Unleashing sustainability in uncertain times: Can we leverage economic complexity, uncertainty, and remittances to combat environmental degradation?

Rapid economic growth and human activities have seriously damaged the environment and hindered the achievement of Sustainable Development Goals (SDGs). Hence, this study aims to explore the impact of economic complexity, uncertainty, and remittance on environmental degradation in 134 countries from 2000 to 2022. In addition, it examines whether uncertainty moderates the relationship between remittance and environmental degradation. Two proxies (ecological footprint and CO2) were used to measure environmental degradation. The analysis was conducted using a cross-sectional dependency test, second-generation unit root test, and panel quantile regression. The results revealed that economic complexity significantly and positively impacted environmental degradation, while uncertainty and remittance significantly and negatively impacted environmental degradation. Furthermore, uncertainty weakened the negative relationship between remittance and environmental degradation. Accordingly, this paper discusses various recommendations and policy implications regarding economic complexity, uncertainty, remittance, and environmental degradation.

Non-parametric quantile regression-based modelling of additive effects to solar irradiation in Southern Africa

Modelling of solar irradiation is paramount to renewable energy management. This warrants the inclusion of additive effects to predict solar irradiation. Modelling of additive effects to solar irradiation can improve the forecasting accuracy of prediction frameworks. To help develop the frameworks, this current study modelled the additive effects using non-parametric quantile regression (QR). The approach applies quantile splines to approximate non-parametric components when finding the best relationships between covariates and the response variable. However, some additive effects are perceived as linear. Thus, the study included the partial linearly additive quantile regression model (PLAQR) in the quest to find how best the additive effects can be modelled. As a result, a comparative investigation on the forecasting performances of the PLAQR, an additive quantile regression (AQR) model and the new quantile generalised additive model (QGAM) using out-of-sample and probabilistic forecasting metric evaluations was done. Forecasted density plots, Murphy diagrams and results from the Diebold-Mariano (DM) hypothesis test were also analysed. The density plot, the curves on the Murphy diagram and most metric scores computed for the QGAM were slightly better than for the PLAQR and AQR models. That is, even though the DM test indicates that the PLAQR and AQR models are less accurate than the QGAM, we could not conclude an outright greater forecasting performance of the QGAM than the PLAQR or AQR models. However, in situations of probabilistic forecasting metric preferences, each model can be prioritised to be applied to the metric where it performed slightly the best. The three models performed differently in different locations, but the location was not a significant factor in their performances. In contrast, forecasting horizon and sample size influenced model performance differently in the three additive models. The performance variations also depended on the metric being evaluated. Therefore, the study has established the best forecasting horizons and sample sizes for the different metrics. It was finally concluded that a 20% forecasting horizon and a minimum sample size of 10000 data points are ideal when modelling additive effects of solar irradiation using non-parametric QR.

Smoothed quantile regression for partially functional linear models in high dimensions

Practitioners of current data analysis are regularly confronted with the situation where the heavy-tailed skewed response is related to both multiple functional predictors and high-dimensional scalar covariates. We propose a new class of partially functional penalized convolution-type smoothed quantile regression to characterize the conditional quantile level between a scalar response and predictors of both functional and scalar types. The new approach overcomes the lack of smoothness and severe convexity of the standard quantile empirical loss, considerably improving the computing efficiency of partially functional quantile regression. We investigate a folded concave penalized estimator for simultaneous variable selection and estimation by the modified local adaptive majorize-minimization (LAMM) algorithm. The functional predictors can be dense or sparse and are approximated by the principal component basis. Under mild conditions, the consistency and oracle properties of the resulting estimators are established. Simulation studies demonstrate a competitive performance against the partially functional standard penalized quantile regression. A real application using Alzheimer's Disease Neuroimaging Initiative data is utilized to illustrate the practicality of the proposed model.

