Investigating the fiscal decentralization’s effect on the carbon intensity of agricultural production may assist the United States in reaching its carbon peak and becoming carbon neutral. This paper delves into the investigation of the spatiotemporal patterns and internal relationships between fiscal decentralization, agricultural carbon intensity, and environmental regulation. The goal was achieved by using the spatial Durbin model using panel data for 49 states of the United States from 2000 to 2019. The study has found that environmental regulations play a significant role in reducing regional carbon emissions in agriculture and contribute positively to carbon emissions control. However, fiscal decentralization, which grants local governments more financial autonomy, has a positive but insignificant impact on carbon emissions, indicating that the prioritization of economic development and carbon control over environmental protection is favored by local governments. In examining the impact of environmental regulations on carbon emissions, the study reveals that fiscal decentralization does not play a substantial role in moderating this relationship. To promote low-carbon agriculture projects and ensure coordinated economic and environmental development, the study recommends optimizing the fiscal decentralization system, formulating different policies for different regions, and regulating the competencies of local governments through an effective examination system. The study concludes that it is crucial to obtain data at the city or county level to accurately understand the relationship between agricultural carbon intensity, environmental regulation, and fiscal decentralization. As a result, the central government must focus on perfecting the fiscal decentralization system, developing a differentiated agricultural carbon emission control system, controlling competition among local governments, and perfecting a political performance assessment system.

Achieving high production with limited resources is a major challenge faced by poultry farmers in countries with developing economies, such as Pakistan. Optimization of the technical efficiency (TE) of poultry business operations is a promising strategy. A representative sample of 210 poultry farms in the province of Punjab in Pakistan was analyzed for TE. The studied sample comprised 105 layer chicken farms (battery cage system, egg production) and 105 broiler chicken farms (environmental control shed system, meat production). A Cobb–Douglas stochastic frontier production analysis approach with the inefficiency effect model was used to simultaneously estimate TE levels and identify factors that influence efficiency. The results indicated that flock size, labor, feed, and water consumption are positively related to egg production, whereas vaccination was found to be insignificant. For broiler businesses, flock size, feed, and water consumption were positively related to the output, whereas labor and vaccination were found to be insignificant. The results of the TE inefficiency effect model revealed that farmer age, education, experience, access to credit, and access to extension services all had a significant and positive influence on the technical efficiency of both layer and broiler farmers. The estimated mean TE level of layer and broiler poultry farmers was 89% and 92%, respectively, evaluated against the benchmark of the identified frontier of efficient production with prevailing systems. The study concludes that it is possible to increase egg production by 11% and meat production by 8% by making more efficient use of the available resources and technology. To improve poultry farmers’ efficiency, policy interventions should focus more on the pronounced effects of variables such as education, farmer experience, credit access, and extension services.

Addressing the global challenge of sustainable waste management, this research investigates the influence of social dynamics and digital connectivity on rural residents’ willingness to adopt waste classification practices, essential for sustainable environmental management. Through a comprehensive analysis of 5413 rural participants surveyed in the China Labor-force Dynamic Survey (CLDS), this study employs a novel mixed-methods approach. It integrates quantitative analysis with the Manski social interaction framework and a Recursive Bivariate Probit model to explore the intricate interplay between community interactions, internet access, and environmental behaviors. Our methodology stands out for its unique combination of social theory and econometric modeling to address a pressing environmental issue. Results highlight a significant effect of mobile internet use and social interactions within communities on enhancing willingness towards waste classification. Notably, digital connectivity emerges as a key facilitator of environmental engagement, mediating social influences, and fostering a collective approach to waste management. Considering these insights, we propose targeted policy interventions that blend digital strategies with traditional community engagement efforts. Recommendations include crafting digital literacy programs and leveraging social media to bolster community-centric environmental governance. By harnessing the synergistic potential of digital tools and social dynamics, these strategies aim to elevate the effectiveness of waste classification initiatives in rural China, offering a scalable model for environmental sustainability.

At the request of the authors, Xiaolan Wang and Nihal Ahmed have been removed from the original publication