Dependence Of Cross Section Research Articles

Socio-economic Development and Carbon Productivity: A Panel Data Analysis of the World’s Largest Carbon-Emitting Countries

AbstractAs global concerns about climate change intensify, assessing the environmental efficiency of production processes through carbon productivity has become increasingly important. This study examines the impact of socio-economic development on carbon productivity in the top 18 CO2-emitting countries, which contribute approximately 82% of global CO2 emissions, over the period 1990–2019. Using a class of econometric tests to address heterogeneity and cross-sectional dependence, we employ the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model for final estimation, ensuring robustness with Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimations. The empirical findings reveal that GDP per capita, Trade, and FDI increase carbon productivity while energy consumption and urbanization curtail carbon productivity. The country-specific effects indicated that 83% of the sample countries exhibit positive relationships between socio-economic development and carbon productivity, suggesting that these nations can serve as models for effective low-carbon policies. Moreover, the results demonstrate bi-directional relationships for GDP per capita, FDI, and energy use with carbon productivity and uni-causal relationship for trade and urbanization. The study highlights the need for implementing stricter regulations to improve energy efficiency and promote the adoption of renewable energy sources such as wind, solar, hydro, and nuclear power. Additionally, countries should incentivize green technology investments through tax breaks and subsidies, enhance international trade agreements that support the exchange of clean technologies, and develop sustainable urban planning initiatives to mitigate the negative impact of urbanization on carbon productivity.

Electromechanical properties and piezoelectric potentials of one-dimensional GaN nanostructures: A bond relaxation investigation

From the viewpoint of atomic bond relaxation, an analytical approach was put forward to elucidate the physical origins of crystal size and cross-sectional shape dependency of piezoelectric potentials in GaN nanowires and nanotubes. It is demonstrated that (i) size-induced increase in piezoelectric potential is attributed to the coupling effect of the rising piezoelectric coefficient and both the reducing dielectric constant and elastic constant caused by the surface atomic coordination number loss, bond energy perturbation, and surface-to-volume ratio rising; (ii) as the number of sides for polygonal nanowires or nanotubes with the same equivalent radius decreases, the surface-to-volume ratio rises, and the piezoelectric potential increases; and (iii) the nanotubes can generate a piezoelectric potential higher than their nanowire counterparts due to their larger surface-to-volume ratios. The proposed formulation offers a scientific basis for the fabrication, optimization, and modulation of one-dimensional GaN-based piezoelectric nanometer devices.

Artificial intelligence and wealth inequality: A comprehensive empirical exploration of socioeconomic implications

This study introduces a global database on artificial intelligence (AI) capital stock and related AI indicators. Using the data constructed, we investigate the impact of AI and capital stock accumulation on wealth inequality, a dimension not extensively explored in the literature. This study contributes to the growing body of literature on the socioeconomic consequences of AI, with implications for scholars, policymakers, and corporate executives. An innovative database detailing AI capital stock is developed by incorporating data from various sources, including corporate reports, industry databases, and scholarly literature. This novel dataset, focusing on the US, the EU, and Japan from 1995 to 2020, is a critical resource for future investigations. The research methodology is centered on an extended Solow–Swan model, conceptualizing AI as a form of capital that can substitute for or complement traditional forms of labor. A panel-corrected standard errors model is used to analyze the data, accounting for potential cross-sectional dependence and heteroscedasticity. Our findings reveal a positive and statistically significant correlation between AI technology adoption, AI capital stock accumulation, and wealth disparity. The analysis further indicates a complex interaction between income and wealth disparities, suggesting a mutually reinforcing cycle. This study fills a significant gap in the existing literature by offering a novel perspective on the distributional impact of AI. Our results underscore the importance of considering the broader socioeconomic implications of AI, extending beyond considerations of immediate productivity and economic growth. This study offers valuable insights for policy formulation and business decision making, emphasizing the necessity of a comprehensive understanding of the influence of AI on wealth distribution.

Economic sanctions and energy efficiency: Evidence from Iranian industrial sub-sectors

Improving energy efficiency is vital for reducing energy consumption and can have substantial impacts on reducing carbon emissions. This study investigates the impact of sanctions on Iran's energy efficiency across different industrial sub-sectors from 2015 to 2019. We compute a sanction intensity index for each industrial sub-sector using principal component analysis. This index measures how much each sub-sector has been affected by sanctions. Additionally, energy efficiency is measured using the directional distance function method, considering the emission impacts as undesirable outputs. We use fixed effects regression analysis, adjusting for various manifestations of cross-sectional (spatial) and temporal dependence and controlling for other sectoral drivers of energy efficiency. We find a significant negative effect of sanction intensity on energy efficiency at the industrial sub-sector level in Iran. The negative effect of sanctions on energy efficiency is significantly larger for industrial units with higher import dependency.

