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This article investigates the impact of foreign trade on carbon emissions of the member countries of the largest trade bloc, the Regional Comprehensive Economic Partnership (RCEP).

The aggregate bilateral trade with members of RCEP during the period 1991–2020 was considered for analysis. The study also examines the impact of foreign trade (between member countries) on economic development, represented by GDP per capita. Dumitrescu–Hurlin panel Granger causality test was conducted to understand the impact of foreign trade on GDP per capita and carbon emissions.

Results indicate that though foreign trade is heterogeneously Granger causing GDP per capita, it also aggravates carbon emissions in RCEP bloc.

The study is of significance to the policymakers in the member countries as it provides evidence to include climate impact in trade agreements. The wealthier RCEP member countries can support the green transition of low-income countries through transfer of eco-friendly technologies.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.