Common Correlated Effects Mean Group Research Articles

How environmental policy stringency, green investment, and renewable energy contribute to environmental pollution and load capacity factor in G11 countries?

AbstractAmid growing concerns about global environmental sustainability, the focus on understanding the interplay between environmental policies, investments in sustainable practices, and renewable energy sources has intensified. This study delves into the relationship between environmental policy stringency, green investment, and the utilization of renewable energy, specifically examining their impact on the load capacity factor (LCF) within G11 countries utilized from 2000 to 2021. Manifestly, LCF integrates both demand‐ and supply‐side aspects of environmental quality. It also utilizes a comprehensive indicator of environmental policy stringency, evaluating its impact alongside green investment. Employing the Cross Sectional‐Augmented Autoregressive Distributed Lag (CS‐ARDL) model and the Common Correlated Effect Mean Group (CCEMG) approach, this research demonstrates that environmental policy stringency, green investment, and renewable energy consumption positively and significantly influence the LCF, consequently fostering improvements in environmental sustainability in both short‐ and long‐term scenarios. It is worth mentioning that environmental policy stringency has the strongest effects on LCF followed by renewable energy. The findings strongly indicate that reinforcing environmental policies, promoting green investment, and embracing renewable energy usage can collectively yield a substantial reduction in environmental pressure for G11 countries.

Measuring How AI Innovations and Financial Accessibility Influence Environmental Sustainability in the G-7: The Role of Globalization with Panel ARDL and Quantile Regression Analysis

This study investigates the impact of AI innovation on environmental sustainability in the G-7 region from 2010 to 2022. Additionally, it tests the Load Capacity Curve (LCC) hypothesis in relation to financial accessibility, globalization, and urbanization. Cross-sectional dependence and slope homogeneity tests reveal the presence of cross-sectional dependence and heterogeneity issues. Panel unit root and panel cointegration tests confirm that the variables are free from unit root problems and are cointegrated in the long run. To identify significant factors influencing environmental sustainability, this study employs Panel ARDL and Quantile Regression methods. Both methods confirm the LCC hypothesis in the G-7 region, demonstrating a U-shaped relationship between income and the load capacity factor. The results indicate that AI innovation and financial accessibility are significantly positively correlated with the load capacity factor, while globalization and urbanization are negatively correlated, leading to lower environmental sustainability. To validate the robustness of the Panel ARDL and Quantile Regression results, Driscoll-Kraay standard errors, Augmented Mean Group, and Common Correlated Effects Mean Group estimation approaches are applied, all of which support the initial findings. Furthermore, the D-H causality test reveals unidirectional causality from economic growth, financial accessibility, globalization, and urbanization to the load capacity factor, and bidirectional causality between AI innovation and the load capacity factor.

The Impact of Ecological Footprint, Urbanization, Education, Health Expenditure, and Industrialization on Child Mortality: Insights for Environment and Public Health in Eastern Europe.

The purpose of this study is to examine the connection between child mortality in Eastern Europe and ecological footprint, urbanization, education, health expenditure, and industrialization. The study acknowledges the significance of understanding how these factors influence the infant mortality rates in this region from 1993 to 2022. The Grossman Health Outcome (H-O) model investigates the theoretical framework. For the existence of the cross-sectional dependency, mixed-order unit root, and cointegration problem, the famous Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) approach is applied. The research also used the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) to check robustness. The findings illustrated that health expenditure and education lessen the infant mortality rate in Eastern European countries. But ecological footprint, industrialization and unemployment raise the infant mortality rate. According to the CS-ARDL findings, expenditure on healthcare significantly reduces child mortality. Still, the ecological footprint significantly impacts increasing child mortality. However, the AMG and CCEMG models demonstrate that investing in education is the most effective strategy for reducing child mortality. Therefore, the government of Eastern European countries should provide more priorities in the sustainable urbanization, health expenditure, and education sectors. The robustness of the AMG and CCEMG also demonstrated the strength of the CS-ARDL findings. This research paper contributes to SDG 3 by examining the environmental and health factors that influence child mortality in Eastern Europe. Policymakers, public health professionals, and other stakeholders can use the findings to inform the development and implementation of programs that specifically target the identified causes of child mortality.

