Panel Quantile Regression Model Research Articles

Can industrial intelligence promote carbon emission efficiency? --empirical research based on the Yangtze River Economic Belt

The rapid development of intelligent technology characterized by robots under the fourth scientific and technological revolution provides a favorable opportunity for the accurate decision-making of urban pollution control and the effective achievement of the goal of carbon emission reduction in China. This research uses the robot penetration rate as a representative and characteristic index of industrial intelligence development, based on panel data from 108 cities in the Yangtze River Economic Belt (YEB) from 2006 to 2020. It then uses panel quantile regression, spatial measurement, and threshold effect models to provide a more thorough theoretical and empirical discussion of the impact, mechanism, and spatial effect of industrial intelligence development on urban carbon emission efficiency (CEE). Urban CEE may be greatly increased with the use of industrial intelligence, and this finding remains true even after endogeneity and robustness tests are controlled; From an action mechanism perspective, industrial intelligence advances technology, optimizes industrial structure, and ultimately enhances regional CEE; There is a Matthew effect on the degree of development of carbon emission efficiency, and the impact of industrial intelligence on CEE is more pronounced in non-resource-based cities and the lower portions of YEB; Urban CEE increases positively with the spatial spillover impact of industrial intelligence development. The ability for regional sustainable development will be significantly increased if cross-regional cooperative prevention and control of environmental governance can be successfully achieved. This study verifies the enabling effect of industrial intelligence development on the improvement of urban CEE, and provides enlightenment for China to improve industrial intelligence development strategies and policies to achieve regional high-quality development.

Foreign direct investment and green innovation in China: An examination of quantile regression

This study employs panel data encompassing 31 provinces in China, spanning the period from 2010 to 2020, to investigate the distributional impact of foreign direct investment (FDI) on green innovation (GI). Employing a panel quantile regression model, the research finds the close association between FDI and GI, particularly considering the local GI foundation. Specifically, FDI plays a crucial and influential role in fostering GI within regions characterized by lower levels of GI. However, it does not have a significant impact on promoting GI in regions with higher levels of GI. Further analysis of regional heterogeneity demonstrates that in coastal areas, FDI exhibits a positive and significant impact on GI in provinces with a moderate level of GI. Conversely, in inland areas, FDI can only facilitate GI in provinces with a high level of GI. The research conclusion suggests that the government should enhance the intensity of FDI and implement tailored policies according to the specific characteristics of coastal and inland regions.

The effect of structural oil shocks on stock returns of Indian renewable energy companies across market conditions

Purpose This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions. Design/methodology/approach This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis. Findings In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles. Originality/value First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

Exploring the Impact of Women Governance on CO2 Emissions in the European Union and Central Asia

The study is novel to contribute to the literature investigating the relationship among CO2 emissions, women governance, economic development, fossil fuel energy consumption and renewable energy consumption, using a balanced panel dataset of 27 European Union and 4 Central Asian countries over the period 1996-2020. As econometric tools, panel quantile and threshold regression models are employed. Overall, the quantile results document that women governance help to mitigate climate change both in European Union and Central Asia. Moreover, threshold findings suggest that women governance negatively impact on CO2 emissions in European Union and Central Asia when economic growth is higher than 9.903 percent. Policy implications are proposed to enhance women governance in European Union and Central Asia.

Decoupling carbon emissions from the economy and low-carbon ecological city construction-based on 196 cities in China

