This study investigates the intricate relationship between CSR, green innovation, and environmental performance within the context of China’s manufacturing industries. Given the pressing environmental challenges faced by this sector, understanding how CSR practices correlate with sustainable innovations is critical for stakeholders aiming to enhance environmental outcomes. This was a survey-based study using a questionnaire and the five-point Likert scale; items were adopted from previous studies. Sampling was drawn through random sampling. Utilizing a sample of 327 respondents, this research employs SPSS and Structural Equation Modeling with Partial Least Squares (SEM-PLS) as analytical tools. The findings reveal a robust positive correlation between CSR practices and green innovation, as evidenced by a path coefficient of 0.704. These data support the stakeholder theory, which posits that organizations attentive to stakeholder expectations are more inclined to adopt sustainable practices. Furthermore, this study underscores the mediating role of green innovation in the relationship between CSR and environmental performance, highlighting its importance in aligning organizational strategies with sustainability-oriented stakeholder interests. This conclusion aligns with the existing literature emphasizing CSR’s significance in improving environmental performance through innovative approaches. However, an unexpected finding emerged: there exists a weak negative relationship between green innovation and organizational agility (−0.080). This suggests that, while firms strive for sustainable innovations, they may inadvertently compromise their flexibility in responding to evolving market demands. By addressing these dynamics, this research contributes valuable insights into how CSR can effectively spur green innovation and promote sustainable practices within China’s manufacturing sector. This study fills a gap in the existing literature by elucidating the mechanisms that connect CSR with enhanced environmental performance while also recognizing the potential trade-offs associated with innovation strategies. Also, the exploration of agility, which is least investigated, can also open various doors towards sustainability and the adaptation of new changes. Future research is encouraged to further explore these relationships across different industries and delve deeper into the mechanisms linking CSR to improved environmental outcomes, ultimately guiding organizations in balancing sustainability efforts with market responsiveness.

Soil salinity and sodicity pose significant challenges to sustainable agriculture by adversely affecting soil properties, crop growth, and yield. The study was conducted in an agricultural field located in Khipro, within the Sanghar district of Sindh Province, Pakistan, to assess the impact of tillage practices and wheat straw incorporation on the organic matter content and structural properties of saline-sodic soil. Field experiments were conducted under shallow (ST) and deep tillage (DT) systems, a conventional practice (NPK fertilizer with gypsum, CK), a control (no straw or gypsum, CTRL), and three wheat straw application rates of 3, 7, and 10 Mg ha ⁻ ¹ and gypsum application rates corresponding to 25%, 50%, and 75% of the gypsum requirement (GR). After two years, soil organic matter (SOM), water-stable aggregates (WSA), mean weight diameter (MWD), and aggregate stability (AS) were significantly improved (P ≤ 0.05) in treatments with incorporated wheat straw. Compared with the control, the combined application of deep tillage, 10 Mg ha ⁻ ¹ straw, and 75% gypsum increased soil organic matter by approximately 35%, water-stable aggregates (>0.25 mm) by 12–18%, aggregate stability by 42%, and mean weight diameter by 31%. Improvements were more pronounced in the upper 0–15 cm soil layer than in deeper layers. Enzymatic activities, including dehydrogenase, urease, and alkaline phosphatase, also increased by 28–46% under the same treatment, indicating enhanced microbial activity and nutrient cycling. These findings demonstrate that integrating straw incorporation with deep tillage and gypsum amendment is an effective management practice to improve soil structure, organic matter content, and biological activity in saline-sodic soils under semi-arid conditions.

In the context of climate change, exploring and predicting the spatio-temporal distribution of flood disasters is crucial for developing effective flood risk management and disaster reduction strategies. This study tackles the shortcomings of traditional methods used to measure the risk of regional flood disasters, which often lack precision. A series of machine learning models enhanced by Particle Swarm Optimization-Machine learning (PSO-ML) models were developed and integrated with General Circulation Model (GCM) data to analyze the temporal and spatial characteristics of flood disasters under different scenarios in Shanxi Province, China. Results indicate that the frequency of days with precipitation exceeding 50 mm in the study area increased from 23 in 1981 to 71 in 2021. The northernmost city, DT, experienced 18 extreme precipitation days, while the southernmost city, YC, recorded 71 such days. A gradual increasing trend in extreme precipitation days was observed from north to south and from distant to near areas. The PSO-ML models demonstrated notably improved performance compared to traditional models across all indices. PSO-XGBoost, PSO-RF, and PSO-KNN exhibited higher prediction accuracy than conventional single models, with AUC values of 0.98, 0.95, and 0.94, respectively. Land use change, elevation, and slope emerged as the most influential factors, with weights of 10.37%, 10.01%, and 8.76%, respectively. Across four scenarios (SSP126, SSP245, SSP370, and SSP585), flood-prone areas were projected to shift southward, with varying degrees of increase in risk areas. The SSP370 scenario showed gradual growth, projecting 7660.116 km² of at-risk area by 2100. The SSP585 scenario exhibited the most rapid growth, with a projected peak of 13,933.69 km² by 2070. This research proposes a novel approach to flood disaster risk assessment and offers insights for effectively mitigating regional flood risks.