Financial inclusion and capital market development are critical components for fostering sustainable economic growth and reducing poverty. Financial inclusion ensures that individuals and businesses, especially those traditionally underserved, have access to useful and affordable financial products and services that meet their needs. Capital market development, on the other hand, facilitates efficient allocation of resources by mobilizing savings and channeling them into productive investments. This study examined the impact of financial inclusion on capital market development of Nigeria. Quarterly time series data from 2000Q1 to 2024Q4 of Capital Market proxied by market capitalization (MCAP), number of microfinance banks (MFB), value of rural deposits as a ratio of total deposits (RDE), value of rural credits as a ratio of total loans (RCR) and interest rate (INT). The Dickey-Fuller unit root test and the Johanson cointegration test were carried out. The result showed that the data set were all stationary at first difference I(1)and the series had a long run relationship with dependent variable. The Fuller modified ordinary least squares (FMOLS) technique was utilized to estimate the model, the result shows that all the variables(MFB, RCR, RDE and INT) had direct relationship with the dependent variable (MCAP) and were all significant at 5% significance level . The result further revealed that the positive impact of financial inclusion on capital market growth suggests that policies aimed at stimulating financial inclusion growth can have a multiplier effect on capital market development. Furthermore, the positive relationship between rural deposits and capital market development indicate that policies aimed at mobilizing rural deposits can increase financial inclusion. This can be achieved through expanding bank branches and mobile banking services in rural areas. The study therefore recommended that Government should develop initiatives to boost rural deposits by creating accessible and attractive savings products, thereby increasing the capital base available for investment in capital markets. Also banks and other financial institutions should implement targeted credit programs that provide subsidized loans or guarantees to rural businesses, particularly in sectors with high growth potential. Banks and other financial institutions should implement monetary policies that keep lending rates low and manageable for borrowers, particularly in rural areas.

Purpose The main objective of this study is to investigate the determinants of net government expenditure of selected “22 Indian States” and public debt from the period 1991 to 2023. The study also examines the comparative effect of central transfers between India's special category states (SCS) and general category states (GCS). Design/methodology/approach This study employs several Cross-Sectional Dependency (CSD) tests for the homogeneity of the sample units. To account the CSD among the units, the study employs the second-generation CIPS unit root test for the stationarity process of the variables. The Kao and Pedroni cointegration are employed to test the long-run cointegration of the variables. Finally, for the result estimation, the highly celebrated autoregressive distributed lag (ARDL) in the panel framework is employed along with the cross-sectional-ARDL, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimator in the robustness analysis. Findings These results demonstrate that all the determinants contribute to net government expenditure positively both in the short and in the long run are able to vary in statistical significance. Moreover, the study results show that central transfer's effect is much higher in the SCS than in the GCS. Research limitations/implications Although the study's emphasis on 24 Indian states may seem selective, the sample was selected to guarantee data availability and comparability over time. These states represent a diverse mix in terms of economic structure, development levels and policy environments, thereby providing a meaningful basis for analysis. However, the findings may not be entirely generalizable to all Indian states or Union Territories. Future research could expand the sample to include additional states or extend the analysis to more recent time periods, subject to data availability. Such extensions would enhance the robustness and external validity of the conclusions drawn in this study. Moreover, this study focuses on quantitative analysis, future work could incorporate qualitative methods such as state-level case studies or stakeholder interviews to further illuminate the mechanisms behind observed fiscal behaviour. Originality/value To the best of our awareness, this investigation represents the initial effort to explore the primary determinants of net government spending in India at the state level. Exploring the comparative effectiveness of central transfers in SCS and GCS further contributes to the study's novelty.

The current study has tried to explore the impact of both renewable as well as non-renewable energy consumption on some of the crucial economic determinants like economic growth, export diversification, trade openness, and human capital in the GCC countries during the time period between 1990-2022. The most relevant second-generation panel econometric techniques, incorporating panel unit root tests, cointegration analysis, Fully Modified Ordinary Least Squares (FMOLS), and quantile regression, have been used. The comprehensive empirical analysis reveals that the consumption of renewable energy is quite significantly impacted by structural variables like trade openness and human capital, while on the other hand, non-renewable energy consumption is principally driven by short-run macroeconomic fluctuations, predominantly GDP and export diversification. The findings of this study highlight the importance of strategic policy realignment in the GCC to promote a sustainable energy transition by investing in human capital, enhancing trade openness, and decoupling economic diversification from carbon-intensive sectors. This paper further offers quite critical insights into the nexus between energy and economy and provides actionable policy recommendations for achieving long-term economic as well as environmental sustainability in fossil-fuel-dependent economies.

