Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.
This paper presents a fresh understanding of the vigorous connection between inward FDI, renewable energy consumption, economic growth and carbon emission in the Chinese economy employing novel Morlet wavelet analysis. Wavelet correlation, continuous wavelet transform and partial and the multiple wavelet coherence analyses are applied on variables under study for data acquired during the period 1979 to 2017. The outcome of these analyses reveals that the connections among the variables progress over frequency and time. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages, although time domain reveals inconsistent associations among the considered variables. The wavelet analysis according to economic point of view supports that inward foreign direct investment (FDI) and renewable energy consumption help to enhance economic condition in Chinese economy. The results also suggested that inward FDI enhances the environmental degradation in medium and long run in China. The results emphasize the significance of having organized strategies by the policymakers to cope with huge environmental degradation occurred for a couple of decades in China.
Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly embracing a sustainable financing mechanism via green bond financing. Green bonds have attracted the attention of the industrial sector and policymakers, however, the impact of green bond financing on environmental and social sustainability has not been confirmed. There is no empirical evidence on how this financial product can contribute to achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact of green bond financing on environmental and social sustainability by considering the S&P 500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index, from October 1, 2010 to 31st July 2020 using a combination of Quantile-on-Quantile Regression and Wavelet Multiscale Decomposition approaches. Our results reveal that green financing mechanisms might have gradual negative transformational impacts on environmental and social responsibility. Furthermore, we attempt to design a policy framework to address the relevant SDG objectives.
In this paper, we analyze the connectedness between the recent spread of COVID-19, oil price volatility shock, the stock market, geopolitical risk and economic policy uncertainty in the US within a time-frequency framework. The coherence wavelet method and the wavelet-based Granger causality tests applied to US recent daily data unveil the unprecedented impact of COVID-19 and oil price shocks on the geopolitical risk levels, economic policy uncertainty and stock market volatility over the low frequency bands. The effect of the COVID-19 on the geopolitical risk substantially higher than on the US economic uncertainty. The COVID-19 risk is perceived differently over the short and the long-run and may be firstly viewed as an economic crisis. Our study offers several urgent prominent implications and endorsements for policymakers and asset managers.
The aim of this study is to identify and highlight the positive and negative indirect environmental impacts of COVID-19, with a particular focus on the most affected economies (USA, China, Spain, and Italy). In this respect, the empirical and theoretical dimensions of the contents of those impacts are analyzed. Research findings reveal a significant relationship between contingency actions and positive indirect impacts such as air quality improvements, clean beaches, and the decline in environmental noise. Besides, negative indirect impacts also exist, such as the rise in waste level and curtailment in recycling, further threatening the physical spaces (land and water), besides air. It is expected that global businesses will revive in the near future (though slowly), but the reduction in greenhouse gas emissions during this short time span is not a sustainable way of environmental mitigation. Thus, long-term mitigation policies should be strengthened to cope with the undesirable deterioration of the environment. Research findings provide an up-to-date glimpse of the pandemic from the perspectives of current and future indirect environmental impacts and the post-pandemic situation. Finally, it is suggested to invent and prepare action plans to induce a sustainable economic and environmental future in the post-pandemic world scenario.
The aim of this research is to explore the association between financial development, research and development (R&D) expenditures, globalization, institutional quality, and energy consumption in India by using the quarterly data of 1995-2018. Quantile Autoregressive Distributed Lag (QARDL) approach is employed to examine the relationship. An application of the QARDL approach suggests that the R&D, financial development, globalization, and institutional quality significantly influence energy utilization in India. R&D and institutional quality have a negative effect on energy utilization which shows that due to the increase in the quality of institutions and R&D in the country, energy utilization is likely to decrease. However, globalization and financial performance have a positive influence on energy which depicts that due to the increase in financial performance and globalization in India the energy consumption is likely to increase. According to the outcomes of this research, India should make a policy to ease the penalties of energy utilization by monitoring resource transfer by means of globalization and by implementing energy conversation procedures through the advancement of the financial sector.
