Nexus of Globalization and Environmental Quality: Investigating Heterogeneous Effects through Quantile Regression Analysis

Purpose – This paper aims to investigate whether there is heterogeneity in the relationship between the bank loan interest rate and its determinants using the quantile regression method and to reconcile some conflicting findings in prior literature. Design/methodology/approach – First, the effects of 18 determinants were examined on the bank loan interest rate using the ordinary least squares method (OLS). Second, it was investigated whether the relationship between the loan rate and its determinants is heterogeneous across quantiles of loan rates using the quantile regression method. Findings – Considerable heterogeneity was found in the relationship between the loan rate and its determinants. Specifically, a determinant that is beneficial for the bank loan rate, on average, as revealed by the OLS method may become unimportant or even detrimental for firms located at extremely high or low loan rate quantiles. By revealing extreme heterogeneity in the relationship between the loan rate and some of its determinants, the authors potentially explain two conflicting findings in prior literature. Originality/value – The conventional OLS method masks the heterogeneity in the relationship between the bank loan interest rate and its determinants. Quantile regression can be used to supplement the OLS estimates to gain a more detailed and complete picture of the relationship between the dependent variable and explanatory variables.

Improving environmental quality is essential for achieving sustainable economic development when nations pursue growth. Although previous studies looked into different factors of sustainability, the precise effects of natural resource rents as well as renewable energy on CO2 emissions are yet to be studied in depth. This dissertation attempts to fill the gap by looking at the relationship between economic growth, natural resource rents, renewable energy, and the level of financial development with the environmental quality in eleven regions of emerged and developing economies over the time period of 1990 to 2022. The findings from the Pedroni cointegration analysis reveal a long-run association among financial development, renewable energy, natural resource rents, economic growth, and carbon emissions. Further analysis using the method of moments quantile regression (MMQREG) indicates that renewable energy and natural resource rents significantly reduce CO2 emissions, particularly at higher quantiles, enhancing environmental quality. Conversely, financial development exacerbates CO2 emissions, negatively affecting environmental sustainability. Economic growth demonstrates a nonsignificant negative relationship with carbon emissions. The study highlights the critical contributions of renewable energy and natural resource rents to improving environmental quality, while emphasizing the adverse environmental effects of financial development. Policymakers are encouraged to prioritize investments in renewable energy and the effective management of natural resources to mitigate carbon emissions and achieve sustainability in these economies.

Impacts of renewable energy, trade globalization, and technological innovation on environmental development in China: Evidence from various environmental indicators and novel quantile methods

Achieving environmental quality through stringent environmental policies: Comparative evidence from G7 countries by multiple environmental indicators

Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.

Particulate matter with diameter less than 10µm (PM10) data usually exhibit different variations as they include normal days and pollution days. This paper applied quantile regression (QR) technique to inspect the changing relationship between predictor variables and PM10 concentrations at Petaling Jaya monitoring station in the year 2014 over different PM10 distributions. For comparative purpose, multiple linear regression (MLR) using ordinary least squares (OLS) estimation approach was also performed. The QR analysis results showed that the interrelationship between predictor variables and PM10 was not consistent across the PM10 quantile distributions and hence, proved discordancy with MLR estimates. The lagged PM10 concentration was the only important factor throughout the quantile distributions of PM10. It was found that the effects of lagged PM10, temperature, carbon monoxide (CO) increased from low to high quantile distributions, while the effects of lagged humidity, east-west wind component, wind speed and nitrogen monoxide (NO) showed the otherwise patterns. The lagged NO associated significantly with PM10 at low quantiles, whereas the lagged temperature and CO associated significantly at high quantiles only. Lagged humidity, east-west wind component and wind speed correlated significantly and negatively with PM10 at low and middle quantiles. Ozone (O3), however, had effect of changing nature from positive association at low PM10 distributions to negative association at high levels. Thus, QR is helpful to provide a more complete description of predictor variable effects on PM10 at different distributions, and may assist in PM10 management especially during haze periods.

Does an environmental stringent policy really matter to achieve environmental sustainability in BRICS-T region? Evidence from novel method of moments quantile regression approach

Financial inclusion and economic uncertainty in developing countries: The role of digitalisation

PurposeThe purpose of this examine the impact of income inequality and shadow economy on environmental degradation given the growing income inequality, shadow economy and ecological degradation in developing countries. Thus, this study is motivated to offer empirical insight into how income inequality and shadow economy influence the environment in African countries.Design/methodology/approachData from 29 countries in Africa between 2000 and 2017 were used, while the novel method of moments quantile regression of Machado and Silva (2019) and Dumitrescu and Hurlin (D-H) (2012) granger causality is used as the estimation techniques.FindingsThe results established the presence of cross-sectional dependence and slope heterogeneity in the panel, while Westerlund panel cointegration confirmed the long-run cointegration among the variables. The results from the quantile regression suggest that income inequality increases environmental degradation from the 5th to the 30th quantiles, while from the 70th quantiles, income inequality reduces ecological degradation. The shadow economy negatively influences environmental degradation across the quantiles, strengthening environmental quality. Per capita income (economic growth) and financial development positively impact environmental degradation throughout the quantiles. However, urbanization reduces environmental degradation from 60th to 95th quantiles. The D-H causality established a two-way relationship between income inequality and environmental degradation, while one-way from shadow economy, per capita income and urbanization to environmental degradation were established.Originality/valueThis study provides fresh insights into the nexus between shadow economy and environmental quality in the presence of higher levels of income inequality for the case of African region. The study applies quantile analysis via moment proposed by Machado and Silva (2019). This technique shows that the impact of income inequality and shadow economy on environmental degradation is heterogeneous across the quantiles of ecological footprints in Africa.