This study examined tax revenue on economic development in Nigeria for a period of 31years, 1993-2023. To achieve this objective, Human Development Index (HDI) served as the dependent variable for this study proxy for economic development, while company income tax (CIT), petroleum profit tax (PPT), personal income tax (PIT), value added tax (VAT), education tax (EDT) and customs and excise duties (CED) served as the independent variables (explanatory variables). Ex-post-facto research design was utilized for this study. Data used were sourced from the United Nations Development Programme (UNDP), Central Bank of Nigeria (CBN) Statistical Bulletin, Central Bank of Nigeria Economic and Annual Report Account, Federal Inland Revenue Service (FIRS), the Federal Reserve Bank of St. Louis, and the World Bank Database Descriptive statistics, Phillips-Perron (PPT) Unit root test, Johansen Co-integration, Granger causality and Vector Autoregressive model (VAR) were the analytical tools for this study. The results revealed that some variables of tax revenue indicators have significant effect; while others have no significant effect on economic development indicators. The variables were normally distributed, and there was a causal relationship between tax revenue indicators and economic development. There exist a long-run relationship between tax revenue indicators and economic development. The Vector Autoregressive results show that tax revenue indicators were statistically significant with HDI. The study concluded that tax revenue indicators had various levels of interaction with economic development index measured in this study with greater outcome indicating positive and significant effect. The study recommended that tax revenue should be spent wisely to fund essential services like affordable housing, good roads, clean water, a reliable electrical supply, education, and basic healthcare. This would encourage the growth of numerous economic sectors, hence boosting economic growth and development. This study contributed to the body of knowledge by extending the scope of existing literature to 2023. Keywords: Tax Revenue, Economic Development, Human Development Index, Company Income Tax, Petroleum Profit Tax, Personal Income Tax And Value Added Tax.

This study examines the relationship between Digital Transformation (DT) and Clean Energy Integration (CEI) in the agro-industrial sectors of emerging economies, with a focus on Kazakhstan and Russia. As these countries pursue industrial growth while meeting climate commitments, the research evaluates how digital maturity and green financial frameworks drive decarbonization. Using secondary data from 2000 to 2024, the study applies advanced econometric methods, including Cross-Sectionally Augmented IPS (CIPS) unit root tests and the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, to address cross-sectional dependence and structural breaks. Results show that both Digital Transformation and Environmental Policy (EP) significantly reduce Greenhouse Gas Emissions (GHGE) in the long term, with coefficients of −1.730 and −2.289, respectively. Green Financial Innovation (GFI) and Circular Capacity (CC) also play key roles in lowering carbon intensity. The Error Correction Term (ECT) of −1.120 suggests a rapid adjustment toward the long-run equilibrium. This research introduces a holistic “Digital-Green Twin” approach, positioning digitalization as the foundation for renewable energy adoption in large-scale agro-industrial processing. The findings offer a scalable roadmap for policymakers to align technological progress with carbon-neutral industrial objectives.

Computed tomography (CT) images are stored at a 12-bit depth. However, many deep learning libraries and pre-trained models are designed for 8-bit images, requiring an intermediate compression step before restoring the original 12-bit physical range. This process causes information loss and can compromise image reliability. This study investigated the impact of two CT resampling methods (8-bit compression; 12-bit decompression) on dose calculation and image quality. Ten total marrow and lymphoid irradiation patients were selected. CT scans were resampled using linear and non-linear look-up tables (l_LUT/nl_LUT). Original and resampled CTs were evaluated considering: (i) Hounsfield unit (HU) root mean squared error (RMSE); (ii) dose-volume histogram (DVH) statistics for target volume and several organs; (iii) 3D gamma passing rate (GPR) with a 1%/1.25 mm criterion; (iv) lymph nodes contouring and diagnostic quality (scale 1–5). The RMSE for l_LUT vs. nl_LUT was 7 ± 1 vs. 10 ± 1 HU. Maximum differences in DVH statistics were 0.4%, with a 3D-GPR = 100% for all cases. CTs resampled with l_LUT exhibited evident brain pixelation (score = 1), whereas nl_LUT matched the original CT quality (score = 4). Both LUTs were acceptable for lymph nodes delineation. The nl_LUT optimized the CT resampling process, providing a more efficient method for possible deep learning applications in synthetic CT generation.