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).
Tourism is one of the important factors that can affect the environmental and economic situation of any economy. This study investigates the relationship between tourist arrivals and CO2 emission in the top 20 tourist destinations using data from quarterly observations from 1995 to 2018. A unique technique via quantile-on-quantile regression and Granger causality in quantiles was used. In particular, how the quantiles of tourist arrivals impact quantiles of CO2 emission was analyzed. The empirical results suggest a combination of both positive and negative effects of tourist arrivals and CO2 emission in most tourist destinations. Predominantly, at both high and low tails, in the USA, Spain, Hong Kong, and Austria, tourist arrival has a positive effect on CO2 emission, whereas in the case of Canada, France, Germany, Mexico, and Malaysia, the association was negative. On the other hand, China, Greece, Russia, Japan, Italy, South Korea, Thailand, and Turkey have both positive and negative effects of tourism on CO2 emissions at low and high tails. Tourism can be an important factor while formulating policy for environmental and climate aspects.
Caseous lymphadenitis (CLA) caused by Corynebacterium pseudotuberculosis is characterized by the development of abscesses, mainly in superficial and internal lymph nodes, visceral and reproductive organs in small ruminants. This study aims to examine the histopathological changes in reproductive organs of goats immunized with killed vaccine of C. pseudotuberculosis. In this study, twenty four (24) clinically healthy bucks and does were divided into four groups A, B, C and D. Animals in groups A and B were immunized with 0.5 and 1% formalin killed vaccine, respectively; followed by a booster dose. After the booster dose of immunization, groups A, B and C were challenged with C. pseudotuberculosis at 106 cfu/ml. Goats in group D were immunize and unchallenged and left as control group. All C. pseudotuberculosis infected animals were euthanized humanely 12 weeks post-challenged. Tissue samples such as testes, epididymis, spermatic cord, penis, pituitary gland, mammary gland, vulva, vagina, cervix, uterus, fallopian tube and ovaries were collected for histopathology study. Microscopic examination of all tissues (testes, seminiferous tubules, spermatic cord, penile tissues and the pituitary gland) in the male reproductive organs of the bucks that were inoculated with 2 ml of 0.5% and 1.0% of C. pseudotuberculosis killed vaccine showed normal (animals inoculated with 1.0%) to mild (animals inoculated with 0.5%) histopathological changes when compared with those from group C which showed varying degrees of histopathological changes (p
The aim of this paper is to examine immigration and environmental degradation using bibliometric analysis. This paper also analyzes sources of publication, authorship, citations, distributions publications and other bibliometric indicators. The study focuses on a total of 1372 articles published from 2000 to 2020. These articles were collected through an automated process from the Scopus database and later analyzed using techniques such as bibliometric indicators analysis, VOSviewer, and Perish or Publish. The research identified 991 articles from varieties of published sources. The topic of immigrants and environmental degradation has been an emerging topic since 1981. Starting in 2000, most of the scholars actively producing an articles pertinent to this topic. Most of the articles were published in journals, and English is the primary language of research. United States is the leading country in contributing the publications. Meanwhile, the most significant fields in which the sources were produced were environmental science, agricultural and biological sciences, arts and humanities and earth and planetary sciences. However, some limitations has been found. It has been suggested for future research, to lengthen this work to other databases, as well as bibliometric analyses of immigration and environmental degradation in developed and developing countries by adding a new keyword such as energy consumption and climate change. This paper aims to assess recent trends in the expansion of academic literature on immigration and environmental degradation using the bibliometric analysis method. Network visualization and bibliometric indicators are used in this paper to present the results.
