In the current competitive market situation, live streaming has become an essential part of the digital transformation of agriculture. In this study, we empirically examine the influence of short video live streaming on the purchase of agricultural products. A regression model was developed using SPSS 23 software. The final results of this study show that short video live streaming has a significant effect on the sales of agricultural products. Consumer cognition and emotion show mediation in the impact between the Key Opinion Leaders (KOL) of charisma, content richness, and interactive evaluation on the purchase of agricultural products. This study explores the path of the impact of short video live streaming on the purchasing of agricultural products. It is beneficial to help agriculture-related practitioners develop new marketing paths, reduce the waste of agricultural products, and improve the economic income of related practitioners.

Given the increasing environmental pressures, it is essential that agriculture achieves the goal of sustainable and low-carbon development. In 2010, China, as the top carbon emitter, introduced a policy on agricultural land lease (ALL), which has been met with considerable approval from farmers and has resulted in a notable surge in the rate of ALL within the country. Nevertheless, the question of how the ALL policy affects agricultural carbon emissions (ACEs) remains unanswered. What are the transmission mechanisms? To answer these questions, this paper presents an equilibrium model that accounts for the heterogeneous production efficiency among farmers. It offers a theoretical analysis of the impact of ALL policy on agricultural carbon emission reduction (ACER) and presents an empirical test of this impact using a difference-in-differences (DID) model. Our research shows that the ALL policy gives impetus to ACER. This conclusion persists even after conducting the robustness and endogeneity tests. The mechanism posits that the policy achieves ACER through reducing the proportion of rural agricultural employees. Heterogeneity analysis indicates that the policy effect is significant in both the northern and southern regions of China. Nonetheless, the effect is only observable in economically developed areas, regions with high chemical fertilizer application rates, and areas with restricted agricultural progress. This study elucidates the connection between land transfer and agricultural carbon emissions, offering empirical evidence to support the advancement of green and low-carbon agricultural development.

Innovation has been elevated beyond the traditional forces of production, by the emergence of a new wave of industrial upgrading and the technological revolution, to become a significant force in the advancement of human society. Can an enterprise group, a significant type of industrial organization, improve the effectiveness of enterprise innovation? Here, a quantitative analysis approach was used to systematically analyze the impact of group control on enterprise innovation effectiveness and its transmission path based on the logical framework of the “policy environment-influence effect-influence path”. The study found that group control significantly improves the effectiveness of enterprise innovation compared to independently listed enterprises. The impact path showed that group control can reduce financing constraints through internal capital markets. It increases the investment in innovation and thus enhances the effectiveness of enterprise innovation. Meanwhile, internal information exchange is accelerated through the internal knowledge market, improving enterprise innovation’s effectiveness. The results of this study were still valid after robustness tests, such as propensity score matching and accounting for lag effects. According to the paper’s findings, to enhance financial support for innovation, financial market reform should be intensified. The growth of manufacturing enterprise groups should also be encouraged. Additionally, the ability of businesses to innovate while improving the internal benefits of enterprise groups and their innovation paths should be strengthened.

Blockchain technology application (BTA) is increasingly deployed in manufacturing supply chains as a digital infrastructure for traceability, cross-organisational coordination, and automated compliance, yet empirical evidence on how BTA converts into green innovation efficiency (GIE) remains fragmented and methodologically narrow. This study develops a multi-factor analytical framework that combines a theoretically grounded structural equation model with a gradient-boosting ensemble and SHAP-based explainable analytics to examine how BTA, supply chain integration (SCI), and three contextual factors — supply chain trust, task complexity, and a green digital learning orientation — jointly shape GIE. Survey data from 380 Singapore manufacturers distributed across seven industry sub-sectors were analysed with a two-stage procedure. Stage one estimates a theory-driven path model showing that SCI fully mediates the BTA–GIE relationship (indirect β = 0.049, 95% bootstrap CI [0.023, 0.078]), with trust amplifying and task complexity attenuating the BTA→SCI pathway. Stage two benchmarks six predictive model families, ranks feature importance through mean absolute SHAP values, and maps nonlinear partial-dependence responses. The stacked ensemble attains R² = 0.598 out-of-sample, outperforming the structural model by 13.7 percentage points and revealing a previously unobserved saturating effect of BTA above the mid-range of the scale. Heterogeneity analysis indicates that the BTA→GIE total effect is substantially larger in large firms, high-pollution sectors, and export-intensive firms. The study advances the literature by integrating confirmatory and exploratory analytics within a single explainable framework and offers managers a concrete, interpretable toolkit for prioritising digital-sustainability investments.

Urban green innovation depends on combinations of digital capabilities, research capacity, finance, and environmental policy, but conventional average-effect models do not directly identify conditions that constrain outcomes or alternative configurations associated with them. This article develops a transparent business data analytics workflow that combines necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). Because no observational urban dataset is available for this study, the workflow is demonstrated with 96 synthetic city profiles generated from a disclosed rule rather than presented as empirical evidence about real cities. Four hypothetical conditions—digital analytics readiness, knowledge capacity, green finance, and policy support—are calibrated using stated anchors. Ceiling-envelopment free-disposal-hull NCA, a permutation test, fuzzy necessity analysis, truth-table construction, and Boolean minimization are implemented in a reproducible Python program. The simulated outcome is constructed to require digital readiness and either knowledge capacity or a conjunction of finance and policy support. In the demonstration, the digital condition has an NCA effect size of 0.476 and fuzzy necessity consistency of 1.000. The high-outcome solution recovers two pathways, digital readiness combined with knowledge capacity and digital readiness combined with finance and policy support, with overall consistency and coverage of approximately 0.993. The results illustrate how to reconcile findings across methods and how high- and low-outcome configurations can be analyzed separately. They reproduce a known data-generating rule and do not establish a causal necessity, policy threshold, or pathway for actual cities. The framework provides a replicable starting point for future research using independently collected urban data.

Key conclusions are as follows: (1) provincial agricultural modernization exhibits pronounced spatial heterogeneity—coastal and major economic provinces generally outperform inland regions in both high-quality development and new quality productivity; (2) model-based indicator scoring reveals clear “knee/fault-line” structures, motivating parsimonious feature-retention thresholds (0.04 for the high-quality development set and 0.05 for the new quality productivity set) while maintaining predictive accuracy; (3) for the lagging cluster (2022 comprehensive score < 1.2), obstacle-degree diagnostics indicate that binding constraints concentrate in green production efficiency, openness-related factor conditions, shared public services, and the digital rural environment rather than a uniform weakness; (4) the stacking ensemble delivers the best overall predictive performance (lower RMSLE and smaller MSE/MAE than any single learner), supporting its use as a monitoring tool for key modernization dimensions. These findings imply that modernization policies should shift from average upgrading to cluster-specific, bottleneck-oriented interventions.