Locally sparse quantile estimation for a partially functional interaction model

Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, while some other variables are functional and have unspecified nonlinear effects. Significantly advancing from the existing literature, we consider a model with interactions between the functional and scalar covariates. To accommodate long-tailed error distributions which are not uncommon in data analysis, we adopt the quantile technique for estimation. To achieve more interpretable estimation, and to accommodate many practical settings, we assume that the functional covariate effects are locally sparse (that is, there exist subregions on which the effects are exactly zero), which naturally leads to a variable/model selection problem. We propose respecting the "main effect, interaction" hierarchy, which postulates that if a subregion has a nonzero effect in an interaction term, then its effect has to be nonzero in the corresponding main functional effect. For estimation, identification of local sparsity, and respect of the hierarchy, we propose a penalization approach. An effective computational algorithm is developed, and the consistency properties are rigorously established under mild regularity conditions. Simulation shows the practical effectiveness of the proposed approach. The analysis of the Tecator data further demonstrates its practical applicability. Overall, this study can deliver a novel and practically useful model and a statistically and numerically satisfactory estimation approach.

Quantile forward regression for high-dimensional survival data

Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates' effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD) and derives the final model based on the extended Bayesian Information Criterion (EBIC). We demonstrate that the proposed method enjoys a sure screening property and selection consistency. We apply it to the national health survey dataset to show the advantages of a quantile-specific prediction model. Finally, we discuss potential extensions of our approach, including the nonlinear model and the globally concerned quantile regression coefficients model.

Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline

The National Alzheimer's Coordinating Center Uniform Data Set includes test results from a battery of cognitive exams. Motivated by the need to model the cognitive ability of low-performing patients we create a composite score from ten tests and propose to model this score using a partially linear quantile regression model for longitudinal studies with non-ignorable dropouts. Quantile regression allows for modeling non-central tendencies. The partially linear model accommodates nonlinear relationships between some of the covariates and cognitive ability. The data set includes patients that leave the study prior to the conclusion. Ignoring such dropouts will result in biased estimates if the probability of dropout depends on the response. To handle this challenge, we propose a weighted quantile regression estimator where the weights are inversely proportional to the estimated probability a subject remains in the study. We prove that this weighted estimator is a consistent and efficient estimator of both linear and nonlinear effects.

Investigating the impact of import, export, and innovation on carbon emission: evidence from Belt and Road Initiative countries

Carbon emissions are a major cause of global climate change. The public is aware that the world must rapidly reduce its windows to avoid the worst effects of climate change. But how this responsibility is distributed between regions, countries, and individuals has become a recurring element of debate in international debates. Most countries are willing to adopt new policies to tackle the global problem of carbon emission. Since China is a real model and the first country of initiating the goal of carbon neutrality, this study aimed to compare the different impacts of export, import, and innovation on carbon emission in 29 selected countries with the Belt and Road Initiative from 2008 to 2019. STIRPAT modeling, cross-sectional analysis, and integrated testing were used to analyze the obtained data. The results show that exports and imports have a negative effect on carbon emission, and population size and energy efficiency increase carbon emission since most countries under the BRI are developing countries, and they tend to emit greatly due to various factors. However, the adoption of green energy via innovation has a significant impact on carbon emissions. In addition, the adoption of modern technologies via innovation reduces carbon emission by increasing energy efficiency. We recommended a set of policies that can efficiently reduce the emission of carbon to achieve an eco-friendly environment in the selected countries. It is important to promote environmental sustainability and the development of professional enterprises in certain countries.

Revisiting global energy efficiency and CO2 emission nexus: fresh evidence from the panel quantile regression model

Given the importance of energy efficiency in environmental degradation, the effects of energy efficiency and renewable and nonrenewable energy consumption on global environmental pollution were investigated. For this purpose, panel data from 107 countries from 1996 to 2014 were examined. In addition, the present study also tested the well-known environmental Kuznets curve (EKC). The long-run relations were estimated by applying a panel quantile regression (PQR) approach, which is useful for finding heterogeneous effects at lower- and upper-level quantiles of CO₂ emissions. The empirical results indicated that energy efficiency had a significantly negative impact on CO₂ emissions with low intensity at higher-level quantiles.Furthermore, the impact of renewable and nonrenewable energy consumption on environmental degradation was significantly negative and positive across all quantiles, respectively. The empirical results provide evidence supporting an inverted U-shaped nexus between GDP and CO₂, whereby the EKC is found valid. Hence, energy efficiency improvement and renewable energy consumption policies must align with strategies to curb environmental degradation.