In pursuit of carbon peaking and carbon neutrality, significant efforts have been directed towards economic, policy, and technical dimensions in China. This study mainly focuses on the decoupling of urban carbon emissions from economic development, and the low-carbon ecological cities (LCEC) construction, aiming at the early realization of high-quality development of urban carbon peaking and carbon neutrality goals. We firstly calculated the Tapio decoupling index of economic development and carbon emission, and the construction level of low carbon ecological cities in 196 cities, then constructed a panel quantile regression model to empirically analyze the main influencing factors of the LCEC construction, and finally predicted the LCEC construction levels in 2030 by using a gray prediction model. The research results show that (1) the majority of the cities achieved carbon emissions decoupling between 2005 and 2019, although a few cities in the Northeast still exhibit negative decoupling. (2) The levels of LCEC construction are notably higher in the eastern and southern regions compared to the west and north, with cities showing higher LCEC construction levels also demonstrating more robust decoupling. (3) Promoting carbon reduction and economic development is crucial for enhancing the LCEC construction levels. However, there are regional differences in the impact factors such as housing investment, energy endowment, foreign investment, and environmental focus on the LCEC construction levels within each economic zone. Therefore, each economic zone should formulate tailored development strategies based on its unique characteristics. (4) The Grey prediction model predicts that 69 cities will achieve medium to high levels of LCEC construction by 2030. These results would provide policy recommendations for local governments to promote comprehensive and sustainable urban development.

How Environmental Policy Stringency, Foreign Direct Investment, and Eco-Innovation Supplement the Energy Transition: New Evidence from NICs

Several researchers have studied the environmental policy stringency and ecological innovation regarding CO2 emissions and renewable energy consumption; however, the impact of environmental policy stringency, technological innovation, FDI, and ecological innovation on energy transition has not been studied in the case of NICs. For this purpose, panel quantile regression models are applied in the context of NICs from 2000 to 2021. Our empirical results show that the effect of foreign direct investment is positive and statistically significant on energy transition. On the other hand the variables environmental policy stringency, eco-innovation, and ICT-trade have an inverse effect on energy transition. Therefore, the findings of the study also provide policy implications that indicate NICs need to optimize their trade structure and re-innovate the latest innovation spillovers, and strict environmental policies should be introduced to facilitate energy transition in NICs.

Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications

Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as ignoring non-desired outputs and slack variables, the efficiency values being limited to the (0, 1) range, and a narrow perspective. To address these shortcomings, this study focuses on the characteristics of the construction industry and introduces the Super-SBM model and Malmquist index into the assessment of safety efficiency in the construction industry. The study analyzes the evolution characteristics of safety efficiency from both static and dynamic perspectives. Furthermore, using panel quantile regression models, the study identifies the factors influencing safety efficiency and analyzes their heterogeneity. Analyzing panel data from 30 provinces in China from 2015 to 2021, the results show that the overall safety efficiency of the construction industry in China is relatively low, with noticeable spatial clustering characteristics. Provinces in the eastern and central regions exhibit higher levels of construction safety efficiency. The Malmquist index demonstrates a declining trend, with technical efficiency being the primary factor limiting the improvement of safety efficiency in construction. Factors such as per capita GDP, urbanization rate, committed contract amounts, and the number of professionals engaged in survey and design, as well as engineering supervision, have an impact on construction safety efficiency, and the effects of these variables vary across different quantile levels of safety efficiency. This research can assist decision-makers in gaining a better understanding of the safety conditions in different regions of the construction industry. It can also assist in developing customized policies to enhance the health and safety environment, thereby promoting the stable development of the construction industry.

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions.

The impact of energy consumption on carbon emissions intensity in China: evidence from a dynamic panel quantile regression model

Abstract China, being the largest global emitter of carbon, faces significant challenges in mitigating carbon emissions from energy consumption. The empirical findings reveal that the impact of energy consumption intensity and energy consumption structure on carbon emissions intensity is positively correlated with quantile levels and shows heterogeneous effects in different regions. Employing an energy consumption structure as a threshold variable to examine the impact of energy consumption intensity on carbon emissions intensity, we identify a significant single threshold effect in both the full sample and the western sample. Based on empirical research findings, this paper proposes specific policy recommendations.

Can industrial co-agglomeration improve carbon emission efficiency? Empirical evidence based on the eastern coastal areas of China.

