The study aimed to highlight the profitability and production function analysis of Penaeus monodon and Litopenaeus vannamei in intensified shrimp farms in Gujarat (India). Two hundred and twenty (220) shrimp farm households were used to identify (principal component and cluster analyses) 8 clusters of management practices that reflected various scales of production intensity ranging from 0–2999 kg/ha/crop to 9000kg/ha/crop and above for both the species. The Cobb-Douglas production function, which relates production output to several independent input variables, was used to determine productivity. The budgeting analysis for both the species showed that more intensively managed farms performed more than the less intensive farm. Empirical results show feed as most significant input for Penaeus monodon and Litopenaeus vannamei seed and labor that affected production. Average net returns/ha/year for Penaeus monodon was $16313.13 and for Litopenaeus vannamei $41640.99. Aquaculture exhibited decreasing returns to scale for both the species and estimates on resource use efficiency revealed that in Penaeus monodon the resources were economically utilized and in case of Litopenaeus vannamei the output was likely to increase if more of seed and less of labor would have been used. The major constraint for the shrimp farmers was diseases which can be mitigated by optimum stocking densities and proper feed management.

The status of data-limited tuna fishery stocks in India has been tested using the latest and most advanced computerized methods, CMSY and BSM. Five tuna fish stocks from both the Eastern and Western Indian Ocean were assessed using both catch and catch per unit effort (CPUE) details available from 1990 to 2015. Both methods help to calculate the maximum sustainable yield (MSY) and exploitation of MSY relative to biomass (B/B_MSY). The results of maximum intrinsic rate (r) and carrying capacity are also estimated. The results revealed that all tuna stocks in both the regions were overfished, with one, the longtail tuna (Thunnus tonggol) in the Western Indian Ocean strongly overfished (B/B_MSY = 0.44). Such observations, although still preliminary since the techniques used to produce them are relatively new, often associated with the situation and exploitation of all the stock in question, making the CMSY and BSM methods promising for stock assessment in data-deficit situations. The study concludes that in order to restore the status of these five tuna stocks in both regions, it would be necessary to reduce the fishing pressure.

The paper adopts the conventional data envelopment analysis (DEA) and bootstrap procedure to analyze the technical efficiency, while tobit regression is applied to identify the factors affecting efficiencies of exotic fish polyculture in Jammu and Kashmir (India). According to the statistical analysis of the variables utilized, there was a lot of variability in the inputs being used by the farmers, with the most variation being in the lime input. The DEA estimated technical efficiency for the sample farms in Jammu and Kashmir is 0.9771 and 0.9741, respectively, with least technical inefficiency of 3%. The bias-corrected (bootstrapped) technical efficiencies found were slightly lower than the ones estimated by conventional DEA. Mean allocative and cost efficiencies for sample farms in the Jammu region were 92% and 75%, respectively, and 84% and 74%, respectively, for farmers in the Kashmir region. Farming experience, age, and education have favorable impacts on technical efficiency of farmers in the state, but family size showed negative impacts. Efficiency improvement will eventually lead to increase in the production providing better scope for marketed surplus. More fisheries extension is suggested for expanding the exotic fish culture in the state making the enterprise a more profitable venture.

Introduction: This study aims to explore the mechanisms by which the digital economy influences urban carbon emissions in China, with a particular focus on potential threshold effects and the mediating role of technology. As the digital economy grows, it impacts various environmental metrics, including carbon emissions, necessitating a deeper understanding of its nonlinear dynamics and implications for sustainable urban development.Methods: Using panel data from 286 prefecture-level cities in China spanning from 2012 to 2021, we apply threshold effect models and mediation effect tests. The threshold effect model is employed to investigate non-linear characteristics of the digital economy’s impact on carbon emissions, while the mediation effect model assesses the role of technology as an intermediary in this relationship.Results: The threshold effect model reveals a single threshold in the impact of the digital economy on urban carbon emissions, indicating a nonlinear relationship. Initially, the influence of the digital economy on emissions is weak, but as the digital economy develops, its effect becomes more pronounced. The mediation effect model demonstrates that technological advancement can offset the increase in emissions associated with digital economic growth, thus showcasing technology’s potential to mitigate environmental impacts.Discussion: The findings suggest that while the digital economy generally promotes urban carbon emissions, its impact is non-linear and mitigatable through technological innovation. To curb emissions in urban areas, fostering technological innovation and supporting green technology research and development are critical. Moreover, enhancing management and supervision within the digital economy sector can contribute to balancing economic growth with environmental goals. These insights are valuable for policymakers striving to harmonize digital economic expansion with sustainable environmental practices.

Introduction: This study investigates the impact of China’s national-level city cluster policy on the synergistic governance of urban pollution reduction and carbon reduction, focusing on both “quantity reduction” (PM2.5 and CO ₂ emissions) and “efficiency improvement” (governance effectiveness). The research explores how the policy, as a quasi-natural experiment, contributes to these dual objectives. Methods: Using panel data from 274 prefecture-level and above cities from 2006 to 2023, the study employs a difference-in-differences (DID) model to estimate the impacts of the policy. Additionally, mediating and moderating effect analyses are incorporated to explore the mechanisms driving the outcomes. Results: The findings reveal that the city cluster policy significantly reduces PM2.5 concentrations and CO2 emissions in pilot cities, while simultaneously enhancing governance efficiency. Mechanism analysis identifies three main drivers: green technology innovation, knowledge spillovers, and industrial structure upgrading. Heterogeneity tests show that the quantitative effects (e.g., pollution reduction) are stronger in hub cities, while efficiency gains are more pronounced in non-resource-based cities. There are no significant differences between hub and non-hub cities for efficiency, and no significant impact of resource endowment on quantity reduction. Discussion: This study contributes to the literature in three key ways: (1) it develops a dual-dimensional framework to assess both “quantity reduction” and “efficiency improvement” in pollution and carbon governance; (2) it uncovers the mechanisms through which the city cluster policy operates; and (3) it identifies regional heterogeneity in the policy’s effects across different types of cities. The findings underscore the dual benefits of the policy, validate the mediating mechanisms, and highlight the role of regional differences in the outcomes. Policy implications include expanding and institutionalizing the pilot programme, fostering green innovation, and tailoring city cluster development strategies to local conditions.

Introduction: Sustainable agricultural productivity is a key priority for China as it navigates environmental challenges and growing food demand. Understanding the impact of agricultural structural adjustments, including crop-livestock integration, service-based agriculture, and digital transformation, on Green Total Factor Productivity (GTFP) is essential for formulating effective agricultural policies.Methods: This study analyzes data from 1,743 counties in China spanning from 2003 to 2022. Using econometric models, we examine the relationship between agricultural structural adjustments and GTFP growth, focusing on the roles of technological progress, structural changes, and digitalization. The study accounts for regional variations in agricultural productivity.Results: Our analysis finds that technological progress is the primary driver of GTFP growth. Agricultural structural changes, particularly crop-livestock integration and service-based models, contribute a “structural dividend,” enhancing sectoral efficiency and advancement. Interestingly, service-oriented models initially slow productivity but yield long-term benefits. Digital transformation significantly amplifies these positive effects. Regional analysis reveals that eastern and western regions benefit more from structural upgrades than central regions.Discussion: These findings highlight the importance of designing policies tailored to local contexts, especially considering regional differences in agricultural productivity. The integration of digital technologies and service-oriented models plays a crucial role in advancing GTFP. Policy-makers should focus on promoting inclusive, sustainable, and technologically-driven agricultural development to meet China’s future food security needs.