Disruptions in the supply of water treatment chemicals can affect water quality, public health, service continuity, and the resilience of water treatment systems. A systematic method is therefore required to assess mitigation strategies under different disruption conditions. A data driven multi criteria framework is developed to assess the risks associated with water treatment chemical supply disruptions in China. A synthetic water quality dataset derived from the China Water Pollution Monitoring Dataset is used to construct indicators for four dimensions: compliance and public health, service continuity, supply vulnerability, and recoverability. The synthetic dataset is used as a controlled and reproducible source for methodological evaluation rather than as direct empirical evidence from Chinese water utilities. Criteria weights are determined using the Best Worst Method, and a quantitative scoring model is applied to evaluate mitigation strategies under multiple disruption scenarios. Compliance receives the highest criterion weight of 0.39. Among the evaluated mitigation strategies, dual sourcing obtains the highest performance score. Its score decreases from 0.74 under lower disruption severity to 0.63 under higher disruption severity, which indicates relatively stable performance across the tested scenarios. Supplier prequalification shows moderate performance and improves under severe disruption conditions, whereas safety stock shows the largest reduction in performance. Sensitivity and robustness analyses indicate that the strategy rankings remain stable under variations in criterion weights and disruption scenarios. The results indicate that the proposed framework provides a consistent and reproducible method for comparing mitigation strategies under controlled synthetic data conditions. The findings also show how multi criteria assessment can integrate water quality, service continuity, supply vulnerability, and recoverability within a common decision model. Since the analysis is based on synthetic data, the results should be interpreted as methodological evidence rather than direct evidence of the performance of Chinese water utilities. Further validation with observed utility and chemical supply data is required before operational application.
Chao Xu, Haibin Gu, Jiandong Sheng, Pingan Jiang, Ze Wang
Bioaugmentation with isolated pollutant-degrading strains is often limited by their poor establishment in native soils. The present study tested the hypothesis that introducing an active “guest” soil with high rac-metalaxyl-degrading capacity into a poorly degrading “host” soil would promote the establishment of degrading microorganisms, reshape the bacterial community, and enhance enantioselective transformation. Host soil (S1) and guest soil (S2) were mixed at various ratios and preincubated for different durations. Microbial biomass carbon (MBC), soil enzyme activities, bacterial community characteristics, and rac-metalaxyl transformation kinetics were evaluated. Soil mixing increased MBC by 10%–15% compared with values predicted from proportional contributions of the individual soils, and bacterial richness in host–guest soils exceeded that of either native soil. Selected enzyme activities and the relative abundances of dominant bacterial phyla responded nonlinearly to the guest soil proportion. Bacterial community dissimilarity increased logarithmically with the proportion of guest soil, maintaining a distinct community structure even after 120 days of preincubation. In the 4:1 host–guest mixture, the initial transformation rate (r 0) reached 48% and 71% of that in guest soil S2 after 14 and 120 days of preincubation, respectively. Transformation enantioselectivity was more closely associated with bacterial community structure than with richness. Active soil can serve as a functionally stable whole-community inoculum, offering a practical bioaugmentation strategy when the establishment of single isolated strains is constrained.
Agriculture in SE Asian coastal lowlands is vulnerable to inundation and associated crop production loss. Quantifying and managing flood risk, surface subsidence, and carbon emissions (in peatlands) requires an understanding of water table depth patterns and controls, for which no comprehensive data currently exists. Here, we present a dataset of 15,575 measurements of canal water table depth below the soil surface (CWD) in large-scale industrial tree plantations across the islands of Sumatra and Borneo, derived from ICESat-2 satellite LiDAR data. A pronounced relation is evident between CWD and land surface elevation, which applies equally in different regions, both in peatlands and mineral soil areas, and in oil palm as well as Acacia plantations. Mean CWD varies consistently across elevation zones, from −0.6 to −0.85 m for oil palm and Acacia plantations, respectively (i.e., ∼-0.5 – ∼-0.75 m in peat soil) where the surface is 5–9 m above global mean sea level, to ∼ -0.2 m at 1–3 m elevation near mean sea level. The area of industrial plantations in the 1–3 m elevation range alone, which likely has the highest flood risk, is 984,700 ha. Accounting for larger areas of smallholder plantations, and for areas at higher elevations with impeded drainage, the total area of lowland plantations likely experiencing regular flooding could exceed 2.8 million hectares. The close relation with elevation indicates that plantation water levels, especially in lowest-lying areas, are often controlled by landscape drainability conditions rather than management targets, and that optimum water table depths for production and peat loss prevention are not achieved over large areas. We propose that such difficult-to-manage landscape elevation controls on water table depth are considered in yield projections and land use planning, as well as fire and peat carbon emission reduction policies. Unproductive flood-prone plantations as well as peatlands with very low water tables may be logical priority targets for restoration planning.