ICT, Energy Intensity, and CO2 Emission Nexus

The relationship between information and communication technology investment (ICT), environmental impacts, and economic growth has received increasing attention in the last 20 years. However, the relationship between ICT, energy intensity, environmental impacts, and economic growth was relatively neglected. In this paper, we aimed to contribute to the environmental literature by simultaneously analyzing the relationship between ICT, energy intensity, economic growth, Carbon dioxide (CO2) emissions, and energy consumption for the period of 1990–2020 in G7 countries. We employed the Panel Quantile Auto Regressive Distributed Lag (PQARDL) method and Panel Quantile Granger Causality (PQGC) methods. According to the results of PQARDL method, energy consumption, ICT, CO2 emission, and energy intensity have effects on economic growth in the long and short run. According to the of PQGC methods allowing causality results for different quantiles, there is evidence of a bidirectional causality between ICT investment and economic growth for all quantiles and evidence of a unidirectional causality from ICT to energy consumption and from CO2 emissions to ICT investment and energy efficiency. Our results indicate that the governments of the G7 countries have placed energy efficiency and ICT investment at the center of their policies while determining their environmental and energy policies, since energy consumption is a continuous process.

Quantile estimation of ecological footprint and economic complexity in emerging economies: The moderating role of increasing energy consumption

There are increasing debates on the relationship between economic complexity and environmental degradation. This study deepens our understanding of this nexus in 11 emerging economies given the moderating role of energy consumption while controlling for economic development, trade openness and population growth. The findings from the quantile regression technique reveal that emerging economies are characteristic of low energy consumption, leading to insignificant contributions of economic complexity to environmental degradation across the spectrum as they also have very low-trade openness. Further results show the invalidity of the EKC between energy use (such as fossil fuels) and environmental degradation in emerging economies. Moreover, the Environmental Kuznets Curve (EKC) between economic development and environmental degradation is valid especially for those countries in the low and median quantiles (Egypt, Indonesia, and Vietnam). Also, the EKC hypothesis between population and environmental degradation is valid only for countries in the high and highest quantiles (Korea Republic, Turkey, Mexico and Iran). Finally, the results revealed that trade openness strictly reduces environmental degradation across the spectrum. Policy implications, limitations of the study and direction for future research are discussed.

The role of bank financing in economic growth and environmental outcomes of sub-Saharan Africa: evidence from novel quantile regression and panel vector autoregressive models

In sub-Saharan Africa, economic expansion and its environmental implications have become major problems. The banking system has been described as a mechanism for decoupling economic expansion from environmental implications. However, the function of bank financing in the growth-environmental consequences in SSA remains undeveloped. This study investigated the role of bank financing in economic growth and environmental outcomes in SSA over the period 1990-2018. We implemented the novel panel quantile regression and panel vector autoregressive models in a generalized method of moments' framework to investigate the influence of bank financing on economic growth and carbon emissions, and the moderating effect of bank financing in growth-environmental consequences among the four regional economies in SSA. The empirical results revealed that bank financing (1) increases economic growth and carbon emissions across quantiles; (2) positively influences economic growth and carbon emissions of East and Central African regions but negatively influences economic growth and carbon emissions of the West African region; (3) mitigates growth-emissions outcomes of low-emission countries but worsens growth-emissions outcomes of median and high emission countries; and (4) worsens growth-emissions outcomes of East and Central African regions but mitigates growth-emissions outcomes of Southern and West African sub-regions. The variance decomposition and impulse response results discovered that the role of bank financing in growth-environmental challenges varies in terms of magnitude and elasticities across the sub-regions over the sampled period. The study also revealed mixed findings regarding the existence of the EKC hypothesis for the sub-regional economies in SSA.

