Panel Quantile Model Research Articles

Foreign direct investment, environmental regulation, and energy transition—An empirical study based on data from 38 OECD countries worldwide

AbstractThis study formulates a theoretical hypothesis regarding the intricate interplay among foreign direct investment (FDI), environmental regulation, and energy transition. To empirically validate this hypothesis, a comprehensive analysis is carried out on 38 member nations of the Organization for Economic Cooperation and Development (OECD) over the span of 2003 to 2020, utilizing a panel fixed effects model, a panel quantile model, and a panel threshold regression model. The findings of the research indicate that (1) FDI exhibits an inhibitory impact on energy transition and, according to the heterogeneity analysis, FDI significantly inhibits energy transition in developed nations. However, the inhibitory influence on energy transition in developing nations is not as pronounced. (2) A considerable amount of dampening influence is exerted by FDI on energy transition at various stages of the transition and is most apparent when the transition is in the growth phase. (3) A threshold effect is evident in FDI and environmental regulation in relation to energy transition owing to the mismatch between the two developments. Notably, the influence of FDI on energy transition significantly varies based on the stringency of environmental regulation. As formal environmental regulation surpasses a designated threshold, FDI shifted from facilitating to inhibiting the energy transition. The same conclusions were reached when informal environmental regulation was considered as the threshold variable. Drawing from the conclusions of this paper, the countries of the OECD can develop a theoretical framework to formulate foreign investment introduction policies and regulate environmental regulation efforts to promote the energy transition.

Effects of Industrial Structure on the Green Utilization Efficiency of Urban Land: A Case Study of the Bohai Rim Region, China

The green utilization of urban land is a critical component of regional high-quality development. Enhancing the green utilization efficiency of urban land (GUEUL) is of great significance to improving the quality of ecological environment and achieving a green lifestyle and low-carbon production. With the process of urbanization, the industrial structure is constantly adjusted, which has an impact on the structure and efficiency of urban land utilization. Taking 43 cities in the Bohai Rim region as an example, the super-efficiency slack-based measure model with undesirable outputs (super-SBM-undesirable model) was employed to evaluate the GUEUL from 2006 to 2021, and the panel quantile model was used to explore the impact of industrial structure on GUEUL. The results showed that the GUEUL in the Bohai Rim region appeared a fluctuating upward trend from 0.664 in 2006 to 0.837 in 2021. High-value zones shifted from western Liaoning province and southern Shandong province to a “C”-shaped belt around the coastline and expanded continuously, while low-value zones decreased significantly. Furthermore, the influence of industrial structure on GUEUL was significantly positive, but there were significant differences among different efficiency levels. Industrial structure upgrading exerted the most significant effect on GUEUL improvement in low-value zones, while industrial structure rationalization was dominant in high-value zones, and the influence of industrial structure technologization was more significant in medium-low-value zones. Therefore, differentiated industrial structure adjustment policies should be formulated based on the actual condition of each city to improve GUEUL.

Spatial–Temporal Evolution and Driving Factors of Agricultural Green Development in China: Evidence from Panel Quantile Approaches

Agricultural green development has become essential for sustainable agriculture and the reduction of carbon dioxide emissions. This study evaluates the total index of agricultural green development by applying the entropy method; it then examines the spatial–temporal evolution of agricultural green development and finally uses the panel quantile model to examine the driving factors of agricultural green development in China. The results indicate that the level of agricultural green development is rising with time, and the differences among the regions have not changed, showing an increasing direction from west to east. The results from the panel quantile regression with nonadditive fixed effects show that the driving factors have different impacts on agricultural green development across quantiles. Industrial structure upgrading, rural informatization, and agricultural marketization have more significant effects in provinces with higher agricultural green development; agricultural finance and the per capita GDP have greater impacts in provinces at a moderate level of agricultural green development; and technology development, rural informatization, and the urbanization rate play a more important role in provinces at a lower level of agricultural green development. Thus, each province should devise policies according to its level of agricultural green development, which would be beneficial in improving the policies’ effectiveness.

