Issue #2 (Volume 17 2026)

Type of the article: Research Article

Abstract
This study examines how foreign direct investment (FDI) has affected manufacturing carbon emissions in eight ASEAN economies from 2005 to 2022 using panel data from the World Bank and UNCTAD and employing random effects and feasible generalized least squares estimators. The preferred specification indicates that a 1 percentage point increase in manufacturing-adjusted FDI inflows (as a share of GDP) is associated with a 0.018-unit rise in log manufacturing CO₂ emissions (approximately 1.8%). Simultaneously, population size (coefficient ≈ 0.94) and fossil fuel energy consumption (coefficient ≈ 0.053) exert strong positive and statistically significant effects. By contrast, per capita income and its squared term are not significant, providing no support for a Kuznets type nonlinear income-emissions relationship, and lagged emissions add little once contemporaneous drivers and error structures are controlled for. The results suggest that FDI has primarily flowed into emissions-intensive manufacturing activities, with limited evidence of broad-based clean technology transfer, thereby risking a lock-in of carbon-intensive development that undermines ASEAN’s Net Zero ambitions and intergenerational equity. The paper argues that tighter environmental standards for FDI, an accelerated energy transition away from fossil fuels, and integrated population planning is needed to reconcile manufacturing-led industrial expansion with sustainability goals in ASEAN. It also offers sector specific evidence to guide FDI governance and energy policy in middle-income countries.

Type of the article: Research Article

Abstract
In the context of growing pressure on forest ecosystems arising from agricultural area expansion and intensification, expanding population, and climate variability, this study aims to identify and quantify the impacts of these changes on forest coverage in the South Asian region. Using a balanced panel dataset for 1990–2023, the analysis employs a regularized fixed-effects estimation to identify the key drivers of forest area change and assess variable importance.
The findings show that a 1% increase in agricultural value added is achieved at the cost of a 0.32% decrease in forest area, making it the most significant driver of forest loss. Use of inorganic fertilizer also exerts a strong negative influence, as forest cover is reduced by 0.18% for every additional percentage usage of fertilizer. Irrigation expansion has a similarly adverse effect, contributing to a 0.21% decline per 1% increase in irrigated area. Population density growth intensifies pressure on forests, with each additional 10 persons per km² corresponding to a 0.05% decrease in forest area. However, pasture share exhibits a positive association: a 1-percentage-point increase corresponds to a 0.14% rise in forest area, and cattle density also shows a modest but positive effect. The results indicate the presence of mixed livestock–forest systems and early forest-transition dynamics in some countries.
Overall, the findings demonstrate that the pattern of agricultural practices determines forest trajectories in South Asia, and achieving sustainability will require country-specific strategies that balance productivity growth with integrated land-use planning and long-term conservation goals.

Type of the article: Research Article

Abstract
This study examines the long-term and short-term dynamic interactions between PM2.5 pollution and its anthropogenic determinants, namely industrial activity, urbanization, economic growth, total energy use, and renewable energy utilization, across five Central Asian countries (Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan) from 1992 to 2023. Preliminary tests validate pronounced cross-sectional dependence and notable slope heterogeneity, substantiating the application of the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model. The Westerlund cointegration results demonstrate a strong long-term equilibrium relationship. The long-term CS-ARDL estimations indicate that industrial activity is the primary driver of PM2.5 pollution, followed by total energy consumption. The analysis reveals evidence supporting the upward-sloping segment of the Environmental Kuznets Curve (EKC), as economic growth significantly elevates PM2.5 levels. In contrast, the squared GDP term is insignificant, suggesting the absence of a turning point in pollution reduction. Renewable energy consumption has a negligible moderating effect. The Error Correction Term is negative and statistically significant, indicating that approximately 24.5% of deviations from the long-term equilibrium are corrected each year. The findings indicate that environmental stability in Central Asia necessitates a strategic transformation of industrial and energy policy, underscoring the importance of coordinated regional initiatives to modernize grids and promote green industrial practices to decouple economic expansion from particulate pollution.

Type of the article: Research Article

Abstract
This study investigates the nonlinear dynamics of the relationship between foreign trade and carbon dioxide (CO₂) emissions in Uzbekistan over the period 1997–2024. The analysis employs annual time-series data from the World Bank’s World Development Indicators (WDI) database, including three key variables: CO₂ emissions per capita (tonnes), exports of goods and services (current USD), and imports of goods and services (current USD). Using the Markov switching regression (MSR) model, the study identifies two distinct economic states: an export-oriented regime (Regime 1) characterized by high industrial production and export activity, and an import-driven regime (Regime 2) characterized by domestic consumption patterns and elevated import flows.
The empirical results demonstrate that the export–emissions relationship is regime-dependent: exports have a statistically significant positive effect on CO₂ emissions only during export-oriented periods (β = 1.54 × 10–10, p < 0.01), while this relationship becomes insignificant during import-driven periods (β = 5.20 × 10–11, p = 0.378). In contrast, imports consistently reduce CO₂ emissions across both regimes (Regime 1: β = −1.05×10–10; Regime 2: β = −1.07 × 10–10, both p < 0.01), indicating a stable import-substitution effect that displaces domestic production-related emissions. The transition probability analysis reveals high persistence in both regimes (P₁₁ = 79.69%, P₂₂ = 81.22%), with structural shifts occurring approximately every five years (expected durations: 4.92 years for Regime 1 and 5.32 years for Regime 2). These findings confirm that the trade–emissions relationship in Uzbekistan is nonlinear and regime-dependent, necessitating the development of regime-sensitive environmental and trade policies.