Do economic policy uncertainty and geopolitical risk surge CO2 emissions? New insights from panel quantile regression approach

In recent times, economic policy uncertainty (EPU) and geopolitical risk (GPR) are increasing significantly where the economy and environment are affected by these factors. Therefore, the goal of this paper is to investigate whether EPU and GPR impede CO₂ emissions in BRICST countries. We employ second-generation panel data methods, AMG and CCEMG estimator, and panel quantile regression model. The conclusions document that most of the variables are integrated at I (1), and there exists co-integration among considered variables of the study. Moreover, we note that EPU and GPR have a heterogeneous effect on CO₂ emissions across different quantiles. EPU adversely affects CO₂ emissions at lower and middle quantiles, while it surges the CO₂ emissions at higher quantiles. On the contrary, geopolitical risk surges CO₂ emissions at lower quartiles, and it plunges CO₂ emissions at middle and higher quantiles. Furthermore, GDP per capita, renewable energy, non-renewable energy, and urbanization also have a heterogeneous impact on CO₂ emissions in the conditional distribution of CO₂ emissions. Based on the results, we discuss the policy direction.

Nexus between Technological Innovation, Renewable Energy, and Human Capital on the Environmental Sustainability in Emerging Asian Economies: A Panel Quantile Regression Approach

The goal of this study was to examine the interlinkage of renewable energy, technology innovation, human capital, and governance on environment quality by using a panel quantile regression in Asian emerging economies over the period of 1990–2019. The results indicated that higher economic growth, population density, technological innovation in renewable energy, and exploitation of natural resources have significantly raised CO2 emissions in emerging Asia. Furthermore, larger capital, more use of renewable energy, green technology, and human capital development can improve environmental sustainability in Asia. As for governances, proxied by corruption rates, no evidence indicated that it has resulted in more damage, unlike earlier studies have suggested. The findings indicated that the three channels exposed in the Kuznets hypothesis can serve as a reference for proposals for environmental policies (scale of consumption, energy composition, and choice of technologies). There are opportunities to reduce CO2 emissions through investments in human development, investing in new technologies to increase efficiency in energy (generation and consumption), increasing working capital (GCF), and migrating to more environmentally friendly energy. The negative link between carbon dioxide emissions and economic growth, increases in population density, and exploitation of natural resources can compromise the achievement of sustainable environmental goals.

The impacts of governance on environmental pollution in some countries of Middle East and sub-Saharan Africa: the evidence from panel quantile regression and causality

Governance is one of the basic determinants of pollution levels through property rights, the effective judicial system, etc. it is accepted as that bad governance because of inefficient regulatory structures, government bureaucracy, weak law enforcement, etc. support environmental pollution. In this context, in some countries of the Middle East and sub-Saharan Africa, it will be studied the impacts of governance on environmental pollution over the period of 1996-2018 by the panel quantile and Granger causality methods. The countries were selected by considering two different measurements, EPI (2020) index and governance index (2020). According to EPI (2020), these countries have low scores in terms of environmental quality, and in the governance index (2020), they have bad governance scores. In this study, in which panel quantile regression model is used, control variables are included in the model to prevent omitted-variable bias. The results of the analysis determined that the effect of governance on carbon emissions is positive, as well as that the effects of independent variables on CO2 emission are heterogeneous across quantities. Panel quantile regression revealed the evidence of the relation among the environmental pollution, two parameters of governance, FDI, financial development, human development index, and trade openness used as the explanatory variable and determined that government has the greatest positive effect on CO2 emission. On the other hand, by using traditional Granger causality and Dumitrescu-Hurlin causality methods, it was found the evidence of causality among governance and environmental pollution in the context of two parameters of governance. Accordingly, it was determined the evidence of unidirectional causality relation from political governance to environmental pollution and besides from economic governance to environmental pollution. And it was determined the evidence of unidirectional causality from FDI and the other explanatory variables to environmental pollution.

