Dynamic Panel Threshold Model Research Articles

The impact of green low-carbon finance on smog reduction and carbon mitigation: evidence from a spatial panel simultaneous equations model

ABSTRACT Mitigating smog and carbon emissions is crucial for promoting sustainable urban development. Based on a comprehensive panel dataset covering 278 Chinese cities from 2008 to 2022, this study employs dynamic spatial panel data models and dynamic panel threshold models to systematically investigate the effects of green low-carbon finance (GCF) on reducing smog and carbon emissions (RSCE), including its underlying mechanisms, spatial spillover effects, and threshold effects. The key findings are as follows: (1) The development of GCF in both local and neighboring cities significantly enhances RSCE in the respective regions. (2) While local GCF indirectly promotes RSCE through green technological innovation, the spillover effects of GCF in neighboring cities on local RSCE via green innovation are not significant. (3) There exists a notable green innovation threshold effect in the relationship between GCF and RSCE, where GCF’s impact on RSCE becomes significantly stronger after surpassing a certain level of green innovation. (4) A temporal analysis reveals that the influence of GCF on RSCE has markedly intensified as GCF-related policies have matured over time. This study provides critical policy implications for strategically leveraging GCF tools to foster high-quality, low-carbon urban development.

Scrutinizing the load capacity curve for a global perspective: The role of Fintech, government effectiveness and renewable energy

With the evolving world order, countries are using financial technologies (Fintech) to access financial resources more effectively, and Fintech can have an impact on the environment. This study aims to investigate the influence of Fintech on ecological sustainability, focusing on the contribution of renewable energy (RE) and government effectiveness (GE) under the Load Capacity Curve (LCC). This research analyzes data from 69 middle-income economies between 2006 and 2022 using the dynamic panel threshold model (DPTR). The outcomes document that GDP reduces the LCF below a certain threshold, but a positive impact above this threshold. Specifically, the model yielded a threshold value of $US 5222.234, which is higher than the average GDP of $US 4276.802. This finding suggests a U-shaped relationship between GDP and LCF, which supports the LCC hypothesis. The outcome also reports that Fintech plays a crucial role in improving ecological sustainability, while government effectiveness has a negative effect. The study emphasizes that middle-income countries should support Fintech and renewable energy along development with economic progress to improve ecological quality.

How Does Industrial Agglomeration Drive High-Quality Green Development in China? New Evidence from a Financial and Manufacturing Co-Clustering

We examine the effect of financial and manufacturing co-clustering on high-quality green development in China’s 30 provinces from 2005 to 2020. The nexus between financial and manufacturing co-clustering and high-quality green development has been comprehensively investigated from linear and non-linear perspectives. We find that financial and manufacturing co-clustering significantly fosters high-quality green development. Mechanism analysis shows that formal environmental regulation has a significant negative moderating effect on high-quality green development, whereas informal environmental regulation plays a significantly positive moderating role. However, empirical results only show the mediation effect of formal environmental regulation in the incentive role of financial and manufacturing co-clustering to high-quality green development. A dynamic panel threshold model also certifies the non-linear effect between financial and manufacturing co-clustering and high-quality green development. Lastly, the promotion effect of financial and manufacturing co-clustering on high-quality green development creates significant heterogeneity.

Responsiveness of United States – China bilateral commodity trade to real exchange rate misalignment: Does the complexity of traded products matter?

This paper assesses the impact of real exchange rate (RER) misalignment on the trade balance between the United States and China, accounting for the complexity of traded products. We employ two recent econometric techniques: the panel autoregressive distributed lag (ARDL) model to estimate both long – and short-run dynamics, and the dynamic panel threshold model of Seo, M. H., and Y. Shin (2016. “Dynamic Panels with Threshold Effect and Endogeneity.” Journal of Econometrics 195 (2): 169–186) to uncover potential non-linearities and identify critical thresholds that could lead to a regime shift. Using highly disaggregated trade data at the HS 06-digit level, encompassing 913 products from 2004 to 2020, our findings reveal a negative impact of RER misalignment on the United States’ trade balance with China. However, this detrimental effect is significant only in industries dealing with less complex products. In contrast, industries handling highly complex products show a notably positive impact. Furthermore, our results suggest that industries can benefit from an undervalued exchange rate only when they have a relatively high level of product complexity. Additionally, heightened product complexity can mitigate any adverse effects overvaluation might have on the trade balance.

Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model

Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government size’s estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels.

Manufacturing and the real exchange rate: natural resource rents matter when measuring misalignments

ABSTRACT We analyse the relationship between the share of manufacturing in GDP and real exchange rate misalignments based on the purchasing power parity criterion (PPP) and a sample of 102 developing and transition economies (2003–2019). In a departure from usual practice, we subtract natural resource rents from GDP in order to correct misalignments for the productivity bias. A dynamic threshold panel model is used and we separate out the impact of undervaluation and overvaluation components in the same regression. Overvaluation has a negative linear effect, while undervaluation stimulates the manufacturing sector in a non-linear way. Above an 18% threshold, the marginal effect of undervaluation diminishes.

Thresholds to sustainability: Globalization's impact on the institutional quality-ecological footprint nexus in Africa

This study explores how globalization influences the relationship between institutional quality and the ecological footprint in Africa. Utilizing dynamic panel threshold models with data from 49 African countries (2002-2022), we investigate how different types of globalization—economic, social, cultural, and political—affect this relationship. Our findings reveal that the impact of institutional quality on the ecological footprint varies significantly depending on globalization thresholds. Specifically, below certain thresholds, improved institutional quality increases the ecological footprint, whereas above these thresholds, it tends to reduce it. For economic globalization, the threshold is 53.510; for social globalization, 48.332; for cultural globalization, 28.446; and for political globalization, 69.663. These results highlight the need for targeted environmental policies that consider the specific effects of each globalization dimension to effectively manage Africa's ecological footprint. The study contributes to the literature by addressing the complex link between institutional quality and globalization and provides practical insights for policymakers to design more effective sustainability strategies in the African context.

The effect of human capital on the trade-growth nexus: A dynamic panel threshold analysis

This paper analyzes the nonlinear effect of trade openness on economic growth over a sample of developed and developing countries during the period 1980–2020. Using a Dynamic Panel Threshold Model advanced by Seo and Shin (2016), and Seo et al., (2019), the results prove the existence of a human capital's threshold level above it openness to trade has a positive effect on economic growth. More precisely, trade can enhance economic performance if the human capital is high, otherwise, the effect is negative.

Renewable Energy Transition and the Paris Agreement: How Governance Quality Makes a Difference?

This paper investigates whether the Paris Agreement affects renewable energy deployment and how institutional quality moderates this relationship. According to a generalized method of moments estimation for panel data for both developed and developing countries over the period 2000–2022, the Paris Agreement positively influences renewable energy deployment, suggesting that countries are promoting renewable energy to align with institutional expectations to maintain their reputations. The results further show that governance quality is the main determinant of renewable energy deployment. However, the moderating role of governance underscores the less-pronounced impact of the Paris Agreement on countries with high governance indicators, suggesting that these countries may have shifted their focus toward other avenues of climate management beyond the deployment of renewable energy. Furthermore, there is strong evidence of the relationship between forest area, CO2 emission, trade openness, domestic credit, and renewable energy deployment. The results are robust with the use of a dynamic panel threshold model.

Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)

This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction. Nonlinear analysis indicates several key insights: (i) a higher proportion of the industrial sector diminishes the inhibitory effect of AI on ecological footprints and carbon emissions but enhances its positive impact on energy transitions; (ii) increased trade openness significantly amplifies AI’s ability to reduce carbon emissions and promote energy transitions; (iii) the environmental benefits of AI are more pronounced at higher levels of AI development, enhancing its ability to reduce ecological footprints and carbon emissions and promote energy transitions; (iv) as the energy transition process deepens, AI’s effectiveness in reducing ecological footprints and carbon emissions increases, while its role in promoting further energy transitions decreases. This study enriches the existing literature by providing a nuanced understanding of AI’s environmental impact and offers a robust scientific foundation for global policymakers to develop sustainable AI management frameworks.