Digital Economy and Urban Green Innovation: Empirical Evidence from Chinese Prefecture-Level Cities

Fully utilizing the ecological advantages of digital economy is of great significance in promoting urban green innovation. This paper discusses the impact of digital economy on urban green innovation based on a two-way fixed-effects model with panel data of 282 Chinese prefecture-level cities from 2011 to 2021. The findings show that digital economy can significantly promote urban green innovation and pass a series of robustness tests such as substitution variables, panel quantile model, and instrumental variables. The heterogeneity test shows that eastern cities and southern cities are more capable of promoting urban green innovation by the digital economy. This paper provides research support for the theory of digital economy in green development and the practice of urban development transformation.

Spatiotemporal Interaction Characteristics and Transition Mechanism of Carbon Intensity in China's Transportation Industry

This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

Quantile estimation of heterogenous panel quantile model with group structure

This paper introduces a heterogeneous panel quantile model whose coefficients have group structure. We propose a penalized minimum weighted absolute deviation estimator in order to estimate the coefficients and identify the group structure simultaneously at any quantiles.

Evaluation the impact of electricity consumption on China's air pollution at the provincial level.

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development.

Research on the spatial effect of digital economy development on urban carbon reduction

Cities are one of the main sources of regional carbon emissions, and reducing urban carbon emission is the key to reducing emissions. The digital economy has transformed the economic operation mode, and it is a significant approach to support the “dual carbon goals” (carbon peaking and carbon neutrality). This article considers the externalities of the digital economy and carbon emissions. And we use spatial econometric models to analyze the effectiveness of digital economy in empowering carbon emissions reduction. Besides, we explore the static and dynamic spillover effects, and use spatial Durbin panel quantile model to analyze the digital economy’s heterogeneity on carbon emissions. Research has shown that the digital economy has a remarkable carbon reduction effect, and the conclusion remains valid after considering robustness tests such as replacing the weight matrices, calculation methods, and proxy variables. The analysis of static and dynamic spillover effects indicates that the degree of the digital economy’s impact on carbon emissions are significantly different. Heterogeneity analysis shows that as the digital economy develops from a low level to a high level, its impact on carbon emissions also shifts from positive promotion to negative suppression. This paper proposes a policy reference to help the development of digital economy and promote carbon neutrality in the face of severe environmental challenges.

Fiscal agricultural expenditures' impact on sustainable agricultural economic development: Dynamic marginal effects and impact mechanism.

Sustainable agricultural economic development is the core task for achieving the objective of rural revitalization strategy in China, which cannot be separated from the support and guidance of fiscal policy, and agricultural industry integration is a key path for the fiscal promotion of sustainable agricultural economic development. This paper systematically examines the interaction mechanism between fiscal agricultural expenditures and sustainable agricultural economic development by using 31 provincial panel data in China from 2008 to 2020 and adopting a two-way fixed effect model, a panel quantile model, and a mediating effect model, respectively. The results show that the impact of fiscal agricultural expenditures on sustainable agricultural economic development is significantly positive, and appears a dynamic increasing trend with the agricultural development stage upgrading. Moreover, heterogeneity analysis shows that the effect of fiscal agricultural expenditures is more obvious for the samples in the central region and with a high share of primary industry. Further, a mediating effect test finds that agricultural industry integration plays a mediating mechanism between fiscal agricultural expenditures and sustainable agricultural economic development. Therefore, this paper proposes constructing a long-term investment mechanism for fiscal agricultural expenditures, formulating differentiated fiscal support policies for agriculture, and prioritizing support for agricultural industry integration, which provides theoretical support and policy inspiration for promoting sustainable agricultural economic development.

Trend prediction and influencing factors of the production comparative advantage of China's main apple-producing provinces.