Accounting for the combined impacts of natural resources rent, income level, and energy consumption on environmental quality of G7 economies: a panel quantile regression approach

As the argument widens on the need to cut down on global carbon emissions, this study addresses environmental degradation using a combination of second-generation empirical methodologies including, quantile regression (QR), augmented mean group (AMG), fully modified ordinal least square (FMOLS), and dynamic ordinal least square (DOLS) to examine the impacts of natural resource rents alongside disaggregated energy consumption on the environmental quality of the G7 economies within the framework of the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The empirical findings reveal that the total natural resources rent indicates a positive significant relationship with pollution in all the quantiles except Q 0.05. Additionally, the findings for renewable energy consumption are adverse and significant throughout the assessed quantiles while fossil fuel energy consumption is reported to have a positive and significant effect on carbon dioxide emissions, thus, increasing environmental degradation experienced in the G7 economies. The extended findings from the Granger causality analysis also show that income levels combined with fossil fuel use have a strong effect on environmental degradation, while the total natural resources rent granger causes clean energy consumption within the G7 countries. This finding supports the assertions that natural resource revenue is mostly channeled into further productivity avenues which consequently lead to further environmental degradation. As such, while maintaining targeted revenue agenda, we strongly recommend that productivity gains from natural resource rents within the G7 economies should be harnessed for investment in clean energy for a more sustainable environment.

Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach

Energy research and development (R&D) and environmental sustainability is often referred to as two interrelated trends, especially in the current context of the 4^th industrial revolution. As a primary input of energy innovations, R&D in the energy sector constitutes a vital tool in addressing global environmental and energy challenges. In this frame, we observe the effects of disaggregated energy R&D on environmental pollution within the Environmental Kuznets Curve (EKC) framework in thirteen developed countries over the period 2003-2018. By employing the panel quantile regression technique, we find an inverted U-shaped nexus between economic growth and carbon emissions only in higher carbon-emitting countries, thus, confirming the EKC hypothesis. However, the U-shaped nexus is more predominant in lower carbon-emitting countries. As such, we demonstrate that there is not any single dynamic in the relationship between economic growth and pollution as reported in previous studies. Contrary to expectations, we find that energy efficiency research and development is more effective in curbing carbon emissions compared to fossil fuels and renewable energy research and development. The empirical results indicate also that only energy efficiency R&D mitigates significantly the CO₂ emissions from the 50^th quantile up to 90^th quantile, although the magnitude of the negative sign is more pronounced (in absolute term) at the highest quantile (90th). In this light, our findings would guide policymakers in the establishment of sustainable energy research and development schemes that will allow the preservation of equilibrium for the environment while also promoting energy innovations.

Bayesian Analysis of Partially Linear Additive Spatial Autoregressive Models with Free-Knot Splines

This article deals with symmetrical data that can be modelled based on Gaussian distribution. We consider a class of partially linear additive spatial autoregressive (PLASAR) models for spatial data. We develop a Bayesian free-knot splines approach to approximate the nonparametric functions. It can be performed to facilitate efficient Markov chain Monte Carlo (MCMC) tools to design a Gibbs sampler to explore the full conditional posterior distributions and analyze the PLASAR models. In order to acquire a rapidly-convergent algorithm, a modified Bayesian free-knot splines approach incorporated with powerful MCMC techniques is employed. The Bayesian estimator (BE) method is more computationally efficient than the generalized method of moments estimator (GMME) and thus capable of handling large scales of spatial data. The performance of the PLASAR model and methodology is illustrated by a simulation, and the model is used to analyze a Sydney real estate dataset.

Analysis of CO2 emissions and energy consumption by sources in MENA countries: evidence from quantile regressions

The development of economies and energy usage can significantly impact the carbon dioxide (CO₂) emissions in the Middle East and North Africa (MENA) countries. Therefore, this study aims to analyze the factors that determine CO₂ emissions in MENA under the environmental Kuznets curve (EKC) framework by applying novel quantile techniques on data for CO₂ emissions, real income, renewable and non-renewable energy consumption, and urbanization over the period from 1990 to 2015. The results from the estimations suggest that renewable energy consumption significantly reduces the level of emissions; furthermore, its impact increases with higher quantiles. In addition, non-renewable energy consumption increases CO₂ emissions, while its magnitude decreases with higher quantiles. The empirical results also confirm the validity of EKC hypothesis for the panel of MENA economies. Policymakers in the region should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions.