Driving low-carbon mechanisms through smart investments in renewable resources and green finance initiatives among G20 nations

In light of the escalating concerns regarding climate change and environmental decline, major nations are actively exploring strategies to mitigate environmental harm and achieve future sustainability. The surge in economic expansion in developed economies is linked to an increase in CO2 emissions. Consequently, in their pursuit of carbon-neutral policies, these countries are increasingly turning towards renewable energy as a means to enhance resource conservation and efficiency. This research investigates the varied impacts of renewable energy investment, green finance, geopolitical risk, GDP growth, foreign direct investment, and gross fixed capital formation on the carbon emissions of G20 countries. The study uses the CUP-FM (Continuously Updated Fully Modified) and CUP-BC (Continuously Updated Bias-Corrected) estimators, which are sophisticated econometric approaches designed to handle non-stationary panel data and cross-sectional dependency, to produce robust long-term parameter estimates. The CUP-FM estimator adjusts for potential endogeneity and serial correlation, improving the accuracy of long-run relationships in panel data. The CUP-BC estimator provides bias-corrected estimates to further enhance the precision of these long-run connections.The long-term parameter estimates reveal a negative correlation between renewable energy investment, green finance, and carbon emissions. In contrast, foreign direct investment, gross fixed capital formation, GDP growth, and geopolitical risk are positively associated with CO2 emissions. This suggests that financial stability often leads to investments in carbon-heavy economic ventures, thereby implicating economic growth as a contributing factor to environmental degradation in G20 countries.

Stabilization of U 5f2 configuration in UTe2 through U 6d dimers in the presence of Te2 chains

We investigate the topological superconductor candidate UTe2 using high-resolution valence-band resonant inelastic x-ray scattering at the U M4,5 edges. We observe atomiclike low-energy excitations that support the correlated nature of this unconventional superconductor. These excitations originate from the U 5f2 configuration, which is unexpected since the short Te2-Te2 distances exclude Te2 being 2−. By utilizing the photoionization cross-section dependence of the photoemission spectra in combination with band structure calculations, we infer that the stabilization of the U 5f2 configuration is due to the U 6d bonding states in the U dimers acting as a charge reservoir. Our results emphasize that the description of the physical properties should commence with a 5f2 ansatz. Published by the American Physical Society 2024

Natural resources, a curse or blessing for international trade? Empirical evidence from CAREC nations

AbstractThere is a huge pile of literature, accessible on economic growth (GDP), natural resource rents (NR), mineral rents (MR), natural gas rents (GR), oil rents (OR), and forest rents (FR) but it does not address the association between trade openness (TO) and the availability of these resources. Thus, this study objectively probes the link among these resources and TO in CAREC nations (Georgia, China, Pakistan, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan) from 2000 to 2022. Mongolia, Afghanistan, and Tajikistan from CAREC countries are exempted in this inquiry due to unavailability of the data for the variables, GR, MR, and OR. The key methods used in this inquiry include method of moment quantile regression (MMQR) followed by Westerlund analysis, slope heterogeneity, CIPS unit root test, cross‐sectional dependence (CSD), and matrix correlation. The MMQR yields valid, trustworthy, and robust results. The empirical estimates demonstrate that MR and OR suggest significant positive impacts on TO. However, NR, GR, FR, and GDP have negative association with TO in CAREC countries. Besides, the research offers pertinent theoretical, and managerial blueprint proposals for global commerce by utilizing MR, OR, FR, and NR contributing to sustainable development goals (SDGs) by fostering economic development, promoting sustainable development, and addressing environmental and social challenges, and highlighting the real impact and association of these resources with TO based on empirical aftermath of the study.