How digitalization, renewable energy, and natural resources shape environmental excellence? Evidence from China using a Quantile-on-Quantile framework

The EAGLE economies face persistent structural challenges such as energy inefficiency, weak institutions, and environmental degradation, alongside heightened vulnerability to global shocks and climate change. These barriers slow progress of EAGLE economies toward the achievement of the Sustainable Development Goals (SDGs). This study investigates how energy efficiency (EEI), Production‐based CO2 Productivity (CP), environment‐related technologies (GDT), and institutional quality (IQI) shape SD in these economies. Using econometric techniques, including the Method of Moments Quantile Regression (MMQR), the study ensures robust estimation. For robustness checks, it applies Bootstrap Quantile Regression, DOLS, FMOLS, and CCR methods. To examine the direction of the relationship, the Dumitrescu–Hurlin panel causality test is employed. The empirical results indicate that EEI, CP, and IQI exert a significant and positive influence on SD performance, thereby supporting progress toward the SDGs. In contrast, GDT exhibits heterogeneous effects across quantiles, showing negative impacts at the lower quantiles but positive and supportive effects at higher quantiles. From a policy perspective, governments should promote cleaner energy transitions, support innovation in green technologies, and strengthen regulatory frameworks to ensure effective environmental governance. Improving institutional capacity and transparency are also essential for successful policy implementation. Additionally, expanding international cooperation, climate finance, and investments in research and education can accelerate the transition toward a low‐carbon economy and support progress toward global sustainability goals.

The 2011 heat wave in Greater Houston: Effects of land use on temperature

In this paper, we examine whether geopolitical risk influences environmental degradation, while controlling for non-renewable energy consumption, economic growth and trade openness, using a quarterly dataset from 1985Q1 to 2019Q4. The choice of India as a case study is based on a number of reasons. India is a developing country, which produces approximately 3.2% of global GDP. Also, India produces almost 17.7% of the world population. The country also emits about 6.8% of global carbon emissions, and according to the 2020 report of the consulting firm Eurasia, India is ranked fifth in terms of geopolitical risk. This study adds to the existing literature by using the quantile-on-quantile (QQR) regression to examine the effect of geopolitical risk on environmental degradation, as well as highlighting the implications of geopolitical risk on environmental sustainability. Based on empirical estimation, we find that geopolitical risk increases and decreases carbon emissions in India. That is, geopolitical risk increases environmental degradation at middle quantiles and decreases environmental degradation at lower and higher quantiles. In addition, we find that non-renewable energy consumption, economic growth and trade openness impede environmental quality in India. Thus, we are of the opinion that policymakers, when making policy decisions on environmental quality, should factor in geopolitical risk in two areas, mitigation and channel of escalation, among other policy suggestions.

The potential of digital trade (DTE) and energy justice (EJ) to support environmental quality requires in-depth exploration because the environmental gains of DTE can be compromised in economies where EJ is not ensured. This study explores the environmental effects of DTE and EJ on ecological footprint (EF) utilizing panel data from 147 economies from 2005 to 2023. The empirical results are estimated using panel data estimators namely fixed effects, random effects, the system generalized method of moments (GMM) and the method of moments quantile regression (MMQR). The GMM estimates suggest that DTE exerts a positive and significant influence on the environment. However, the MMQR results demonstrate that the effect of DTE on EF varies in sign and magnitude across quantiles. The positive effect is observed in median and higher quantiles while the negative effect is noted in lower quantiles. Finally, the results suggest a moderating role of EJ in the environmental effect of DTE. That is, the interactive effect of DT with EJ exerts a significantly positive effect on environmental quality. This finding suggests that ensuring EJ in home economies strengthens the environmental benefits of digital trade integration. These findings offer important policy implications for advancing digitalization strategies along with ensuring EJ to promote global environmental sustainability.

This study, with its significant findings, delves into the impact of women's status on environmental quality in BRICS economies (Brazil, Russia, India, China, South Africa) from 1960 to 2022. Using a novel method, the Method of Moments Quantile Regression, this study has been able to analyse the relationship. The results, which are of utmost importance, show that women's political empowerment and leadership positions (government chief executives) significantly reduce carbon emissions, while the impact of women's civil liberties along with population growth increases emissions across the analysed quantiles. Economic growth is insignificantly negatively associated with environmental quality. The paper's findings reveal a unidirectional causal relationship from women's political empowerment to emissions, carbon emissions to women's civil liberties and emissions to economic growth. Additionally, a bi-directional causality connection is evident between population growth and environmental quality. These insights, which are crucial for policymaking, suggest that promoting improved women's status is a crucial policy strategy for mitigating climate change in BRICS economies. Thus, this paper suggests that empowerment of women is an effective strategy for reducing carbon emissions. It emphasizes the need for climate policy to promote gender equality, prioritise women's leadership in the clean energy industry, and enhance their access to resources and opportunities.