Urban agglomeration is a primary source of global energy consumption and CO₂ emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO₂ emissions in urban agglomerations and prediction of the future trends of CO₂ emissions in urban agglomerations will help in the implementation of CO₂ reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO₂ emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO₂ emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO₂ emission scenarios to predict the energy CO₂ emission of the region and its cities. The results show that, first, the CO₂ emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO₂ emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO₂ emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO₂ emissions in the baseline scenario are low in the short term, but the CO₂ emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO₂ emissions.

Data complexity directly affects the dynamics of complex systems, which in turn influences the accuracy and robustness of forecasting models. However, the load data exhibit complex features such as self-similarity, long-term memory, randomness, and chaos. This study aims to quantify and evaluate the complexity features of natural gas loads and to develop a multi-step-ahead forecasting model that integrates data decomposition and ensemble deep learning while considering these complexity features. Firstly, the complexity features of the series are quantified by rolling the fractal dimension, Hurst exponent, sample entropy, and maximum Lyapunov exponent. The analysis contributes to understanding data characteristics and provides information on complex features. Secondly, the ensemble learning eXtreme Gradient Boosting (XGBoost) can effectively screen complexity features and meteorological factors. Concurrently, variational mode decomposition (VMD) provides frequency-domain decomposition capability, while the gated recurrent unit (GRU) captures long-term dependencies. This synergy enables effective learning of local features and long-term temporal patterns, resulting in precise predictions. The results indicate that compared to other models, the proposed method (XGBoost-VMD-GRU considering complex features) demonstrates superior performance in forecasting, with R² of 0.9922, 0.9860, and 0.9679 for one-step, three-step, and six-step prediction, respectively. This study aims to bring innovative ideas to load forecasting by integrating complex features into the decomposition forecasting framework.

As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essential for effective BPM. However, the traditional manual-based modeling approach is time-consuming and error-prone. To overcome this, process recommendation technology can improve the intelligence and efficiency of modeling to a certain extent. However, existing process modeling recommendation methods suffer from the problem of low accuracy and neglecting short-process models. Therefore, a novel process modeling recommendation method that integrates disjoint paths and sequential patterns was proposed. This method uses edge-disjoint paths for the first time to represent the behavioral semantics of processes, and an improved contiguous sequential pattern mining algorithm was proposed to mine the contiguous path sequential patterns (CPSPs) of edge-disjoint paths. In the process modeling recommendation stage, the k CPSPs with the highest matching degree with the current reference model process were calculated, and the last node in these CPSPs was used as the set of recommendation nodes. In cases with CPSPs with the same matching degree, the one with the higher value was recommended according to their corresponding lift, confidence, and support degrees. Through experimental evaluation and comparison, it was shown that the proposed method effectively improved the accuracy of the recommendation of both short-process and long-process models while ensuring effectiveness and time efficiency.

Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi’an, simulation results show that the model reduces gas supply costs by 3.45% for residential users and 6.82% for non-residential users, increases user welfare by 4.64% and 88.87%, and decreases carbon emissions by 115.18 kg and 2156.8 kg, respectively. Moreover, non-residential users achieve an additional reduction in carbon trading costs of 183.85 CNY. The findings demonstrate the effectiveness of integrating dynamic price signals, user behavior modeling, and carbon constraints into a unified optimization framework, offering decision support for sustainable and flexible natural gas scheduling.

Wind power integration into mountainous power grids amplifies probabilistic forecasting challenges arising from strong non-stationarity, multi-source meteorological redundancy and frequent curtailment events. To address the limitations of existing approaches, this paper proposes a multi-horizon probabilistic forecasting framework integrating multi-perspective entropy-weighted fusion, permutation-entropy-guided decomposition, and residual-anchored probability modelling. First, an Entropy-Weighted Multi-criteria Permutation Feature Importance (EW-MPFI) module fuses KSG mutual information, Tree-SHAP, and elastic-net permutation importance through entropy-based weighted aggregation, distilling 23-dimensional meteorological inputs into eight informative features while suppressing single-criterion bias. Then, a three-stage decomposition strategy applies ICEEMDAN primary decomposition, permutation-entropy and sample-entropy guided band reconstruction, and SSA secondary refinement on high-frequency components, achieving complexity-aligned multi-scale separation. Finally, a decomposition-aware patch-based Transformer backbone (DPC-Former) generates three-quantile point forecasts, upon which an NGBoost residual layer models the conditional distribution via natural-gradient optimization in the information-geometric parameter space. Case studies on a 130 MW mountainous wind farm in Sichuan, China, covering 8736 15-min samples with 566 curtailment samples (6.48% of the dataset), show that, under the partition-wise offline batch-evaluation protocol, the proposed framework achieves an NMAE of 5.21%, an NCRPS of 3.74%, and a PICP80 of 0.84 across forecasting horizons from 15 min to 4 h. Ablation analysis attributes NMAE improvements of 25.36% and 24.57% to the decomposition and feature-selection modules, respectively, while 50-seed ensembling further reduces NCRPS, NMAE, and NRMSE by 7.40%, 7.00%, and 13.70% relative to single-seed training. A fixed-checkpoint test-block diagnostic further shows limited sensitivity at approximately weekly and three-day decomposition cadences, but a material degradation at a one-day cadence. The reported metrics should therefore be interpreted as offline best-case results rather than as performance under strictly causal real-time deployment.

Land-use change affects carbon emissions and air quality through coupled ecological and urban processes, yet their coordinated mitigation remains insufficiently understood in heavily industrialized inland regions. Using 113 district-county units in the Fenwei Plain, China, this study developed a regional assessment framework for carbon-PM_2.5 co-mitigation under land-use change from 2013 to 2023. The framework integrates dynamic coordination diagnosis, model-estimated improvement-potential assessment, and governance-oriented classification to identify regional differences in coordination status and adjustment capacity. The results showed a phase-wise evolution of the carbon–pollution relationship, with the Synergetic Evolution Index (SEI) shifting toward higher coordination levels in the later period and becoming more stable after 2020. High-synergy areas were concentrated along the Xi’an–Xianyang–Weinan corridor, whereas several peripheral counties remained under combined carbon–pollution pressure. Model-estimated improvement potential showed pronounced spatial heterogeneity and was concentrated mainly in transition zones with moderate coordination levels and relatively strong structural responsiveness. Land-use-related carbon emissions and PM_2.5 pollution were the dominant constraints on responsiveness, whereas ecological conditions and urban expansion played secondary roles. By integrating coordination status, model-estimated improvement potential, and structural responsiveness, four governance-relevant types were identified: consolidation areas, transformation areas, ecological mismatch areas, and urbanization lock-in areas. This study provides a regionally differentiated framework for diagnosing carbon–pollution co-mitigation under land-use change and supports adaptive environmental governance in industrializing inland regions.