The apple industry is an essential industry to assist in rural revitalization. However, in recent years, the urbanization, industrialization, globalization and climate change have brought various challenges to the apple industry in China's main apple-producing provinces. Given this, effectively identifying, enhancing on apple production comparative advantage (APCA) is imperative to safeguard the long-term sustainable development of China's apple industry. This study aims to explore the evolutionary trends and influencing factors of APCA, and to provide quantitative support for the formulation of scientific and effective apple production policies. In this paper, the APCA of China's eight main apple-producing provinces from 2013 to 2022 was measured by using a aggregate comparative advantage index. The spatio-temporal dynamic evolution characteristics of APCA were revealed by adopted Arc GIS and kernel density estimation method. Second, the transfer probabilities of different types of APCA were predicted by empolyed traditional and spatial Markov chains. Finally, the driving mechanism of APCA is explored with the panel quantile model. 1) The average value of APCA of the main producing provinces increased from 1.330 in 2013 to 1.419 in 2022. 2) The probabilities of provinces with low, primary and middle level of advantage jumping to the next level are 31.58%, 16.67% and 11.76%, respectively. When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile. 1) Temporally, APCA shows a trend of slow growth, ups and downs. Spatially, APCA shows a distribution pattern of "west high, east low". 2) APCA mainly shifted sequentially between neighbouring ranks. Besides, the change of APCA had significant spatial spillover effect, and highly advantage provinces featured more prominent proactive spillovers. 3) There is significant heterogeneity among the influencing factors.

The impact of rural revitalization policies on the diversification benefits of listed agricultural companies

This paper investigates the effect of the implementation of rural revitalization policy on the diversification of listed agricultural companies, using panel quantile model with OLS regression model, and examines the heterogeneity of tax incentives on the above effect. And the manuscript find that the regression coefficient of the explanatory variables of tax incentives is 0.3367, which is significant at 1 % level. It indicates that tax incentives have a significant positive effect on the business performance of agricultural listed companies, which in turn promotes the transformation of agricultural listed companies to modern agriculture.

On the influencing factors of AH shares linkage in China under new regulation policy of reduction

AH stock premium and its price linkage have always been the key problems of concern for scholars and investors. Taking the stock price yield of AH stock as the explanatory variable, this paper adopts the panel quantile model to probe into the linkage mechanism of AH stock price from five aspects such as risk difference, liquidity difference, demand elasticity difference, information asymmetry and new regulation policy of reduction. Due to the herding effect, information asymmetry has a negative effect on the H-share yield in the income and loss intervals, and a positive effect in the stable interval. The negative effect of liquidity difference on the H-share yield in the loss interval is converted into the positive effect in the income interval, but it is the most sensitive in high risk areas. The influence of demand elasticity on H-share yield is characterized by the typical characteristics of Chinese stock market “chasing after go up and killing cheapen” or “buying the winners”. Risk difference is not obvious because the behavior of risk lover and risk averter is offset by each other. The introduction of a 2017-year reduction in the new rules has significantly reduced the share price yield of H-shares. Finally, some suggestions are put forward for investors to invest and avoid risk in AH stocks and some specific policy implications for financial regulators are also provided based on these results.

Spatiotemporal evolution of carbon emission intensity and the driving effect of green technology innovation: Evidence from China.

The "double carbon" goal has proposed new "green" requirements for China's low-carbon economic development, and green technology innovation (GTI) has become an important way to coordinate economic and sustainable development. The study explores the spatial-temporal evolution of carbon emission intensity (CEI) of Chinese prefecture-level cities, analyses the nonlinear impact of GTI on the CEI by constructing a panel quantile model, and draws the following conclusions. First, CEI shows a decreasing trend from 2006 to 2019 and a spatial distribution pattern of "high in the north and low in the south, high in the west and low in the east". Second, GTI significantly reduces CEI, and as the quantile point increases, the carbon reduction effect of GTI is characterized by a U-shaped change, decreasing first and then increasing. Overall, GTI has a significantly more profound inhibiting effect on high CEI regions than on low CEI regions. Third, there is spatial heterogeneity in the impact of GTI on CEI across the four major regions and diverse policy contexts. The study proposes countermeasures for low-carbon development in terms of tapping the potential of GTI, strengthening its regional synergy, and applying locally appropriate measures, to gain the great practical significance for achieving the double carbon target.