The research explores the complicated and dynamic link between tax revenues, financial development (FD), and economic growth (EG) in rising countries like the BRICS. The research uses second-generation panel econometric techniques using a sample from 2010 to 2024. The BRICS nations show high CSD and slope heterogeneity, according to the preliminary dataset. Panel unit root tests show that most variables are I(1) while economic growth (EG) is I(0). Panel co-integration experiments (Kao, Pedroni, and Westerlund) showed a strong long-run connection between these variables. Regression analysis (Fixed Effect and FGLS) shows that tax income positively and statistically significantly affects financial development. Tax revenue boosts economic development. Positive control elements were FDI and trade openness, whereas negative control elements were inflation in financial development and economic growth. BRICS policymakers need these findings to understand that an effective and strong tax framework boosts government revenue and financial sector stability and depth, which are necessary for long-term economic success. The research adds to the field by examining taxes and financial growth in developing countries.

This study examined the impact of interest rate liberalization on investment in Nigeria from 1986-2023 using data from the CBN Statistical Bulletin. The data were subjected to various diagnostic tests such as Unit Root, ARCH, Normality, among others, and the result revealed that the data were suitable for estimation. The dependent variable was represented by Gross Fixed Capital Formation used as a proxy for investment, while Exchange Rate, Government Expenditure, Interest Rate, Inflation and Money Supply were the independent variables. The Auto Regressive Distributed Lag (ARDL) estimation technique was employed to test the short and long run impact of the independent variables on the dependent variable. The ARDL long and short run results revealed that Government Expenditure and Interest Rate significantly impacted on investment during the period under review showcasing the importance of these variables in promoting investment in Nigeria. The result further revealed the need to. strengthen policies that would promote stable exchange and inflation rate that would enhance domestic investment. Based on the findings of this study, it is pertinent to maintain macroeconomic stability through sound monetary and fiscal policies as well as strategic investments in infrastructure, education, and regulatory reforms, for a more robust and diversified economy.

Unit roots of the unit root L-functions

Abstract The present study investigates the impact of the level of governance on long-run economic growth in the G7 and E7 economies from 2002 to 2023. Institutional variables such as rule of law, regulatory quality and accountability are incorporated into the neoclassical growth framework. The empirical study uses data from the World Bank and the IMF, with advanced panel data techniques including Pesaran’s (2007) CADF unit root test, Pedroni’s (1999) cointegration test and a panel vector error correction model (VECM). To ensure robustness of the analysis, both Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators are used. The results show that there is a stable long-run equilibrium relationship which exists between the governance and some measure of economic growth for both sets of economies. However, the G7 economies tend to adjust to this equilibrium nearly five times faster than the E7 economies. Rule of law and regulatory quality are consistently found to be important determinants of the level of economic growth while weak accountability also constrains performance, especially in emerging markets. These results indicate the important role played by the governance of an economy in this regard, especially in the case of advanced economies but point to the need for judicial independence and regulatory stability before a country can inaugurate a series of reforms and hope to derive a sustainable rate of growth.

Purpose The purpose of this study is to discuss how knowledge management (KM) practices can help in sustainable infrastructure growth in various regions with specific reference to energy consumption and economic development. Design/methodology/approach The study employs the panel data of 65 Belt and Road Initiative (BRI) countries across the period between 1981 and 2016 to analyze using the econometric modeling techniques. Energy consumption, financial development, utilization of renewable energy and technological advancement are key variables. A theoretical lens of KM is incorporated in comprehending the data-driven decision-making and knowledge-based policy implementation. To ensure robust results, advanced econometric techniques are applied, including cross-sectional dependence tests (LM Breusch–Pagan and CD test of Pesaran), unit root tests (ADF Fisher Chi-square and CIPS CADF) to capture long-run relationships. Findings The findings indicate that energy consumption and economic expansion have significant implications to the quality of the environment whereas use of renewable energy and economic development have positive environmental consequences within the chosen regions. The research reveals that the regional differences influence the manner in which the KM mediates the interrelation of development and sustainability. Research limitations/implications The research depends on secondary sources of KM inferences and is restricted to the chosen BRI countries. Practical implications The study also gives policymakers, planners of infrastructure and development institutions recommendations of how to incorporate KM in infrastructure policy in line with the sustainability objectives. Social implications International organizations and development partners should fund capacity-building programs and knowledge-sharing platforms to enable BRI nations to execute sustainable development objectives. Originality/value The research examines how KM is linked to sustainable development in 65 BRI countries, taking into account differences among areas and difficulties in using cross-sectional information.

