Purpose: In the 21st century, the agricultural and food industries have faced the critical challenge of meeting the needs of a rapidly growing global population. Food production is estimated to increase by 70% by 2050, thus supporting this growth. Limited farmland availability, unpredictable climate conditions and widespread food insecurity make this challenge even more difficult. This study examines how advanced digital technologies can help address challenges in the agricultural sector. Design/methodology/approach: A comprehensive bibliometric analysis examines agricultural digitalisation trends from 2013 to 2022. Findings: The study uncovers promising trend-developed regions, such as Europe, Germany, the UK and the United States, at the forefront of digital agricultural practices. The analysis underscores the transformative power of digitalization in agriculture, with technologies such as artificial intelligence, smart farming, big data reshaping strategies, enhancing connectivity and fostering transparency. This study underscores the need for responsible innovation, inclusive development and targeted policies to harness the full potential of digitalization, ensuring sustainability and equity in the agricultural sector. It also identifies emerging trends like Industry 4.0 and precision livestock farming as pivotal components of agriculture in the digital age. Research limitations/implications: This study offers insights into the digitalization of agriculture through bibliometric analysis, but it has limitations. It relies solely on published literature and trends and lacks real-world data and empirical case studies. Future research should incorporate secondary data and case studies to determine the significance of adapting digital agriculture to help overcome various obstacles in agriculture. Originality/value: Several researchers have examined the use of technologies, such as UAVs, IoT, machine learning and other advancements in agriculture. Recent studies have highlighted the digital agriculture revolution, emphasizing IoT, SSM, CSA, RS and AI in Agriculture 4.0. These studies offer a comprehensive roadmap for the digital agriculture revolution, covering performance analysis and scientific mapping.

In the wake of the 2015 Paris Agreement, green finance has emerged as a pivotal mechanism for addressing environmental challenges and achieving sustainable development goals (SDGs). This study employs a combined meta-analysis and bibliometric analysis to assess the evolving research landscape of green finance with a comparative lens on developed and developing economies. Based on 51 studies from Scopus (1990–2024) for bibliometric analysis and 17 studies for meta-analysis, the findings highlight distinct research patterns. Developing economies, particularly China and India, exhibit a rising trend in green finance research, emphasizing practical environmental solutions, whereas developed economies, including the United States, Sweden, and South Africa, focus on long-term strategies and foundational policy frameworks. The meta-regression analysis indicates that integrating green finance with financial development and renewable energy investments significantly enhances carbon reduction efforts. Model 2 results show that fintech and renewable energy investments contribute to emission reductions by 84% and 92%, respectively, at a 1% significance level. Moreover, interaction effects suggest that these investments yield greater benefits in developing economies, reducing emissions by 56% and 118%, respectively, at the 1% significance level. This study underscores the importance of Green Finance policies tailored to the specific economic contexts of nations to maximize environmental sustainability.

This study examines the changing climate patterns in Punjab from 1981 to 2020, focusing on precipitation concentration, seasonality, and temperature variability. These factors are important for the region’s agriculture, particularly wheat and rice production. The analysis utilizes the Precipitation Concentration Index (PCI), Seasonality Index (SI), and Standardized Anomaly Index (SAI) to identify long-term trends. The results show that after 2000, there were no years with low or moderate precipitation (PCI < 10 or PCI 10–15), with 10 years consistently experiencing highly irregular precipitation (PCI > 20). The Seasonality Index reveals that seven years between 2001 and 2020 had rainfall concentrated within three months (SI 1.0–1.19), compared to six years in the previous period. Temperature analysis indicates that summer maximum temperature anomalies reached + 8.763 °C and spring minimum temperatures peaked at + 17.11 °C. Further, the VAR (5) model forecasts slight increases in rainfall, with maximum and minimum temperatures expected to rise by 0.5 °C to 1.2 °C and 1.1 °C to 2.6 °C, respectively, by mid-century. These findings highlight the urgent need for climate-resilient agricultural practices and improved water management strategies to protect wheat and rice production in Punjab, as both crops are susceptible to these climate shifts.