Combining the biennial Malmquist-Luenberger index and panel quantile regression to analyze the green total factor productivity of the industrial sector in China

Improving the green total-factor productivity (GTFP) is a key measure to coordinate industrial development and environmental protection in China. This study adopts the biennial Malmquist-Luenberger (BML) productivity index to estimate the GTFP change of China's 34 industrial subsectors covering the period 2005-2015. Subsequently, fixed-effect panel quantile regression is applied to analyze the heterogeneous effects of eight selected influencing factors on China's industrial GTFP change. The results show that China's overall industrial GTFP exhibited an increasing trend during the study period and varied greatly in different sub-sectors. Moreover, technological innovation rather than efficiency promotion was the main contributor to the improvement of industrial GTFP in China. The impact of the scale structure (SS) was significantly positive across the quantiles and maintained a slightly downward trend. The impact of the property rights structure (PTS) was significantly negative and showed an increasing trend across the quantiles. The impact of the energy intensity (EI) slightly increased and was significantly negative at most quantiles. The energy consumption structure (ECS) exhibited an increasing trend and had a significantly negative effect at the middle quantiles. Technological innovation (TI) exerted a significantly positive effect and displayed a downward trend across the quantiles, and it was the most important factor to drive industrial GTFP growth. The "pollution halo" hypothesis and the Porter hypothesis were both verified with a certain range from the analysis of foreign direct investment (FDI) and environmental regulation (ER), as well as the interaction between ER and TI. Our results stress the importance of the heterogeneous effects of these influencing factors on different quantile subsectors when formulating the related measures and policies.

Energy consumption, FDI, and urbanization linkage in coastal Mediterranean countries: re-assessing the pollution haven hypothesis

Foreign direct investment (FDI) and the consumption of non-renewable energy have been on the increase in the coastal Mediterranean countries (CMCs) over the last few decades. Both trigger growth, but the environmental impact could be far-reaching as environmental distortions are mainly human-induced. This study examines the environmental issues facing CMCs. Specifically, we investigate whether the pollution haven hypothesis holds for CMCs. We employ a quantile panel data analysis for CMCs to account for heterogeneity and distributional effects of socioeconomic factors. The result reveals that the influence of FDI on environmental degradation is a function of the indicators utilized and also depends on the initial levels of environmental degradation. The results suggest that the pollution haven hypothesis does not hold for CMCs. However, we also find that energy consumption significantly increases environmental degradation for all indicators and across the observed quantiles. The effects of economic growth and urbanization on the environment were mixed for the different indicators and across quantiles. We recommend that it is pertinent for CMCs to limit their "dirty" energy sources and substitute them with renewables to promote environmental sustainability.

An empirical study on the determinants of health care expenses in emerging economies

BACKGROUND

Emerging countries continue to suffer gravely from insufficient healthcare funding, which adversely affects access to quality healthcare and ultimately the health status of citizens. By using panel data from the World Development Indicators, the study examined the determinants of health care expenditure among twenty-two (22) emerging countries from the year 2000 to 2018.

METHODS

The study employed cross-section dependence and homogeneity tests to confirm cross-sectional dependence and to deal with homogeneity issues. The Quantile regression technique is employed to test for the relationship between private and public health care expenses and its determinants. The Pooled mean group causality test is used to examine the causal connections among the variables.

RESULTS

The outcome of the quantile regression test revealed that economic growth and aging population could induce healthcare costs in emerging countries. However, the impact of industrialization, agricultural activities, and technological advancement on health expenses are found to be noticeably heterogeneous at the various quantile levels. Unidirectional causality was found between industrialization and public health expenses; whereas two-way causal influence was reveled amongst public health expenditure and GDP per capita; public health expenditure and agricultural activities.

CONCLUSION

It is therefore suggested that effective and integrated strategies should be considered by industries and agricultural sectors to help reduce preventable diseases that will ultimately reduce healthcare costs among the emerging countries.

