, natural resources, financial development, and carbon emissions from 1980 to 2017. The NARDL method has the benefit of discriminating the long-term and short-term asymmetric carbon emission responses due to a positive and negative shock in our primary variables of interest. Mainly, the findings of NARDL estimations confirm that positive shocks in globalization and financial developments have a significant positive impact on carbon emissions, whereas negative shock in natural resources has a significant positive impact on carbon emissions. Similarly, the outcomes of continuous wavelet transformation and wavelet transformation coherence confirm the causal linkages between covariates; however, this effect varies across different time and frequency domains. These results imply that environmental researchers should consider asymmetric transmission channels and time-frequency associations among variables to devise long-term sustainable policies.

Dynamic and causality linkages from transportation services and tourism development to economic growth and carbon emissions: New insights from Quantile ARDL approach.

The present study investigates the pathway toward sustainability in Malaysia while observing the effects of tourism and transportation services on economic growth and carbon emissions. The study applied the quantile autoregressive distributed lag approach during the period 1970-2018, along with Granger causality to explore dynamic and asymmetric causal associations between the proposed variables. The empirical outcomes indicate that the error correction parameters are significant across major quantiles, confirming the presence of steady-state equilibrium in the long run. The results demonstrate that tourism and transportation services contribute significantly to economic growth in the long run; however, their contribution varies at different quantiles. On the other hand, tourism and transportation services were found to mitigate carbon dioxide emissions mainly across higher emission quantiles, confirming the sustainability of the transportation and tourism sector in Malaysia. We also observe a bidirectional causality between model variables. These results suggest important policy implications. Integr Environ Assess Manag 2022;18:1272-1287. © 2021 SETAC.

The asymmetric effect eco-innovation and tourism towards carbon neutrality target in Turkey.

After the Paris Climate Conference (Conference of the Paris COP21), emerging countries also start progressing as the world has shown serious concern towards carbon neutrality targets. In doing this, the tourism and hospitality businesses have also emerged as an industry that has shown tremendous economic positive outputs and support to an economy with the lease inputs. However, tourism has also been reported to lead to increased environmental degradation as foreigners came to the host country, whereby ecological innovation emerged as a potential solution for eliminating the level of carbon emissions. In contrast, researchers are in agreement in terms of its beneficial relationship. Therefore, based on Turkey as the contextual gap and the application of quantile autoregressive distributed lag (QARDL) as the methodological contribution, the present study explores the relationship of tourism and ecological innovation within the framework of Environment Kuznets Curve (EKC) on the environment using two different proxies (carbon emissions and ecological footprint) spanning from 1995 to 2018. The results confirm the EKC curve whereby the parameter estimating tourism and ecological innovation on carbon emissions and ecological footprint, both of the integrations are found negative and statistically significant across most quantiles. Based on the findings, the study is accordingly concluded, followed by the managerial implications.