Purpose The purpose of this study is to unveil the dynamic relationship between renewable energy generation (REG), carbon emissions, infrastructure concerning electric consumption, internet users and highway networks, macroeconomic factors and foreign direct investment (FDI) inflows in India from 1990 to 2023. Design/methodology/approach Ensuring the outcomes of the autoregressive distributed lag (ARDL) bound test of overlong co-integration, fully modified ordinary least squares (FMOLS) attested to the relationship among the variables followed by the augmented Dickey–Fuller test for unit roots. The error correction method (ECM) under the ARDL framework captures the short-run dynamics while a causal link has been established through the Vector Error Correction Granger causality test. Findings Contrary to the FMOLS findings, the ARDL bound test supported the hypothesis that infrastructure, trade and REG have a positive long-term impact on FDI, but domestic investment, market size and instability have a negative one. ECM regression shows that while market size promotes FDI, emissions deter it in the short term. The Granger causality test confirmed that market size, FDI and REG drive domestic investment, while trade leads to instability and emissions. In addition, the analysis verified that trade and FDI, infrastructure and instability and FDI and instability are all causes of one another. Moreover, emissions and infrastructure are the main causes of instability. Practical implications Policymakers must prioritize renewable energy, robust infrastructure and stability in their efforts to address emissions and instability. Policies that effectively integrate energy, environmental considerations and economic growth are crucial for attracting and sustaining higher FDI inflows in India. Originality/value This study expands novel insights regarding the significance of renewable energy, emissions, infrastructure and macroeconomic factors for FDI inflows in Indian phenomena. Although previous research has mostly focused on how economic factors encourage FDI inflows, this study is an infrequent attempt to determine whether REG, emissions and infrastructure that represent individual internet users, per capita electricity usage and the length of the national highways have any interplaying relationship on inbound FDI, thereby increasing environmental quality.

Purpose The purpose of this paper is to explore market efficiency in the Metaverse economy by analyzing the behavior of major Metaverse tokens. This study examines how information diffusion, price discovery and systemic dynamics shape efficiency and speculative activity in this emerging digital ecosystem. Design/methodology/approach The methodology used in this paper, applied to five Metaverse tokens, is structured in three sequential phases. Firstly, the authors test the efficient market hypothesis in its weak form and the Random Walk Theory to assess the integration of historical information into prices. Secondly, the authors carry out Bubble Timestamping using explosive unit root tests (GSADF) and price/fundamental gap analysis. Thirdly, the authors quantify the magnitude of market inefficiency through the Adjusted Market Inefficiency Magnitude (AMIM), capturing its dynamic variations. Findings The results of this study reveal persistent inefficiencies and recurring speculative bubbles across all tokens. Explosive price trends often coincide with major announcements, amplifying volatility and distorting valuations. These patterns confirm the adaptive market hypothesis, showing that Metaverse markets evolve through adaptive cycles periods of speculation followed by phases of improved efficiency. Short-term price movements frequently overshadow fundamentals, creating systemic vulnerabilities and raising concerns about market integrity and investor protection. The findings of this study highlight the need for adaptive investment strategies and regulatory frameworks to manage systemic risks in rapidly evolving digital ecosystems. Originality/value This study offers a detailed analysis of the efficiency of the Metaverse market, a field that remains largely unexplored. This study goes beyond traditional approaches by examining speculative dynamics, responsiveness to global shocks and adaptability through the adaptive market hypothesis. By incorporating AMIM, a tool designed for emerging markets, into efficiency tests, this paper provides a dynamic interpretation of bubbles and inefficiency. AMIM also serves as a practical monitoring tool for regulators and helps investors anticipate risks in volatile virtual environments.

Growing global population and climate change concerns have caused major challenges for sustainable food production to meet the growing food demand. High mechanization, drought intensity, fertilizer application, energy use, and irregular climate change adaptation are the major challenges faced by agriculture. In the Kingdom of Saudi Arabia, the climate is very harsh, and the country depends highly on agriculture to meet domestic food demands. This generates major challenges for the agricultural system to produce sufficient quantities of food with minimal carbon footprints. Therefore, this study aims to analyze the impact of growing mechanization (Mech), water productivity (WP), climate change technology adaptation (CCA), fertilizer consumption (FC), agriculture growth (AG), and energy consumption in agriculture (ENA) on CO 2 emissions in agriculture in both the short and long run. For this purpose, an autoregressive distributed lag (ARDL) model was applied to the data for 1992–2021. According to the unit root test, all variables were integrated in order I(1), and the ARDL bound test for the cointegration tests ensured the long-run relationships among the variables. Moreover, the ARDL model passed all diagnostic tests, and CUSUM and CUSUMQ confirmed the stability of the model. The findings revealed that Mech has a strong negative impact in the long run while significant positive impact in the short run on CO 2 emission. Both WP and CCA significantly reduced CO 2 emissions in both the short and long run. FC causes an increase in CO 2 emissions in both the short and long run. AG reduces CO 2 emissions, whereas ENA increases CO 2 emissions only in the long run. The findings suggest that the government should focus on efficient and eco-friendly mechanization, energy sources, adoption of efficient irrigation systems, and promote R&D for advanced climate change-oriented technologies to enhance agricultural environmental sustainability by reducing CO 2 emissions in agriculture.

Purpose: This research aims to explore the factors influencing capital structure and the effect of the COVID-19 pandemic on leverage decisions. Due to the disruptions caused by COVID-19, this study identifies the impact of pandemic on the capital structure choices and their determinants, as well as the adjustment dynamics, among listed companies of pharmaceutical and chemical in Bangladesh. Design/Methodology/Approach: To perform the study has undertaken 196 firm year observation sample extending from 2015 to 2024 and a panel data from listed firms in the Pharmaceuticals and Chemicals sector on the Dhaka Stock Exchange (DSE) in Bangladesh. A fixed effects panel regression model, incorporating a lagged dependent variable, is estimated to account for dynamic adjustments while controlling for unobserved, time-invariant firm-specific effects. A comparative analysis of pre-post covid impact on the capital structure has been also analyzed. For examining the stationarity of the variables, the Fisher-type Augmented Dickey-Fuller (ADF) panel unit root test is utilized in this study. Findings: The findings of the study reveal that lagged leverage and firm size have a significant impact on all the measures at 5% level of confidence interval which confirms the path dependency nature of capital structure decisions. In oppose to that tangibility and profitability shows insignificant while liquidity reveals a weak negative effect which interprets conservative liquidity management during the periods of crisis. However, The COVID-19 dummy variable shows negative impact on leverage, highlighting a shift towards internal financing. In addition to that pre- and post-pandemic analysis reveal increased leverage persistence and a greater importance of firm size, along with a diminished role of liquidity as firms prioritize cash reserves. Overall, capital structure decisions in Bangladesh are influenced more by institutional constraints and risk aversion than by traditional collateral-based mechanisms, providing partial support for both trade-off and pecking-order theories. Originality/Value: The article adds to the body of knowledge on capital structure by providing sector-specific insights and examining capital structure during crises in an under-researched emerging market, thereby expanding the discourse on dynamic capital structure to include Bangladesh.

India is an agrarian economy, and products like spices and cashews are contributing a substantial amount to foreign earnings. India has the potential to offer goods at competitive prices not only in developed markets but also in developing markets around the world. The trade reforms and new economic scenario provide both opportunities and challenges in the world market. In this context, it is essential to examine which factors contribute to the export success of spices and cashews in the world market. The main objective of this chapter was to investigate the determinants of spices and cashew exports (CEs). The determinants of exports are identified as the exchange rate, domestic consumption, world demand and production. The study was carried out using the fully modified ordinary least squares (FMOLS) method. The augmented Dickey–Fuller (ADF) unit root test was used to test stationary, and the Johansen and Juselius cointegration test was used to examine long-run relationships among variables. All time series variables were integrated at order one. The Johansen cointegration test confirmed that there exists a long-run equilibrium relationship between variables. The FMOLS results reveal that variables like domestic consumption, exchange rate and production are the major determining factors for spice export. As far as aggregated CE is concerned, domestic consumption and exchange rate are the major determining factors during the study period.