Unlocking the potential of FinTech: A pathway to sustainable resource management in the EU

The rapid growth of the ecological footprint within the European Union (EU) has indicated a strong need for new and innovative approaches to sustainably managing natural resources. To this end, this study delves into how to harness the power of financial technology (FinTech) by infusing the principles of circular economy and technological innovation to address the growing ecological footprint. This study aims to explore the intricate interplay of natural resources, circular economy, and technological innovation on the effect of ecological footprint, particularly focusing on FinTech as a moderator in these relationships. The common correlated effects mean group (CCGMG) estimator addresses panel data's heterogeneity and cross-sectional dependence, which is essential for understanding the complex nature of ecological footprint issues. The evidence from 27 EU countries spanning 2013 to 2021 suggests that while more natural resources are used, the ecological footprint will increase, but improving circular economy practices and technological innovation will massively reduce this increase. Specifically, a 1% increase in natural resources leads to a corresponding 0.079% rise in ecological footprint, while a 1% increase in the circular economy and technological innovation reduces ecological footprint by 0.328% and 0.326%, respectively. Furthermore, FinTech stands out as a pivotal moderating force in amplifying the positive effects of circular economy and technological innovation towards ecological sustainability. Notably, it cushions the negative impacts of natural resource use and reinforces the power of sustainable practices on the footprint-reducing effect. From that angle, several targeted policy recommendations are proposed to harness FinTech's capabilities of resources and waste management, optimizing financial capital allocation, and enhancing financing efficiency. The study provides valuable insights and practical recommendations for promoting green initiatives within the EU and globally, aligning with the overall objectives of environmental sustainability and economic resilience.

Towards achieving inclusive energy in SSA: The role of financial inclusion and governance quality

Energy poverty is a serious impediment to achieving Sustainable Development Goal 7 (SDG-7), thereby compelling policymakers and the scientific community to make collaborative commitments to address energy issues worldwide. While there is growing debate on the prevalent determinants of energy poverty, the empirical scrutiny of the contributing role of financial inclusion and governance quality remains elusive in existing literature. Therefore, this study investigates the impact of financial inclusion and governance quality on energy poverty in 29 sub-Saharan Africa (SSA) spanning 2004–2022. The empirical analysis was accomplished using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) techniques with the Driscoll-Kraay standard error (DKSE) approach as a robustness check. The empirical outcomes indicate that a well-developed financial system that fosters easy and quick access to credit, especially among marginalized households, leads to the affordability of modern energy resources, thereby ameliorating energy poverty in the region. Additionally, the findings demonstrate the undeniable role of good governance in fostering a sound and inclusive financial system that enables households to access energy resources, ultimately reducing energy poverty in the region. Based on these outcomes, this study proposes important policy insights for the design and implementation of inclusive financial and energy policies.

Exploring the impact of FDI on environmental innovation in China: An empirical investigation

Today, firms face several environmental concerns, including but not limited to: decreasing natural resources, pollution management, and climate change. Little is known about the factors that motivate the many forms of environmental innovation or the effects of these endeavors on the financial success of businesses, despite the growing demand for eco-friendly goods and services. For the first time, this study introduces the core environmental determinants for China's 30 provinces covering the period of 2005-2022. In these determinants, this study considers foreign direct investment (FDI), financial development (FDI), natural resources (NRs), human capital (HC), and green energy (GE). However, to investigate the study objectives, the present study utilizes an advanced series of estimators that can overcome all panel data problems. In these estimators, the Augmented Mean Group (AMG), the Common Correlated Effect Mean Group (CCE-MG), and the Panel Quantile Regression (P-QR) are being considered. However, the results of the long-term investigation obtained interesting outcomes for selected provinces. For example, financial proxies such as economic development and foreign direct investment significantly increase the environmental innovations across the selected regions. Conversely, natural resources negatively affect environmental innovations and become a leading hurdle in the clean & green innovation process. Finally, empirical analysis finds a positive response of ecological regulations and green energy to an explained variable. This article outlined policy proposals for a low-carbon economy that would guarantee environmental innovation in certain provinces by reducing emissions; these proposals included increasing the use of renewable energy sources, sustainable technology, and environmentally friendly technology.

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.

