Based on China’s new development philosophy and the connotation of high-quality agricultural development, this study constructed a six-dimensional comprehensive evaluation framework: innovation-effectiveness-sustainability-coordination-openness-sharing. The spatio-temporal-range-improved entropy approach and the Dagum Gini coefficient were applied to evaluate and analyze the level of regional disparities and the dynamic distribution characteristics of high-quality agricultural development in China in the period from 2010 to 2018. The result shows that the level of high-quality agricultural development in China has steadily improved in general, but there exist prominent structural problems. Concerning regional differences, it indicated a pattern dominated by the pattern of “high in the east and low in the west”, mainly arising from the inter-regional disparity, with a gradual downward trend during the selected period. This study also comprehensively explored the four-dimensional driving mechanisms (production conditions, productivity, production relations, and production efficiency), and further examined the driving paths of various variables and regional heterogeneity using a panel Tobit model.

The coupling and coordination of high-quality agricultural development (HQAD) and rural revitalization is an inevitable choice to accelerate the realization of Chinese-style agricultural and rural modernization. Based on system theory, this study reconstructs the indicator systems of both and conducts measurements by applying the improved AHP–entropy weight method. This study has extended the analytical methods of kernel density estimation, Dagum Gini coefficient, σ convergence, and spatial β convergence to further investigate the spatio-temporal evolution, regional disparities, and convergence effect of the coupling coordination degree (CCD) of HQAD and rural revitalization in China from 2010 to 2020. The results show that the CCD has a tendency to increase year by year, presenting the characteristics of ‘high coupling degree–low comprehensive development level–low coupling coordination degree’, and also has the spatial distribution pattern of ‘high in the east and low in the west’. In addition, most of the provinces have a tendency to jump to a higher stage of coupling coordination; the overall trend of the kernel density curves is favorable; the results of Dagum’s Gini coefficient show that inter-regional disparities contribute the most to regional spatial disparities; and there is a significant tendency towards σ convergence and spatial β convergence of the CCD in China and the four regions. This study stimulates a broader discussion of rural revitalization, with potential implications for decision making in practice.

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification.

Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems.