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This research investigates the threshold effects of the interplay between finance, development and carbon emissions across 97 countries, including 50 low-income and 47 high-income countries, during the period from 1991 to 2019.

Employing various econometric modeling techniques such as dynamic linear regression, dynamic panel threshold regression and in/out of sample splitting, this study analyzes the data obtained from the World Bank's world development indicators.

The results indicate that low-income countries require a minimum financial development threshold of 0.354 to effectively reduce carbon emissions. Conversely, high-income countries require a higher financial development threshold of 0.662 to mitigate finance-induced carbon emissions. These findings validate the presence of a finance-led Environmental Kuznet Curve (EKC). Furthermore, the study highlights those high-income countries exhibit greater environmental concern compared to their low-income counterparts. Additionally, a minimum GDP per capita of US$ 10,067 is necessary to facilitate economic development and subsequently reduce carbon emissions. Once GDP per capita surpasses this threshold, a rise in economic development by a certain percentage could lead to a 0.96% reduction in carbon emissions across all income levels.

This study provides a novel contribution by estimating practical financial and economic thresholds essential for reducing carbon emissions within countries at varying levels of development.

The authors employ the vessels that comprise the dry bulk segment of the maritime industry and examine how market sentiment affects the herding behavior of shipping investors in a real asset market.

The authors employ a threshold regression model to examine how changes in market sentiment can affect herding behavior in oceanic dry bulk shipping.

The results show that the behavioral aspect of investing, measured through intentional and unintentional herding, contrary to the results for financial markets, is affected by sentiment on the buy side (newbuildings) but not on the sell side (scrapping). Furthermore, the authors provide evidence that when market sentiment is negative, investors tend to follow market leaders (intentional herding), while, when sentiment is positive, unintentional herding leads to common investment practices among shipping investors.

The results have significant implications both for academics and for practitioners since they reflect a clear distinction of the pattern of investment decisions for real assets, compared to financial assets.

The purpose of this paper is to investigate sustainable green economy in sub-Saharan African (SSA) countries over the period 1990–2019 using a quantile regression approach, considering the nexus between urbanization, economic growth, renewable energy, trade and carbon dioxide (CO₂) emissions.

The study used a dynamic panel quantile regression to investigate the conditional distribution of CO₂ emissions along the turn-points of urbanization, economic growth, renewable energy, trade and the regressors via quadratic modeling specifications.

The main findings are established as follows. There is strong evidence of the Kuznets curve in the nexus between urbanization, economic growth, renewable energy, trade and CO₂ emissions, respectively. Second, urbanization thresholds that should not be exceeded for sustainability to reduce CO₂ emissions are 0.21%, and 2.70% for the 20th and 75th quantiles of the CO₂ emissions distribution. Third, growth thresholds of 3.64%, 3.84%, 4.01%, 4.36% and 5.87% across the quantiles of the CO₂ emissions distribution. Fourth, energy thresholds of 3.64%, 3.61%, 3.70%, 4.02% and 4.34% across the quantiles of the CO₂ emissions distribution. Fifth, trade thresholds of 3.37% and 4.47% for the 20th and median quantiles of the CO₂ emissions distribution, respectively.

The empirical shreds of evidence offer policy implications in such that building sustainable development and environment requires maintaining the critical mass, not beyond those insightful thresholds to achieving sustainable development and environmentally friendly SSA countries.

Sustainable cities and communities in an era of economic recovery path COVID-19 mitigate greenhouse gas. The policy relevance is of particular concern to the sustainable development goals.

The study is novel considering the extant literature by providing policymakers with avoidable thresholds for policy formulations and implementations in the nexus between urbanization, economic growth, renewable energy and trade openness.

Most emerging economies have made conscious efforts through policy initiatives to attract foreign direct investment (FDI). However, a significant obstacle to FDI inflow has been the prevalence of corruption in the host country. This study, therefore, aims to examine whether there is an optimum corruption value that results in threshold effects of corruption on FDI.

To achieve this objective, this study used Hansen’s (1999) panel threshold regression (PTR) model by using a panel data of 30 sub-Saharan African (SSA) countries from 2000 to 2021.

This study finds that the nexus between corruption and FDI has a single threshold effect, with a 5.37% optimum corruption threshold value. At this threshold value, corruption affects FDI negatively. Any corruption value that is below the threshold value also elicits a negative corruption–FDI relationship. Despite having a negative relationship when the corruption value is above the optimum corruption threshold, it is not statistically significant.

The implication of the results is that it is deleterious to use corrupt practices to draw FDI to SSA nations.

To the best of the authors’ knowledge, this study is one of the first in the corruption–FDI nexus literature to use Hansen’s PTR model to estimate an optimal corruption threshold. The authors recommend that policymakers in the selected SSA countries reconsider the use of corruption to attract FDI because there is an optimal corruption threshold that could impact FDI in the host country.

This manuscript reports evidence on how debt-taking decisions of top management in a multi-country setting do affect credit rating scores assigned by credit rating agencies (CRAs) as global monitors of creditworthiness of borrowers. This aspect has been long ignored by researchers in the literature. The purpose of this paper is twofold. A test model is specified first using theories to connect debt-taking behavior to credit rating scores. Once that model helps to identify a number of statistically significant factors, the next step helps to identify threshold values at which the variables driving debt-taking behavior would worsen the credit rating scores as turning points of the thresholds.

The study identifies factors driving creditworthiness scores due to debt-taking behavior of countries and develops a correct research design to identify a model that explains (1) credit rating scores and the factors driving the scores and runs (2) panel-type regressions to test model fit. Having found factors driving debt-taking behavior by observed units, the next step identifies threshold values of factors at which point further debt-taking is likely to worsen credit rating risk of the observed units. This is a robustness test of the methodology used. The observed units are 20 countries with data series across 14 years.

First, new findings suggest there are about six major factors associated with debt-taking behavior and credit rating changes. Second, the model developed in this study is able to account for substantial variability while the identified factors are statistically significant within the normal p-values for acceptance of hypotheses. Finally, the threshold values of factors identified are likely to be useful for managerial decisions to judge the levels at which further debt-taking would worsen the credit rating scores of the observed units.

The observed units are from 20 countries over 14 years of annual data available on credit rating scores (privately obtained from Standard and Poor [S&P]). The sample represents major economies but did not include emerging countries. In that regard, it will be worthwhile to explore the debt-taking behavior of emerging economies in a future study using the methodology verified in this study.

The findings help add few useful guidelines for top management decisions. (1) There are actually factors that are associated with debt-taking behavior, so the authors now know these factors as guides for managerial actions. (2) The authors are free to state that the credit rating changes occur on objective changes in the factors found as significantly related to the debt-taking behavior. (3) The threshold values of key factors are known, so top management could use these threshold values of named factors to monitor if a debt-taking decision is going to push the credit rating to a worse score.

There are society-wide implications. Knowing that the world's debt level is high at US$2.2 for each gross domestic product (GDP) dollar across almost 200 countries, any knowledge on what factors help drive creditworthiness scores, thus credit riskiness, is revealed in this paper. Knowing those factors and also knowing the turning points of the factors – the threshold values – likely to worsen creditworthiness scores is a powerful tool for controlling excessive debt-taking by an observation unit included in this study (The dataset in this research can also be used to see inter-temporal movement on debt-taking in a future study).

In the authors' view, there are many studies on debt-taking behavior. But none has connected debt-taking on how (1) named factors are observable to management that affect credit rating changes and (2) if a factor affects creditworthiness, at which point of the factor value, the creditworthiness will flip to worsen the score. These aspects are seldom found in the literature. Hence, the paper is original with practical value at the global level.

The study aims to complement extant literature by assessing linkages between financial development, external flows and CO₂ emissions in 27 sub-Saharan African countries for the period 2002 to 2018.

The empirical evidence is based on interactive quantile regressions and external flows consist of remittances, foreign aid, trade openness and foreign investment.

The findings show minimum levels of external flows that should be reached in order for the interaction between external flows and financial development to promote environmental sustainability in terms of reducing CO₂ emissions. The minimum thresholds are critical levels of external flows that should be reached before financial development promotes environmental sustainability.

Policy implications – The disclosed external flow (i.e. FDI, foreign aid, trade and remittances) thresholds are actionable policy thresholds that the government can act upon in order to influence environmental sustainability by means of financial development. Theoretical implications – The findings below the external flow thresholds are consistent with the dependency theory in that external flows are harmful to socio-economic progress and environmental sustainability. When external flows are consolidated to the established critical masses or thresholds in the long run, the corresponding findings are in line with the extant neoclassical and endogenous growth theories, not least, because in the long run, external flows are associated with technological progress and adoption of stronger environmental legislation at the domestic level which are worthwhile in promoting environmental performance.

To reach the minimum trade and FDI levels that are worthwhile for the promotion of environmental sustainability, corporations should set targets on exports and imports as well as foreign investment levels that they have to attain in contributing to the national target of external flows needed to reduce CO₂ emissions. Such trade and FDI targets should be set in industries of various economic sectors.