Governance and the relationship between corruption and FDI in Africa: a threshold regression analysis

ABSTRACT Research on the institutional environment, corruption, and FDI highlights an important facilitating relationship between these factors. The effect of corruption on FDI vis-à-vis the grabbing hand vs. the helping hand hypotheses has been previously examined with suggestions that both hypotheses can co-exist under the assumption that the FDI-corruption relationship depends on the level of institutions. This study revisits this relationship for 34 African countries over the 2005 to 2019 period using the dynamic panel threshold model, which allows for an endogenous threshold variable. Previous studies that have examined this relationship using a threshold regression approach are either not based exclusively on African countries (where the implication of this relationship is more salient) or use a threshold regression that assumes exogeneity of the threshold variable. This study examines the facilitating nature of governance measures – political stability, government effectiveness, rule of law and regulatory quality – on the corruption-FDI relationship. The results indicate significant threshold effects and shows that while the grabbing hand hypothesis is consistent with the data irrespective of the institutional proxy used, the helping hand hypothesis is sensitive to the choice of governance. These results agree with the strand of literature that supports a weak helping hand hypothesis.

Non-Linear Impact of Income Inequality on Mental Health: Evidence from Low and Middle-Income Countries

Abstract In this study, we examine the relationship between income inequality and mental health using a sample of low and middle-income countries over the period 1990–2019. Using a dynamic panel threshold model that allows for endogeneity in both the regressors and threshold variable, we find a non-linear relationship between income inequality and the prevalence of mental health disorders. Specifically, income inequality is associated with reduced prevalence of mental health disorders at low levels of income inequality but after it surpasses a threshold Gini coefficient (estimated between 39 and 49), it has an adverse effect on mental health. The impact is more pronounced in low income and lower middle-income countries. We also find evidence of heterogenous effects by age and gender. Our findings indicate the importance of modelling non-linearity in the income inequality-health relationship and highlight the importance of keeping income inequality within reasonable bounds.

Does immigration increase crime? The advantage of dynamic threshold models with finer geographic units

This study investigates whether immigration and crime rates are positively related, by applying dynamic panel threshold models to fine geographic units. This is distinct from previous studies, in which static and continuous models are applied to coarse geographic units. This analysis reveals that the immigration-crime relationship has structural breaks (or discontinuities). The favorable ethnic network externalities on crime emerge after the immigrant share reaches a certain level in the community. This analysis also shows that the immigration-crime relationship observed in fine geographic units disappears when coarsely classified units are used. If geographic aggregation obscures heterogeneity among cities, the immigration-crime relationship is underestimated. These results suggest the advantage of dynamic threshold models with fine geographic units compared to traditional static and continuous models with coarse geographic units when discussing the immigration-crime relationship.

Does natural resource dependence restrict green development? An investigation from the “Belt and road” countries

Addressing natural resource dependence is integral to achieving the Sustainable Development Goals by promoting economic diversification, environmental sustainability, and climate resilience. This study explores the effect of natural resource dependence on green development by adopting the balanced panel dataset from the “Belt and Road” countries from 2005 to 2019. Notably, the novelty of our analysis lies in the empirical analysis using instrument-based techniques that consolidate the “green development curse hypothesis” in the Belt and Road countries. The mechanism analysis reveals that natural resource dependence curbs green development by weakening innovative capability, disturbing institutional quality, reducing population density, and crowding out human capital. Further, the dynamic panel threshold model handling endogeneity verifies the nonlinear relationship between natural resource dependence and green development. Interestingly, digital trade offers greater “resilience” than traditional trade, correcting the resource curse dilemma. Finally, heterogeneity analyses indicate that the green development curse hypothesis only exists in countries with high-level environmental regulations and resource-based countries.