Club Convergence in Productivity among European Regions: Recent Evidence and Policy Implications

We use data on 155 NUTS regions, with the objective of i) generating TFP series over 1996-2018, and ii) testing for club-convergence. We also employ a logistic regression to identify the significant club-membership conditioning factors. We contribute to the literature on two main grounds. Firstly, we are the first to extract TFP series while applying a methodology that allows for heterogeneous production functions and accounts for cross-section dependence and nonstationarity. Secondly, we are the first to apply a TFP-club convergence analysis in the context of European regions over a large period of time that witnessed several shocks affecting the European integration. Our analysis reveals that: (i) most of the regions of the top TFP convergence clubs are located in the "old" EU-member states, with few regions situated in countries that joined the EU after 2004; (ii) several regions in the South are trailing with the risk of falling in a "productivity trap"; (iii) regions situated in new EU-member countries registered remarkable TFP growth rates; (iv) larger regional research and development resources, and higher employment in high-technology sectors are associated with a greater probability of being member of top-ranked TFP convergence clubs. Our findings suggest that the needs of the lagging regions should be prioritized in the agenda of the EU. In this respect, we suggest several recommendations.

The dynamics of non-intervention: applying the dynamic common correlated effects estimator to state-level economic freedom

ABSTRACT The relationship between economic freedom and economic performance is well-studied. We contribute to a dimension of the literature concerned with subnational economic freedom and subnational economic performance through the application of the Dynamic Common Correlated Effects estimator, which allows estimation for the first time to take into account cross-sectional dependence and heterogeneous slopes among US states. We find a positive relationship between economic freedom and economic growth, though when applying a battery of control variables, some specifications lose statistical significance.

Impact of Classical and Quantum Light on Donor-Acceptor-Donor Molecules.

Investigations of entangled and classical two-photon absorption have been carried out for six donor (D)-acceptor (A)-donor (D) compounds containing the dithieno pyrrole (DTP) unit as donor and acceptors with systematically varied electronic properties. Comparing ETPA (quantum) and TPA (classical) results reveals that the ETPA cross section decreases with increasing TPA cross section for molecules with highly off-resonant excited states for single-photon excitation. Theory (TDDFT) results are in semiquantitative agreement with this anticorrelated behavior due to the dependence of the ETPA cross section but not TPA on the two-photon excited state lifetime. The largest cross section is found for a DTP derivative that has a single photon excitation energy closest to resonance with half the two-photon excitation energy. These results are important for the possible use of quantum light for low-intensity energy-conversion applications.

Interplay of Foreign Direct Investment, Remittances, and Economic Growth: Insights from SAARC Countries

The current study focuses on finding and analyzing the nexus between remittances, foreign direct investment, and economic growth in SAARC countries. This study uses secondary balanced data of selected SAARC countries for the years 2000 to 2019. This study uses gross domestic product i.e., GDP, which serves as the proxy of economic growth, as the dependent variable. The core independent variables of this study include (FDI) foreign direct investment and remittances (RMTN). Additionally, gross capital formation (GCF) and carbon dioxide emissions (CO2) are the other two independent variables in this study. For the proxy for environmental degradation the study uses data of CO2 emissions. The data is collected from the world development indicators, WDI. The study focuses on how all the independent variables affect the growth of economy in selected SAARC countries which include Pakistan, India, Bangladesh, Sri Lanka, and Nepal. The study applies cross sectional dependence test, panel cointegration test, unit root tests, and PARDL (panel auto regressive distributed lag model), also called pooled mean group methodology, to obtain empirical estimates. The results of PARDL show that FDI, RMTN, and GCF are all positively and significantly related to GDP. This means that GDP or alternatively economic growth, is improving with rise in FDI, RMTN, and GCF in selected SAARC countries. The results show that CO2 emissions have a negative significant impact on GDP i.e., increase in pollution levels is harmful for the growth of the selected economies.

Analyzing the Dynamics: Asymmetric Effects of Economic Growth, Technological Innovation, and Renewable Energy on Carbon Emissions in Africa

This study examines the interactions among economic growth, technological innovation, renewable energy consumption, and carbon emissions in Africa using advanced econometric methods. Panel data analysis reveals both symmetric and asymmetric impacts on carbon emissions, showing how economic and technological variables contribute to environmental outcomes. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators address cross-sectional dependence and heterogeneity, ensuring robust results. Findings indicate that positive economic growth increases emissions, while economic downturns significantly reduce emissions. The study underscores the need for strategic policies that leverage technological innovations and renewable energy to promote sustainable development in African economies, balancing economic growth with environmental protection.