The goal of "carbon peak and carbon neutrality" is the key to coping with global warming and achieving high-quality development. Producer services and manufacturing co-agglomeration (Coagglo) is an important path to achieve low-carbon development. Therefore, the relationship between industrial co-agglomeration and carbon emission efficiency (CEE) needs to be discussed. Based on the panel data of 114 cities along the eastern coast of China from 2006 to 2021, this study uses a panel quantile regression model and dynamic spatial Durbin model to evaluate the impact and spatial effect of Coagglo on CEE. The results show that there is a nonlinear relationship between Coagglo and CEE. When it exceeds the 50th quantile, the degree of influence decreases slightly, but it still shows a significant positive correlation. When considering industry heterogeneity, we find that the co-agglomeration of warehousing and postal industry (TRA) and manufacturing has the most significant impact on CEE, while the co-agglomeration of leasing and commercial service industry (LEA) and manufacturing has the least impact on CEE. Regional heterogeneity shows that the Coagglo has a greater impact on carbon emission efficiency in the northern region than in the southern region. In addition, Coagglo promotes the spillover of knowledge and technology and has a positive spatial spillover effect on CEE. This conclusion provides a theoretical reference for carbon emission reduction in eastern coastal areas of China.

The Influence of Internal Control Quality on Corporate Financial Performance: An Empirical Analysis based on Panel Quantile Regression Model

This study delves into the relationship between internal control quality and the financial performance of Chinese-listed corporations. Employing a distinctive research approach featuring panel quantile regression, this study meticulously examines data spanning the period from 2011 to 2020, encompassing 953 Chinese-listed entities. The analysis, in comparison to conventional methods such as ordinary least squares (OLS) regression and fixed-effects models, consistently reveals a robust and statistically significant positive correlation between internal control quality and corporate financial performance. Notably, the findings of the panel quantile regression bring to the forefront a nuanced perspective, elucidating an enhancing effect of internal control quality on Return on Assets. Nevertheless, this effect diminishes as one ascends the quantile distribution, elucidating varying degrees of influence contingent upon the internal control quality spectrum. These findings underscore the pivotal role of effective internal control systems in augmenting financial performance, with implications for corporate governance. Furthermore, this research underscores the evolving nature of internal control frameworks, particularly in the context of digitalization, thus delineating an imperative area for prospective scholarly exploration.

Energy efficiency, financial inclusion, and socio-economic outcomes: Evidence across advanced, emerging, and developing countries

This study analyzes the effects of financial inclusion and energy efficiency on socio-economic performance across advanced, emerging, and developing economies. We view financial inclusion from the lens of access to and usage of financial services, while energy intensity represents energy efficiency. The panel quantile regression model is employed to capture the nonlinear impacts of financial inclusion and energy efficiency across the conditional distributions of the socio-economic indicators. We find that financial inclusion broadly improves the socio-economic markers, except employment in developing economies. Comparatively, the effect of access to financial services is stronger than the effect of the usage of financial services. On the other hand, the impact of energy efficiency is quite heterogeneous across the country groups and the socio-economic indicators, being sensitive to the level of economic development of the countries. Overall, energy efficiency has the strongest influence in advanced economies, but a moderate influence in the emerging and developing economies. Our results provide imperative policy reflections for the policymakers in the underlying countries, particularly on properly channeling financial resources and enhancing energy efficiency to benefit the various components of their economies.

Revisiting the Environmental Kuznets Curve (EKC) Hypothesis of Carbon Emissions: Exploring the Impact of Geopolitical Risks, Natural Resource Rents, Corrupt Governance, and Energy Intensity

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions.