The determinants of environmental degradation have been investigated many times by utilizing carbon dioxide emissions and/or ecological footprint. However, these traditional environmental degradation indicators do not consider the supply side of environmental problems. Therefore, this study focuses on the dynamic influence of financial development, energy efficiency, economic growth, and technological innovation on environmental degradation in India through the load capacity factor, including both the supply and demand sides of environmental problems. For that purpose, the recently developed dynamically simulated autoregressive distributed lag (ARDL) method is employed using the annual time-series data extending from 1980-2020. The dynamically simulated ARDL results demonstrate that financial development, economic growth, and technological innovation have a dynamic adverse impact on the load capacity factor, whereas energy efficiency has a positive dynamic influence on environmental quality. In addition, the results support the validity of the environmental Kuznets curve hypothesis as the negative effect of economic growth on environmental quality decreases over time. Based on the study findings, policy recommendations are provided for India. Finally, this study utilizing load capacity factor as an indicator for environmental quality will provide new topics in exploring the determinants of environmental degradation.
The present paper implements the quantile autoregressive lagged (QARDL) approach of Cho et al. (2015) and the Granger causality in quantiles tests of Troster et al. (2018) to explore the nonlinear effects of US energy consumption, economic growth, and tourist arrivals on carbon dioxide (CO2) emission. Our results unveil the existence of substantial reversion to the long-run equilibrium connectedness between the variables of interest and CO2 emissions. The outcomes show that tourist arrivals decrease CO2 emissions in the long term for each quantile. In addition, we found that the output growth positively influences the carbon emissions at lower quantiles but negatively influences the carbon emissions at upper quantiles. Moreover, our findings of short-term dynamics validate an asymmetric short-run effect of tourist arrivals and economic growth on CO2 emissions in the US economy. Further results and their corresponding policy implications are discussed.
Energy is one of the prime factors in influencing the sustainable development of a country. Different energy sources play important roles in driving the income growth of different economic sectors such as industrial, agricultural, and services. Fossil fuels, however, have come under strong criticism for actively accelerating climate change. As such, it is imperative to investigate the contributions of various energy sources toward sustainable growth. With Malaysia as the test-bed, the present study analyzes the impact of energy prices on economic stability using the novel wavelet-based analysis. Specifically, the study analyzed the impact of crude oil, natural gas, and gasoline prices on the economic (brown) and green growth from 1995 to 2020. The results show that in continuous wavelet transform, the cone of influence of all five factors exhibits strong short-run variance and fluctuations from 2005 to 2013. However, the intensity of brown growth is more influential than green growth. Similarly, in wavelet coherence graphs, the downward right arrows indicate positively significant associations between crude oil prices, natural gas prices, and gasoline prices with brown and green growth. Additionally, wavelet-based Granger causality reveals a bidirectional causal relationship between all variables. The results thus strongly suggest that energy prices predominantly affect the economic (brown) and green growth progression of the Malaysian economy. The study concludes with some suggested implications to augment the country's sustainable growth.
A key objective of renewable energy development in the USA is to reduce CO2 emissions by decreasing reliance on fossil fuels in the coming decades. Using quantile-on-quantile regressions, this research examines the relationship between disaggregated sources of renewable energy (biomass, biofuel, geothermal, hydroelectric, solar, wind, wood, and waste) and CO2 emissions in the USA during the period from 1995 to 2017. Our findings support the deployment of various types of renewables in combating CO2 emissions for each quantile. In particular, a negative effect of renewable energy consumption on CO2 emissions is observed for the lower quantiles in almost all types of renewables. The effect of all the renewable energy sources taken together is significant for the lower and upper quantiles of the provisional distribution of CO2 emissions. The effect of renewable energy becomes stronger and more significant in the middle quantiles, where a pronounced causal effect of return and volatility is detected for the lower and upper middle quantiles. At the same time, heterogeneity in the findings across various types of renewable energy sources reveals differences in the relative importance of each type within the energy sector taken as a whole. Future US initiatives in renewable energy deployment at both the federal and the state levels should take into consideration the relative importance of each type, so as to maximize the efficacy of renewable energy policies in combating CO2 emissions.