Economic Diversification, Oil Revenue and Energy Transition in Oil Dependent Countries: A Wavelet Decomposition and Panel Data Approach

In oil-dependent countries, the energy transition process is ongoing and it appeals substantial and necessary adjustments in terms of diversification. We attempt to provide a systematic analysis of the connectedness between energy transition, oil revenue and economic diversification using novel econometric approach, wavelet decomposition and panel data estimation. Our attention will be focused on the dynamics and causal relationships between energy transition process and economic diversification by checking the role of oil revenue. We use second generation unit roots, and a particular attention is given to cross-sectional dependence of the series. Results show that correlations between wavelet components are absent in the short term, weak in the medium term, and moderate in the long-run. Causality tests support a bidirectional causality between economic diversification and energy transition and between economic diversification and net oil revenue, in the medium and long-run scale levels. Our results would have several prominent implications for policy makers, in oil-dependent countries, when designing energy transitions and economic diversifications strategies, and gives and insightful look about the future of these countries.

Unleashing the potential: a quest to understand and examine the factors enriching research and innovation productivities of South Asian universities

South Asian universities play a crucial role in driving economic development through research and innovation. However, these institutions often face challenges that hinder their productivity in these areas. To address these challenges, this study aims to understand and examine the factors enhancing research and innovation productivity in universities across South Asian countries, specifically Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Using panel data from 2009 to 2021, the study employs the newly developed dynamic common correlated effects (DCCE) approach by Chudik and Pesaran (2015a). The results revealed that the factors found to be enriching research productivity, though the impact is negligible, are patent rights, funding for tertiary education, information technology, and publications. However, tertiary school enrollment is not a significant factor. On the other hand, for innovation productivity, the influencing factors with a negligible impact are patent rights, funding for tertiary education, and information technology. In this case, neither tertiary school enrollment nor publications play a significant role. Furthermore, the research productivity of the universities in South Asian countries is negligibly stimulating the countries’ GDP per capita, while the innovation productivity of the universities is not. Moreover, examining these relations using models such as mean group (MG), pooled mean group (PMG), and augmented mean group (AMG) can produce misleading results due to cross-sectional dependence among the units. Nonetheless, PMG outperformed AMG, followed by MG. Policy recommendations were suggested based on the findings.

Navigating poverty in developing nations: unraveling the impact of political dynamics on sustainable well-being

Political instability, dismal governance, and corruption are among the factors that currently distress poverty. The primary objective of this research was to determine how political factors distress poverty in developing nations, which has perhaps not been investigated yet. The main objective was also to observe if the effect of political factors on poverty is a dilemma or a reality. Poverty (dependent variable) has been divided into two segments: income poverty index and Human Health Poverty Index; however, political factors (independent variables) studied were corruption, democracy, governance, and political globalization. Twenty-six years of data were taken from 1997 to 2022 of twenty-four developing nations. The poverty and institutional quality indices were constructed through Principal Component Analysis (PCA). The Fixed Effect, System GMM, and 2SLS approaches were used to determine the dynamic impact on poverty. Furthermore, this study incorporated fixed effects with Driscoll-Kraay standard errors to address cross-sectional dependence. The findings indicated that, although there was a significant connection between governance and income and human poverty. Democracy also showed a negative and significant relationship in the income poverty model but insignificant in the human poverty model. Political globalization showed negative and significant associations with poverty models (income and human poverty). Conversely, corruption showed a significant positive relationship in poverty models, i.e., income and human poverty.

Enthalpy of the Cerium (Ce) Chemi-Ionization Reaction and CeO+, CeC+, and CeCO+ Bond Energies Determined by Energy-Resolved Guided Ion Beam Mass Spectrometry Experiments.

Cerium (Ce) oxide, carbide, and carbonyl cation bond energies and the exothermicity of the Ce chemi-ionization (CI) reaction with atomic oxygen were investigated using guided-ion beam tandem mass spectrometry (GIBMS). The kinetic energy dependent product cross sections for reactions of Ce+ with O2, CO, and CO2 and CeO+ with O2, CO, Xe, and Ar were measured using GIBMS. For the reactions of Ce+ with O2 and CO2, CeO+ is formed through an exothermic reaction, whereas CeO+ formation is endothermic in the reaction with CO. This reaction also forms CeC+ and CeCO+ is formed in the reaction of Ce+ with CO2, both in endothermic processes. Reactions of CeO+ with all four gases led to endothermic collision-induced dissociation as well as exchange reactions to form CeO2+ for the O2 and CO reactants. Bond dissociation energies (BDEs) at 0 K were determined through analyses of the kinetic energy dependent cross sections for all endothermic reactions. We determined that the BDEs of CeO+, CeC+, and CeCO+ are 8.46 ± 0.15, 3.93 ± 0.16, and ≥0.25 ± 0.07 eV, respectively, where the CeO+ BDE is a weighted average of 6 independent 0 K threshold measurements. The CeO+ BDE is combined with the ionization energy (IE) of Ce to determine an exothermicity of 2.91 ± 0.15 eV for the Ce + O → CeO+ + e- chemi-ionization reaction. Combined with neutral BDEs from the literature, the thermochemistry determined here also provides IE(CeO) = 5.03 ± 0.12 eV and IE(CeC) = 6.50 ± 0.16 eV. Theoretical calculations for ground and some excited states were performed for CeO+, CeC+, and CeCO+ to provide insight into the bonding and a comparison with the experimentally determined BDEs for each molecule.