The Impact of Geopolitical Risk on Portuguese Exports

This investigation evaluates the performance of Portuguese exports by focusing on the 11 main partners for 1990–2021, considering panel data. Country risk analysis has been frequently used to assess the determinants of international trade in recent years. Empirical studies demonstrate that country risk can affect bilateral relationships between economies, especially in economies with greater geopolitical risk. Next, we refer to the methodology used in this research. In this context, we assessed the stationarity of the variables used in this study. Subsequently, models were used to eliminate bias and endogeneity between the variables. The panel quantile regression model allows us to understand the behaviour of variables across different quartiles. The empirical study shows that countries with low country risk promote the performance of Portuguese exports. On the other hand, the size of the economies, both the exporting country (Portugal) and the importing countries (commercial partners), is decisive for increasing Portuguese exports. This finding can be explained as a monopolistic competition with the economy’s scale and industrial concentration serving as theoretical support. As noted with previous studies on the gravity model, the common language of Portuguese-speaking countries reduces communication costs and increases Portuguese exports. Furthermore, the econometric model also validates the issue of geographical distance, where this variable has a negative impact on exports, demonstrating that geographical proximity reduces transport costs.

Dynamic Evolution Characteristics and Drivers of Tourism-Related Ecological Security in the Beijing–Tianjin–Hebei Region

Revealing the characteristics of the spatial and temporal evolution of regional tourism-related ecological security and exploring its driving factors are of great theoretical and practical value to promoting the coordinated and sustainable development of the tourism economy and the ecological environment. Taking the Beijing–Tianjin–Hebei region as a case study, this study constructed a tourism-related ecological security evaluation index system based on the DPSIR theoretical framework. The tourism-related ecological security index was measured from 2011 to 2022 and its spatiotemporal characteristics and dynamic evolutionary process were analyzed. Finally, the panel quantile regression model was used to analyze its driving factors. The results show that: (1) in time, the average value of tourism-related ecological security fluctuates and rises, and the differences between the units show a convergent trend, which is mainly manifested in the catching-up effect from low-value cities to higher-value cities; (2) in space, Beijing and its southeastern cities have a high level of tourism-related ecological security, and although the study area is mainly at the less secure level, it is developing continuously and progressively; (3) in terms of the dynamic evolution characteristics, the type transfer of tourism-related ecological security has certain “path dependence” and “self-locking” effects, often occurring between neighboring levels, and the type transfer under the influence of different neighborhoods has significant differences; (4) in terms of driving factors, environmental pollution has a significant inhibitory effect on the level of tourism-related ecological security, and the level of economic development has the largest positive marginal effect on tourism-related ecological security, while other influencing factors such as the level of tourism development have certain differences in terms of their positive role in the promotion of the level of tourism-related ecological security. This study can provide a reference for decisionmakers to promote ecological protection and high-quality tourism development in the Beijing–Tianjin–Hebei region.

Environmental technology import and carbon emissions intensity convergence: Analysis for the Belt and Road Initiative countries

Since the official launch of the Belt and Road Initiative (BRI) in 2013, China and the BRI countries have been working for the implementation of certain environmental measures to make the BRI project green and clean. For this purpose, China and the BRI countries have planned to implement certain environmental measures. Although China can efficiently implement these measures, most of the BRI countries face technological deficiencies and lack of proper environmental plannings. To tackle these deficiencies, the BRI countries can import environmental technology from China. Moreover, they can communicate their environmental protection policies with China for better policy guidance. The current study, therefore, aims to examine whether the BRI countries’ import of environmental technology from China can reduce carbon emissions in these countries. Moreover, it also examines that whether these countries should follow the environmental policy and the import policy of China or they should follow the six European countries (EU-6) with minimum carbon emissions intensity. This study considers a sample of 88 selected BRI countries (BRI-88) for the period 2001–2019. The results obtained with β convergence (based on the panel quantile regression model) suggest that not all the BRI countries but only the BRI countries with relatively higher carbon emissions intensity can significantly reduce their average carbon emissions intensity by importing environmental technology from China. Moreover, BRI countries can follow the environmental policy of China which is more feasible for them. However, regarding the environmental goods import policy, BRI countries can follow both China and the EU-6.