Can Service Trade Effectively Promote Carbon Emission Reduction?—Evidence from China

Carbon emissions have become a global issue of increasing concern due to their detrimental impact on the environment. Efforts to combat rising emissions have been taken globally. Despite China’s commitment to globalization, policymakers have faced challenges in adequately addressing this pressing issue. This paper aims to fill this gap by exploring a specific aspect of international trade, namely service trade. We theoretically analyzed the impact of service trade on carbon emissions and then empirically examined the impact using panel data from 2009 to 2019 of 30 provincial regions in China. Specifically, a non-linear model was used to capture the direct effect, particularly the potential non-linear relationship; a mediating effect model was applied to investigate the indirect effects; and a panel quantile model was adopted to examine the heterogeneity of the impact across different levels of carbon emissions. The research revealed: (1) The impact of service trade on carbon emissions exhibits a non-linear characteristic with a significant inverted U-shaped relationship being evident, indicating that the development of service trade can ultimately contribute to carbon reduction; (2) service trade can directly impact carbon emissions through its scale effect, while mechanism analysis showed that service trade can indirectly affect carbon emissions through its technological and structural effects, with carbon emission reduction also relying on these mechanisms; (3) There is significant heterogeneity in the impact of service trade on carbon emissions across geographic regions and at different levels of carbon emissions in China.

How does geopolitical risk affect carbon emissions?: An empirical study from the perspective of mineral resources extraction in OECD countries

Faced with the severe climate change situation, the major economies of the world have gradually developed carbon reduction strategies to combat climate change. Mineral resources security has become a hot issue because of the frequent occurrence of geopolitical risk (GPR) events, which will further have an inevitable impact on carbon dioxide (CO2) emissions. However, there is little empirical evidence to support the issue. This paper explores the impact of GPR on CO2 emissions in OECD countries from the perspective of mineral resources extraction. The panel data from 25 OECD countries in the period of 1990–2019 is applied in the empirical study. Three primary conclusions are obtained using panel fixed-effects (FE) model and panel quantile model. First, GPR increases CO2 emissions in OECD countries. Second, the positive impact of GPR is heterogeneous. Third, the positive impact on CO2 emissions occurs mainly due to the exacerbated risks of mineral resources. Increased GPR inhibits the improvement of energy structure and promotes fuel imports, which further increase CO2 emissions. Finally, we provide some policy recommendations to allay these concerns.

Evolution of Industrial Ecology and Analysis of Influencing Factors: The Yellow River Basin in China

The Yellow River Basin is an important ecological function area and economic zone in China that faces the dual pressure of economic development and ecological protection. Industrial ecology designs industrial activities by imitating the ecosystem and would solve the dilemma in the development process of the Yellow River Basin. In this study, we evaluated the industrial ecology of 100 prefecture-level cities along the Yellow River Basin from 2003 to 2020 using the entropy weight method and analyzed their long-term spatial and temporal evolution trends. We also deconstructed the driving factors affecting the industrial ecology in the Yellow River Basin, regarding both internal and external aspects, using the coupled coordination model and the panel quantile model. We found the following: (1) The industrial ecology in the Yellow River Basin was slowly increasing in a “N”-type transformation trajectory, but the overall was still relatively low. (2) The slow improvement in ecosystem efficiency and the low coordination between the industrial system and the ecosystem are the main internal factors limiting the improvement in industrial ecology. (3) The population agglomeration, human capital accumulation, government guidance, and technology level are all important for improvements in industrial ecology in the Yellow River Basin. The impact of the financial level on industrial ecology in the Yellow River Basin was found to be negative. Therefore, it is necessary to take the correlation and coordination between the industrial system and the ecosystem as the main means and actively and positively promote the role of the external influencing factors to improve the industrial ecology in the Yellow River Basin.

The impact of sustainability‐oriented dynamic capabilities on firm growth: Investigating the green supply chain management and green political capabilities

AbstractBuilding on the dynamic capabilities literature and natural‐resource‐based view, the paper examines whether firms can attain sales growth through a range of sustainability‐oriented dynamic capabilities including (1) internal green supply chain management capabilities, (2) external green supply chain management capabilities and (3) green political capabilities. Based on a dataset of 277 public US firms between 2010 and 2020, a panel quantile model of firm growth showcases that while internal green supply chain capabilities and green political capabilities affect firms' growth performance positively, external green supply chain capabilities are associated with slower growth. Importantly, the results indicate that the positive growth effects of green political capabilities are short‐lived, while those of internal green supply chain capabilities are long‐lived. The study contributes to the sustainability‐oriented dynamic capabilities literature by showing that different capabilities have different implications for firm growth depending on the firm's base performance and the time periods under consideration.