COP 28 Policy Perspectives: Achieving Environmental Sustainability through FDI, Technological Innovation Index, Trade Openness, Energy Consumption, and Economic Development in N-11 Emerging Economies

Despite remarkable success in attracting foreign direct investment (FDI) to achieve maximum economic growth, the Next-11 emerging economies grappling with an undesirable situation of environmental degradation have become a hot topic at COP28. Researchers have long focused on this connection, emphasizing the urgent need for international and national environmentalists to promote sustainable development (SD) in these rapidly growing economies under the United Nations (UN) Framework Convention on Climate Change action plans. As a result, this study examines the role of FDI in the N-11 emerging economies, focusing on energy usage and technological innovation within the theoretical framework of the Halo-Haven hypothesis, covering the period from 1990 to 2022. We utilize ARDL, FMOLS, and DOLS techniques to analyze both short-term dynamics and long-term equilibrium relationships, effectively managing heterogeneity, time dynamics, and cross-sectional dependence issues to produce comprehensive results. The long-term analysis supports the haven hypothesis, demonstrating an affirmative relationship between FDI, economic growth, and carbon emissions, whereas energy usage is negatively associated with carbon emissions. Furthermore, the D-H test established a reciprocal causal relationship between variables such as FDI, economic growth, trade openness, and environmental pollution. However, we found a one-way causal correspondence in the usage of green energy, the technological innovation index, and carbon emissions. Given the mixed findings, policymakers should focus on attracting FDI to the green energy sector while reinforcing regulations and implementing stringent oversight for FDI in energy-intensive industries. This approach will ensure that such investments adhere to high environmental standards, thereby benefiting future generations.

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.

Co-movements between Stock Markets and Exchange Rates: Empirical Evidence from the G20 Nations

This study investigates the relationship between stock markets and exchange rate markets in the short and long run for G20 countries by analyzing weekly stock market returns and exchange rates from January 2008 to March 2023. This study uses the CD test for testing cross-sectional dependence in all the panel series, and unit root tests are employed to verify the stationarity properties of variables. Based on the above results, the panel ARDL model and PMG estimator are used to establish the long-run and short-run cointegrating relation between the stock and exchange rate markets. We have found a stable positive relationship between the stock markets and the exchange rate of G20 countries in the long run, but a negative association was found in the short run. These findings highlight the intricate dynamics between stock markets and exchange rates and the interconnectedness of G20 nations. The robust positive long-term correlation and the short-term negative association exhibit the differential impacts of macroeconomic variables over varying time horizons

Empirical Evaluation of The Pollution Haven Hypothesis for Seven Largest Emerging Economies within The Framework of The New Global Climate Agreements

This study aims to determine whether the Pollution Haven Hypothesis (PHavenH), which suggests the negative impact of the FDI on environmental quality (EQ), is valid in seven selected emerging market economies for the period 1990-2020. To achieve this goal, the CO2 emission (CE) model includes renewable energy consumption (REC) in addition to the FDI variable. The elasticity coefficients of the model, in which cross-sectional dependence (CSD) and slope heterogeneity were determined, were estimated by the AMG method. The study found that FDI and REC positively influenced the EQ of the countries examined. The Dumitrescu-Hurlin (D-H) test results indicated a bidirectional causality relationship between FDI and CE. Additionally, a unidirectional causality was observed from REC to CE. These results suggest that both FDI and REC play a role in improving EQ. The results of the analysis show that the PHavenH, which expresses the view that FDI in the relevant countries causes environmental degradation (ED) in developing countries, is not valid. In other words, it confirms the Pollution Halo Hypothesis (PHaloH), which points to the finding that FDI improves EQ in the countries concerned. In this context, it has been concluded that REC has an important contribution to determining the validity of this hypothesis. The findings of the study suggest that it is essential to formulate policy recommendations aimed at boosting the production and utilization of renewable energy in the seven emerging market economies under investigation. Such initiatives can help enhance environmental sustainability and contribute to a cleaner and greener future for these countries.