The study complements the extant literature by assessing how external flows interact with financial development to influence CO₂ emissions.

This study aims to investigate the impact of government support on the coupling coordination degree of innovation chain and capital chain in integrated circuit (IC) enterprises and to explore the mechanism for considering talent in the influence path.

This paper uses coupling coordination degree model to estimate the coupling of two chains, and applies dynamic panel system generalized method of moments (system-GMM) to analyze the impact of government support on coupling of two chains and conducts dynamic panel threshold regression to explore the threshold effect of talent in the influence of government support on coupling coordination degree.

Serious imbalance in the coupling of two chains is a major obstacle in IC enterprises. Government support significantly reduces the coupling coordination degree. The talent in IC enterprises has a significant threshold effect. When the number of talent is lower than the threshold value, government support has a negative impact. Once the number of talent reaches a certain level, government support can significantly enhance the coupling of two chains. Compared with state-owned enterprises, government support has a greater negative impact on the coupling of the two chains in non-state-owned enterprises. The former needs more talent to take advantage of government support.

This paper applies the concept of coupling into enterprises and deeply studies the coupling coordination degree of two chains. The influence mechanism of government support and talent on the coupling of two chains is explored, which reveals that government support cannot achieve the expected incentive effect without the support of talent. We also discuss the heterogeneous effect of government support and of talent in enterprises of different ownership types.

This paper aims to explore the impact of business environment on high-quality economic development (HQED) and clarify the role of technological innovation and government intervention in this relationship.

Based on China’s provincial panel data from 2010 to 2019, this paper adopts the system generalized method of moments to empirically analyze the influential mechanism and heterogeneity of the business environment on HQED. Furthermore, the authors construct a dynamic panel threshold model to test the threshold effect of government intervention.

The results indicate that optimizing the business environment can significantly promote HQED, technological innovation plays a partial mediating role in the impact of business environment on HQED, mainly by enhancing the intensity of innovation input and increasing innovation output to facilitate HQED. Government intervention can regulate the impact of business environment on HQED, and there is a double threshold effect, and it possesses an inverted U-shaped feature of first promoting and then inhibiting.

This paper examines the influence path of business environment on HQED from the perspective of technological innovation and government intervention, filling the gap in the study of provincial business environment. Moreover, the conclusions furnish a theoretical basis for optimizing the business environment and facilitating the HQED in China.

This study aims to examine the nexuses between economic growth, trade openness, renewable energy consumption and environmental degradation among organization of petroleum exporting countries (OPEC) members over the period 1990–2019.

The empirical strategy for the study includes dynamic heterogeneous panel pooled mean group (PMG), mean group (MG) estimators and dynamic panel threshold regression (TR) analysis. For clarity, PMG and MG are used to explore the long-run relationship between the variables, whereas TR is used to uncover the actionable and complementary policy thresholds in the nexuses between green growth and environmental degradation.

The empirical evidence is based on the significant estimates from PMG and TR. First, using PMG, the study finding revealed a long-run relationship between economic growth and environmental degradation via the PMG estimator. Second, using TR, the study revealed an actionable threshold for carbon dioxide emissions (CO₂) metrics tons per capita (mtpc) not beyond a critical mass of 4.88mtpc, and the complementary policy threshold of 85% of the share of trade to gross domestic product, respectively.

The policy relevance of the thresholds is apparent to policymakers in the cartel and for policy formulation. The policy implication of this study is straightforward.

The novelty of this study stalk in the extant literature on providing policymakers with an actionable threshold for CO₂ emissions with the corresponding complementary threshold for trade policies in the nexuses between green growth and the environment.

The study examines whether the growth effect of government spending is contingent on the level of institutional environment prevalent in Economic Community of West African States (ECOWAS).

The study adopts the more refined and more appropriate dynamic threshold panel by Seo and Shin (2016) and made applicable be Seo et al. (2019). The technique models a nonlinear asymmetric dynamics and cross-sectional heterogeneity simultaneously in a dynamic threshold panel data framework.

The results show that there is a threshold effect in the government spending-growth relationship. Specifically, the authors found that the impact of government spending on economic growth is positive and statistically significant only above a certain threshold level of institutional development. Below that threshold, the effect of government spending on growth is insignificant and negative at best. The findings suggest that government spending-growth nexus is contingent on the level of Institutional quality.

Unlike previous studies that adopt the linear interaction model which pre-impose a priori conditional restrictions, this study adopts the dynamic threshold panel framework which allows the lagged dependent variable and endogenous covariates.