Type of the article: Research ArticleAbstractFinancial institutions can play a critical role in promoting energy efficiency by facilitating investment in energy-saving technologies and infrastructure, as per the Sustainable Development Goals (SDGs). This study aims to investigate the impact of Financial Institutions’ Depth (FID), Access (FIA), and Efficiency (FIE) on Energy Intensity (EI) in the GCC countries from 2000 to 2021 within the Environmental Kuznets Curve (EKC) framework. To add novelty to the analysis, regulatory quality’s moderating role is also tested in the relationship between FID, FIA, FIE, and EI. Given the financial, economic, and institutional interconnectedness of the GCC region, second-generation panel econometric techniques, such as Cross-sectional Dependence (CD) unit root tests, cointegration, and Autoregressive Distributed Lag (ARDL), are employed. The findings confirm the long-run validity of the EKC in all models. In the short term, the EKC is only supported by an FID model. FIA and FID do not directly influence EI in either the short or long run. However, regulatory quality significantly moderated these relationships with long-run coefficients of –0.096 (FID) and –0.063 (FIA). FIE is found to reduce EI with the long-run coefficient of –0.109, and this effect is also moderated by better regulatory quality with the long-run coefficient of –0.087. However, FIE and its interaction with regulatory quality do not significantly impact EI in the short run. The results conclude that regulatory quality directly and consistently reduces EI in all models. These results emphasize the need to improve the financial institutions and regulatory governance to achieve energy efficiency in the GCC region.

This paper explores the connection between the growth rate of India's pharmaceutical exports over a year and various economic markers foreign direct investment (FDI) in pharmaceuticals, patent applications, and the exchange rate. With the use of annual data from 1995 to 2023, the investigation uses Ordinary Least Squares (OLS) estimation following stationarity confirmation via unit root tests. The results indicate that there is a statistically significant adverse effect of exchange rate volatility on export performance, whereas the FDI inflows and patent filings effects are statistically not significant. The results indicate that although macroeconomic stability in currency value is crucial to export growth, other variables such as FDI and innovation indicators are probably too short-term or will need complementary enabling policies to show their export contributions. The research makes a contribution to the literature by presenting empirical evidence on short-run dynamics among innovation, investment, and trade in India's pharmaceutical industry and presenting policy implications for improving global competitiveness.

Type of the article: Research ArticleAbstractThe private sector remains the engine room for inclusive economic development, and its interaction with Foreign Capital Inflows (FCIs) is crucial for growth in emerging markets like Nigeria. This study examined the impact of FCIs (Foreign Direct Investment, Foreign Portfolio Investment, Foreign Debt, Foreign Aid & Foreign Remittances) on Private Sector Credit in Nigeria. To achieve this objective, time series data spanning a 26-year period (1998–2023) were harvested and used. The Augmented Dickey-Fuller test was used to ascertain the unit root, while the hierarchical regression technique provided the model estimates. From the results, foreign remittances emerged as the only significant contributor to private sector credit growth (β = 0.993, p < 0.05). This underscores the critical role of diaspora remittances in supporting financial intermediation and private sector development. The study concluded that foreign remittance is a major driver of private sector credit expansion in Nigeria. It is recommended that policy efforts should prioritize facilitating remittance inflows through a supportive regulatory framework. Emphasis should also be placed on leveraging remittances as a stable and development-oriented source of capital.

The study examines the relationship between environmental sustainability and economic growth across countries and income levels by testing the Environmental Kuznets Curve hypothesis using panel data methods. In the analysis, CO₂, agricultural CH4 and agricultural N20 emissions are used as dependent variables and three models are constructed based on various economic and agricultural indicators. The dataset covers broad country groups such as the Arab World, Euro Area, European Union, OECD, IBRD, and East Asia-Pacific, as well as low, middle and high income economies. Unit root tests show that most variables are stationary at level. Findings related to CO₂ emissions reveal that the classical inverted U shaped Environmental Kuznets Curve does not hold for any country group, instead U, N and inverted N shaped relationships emerge. By income level, low-income countries exhibit an N type curve, middle income countries an inverted N structure, and high income countries a U shaped pattern. This indicates that environmental pressures do not consistently decline with rising income but fluctuate depending on economic structure, energy policies and technical capacity. For agricultural CH4 emissions, only the European Union and Euro Area support the classical inverted U form, while U, N, and inverted N shaped structures appear in other regions. By income group, low income countries exhibit a U shaped curve, middle income countries an inverted N curve, and high income countries an inverted U structure. For agricultural N₂O emissions, the dominant curve type is N shaped across both country groups and income levels. Overall, the results demonstrate that environmental sustainability is determined less by income growth and more by structural transformation, environmental regulation, technological development, and sectoral characteristics. These findings highlight the necessity of country and income specific policy strategies instead of uniform global environmental policies.