Against the backdrop of sustainable development and China’s Dual Carbon Goals, Digitalization, Urban Development, and Low-carbon Transition continuously interact to shape urban sustainable development. However, the long-term coordination of the Digitalization–Urban Development–Low-carbon Transition (DUL) system and its underlying mechanisms remain insufficiently understood. To address this research gap, using panel data for 289 prefecture-level and above cities in China from 2010 to 2024, this study constructs the DUL system and applies the CRITIC-Entropy Weight Method to evaluate the development levels of the three subsystems and the overall DUL system. The Local and Tele-coupling Coordination Degree (LTCCD) is employed to characterize long-term coordination, while the Dagum Gini coefficient and GeoDetector are used to examine regional disparities and driving mechanisms. Based on this analytical framework, the results show that: (1) the development levels of Digitalization, Urban Development, and Low-carbon Transition all improved continuously, but their evolutionary trajectories differed, resulting in asynchronous development of the DUL system; (2) LTCCD exhibited a sustained upward trend, with coordination gradually evolving from imbalance to coordination, although eastern China consistently outperformed the central and western regions; (3) regional disparities in LTCCD generally narrowed, while interregional differences remained the primary source of spatial disparities, with the largest gap occurring between eastern and western China; and (4) the driving mechanisms of LTCCD evolved over time, with the explanatory power of individual factors changing dynamically, whereas the interaction effects among multiple factors consistently exceeded those of single factors, indicating that the long-term coordinated development of the DUL system mainly depends on multi-factor synergy. Taken together, these findings reveal the spatiotemporal evolution and formation mechanisms of long-term coordinated development in the DUL system and provide theoretical insights for promoting coordinated urban development and low-carbon transition in the digital era.
Using the COVID-19 pandemic as a common macroeconomic shock to which firms were differentially exposed, we investigate whether pre-crisis environmental, social, and governance (ESG) performance was associated with corporate financial resilience, measured through return on assets (ROA). Two competing directional hypotheses are evaluated: the insurance-value hypothesis (H1a), under which accumulated stakeholder trust protects profitability in crises, and the short-run cost hypothesis (H1b), under which relatively rigid ESG-related commitments depress accounting returns when demand contracts. Using a panel of 70 US industrial firms over 2012–2021 (690 firm-year observations), a two-way fixed-effects (TWFE) difference-in-differences (DiD) framework compares changes in ROA across firms with stronger versus weaker pre-crisis ESG performance. The results are consistent with the short-run cost hypothesis: a one-standard-deviation higher pre-crisis ESG score is associated with an approximately 1.06 percentage-point larger relative decline in crisis-period ROA (continuous specification, p = 0.006), and high-ESG firms experienced a 1.97 percentage-point relative decline compared to low-ESG counterparts (p = 0.019; 95% confidence interval (CI) = −3.60 to −0.33; N = 70 firms), approximately 24% of mean ROA. Dynamic event-study estimates show no differential pre-trends and significant high-ESG differentials in both 2020 and 2021; the penalty is unchanged by controls for research-and-development (R&D) and advertising intensity, is imprecisely larger in the most pandemic-exposed industries in a triple-difference specification, and is attenuated within industry-by-year cells. Pillar-level estimates are precision-limited nulls. Where included, financial leverage is the strongest single predictor of crisis-period profitability. The findings are interpreted through a sustainability trade-off mechanism in which environmental and social commitments behave like fixed capacity investments that build relational value over the long run but increase operating leverage in the short run; they identify a robust profitability pattern consistent with this mechanism, rather than a direct evaluation of it, and caution against treating ESG ratings as guarantees of short-run accounting resilience.