Financial sector development and renewable energy consumption nexus: Evidence from global dynamic panel threshold analysis

AbstractDecarbonizing economies requires an energy transition from conventional energy resources to renewable and clean energy resources. However, this transition largely depends upon the availability of huge investments to manage high start‐up costs and operational infrastructure. In this respect, the financial sector can play a vital role. This study explores the financial sector's role in renewable energy consumption utilizing a comprehensive measure of financial sector development constituting both financial institutions and financial markets. Moreover, the study utilizes an advanced econometric technique “dynamic panel threshold model” on panel data of 165 countries ranging from 1980 to 2019. The empirical analysis reveals the presence of a threshold value of 0.191, 0.196, and 0.008 for the overall financial development index, financial institutions index, and financial market index, respectively. This finding confirms the presence of a U‐shaped curve between financial sector development and renewable energy consumption, validating the existence of the financial Kuznets curve. Thus, initially, financial sector development results in lower renewable energy consumption while after reaching the threshold level it boosts renewable energy consumption. Furthermore, the study also shows the statistically significant role of economic growth, trade openness, and inflation in explaining renewable energy usage. The obtained outcomes suggest a pressing necessity to improve both financial institutions and markets to surpass the threshold levels of financial sector performance within the financial sector, thus supporting a rise in renewable energy consumption.

The potential impact of digital economy on energy poverty in the context of Chinese provinces

This study focused on exploring the impact of the digital economy (DE) on energy poverty (EP) across Chinese provinces from 2004 to 2018, motivated by the critical need to understand how technological advancements in the digital sector influence energy accessibility and sustainability. Conducted against the backdrop of global digital transformation, the research aimed to dissect the nuanced ways in which the DE contributes to mitigating EP, employing dynamic panel threshold and indirect effect models to capture both the direct and nuanced, and intermediate effects of digital progress on energy deprivation. Key findings revealed a significant reduction in EP attributed to the advancements in DE, with the most notable improvements observed in Eastern China where strategic energy policies and management practices enhanced the positive impacts of digitalization. The study highlighted the DE's role in improving energy access, efficiency, and environmental sustainability, although it also pointed out the potential for regressive effects in areas with lower levels of technological advancement. These findings are of substantial value as they offer empirical evidence of the DE's capacity to alleviate EP, underlining the importance of integrating digital strategies into energy policy planning. The research provides critical insights for policymakers, stakeholders in the energy sector, and scholars interested in the synergies between digital innovation and energy security, suggesting that leveraging digital technologies could accelerate efforts towards achieving sustainable energy access and combating energy poverty in China and potentially in other contexts facing similar challenges.

Impact of Green Infrastructure Investment on Urban Carbon Emissions in China

Given the increasingly severe global climate change, the reduction in urban greenhouse gas emissions has become the common goal of all nations. As a widely concerned sustainable development strategy, green infrastructure investment (GII) aims to reduce urban carbon emissions, improve the efficiency of resource utilization, and improve environmental quality. However, the construction cycle of green infrastructure is long, and the construction process itself may produce carbon emissions; so, the final effect of GII on urban carbon emissions is unclear, which deserves our in-depth study. Further, is this effect having a time-lag effect? Is there only a simple linear relationship between GII and urban carbon emissions? Based on panel data from 235 Chinese cities from 2006 to 2019, this study conducted an econometric regression analysis using time-lag-effect and threshold-effect models. The results showed the following: (1) GII had a negative inhibitory effect on urban CO2 emissions. Adding one unit to the GII could reduce urban CO2 emissions by 0.032 units. (2) GII exhibited a time-lag effect on urban CO2 emissions, and the greatest reduction in CO2 emissions occurred in the third lag period. (3) GII had a threshold effect on urban CO2 emissions based on technological progress (TP). This paper used the static and dynamic panel threshold models to research separately, and obtained the corresponding regression results. In the static panel, the double threshold values for TP were 3.9120 and 6.8035. At different TP levels, GII had an inhibitory effect on CO2 emissions, but the coefficients were different. However, in the dynamic panel, the threshold value was 3.666. The threshold changed over time and the effect of GII on CO2 emissions shifted from facilitation to inhibition.