Greenflation, a myth or fact? Empirical evidence from 26 OECD countries

Among the various effects of the green transition on the macroeconomy, we examined the unintended effects of the green transition on inflation, the so-called “greenflation”. We analyzed panel data from 26 OECD countries covering the period from 2005 to 2019 using four models based on the New Keynesian Phillips curve model. To date, only a few studies have examined greenflation, particularly in terms of empirical investigation. The main contribution of this study to the literature is to provide empirical evidence of greenflation. We also provide empirical evidence on the issues in the NKPC literature that the output gap better explains inflation dynamics than unit labor costs and confirm the demise of the Phillips curve. Estimates from the Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG), and Panel Corrected Standard Error (PCSE) models indicate that the green transition has an upward inflationary impact on headline inflation, but no significant impact on core inflation. In addition, we have examined four circumstances under which the green transition may affect inflation that have not been sufficiently discussed in the literature: increased green transition spending, carbon pricing, increased demand for critical minerals, and the low energy density of renewable energy. Each of these circumstances has the potential to exert both upward and downward pressures simultaneously. Our suggestion to policymakers is that they do not yet need to change their current view of monetary policy with respect to greenflation, but they should carefully consider policy designs that take into account the unintended effects of the energy transition.

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.

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors.

Empowering China's energy renaissance: Electrochemical storage strategies driving economic growth and policy evolution

This study presents an innovative framework to examine the interplay between foreign direct investment and economic development from 1996 to 2021. Using the Panel NARDL approach, our study provides an empirical framework for exploring these complex relationships. We enhance the reliability of our results by integrating additional methodologies, such as augmented mean group and common correlated effects mean group. A decline in carbon emissions is commonly hypothesized to be correlated with economic expansion, foreign direct investment inflows, advancements in renewable energy, and implementation of environmental technologies. On the contrary, detrimental disturbances have the potential to result in protracted ecological deterioration, especially in situations where there are setbacks in economic expansion and foreign direct investment. It is worth noting that although short-run and long-run coefficients exhibit comparable directional trends, there are discrepancies in their magnitudes and levels of statistical significance. This sophisticated comprehension empowers countries such as China, among others, to allocate resources strategically towards sustainable energy initiatives, promoting a systematic approach that prioritizes the adoption of renewable energy, advancements in technology, attraction of foreign investment, and transformation of structures.

Climate change and food security causality in ECOWAS region: Do countries interdependence matter?

Studies abound on the causal relationship between climate change and food security; however, the ECOWAS sub-region, despite its unique climatic, agricultural, and socioeconomic characteristics, remains insufficiently explored. Utilizing data from 14 ECOWAS countries spanning 1986 to 2022, this study departs from earlier studies by incorporating the Dumitrescu–Hurlin Panel Causality test, the Pooled Mean Group (PMG), Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG) and the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique to analyze the complex relationship between climate change and food security in the ECOWAS sub-region. Unlike the prior empirical studies, the findings reveal a robust mutual influence of climate change and food security in the ECOWAS sub-region. Furthermore, the findings reported a positive impact of climate change on food security, aided by increased cropland area and population growth. At the same time, drought incidence and erratic rainfall between July and September annually remained a severe concern in the region. However, while the conversion of forests and natural habitats into cropland in the sub-region can lead to heightened carbon emissions and reduced ecosystem services, policy recommendations advocate the need for ECOWAS sub-regional policymakers to carefully balance the benefits of food security through sustainable practices and environmental considerations to ensure long-term food security benefits.

Economic Complexity, Green Technologies and Environmental Degradation in World’s Top Polluting Countries: Evidence from Advance Panel Estimation

The current study empirically probes into the role of economic complexity and green technologies on environmental degradation under STIRPAT framework in world’s top polluting countries over 2000 to 2020 period. To empirically analyze the relationships, Common Correlated Effects Mean Group (CCEMG) estimation technique is used because of the existence of the cross sectional dependence issue in panel data. The findings reveal that economic complexity increases but green technologies decrease CO2 emissions in studied economies. By providing the comprehensive understanding on the relationship between green technologies, economic complexity and CO2 emissions, the present study recommends the governments of the concerned countries to implement relevant policies and practices for improving the production structure and foster the research and development activities to promote the development and use of green technologies to curb CO2 emissions in studied economies.