This paper assesses the Environmental Kuznets curve based on quantile behavior of the relationship between economic growth, forest area, agriculture production, renewable energy, and environmental degradation. The current literature generally used a single indicator to address environmental issues; however single indicator neither measures overall environmental conditions nor does specify that the environment issue is generally diminishing. Our study is the first one that used ecological footprint (EF) as an indicator to test environmental Kuznets curve (EKC) hypothesis for Pakistan by employing recent approach of quantile autoregressive distributed lag (QARDL) initiated by Cho et al. (J Econ 188(1):281-300, 2015). The result of this study validates the EKC hypothesis for Pakistan and shows quantile-dependent relationship, and in that case, using the conventional methods may somewhat lead to biased results. Moreover, the rejection of the null hypothesis of parameter constancy is also confirmed by Wald test. In the long run, the findings of renewable energy consumption and forest area show significant negative effects on ecological footprints, which indicates that by increasing renewable energy usage and forest area, ecological footprints can be minimized. Interestingly, the short-term effects of agricultural production findings on EF show statistically negative results. This illustrates that EF can also be reduced in the agriculture sector by adopting environment-friendly technologies. In order to create efficient policies for environment deterioration, the empirical findings of the current analysis can be used as a guideline for policy implications.
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.
Being a prominent tourist destination, the hotel industry's demand in Malaysia has been increasing day by day. There is still a shortage of studies focusing on how hotels can make environmental management routine work, take environmental laws seriously and be more responsive to the environment. This study focused on the connections between green employee involvement, green performance management and green dynamic capability in implementing environmental law in the hospitality industry. The study also evaluates the mediating role of implementing environmental law between green employee involvement, green performance management, green dynamic capability and organisational citizenship behaviour to reduce pollution. This study employed a quantitative approach to test the hypotheses and a convenient sampling method to collect the data from hotel employees. Out of 600 distributed questionnaires, useable responses were 253 to proceed with data analysis. Data were analysed through structural equation modelling (SEM) using the Smart-PLS and SPSS. The relationship between green employee involvement, green performance management, green dynamic capability and implementation of environmental laws was discovered and considered unique in the hotel industry in Malaysia. The study further established the mediating role of environmental law between independent and dependent variables.
Globalization persists the tendency to alter numerous aspects of today's world including religion, transport, language, living styles, and international relations; however, its potential to influence quality of environment is the prime concern for trade and environmental policies guidelines (Audi and Ali 2018). In response to the growing interest for identifying the dynamic relationship between globalization and environmental performance, the present study seeks to investigate the critical link between globalization and ecological footprints in top 15 globalized countries between 1970 and 2016. Applying the novel methods of quantile-on-quantile regression (QQ) and Granger causality in quantiles, the findings examine the manners in which quantiles of globalization affect the quantiles of ecological footprints and vice versa. The empirical results suggest that globalization has a long-term positive effect on ecological footprint and vice versa in case of Belgium, the Netherlands, Sweden, Switzerland, Denmark, Norway, Canada, and Portugal. On the other hand, the estimated results indicate a negative effect between globalization and ecological footprint in the case of France, Germany, the UK, and Hungary. These results extend the recent findings on the globalization-environment nexus implying that the magnitude of relationship among both variables varies with countries demanding individual focus and cautions for postulating environmental and trade policies.
A country's financing system is essential in addressing sustainable development requirements. National sources and international financial flows contribute to economic growth and environmental quality in many ways, and their impact can be critical. This paper applied panel data analysis using a comparative approach of Pooled Mean Group Auto Regressive Distribute Lags (PMG-ARDL) and Cross Sectionally ARDL (CS-ARDL) to estimate the effects of FDI, renewable energy, and remittance on environmental quality in the top remittance-receiving countries, during 2000-2021. The study emphasized the positive relationship between FDI and carbon emissions. Moreover, renewable energy and remittances revealed an inverted U-shaped relationship with carbon emissions. In the case of developing countries from the panel, remittance improves environmental quality after reaching the threshold. Moreover, for some of the developing countries included in the panel, we found that they do not achieve the desired carbon mitigation effect in their early stages of renewable energy implementation. However, renewable energy becomes a key factor for tackling environmental pollution after a certain threshold. The mixed results determined diverse policy recommendations for various stakeholders.