Debt, economic growth and threshold effects: Evidence from developing countries

PurposeMost empirical papers on threshold effects between debt and growth focus on developed countries or a mix of developing and developed economies, often using public debt. Evidence for developing economies is inconclusive, as is the analysis of other threshold effects such as those probably caused by the level of relative development or the repayment capacity. The objective of this study was to examine threshold effects for developing economies, including external and total debt, and identify them in the debt-growth relation considering three determinants: debt itself, initial real Gross Domestic Product (GDP) per capita and debt to exports ratio.Design/methodology/approachWe used a panel threshold regression model (PTRM) and a dynamic panel threshold model (DPTM) for a sample of 47 developing countries from 1970 to 2019.FindingsWe found (1) no evidence of threshold effects applying total debt as a threshold variable; (2) one critical value for external debt of 42.32% (using PTRM) and 67.11% (using DPTM), above which this factor is detrimental to growth; (3) two turning points for initial GDP as a threshold variable, where total and external debt positively affects growth at a very low initial GDP, it becomes nonsignificant between critical values, and it negatively influences growth above the second threshold; (4) one critical value for external debt to exports using PTRM and DPTM, below which external debt positively affects growth and negatively above it.Originality/valueThe outcome suggests that only poorer economies can leverage credits. The level of the threshold for the debt to exports ratio is higher than that found in previous literature, implying that the external restriction could be less relevant in recent periods. However, the threshold for the external debt-to-GDP ratio is lower compared to previous evidence.

How aging impacts environmental sustainability—insights from the effects of social consumption and labor supply

Population aging is discreetly reshaping the dynamics of social demand and labor supply, introducing potential turbulence to global carbon emissions. Understanding the impact of aging on carbon emissions is imperative for steering the next phase of sustainable development. This study, focusing on China, the world’s largest carbon emitter, delved into the intricacies of how population aging influences carbon emissions using a dynamic panel threshold model and a mediated effects model. Geographic heterogeneity within China was also considered. On the one hand, this study incorporated three consumer-side intermediation mechanisms: energy consumption, residential consumption and medical consumption. It was found that the positive driving effect of consumption-side variables on carbon emissions was characterized by an inverted “U”-shaped change in China’s highly aging regions, while an asymptotic upward trend of 7.65% was observed in regions with moderate and low aging. On the other hand, this study scrutinized three supply-side mediating mechanisms: industrial structure, R&D innovation and labor supply. The mechanism of supply-side variables on carbon emissions exhibited a shift from robustly positive driving to more nuanced weak positive driving or even negative inhibiting in highly aging regions, while inhibiting effects dominated in regions with moderate and low aging. This study offers a dual perspective encompassing both the production and consumption sides, which lays a foundation for exploring the internal mechanism of aging on carbon emission.

A dynamic panel threshold model analysis on heterogeneous environmental regulation, R&D investment, and enterprise green total factor productivity

Environmental regulations are important means to influence manufacturing enterprise green development. However, there are two completely different conclusions both in theoretical and in empirical research, namely the “Follow Cost” theory and the “Porter Hypothesis”. The nonlinear mechanism needs to be considered. Therefore, this study aims to explain the threshold impact of heterogeneous environmental regulations on enterprise green total factor productivity. Environmental regulations are divided into different sub-categories, then based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on Chinese manufacturing enterprise Green Total Factor Productivity. The empirical results show that: (1) Command-controlled, market-incentive and voluntary-agreement environmental regulation all have a significant nonlinear impact on enterprise Green Total Factor Productivity. (2) Enterprise R&D investment plays a threshold role in the impact. (3) There are industry and equity type differences in the impact process. This study focuses on the micro level of enterprises and tests the threshold mechanism, which make some theoretical complement to previous researches. The research results are not only beneficial for the government to propose appropriate environmental regulatory policies, but also for enterprises to achieve green growth through heterogeneous R&D investment.