The Interaction Effect between ESG and Green Innovation and Its Impact on Firm Value from the Perspective of Information Disclosure

Few studies have been conducted on whether the coexistence of green innovation and corporate social responsibility (CSR) has a favorable interaction effect on firm value. This interaction effect is of great significance for enterprises balancing resource allocation between two factors in the future. Meanwhile, information disclosure can reflect the efforts of enterprises in taking on CSR. Therefore, taking China’s listed companies as an example, this paper studies the interaction effect of CSR after being divided into the three different dimensions of environment, society, and governance (ESG) and green innovation on firm value. The quantile regression method can reflect the impact of CSR and green innovation on the firm value of different levels. The study finds that: (1) green innovation can promote the improvement of medium- and high-level firm value; (2) only the disclosure of environmental and social information can have a positive impact on firm value; (3) the interaction effect between green innovation and social disclosure on firm value is a substitution effect, which will gradually weaken with the increase of firm value. This paper proposes that relevant departments should guide green funds into enterprises with capital constraints to alleviate the issue of fund crowding into CSR and green innovation.

Different impacts of export and import on carbon emissions across 7 ASEAN countries: A panel quantile regression approach

ASEAN (Association of Southeast Asian Nations) has contributed numerous carbon emissions during the phase of industrialization. This study mainly compares the different effects of export and import on CO₂ emissions across 7 ASEAN countries over 1990-2017. In addition, we investigate how technological innovation affects carbon emissions. Stationary tests are conducted through cross section dependence, unit root of panel data, and Westerlund cointegration. The results of panel quantile regression show that export and import both have adverse effects on CO₂ emissions. EKC is valid in these countries. Moreover, population size and energy intensity increase carbon emissions. In particular, technology innovation significantly reduces carbon emissions by augmenting energy efficiency. It is important to improve eco-innovation, and expand knowledge-intensive industries in ASEAN countries.

The Impacts of Non-Fossil Energy, Economic Growth, Energy Consumption, and Oil Price on Carbon Intensity: Evidence from a Panel Quantile Regression Analysis of EU 28

This study investigates some determinants of carbon intensity in 28 countries in the European Union (EU), including non-fossil energy, economic growth, energy consumption, and oil price. A panel quantile regression method, which considers both individual heterogeneity and distributional heterogeneity, is applied in this paper. The empirical results imply that the influences of these determinants on carbon intensity are heterogeneous and asymmetric across different quantiles. Specifically, non-fossil energy can significantly decrease carbon intensity, but shows a U-shaped relationship. Economic growth has a negative impact on carbon intensity, especially for medium-emission and high-emission countries. The effects of heating degree days on carbon intensity are positive, although the coefficients are not significant at low quantiles, they become significant from medium quantiles. Besides, we find an inverse U-shaped relationship between crude oil price and carbon intensity. Finally, several relevant policy recommendations are proposed based on the empirical results.

New semiparametric method for predicting high-cost patients

Motivated by the Medical Expenditure Panel Survey containing data from individuals' medical providers and employers across the United States, we propose a new semiparametric procedure for predicting whether a patient will incur high medical expenditure. Problems of the same nature arise in many other important applications where one would like to predict if a future response occurs at the upper (or lower) tail of the response distribution. The common practice is to artificially dichotomize the response variable and then apply an existing classification method such as binomial regression or a classification tree. We propose a new semiparametric prediction rule to classify whether a future response occurs at the upper tail of the response distribution. The new method can be considered a semiparametric estimator of the Bayes rule for classification and enjoys some nice features. It does not require an artificially dichotomized response and better uses the information contained in the data. It does not require any parametric distributional assumptions and tends to be more robust. It incorporates nonlinear covariate effects and can be adapted to construct a prediction interval and hence provides more information about the future response. We provide an R package plaqr to implement the proposed procedure and demonstrate its performance in Monte Carlo simulations. We illustrate the application of the new method on a subset of the Medical Expenditure Panel Survey data.