The role of financial inclusion and tourism in tackling environmental challenges of industrialization and energy consumption: Redesigning Sustainable Development Goals policies

AbstractThe Next 11 (N‐11) countries are facing many challenges in achieving the objectives of Sustainable Development Goals (SDGs), as preserving environmental quality has become a major challenge for them. Specifically, achieving SDG 3, SDG 7, SDG 9, and SDG 13, the current research scrutinizes the influence of industrialization, tourism, and renewable and fossil fuel energy consumption with the moderating role of financial development on the ecological footprint in the N‐11 countries during 1995–2018. By testing all nine hypotheses through the augmented mean group, common correlated effects mean group estimators, and the Dumitrescu and Hurlin causality approach, various interesting findings are discovered. The findings verified that industrialization, tourism, and non‐renewable energy play major roles in driving environmental pollution. However, renewables and financial development increase environmental quality. Moreover, the interacting role of financial expansion with industrialization and non‐renewables significantly deteriorates the environment. In contrast, the moderating role of financial development with tourism and renewable energy protects environmental excellence. In addition, the growth hypothesis is discovered from tourism and renewables to ecological footprint, and the feedback hypothesis is discovered between industrialization, financial development, and ecological footprint. Following the empirical findings, we recommend several policy implications to address the objectives of SDG 3, SDG 7, SDG 9, and SDG 13.

Unraveling consumption‐based carbon dioxide emissions: Sectoral analysis and temporal dynamics in G7 economies

AbstractThis research investigates consumption‐based carbon dioxide emissions (CBE) within the Group of Seven (G7) economies from 1990 to 2022, particularly under the Paris Agreement. The main focus of the study is on key determinants such as trade, income, renewable energy consumption, foreign direct investment, and technological innovation. The study aims to unravel the complexities underlying the environmental footprint of consumption. A novel aspect of this research is its intent to fill existing gaps in the literature by providing a sectoral analysis and exploring the temporal dynamics of consumption patterns within these advanced industrial nations. Advanced econometric methods, specifically the Cross‐Section Augmented Autoregressive Distributed Lag (CS‐ARDL), are employed to enhance the robustness and reliability of the analysis. Cointegration relationships among CBE and its determinants shed light on the short and long‐term interdependencies among these factors. Using the Augmented Mean Group (AMG) and Common Correlated Effect Mean Group (CCEMG) methods to conduct robustness checks makes the models even more substantial and ensures the results' validity. The implications of this research are far‐reaching, extending to various stakeholders, including policymakers, businesses, industries, and supply chain decision makers. Our study identifies significant long‐term relationships between CBE and key determinants within G7 economies. The findings emphasize the urgency of adopting strategies that promote the sustainable management of natural resources. As global efforts intensify to address climate change, this research provides practical implications for shaping sustainable policy strategies and environmental initiatives on a global scale.

Sustainable development in ASEAN: The role of trade diversification, government revenue, and natural resources

AbstractOver the past few years, the Association of Southeast Asian Nations (ASEAN) has experienced great economic expansion, which has resulted in varied degrees of diversified commerce, an elevated level of government revenue, and an increase in the demand for energy. The purpose of this study is to provide a solution to this conundrum by analyzing the effects of trade diversification (TDF), government revenues (GRN), gross domestic product (GDP), and natural resource rent (NTR) on the sustainable development of the ASEAN countries between the years 1981 and 2022. In order to accurately portray the concept of environmental sustainability, the ecological footprint (EFP) is utilized to represent sustainable development. The quantile‐based econometrics technique known as the Method of the Moments Quantile Regression (MMQR) has been utilized in order to investigate the direction and amplitude of the asymmetric correlation that exists between the interaction of GRN, TDF, NTR, and EFP. According to the estimations of the MMQR, it is proposed that government revenues, which include significant financial incentives that promote the stringent execution of environmental rules, hence avoiding deleterious impacts on the environment, have negative coefficients at all quantiles (Q0.25 − Q0.90). Conversely, TDF and GDP have a positive and statistically strong significant correlation across all quantiles (Q0.25 − Q0.90), revealing that TDF reduces environmental sustainability and expands the size of the world's EFP by making energy‐intensive products more accessible. In addition, the Augmented Mean Group (AMG) and the Common Correlated Effect Mean Group (CCEMG) both provide evidence that supports the correlation study by demonstrating that there is a similar pattern of causality across variables.

