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Effective coordination of water, energy, and carbon is vital for the sustainable development of irrigated agriculture. However, limited research has been conducted on the impact of irrigation technology on the coupling and coordination relationship of these elements, especially on the North China Plain (NCP) where irrigation is applied extensively. This study establishes a water-energy‑carbon (WEC) nexus framework based on footprint theory and energy analysis. Water utilization, energy consumption, and carbon emissions in a wheat production system under conventional irrigation (CI), sprinkler irrigation (SI), and drip irrigation (DI) technology on the NCP from 2000 to 2019 were quantified. Subsequently, the coupling coordination degree (CCD) model is used to analyze the interactions and correlations of the WEC nexus. Results indicated that SI and CI effectively reduced water consumption and mitigated water degradation, but this came at the expense of increased energy consumption and carbon emissions. The irrigation process represented the predominant share of energy consumption, representing 40.54 % and 37.64 % of the production-based energy consumption under SI and DI, respectively. The primary contributors to the production-based carbon footprint under CI, SI, and DI were N fertilizer (23.67 %), pipeline production (59.10 %), and irrigation electricity (21.85 %), respectively. The CCD range of WEC systems under the three irrigation technologies varied from 0.35 to 0.50 on the NCP during the investigation period. There were some slight differences in the average annual CCD between each irrigation technology, with DI (0.43) > SI (0.40) > CI (0.39). SI and DI was in basic coordination, while CI was in imbalanced type. Meanwhile, the spatial heterogeneity of CCD was fully reflected over time. Promoting water-saving irrigation technologies, developing clean energy, controlling the expansion of irrigation areas, and strengthening the connections among various subsystems are crucial measure to achieve regional WEC nexus coupling coordination., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Terrestrial evapotranspiration (ET a ) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ET a remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ET a data, the interannual variability of ET a and its ties to precipitation (P), potential evaporation (ET p ) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ET a to climate change's direct (P and ET p ) and indirect (NDVI) impacts. The ET a , P, ET p and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr -1 , 3.18 mm yr -1 , 0.89 mm yr -1 and 4.0 × 10 -4  yr -1 , respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ET p to the change in ET a (∆ET a ). The positive ∆ET a between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ET a differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ET a, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ET p was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ET a are modulated by both the sensitivity of impact factors acting on ET a as well as the magnitudes of factor changes. The greening of vegetation can influence ET a by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Crown Copyright © 2020. Published by Elsevier B.V. All rights reserved.)

The relationship between climate and human society has frequently been investigated to ascertain whether climate variability can trigger social crises (e.g., migration and armed conflicts). In the current study, statistical methods (e.g., correlation analysis and Granger Causality Analysis) are used in a systematic analysis of the potential causality of climate variability on migration and armed conflicts. Specifically, the statistical methods are applied to determine the relationships between long-term fine-grained temperature and precipitation data and contemporary social conditions, gleaned from historical documents covering the last two millennia in China's Hexi Corridor. Results found the region's reconstructed temperature to be strongly coupled with precipitation dynamics, i.e., a warming climate was associated with a greater supply of moisture, whereas a cooling period was associated with more frequent drought. A prolonged cold period tended to coincide with societal instability, such as a shift from unification towards fragmentation. In contrast, a prolonged warm period coincided with rapid development, i.e., a shift from separation to unification. The statistical significance of the causality linkages between climate variability, bio-productivity, grain yield, migration and conflict suggests that climate variability is not the direct causative agent of these phenomena, but that climate reduced food production which gradually lead to migration and conflicts. A conceptual causal model developed through this study describes the causative pathway of climate variability impacts on migration and conflicts in the Hexi Corridor. Applied to current conditions, the model suggests that steady and proactive promotion of the nation's economic buffering capacity might best address the uncertainty brought on by a range of potential future climate scenarios and their potential impacts., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model's absolute errors were small in magnitude (±0.25), whereas the MARS and M5 Model Tree models generated 53% and 48% of such errors, respectively, indicating the latter models' errors to be distributed in larger magnitude error range. In terms of peak global UVI forecasting, with half the level of error, the ELM model outperformed MARS and M5 Model Tree. A comparison of the magnitude of hourly-cumulated errors of 10-min lead time forecasts for diffuse and global UVI highlighted ELM model's greater accuracy compared to MARS, M5 Model Tree or Pro6UV models. This confirmed the versatility of an ELM model drawing on θ s data for VSTR forecasting of UVI at near real-time horizon. When applied to the goal of enhancing expert systems, ELM-based accurate forecasts capable of reacting quickly to measured conditions can enhance real-time exposure advice for the public, mitigating the potential for solar UV-exposure-related disease., (Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.)