With the growing environmental concerns, the existing literature mostly highlights the industrial pollution while neglecting the factors associated with the agriculture-related greenhouse gas emissions. Regarding this, the study explores the impacts of green energy, tech development, and urbanization on agriculture's greenhouse gas emissions. The prime objective of the current research is to unveil the green energy, tech innovations, and environmental sustainability nexus to draw novel implications in the context of Sustainable Development Goals (SDGs). In doing so, the authors employ the quarterly data of China from 1990Q1 to 2020Q4. For the long-run empirical analysis, the authors utilize various time-series estimating approaches, such as Quantile regression, which performs better in testing the nexus at different quantiles. However, the Fully Modified OLS, Dynamic OLS, and Canonical Cointegration Regression methods are used as robustness tools to authenticate the estimate of the primary approach. The results suggest that greener energy and technological innovations significantly reduce agriculture sector emissions. Furthermore, the presence of green energy transforms its negative influence into a positive one. Contrastingly, the use of traditional fossil fuel energy, urbanization, and financial development are significant drivers of emissions. This study's findings support SDGs, particularly SDG-2, which supports the stance of sustainable agriculture and encourages green energy use. Overall, the study discourses policy-related suggestions in the sustainability's context.

To tackle global climate change and fulfill China's dual carbon objectives, the coordinated abatement of pollution and carbon emissions has emerged as a pivotal focus for sustainable urban development. Land use patterns and pollutant emissions are deeply connected within cities, making them a crucial lever for advancing synergistic pollution and carbon reduction. To investigate this relationship, this study constructs a synergy index (Syg) and a territorial land use compactness index (TD) using the Entropy Weighted TOPSIS method based on land raster data from 2012 to 2023, LandScan population raster data, and prefecture-level city panel data. The findings indicate that the compactness of urban land use patterns positively influences the synergistic effects of pollution and carbon emissions reduction. This conclusion remains valid after undergoing benchmark regression analysis, robustness testing, and endogeneity treatment. Regarding the operational mechanism, the compactness of urban land use patterns primarily exerts its effects through two pathways: the aggregation of innovative factors and overall industrial structure upgrading. Heterogeneity analysis further reveals that the synergistic effects of pollution and carbon emission reduction are more pronounced in eastern and western cities, non-transport hub cities, non-resource-based cities, and mature resource cities. Thus, it is recommended that differentiated land policies and green innovation clusters be established, and that a synergistic assessment system for land use, as well as pollution and carbon emission reduction, be developed to help achieve the dual carbon goals.