Technology Oriented Struggle Against Climate Change in Transportation Sector: An Empirical Investigation

The transportation sector is one of the most important sectors in which greenhouse gas emissions (GHG) are the highest, thus causing the global warming problem to rise. One of the most effective and international solutions to this problem is considered to be a technology-oriented struggle, and the development of green technologies is encouraged by global authorities. The study aims to investigate the success of the technology-oriented struggle against global warming in the transport sector. In analyses, data on transportation-related greenhouse gas emissions, the number of patents (transport-related climate change mitigation technologies), trade openness, and GDP per capita of 12 OECD countries years between 1999-2017 were used. To identify the long-run and short-run relationship among variables, the Cross-Sectional Autoregressive Distributed Lags Estimator (CSARDL) and also the Mean Group (MG), Augmented Mean Group (AMG), and Common Correlated Effects Mean Group Estimators (CCE) were applied. According to the estimators' findings, no evidence was found that the number of patents and trade openness affected greenhouse gas emissions, but it was determined that GDP positively affected greenhouse gas emissions. As a result, it can be said that the technology-oriented struggle against climate change in the transportation sector alone isn't sufficient to reduce transportation-related GHG emissions.

The combine impact of stock market, international investment and clean energy consumption on ecological footprint in emerging countries

PurposeEmerging nations strive to diminish their ecological impact to meet net-zero targets, yet encounter formidable hurdles in curbing their environmental footprint. This purpose necessitated the study into impact of stock market, renewable energy and international investment on the ecological footprint in emerging countries from 1990 to 2020.Design/methodology/approachThe study used augmented mean group (AMG) estimator, cointegration and heterogenous panel causality approach.FindingsResults from the AMG show that renewable energy consumption reduces environmental pollution in most countries except Mexico. The study disclosed that stock market capitalization decreases ecological footprint in emerging countries. Using both the Kao and Pedroni cointegration methods, the study affirms the existence of stable equilibrium relationship in the long term. The causality test concluded a bidirectional relationship between stock market and ecological footprint and a unidirectional link between international investment, clean energy and ecological footprint.Research limitations/implicationsThe research is limited to only emerging countries. Therefore, future research should examine the environmental impacts of renewable energy consumption in different countries and regions, taking into account the local environmental conditions, policies and practices. This would help to identify the best practices and standards for minimizing the ecological footprint of renewable energy technologies and maximizing their benefits for environmental sustainability.Practical implicationsThe study found that stock market capitalization reduces ecological footprint in Brazil, China, Turkey and India. To foster a culture of sustainability in stock market development impact, academic policies should emphasize the integration of environmental education across disciplines. By promoting awareness of the ecological consequences of stock market activities, societies can cultivate a mindset that values responsible economic practices. This, in turn, can lead to informed decision-making at individual and institutional levels.Social implicationsFirst, since the study found that clean energy reduces ecological footprint, advocating for utilization of clean energy sources could be a key priority in emerging countries. Governments should incentivize the development and adoption of renewable energy technologies, such as wind and solar power, by providing subsidies and tax benefits. Furthermore, increasing awareness among residents about the benefits of clean energy and promoting its utilization in both residential and commercial environments can expedite the transition to a more environmentally friendly energy combination.Originality/valueFirst, it pioneers an exploration into the interplay between stock market capitalization, international investment, clean energy and ecological footprint in emerging countries. Secondary unlike, unlike prior research, this study uses methodologies that account for cross-sectional dependencies and a unique characteristic specific to each country. In addition, by using common correlated effects mean group, AMG, cointegration and causality procedures, this study distinctly isolates and analyzes empirical findings for each country, leading to policy-oriented outcomes.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

Bridging the digital divide: the impact of technological innovation on income inequality and human interactions

This study seeks to understand the nuanced relationship between technological innovation and income inequality with an emphasis on the broader implications of this interplay on human–technology interactions in diverse socioeconomic settings. Using cross-country panel data from 59 nations (31 developed and 28 developing) from 1995 to 2020, the study employed the common correlated effect mean group (CCEMG) estimator. The robustness of our findings was validated using the augmented mean group (AMG) estimator and the panel causality test. The results indicate that technological innovation, while heralded for its potential to bridge communication and operational gaps, inadvertently exacerbates income disparities, with a pronounced effect in developed economies. Moreover, interactions between technological innovation and variables such as economic growth, globalisation and export trade introduce additional complexities, including both buffering and acceleration effects on the primary relationship. These findings shed light on the double-edged nature of technological advancements, underscoring the need for informed policy-making that harnesses the benefits of innovation while mitigating its unintended socioeconomic consequences. The study sets the stage for domain-specific explorations such as in education, public health and business. It also invites interdisciplinary discourse on the ethical and behavioural dimensions of technology adoption, especially user experiences and societal outcomes.