Energy-saving effect of financial development and its dynamic heterogeneity: Empirical evidence from the dynamic panel quantile model

The energy-saving effect of financial development is directly related to the formulation and implementation of financial policies. Considering the inertial characteristics of energy consumption, this study tested the energy-saving effect of financial development and examined its heterogeneity in terms of low-carbon cleaning and policy change. The results were as follows: First, when energy consumption was at the lower quantile, as consumption increased, the promoting impact of financial development on energy consumption decreased. When energy consumption was at the upper quantile, as consumption increased, the restraining impact of financial development on energy consumption increased. Second, an increase in the quantile level showed that financial development exerted an increasingly stronger influence on promoting clean energy consumption. When non-clean energy consumption was at the upper quantile, financial development exerted an increasingly strong inhibitory effect on non-clean energy consumption. Third, before green credit policy changed, the energy-saving effect of financial development was not widespread and obvious. After green credit policy changed, the restraining impact of financial development on energy consumption increased with the level of consumption. Fourth, after green credit policy changed, compared with the increase of financial development toward promoting clean energy consumption, the inhibitory effect of financial development on non-clean energy consumption significantly improved relative to the second case.

Are Islamic and conventional banks decoupled? Empirical evidence from Turkey

PurposeThis study investigates the determinants of net interest margin (NIM) and tests the decoupling hypothesis in Turkey's Islamic and conventional banks.Design/methodology/approachThis study has employed a panel quantile model (PQM) to assess the net interest margin (NIM) and test the decoupling hypothesis in the dual banking system of Turkey.FindingsThe empirical results show that the impact of equity is positive for both Islamic and conventional banks but relatively more robust for Islamic banks. Moreover, it is observed that return on assets has a positive association with NIM in both types of banking systems. Interestingly, the impact increases from lower to higher quantiles, but a higher acceleration rate is observed for Islamic banks. The study also finds that, as bank stability increases, NIM decreases for both groups of banks but more stably for Islamic banks, resulting in lower margins than conventional banks. Thus, the paper confirms the decoupling hypothesis and suggests that, to increase profit margins, Islamic banks need to increase assets and equity.Practical implicationsThe paper confirms the decoupling hypothesis and suggests that to increase profit margin, Islamic banks need to increase assets and equity.Social implicationsSince both equity and assets contribute positively to interest margins, policymakers in the industry need to increase the size of equity and assets to get maximum returns.Originality/valueThis is one of the first studies to investigate NIM's determinants and test the decoupling hypothesis in the Turkish dual banking system using a non-parametric MCMC panel quantile regression (QRM) model.

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Since the beginning of the new century, China’s carbon emissions have increased significantly, and the country has become the world’s largest carbon emitter. Therefore, determining the influencing factors of carbon emissions is an important issue for policymakers. Based on the panel data of 30 provinces and cities across the country from 2000 to 2018, this study empirically tested how per capita disposable income, industrial structure, urbanization level, average family size, and technological innovation level impacts carbon emissions at different quantile levels by using the panel quantile STIRPAT model. The results showed that per capita disposable income and industrial structure had significant promoting effects on carbon emissions, while urbanization level, average family size, and technological innovation level had significant inhibitory effects on carbon emissions. The main thing is that the emission distributions of the 10th and 90th quantiles of the independent variables were quite different, which shows that the influence of each factor on carbon emissions has obvious heterogeneity at different levels. Specifically, the impact of per capita disposable income and technological innovation level on carbon emissions in low carbon emission areas were higher than that in high carbon emission areas, and the impact of industrial structure, urbanization level, and average household size on carbon emissions in high carbon emission areas was higher. Finally, specific policy implications are provided based on these results.