International tourism development in the digital economy era: the role of ICT services and digital finance

PurposeThis paper aims to investigate how information and communication technology (ICT) services and digital finance affect the development of international tourism.Design/methodology/approachThe two-way fixed effect panel regression model, spatial econometric model, panel threshold regression model and panel quantile regression model are used. Data on tourism, economic and social development in 198 Chinese cities from 2011 to 2020 are analyzed.FindingsThis study finds that digital economy including ICT services and digital finance has significantly promoted the development of international tourism industry, while there is a negative spatial spillover effect. The promotion effect of international tourism increases significantly after digital innovation reaches the threshold value. International tourism is benefiting more from digital economy with the development of international tourism industry.Research limitations/implicationsThe development quality of international tourism industry has not been analyzed due to data limitations, and the mechanism has not been tested.Originality/valueThis study creatively reveals the development of international tourism industry in the digital economy era from ICT services and digital finance perspectives. This study also shows the spatial, nonlinear and asymmetric relationship between digital economy and international tourism.

The Relationship between Bank Capital, Risk-Taking and Profitability: Fresh Evidence from Panel Quantile Approach

This study aims to reveal the relationship between bank capital (BC), risk-taking, and profitability for commercial banks in Turkey using panel quantile regression models (QRPD) with non-additive fixed effects (NAFE). Accordingly, we consider the data of 18 commercial banks for 2012-2022. Firstly, we concluded a positive relationship between banks' risk-based capital (RBC) and traditional capital ratios (CR) and return on assets (ROA); in contrast there is a negative relationship between RBC and risk-weighted assets (RWATA) under all market conditions. Secondly, we found a positive relationship between the RBC and the provision for loan losses ratio (LLPTA) in other periods except for the contraction period; at the same time, there is a positive relationship between CR and LLPTA in other periods except for the expansion period. The findings also showed an inverted U-shaped relationship between RBC and the LLPTA and return on equity (ROE) and an N-shaped relationship between CR and ROE. Lastly, we discovered a positive relationship between RWATA and ROA and ROE in all market conditions, whereas a negative relationship exists between LLPTA and ROA. The findings provide valuable insights into the validity of the moral hazard and cost-skimping hypotheses, regulatory assumptions, agency, portfolio and risk-bearing profit theories in the commercial banking sector, and that risk, capital and profitability indicators are leading factors in banks' stability.

Financial development and growth nexus in Asian countries: mediating role of FDI, foreign aid and trade

PurposeThis paper analyzes the direct effect of financial development and the mediating impact of financial development through foreign direct investment (FDI), foreign aid and trade on economic growth for all Asian countries.Design/methodology/approachA fixed-effect model with Driscoll–Kraay panel corrected estimators was employed to find the direct and mediating impact of financial developments on growth for all 47 Asian economies from 1980 to 2020. The bootstrapped panel-quantile regression (BPQR) model is used to check how this effect varies for different income groups of countries.FindingsThe results demonstrated that financial development positively impacts countries' economic growth. The interaction effect of financial development with FDI, foreign aid and foreign trade negatively impacts economic growth. The BPQR results showed that FDI and foreign aid help in the growth of lower quantile economies; however, the impact is negative for middle- and upper-income countries. Trade impacts growth positively for all the quantiles of economies.Research limitations/implicationsThe results suggest that the Asian economies must continue to provide thrust on the financial development of their own countries to achieve better growth. It also implied that the dependence on external finance is good for low-income countries and not advisable for middle- and upper-income countries.Originality/valueTo the best of the authors’ knowledge, the current study is the first to provide empirical evidence on analyzing both the direct and interaction effect of financial development on economic growth by considering all the Asian economies.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0587

Evaluating the Impact of Economic-Institutional-Energy Variables on the Ecological Footprint: The Application of the Panel Quantile Regression Model in Selected Countries of the MENA Region

Evaluating the Impact of Economic-Institutional-Energy Variables on the Ecological Footprint: The Application of the Panel Quantile Regression Model in Selected Countries of the MENA Region